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Temperature excursion management in pharmaceutical storage.

AKCP 06.2020 Articles , Blog

pharma temperature

The quality of pharmaceuticals relies on environmental controls during their storage and handling. Every pharmaceutical item ought to be taken care of and stored under manufacturer-recommended storage conditions marked on the data information sheet or packaging.

Temperature Excursion Management in Pharmaceutical Storage is important during receipt of raw materials, manufacturing, and distribution of drugs.

System failures or human carelessness can cause circumstances leading to temperature excursions . The most significant environmental condition having the capacity to affect the nature of the pharmaceutical product is temperature. If the temperature excursions are not taken care of efficiently, there will be an adverse impact on product quality . There is a developing need to monitor for environmental excursions during pharmaceutical logistics to negate effects on the quality of the product. Quality Management System (QMS) should be implemented to avoid temperature deviations during the storage, transport, and distribution of pharmaceuticals.

QMS Proecdures for factory energy management

Pharmaceutical Temperature Excursion : An integrated approach for managing the quality system should include temperature excursion management. The overall temperature excursion management can be laid down in the following steps:

High Breach Temperature Graph

  • Storage temperature and humidity limits: Specified directions are stated for pharmaceutical products with reference to the temperature and humidity at which articles shall be stored, transported, and distributed. Pharmaceutical supply chain products require cold storage or climate-controlled storage to meet the manufacturer’s recommendations. For products from the class of cold chain, the storage condition is maintained at 2°C–8 °C. Similarly, the relative humidity shall be maintained below 60% and above 40% depending upon the hygroscopic nature of the product. When environmental stability data indicates there has been temperature deviation with storage and distribution at a lower or higher temperature and humidity there are protocols in place that may require the disposal of the product, or at the least taken out of circulation until tested and verified.
  • Measurement devices: Temperature and Humidity measuring devices (popularly referred to as data loggers) are available. They log the temperature at a preset interval which will be downloaded to a computer system or QMS for review, evaluation, and recording. Periodic verification of the calibration status of temperature data loggers and upgrading of software is a prerequisite for uninterrupted and accurate information about product storage conditions. The temperature and relative humidity sensor should be placed on the hottest spot, concluded after temperature mapping of the area.
  • Reason for Temperature excursion: In pharmaceutical factories and cargo areas, the required temperature is maintained with help of air handling units (AHU). The design and capacity of AHU are selected on the temperature required to be maintained.

Temperature excursions in a manufacturing area are caused due to the following reasons (not limited to):

  • An inadequate number of air handling units (AHU) were installed to maintain the desired temperature conditions inside the manufacturing shop floor.
  • Leakage or rupture from the air duct, resulting in an insufficient cooling effect.
  • Mechanical failure in air handling unit (AHU).  unprecedented temperature fluctuations.
  •   Power failure makes the AHU operation defunct.
  • Lack of quality system and weak discipline of Good Manufacturing Practices (GMP) on the production shop floor.
  • General awareness about the consequences of not maintaining the temperature within limits.
  • Extreme weather changes and obsolete contingency plans to handle

Temperature excursions during transport are caused due to the following reasons:

  • An unexpected delay in transportation due to which the temperature control cannot be maintained effectively
  • Product pallets are kept in hot zones of airports or shipping yards.
  • Reefer containers or refrigerated control vans are not deployed for transportation.
  • The transport agency fails to maintain the planned transport condition.
  • Higher cost to maintain the temperature within limits.
  •   Power failure due to short longevity of power bank during longer travel time.
  • Good Distribution Practices (GDP) understanding amongst supply chain personnel about the adverse impact on product quality.

Consequences Of Temperature Excursion

The storage condition for the product is assigned based on scientific studies into the deterioration of the product during its life cycle. If the temperature excursion isn’t addressed immediately, the subsequent negative impacts are common:

  • Loss of product.
  • Decreased efficacy.
  • Separation of layers in liquid products.
  • Change in dissolution pattern of solid dosage.
  • Discoloration of products.

Control of Temperature Excursions

To handle the temperature excursion strategic planning , effective packaging, and well-documented procedures are recommended. The development of a QMS database of pharmaceutical products is beneficial to assign quality storage conditions.

The storage conditions suitable for the product are assigned through the following tests:

At the merchandise development stage, the semi-finished product and final pharmaceutical product dosage are subjected to challenging conditions to monitor the potential impact on quality attributes.

  • Hold time studies are administered to determine the allowable period of time at a specified storage condition without impacting quality. the standard attributes of product intermediates include chemical, microbiological and pharmacological determinants at various time points of stability.
  • Accelerated condition stability study data of the product form an assurance for the storage condition that shall be suitable to product safety. The time stability data is generated in the laboratory to assess the product’s change in quality and attributes throughout the expiration date. The accelerated stability data is generated to gauge the impact on the quality of the product under a stressed condition.
  • A freeze/thaw study for multiple cycles should be conducted to specify the effect of freezing, if any, and therefore the subsequent thawing. Samples from different layers (top, middle)

Thermal Packing During Transportations:

Vaccine storage temperature monitoring

Packaging of pharmaceutical products for transport should include the supply of thermal packing and display of appropriate storage conditions. Caution notes to  avoid storage outside the specified conditions shall help supply chain personnel protect the merchandise quality. The storage condition should be effectively displayed on the packaging of the pharmaceutical product . The packaging configuration card must contain the small print of knowledge loggers.

Procedure To Minimize Temperature Excursions: The temperature excursion has regulatory implications as well as an impact on business operations. A standard procedure (SOP) should be established and adherence ensured through adequate training to concerned personnel. An operational checklist of the Integrated Quality Management System (QMS) approach should include the qualification status of a manufacturing facility with special attention to environmental controls during storage and transportation.

Control of Temperature Excursion By Using AKCP Wireless Temperature and Humidity Sensor:

With AKCP wireless environmental monitoring solution, you can monitor temperature excursions with real-time alerts, data logging , and reporting.

A monitoring system gives an accurate picture of the temperature and humidity conditions of drug storage . Have confidence in the quality and safety of pharmaceutical products. End-to-end monitoring prevents the loss of thousands of dollars of perished products

Wireless Tunnel radio technology penetrates even thick secure storage and refrigerated cabinets. Battery-powered sensors with a 10-year battery life guarantee easy installation.

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How to Troubleshoot Temperature Excursions?

Download our free temperature excursions poster below detailing everything from this blog including:

  • Warning signs of a temperature excursion
  • Actions items to take during an excursion event
  • Preventative measures you can take

  As a primer, we’ve detailed below everything you need to know about temperature excursions so you can be prepared if one happens inside your healthcare facility.

What Is a Temperature Excursion?

In the United States, the CDC defines a temperature excursion as "any break in the cold chain of a pharmaceutical product" . The "cold chain", is a series of refrigerated production , storage and distribution activities. These processes ensure that vaccines and other pharmaceuticals remain at the correct temperature. Maintaining proper temperatures and avoiding excursions ensures that materials remain safe and effective.

Meanwhile, according to the European Compliance Academy , a temperature excursion is defined as: " a deviation from the labeled storage condition of a product for any duration " .

This includes excursions during transportation or distribution.

data excursion definition

As a result of this degradation, there will be a generation of impurities in the product. Degradation products like this are not wanted in the manufacturing chain. Additionally, they present a threat to patients' health.

As such, the raw materials needed to produce pharmaceuticals need to be maintained as well . Active pharmaceutical ingredients (API) need proper temperatures during the manufacturing process. If not properly handled, they can lose potency, effectiveness and safety before they're even distributed.

Deviating from optimal storage conditions can result in significant changes in the API. This includes degradation, decay, polymerization, and an increase in impurity levels.

Reasons Why Temperature Excursions Occur

Air handling units (AHU) maintain the required temperature in pharmaceutical factories. So, the design and capacity of these units reflect the API manufacturers temperature requirements during the production process. Regardless, temperature excursions are unavoidable. Even in the manufacturing area and during transportation.

data excursion definition

  • An insufficient number of air handling units to maintain the desired temperature conditions
  • A leaked or ruptured air duct
  • Mechanical failure in air handling units
  • Unprecedented temperature fluctuations.
  • Power outages interrupting the AHU's operation
  • Poor adherence to good manufacturing practices (GMP) on the production floor
  • Poor staff oversight of temperatures/ Poor Quality Control
  • Extreme weather conditions and poor or absent SOPs for handling them

data excursion definition

Effects Of Temperature Excursion

A temperature excursion affects product quality in two potential ways. But, the impacts of high temperatures are different than those of temperatures that are too low. Though the result is the same nonetheless - a compromised product.

Products sensitive to high temperatures experience degradation that can cause a  decrease in the active ingredient content of the product .

This happens due transformations in the affected, degraded components within. The transformation could be oxidative, hydrolytic, or some other form. As a result, much more toxic variants of the compound begin to appear. The amount of degradation/toxicity is dependent on length and severity of the temperature excursion, which can result in:

  • Components becoming discolored.
  • Changes in dissolution rates
  • Separation of emulsions

Products sensitive to a low temperature usually get damaged by phase changes caused by the freezing process . As a result, the physical atomic structures of the chemicals experience permanent changes.

Products with large quantities of water, like creams or biologicals, are especially prone. Often, they'll lose their properties after experiencing excessive freeze-thaw or temperature cycles. Because water can easily change temperature, the presence of ice quickly damages product.

How To Measure Temperature Excursions

One responsibility of pharmaceutical manufacturers is guaranteeing every batch is a safe and effective high quality product. One of the ways to make this happen is through continuous temperature monitoring. By monitoring the temperature range, they can catch any excursions that occur. After which, they can take the necessary steps to address them immediately.

The Center for Disease Control recommends using a continuous temperature monitoring device (TMD) to track excursions . The recommended TMD is a digital data logger (DDL) that has the following characteristics:

  • Ability to record the logged temperature values to a computer system for review.
  • The digital data logger's calibration status must be regularly verified, with up-to-date software. This ensures accurate information about storage conditions.

The following information is required when documenting temperature excursions:

  • The date and time the temperature excursion occurred
  • An inventory of affected products
  • The storage unit air temperatures. Including the minimum and maximum temperatures observed during the temperature excursion, if available
  • Ambient temperature (also referred to as “room temperature”).
  • A general description of the event, such as the length of the temperature event. The digital data logger should provide this data.
  • A list of any other items in the storage unit
  • Documentation of any problems with the storage unit

All the data should be compiled, and a copy given to the distributor and recipient of the affected product(s).

data excursion definition

How To Avoid and Combat Temperature Excursions

Simply put, any temperature excursion is a result of prolonged exposure to air that is room temperature or above. For end users of pharmaceuticals, the most likely scenario for them to experience a temperature excursion is because they’ve left the door open to a medical refrigerator/medical freezer or it has stopped running.

If Your refrigerator/freezer temperature has dropped below 35°F (2.0°C) for 15 or more minutes consecutively:

  • Check the Placement of the Thermometer probe – Place in the middle and monitor temperature in 30-minute intervals
  • Adjust Refrigerator/Freezer Temperature - Change temperature of appliance, if possible, to a warmer setting. Monitor and record = temperature every 30 minutes for next 2 hours.
  • If temperatures remain out of range — implement facilities’ relocation plan. Immediately call vaccine manufacturer/distributor.

If your Refrigerator has risen above 46°F (7.7°C) or Freezer has risen above 5°F(-15°C) for 60 minutes or more consecutively:

  • Check your power supply — if your area is experiencing a power outage estimated to last 2 or more hours and your facility does NOT have a dedicated backup power source for your cold storage appliances, implement emergency relocation plan
  • Check the door to the storage unit — ensure nothing is preventing door from securely closing. If so, adjust accordingly and shut door completely.
  • Adjust the refrigerator/freezer temperature — Change temperature of appliance to a cooler temperature is possible. Monitor and record temperature every 30 minutes for next 2 hours.
  • If temperatures remain out of range — implement facilities’ relocation plan. Immediately call vaccine manufacturer/distributor. In short, unless total failure of the appliance has occurred, the most common solutions to these problems are:
  • A door alarm to ensure staff always close it
  • A backup power solution to ensure any medical and pharmaceutical refrigerators continue to operate as normal.

While door alarms are fairly easy to source tools, a reliable and powerful backup power system for refrigerators can be harder to find.

Luckily, battery backup systems offer instant and automatic power for medical appliances as soon as the power goes out. As a result, no staff needs to be on-site to keep track of or start the generator and vaccines will continue to remain safe—with no extra work required.

Additionally, their vertical, cabinet-like design and leak-proof batteries mean they can be installed in even the tightest spaces and oriented in anyway to make them fit. Plus, if your medication or vaccine room is truly tight on space, a hardwired backup power unit can instantly supply remote power to your appliance—directly via the outlet its already plugged into.

Regardless of what kind of system is the best fit, they ensure that your entire stock of vaccines are protected from a sudden loss of power (and the resulting temperature excursions) by guaranteeing a seamless transition from utility power to backup power.

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  • Over a weekend
  • Or even for a whole week.

So, to protect your facility from tens of thousands of dollars in lost vaccine stock, speak to a Medi-Products battery backup expert.

They’ll help design you a system that both meets your power needs and will fit inside your facility—for a much lower cost than what your vaccines are worth. So a backup power system pays for itself the first time your power goes out.

Designing a system for you is as easy as taking a picture of your appliance’s nameplate, and a photo of the room where it’s in.

Then, you just email both photos to our Product experts, and we’ll provide you with multiple options for backup power protection.

For more information contact:

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Battery Backup for Vaccine Refrigerators and Freezers.

data excursion definition

Our powerful battery backup systems will instantly power multiple appliances during a power outage. These custom sized systems can provide power for up to 72 hours of runtime!

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Temperature excursion management: A novel approach of quality system in pharmaceutical industry

Quality of pharmaceutical product largely depends upon the environment controls during its storage and handling. Each pharmaceutical product should be handled and stored under specified storage condition labelled on product information data sheet or product pack. Hence the temperature excursions during receipt of raw materials, manufacturing of pharmaceutical products and distribution should be managed during entire product life cycle with holistic approach. The research is based on primary data and exploratory study through literature review. The temperature excursion may be observed during transportation of raw materials manufacturing as well as distribution of pharmaceutical products, which have potential to deteriorate the product quality. Temperature excursion in pharmaceutical industry should be recorded and reported to the manufacturer for further investigation and risk analysis. The concept of temperature excursions, its reasons, consequences and handling mechanism should be well understood to ensure the concerted efforts under the aegis of Quality Management System. Based on the reasons and consequences of temperature excursions during pharmaceutical operations, a system based quality management has been envisaged through this study. The concept and procedure to handle temperature excursion have evolved after this study which shall be useful to pharmaceutical industry as well as to medicine distributors and consumers.

1. Introduction

The pharmaceutical product quality largely depends upon the storage environmental conditions. Natural reasons or human negligence could create uncalled-for situation causing temperature excursions. The most important environmental parameter having significant potential to impact quality of pharmaceutical product is temperature. If the temperature excursions are not handled systematically, there shall be an adverse impact on product quality.

There is a growing need to manage the environment excursions during pharmaceutical operations and its impacts on quality of products. In an era of Quality by Design (QbD) for pharmaceutical products, the attention is paid towards inbuilt quality instead of inspected quantity ( Roy et al., 2012 ). As manufacturers have extensive knowledge about critical product and process parameters and quality attributes, the impact assessment has to be extended to temperature excursions. The temperature and relative humidity (RH) beyond limit shall lead to product degradation rate and microbial growth. This concept is the theoretical basis for the pharmaceutical guidelines that provide recommendations for long-term, intermediate, and accelerated storage conditions and for establishing shelf life periods or expiry dates of products ( Scrivens, 2012 ).

Pharmaceutical regulatory bodies expect strict adherence of Good Manufacturing Practices (GMP) and Good Distribution Practices (GDP) during plant manufacturing and product distribution processes. GMP and GDP are deemed as synonyms of Quality System in pharmaceutical business. Since temperature excursions are observed during raw material receipt, manufacturing operation and distribution of pharmaceutical products, there is a need of holistic approach of quality system which shall be based on both GMP and GDP.

1.1. Research methodology

The following instruments have been used to generate data for the study:

  • (a) A survey has been conducted amongst pharmaceutical professionals to understand their experience regarding environmental condition during pharmaceutical manufacturing and that during distribution process.
  • (b) The guidance papers issued by drug regulatory agencies and related literature and scientific search engines such as Google were searched for pharmaceutical supply chain risk management studies in English language. Searching through databases was done with different keywords: supply chain risk, Good Distribution Practices, Quality Risk Management, and pharmaceutical. Searching in each database was adapted to databases characteristics and additionally pharmaceutical risk. The result studies and meeting abstracts were screened at 4 steps and exclusion process was based on consensus of both the authors.

1.2. Data and analysis

The research survey study amongst pharmaceutical professionals in India reveals that the records of environment condition (EC) monitoring during manufacturing and distribution operations follow a contrast trend (refer Chart 1 ). The survey alludes that deployment and monitoring of data logger results during manufacturing as per GMP are in place, whereas that during distribution operation is not so methodological.

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Trend of Environment Condition (EC) monitoring practices during manufacturing and distribution operations.

As a part of Quality Management System (QMS), pharmaceutical industry has identified a number of core quality elements which are followed at manufacturing site level. Few of such core elements of plant quality system may be listed as,

  • a. Documents and Record Control: There shall be good documentation practices in organization to ensure the document and online records are adequately maintained.
  • b. Deviation Control: Incidences leading to departure from documented and approved instructions shall be recorded and evaluated for potential impact on product quality.
  • c. Change Control: The changes to an approved design, equipment or system in pharmaceutical facility shall be adequately reviewed and validated.
  • d. Validation Master Plan: The validation master plan shall exhibit management philosophy, strategy and commitment of organization towards validations of processes and qualification of equipment.
  • e. Quality Risk Management: Quality risk to product shall be identified and evaluation shall be made to estimate the severity, occurrence and detectability. A robust quality risk management.
  • f. Training and Awareness: The organization shall develop and implement robust training programme for personnel engaged in GMP operations.
  • g. Market Complaint Handling system: There shall be a documented procedure to receive, log and investigate each market complaint to further facilitate necessary corrective and preventive action.
  • h. Recall Management, etc.: There shall be documented procedure to handle the recall or market returned goods.

It is observed that inadequate QMS components have direct impact on consistency of storage conditions with respect to temperature and humidity. The temperature excursion is a common notion which signified the general environmental excursions (see Fig. 1 ).

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Object name is gr1.jpg

Effect of inadequate Quality System element can cause Environmental Excursions (EC).

A research survey amongst pharmaceutical professionals finds that the core quality assurance aspects are followed by each manufacturing site as a part of cGMP but during distribution practices (out of plant) the above quality elements are not followed meticulously. The environmental excursion management is interlinked with Product Complaint Management, Quality Risk Management, Deviation Management and Change Control Management, sometimes as a cause or else as an effect.

2. Discussions – facets of temperature excursion

Quality and environmental excursions are two important aspects of pharmaceutical operational excellence. An integrated approach for managing the quality system should include the temperature excursion management. The overall temperature excursion management can be laid down in following steps.

2.1. Understanding temperature excursion

The environmental condition during pharmaceutical business is defined by temperature (T) and relative humidity (RH). The temperature and relative humidity are used and monitored during manufacturing processes particularly when the product intermediates are exposed to environment. During packaged conditions temperature is measured, monitored and controlled. Any spike in measured value of T and RH shall be construed as environmental excursion. During manufacturing the temperature is always maintained below 27 °C, and whenever any deviation from this limit is observed, the case is thoroughly investigated and impact on product quality is mitigated.

According to European Compliance Academy, a temperature excursion is the deviation from the labelled storage condition of a product for any duration whether during transportation or distribution. Studies indicate that if there is exposure of product or intermediate beyond specified environmental limits for substantial time, there shall be generation of impurities as result of product degradation. Such degradation products are not only regarded as undesired but also shall have adverse reaction to the patient’s health.

The temperature excursion phenomena are also applicable during manufacturing and transportation of active pharmaceutical ingredients (API) prior to receipt at pharmaceutical manufacturing site. Deviation against storage condition can lead to significant qualitative change in API, such as degradation, decomposition, polymerization and impurity level increase.

2.2. Storage temperature and humidity limits

Specified directions are stated in some monographs with respect to temperature and humidity at which official articles shall be stored and distributed (including the shipment of articles to the customer), when stability data indicate that storage and distribution at a lower or higher temperature and humidity produce undesired results.

Cold chain products are those products which have to be necessarily stored under cold condition (refer Table 1 ). For products from class of cold chain, the storage condition is maintained at 2–8 °C. Similarly the relative humidity shall be maintained below 60% and 40% depending upon the hygroscopic nature of product.

Typical storage conditions.

United States Pharmacopoeia (USP) has described the different labelling terminologies related to temperature conditions. The general chapter of USP clarified various storage labelling conditions.

2.3. Measurement devices

Temperature and Humidity measuring devices (popularly known as data loggers) are available in market, which log the temperature at a defined (preset) interval that can be downloaded in computer system for review, evaluation, investigation and record. Periodic verification of calibration status of temperature data loggers and upgradation of software is the prerequisite of uninterrupted and accurate information about product storage condition.

Calibration and periodic verification of measurement device are keys of correct recording. In addition to that periodic cleaning of measuring device and appropriate precautions should be taken to avoid blockage or damage of sensor. It is recommended to use a temperature measurement reference instrument which is of higher accuracy than the device to be checked. The temperature and relative humidity sensor should be placed on the hottest spot, concluded after temperature mapping of the area.

2.4. Locations where environmental or temperature excursions may take place

To manage the temperature excursion related issue, it is important to know the places where temperature excursion can occur. The deviations can be observed against the temperature limits not only at manufacturing sites or during transportation or distribution rather, it can be caused at the end of business i.e., retail outlets and drug shops (see Fig. 2 ).

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Locations wherein excursions can occur.

2.5. Reason of temperature excursion

In pharmaceutical manufacturing plant and storage area, the specified temperature is maintained with help of air handling units (AHU). The design and capacity of AHU are selected on the basis of volume of work area or manufacturing cubical.

2.5.1. Temperature excursions in manufacturing area caused due to following reasons (not limited to)

  • a. Inadequate number of air handling unit (AHU) installed which are actually required to maintain the desired temperature conditions inside manufacturing shop floor.
  • b. Leakage or rupture from air duct, resulting in insufficient cooling effect.
  • c. Mechanical failure in air handling units (AHU), such as breakdown.
  • d. Power failure making the AHU operation defunct.
  • e. Lack of quality system and weak discipline of Good Manufacturing Practices (GMP) on production shopfloor.
  • f. General awareness about the consequences of not maintaining temperature within limit and undue keenness to prevent power consumption.
  • g. Extreme weather changes and obsolete contingency plan to handle unprecedented temperature fluctuations.

2.5.2. During transportation of pharmaceutical product, following reasons (not limited to) cause temperature excursion

  • a. Unexpected delay in transportation due to which the temperature control cannot be maintained effectively.
  • b. Product pallets are kept in hot zones of airport or shipping yards.
  • c. Reefer containers or refrigerated control vans are not deployed for transportation.
  • d. The transport agency fails to maintain the planned transport condition.
  • e. Higher cost to maintain temperature within limits and other business issues.
  • f. Power failure due to short longevity of power bank during longer travel time.
  • g. Good Distribution Practices (GDP) understanding amongst supply chain personnel about the adverse impact on product quality.

2.6. Consequences of temperature excursion

The storage condition for product is assigned on the basis of scientific studies to avoid deterioration during product life cycle. If the temperature excursion is not taken due care the following negative impacts are commonly noticed :

  • a. Loss of assay.
  • b. Increase of impurity.
  • c. Separation of layers of liquid products.
  • d. Change in dissolution pattern of solid dosage.
  • e. Discolouration of products.

Achieving the stability by design for solid dosage pharmaceutical products shall require establishment of limits for storage temperature (T) and relative humidity (RH). Within these prescribed temperature (T) and relative humidity (RH) limit, the kinetics of degradation of a pharmaceutical product can be estimated using and extended Arrhenius model ( Porter, 2013 ).

An excursion can have a significant impact on quality of products, which can be investigated as a part of therapeutic drug properties. As a consequence of excursion, the following impact can be observed (see Fig. 3 ).

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Impact on quality and therapeutic drug properties due to temperature.

2.7. Review of sensitivity of drug products towards temperature excursions

To control temperature excursions is often complicated because there is no way to predict the condition that the product will be exposed to. The management of temperature excursion becomes more important particularly for the drug products, which are sensitive towards temperature conditions. Due care against potential excursions is mandatory during the course of drug manufacturing as well as during distribution process.

2.7.1. Product sensitive towards higher temperature

Drug product sensitive to higher temperature excursions can depreciate the intended action by receiving the thermal energy. This may instigate,

  • • Transformation of degraded ingredients because of oxidation, hydrolysis, decomposition, polymerization. As longer as the time of exposure of product to unspecified temperature, higher would be the impact on quality.
  • • Modification of the drug dissolution pattern (either higher or lower) in case of solid dosage drug product.
  • • Separation of emulsions.

2.7.2. Product sensitive towards lower temperature

The adverse impact on product quality is not observed only due to exposure to higher temperature. Drug product sensitive to lower temperature can also depreciate the intended action by losing their therapeutic characters. This may cause,

  • • Lattice positions dislocation due to extremely lower temperature.
  • • Change in property of biological products.

2.7.3. Cold chain products

A deficiency in monitoring and maintenance system usually affects the products’ therapeutic properties and causes quality risks such as lack of effect, intoxications. In case of cold chain products the challenge of temperature excursions is bigger, because there is a task to preserve the adequate storage and temperature conditions throughout the product life cycle. Quality Assurance personnel must make sure that conditions of storage are observed at any time during manufacturing, transport and distribution.

2.7.4. In case of lyophilized drug products

Lyophilization process is commonly used for pharmaceuticals/biopharmaceuticals to improve the stability and shelf life. A disruption of the initial freezing rate due to temperature excursion can potentially lead to incomplete crystallization of crystalline excipients or heterogeneous moisture distribution in the lyophilized products. Temperature excursion during drying can lead to collapse or there could be melt back related negative impact on product quality.

3. Result – solution strategy and model for temperature excursion management

3.1. solution strategy against excursions.

In view of significant impact on product quality due to environmental excursions, the pharmaceutical industry should establish a documented programme for ‘Temperature Excursion Management’. As a part of this programme, the storage condition database and standard operating procedure (SOP) are required. The details of strategy are as under.

3.1.1. Development of storage condition database

To handle the environment excursion, the strategic planning, effective packaging and well documented procedure are recommended. Development of storage database of pharmaceutical product is useful to assign the standard storage condition.

The storage conditions suitable for product are assigned through the following information:

  • a. At the product development stage, the semi finished product and final pharmaceutical product dosage are subjected to challenging conditions (i.e., forced conditions) to observe the potential impact on quality attributes.
  • b. Hold time studies are carried out to establish the database for allowable time period at specified storage condition without impacting quality. The quality attributes of product intermediates include chemical, microbiological and pharmacological determinants at various time points of stability.
  • c. Studies of the long term (i.e., real time) and accelerated condition stability study data of each product formula form an assurance for the storage condition that shall be suitable to product safety. The real time stability data are generated in laboratory to assess the change in quality attributes of product throughout the expiration date. The accelerated stability data are generated to evaluate the impact on quality of product under slightly stressed environmental condition.

A freeze/thaw study for multiple cycles should be conducted to specify the effect of freezing, if any, and the subsequent thawing. Samples from different layers (top, middle and bottom) of container are drawn for analysis at the end of the cycle (see Fig. 4 ).

  • a. Know your formula.
  • b. Carryout Quality Risk Analysis.
  • c. Establish Stability Study Protocol.
  • d. Evaluate Stability Study Data.
  • e. Improve Product Formula.
  • f. Redefine Product Formula.

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Temperature Management and Control Cycle.

3.1.2. Thermal packing during transportations

Packaging configuration should include the provision of thermal packing and display of appropriate storage condition. Caution notes to avoid storage outside the specified conditions shall help supply chain personnel to protect the product quality.

The storage condition should be effectively displayed on the packaging of pharmaceutical product. The packaging configuration card must contain the details of data loggers.

3.1.3. Ten step procedure to deal with temperature excursions

The temperature excursion has regulatory implications as well as business impact due to impact on quality.

A standard operating procedure (SOP) compromising of following steps should be established and adherence should be ensured through adequate training to concerned personnel:

  • i. The provision of technical agreement should be laid down in SOP. Technical agreement between manufacturer and distributor should clearly assign the responsibility of notifying the details of environmental excursions during transit, storage and distribution process. A contract is a written agreement between two or more interested parties which creates obligations that are enforceable by law. Before services are provided by a vendor, a contract must be put in place and executed by the parties.
  • – Active Pharmaceutical Ingredient (API) supplier – Pharmaceutical product manufacturer.
  • – Active Pharmaceutical Ingredient (API) supplier – transporter, cargo.
  • – Pharmaceutical product manufacturer – transporter, cargo, etc.
  • ii. Product characteristics database should be accessible to all concerned personnel for ready reference. Such database should clearly mention the storage instruction and precautions prominently highlighted on packs of product. Ideally larger display of storage condition on shipment pack shall alert the distribution agencies to avoid potential excursions.
  • (a) manufacturing facility with special attention on environmental controls,
  • (b) transport media (motor van, shipping containers, air cargo, etc.),
  • (c) replenishment and handling devices,
  • (d) storage warehouse in loaded conditions,
  • (e) distribution centres, and
  • (f) drug seller’s outlet and pharmacy.
  • iv. Standard Operating Procedure (SOP) should be in place for each critical steps of manufacturing and distribution that may have trigger temperature excursions. Adequate training (in the form of personalized, awareness or document read) should be imparted to all operation executives. The key personnel across the pharmaceutical business should be aware about the SOP on investigation, handling and managing the temperature excursions.
  • v. Records related to temperature excursions and duration should be regularly reviewed and approved to ascertain whether an excursion may have occurred and a systematic investigation should be performed. This should be formally signed off physically or electronically.
  • vi. Investigation report against temperature excursions and duration should be notified immediately to the responsible person (as per the technical agreement) or manufacturer in a timely fashion.
  • vii. Product quality risk analysis should be carried out against each case of temperature excursions. As a part of risk evaluation, the specimen complaint sample and control sample (retained by manufacturer) may be simultaneously analysed by using the validated analytical method. Due focus should be there to estimate the loss of assay as well as increase in impurity in sample impacted due to temperature excursion.
  • viii. The stability data should be available with manufacturer against each excursion to evaluate and justify that there is no impact on product quality due to the excursions. The stability study under accelerated condition and freeze–thaw study are the relevant to evaluate the impact on product quality in a scientific manner.
  • An accelerated stability study programme in line with ICH:Q1 guidelines should be carried out.
  • A typical freeze–thaw study comprises of estimating the quality impact due to storage of product at extremely low and high temperatures, such as −20 and +50 °C for a duration up to 12 days in multiple cycles depending upon the proposed route, time and length of travel ( Adadevoh, 2002 , GCC Guidelines, 2007 ).
  • ix. The statistics of temperature excursion cases should be evaluated periodically by quality professionals and in case of recurring observation of excursions are noticed from a particular facility, that particular mode or facility should be subjected to requalification.
  • x. Appropriate corrective actions should be taken to avoid the recurrence of temperature excursions, if any. Modify the storage and transportation conditions on the basis of quality risk management programme.

4. Conclusion

Temperature excursion is a general term that represents the environmental excursions. There is a need of holistic approach to handle the temperature excursions starting from raw material manufacturing site to medicine retailers shop to protect quality of product. The temperature excursion at any stage of pharmaceutical business operation must be reported as soon as possible and investigated appropriately. The consequences of deviation against temperature and humidity limits should be studied appropriately by quality assurance personnel. The risk of temperature excursions cannot be ruled out, but it can be minimized through effective system. Alternative is to use thermal resistant packaging and stringent control measures during transit and shipment, to avert the undesired quality impact on pharmaceutical product. The systematic approach to handle the issues related temperature excursions becomes inevitable for pharmaceutical manufacturers.

Peer review under responsibility of King Saud University.

  • Adadevoh, K., 2002. Short-term, Freeze Thaw and Shipping Studies. ARMWG RFP11 Report.
  • The GCC Guidelines for Stability Testing of Drug Substances and Pharmaceutical Products, 2007, Edition Two (1428 H – 2007G).
  • Porter William R. Degradation of pharmaceutical solids accelerated by changes in both relative humidity and temperature and combined storage temperature and storage relative humidity (T × h) design space for solid products. J. Valid. Technol. 2013; 19 (2) [ Google Scholar ]
  • Roy Souvik, Ruitberg Chiristian, Sethuraman Ananth. Troubleshooting during the manufacturing of lyophilized drug product – being prepared for the unexpected. Am. Pharm. Rev. 2012 [ Google Scholar ]
  • Scrivens G. Mean kinetic relative humidity: A new concept for assessing the impact of variable relative humidity on pharmaceuticals. Pharm. Technol. 2012; 36 (11) [ Google Scholar ]
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Documenting Temperature Excursions

It is the responsibility of every pharmaceutical manufacturer to guarantee every medicine they deliver to a patient is of the highest quality. Maintaining the quality of a pharmaceutical product requires adhering to strict precautions, including storing or transporting the medication at specified temperatures.

If a shipment is complete, a decision needs to be made to release or quarantine the product. The following data must be available:

Complete measurement record from a calibrated sensor

Start time stamp (clear date and time)

Stop time stamp (clear date and time)

Stability budget (assessment criteria) with clear conditions of temperature zones/limits and allowed times

Sometimes additional criteria are defined, like number of allowed excursions or number of freeze-thaw cycles. As soon as all the data is available, the assessment can be performed a clear OK (= release) or ALARM (=quarantine) decision can be made. Information between stakeholders usually takes place via email or SMS.

A temperature excursion could result in patients ending up with unsafe products that may cause adverse reactions. Therefore, every pharmaceutical manufacturer must ensure all products are kept in the specified conditions until they reach the patient.

So, what are the right tools to use to guarantee that patients are getting the right quality and, most importantly, safe medicine?

Temperature Excursion Allowance: A GxP Compliant Solution

By programming stability information into data loggers, you can prevent products from being discarded prematurely.

What Are Temperature Excursions and Why Do They Even Matter?

Under the World Health Organization (WHO) Model Guidance, temperature excursion is “an excursion event in which a Time Temperature Sensitive Pharmaceutical Product (TTSPP) is exposed to temperatures outside the range(s) prescribed for storage and/or transport. Temperature ranges for storage and transport may be the same or different, as they are based on individual product stability data.”

Like many products, some medicinal products are sensitive to temperature and need to be stored and/or transported within a limited temperature range until expiration. However, unlike a flower that has lost the vibrancy and aromas, it isn't easy to tell when pharmaceutical products have lost their quality or efficacy.

Whenever temperature-sensitive pharmaceutical products are exposed to temperatures above or below the specified range, they experience temperature excursions. These excursions can affect their potency or result in an adverse reaction to the patient’s health. A temperature excursion may occur at the manufacturing site, in transit, or at the repository.

The Role of Regulators

Both the European and United States Pharmacopeia outline that pharmaceutical manufacturers are accountable for a product’s quality until it reaches the patient. While temperature conditions at manufacturing and packaging sites are usually under strict control (GMP regulation), this control is likely to decrease as soon as the product is dispatched for transportation.

As such, manufacturers should guarantee the safety, efficacy, and quality of the product until its final use. When temperature excursions occur, they have to take the required measures.

5 Examples of False Excursions and How to Correct Them 

Temperature alarms may not be a final result. Sometimes there are false/positive excursions that can be corrected by a cold chain database. From the experience of millions of pharma shipments analyzed in our database, we know that less in than 10 percent of all cases, a true temperature alert (excursion outside defined shipping conditions) is found.

Typically, half of the temperature excursion cases are caused by a late stop or other mistakes that happen at destination – which could be considered false excursions.

The following are examples of common causes of false alarms and their interventions:

Early start, sensor measures too early (before product has been loaded or conditioned)

Intervention:   Cold chain database can reassess the data using the correct time stamps.

No temperature values available due to sensor failure (sensor not started or no sensor added)

Intervention:   If the shipment contains more than one device, it might be possible to use this data to release the entire shipment.

Minor temperature excursion during shipment

Intervention:   Cold chain database can reassess the data using the stability data of the product.

Wrong sensor setting triggers a temperature alarm

Intervention:   Cold chain database can reassess the data using the correct stability data.

Late stop at destination, sensor continues to measure (at room temperature)

In every case, it is important to have a two-level process in place whereby both logistics and quality review excursions before the product is released.

How to Document Temperature Excursions 

Manufacturers that wish to follow GDP and GMP regulations are required to have procedures in place to document, investigate, and handle temperature excursions. The approach below outlines best practices for the documentation of temperature excursions at the manufacturing site, during transportation, or in warehouse storage.

Collect the following information:

  • Details of the person completing the report
  • Date and time of the temperature excursion
  • Inventory of affected products
  • Storage unit temperature (including minimum/maximum temperatures during the time of the event, if available)
  • Room temperature (if available)
  • General description of the event (i.e., what happened) • The length of the exposure if using a digital data logger (DDL)
  • List of other items in the unit
  • Any problems with the storage unit or affected products before the event
  • Other relevant information

The distributor and recipient of the affected product(s) should receive a notification should a temperature excursion occur.

7 Components of a Cost-Saving CAPAs for Temperature Excursions

In case of a temperature excursion, GMP and GDP regulations require a Corrective Action & Preventive Action (CAPA). It is a structured process, which investigates and identifies root causes of problems and defines corrective action to prevent recurrences.

To create a useful CAPA, ask these questions:

Who found the temperature excursion, when, and where?

What is the scope of the case (shipment number, delivery, handling unit, pallet, product, batch)?

What is the severity of the excursion?

What was the label/transport condition?

What was the highest (or lowest) temperature measure?

What was the number of excursion hours (can the product still be released based on stability budget)?

What was the root cause of the excursion?

What are corrective actions to eliminate this specific problem?

Have similar cases happened before? Are there patterns in the data?

Can we define preventative actions to make sure similar root causes are eliminated?

For a reliable CAPA process in cold chain management, a database is necessary where all data is available in a structured and well-documented way.

Practical Guide: Cold Chain Logistics Choices

Learn how the choice of transportation mode & equipment affect products, & how to best handle temperature excursions & subsequent CAPAs.

Related Articles

Stability budget explained, making the right cold chain logistics and packaging choices ..., get everything under control with the right software, let's talk about temperature monitoring .

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June 21, 2017

6 Steps for Handling Temperature Excursions

Written by SmartSense | Pharmacy Safety

What is a temperature excursion? In the pharmaceutical industry , it refers to any temperature reading outside recommended ranges from the manufacturer’s package insert. Why is it important? Because out-of-range storage temperatures or inappropriate conditions for any vaccine can negatively impact the efficacy, and will require immediate action.

Fortunately, the Centers for Disease Control (CDC) have developed best practices to handle this emergency situation. If there is any question about whether vaccines may have been exposed to a temperature excursion due to the unit becoming too cold or too hot, take the following steps.

Step 1: Notify Supervisors

Any staff member who hears an alarm, receives an alert message, or notices a temperature excursion should notify the vaccine coordinator immediately or report the problem to a supervisor.

Step 2: Quarantine Vaccines

If a vaccine has been compromised, quarantine it immediately. Label exposed vaccines, “DO NOT USE,” and place them separately from other vaccines in the storage unit. Do NOT discard the compromised vaccines.

Step 3: Document the Event

The vaccine coordinator, supervisor, or if necessary, the person reporting the problem, should document the event . Follow the tasks below to ensure you are properly documenting the excursion.

  • Name of the person completing the report
  • Date and time of the temperature excursion 

  • Inventory of affected vaccines
  • Description of the event
  • Minimum and maximum storage unit temperature and room temperature during the time of the event
  • Length of time vaccine may have been affected 

  • Listing of items in the unit (including water bottles) other than vaccines 

  • Any problems with the storage unit and/or affected vaccines before the event 

  • Other relevant information

patient safety brochure

Step 4: Get Guidance  

Contact your immunization program or vaccine manufacturer for additional guidance on whether to use affected vaccines and for information about whether patients will need to be recalled for re-vaccinations. Be prepared to provide documentation of the event (e.g., temperature log data). 


Step 5: Implement SOPs

Implement your facility’s Standard Operating Procedures (SOPs) to adjust the unit temperature to the appropriate range. At a minimum, check the temperature monitoring device to make sure it is appropriately placed in the center of the fridge.

Step 6: Wrap Up

Complete your documentation of the event, including the following:

  • What happened to affected vaccines
  • What you did with the vaccine and when
  • Whom you have contacted and instructions received
  • What you have done to prevent a similar future event

You never know when a temperature excursion may happen. If you have not yet incorporated these best practices into your monitoring program, now is a good time to start!

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Allowable room temperature excursions for refrigerated medications: A 20-year review

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Lucas E Orth, Amanda S Ellingson, Sara F Azimi, Joseph T Martinez, Amal A Alhadad, Brenda C Tran, Chase W Allen, Cecilia T Nguyen, Tony Duong, Jordan S Burkdoll, Jenny Yoo, Allison B Blackmer, Meghan N Jeffres, Allowable room temperature excursions for refrigerated medications: A 20-year review, American Journal of Health-System Pharmacy , Volume 79, Issue 15, 1 August 2022, Pages 1296–1300, https://doi.org/10.1093/ajhp/zxac118

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The aim of this review was to build upon previous literature describing the maximum duration for which refrigerated medications can tolerate room temperature excursions while maintaining stability and potency.

During a 12-month period ending in June 2021, the prescribing information and published monographs from multiple pharmacy compendia were reviewed for all medications and biologic products approved by the US Food and Drug Administration (FDA) for human use since January 2000. Products that were subsequently withdrawn from the US market were excluded. When temperature excursion data was unavailable in published form, product manufacturers were surveyed via telephone and/or email. Acceptable storage information for all products for which storage is recommended at temperatures below room temperature (20-25 °C [68-77 °F]) was compiled and arranged in tabular format.

Of the 705 products or formulations approved by FDA during the predefined time period, 246 were identified as requiring storage at temperatures below room temperature. After review of available prescribing information and manufacturer communications, if applicable, acceptable periods of excursion to temperatures at room temperature or higher were identified for 214 products (87%).

Information related to acceptable periods of room temperature excursion was compiled for a total of 214 products approved for US distribution since 2000. The included tables may increase patient safety and decrease medication loss or related expenditures.

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data excursion definition

Data-driven supply chains: improving your excursion rate and temperature performance in 5 steps

data excursion definition

Data-driven supply chains

Good data helps tell the stories that we often struggle to see. For example, one of the main issues faced by supply chain, logistics and temperature management professionals is the number of excursions that occur when products arrive at their destination. Having more visibility and automating more of the manual processes that characterize activities at these warehouses or sites can not only increase site efficiency but reduce the number of costly quarantines too.  

But where is the best place to start? Our colleague supply chain data expert, Marina Mladenovic, works with supply chain managers on using their data to drive improvements in their logistical processes.  

Here are Marina’s five key steps:

Cleaning your data

The amount of data that companies are able to collect about their processes, internal resources and end users has never been greater. But without structure, this growing pool of raw data is not much use. And with a growing number of devices, data streams and users feeding into them, these pools of data are only going to get bigger.  

A critical first step in increasing data efficiency is making sure that your data-collection processes are set up in a way that is going to help you achieve your goals. Often, this means cleaning up your databases and internal systems.  

“I always recommend that clients take a look at the data they have before trying to collect more,” says Marina. “Often there are a huge number of duplications, inactive user accounts, outdated or incorrect data that are actively hampering the ability of these teams to enable effective data analytics.”  

Automation is reducing the need for manual data input too. This not only reduces the amount of time needed to update and maintain datasets, but can also make its simpler to provide decision makers with a continuous stream of high-quality supply chain data.  

Understanding your data

Implementing automation into the data-collection process can mean that information on regular shipments and freight forwarders can be added to the database. This makes comparing the performance of individual shipments and identifying areas that could be improved much simpler. This is one example of starting to use structured data to understand what is happening in the real world.  

When your data is easily accessible and organised, you can begin to drill down to see specific locations or shipping partners. For shipments with multiple routes and temperature limitations, this removes the guesswork if an excursion does occur and allows you to move to address issues much faster.  

“Companies need to specify their targets and what exactly they need to know in order to achieve them,” says Marina. “A common problem for supply chains, and temperature management in particular, is that people often don’t have a clear view of what the issues actually are. Without clear visibility, it’s much harder to create effective strategies for reducing excursions going forward.”  

Developing data-driven strategies

Sites experience a range of different challenges and issues when it comes to temperature management. While there are some common best practice steps all sites can take, there is no one-size-fits-all approach that is going to work. This is where having accurate data really starts to add value. For example, in really hot locations, simply leaving product storage out in the sun while uploading for a few minutes can trigger an alarm. And while this excursion is unlikely to result in the need for a product recall or reshipment, it could necessitate a lengthy quarantine.  

“Having data on specific sites and shipment routes lets us create tailored solutions to specific problems,” says Marina. “We may need to work closely with site teams to deliver targeted training solutions based on the issues they are having.”  

Addressing weak points in your supply chain

At TSS, we help our clients with regular reporting on how their sites are performing. We then help them use this temperature monitoring data to address issues within their supply chains. One example of this is route and storage optimization. Often relatively small adjustments to the way that products are packaged during shipment, delivered to, and stored can have large-scale impacts on the number of excursions taking place.  

But enabling clients to utilize their data is also critical. “We develop tailored data analytics and business intelligence dashboards for our clients based on their needs,” says Marina. “Ultimately, working with clients to develop their internal capabilities is what is really going to drive down the excursion rates in their supply chains.”

Data connecting dots

Building continuous improvement into your operations

Once your data is actively helping you optimize the delivery, storage and distribution processes of your supply chain and sites, you should start seeing some benefits. It’s important not to lose momentum though. Embedding the idea of continuous data review and process improvement into your regular business decision making is what separates innovators and market leaders from the rest of the sector.  

“The dashboards we create for clients are important tools for enabling them to identify issues and implement solutions to address them,” says Marina. “When all of your data is locked up in complex spreadsheets and databases it can be hard to see changes as they occur. By pulling that data out and making it simple to understand, you don’t just remove manual work but you actively increase organizational agility.”

Innovations in temperature monitoring and IoT are helping pharma companies to see exactly what is happening at every stage of their supply chains. At TSS, we are working with the industry to help turn temperature data into actionable insight that reduces costs, site burden and waste and ultimately delivers better outcomes for patients.  

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Definition of excursion

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In Latin, the prefix ex- means "out of" and the verb currere means "to run." When the two are put together, they form the verb excurrere , literally "to run out" or "to extend." Excurrere gave rise not only to excursion but also to excurrent (an adjective for things having channels or currents that run outward) and excursus (meaning "an appendix or digression that contains further exposition of some point or topic"). Other words deriving from currere include corridor , curriculum , and among newer words, parkour .

Examples of excursion in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'excursion.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Latin excursion-, excursio , from excurrere

circa 1587, in the meaning defined at sense 1a

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Dictionary Entries Near excursion

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Cite this Entry

“Excursion.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/excursion. Accessed 26 Mar. 2024.

Kids Definition

Kids definition of excursion.

from Latin excursio, excursion- "a going out," from excurrere "to run out, make an excursion, extend," from ex- "out, forth" and currere "to run" — related to current

Medical Definition

Medical definition of excursion, more from merriam-webster on excursion.

Nglish: Translation of excursion for Spanish Speakers

Britannica English: Translation of excursion for Arabic Speakers

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How Ontology and Data Go Hand-in-Hand

data excursion definition

Data ontology is a way of linking data in various formats based on various concepts. In the early days of the internet, data were linked using HTTP protocols. Nowadays, one can add another layer, an ontology, to define a specific concept, and then automatically link data points that are pertinent to that concept.

If you’ve seen the word recently and thought that ontology is a new thing, rest assured that it’s more ancient than the oldest sweater you own. Aristotle , a Greek philosopher who lived in the fourth century B.C.E., called it the “first philosophy” in his work Metaphysics . 

Truth be told, it took a little while for this concept to get popular again. The German rationalist philosopher Christian Wolff eventually got ontology back into mainstream discussions of philosophy in the 18th century. Since then, philosophers have consistently debated the topic — as have patrons in bars, when the hour is late and the liquor is flowing. In this article, we ’ ll take the concept out of the philosophy seminar and the tavern to explore data ontology and how it works in practice.

What Is Ontology? How Does it Relate to Data?

At its core, ontology is the study of what is. The concept has recently begun to gain traction in the world of computer science through the concept of data ontology. 

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Ontology and Data

In the early 2000s, ontology migrated out of the realm of the philosophers when it struck a chord with computer scientists. Veterans like Tim Berners-Lee started advocating for what they called “linked data.” The idea is that data should not solely exist in the form of hypertext documents and hyperlinks between them. Rather, data should be viewed as what it represents — people, places, events, ideas, activities, and so on — and linked in a human-readable way.

This ontological view is quite an advanced way of thinking about data. Perhaps it’s not surprising, then, that the necessary tools to put these thoughts into practice weren’t available yet at that time. 

Now, however, the internet has matured. The tools are available. And ontology is experiencing a renaissance in computer science.

What Is Ontology?

Before we get into the details of how ontology works in the world of data, let’s have a look at what a philosopher would say about it. 

At its core, ontology is the study of what is. To make this a little more concrete, one could also say ontology is the study of what exists or of what is real. “Does God exist?,” “Are my feelings real?”, “What is ‘nothing,’ and does it exist?” are all examples of ontological questions. 

Those are questions you might be asking when it’s particularly late at night or when you’ve had an exceptionally tough day. But for philosophers and, increasingly, computer scientists, these are everyday questions thanks to ontology. 

Philosophers like to make assumptions in order to explore such questions further. For example, they might assume that God exists. Then they might ask something like, “What is the relationship of God to humans, animals, plants, the ocean and the sky?” The answers to these questions provide information not only about what exists (e.g., God and humans), but also about the relationship between these things (e.g., God gives kindness to humans). 

What Is Data Ontology?

See what I did in that sub-header? It’s an ontological question about ontology and data! 

Mind blown … But let’s get back to business.

The common ground between ontology in philosophy and in computer science is that it’s an attempt to describe everything that is, i.e., entities, ideas, and events, and all the relations between these things. 

For example, if you searched for “Paris” a decade ago, your favorite search engine spat out a list of links that seemed particularly relevant for your query. Their relevance was determined by the amount of times the word “Paris” was mentioned, the number of backlinks to these sites, and a bunch of other criteria that SEO experts can explain much better than I can.

Fast-forward to today: If you tap in “Paris” now, your search machine recognizes that it is a city — and knows what a city is — and will propose data points pertaining to cities, like demographics, districts, and so on. It might also propose train lines that bring you to Paris because trains are things that exist for ontologists, and because your relationship to Paris might be wanting to visit.

This is ontology in action.

More in Data Science What Is a Data Set?

How Ontologies Make Data Easier to Use

There are, of course, many other ways to organize your data. These include vocabularies, taxonomies, thesauri, topic maps, logical models and relational databases. These come with the added benefit that you don’t need to know anything about philosophy to understand them. 

What makes ontologies special is how flexible they are. If you wanted to change a property in a relational database from an integer to a floating-point number, you would have to delete the entire column for that property and recreate it using the new property. In the worst case, you’d have to recreate your whole data set because you can’t always add new columns in relational databases . It’s a mess!

With an ontology, however, changing a property is as easy as changing the semantic concept that underpins that property. That might sound complicated, but in practice it’s as easy as redefining the column that holds this property. The original data set doesn’t get lost, nor do any links or indices that deal with it. 

Data Ontology Example

To give you a concrete example, let’s say you have a data set of contracts . If you knew nothing about ontology, you might put all data points about your contracts in a table. This table might contain columns like “Contract owner,” “Coverage,” and “Confidentiality.” The problem is that, if you want to change one of these columns or add a new column later on, you’d have to recreate the whole table to make sure that all entries are in the right format. Also, some contracts might need certain columns while others do not. But filling all the columns for all contracts is a huge time-waster.

With an ontology about contracts, on the other hand, you might have classes like “Business contract” or “Tenancy contract,” which each have properties of their own. Think of it more like a tree diagram than a rigid table. If you want to add a new property, it’s as easy as adding a branch in the appropriate place. You might want to add “Student tenancy agreement” as a branch under “Tenancy contract.” It doesn’t make sense, though, to add this as a whole column to all sorts of contracts because something like a business NDA has absolutely nothing to do with a student tenancy agreement.

Ontologies are also extremely useful for machine learning . It’s tough even for a large language model to understand all of these things: that “Paris” is a city, and as such has certain properties, and that you’re not in this city but might want to be, and that it should, therefore, propose some appropriate train lines to you. All this information is directly fed to the machine learning model if you’re using an ontology. This way, the model can focus its capabilities on proposing the best train lines and tourist venues to you.

Ontology Modeling and the Semantic Web

Ontologies are one of the building blocks of the semantic web . This is basically a fancy word for expressing the wish that the web should be human-readable and work with linked data, rather than a scattered mess of https URLs pointing to one another. For example, if you search for “Paris,” you won’t just get a list of links to pages that mention the word “Paris” a lot, but you’ll get pertinent information about the city, its inhabitants, and ways to go there.

What Is the Semantic Web?

The semantic web reflects the idea that the web should be human-readable and work with linked data, rather than a scattered mess of https URLs pointing to one another. Data ontology is a key part of bringing this new, improved web to life.

With the semantic web, distributed and heterogeneous databases can interact with one another because they speak a common language. Distributed, in this context, means that the databases live on many different servers. Heterogeneous means that they might vary in terms of their architecture, data formats, and so on. What these databases have in common, though, is that they all know what a person or a city is, for example. So a database about the largest cities in the world can be matched with the database of the richest people in the world without having to deal with too many technical fiddles. And you’ll know which major city has the most rich people in the blink of an eye if that’s what you queried.

Put in more fancy technical terms, ontologies and the semantic web ensure interoperability, cross-database search and smooth knowledge management. Interoperability means that the databases are able to work with one another. Cross-database search means that you can search for results in several databases at once and infer logical conclusions from them. Finally, smooth knowledge management means that information is stored and used in ways that are straightforward and user-friendly. Think about how your search results and social media feeds might have changed in the last few years. The semantic web is well on its way!

Practical Applications of Ontology

We’ve talked about web search a bit, but the scope of ontology goes much further than that. In the pharmaceutical industry, AstraZeneca has used ontology to test its early hypotheses . They build a large data set following ontological principles (i.e., different things like proteins, genes, and diseases exist, and they have certain relationships to one another). This data set was accompanied by a user interface so that researchers at AstraZeneca could explore all things and their relationships before starting any drug development.

In another use case, health records were organized in an ontological way to help people make better food choices . And in a further case, financial data was used to uncover financial crime . 

What all these applications have in common is that they’re quite user-centric and based on real-world problems. The internet is becoming less about “Hey, why is this URL broken?” and more about “Hi internet, I have a problem, can you solve it for me?”

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The Age of Ontology

Ontology is sometimes marketed as the next big thing in data science . In reality, it’s an age-old discipline. The idea of using it for data is certainly disruptive; however, it’s already being deployed on data wherever you look.

The question you’re asking now should no longer be “What is ontology and why do I need it?” but rather “Why does my company not work with ontology yet?” 

You don’t have to pick up a philosophy book to answer that question. You might want to take a critical look at how your company is doing this today, however, and reflect on how ontology might improve its current business processes.

You shouldn’t be jumping on every buzzword and every fad in tech ( remember NFTs ?). You should, however, respect age-old principles when they revolutionize the way we tech. 

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Runway Excursions

What is a runway excursion.

A runway excursion ( RE ) is a veer off or overrun from the runway surface ( ICAO ). These surface events occur while an aircraft is taking off or landing, and involve many factors ranging from unstable approaches to the condition of the runway. It is important that all parties involved (Pilots, Air Traffic Controllers, Airport Authorities, etc.) work together to mitigate the hazards that result in an RE . The FAA Runway Safety program is committed to reducing RE risk through analysis, awareness, and action.

The following web sites provide useful information and help provide a foundation to understanding REs and the factors that contribute to them.

  • Skybrary - Runway Excursion
  • Flight Safety Foundation – Reducing the Risk of Runway Excursions

More From Forbes

What is true interoperability in data collaboration.

Forbes Technology Council

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Sadegh Riazi is the cofounder and CEO of Pyte .

As technologies continue to advance, organizations are managing increasingly complex tech stacks. According to a 2022 survey of IT and security professionals , organizations are now using an average of 130 SaaS apps, up from just eight in 2015.

In this environment of overlapping platforms and vendors, technical interoperability in data collaboration is more important than ever. The problem is that there is no standard definition of interoperability. It’s often perceived as binary: Either a tool is interoperable or it isn’t. But interoperability is actually a spectrum, and it’s important to recognize its nuances. The sophisticated tools we use today don’t fit into simple “yes” or “no” buckets.

Since there is not yet a clear definition of interoperability, my team and I are taking the first steps toward creating one. We want to open up the conversation and ask others in the industry to contribute to an evolving definition of the spectrum of interoperability.

Levels Of Interoperability

We propose a framework that recognizes three levels of interoperability:

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• Level 1: Vendors can install their software in any cloud environment (for example, Microsoft Azure, Amazon Web Services or Google Cloud Platform).

• Level 2: Vendors can facilitate data in and out of their software in any cloud environment or SaaS provider, such as data clean rooms.

• Level 3: Vendors can facilitate distributed data processing in any cloud or data cloud environment, without moving plaintext data in or out.

I believe there is value in achieving each level of interoperability. Level 1 is now becoming table stakes since the market share for each of the cloud providers is leveling out. It is also what many organizations believe interoperability is. They need their software to support a wide range of clouds. Some software companies and data clean room providers are also moving toward Level 2, making sure they have data connectors in place that can process incoming and outgoing data from different environments. But we think Level 3 is the holy grail of interoperability that every company should strive for. In Level 3, data computation happens in a distributed fashion, guaranteeing both full control and no plaintext data movement. This means that if you are working with Company A in their cloud environment and Company B in a completely different environment, you can use a truly interoperable software that allows them to do data collaboration, without ever moving plaintext data into another environment and giving up control. Level 3 would also allow Company A to use Clean Room 1 and Company B to use Clean Room 2, with both companies still able to process data in a distributed way.

Businesses have two goals that are often contradictory: maximizing revenue and profit while also maintaining data confidentiality. It’s a tricky balance to manage; you want to provide more access to your data and monetize it, but you also feel pressure to protect and control your data for regulatory, legal and brand reputation reasons. I believe Level 3 allows you to have the best of both worlds.

Understanding And Achieving Interoperability

Interoperability in data collaboration is often oversimplified and misunderstood. Here are three steps you can take now to understand its nuances and move your business toward more seamless and secure collaboration.

Move Beyond The Binary

Recognize that interoperability isn't a “yes” or “no” binary. It operates on a spectrum with distinct levels, each offering different capabilities and benefits. Learning how to differentiate between these levels is the first step toward making informed decisions about your data collaboration tools.

Ask The Right Questions

When evaluating vendors, don’t simply accept their claims that they are interoperable. Ask questions about where they are on the interoperability spectrum. Do their capabilities put them at Level 1, Level 2 or Level 3? What does that mean in practical terms?

Choose Wisely

Evaluate whether a vendor's interoperability aligns with your business needs and objectives. If there is a mismatch, you risk encountering problems such as integration challenges, increased costs and reduced efficiency.

When you have a clear understanding of interoperability, you are empowered to make well-informed buying decisions. Many vendors fall short of fulfilling the big promises they make about integration and interoperability—and once you sign a contract, you are left to build the custom integrations that will allow your vendors to work together. You can save significant costs by reducing the number of vendors you require for data collaboration and the amount of custom integration work you need to do on your vendors’ behalf.

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Sadegh Riazi

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  • Published: 09 January 2024

Recurrence of post-traumatic stress disorder: systematic review of definitions, prevalence and predictors

  • Samantha K Brooks 1 &
  • Neil Greenberg 1  

BMC Psychiatry volume  24 , Article number:  37 ( 2024 ) Cite this article

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Many people will experience a potentially traumatic event in their lifetime and a minority will go on to develop post-traumatic stress disorder (PTSD). A wealth of literature explores different trajectories of PTSD, focusing mostly on resilient, chronic, recovered and delayed-onset trajectories. Less is known about other potential trajectories such as recurring episodes of PTSD after initial recovery, and to date there has been no estimate of what percentage of those who initially recover from PTSD later go on to experience a recurrence. This systematic review aimed to synthesise existing literature to identify (i) how ‘recurrence’ of PTSD is defined in the literature; (ii) the prevalence of recurrent episodes of PTSD; and (iii) factors associated with recurrence.

A literature search of five electronic databases identified primary, quantitative studies relevant to the research aims. Reference lists of studies meeting pre-defined inclusion criteria were also hand-searched. Relevant data were extracted systematically from the included studies and results are reported narratively.

Searches identified 5,398 studies, and 35 were deemed relevant to the aims of the review. Results showed there is little consensus in the terminology or definitions used to refer to recurrence of PTSD. Because recurrence was defined and measured in different ways across the literature, and prevalence rates were reported in numerous different ways, it was not possible to perform meta-analysis to estimate the prevalence of recurrence. We also found no consistent evidence regarding predictors of PTSD recurrence.

A clear and consistent evidence-based definition of recurrence is urgently needed before the prevalence and predictors of recurrence can be truly understood.

Peer Review reports

Potentially traumatic events are common. Research suggests that over 70% of people will experience a potentially traumatic event (such as witnessing death or serious injury, automobile accident, life-threatening illness or injury, or violent encounter) in their lifetime [ 1 ]. Understandably, these events can be very distressing in the short-term and many people will experience acute post-traumatic symptoms in the immediate aftermath of a traumatic event, including intrusive symptoms (e.g. recurrent unwanted thoughts, nightmares); avoidance symptoms (e.g. emotional numbing, social withdrawing); hyperarousal (e.g. easily startled, feeling ‘on edge’); and physical symptoms (e.g. chest pain, dizziness) [ 2 ]. For the majority, these symptoms will decline naturally without intervention [ 3 ], typically within the first four weeks [ 2 ]. An important minority will find their symptoms persist for longer than a month. Those who continue to experience persistent re-experiencing of the traumatic event; avoidance of stimuli associated with the event; negative alterations in cognitions and mood and alterations in arousal and reactivity, causing clinical distress or functional impairment and not attributable to any other medical condition, are likely to be diagnosed with post-traumatic stress disorder (PTSD) [ 4 ]. Although only a minority of people who experience potentially traumatic events will go on to develop PTSD, it remains one of the most common mental disorders with lifetime prevalence estimated to be between 8% [ 5 ] and 12% [ 6 ]. PTSD is associated with reduced health-related quality of life and physical comorbidities, as well as major socio-economic costs [ 7 ].

The early 2000s saw a shift from studying PTSD itself as an outcome to studying change in symptoms as an outcome [ 8 ], with a wealth of studies using modelling approaches such as latent class growth analysis and latent growth mixture modelling to identify different trajectories of PTSD. Most of this literature identifies four trajectories, two of which are relatively stable trajectories ( chronic , a stable trajectory of post-traumatic stress symptoms, and resilient , a stable trajectory of healthy functioning after an adverse event), and two which display dynamic symptom patterns ( recovered , i.e. decreasing symptoms after an initial diagnosis of PTSD, and delayed-onset , i.e. increasing symptoms not meeting the diagnostic criteria for PTSD until potentially months or even years after traumatic exposure) [ 9 ]. Van de Schoot et al. [ 10 ] suggest that the two trajectories which typically occur less often (chronic and delayed-onset) are at risk of being overlooked by researchers or overwhelmed within the data by the larger trajectories. There may also be other less-researched or less-understood trajectories overlooked to an even greater extent. For example, one previous review [ 11 ] identified limited evidence of another, smaller trajectory referred to as a ‘relapsing’ or ‘recurring’ PTSD trajectory, in which individuals develop PTSD, are free from symptoms for long enough to be considered ‘recovered’, and then experience a recurrence of symptoms.

Recurrence is given relatively little attention in the PTSD literature, perhaps due to limitations of study methodologies and the complexities of studying recurrence. For example, Santiago et al. [ 11 ] note that few studies of PTSD follow participants for more than a year or with more than two assessments. Clearly, it would not be possible for researchers to identify recurrence of PTSD if data is only collected for two time-points: the only possible outcomes would be low symptom levels at each time-point (‘resilience’), high symptoms at each time-point (‘chronic’), or low level of symptoms at one time-point and a high level at the other (either ‘recovery’ or ‘delayed-onset’ depending on time-point at which symptoms were experienced). Additionally, studies which only follow up participants for a year or less are unlikely to clearly identify a recurrent trajectory of PTSD given the time needed to both recover and to experience a recurrent episode. The timing of PTSD assessment is also important: identification of PTSD recurrence relies on studies capturing the presence of symptoms during the recurrence, rather than before it occurs or after recurring symptoms have subsided. Therefore, it is perhaps unsurprising that the majority of the literature does not identify a ‘recurring’ trajectory of PTSD. Even studies which do identify recurrences often group these in with other trajectories: for example, Mota et al. [ 12 ] identified ‘recurrent’ cases of PTSD (individuals who had a lifetime diagnosis in 2002 and another post-2002 diagnosis reported in 2018), but grouped ‘persistent’ and ‘recurrent’ cases of PTSD together. Magruder et al. [ 13 ] identified a group of recurrent cases of PTSD – individuals who had lifetime PTSD pre-1992 but not a current diagnosis in 2002, who then had a diagnosis again in 2021, but these were grouped with ‘chronic’ cases. Karamustafalioglu et al. [ 14 ] simply include an ‘other’ group constituting both recurrent cases (individuals who met the criteria for PTSD diagnosis 1–3 months post-trauma and at the third follow-up 18–20 months post-trauma, but not at the second follow-up 6–10 months post-trauma) and others with delayed-onset PTSD which resolved. Boe et al. [ 15 ] identified a group of individuals with ‘reactivated’ PTSD who reported remission from PTSD in the first five years after the North Sea oil rig disaster of 1980 and a new episode at any point between 1985 and 2007. However, the authors suggest that there are blurred boundaries between delayed-onset and ‘reactivated’ PTSD, going on to include ‘possible delayed cases’ in their analysis of reactivated PTSD.

It is important to note that even the definitions of the more well-established trajectories of PTSD are not without their controversies. For example, Andrews et al. [ 16 ] point out the ambiguity in the criterion for delayed-onset PTSD, questioning whether ‘the onset of symptoms’ refers to any symptoms which might eventually lead to PTSD or only to full-blown PTSD itself. North et al. [ 17 ] comment on the ambiguities involved in the term remission (i.e. whether remission should be symptom-based or threshold-based) as well as the term onset (i.e. whether onset refers to first symptoms or first meeting diagnostic criteria). Definition of recovery also appears to differ from study to study, with some authors considering recovery to be symptom-based (i.e. no symptoms of the disorder remain) and others considering it to be threshold-based (i.e. some symptoms may remain, but they are beneath the diagnostic threshold) [ 18 ].

To date, several systematic reviews have been published which focus solely on only one PTSD trajectory. For example, previous reviews have focused on the delayed-onset trajectory [ 16 , 19 ]; the recovery trajectory [ 20 ]; and the resilient trajectory [ 21 ]. To date there has not been a literature review examining evidence of a recurrent trajectory of PTSD. Berge et al. [ 22 ] aimed to systematically review research on relapse in veterans but found no studies reporting actual rates of relapse or recurrence. Reviews have also explored the risk of relapse of various anxiety disorders, including PTSD, after discontinuation of antidepressants [ 23 ] and after cognitive behavioural therapy [ 24 ]. However, there have been no reviews attempting to quantify the risk of PTSD recurring, establish the predictors of recurrence, or quantify how much each predictive factor contributes to the risk of recurrence. The current review aimed to fill this gap in the literature by synthesising existing published data on how researchers define ‘recurrence’ of PTSD, recurrence rates of PTSD, and predictive factors of recurrence.

Having an appropriate understanding of recurrence is important as the concept needs to be properly understood in order to take steps to mitigate the risks of recurrent PTSD episodes. Mitigating the risk of PTSD recurring could benefit the health and wellbeing of trauma-exposed individuals and could reduce the socio-economic costs to the wider society [ 7 ]. The prevalence of recurrence is of particular importance to occupational medicine: regularly trauma-exposed organisations, for example, are often faced with decisions about when (and if) staff who have had and recovered from PTSD should return to the frontline duties. Understanding the risk of recurrent episodes may therefore have implications for those in charge of making such decisions. The present time is also a particularly relevant time to develop our understanding of recurrence of PTSD, as it is possible that the COVID-19 pandemic could contribute to recurrence. The pandemic has been declared a potential traumatic stressor, with research suggesting that COVID-19 survivors are at elevated risk of experiencing PTSD [ 25 ] and that PTSD symptoms may also develop due to quarantine [ 26 ], concerns about the health of loved ones, or economic loss as a result of the pandemic [ 27 ]. Hori et al. [ 28 ] suggest that the daily television updates regarding COVID-19 could trigger memories of surviving a previous traumatic situation, and exacerbate subthreshold PTSD symptoms. Therefore, experiencing the pandemic could potentially cause a recurrence of symptoms in people who have previously been diagnosed with PTSD.

The aim of this review was to collate literature which provides evidence of the lesser-studied ‘recurrent’ trajectory of PTSD and to identify: (i) the definitions of ‘recurrence’ used throughout the literature; (ii) prevalence of recurrence; and (iii) risk and protective factors for the recurrent trajectory of PTSD.

This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 29 ]. Our population of interest were people who had been diagnosed with, recovered from, and experienced a recurrence of PTSD (as diagnosed by a clinician or validated PTSD assessment tool). For the aim relating to prevalence of recurrent episodes, studies needed to involve a suitable design allowing prevalence to be assessed: for example, studies involving a population of people who had recovered from PTSD, followed over time to show how many had a recurrent episode and how many did not. For the other aims (i.e., definitions of recurrence and factors associated with recurrence), a comparison group was not necessary.

Registering the review

A protocol for the current review was developed and registered with PROSPERO on March 9th 2023 (registration number CRD42023405752). The only deviation from the protocol was the addition of another quality appraisal tool, due to finding a study design (retrospective analysis of existing health data) which we had not anticipated.

Eligibility criteria

To be included in the review, studies needed to (1) be published in peer-reviewed journals, (2) be published in the English language, (3) use quantitative methodology, (4) use a standardised tool to assess PTSD and (5) present data on recurrence rates of PTSD and/or factors associated with PTSD recurrence. There were no limitations relating to publication date or location of the studies. Case studies were excluded but there were no other exclusion criteria relating to population size.

Data searching and screening

A systematic literature search was carried out to examine definitions, prevalence rates and predictors of PTSD recurrence. Four electronic databases (Embase, PsycInfo, Medline and Web of Science) were searched on 24th November 2022, using a combination of search terms relating to PTSD, recurrence, and prevalence/predictors which were combined using Boolean operators. The full list of search terms is presented in Appendix 1 . The US Department of Veterans Affairs National Center for Post-Traumatic Stress Disorder’s PTSDPubs database (formerly PILOTS) was searched separately on the same date using the individual terms ‘recurrence’ and ‘recurrent’ and limited to peer-reviewed articles. Reference lists of articles deemed to meet the inclusion criteria were also hand-searched.

All citations resulting from the literature searches were downloaded to an EndNote library where duplicates were removed. The titles of all citations were then screened for relevance to the review, with any clearly not relevant being excluded. Abstracts were then screened for eligibility and the full texts of all remaining citations after abstract screening were located and read in their entirety to identify studies meeting all inclusion criteria. The literature searches and screening were carried out by the first author. The two authors met regularly throughout the screening process to discuss any uncertainties about inclusion or exclusion until a decision was reached.

Data extraction

The first author carried out data extraction of all citations deemed to meet the inclusion criteria. Data were extracted to a Microsoft Excel spreadsheet with the following headings: authors, year of publication, country, study design, sampling method, inclusion/exclusion criteria, study population size, socio-demographic characteristics of participants, type of trauma exposure, time-points at which PTSD was assessed, tools for assessing PTSD, definitions of recovery and recurrence, whether any PTSD treatment was received, prevalence rates of recurrence, and factors examined as potential predictors of recurrence.

Data synthesis

For the first aim of the review (relating to definitions of recurrence), we designed a table to present data relating to how ‘recurrence’ was understood and defined in each study. The tools used to diagnose and measure PTSD symptoms in the first place are important in understanding how PTSD is defined, so first the assessment tools used in each study were extracted into the table. Given that we wanted to understand the length of time an individual needs to be free of PTSD in order to be considered ‘recovered’, for each study we also included the time-points of PTSD assessment in the table. Next, we included the definitions of recovery and recurrence from each study, explained narratively in the table. We also added information to this table to report whether participants had received PTSD treatment during each study, as some studies focusing on interventions used ‘response to treatment’ in their definitions of recovery. We compared the different definitions used within the studies to establish whether there was consensus within the literature around (i) whether recovery and recurrence are symptom-based or threshold-based and (ii) how long the recovery period between initial diagnosis and recurrent episodes needs to be in order to be considered recurrent rather than chronic PTSD.

The second aim related to prevalence of PTSD recurrence. Due to the various research designs and definitions of ‘recurrence’ in the literature, as well as the different ways in which prevalence was reported, meta-analytic techniques could not be used. Rather, we presented the prevalence data as it was reported in each study. This sometimes meant presenting the prevalence of PTSD recurrence within an entire trauma-exposed population, including those who never experienced PTSD at any time. Other times, this meant presenting the prevalence of PTSD within a population who all had PTSD at one time-point, and other times this meant presenting the prevalence of PTSD within a group who had recovered from PTSD.

Finally, in order to explore factors associated with PTSD recurrence, all variables considered as potential covariates were recorded individually for each study. Each potential predictive factor was descriptively reported in a table, and any found to be significantly associated with experiencing PTSD recurrence were bolded to differentiate between non-significant and significant findings. Factors are also described narratively within the results section. Insights from thematic analysis [ 30 ] were used to group similar data together. For example, data relating to gender or age as predictors of recurrence were coded ‘socio-demographic’ and discussed together within the results.

Quality appraisal

We appraised the quality of studies using National Institutes of Health (NIH) tools: either the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies or the Quality Assessment of Controlled Intervention Studies tool, depending on study design. Concurrent with other reviews [e.g. 31 ] we rated quality as ‘poor’ if studies scored 0–4/14, ‘fair’ if they scored 5–10/14 and ‘good’ if they scored 11–14/14. One study used retrospective analysis of existing health data, and for this study we used the MetaQAT Critical Appraisal Tool [ 32 ]. To keep the ratings consistent with our rating system for the studies appraised by NIH tools, we defined ‘poor’ quality as a score of 0–34%, ‘fair’ quality as a score of 35–72% and ‘good’ quality as a score of 78% or higher.

Literature searches yielded 5,398 citations of which 1,083 were duplicates. After title and abstract screening, 4,210 citations were excluded leaving 105 citations for full-text screening. After reading full texts of the remaining citations, 75 were excluded and an additional five studies were added after hand-searching reference lists. A total of 35 citations were included in the review [ 15 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 ]. Figure  1 illustrates the screening process in a PRISMA flow diagram.

figure 1

PRISMA flow diagram of screening process

Table  1 provides an overview of key characteristics of all included studies. Studies originated from the United States of America (n = 13), Denmark (n = 5), Israel (n = 4), China (n = 4), Norway (n = 2), the United Kingdom (n = 2), Japan (n = 1), the Netherlands (n = 1), Switzerland (n = 1), and Turkey (n = 1). The remaining study included participants in multiple different countries across Europe and Asia. Study populations ranged from 35 to 7,918 and included military personnel (n = 15), civilian adults (n = 14), children or adolescents (n = 4) or a combination of military and civilian adults (n = 2). Only three studies were rated as ‘good’ quality; the majority were rated ‘fair’.

Definitions of recurrence

Table  2 reports, for each study, the tools used to assess PTSD; time-points at which PTSD was assessed; definitions of recovery and recurrence; and whether the participants received PTSD treatment or not.

Terminology

The first aim of the review was to explore how ‘recurrence’ is defined in the literature. We found no consensus in terms of how this is defined. In fact, the studies used a variety of different terms to describe the emergence of new PTSD episodes after initial ‘recovery’, including ‘recurrence’ [ 33 , 37 , 44 , 47 , 64 , 65 ]; ‘relapse’ [ 35 , 36 , 40 , 49 , 50 , 52 , 53 , 57 ]; ‘reactivation’ [ 15 , 60 , 62 ]; ‘exacerbation/reactivation’ [ 61 ]; ‘relieved-worsening PTSD’ [ 34 , 48 , 51 , 63 ]; ‘response-remit’ trajectory [ 54 ]; ‘fluctuating course’ [ 58 ]; ‘intermittent cases’ [ 43 ]; ‘delayed increase in symptoms’ [ 46 ]; and the ‘relapsing/remitting’ trajectory [ 42 , 55 ]. Many others simply described recurrence as ‘symptom increase’ [ 38 ], ‘initial declines followed by symptom increases’ [ 56 ] or ‘exacerbation of symptoms’ [ 41 , 60 ]. Some studies did not name the trajectory at all; rather, they presented tables or flow charts showing the number of participants with PTSD at each time-point, from which it was possible for us to identify a sub-group of participants who were described as having PTSD at one time-point, not having it at least one follow-up, and then having it again at subsequent time-points [ 39 , 59 ]. Similarly, Hansen et al. [ 45 ] identified and commented on a sub-group of participants who met the criteria for PTSD, did not meet the criteria at a subsequent time-point, and then met the criteria again later, but they did not give this a name.

Criteria for recurrence

Several studies defined recurrence (or equivalent terminology such as relapse) as meeting diagnostic criteria for PTSD at a follow-up time-point after an initial ‘recovery’ period where they did not meet the cut-off for PTSD [ 33 , 35 , 37 , 39 , 43 , 45 , 46 , 58 , 65 ]. Holliday et al. [ 47 ] referred to ‘clinically meaningful change in PTSD symptoms’, which was also assumed to refer to clinical cut-off scores. Markowitz et al. [ 52 ] based the definition of relapse on similarity to baseline scores. Sungur and Kaya [ 64 ] defined recovery and recurrence as being asymptomatic and then symptomatic again, but it is not clear whether this referred to clinical cut-offs. One study defined ‘reactivation’ of PTSD as meeting full diagnostic criteria or being a sub-syndromal case [ 15 ]. Others were more vague and did not mention cut-offs, instead referring to dramatic or steep symptom increases [ 34 , 38 , 56 , 63 ], fluctuating symptoms [ 42 , 55 ], returning to pre-treatment levels of PTSD [ 54 ], symptoms which ‘decreased somewhat and increased drastically’ [ 48 ], symptoms which ‘decreased to a low level and increased again’ [ 49 , 50 ] or ‘steadily worsening’ symptoms [ 36 ]. DenVelde et al. [ 41 ] simply asked participants to self-report whether they had ‘experienced remissions and exacerbations’. Martenyi et al. [ 53 ] had multiple definitions of relapse, including increases in scores on their PTSD measures or ‘the clinical judgement of the investigator’. Others labelled the trajectory but did not specify the parameters of their definitions [ 51 , 60 , 61 , 62 , 66 ]. One study [ 57 ] used ‘being hospitalised’ as a proxy measure of PTSD recurrence, although this way of defining recurrence would obviously not capture individuals who developed recurring symptoms which were not severe enough to warrant hospitalisation; additionally, no criteria for hospitalisation were described. Similarly, Davidson et al. [ 40 ] described ‘relapse’ as PTSD scores reverting back to baseline or worse, or experiencing an ‘untoward clinical event’ including suicidality, hospitalisation, or dropping out of the study due to feeling progress was not being made.

We found little consensus as to how long participants needed to be symptom-free (or have reduced symptoms) in order to be considered ‘recovered’ prior to recurrence. The majority of studies simply based their definitions on the time-points of the study, suggesting that recurrence was identified if participants had PTSD at baseline, did not have PTSD during at least one follow-up, and then had PTSD again at a later follow-up. The time-points of follow-ups ranged from weeks to months to years. Only four studies suggested specific timeframes: three studies claimed that participants needed to be ‘recovered’ for eight weeks in order for later reports of PTSD to count as ‘recurrence’ rather than symptom fluctuation [ 35 , 37 , 66 ] whereas Zanarini et al. [ 65 ] reported that participants needed to be not meeting the PTSD criteria for at least two years in order to be considered ‘recovered’. Similarly, most studies did not clarify a time-scale for how long symptoms needed to be experienced in order to be considered a ‘recurrence’. Most studies again simply based their diagnosis on the scores participants happened to report on the days they were assessed. Few studies specified a time-frame: three [ 35 , 43 , 65 ] suggested a duration of four consecutive weeks of meeting their criteria for PTSD, while Benítez et al. [ 37 ] suggested two weeks of symptoms was sufficient to identify a recurrent episode.

Prevalence of recurrence

The review’s second aim was to explore PTSD recurrence rates. Table  3 presents data on the prevalence of recurrence of PTSD for each study. The second column of Table  3 presents the data that is reported in the original studies. The findings reported in this column are not easily comparable because studies reported recurrence rates in different ways. Some reported the percentage of the entire trauma-exposed sample who experienced PTSD recurrence (column 3 of Table  3 ). Others reported the percentage of those with PTSD who experienced recurrence (column 4 of Table  3 ) and the remaining studies reported the percentage of those who recovered from PTSD who experienced recurrence (column 5 of Table  3 ). Three studies [ 44 , 47 , 57 ] did not report the prevalence of recurrence, but were still included in the review as they included definitions and/or predictors of recurrence. One study [ 60 ] deliberately chose a sample who had all experienced recurrence; therefore, recurrence prevalence data for this study was not recorded in Table  3 as it would, by design, be 100%.

Most studies (19/35) reported the prevalence of recurrence within the entire trauma-exposed population. We would therefore expect prevalence rates to be extremely small, given that the majority of trauma-exposed people will not develop PTSD in the first place [ 3 ], let alone have recurrent episodes. However, in several studies this was not the case. Prevalence of recurrence ranged from 0.2% (for a sub-set of participants who did not directly witness the disaster in question) [ 45 ] to 57% of 63 women newly-diagnosed with ovarian cancer [ 43 ]. The latter study was carried out over 27 weeks and identified ‘intermittent cases’ who had PTSD at one time-point, no PTSD at a later time-point, and then PTSD again later on. We note that 27 weeks is a fairly short period of time for both recovery and recurrence to occur, and it is therefore possible that the data reflects symptom fluctuations rather than true recovery or recurrence. Overall, the mean prevalence of recurrent PTSD in trauma-exposed populations was 13.1%, and the median was 3.8%.

Five studies presented the prevalence of recurrence within populations diagnosed with PTSD. We would expect these prevalence rates to be higher than the prevalence rates of recurrence within full trauma-exposed samples, as they are based on populations who developed PTSD only. The rates were 4.9% [ 39 ], 15.4% [ 66 ], 24.5% [ 36 ], 28% [ 46 ] and 49.6% [ 41 ]. Mean and median prevalence of recurrent PTSD were both 24.5%.

Seven studies presented data on the prevalence of recurrence within sub-sets of study populations who had recovered from PTSD; therefore, the only possible trajectories for these participants would be recurrence or maintenance of recovery. Recurrence rates ranged from 5.8% (for a sub-set of participants treated with fluoxetine) [ 53 ] to 50% (for a sub-group treated with a placebo) [ 40 ]. Mean prevalence of recurrent PTSD was 25.4% and the median was 22.2%.

The three studies rated highest in quality [ 34 , 47 , 55 ] did not report similar findings relating to prevalence. Holliday et al. [ 47 ] did not present prevalence data at all. Andersen et al. [ 34 ] reported that 2% of participants followed the ‘relieved-worsening’ trajectory, whereas Osenbach et al. [ 55 ] reported that 35% of participants followed the ‘relapsing-remitting’ trajectory. Notably, Andersen et al.’s [ 34 ] participants were military personnel, whilst Osenbach et al.’s [ 55 ] participants were civilian trauma survivors. For this reason, we decided to look separately at recurrence rates in military and civilian participants. We also decided to look separately at data on children as children’s experiences during and after potentially traumatic events are likely to be distinct from those of adults [ 67 ]. Table  4 presents the mean and median recurrence rates for different populations.

Prevalence of PTSD recurrence in military populations

Fifteen studies focused on military personnel and veterans, three of which did not provide prevalence data and one of which included only participants with PTSD recurrence. Military studies which presented rates of recurrence in trauma-exposed populations (rather than focusing on people diagnosed with PTSD only) typically found low prevalence of recurrence: seven studies found prevalence rates under 4% [ 34 , 48 , 51 , 54 , 61 , 62 ]. Another study found a prevalence rate of 6% [ 38 ]. The only higher prevalence rates were reported by Solomon & Mikulincer [ 59 ], who reported recurrence rates of 24.4% for those with combat stress reactions (people referred for psychiatric intervention during the war) and 13.2% for participants who participated in combat in the same units but without need for psychiatric intervention during the war. This study assessed participants over twenty years, which may explain its higher prevalence rate than the majority of studies which were completed within two-and-a-half years or less. However, the study period was shorter than the forty-seven years of Solomon et al.’s [ 62 ] study, which reported only a 1.6% rate of recurrence. It is unclear why Solomon and Mikulincer [ 59 ] found much higher rates of recurrence.

Two military studies reported recurrence rates for PTSD-populations. These were 24.5% [ 36 ] and 49.6% [ 41 ]. We note that all of Armenta et al.’s [ 36 ] participants had comorbid depression at baseline. We also note some concerns about the reliability of DenVelde et al.’s study [ 41 ], which was a retrospective study asking participants to give complete life-history data at one time-point only.

One military study reported on the prevalence of recurrence in a sub-group of participants who had recovered. Solomon et al. [ 62 ], who reported a prevalence rate of 1.6% (out of the entire trauma-exposed sample) over the first forty-two years of the study, found in a follow-up at forty-seven years that 16.7% of those who had initially recovered experienced recurrence of PTSD during the COVID-19 pandemic.

Prevalence of PTSD recurrence in civilian adult populations

Fourteen studies focused on civilian adults. Findings relating to recurrence prevalence in entire trauma-exposed samples were mixed. Two studies reported rates of under 5% [ 45 , 58 ] in survivors of a terrorist attack and an earthquake respectively. Sungur and Kaya [ 64 ] reported a recurrence rate of 8.9% in survivors of the Sivas disaster, a religious fundamentalist protest which resulted in civilian deaths. Higher rates of recurrence were reported for survivors of an oil rig disaster (18.8%) [ 15 ], survivors of an oil spill (32%) [ 56 ], acutely injured trauma survivors (35%) [ 55 ] and women recently diagnosed with ovarian cancer (57%) [ 43 ].

For populations of civilians with PTSD only, recurrence rates were 4.9% [ 39 ] (type of trauma not reported), 15.4% [ 66 ] (trauma type varied), and 28% [ 46 ] (participants severely injured in accidents). Four studies reported data on the prevalence of recurrence in populations who had previously recovered from PTSD. Reported rates were 14% [ 52 ] (trauma type varied), 29.5% [ 37 ] (trauma type varied), 34% [ 35 ] (trauma type not reported) and 40% [ 65 ] (trauma type varied).

Prevalence of PTSD recurrence in children

Four studies focused on recurrence in adolescents / children, with mixed findings. Fan et al. [ 42 ] found that 3.3% of 1,573 earthquake survivors experienced ‘relapsing/remitting’ PTSD. Liang et al. [ 49 , 50 ] found that 17.7% of 301 earthquake survivors experienced the ‘relapsing’ trajectory of PTSD. An et al. [ 33 ] found that 37% of 246 adolescents experienced ‘recurrent dysfunction’ after experiencing an earthquake.

Prevalence of PTSD recurrence in combined military and civilian populations

Finally, two studies included both military and civilian participants; both of these studies were trials comparing fluoxetine to placebo treatment in people with PTSD. Davidson et al. [ 40 ] found that half of the placebo group relapsed after recovery, compared to 22.2% of the fluoxetine group. Martenyi et al. [ 53 ] reported lower rates of ‘relapse’: 16.1% of the placebo group and 5.8% of the fluoxetine group. The latter study followed up participants after 36 months, while Davidson et al. [ 40 ] followed up participants for a year after treatment.

Predictors of PTSD recurrence

The third and final aim of the present review was to identify factors associated with PTSD recurrence. Firstly, we note that (as shown in Table  2 ), participants in a number of studies had received some type of intervention during the study period, which was typically not accounted for in analyses of predictors. Many other studies did not report whether participants received treatment or not. Having treatment, whether it be medication, therapy, or a combination, is likely to be an important factor influencing PTSD trajectory, given that there are evidence-based treatments for the condition [ 68 ], but this was typically not explored.

Table  5 shows the factors considered as predictors in each study, with significant associations presented in bold. The majority of included studies (22/35) explored at least one covariate; the remaining studies either did not explore covariates or combined recurrent trajectories with other trajectories in their analyses of predictors. Of those studies which did explore covariates of recurrence, we found little consensus.

Sociodemographic factors

Gender was considered as a potential covariate by six studies; one [ 33 ] found that recurrent PTSD was associated with female gender while five studies (including two based on the same data-set) [ 49 , 50 ] found no significant gender association [ 35 , 36 , 42 , 49 , 50 ]. None of the three studies testing age as a covariate found a significant association [ 35 , 36 , 57 ]. One study of school-aged children found that children in a higher grade (i.e. older in age) were more likely to experience PTSD recurrence [ 33 ], while three studies of two cohorts [ 42 , 49 , 50 ] found no significant association between recurrence and school grade. Three studies considered race as a covariate, finding no significant association between PTSD recurrence and race [ 36 , 44 , 55 ]. Other socio-demographic characteristics considered included number of children in the family [ 42 ], marital status and level of education [ 36 ], none of which were found to be associated with PTSD recurrence. For military participants, there were no significant differences in service branch, service component or pay grade between the recurrent and rapid recovery groups [ 36 ].

Psychiatric history

Seven studies considered psychiatric history and concurrent diagnoses as potential covariates of PTSD recurrence, again with mixed findings. Recurrence was not found to be associated with other anxiety syndromes [ 36 ], baseline levels of anxiety [ 54 ], depressive symptoms [ 55 ], baseline levels of depression [ 54 ] or psychiatric history [ 55 ]. Ansell et al. [ 35 ] found that diagnoses of a number of co-morbid mental health disorders such as major depressive disorder and personality disorders such as schizotypal personality disorder, avoidant personality disorder and borderline personality disorder were not associated with recurrence, but participants with a baseline diagnosis of obsessive-compulsive personality disorder were significantly less likely to experience PTSD recurrence. Conversely, Perconte et al. [ 57 ] found that those who experienced recurrence were significantly more likely to report obsessive-compulsive symptoms than those whose symptoms improved without recurrence. Sakuma et al. [ 58 ] found that pre-disaster treatment for mental illness was significantly associated with PTSD recurrence, but note that the results should be interpreted carefully due to the very small number of participants in the ‘fluctuating symptoms’ group who appeared to have experienced recurrent episodes. Perconte et al. [ 57 ] found that, versus the improved symptoms group, those with PTSD recurrence were more likely to report depression, anxiety, hostility, phobic anxiety, somaticism and psychoticism; however, previous psychiatric hospitalisations and pre-treatment ratings of global pathology on a psychiatric scale did not predict recurrence. Finally, Madsen et al. [ 51 ] found that suicidal ideation was significantly higher in the ‘relieved-worsening PTSD’ group than the ‘low-stable’ group and that suicidal ideation was in fact highest in the recurrent (termed ‘relieved-worsening’) group than any other. However, it should be noted that suicidality was not assessed at baseline in this study, therefore it is not clear whether suicidal ideation is a cause or a consequence of PTSD recurrence.

Physical health

Fewer studies considered physical health as a potential predictor of PTSD recurrence. One study found no association between recurrence and disabling injury/illness, somatic symptoms or bodily pain [ 36 ] and another found no association between recurrence and prior treatment for physical illness [ 57 ]. However, obesity was a significant predictor of PTSD recurrence [ 36 ]. In terms of health-related behaviours, Armenta et al. [ 36 ] found no association between PTSD recurrence and smoking status, alcohol problems or sleep duration. However, Perconte et al. [ 57 ] found that higher weekly alcohol intake both before and at termination of PTSD treatment predicted recurrence.

Cognitive ability

Only one study [ 63 ] explored cognitive ability as a potential covariate, finding that the participants who were in the recurrent (termed ‘relieved-worsening PTSD’) group had significantly lower cognitive ability scores than those in the ‘low-stable’ group.

Trauma history and pre-trauma experiences

The review also found mixed evidence for trauma history as a predictor of PTSD recurrence. Liang et al. [ 49 , 50 ] found no association between pre-disaster traumatic experience and PTSD recurrence. Armenta et al. [ 36 ] found no association between recurrence and childhood sexual abuse, childhood verbal abuse, childhood neglect, sexual assault, physical assault, or ‘other life events’, but did find that participants reporting a history of childhood physical abuse were significantly more likely to experience PTSD recurrence. Holliday et al. [ 47 ] found that veterans who had experienced military sexual trauma (MST) had greater initial reductions in PTSD symptoms than those who had not experienced MST, but also experienced a ‘modestly greater’ recurrence of symptoms than those without MST, although this difference did not appear to reach statistical significance. Zanarini et al. [ 65 ] found that the presence of childhood sexual abuse history did not significantly predict time-to-recurrence, but severity of childhood sexual abuse, adult rape history, combination of childhood sexual abuse history and adult rape history, and experiencing sexual assault during study follow-up were associated with less time-to-recurrence. Osofsky et al. [ 56 ] found that abuse, emotional abuse, domestic violence, and greater number of traumas experienced were associated with recurrence of PTSD, and Osenbach et al. [ 55 ] found that recurrent life stressors significantly increased the odds of membership in chronic, relapsing or recovery groups rather than the resilient group. For military participants, one study found combat deployment was significantly associated with recurrent PTSD [ 36 ] while others found combat exposure was not associated with recurrence [ 54 , 57 ]. Finally, Fan et al. [ 42 ] found that compared to the recovery group, relapsing participants experienced significantly fewer negative life events 6-months post-disaster, but significantly more such events at the 24-month follow-up.

Few other pre-trauma experiences were considered. An et al. [ 33 ] found that those with recurrent PTSD were significantly more likely to have experienced academic burnout than those in the recovery trajectory, although there was no difference between the recurrent and delayed trajectories.

Experiences during and immediately after the traumatic experience

The review also found mixed evidence for an association between peri-traumatic experiences and PTSD recurrence. The most consistent finding related to how stressful the traumatic experience was perceived to be at the time. For example, risk of recurrence was significantly higher in those with combat stress reactions [ 59 ] and in those with higher stress relating to the disaster they had experienced [ 56 ], as well as with greater trauma severity [ 49 , 50 ]. However, recurrence was not found to be associated with subjective fear during the event [ 33 ]; directly witnessing a disaster [ 42 ]; property loss during the event [ 33 , 42 ]; property damage [ 42 ]; displacement due to property damage [ 58 ]; near-death experience [ 58 ]; or having a family member injured, killed or missing [ 42 , 58 ].

There was some evidence that initial post-traumatic stress symptoms immediately after the traumatic event could predict PTSD trajectory. Liang et al. [ 49 , 50 ], in a study of PTSD in children from two schools affected by an earthquake, found that children from one of the two schools (‘School 2’) were significantly more likely to experience PTSD recurrence than children from the other school (‘School 1’). Further investigations revealed that after adjusting for immediate post-traumatic stress symptoms the school no longer predicted relapse; those from School 2 had significantly greater post-traumatic stress symptoms immediately after the disaster, which the authors suggest might be due to School 1 providing sufficient psychological services as well as having the same students and teachers before and after the earthquake (therefore perhaps greater social support available), whereas School 2 had insufficient psychological services and consisted of teachers and students from several different schools which could not be reconstructed after the earthquake.

One study [ 58 ] considered occupational-related covariates of PTSD recurrence for disaster recovery workers. They found that having mainly disaster-related occupational duties and lack of rest due to occupational duties were not associated with recurrence, but perceived poor workplace communication did predict recurrence.

Post-trauma experiences and symptoms

An et al. [ 33 ] found that, compared to the delayed PTSD trajectory, those who experienced recurrence were less likely to have experienced post-traumatic growth after the traumatic event; however, there were no differences in post-traumatic growth between the recurrent and recovery groups. Fan et al. [ 42 ] found that neither positive coping nor negative coping six months post-disaster were associated with PTSD recurrence. In a military study, Karstoft et al. [ 48 ] found that poor adjustment to civilian life (i.e. difficulties with community reintegration after deployment) was significantly higher for the recurrent (‘relieved-worsening PTSD’) group than all other groups. However, it is not clear whether poor adjustment was a cause or an effect of PTSD symptoms worsening after initial improvement.

Two studies explored specific cluster symptoms. Murphy and Smith [ 54 ] found PTSD recurrence was not predicted by the magnitude of re-experiencing, avoidance, or hyperarousal symptoms. Boe et al. [ 15 ] found that the number of intrusion and avoidance symptoms five-and-a-half months post-trauma did not predict recurrence, but the number of intrusion and avoidance symptoms both fourteen months and five years after the disaster did predict recurrence.

Social support

Only three studies directly considered social support as a potential covariate. Armenta et al. [ 36 ] found no association between social support and PTSD recurrence, and Perconte et al. [ 57 ] found that family support did not predict recurrence. Fan et al. [ 42 ] found that level of social support six months after experiencing an earthquake was not associated with PTSD recurrence, but those in the ‘relapsing’ group reported significantly less social support 24 months after the earthquake than those in the ‘recovery’ group.

PTSD treatment

Most of the studies investigating treatment for PTSD found that not receiving interventions, or discontinuing treatment, were associated with PTSD recurrence. For example, Osenbach et al. [ 55 ] found that those who received ‘usual care’ only were significantly more likely to experience recurrence than those who received interventions designed to reduce post-traumatic symptoms. Davidson et al. [ 40 ] found that those who received placebo treatment were significantly more likely to experience recurrence than those who received fluoxetine. Martenyi et al. [ 53 ] found that those who discontinued fluoxetine treatment were significantly more likely to experience recurrence, especially for those with combat-related PTSD. However, Perconte et al. [ 57 ] found that number of weeks enrolled in treatment and number of treatment sessions attended did not significantly affect risk of recurrence. In this study, though, being hospitalised at least once since the termination of treatment was used as a proxy measure of ‘recurrence’ and so the findings are arguably not truly representative of actual recurrent episodes of PTSD. Overall, our findings indicated some evidence that treatment helped to avoid recurrent episodes.

In this study, we systematically reviewed 35 studies to identify definitions and prevalence of recurrent PTSD and factors associated with recurrence. It is important to define and operationalise recurrence as the concept needs to be understood in order to make prevention efforts. The health-related, social and economic costs of PTSD can be substantial. PTSD negatively affects individuals’ emotional wellbeing and physical health [ 7 ], impedes social relationships [ 69 ], limits productivity at work and increases sickness absence [ 70 ]. The direct costs (e.g., medical care costs) and indirect costs (e.g., costs of unemployment or reduced productivity) of PTSD can create substantial economic burden [ 7 , 71 ]. Determining the predictors of recurrence of PTSD (which can only be properly understood if ‘recurrence’ itself has a clear definition) is important for prevention efforts: identifying those most at risk for recurrent episodes would allow for the subsequent investigation of ways of mitigating or preventing the risk. However, we found little consensus as to how recurrence is defined, mixed evidence on the prevalence of recurrence and inconsistent findings relating to predictors of recurrence. This lack of clarity about what relapse or recurrence is, and is not, is a major barrier to understanding this important topic.

In a previous review exploring PTSD recurrence in veterans, Berge et al. [ 22 ] acknowledge that there is no generally accepted or used definition of recovery relating to psychological trauma. The definition of recurrence used in their review was the return of symptoms following a period of complete recovery, representing the start of a new and separate episode . However, it is not clear what length of time is covered by ‘a period of complete recovery’ nor what ‘complete recovery’ means. How many days, weeks, or months does an individual need to be free of symptoms of PTSD in order to be considered truly recovered? Is ‘symptom-free’ the only definition of recovery, or is ‘not meeting the criteria for PTSD’ enough? Our own review revealed that there is little consensus as to what recurrence means and the parameters for its definition. Even the terminology used varied across studies, with ‘relapse’, ‘recurrence’, ‘reactivation’ and numerous other terms often used to describe what essentially appeared to be the same concept. There was no consensus as to how long an individual needed to be free of symptoms in order to be considered recovered, nor for how long symptoms needed to recur in order to be considered a recurrent episode. Most studies simply defined recurrence as a change in symptoms between assessments, meaning that whether or not an individual was defined as having a recurrent episode or not very much depended on the scores they reported at arbitrary time-points. Even minor symptom fluctuations could cause someone to change from being identified as a ‘case’ to ‘recovered’ and vice versa. Because PTSD tended to be examined using prospective studies where symptoms were assessed at predetermined assessment points, it is possible that individuals may have onsets of PTSD after one assessment and then remit before the next. With no retrospective assessment between time-points, it is difficult to assess the true prevalence of recurrence. Andrews et al. [ 16 ] make a similar point in relation to delayed onset PTSD, suggesting the absence of information about symptoms outside of the predetermined time-points of studies means that estimates of delayed onset PTSD may be unreliable.

The second aim of the review was to examine the prevalence of PTSD recurrence in existing literature. Given the numerous different ways of assessing PTSD, defining initial recovery and defining recurrence, as well as the differing time-points at which PTSD was assessed across studies, we suggest that the current data on recurrence prevalence is not especially meaningful. We found very different prevalence rates reported within the literature, with data suggesting that anywhere between 0.2% and 57% of trauma-exposed populations might experience recurrent episodes of PTSD. Some of the higher percentages we found seem greater than we would expect, given that only a minority of trauma-exposed people are likely to develop PTSD in the first place – let alone suffer from it, recover from it, and experience a recurrent episode. We would expect that studies carried out over a longer period of time would find higher recurrence rates, simply because in these studies there is more time for recurrent episodes to occur. However, the highest prevalence rate (57%) was found in a study which took place over only 27 weeks [ 43 ]; the authors labelled these participants as ‘intermittent cases’ and it appears likely that symptom fluctuation, rather than true recovery and recurrence, occurred in this study – and potentially many others. Additionally, studies did not typically control for exposure to subsequent trauma, meaning that ‘recurrences’ of PTSD identified may actually be new episodes, rather than a relapse. Further research studies, especially research involving assessments over a number of years, are needed to establish the true prevalence of recurrent PTSD which also needs to be clearly defined with an agreed time period between remission and relapse.

It has been proposed that recurrence rates might increase with old age. Murray [ 72 ] suggests that PTSD can be ‘reactivated’ in older age because physical illnesses become more common, which can reactivate traumatic memories; increased dependence on others due to ageing can reactivate feelings of helplessness; and loss of structure and identity caused by retirement can similarly reactivate traumatic symptoms. Other factors relating to ageing such as decline of cognitive function, difficulty controlling ruminations, reminiscing, and late-life stressors such as serious illness, surgical procedures and death of spouses, siblings or close friends can either directly remind the person of their previous traumatic experience(s) or can induce similar feelings of vulnerability [ 73 ]. Three studies of adults in this review did not find age predicted recurrence [ 35 , 36 , 57 ]; however, the populations trended young overall, with each of the three studies reporting the mean age of participants was under 40. We suggest, then, that more studies of older adults with lifetime PTSD are needed to establish whether this group are at increased risk of recurrence.

The third aim of this review was to understand factors associated with PTSD recurrence. Although a number of potential covariates were considered, most were not investigated by more than a few studies, and findings were varied and inconsistent. Of the covariates investigated by multiple studies, none were found to have significant associations with recurrence across all studies. It was therefore not possible to quantify the extent to which potential risk factors contribute to the risk of recurrence. One reason for the inconsistent findings might be the relatively small numbers of participants with recurrent PTSD in many of the studies. We note also that most studies did not consider either subsequent trauma or treatment impact in their analysis of predictors of recurrence.

We did not find strong evidence of an association between PTSD recurrence and comorbid psychiatric conditions. Recurrence of other mental health disorders, such as anxiety, is reportedly associated with comorbid psychiatric conditions including major depression, alcohol and substance use disorders [ 74 ]. Additionally, comorbid disorders have been found to be associated with an ‘unfavourable long-term course’ of PTSD [ 18 ]. However, in a review of predictors of developing PTSD, Brewin et al. [ 75 ] found that while psychiatric history was associated with development of PTSD, it was not a strong risk factor – factors operating during or after the traumatic exposure had greater effects than the pre-trauma factors. Many studies in this review found no evidence of a relationship between PTSD recurrence and other mental health conditions; in those that did find a relationship, it was not always clear whether the other conditions pre-dated the recurrent PTSD episode or not. Overall, the most consistent evidence we found indicated that recurrence of PTSD was associated with greater stress and traumatic response at the time of the traumatic experience.

We did not find evidence to suggest that trauma type may affect recurrence. Many studies examined PTSD trajectories after a single traumatic event. Those that did include participants who had experienced various different types of trauma did not consider trauma type as a potential predictor of recurrence. Given the wide variations in methodology, it was not appropriate for us to compare recurrence rates for different trauma types within the review. Future research should include participants who have experienced different types of trauma and should consider trauma type as a potential predictor of PTSD trajectory.

Only one study assessed PTSD during the COVID-19 pandemic, with Solomon et al. [ 62 ] reporting that 16.7% of initially-recovered participants experienced recurrence during the pandemic. However, it is not clear how many of this cohort may also have experienced recurrence before the pandemic, and without being able to make that comparison, we cannot ascertain the extent to which recurrence was exacerbated by the pandemic. Additionally, the percentage (16.7%) is similar to recurrence rates in several other, non-COVID studies. Ideally, future studies will present data on PTSD recurrence rates for one cohort at regular intervals, including data collected during or after the COVID-19 pandemic, to ascertain whether the pandemic did affect recurrence rates.

In their review, Steinert et al. [ 18 ] identified older age, higher education, greater trauma severity, higher baseline symptoms, more physical/functional impairments, and poorer social support as predictors of ‘unfavourable’ long-term course of PTSD. These were identified as predictors due to being reported in at least two studies within their review. The current review did not find consistent evidence that age, education, trauma severity, baseline symptoms, impairments or social support predicted recurrence – although age was only considered in studies of young people. We found some evidence from treatment studies that fluoxetine reduced the risk of recurrence, as did participation in an intervention involving a combination of motivational interviewing, behavioural activation and pharmacotherapy. It is therefore difficult to make recommendations relevant to occupational health, as we had hoped to do. Managers of trauma-exposed employees who have developed PTSD may have questions around whether recovered individuals can go back to frontline work, or whether they risk experiencing a recurrence of PTSD. Our findings tentatively suggest that recurrence might be relatively rare (rates of recurrence ranged from 0.2 − 57% in full trauma-exposed samples, mean 13.1%; 4.9 − 49.6% in PTSD-only subgroups, mean 24.5%; and 5.8 − 50% for recovered subgroups, mean 25.4%) but clearer definitions and assessments of recurrence are needed to substantiate that claim. As we found no consistent evidence of predictors of recurrence, it was therefore not possible to identify which sub-groups of people might be more likely to have their PTSD recur. We did find evidence from two studies that recurrence was more prevalent in groups of PTSD patients treated with placebos compared to PTSD patients treated with fluoxetine, suggesting that medication appears at least somewhat effective in reducing the risk of recurrence. However, we found no studies looking at the impact of first-line treatments on relapse (i.e. trauma-focused cognitive behavioural therapy [ 76 ] or eye movement desensitisation and reprocessing [ 77 , 78 ]) which is a major gap in the literature. Whilst more, high-quality studies are carried out, employers should ensure that workers get evidence-based treatments and have an occupational mental health assessment on completion of potentially traumatic work to provide an expert judgement, given that we cannot identify any clear risk factors from the literature.

The key limitation of the literature on PTSD recurrence is that it is not always easy to differentiate between recurrence and symptom fluctuation, and it is also difficult to know what ‘recovery’ truly means. It is not clear how many of the so-called ‘recovered’ participants within the reviewed studies may have been close to clinical thresholds for PTSD at the assessment points. Rather than moving from distinct ‘recovered’ to ‘recurrent episodes’, it may be that individuals only experienced small fluctuations in PTSD symptoms, moving them above and below the symptom thresholds. Indeed, the authors of several of the included studies remarked on the difficulties in identifying PTSD trajectories. In Boe et al.’s [ 15 ] study, clinical interviews were conducted by two clinical psychologists who were trained and supervised by an experienced clinician and trauma researcher and even these experienced individuals had difficulties identifying recurrence of PTSD, with one case being recategorised from ‘full-blown PTSD reactivation’ to ‘sub-syndromal reactivation’ after discussion between the researchers. Markowitz et al. [ 52 ] pointed out that, as they defined relapse as ‘loss of response (to treatment) status’, relapse might reflect barely crossing that threshold: indeed, more in-depth analysis of their six ‘relapsers’ showed that all but one still showed some, albeit more modest, treatment benefit relative to their baseline PTSD severity.

Sakuma et al. [ 58 ] discussed their finding of a ‘fluctuating’ trajectory (and lack of a delayed-onset trajectory), differing from the typical four trajectories widely accepted within the PTSD literature. They suggested the difference may be due to variations in the duration of study periods and characteristics of the study samples. The majority of studies which produce the typical four trajectories are conducted over short periods between a few months and two years [ 9 ], compared to the longer (54-month) period of Sakuma et al.’s [ 58 ] study: the trajectory commonly identified as ‘delayed onset’ could really be a fluctuating trajectory if examined over a longer period. Or, it could reflect a gradual accumulation of symptoms resulting in a delayed presentation of PTSD, rather than delayed onset.

The time-points of assessments could also affect reported prevalence rates. For example, Sungur & Kaya [ 64 ] pointed out that some of their ‘recurrent’ cases would have been considered ‘recovered’ if the study period had been shorter or if participants had not been reassessed at the particular time-points chosen. They also noted that symptoms across the entire participant population seemed to be higher at particular times during the study (namely, at the anniversary of the event and at the time of a disappointing result of a court hearing for compensation), suggesting that the nature and course of PTSD might be influenced by particular events which might trigger unwanted memories of the traumatic event. In the current review, most studies assessed participants for at least a year, but not all: five [ 38 , 39 , 43 , 52 , 53 ] followed participants for less than a year. Additionally, two studies [ 44 , 47 ] reported assessing participants pre-treatment and four months post-treatment but it was not clear how long treatment lasted.

We suggest that PTSD recurrence may not have been adequately assessed in many of the included studies. For example, Chopra et al. [ 39 ] described how, in order to minimise respondent burden, assessors were expected to stop inquiring about PTSD symptoms if participants were unlikely to meet the criteria and if they answered no to particular questions on the assessment tool. This could mean that some individuals who did have recurrent episodes of PTSD were not identified as they did not complete the full measures. Additionally, we found that a number of studies had very vague definitions of recurrence, such as ‘increasing symptoms’, where it was unclear what exactly this meant. Others used hospitalisation as a proxy measure for recurrence, or simply asked participants whether they perceived their symptoms had been exacerbated and in one case used the investigator’s own judgements as a way of determining recurrence. It is therefore likely that some recurrent cases may have been missed while others who never truly ‘recovered’ at all may have been reported to have experienced recurrence. Overall, the vague and inconsistent ways of assessing recurrence mean it is currently impossible to ascertain true recurrence rates within existing literature.

It is also possible that recurrent trajectories of PTSD appear in studies which do not identify them as such. For example, in Andrews et al.’s [ 16 ] review, the authors note that some cases of ‘delayed-onset PTSD’ in veterans of relatively old age with long intervals to first onset may in fact have had episodes of PTSD soon after their traumatic experiences which were undisclosed or forgotten. In other words, some cases of supposedly ‘delayed-onset’ PTSD might actually be recurrent cases. Andrews et al. [ 16 ] also point out that many of the studies included in their review of delayed-onset PTSD did not assess whether respondents could have had onsets of PTSD and then remitted before the next assessment point – which could lead to both over- and under-estimates of delayed-onset rates of PTSD. Indeed, the studies included in our own review tended to focus only on the scores at the various time-points and did not explore participants’ perceptions of symptom fluctuations outside of the time-points set by the study.

Limitations

There are a number of limitations of the literature reviewed. Many did not collect data on whether participants had undergone any intervention or not, and those that did tended not to include this as a potential confounding variable. The majority of studies did not assess whether participants experienced additional potentially traumatic experiences between PTSD assessments. Many did not define the parameters of ‘recovery’ and ‘recurrence’ and it is not clear whether recurrent episodes identified were truly recurrent episodes or merely symptom fluctuations. Many did not collect data on whether or not participants received any treatment for PTSD between data collection time-points, and many of those which did ask participants whether they had received any treatment did not distinguish between types of treatment. It is therefore unclear if, and how many, participants in many studies received any evidence-based PTSD treatment or not. Additionally, the majority of studies did not collect data on the time period of any treatment received. Some studies had extremely long gaps (e.g., decades) between assessments which could mean that recurrences were missed.

There are also limitations of the review process itself. Firstly, the screening, data extraction and quality appraisal were carried out by one author. Although decisions about exclusion or inclusion were discussed with the second author, it would have been preferable to have multiple screeners. We limited the review to English-language studies only, meaning that important studies published in other languages would have been missed. We included only studies which identified ‘recurrent episodes’ (or equivalent terminology e.g. relapse, reactivation); studies which identified no recurrent trajectory were not reviewed. It may be that these studies did not include a sufficient number of assessments to pick up on recurrent episodes, but it may also be that no participants in these studies experienced recurrence and therefore the true prevalence of recurrence may be lower than this review suggests.

Conclusions and implications

The main conclusion that can be drawn from the current review is that, moving forward, better clarity and consensus regarding the definition and identification of recurrent PTSD are urgently needed. Berge et al. [ 22 ] suggest that consistent definitions of relapse-related terms, supported by empirical research, are required in order to make studies of PTSD trajectories more robust. The findings of this review support this suggestion. Experts in the field should agree on an appropriate definition of recurrence (i.e. symptom-based or threshold-based) and should agree how long an individual needs to be ‘better’ for in order to be considered recovered as well as how long an individual needs to experience symptoms for in order to be considered as having a recurrent episode. Recurrence is arguably better-defined for recurrent depressive disorder, with the ICD-11 stating that recurrence is characterised by a history of depressive episodes separated by at least several months without significant mood disturbance [ 79 ]. However, further clarity is still needed. How many months is ‘several’? What are ‘significant’ symptoms? Still, we suggest this might be a useful starting point for a working definition of recurrent PTSD: a history of episodes of PTSD separated by at least several (i.e., three) months without significant (i.e., meeting diagnostic criteria) PTSD symptoms . However, further research is necessary to clarify whether these parameters (i.e. three months as a time period, symptom thresholds as a diagnostic tool) are the most appropriate to use. Using consistent terminology within the literature would make it easier to researchers in the future to understand true prevalence rates of PTSD recurrence and to compare them across studies. Further research allowing for the identification of recurrent PTSD episodes is needed. We believe the gold standard for assessing PTSD and properly identifying its trajectories, including recurrent trajectories, would be using the Clinician Administered PTSD Scale (CAPS) [ 80 ], or other validated questionnaires, at multiple specific time points over a long period of time. Figure  2 summarises the findings of the review and the proposed next steps based on our findings.

figure 2

Summary of review and suggested next steps

It is important to understand recurrence in order to take steps towards reducing the risk of PTSD recurring. However, due to the inconsistent findings relating to predictors of recurrence, it is difficult to draw conclusions about the best ways of preventing or minimising recurrence. We suggest that ensuring that people who develop PTSD are provided with timely, evidence-based treatments is a logical first step [ 68 ]. Second, awareness of ‘early warning sign’ symptoms and ‘triggers’ might be useful, as well as awareness of effective coping strategies and how to access support. That is, if people with PTSD are able to recognise when they are struggling more and acknowledge that they need to be proactive in ensuring symptoms do not develop into full-blown PTSD again, they may be able to draw on their coping skills or reach out for formal or informal support when a recurrent episode seems imminent and may be able to stave off the recurrent episode. We also suggest that reframing the re-emergence of symptoms in a more positive way might be useful: instead of feeling defeated that symptoms have recurred, people could remind themselves that they have recovered once and therefore know that they are capable of doing so again. Within organisational settings, it is also important to foster an environment in which people who have any mental health condition, including PTSD, feel confident that asking for help will not lead to stigmatisation or increase the likelihood of inappropriate job loss. It may also be helpful to incorporate relapse prevention, understanding ‘warning signs’ of recurrent episodes and positive reframing into PTSD treatment programmes.

Data availability

All data generated or analysed during this study are included in this published article.

Abbreviations

Clinician-Administered PTSD Scale

Military sexual trauma

National Institutes for Health

Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Post-Traumatic Stress Disorder

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This study was funded by the National Institute for Health and Care Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response, a partnership between the UK Health Security Agency, King’s College London and the University of East Anglia. The views expressed are those of the author(s) and not necessarily those of the NIHR, UKHSA or the Department of Health and Social Care. For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. The funders had no role in carrying out the review or preparing the manuscript for publication.

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Brooks, S.K., Greenberg, N. Recurrence of post-traumatic stress disorder: systematic review of definitions, prevalence and predictors. BMC Psychiatry 24 , 37 (2024). https://doi.org/10.1186/s12888-023-05460-x

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data excursion definition

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Computer Science > Cryptography and Security

Title: jailbreaking is best solved by definition.

Abstract: The rise of "jailbreak" attacks on language models has led to a flurry of defenses aimed at preventing the output of undesirable responses. In this work, we critically examine the two stages of the defense pipeline: (i) the definition of what constitutes unsafe outputs, and (ii) the enforcement of the definition via methods such as input processing or fine-tuning. We cast severe doubt on the efficacy of existing enforcement mechanisms by showing that they fail to defend even for a simple definition of unsafe outputs--outputs that contain the word "purple". In contrast, post-processing outputs is perfectly robust for such a definition. Drawing on our results, we present our position that the real challenge in defending jailbreaks lies in obtaining a good definition of unsafe responses: without a good definition, no enforcement strategy can succeed, but with a good definition, output processing already serves as a robust baseline albeit with inference-time overheads.

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  1. Handling Temperature Excursions and the Role of Stability Data

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    The stability data should be available with manufacturer against each excursion to evaluate and justify that there is no impact on product quality due to the excursions. The stability study under accelerated condition and freeze-thaw study are the relevant to evaluate the impact on product quality in a scientific manner.

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    The approach below outlines best practices for the documentation of temperature excursions at the manufacturing site, during transportation, or in warehouse storage. Collect the following information: Details of the person completing the report. Date and time of the temperature excursion. Inventory of affected products.

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    Step 3: Document the Event. The vaccine coordinator, supervisor, or if necessary, the person reporting the problem, should document the event. Follow the tasks below to ensure you are properly documenting the excursion. Name of the person completing the report. Date and time of the temperature excursion.

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    When temperature excursion data was unavailable in published form, product manufacturers were surveyed via telephone and/or email. Acceptable storage information for all products for which storage is recommended at temperatures below room temperature (20-25 °C [68-77 °F]) was compiled and arranged in tabular format.

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    A temperature cycling study intended to identify those articles affected by multiple, short-term excursions beyond the storage temperature limits should be performed. These data provide wholesalers and distributors with clearer identification of those drug products that may require special handling during particular climate conditions.

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    Temperature excursion queries that may arise during this stage due to improper handling (as communicated by the drug product user) may be handled by the manufacture's Medical Information department, which may have data on file to provide information in response to specific questions on temperature excursions (e.g., information based on in-use ...

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    How/When Thermal Cycling Study Data is Used to Support Temperature Excursions. Thermal cycling study data (from development studies and in some cases from formal thermal cycling on cGMP drug product batches) and associated impact toward product quality and stability may be included in a New Drug Application (NDA). Pharmaceutical companies may ...

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    In the five (5) year period, there was an average of (31.8) runway safety accidents per year. Figure 9 shows the frequency of runway safety accidents by year between 2010 and 2014. The lowest number occurred in 2014 when there were 28 runway safety accidents. The 28 accidents were below the five-year average.

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    The following are examples of data trends that may constitute an Adverse Trend. These may differ depending upon how conservatively a firm approaches trend analy­ ses. • Two or more excursions for a given sample type (e.g., viable air) in an area or room on the same day; • Two or more consecutive excursions for a given sample type

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