Prevalence of post-traumatic stress disorder in Peruvian military: a cross-sectional study

Introduction: The COVID-19 pandemic has led us to stay at home in order to mitigate the increase in contagion, which has modified military personnel’s work as they had to be on the front-line of the global fight. Post-traumatic stress disorder (PTSD) is a psychiatric condition that has become a challenge for public health. Little research has been undertaken in military population, even less in the Latin American context. The objective is determining the prevalence and factors associated with post-traumatic stress disorder in military personnel in Lambayeque, Peru.

Methods: This is an analytic cross-sectional observational study in military staff that carried out first-line activities on the defense against COVID-19. The study population was comprised of 820 military personnel. To evaluate the factors associated with PTSD, we calculated prevalence ratios (PR) and confidence intervals at 95%, using simple and multiple regression models.

Results: The prevalence of PTSD was 7.8% (95% CI: 5.8%–10.2%). The multiple regression model showed that smoking (PR: 2.84; 95% IC: 1.14-7.09), having worked between 13 and 18 months (PR: 2.62; 95% IC: 1.20-5.75), insomnia (PR: 4.09; 95% IC: 1.90-8.83), and fear of COVID (PR: 6.20; 95% IC: 2.70-14.22) were associated with a higher prevalence of PTSD in military personnel.

Conclusion: We found that nearly one in ten military personnel presented PTSD. Factors associated with a higher prevalence included longer service time, smoking, insomnia, and fear of COVID-19. Although resilience showed a protective trend in crude analyses, this association was not significant after adjustment.

Introduction

The COVID-19 pandemic has caused negative repercussions for mental health worldwide (1–7). Among these negative consequences, we can highlight post-traumatic stress disorder (PTSD), which emerges after exposure to a severe trauma or life-threatening event such as the COVID-19 pandemic. PTSD occurs when the nervous system remains in a persistent state of stress activation, meaning that although the immediate danger has passed, the individual continues to experience heightened arousal and is unable to fully return to baseline functioning (8).

The pandemic has made us stay at home to mitigate the increase in contagion through quarantines and isolation, which have modified work for military personnel who stood in the front-line of defense worldwide to ensure the compliance of the mentioned measures (9). Among the factors associated with the highest prevalence of PTSD in military members, we can mention bad sleep quality, a low level of social support (10–13), being an older adult (10, 14), lower official ranks (10, 12, 14). On the other hand, among the factors associated with low prevalence we can mention a long time of service (14), and having a high level of social network support (15).

According to current literature, PTSD is a common psychiatric condition in military personnel worldwide and has become a challenge for public health (16). However, the prevalence reported before the pandemic ranged from 3.0% to 30.0% in countries such as the United States, United Kingdom, Canada, and the Netherlands (17–20). At Latin American level it has been reported that, in Colombia, the prevalence of PTSD varies between 4.90% and 36.0% (21, 22); in Mexico, 4.3% (23); in Peru, 22% (24); and Chile, 15.0% (25). This situation differs from the studies conducted in the pandemic since they have reported PTSD prevalence from 12.8% to 31.2% among active military personnel and veterans (15, 26, 27). However, the existent literature has focused on other realities different from Latin America such as in veterans (military personnel that do not have deployment activity on the first line of defense against COVID-19) (26–29), or other population groups such as health personnel, general population, or infected patients (30–33).

In addition, there have been limitations identified in some studies such as not having measured variables such as uncertainty intolerance or income level (34). In other studies, a four-item instrument which do not have a high reliability was used (35); data collection was performed online (36); the sample size used was small (22, 37); the studied populations corresponded to veterans (not active military personnel) (38, 39); thus, the results cannot be generalized in groups with an active status. Additionally, the studies conducted during the pandemic have not evaluated variables such as comorbidities (40), mental health support (41), time of service, food insecurity (42), or resilience (43), which were evaluated in our study.

Therefore, our study aims to identify the prevalence and factors associated with post-traumatic stress disorder in Peruvian military personnel on the first line of defense in the context of the pandemic.

Methods

Study design

This is an analytic cross-sectional observational study conducted in military staff that carried out front-line defense activities against COVID-19 in the infantry brigade in Lambayeque, Peru. The study was undertaken between November 02 to 09, with the objective of assessing the prevalence and factors associated with post-traumatic stress disorder.

Population and sample

The study population was comprised of 820 military personnel of the infantry brigade located in the region of Lambayeque, Peru. This region is in the north of Peru and was severely hit by the COVID-19 pandemic as it showed a high level of seroprevalence (44) and mortality (45) due to SARS-CoV-2.

We calculated a sample size of 582 participants. The study used an expected prevalence of 12.8%, a confidence level at 99%, a precision of 2.5% and a rejection and loss rate of 20%. However, we obtained a higher participation in the execution (n=710 military members) and the sample gave their consent to participate in the study, which represented 86.6% of the population. We applied a snowball sampling strategy. Initially, the survey link was distributed to military supervisors, who subsequently shared it with other eligible members of their units. Although snowball sampling is a non-probability method and carries a risk of selection bias, it was the only feasible strategy during the COVID-19 state of emergency due to operational restrictions and the hierarchical structure of the military. To reduce this potential bias, recruitment was coordinated directly with unit supervisors, and coverage was maximized by including personnel from all service groups within the brigade.

The military members included were actively working during the COVID-19 health emergency in Lambayeque when they participated in the study and had at least one month of service. The military personnel excluded were the following: those who requested temporary layoff due to factors of risk of COVID-19, those vulnerable military members that were working remotely, and those who were isolated for having tested positive for COVID-19. Additionally, for this analysis, we excluded 95 participants for having incomplete variables in questions of the PCL-C questionnaire, given that it measures the dependent variable. Thus, we analyzed 615 military records.

Variables and instruments

They have seven sections that covered 1) sociodemographic data, 2) Household Food Insecurity Access Scale (HFIAS), 3) Insomnia Scale (ISI), 4) Physical Activity Questionnaires (I-PAQS), 5) The Connor-Davidson Resilience Scale (CD-RISC), 6) Fear of COVID-19 Scale, 7) Post-Traumatic Stress Disorder Scale (PCL-C).

In the general data, we obtained information about age in years, gender (male, female), civil status: single (no, yes), religion (none, Catholic, Non-Catholic), previous disorders (hypertension, diabetes), report of frequent alcohol consumption and smoking, self-reported weight and height, previous personal and family history of a mental disorder, mental health support during the pandemic, trust in the government to manage the pandemic, and time of service on the frontline of defense against COVID-19 pandemic in the military site (1 to 6 months, 7 to 12 months, 13 to 18 months, 19 months or more).

Dependent variable

Post-traumatic stress disorder (PCL-C): PTSD symptoms were assessed using the Posttraumatic Stress Disorder Checklist – Civilian Version (PCL-C), a validated 17-item self-report instrument based on DSM-IV diagnostic criteria. Each item is scored on a 5-point Likert scale (1 = not at all to 5 = extremely), yielding a total score between 17 and 85. A cut-off score of ≥44 was applied to classify probable PTSD (46), following international studies in Spanish-speaking populations and prior applications in military and healthcare settings. Although this threshold has not yet been locally validated in Peruvian military personnel, it has demonstrated good diagnostic accuracy in other populations and was selected to ensure comparability with existing literature. Although no structured clinical interview (e.g., CAPS-5) was conducted, the PCL-C has demonstrated strong psychometric properties (internal consistency α = 0.94; test–retest reliability r = 0.82) (47) and is widely used in epidemiological studies where clinical confirmation is not feasible.

Independent variables

Household Food Insecurity Access Scale (HFIAS): It was developed by the US Agency for International Development. It includes nine items, which correspond to questions about the food consumed in the last four weeks. The questions ask whether having food insecurity, anxiety, quality and consumption of enough food, and physical consequences (48). The questions are categorized as follows: food security (Question 1), mild food insecurity (questions 2-4), moderate food insecurity (questions 5 or 6), and severe food insecurity (questions 6 - 9) (49). The instrument showed high internal consistency (α = 0.74) (50).

Insomnia scale (ISI): It includes seven items that evaluate the nature, severity, and impact of insomnia. It has been validated in older adults, in primary health care patients, and in Spanish-speaking general population. This instrument showed a reliability coefficient of 0,82 through the Cronbach’s alpha (51). The ISI has shown solid psychometric properties, including high internal consistency (α = 0.91) and convergent validity in Spanish older adults (52), factorial validity in Spanish-speaking medical students (51), and construct/diagnostic validity in U.S. veterans with traumatic brain injury (sensitivity 0.81, specificity 0.71) (53), as well as other clinical populations (54, 55). Insomnia presence was valued with a score superior to eight points (53, 56, 57).

Physical activity Questionnaires (IPAQ-S): It considers the four components of physical activity (leisure, house maintenance, work, and transportation) (58). It has nine items and evaluates physical activity declared in the last seven days. This instrument allows a weighted estimation of physical activity declared by week. The physical activity level is classified as low, moderate, and high. It has been validated in Spanish-speaking populations and has been administered to Latin American population (59).

The Connor-Davidson Resilience Scale (abbreviated version of CD-RISC): This questionnaire is composed of ten questions. It has a Cronbach’s alpha coefficient of 0,89 in general population and a test-retest reliability of 0,87 in people with post-traumatic stress disorder (PTSD) (60). It was evaluated through a five-point Likert scale. A cut-off point of 30 was used to categorize resilience as high (>30) and low (<30) (61). It has been validated in military populations, showing excellent psychometric properties, including internal consistency (α >0.93), test–retest reliability (ICC = 0.88), and measurement invariance across rank, gender, and time in Chinese military personnel (62), as well as good validity and reliability in male soldiers with and without PTSD (63). In Latin America, it has been validated in Peruvian university students, demonstrating unidimensional structure, acceptable reliability (ω > 0.85), and measurement invariance by sex (64). Likewise, evidence from Colombian university populations confirmed adequate factorial validity and reliability of the scale (65). The cut-off point of ≥30 for high resilience on the CD-RISC-10 was selected based on its use in Peruvian studies during the COVID-19 pandemic (66, 67), including research with healthcare workers (61). Although the scale has no universal threshold, categorizations in international studies (e.g., terciles or quartiles) also support interpreting scores ≥30 as indicative of high resilience (68, 69).

Fear of COVID-19 scale: It is a reliable instrument with seven items to assess fear of COVID-19 among general population. This instrument has a Cronbach’s alpha coefficient of 0,82 (70). In the present study we used a higher score than 16,5 as a cut-off point to assume the presence of fear of COVID-19 (71). Additionally, cut-off scores have been proposed in other populations, such as 17.5 in the Arabic version validated among the Syrian general population (α = 0.89) (72) and 17.5 in Chinese university students, where the scale showed good reliability (α = 0.87) and diagnostic accuracy (AUC = 0.70) (73). A study of the psychometric properties of the Spanish version of the Fear of COVID-19 scale in a sample of Peruvian population showed adequate measure properties in terms of reliability and validity (74). In Latin America, the FCV-19S has been validated in different populations. In Peru, it showed good internal consistency (Cronbach’s α = 0.83) and unidimensional structure in university students (75). In Colombia, psychometric evaluations in physicians revealed limitations in the original seven-item version but acceptable internal consistency in the five-item version (ω = 0.68) (76).

Procedures

We conducted face-to-face interviews using self-administered questionnaires in order to obtain sociodemographic, psychosocial, physical and mental health information to measure post-traumatic stress disorder (PTSD).

A field team was formed to conduct the research in the military site in Lambayeque, Peru, during the context of the COVID-19 health emergency. We requested authorization to the officer in charge of the military site. Then, the questionnaire was designed online in the data entry system REDCap. Subsequently, the field team coordinated the interviews in three groups during two different shifts (morning and afternoon). The main researcher explained the objective of the research and invited the military personnel to participate in the study through filling out the questionnaire. This happened while they were in ventilated environments, complying with biosecurity measures (correct use of masks, social distancing, and hand washing). Immediately, the main researcher sent the questionnaire link online to the supervisors of the shift. The supervisors sent the questionnaires to the different WhatsApp groups of coordination; thus, we ensured that the link was shared to all the study population. The first part of the questionnaire contains the informed consent; once the participant accepted to participate, he/she was automatically directed to the self-administered questionnaires of the variables of interest. The average time to fill out the questionnaire was 20 minutes.

Analysis plan

We exported the Excel database from the REDcap system. Then, we carried out a process of cleaning of the database to identify missing data and/or inconsistent or out of rank data.

The statistical analysis was performed in the STATA v.17.0 program (StataCorp, TX, USA).

In the univariate analysis, we present the best measures of central tendency and dispersion of the numerical variables, after the evaluation of normal distribution. The categorical variables were shown as absolute and relative frequencies.

To compare numerical variables and the outcome of interest (PTSD), we used the Mann- Whitney U test, after evaluating the normal distribution assumption. To compare categorical variables and PTSD, we used the Chi-squared test after having evaluated the assumption of expected frequencies previously.

To evaluate the factors associated with PTSD, we calculated prevalence ratios (PR) and confidence intervals at 95%, using simple and multiple regression models. In the multiple models, the variables that were statistically significant (p<0.05) in the simple model were included. Additionally, multicollinearity was evaluated in the variables of interest. PTSD symptoms (e.g., re-experiencing, avoidance, hyperarousal) were measured using the PCL-C instrument, while smoking, insomnia, and fear of COVID-19 were treated as independent variables rather than components of PTSD.

Multiple regression results for factors associated with PTSD were summarized using a forest plot constructed in RStudio.

Ethical aspects

The protocol study was approved by the Ethics Committee of the Universidad San Martin de Porres (File N°269-2021-CIEI-FMH-USMP). All participating military personnel provided their informed consent prior to completing the survey, which was anonymous, and confidentiality of the respondents was guaranteed. We respected the ethical principles of the Helsinki Declaration.

Results

General characteristics of military personnel

Of the 615 military members, most of them were male (93.7%) and were between 19 and 32 years old. In addition, 73.3% were single; 27.5% had children; 17.4% reported that they consumed alcohol frequently; and 9.1% informed they had hypertension. The majority had a normal body mass index (57.5%), and almost the third part were overweight (34.5%). Moreover, 48.6% experienced food insecurity while 18.4% suffered from subclinical insomnia, and 43.2% showed high resilience (Table 1).

Table 1

Table 1. Participant characteristics (n=615).

Post-traumatic stress disorder - PCL-C

The prevalence of PTSD was 7.8% (95% CI: 5.8%–10.2%). Of the total, 10.7% mentioned that they felt moderately distant from other people. In addition, 9.6% informed that they avoided moderately activities that remind them of the stressful experience of COVID-19. Finally, 7.8% reported moderately having memories and/or thoughts that occur repeatedly due to past stressful experiences (Figure 1).

Figure 1

Figure 1. PCL-C Instrument in military personnel.

Factors associated with post-traumatic stress disorder in the bivariate analysis

The military personnel with fear of COVID-19 had a higher frequency of PTSD, in comparison with those who did not informed fear (29.7% vs 2.1%; p<0.001). PTSD frequency was superior in military personnel with a low level of resilience (9.9% vs. 4.4%; p=0.011), in comparison with military members with a high level of resilience. The military personnel with severe insomnia had a higher frequency of PTSD, in comparison with those without insomnia (55.6% vs 2.1%; p<0.001). Additionally, we found that alcoholism (p=0.025), smoking (p<0.001), previous history of mental health (p=0.039), trust in the government to manage the pandemic (p=0.033), level of physical activity (p=0.003), and time of service (p<0.001) were factors significantly associated with PTSD (Table 2).

Table 2

Table 2. Characteristics associated with post-traumatic stress disorder in the bivariate analysis.

Factors associated with post-traumatic stress disorder in the simple and multiple regression model analysis

Through simple regression analysis, we could find that the factors associated with a higher prevalence of PTSD were alcoholism (PR: 1.95; 95% IC: 1.09-3.52), smoking (PR: 4.27; 95% IC: 2.18-8.38), history of mental health (PR: 3.78; 95% IC: 1.13-12.60), trust in the government to manage the pandemic (PR: 1.81; 95% IC: 1.04-3.17), having been working between 13 and 18 months (PR: 4.21; 95% IC: 1.75-10.12), insomnia (PR: 12.30; 95% IC: 6.04-25.04) fear of COVID-19 (PR: 13.99; 95% IC: 6.86-28.56). On the contrary, the factors associated with a lower level of prevalence of PTSD were the following: having a high level of physical activity (PR: 0.34; 95% IC: 0.19-0.63), and a high level of resilience (PR: 0.44; 95% IC: 0.23-0.85) (Table 3).

Table 3

Table 3. Factors associated with post-traumatic stress disorder in Lambayeque, in the analysis of simple regression model.

The analysis of the multiple regression model showed that smoking (PR: 2.84; 95% IC: 1.14-7.09), having between seven and 12 months of service (PR: 2.52; 95% IC: 1.12-5.66), and from 13 to 18 months (PR: 2.62; 95% IC: 1.20-5.75) were associated with a higher level of prevalence of PTSD in military personnel. Military members with insomnia were associated with a 309% higher prevalence of PTSD (PR: 4.09; 95% IC: 1.90-8.83). Likewise, experiencing fear of COVID-19 was associated with a 520% higher prevalence of PTSD (PR: 6.20; 95% IC: 2.70-14.22), compared with those who did not report this fear (Figure 2).

Figure 2

Figure 2. Factors associated with PTSD in military personnel.

Correlation analysis

PCL-C scores were positively correlated with insomnia severity (ρ = 0.55, p < 0.001) and fear of COVID-19 (ρ = 0.50, p < 0.001), while a weak negative correlation was observed with resilience (ρ = –0.11, p = 0.011). Table 4.

Table 4

Table 4. Spearman’s correlations between PCL-C scores and key variables.

Discussion

Prevalence of post-traumatic stress disorder

As far as we know, this is the first study that assesses PTSD in a representative sample of military personnel from a Latin American country. We found that almost one out of ten participants (7.8%) had PTSD. This prevalence is lower than that reported by Corzo P. et al. (77), who reported a prevalence of 16.6% in Colombian combatants. It is important to highlight that these authors used a different instrument (CAPS) and a smaller sample (42 participants). Similarly, Almanza Muñoz et al. (23), in Mexico, estimated a prevalence of 4.3% in the Mexican army. We can remark that this is the only study conducted in Latin America. Kaspersen y Matthiesen (78) reported a prevalence of 2.8 to 7.8% among the Norwegian soldiers. Marmar et al. (39) reported a prevalence of 4.5% and 11.2% in Vietnamese military members. We have to mention that these studies were conducted in pre-pandemic contexts and used different instruments (psychiatric interview, PSTSS-10, and M-PSTD). The results were supported by the meta-analysis carried out by Petereit-Haack et al. (79), who found a prevalence of PTSD of 1.67% after analyzing 22 studies that used PCL-C as an evaluation instrument.

In contrast, studies conducted during the COVID-19 pandemic have generally reported higher prevalence levels. Hill et al., who reported a prevalence of 16.6% of PTSD among American military personnel (18 to 44 years old) (15). Similarly, Pedersen et al. reported that 31.2% of combat veterans had PTSD (26). Pietrzak et al. reported that 12.8% of military members had PTSD symptoms (27). We can note that these studies were conducted in the first wave of the pandemic and used the PCL-5 instrument. Our results are different from the ones found in studies among health personnel on the first line of defense with a prevalence of 10.7 to 19.9%, in which the Chinese version of the Essen Trauma Inventory (ETI) and PCL-5 were used as measurement instruments, respectively (80, 81). Similarly, in the general population, prevalence values of 4.6 to 55.3% have been reported, in which the Acute Stress Disorder Scale instrument was used (82, 83). We can highlight that these studies were not conducted in the first wave and in populations who are not Latin American. Therefore, our results contribute to the literature about this topic and with this population group in particular, as they may be more vulnerable to developing mental health disorders such as PTSD. This vulnerability may be related to factors such as sleep quality disturbances (84), isolation and family distancing (85) that were more frequently reported during the pandemic.

Factors associated with post-traumatic stress

Through this research, we found that a greater number of months working on the frontline of the COVID-19 health emergency was associated with a higher prevalence of PTSD, as participants who had between seven and 12 months and between 13 and 18 months of service showed 152% and 162% higher prevalence, respectively. This is similar to what was reported by Gonzáles-Penagos et al. (86) who showed that the participants with the highest working time (eight and 12 hours a day) had higher levels of prevalence (PR = 4,24; 95% IC, 0,71-25,3) of PTSD in Colombian forces who are exposed to public order disturbances. Similarly, Grieger et al. (87) identified that the active American soldiers had PTSD at the 1rst, 4th, and 7th month (4.4%, 12.2%, and 12.0%, respectively). In addition, Grieger et. al (13)., in their meta-analysis of 32 studies, showed that a greater number of time of service is directly correlated with the increase in the probability of developing PTSD (OR = 1,28; 95% IC, 1,01-1,26) in active military personnel and veterans. We can highlight that these three studies were not conducted in the pandemic context for which our result contributes with evidence to current literature. Those findings may be understood in light of the fact that time of service, deployment, and the re-experiencing of active duty tend to be associated with higher levels of PTSD symptoms. These associations may have been further reinforced by the constant stress experienced during the pandemic (the study population corresponded to war veterans) (88).

Participants who reported frequent smoking were associated with a 184% higher prevalence of PTSD. Similarly, the study conducted by Currie Cheryl in Canadian general population in the pandemic context found that females and males that consumed substances were 2.2 and 2.3 times more likely to have post-traumatic stress symptoms, respectively (89). This is similar to what was reported by Dabs et al. (90), who showed that active military personnel with substance dependency (opiates) had higher probability (OR = 28; CI: 21,2-37,7) of developing post-traumatic stress. In addition, Cotter LB et al. (91) showed a positively significant association between substance use (cocaine/opiates) and PTSD symptoms in general population. Moreover, it has been noted that the onset of substance use occurs in parallel with PTSD symptoms (92). We can highlight that the data reported in these three studies come from non-pandemic contexts. Models of association between these variables have been proposed, in a bidirectional manner, whereby patients with PTSD may use substances as a coping strategy to alleviate symptoms, while substance use itself may be related to an increased likelihood of experiencing PTSD symptoms (68, 71), as in our study. Among the proposed mechanisms is “self-medication” referring to people with PTSD who smoke cigarettes in order to alleviate and cope with the symptoms of the disorder (72). However, the number of studies examining these associations remains limited (90–95).

Similarly, those who had insomnia showed an association with a 309% higher prevalence of PTSD. This result is consistent with a study that was conducted in people infected with COVID-19 in the United States, which showed that the relationship between the long-term variable increased significantly in a positive direction (96). In addition, a study carried out in the pandemic context in general population that evidenced insomnia as a risk factor for PTSD according to the time since the start of the pandemic, which was a 26% higher after four months and 40.0% after nine months, different from those who did not have insomnia (97). This is similar to what was reported by McLay et al. (98), who reported insomnia as the main comorbidity that is associated with developing PTSD among military members returning from deployment activities. Armenta et al. (10) identified that military personnel that slept less than four hours a night had a higher risk of developing persistent PTSD. Miles et al. (99) demonstrated the PTSD severity was associated with severity of nightmares, sleep time, and insomnia. It is important to highlight that these studies were performed in pre-pandemic contexts. The findings could be explained by the fact that having comorbidity conditions lead to a dysregulation of the neuroendocrine system (100), where the exact mechanisms are still unknown in the context of the pandemic (101). Furthermore, it has been postulated that insomnia is a predictor of long-term PTSD; however, the bidirectional relationship between the variables is highlighted (10). Finally, it has been proved that the Mindfulness-Based Cognitive Therapy focused on sleep (MBCT) reduces PTSD symptoms (102).

Fear of COVID-19 was strongly associated with a 520% higher prevalence of PTSD. This is similar to what was reported by McLay et al. (103), who proved that those American military veterans were concerned about the pandemic were more likely to have PTSD (OR = 1,10, 95% IC:1.05-1.14). Similarly, Hendrikx et al., during the pandemic, reported that those military veterans who were more worried as a stressor increased the probability of PTSD (OR = 6,30, 95% IC:1.97-20.13). We can mention that they used the PCL-5 instrument to measure PTSD and did not use a validated instrument to measure fear of COVID-19. On the other hand, they formulated a variety of questions related with the effects of the pandemic (104). Supporting our result, it has been identified that there exists a relationship between fear of COVID-19 and the development of mental health disorders (PTSD, depression, anxiety, and stress) in Canada, Italy and Peru (34–36). However, we did not find studies that evaluate the same variables in military population; thus, our study contributes to the existent literature. This association may be related to fear of death from COVID-19 or the loss of a relative, which can constitute traumatic experiences and intense grief, and both situations may act as potential risk factors for chronic PTSD symptoms (105).

Finally, the prevalence of PTSD appeared to be reduced by 56% in participants with high resilience; however, this protective association was not statistically significant in the final adjusted model. This is similar to what was reported by de Roon-Cassini et al. (106) who identified that people who reported high resilience did not present PSTD symptoms. Moreover, Ghaffarzadegan et al. (107) suggested that resilience may be effective in mitigating PTSD symptoms in military personnel and veterans. One possible explanation for the lack of statistical significance in our final model is that resilience may overlap with other proximal determinants such as insomnia and fear of COVID-19, which exert stronger direct associations with PTSD. In this sense, resilience could act as a distal coping factor whose apparent protective role becomes attenuated once acute stressors and comorbidities are accounted for. Another explanation may involve residual confounding, as unmeasured psychosocial resources (e.g., social support, prior trauma exposure) were not included in our models. This potential effect may be related to adaptive coping and positive personality traits that are associated with a lower frequency of PTSD symptoms (104). It should be noted that the studies were conducted in a pre-pandemic context. Overall, our findings suggest that resilience alone may not be sufficient to buffer PTSD risk in high-intensity contexts such as the pandemic, highlighting the need for integrated interventions that combine resilience-building strategies with approaches targeting sleep health, stress management, and broader psychosocial support. One interpretation of this finding is that military training and the acquisition of adaptive coping skills in the management of previous stressors could help enhance resilience in regard to pandemic-related stressors (108). Although resilience was analyzed as a covariate in our models, it may also act as a moderator, influencing how stressors such as insomnia or fear of COVID-19 translate into PTSD symptoms. Future studies with longitudinal designs are needed to examine these potential interaction effects. Furthermore, while we applied the ≥30 threshold to ensure comparability with local literature, alternative approaches such as tertile- or percentile-based classifications could yield different patterns and should be considered in future research.

In our study, smoking, insomnia, and fear of COVID-19 emerged as factors associated with higher PTSD prevalence. These should not be interpreted as PTSD symptoms themselves. PTSD symptoms—such as flashbacks, avoidance, and hyperarousal—were assessed directly through the PCL-C instrument. Our findings therefore highlight how external behavioral and psychosocial exposures can increase vulnerability to PTSD, beyond the disorder’s intrinsic symptomatology.

Implications of findings for mental health

Our results show that PSTD is prevalent in military populations. Post-pandemic interventions should focus on detection and treatment in order to mitigate the psychiatric sequelae of an unanticipated event and prevent the occurrence of persistent PTSD. Given that there is a high level of prevalence in active military personnel, screening methods become a public health necessity, such as those carried out in our country through the directives of the Ministry of Health. These directives allocate a monthly budget for mental health screening (109) and the Peruvian army, which implements a comprehensive health care model for the military community at the primary health care level (110). In addition, our results could serve as a precedent for having been carried out in Peruvian military personnel, which adds Latin American evidence to the world literature.

From a policy perspective, our findings support the need for annual screening of PTSD symptoms among active-duty military personnel, with particular attention to individuals with insomnia, smoking habits, or elevated fear of infectious threats. Referral pathways to mental health professionals should be standardized, ensuring timely access to specialized care. At the unit level, insomnia reduction and sleep-hygiene programs could be implemented to address one of the strongest predictors identified in our analysis. Furthermore, resilience-building interventions (e.g., stress management workshops, peer support groups) should be incorporated into ongoing military training, strengthening protective factors against PTSD in future crises.

Limitations and strengths

Our findings are subject to several limitations that should be considered. First, the cross-sectional design precludes establishing causality between independent variables and PTSD. As a result, the associations identified should not be interpreted as causal effects, and it remains uncertain whether factors such as smoking, insomnia, and fear of COVID-19 preceded the development of PTSD or emerged as consequences of it. We also emphasize that neither correlations nor prevalence ratios establish causality, and future longitudinal cohort or case–control studies are warranted to confirm temporal relationships. Nevertheless, we used validated instruments with strong internal consistency, such as the PCL-C, which strengthens measurement validity (111). Second, the use of self-administered questionnaires may have introduced recall or reporting bias. To minimize this risk, participants were recruited during active service and data collection was coordinated in person, ensuring comprehension of the questions. Third, limitation relates to the sampling strategy. Because snowball sampling was used, there is potential for selection bias, which may have affected the representativeness of the sample and limited the external validity of the findings. Although this approach was the only feasible strategy during the COVID-19 state of emergency, efforts were made to reduce bias by coordinating recruitment through unit supervisors, achieving a high participation rate (86.6%), and including personnel from all service groups within the brigade. Fourth, unmeasured confounders (e.g., previous trauma exposure, social support, coping strategies) were not captured in our models, which may have influenced the observed associations. Another limitation is that the psychometric instruments used (ISI, CD-RISC-10, and FCV-19S) have not been specifically validated in Peruvian military populations. However, they have demonstrated robust validity and reliability in culturally and contextually relevant groups, such as Spanish- and Latin American university students, healthcare workers, and military samples in other countries, including during pandemic contexts (2, 66, 84, 112). This supports their applicability to our study population, although further validation in Peruvian military personnel remains warranted. Also, PTSD was assessed using the PCL-C with a cut-off of ≥44 points. While this threshold is widely used and has been validated in other Spanish-speaking and military populations (67), it has not been specifically validated in Peruvian military personnel, which could have led to a slight misclassification of cases. Furthermore, our findings may not be generalizable to other military populations or regions, as cultural norms, operational structures, and context-specific stressors vary widely across armed forces. Therefore, the results should be interpreted within the context of military personnel from northern Peru, a region heavily impacted by COVID-19 morbidity and mortality during the pandemic, and future research is needed to examine whether similar patterns are observed in other Peruvian or international military settings. Another limitation is that PTSD was assessed through the PCL-C self-report scale rather than a structured clinical interview (e.g., CAPS-5), which may slightly over- or underestimate prevalence; however, the PCL-C has strong psychometric validity and is widely used in epidemiological research. Finally, another limitation is the reliance on self-administered questionnaires, which are vulnerable to information bias, including recall errors and social desirability effects. These biases may have led to underreporting of sensitive behaviors (e.g., smoking, alcohol use) or mental health symptoms. These factors could have affected both the prevalence estimates and the observed associations.

This study presents several strengths. First, it addresses PTSD in military personnel during the COVID-19 pandemic, a critical and underexplored issue in Latin America. Second, the relatively large sample of 615 participants, collected during the second wave when military personnel were actively on duty, enhances statistical power and contextual relevance. Third, we employed well-established psychometric instruments (e.g., PCL-C, ISI, CD-RISC, FCV-19S), many of which have been validated in Spanish-speaking populations, ensuring cultural and linguistic applicability. Fourth, the use of both bivariate and multivariate regression models strengthened the robustness of the analyses.

Conclusions

It was found that almost one out of ten participants had PTSD. Among the factors associated with PTSD, we can mention number of years of service, smoking, insomnia, and fear of COVID-19. High resilience was associated with lower prevalence in crude models but was not statistically significant after adjustment. The found results highlight the importance of evaluating, following, and treating mental health disorders in active military members.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics statement

The protocol study was approved by the Ethics Committee of the Universidad San Martin de Porres (File N°269-2021-CIEI-FMH-USMP). All participating military personnel provided their informed consent prior to completing the survey, which was anonymous, and confidentiality of the respondents was guaranteed. We respected the ethical principles of the Helsinki Declaration. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

MV-G: Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. CP-R: Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. JZ-V: Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. VF-R: Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. CP: Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. DV-G: Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing. VV-P: Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declare financial support was received for the research and/or publication of this article. The study was self-financed. MV-G was supported by the Fogarty International Center of the National Institutes of Mental Health (NIMH) under Award Number D43TW009343 and the University of California Global Health Institute.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

References

1. Fukase Y, Ichikura K, and Tagaya H. Symptoms and risk factors of depression and PTSD in the prolonged COVID-19 pandemic: a longitudinal survey conducted from 2020 to 2022 in Japan. BMC Psychiatry. (2023) 23:180. doi: 10.1186/s12888-023-04670-7

PubMed Abstract | Crossref Full Text | Google Scholar

2. Valladares-Garrido MJ, Picón-Reátegui CK, Zila-Velasque JP, Grados-Espinoza P, Hinostroza-Zarate CM, Failoc-Rojas VE, et al. Suicide risk in military personnel during the COVID-19 health emergency in a Peruvian region: A cross-sectional study. Int J Environ Res Public Health. (2022) 19:13502. doi: 10.3390/ijerph192013502

PubMed Abstract | Crossref Full Text | Google Scholar

3. Hernández-Yépez PJ, Muñoz-Pino CO, Ayala-Laurel V, Contreras-Carmona PJ, Inga-Berrospi F, Vera-Ponce VJ, et al. Factors associated with anxiety, depression, and stress in Peruvian university students during the COVID-19 pandemic. Int J Environ Res Public Health. (2022) 19:14591. doi: 10.3390/ijerph192114591

PubMed Abstract | Crossref Full Text | Google Scholar

4. Aveiro-Róbalo TR, Garlisi-Torales LD, Chumán-Sánchez M, Pereira-Victorio CJ, Huaman-Garcia M, Failoc-Rojas VE, et al. Prevalence and associated factors of depression, anxiety, and stress in university students in Paraguay during the COVID-19 pandemic. Int J Environ Res Public Health. (2022) 19. doi: 10.3390/ijerph191912930

PubMed Abstract | Crossref Full Text | Google Scholar

5. Santander-Hernández FM, Peralta CI, Guevara-Morales MA, Díaz-Vélez C, and Valladares-Garrido MJ. Smartphone overuse, depression & anxiety in medical students during the COVID-19 pandemic. PloS One. (2022) 17:e0273575. doi: 10.1371/journal.pone.0273575

PubMed Abstract | Crossref Full Text | Google Scholar

6. Hansen KT, Plouffe RA, Walker DL, Wanklyn SG, Lamrock L, Maher P, et al. Impact of the COVID-19 pandemic on the mental health and well-being of Veterans’ spouses: a cross sectional analysis. BMC Psychiatry. (2023) 23:188. doi: 10.1186/s12888-023-04687-y

PubMed Abstract | Crossref Full Text | Google Scholar

7. Fernandez-Canani MA, Burga-Cachay SC, and Valladares-Garrido MJ. Association between family dysfunction and post-traumatic stress disorder in school students during the second COVID-19 epidemic wave in Peru. Int J Environ Res Public Health. (2022) 19:9343. doi: 10.3390/ijerph19159343

PubMed Abstract | Crossref Full Text | Google Scholar

8. PTSD in military veterans - helpGuide.org . Available online at: https://www.helpguide.orghttps://www.helpguide.org/articles/ptsd-trauma/ptsd-in-military-veterans.htm (Accessed July 14, 2022).

Google Scholar

9. Wynn G, Morganstein JC, Jetly R, Ford SC, Vance MC, Meyer EG, et al. Military mental health and COVID-19. J Military Veteran Family Health. (2020) 6:21–6. doi: 10.3138/jmvfh-2020-0048

Crossref Full Text | Google Scholar

10. Armenta RF, Rush T, LeardMann CA, Millegan J, Cooper A, Hoge CW, et al. Factors associated with persistent posttraumatic stress disorder among U.S. military service members and veterans. BMC Psychiatry. (2018) 18:48. doi: 10.1186/s12888-018-1590-5

PubMed Abstract | Crossref Full Text | Google Scholar

11. Schultebraucks K, Qian M, Abu-Amara D, Dean K, Laska E, Siegel C, et al. Pre-deployment risk factors for PTSD in active-duty personnel deployed to Afghanistan: a machine-learning approach for analyzing multivariate predictors. Mol Psychiatry. (2021) 26:5011–22. doi: 10.1038/s41380-020-0789-2

PubMed Abstract | Crossref Full Text | Google Scholar

12. Wal SJ, van der, Vermetten E, and Elbert G. Long-term development of post-traumatic stress symptoms and associated risk factors in military service members deployed to Afghanistan: Results from the PRISMO 10-year follow-up. Eur Psychiatry. (2021) 64. doi: 10.1192/j.eurpsy.2020.113

PubMed Abstract | Crossref Full Text | Google Scholar

13. Xue C, Ge Y, Tang B, Liu Y, Kang P, Wang M, et al. A meta-analysis of risk factors for combat-related PTSD among military personnel and veterans. PloS One. (2015) 10:e0120270. doi: 10.1371/journal.pone.0120270

PubMed Abstract | Crossref Full Text | Google Scholar

14. Richardson A, Gurung G, Samaranayaka A, Gardner D, deGraaf B, Wyeth EH, et al. Risk and protective factors for post-traumatic stress among New Zealand military personnel: A cross sectional study. PloS One. (2020) 15:e0231460. doi: 10.1371/journal.pone.0231460

PubMed Abstract | Crossref Full Text | Google Scholar

15. Hill ML, Nichter B, Na PJ, et al. Mental health impact of the COVID-19 pandemic in U.S. military veterans: a population-based, prospective cohort study. Psychol Med. (2021), 1–12. doi: 10.1017/S0033291721002361

PubMed Abstract | Crossref Full Text | Google Scholar

16. Steenkamp MM, Litz BT, Hoge CW, and Marmar CR. Psychotherapy for military-related PTSD: A review of randomized clinical trials. JAMA. (2015) 314:489–500. doi: 10.1001/jama.2015.8370

PubMed Abstract | Crossref Full Text | Google Scholar

17. Richardson LK, Frueh BC, and Acierno R. Prevalence estimates of combat-related post-traumatic stress disorder: critical review. Aust N Z J Psychiatry. (2010) 44:4–19. doi: 10.3109/00048670903393597

PubMed Abstract | Crossref Full Text | Google Scholar

18. Stevelink SAM, Jones M, Hull L, Pernet D, MacCrimmon S, Goodwin L, et al. Mental health outcomes at the end of the British involvement in the Iraq and Afghanistan conflicts: a cohort study. Br J Psychiatry. (2018) 213:690–7. doi: 10.1192/bjp.2018.175

PubMed Abstract | Crossref Full Text | Google Scholar

19. Richardson JD, Thompson A, King L, et al. Comorbidity patterns of psychiatric conditions in canadian armed forces personnel. Can J Psychiatry. (2019) 64:501–10. doi: 10.1177/0706743718816057

PubMed Abstract | Crossref Full Text | Google Scholar

20. Engelhard IM, van den Hout MA, Weerts J, Arntz A, Hox JJCM, and McNally RJ. Deployment-related stress and trauma in Dutch soldiers returning from Iraq. Prospective study. Br J Psychiatry. (2007) 191:140–5. doi: 10.1192/bjp.bp.106.034884

PubMed Abstract | Crossref Full Text | Google Scholar

21. Garzon M, Monroy Galeano JP, Castiblanco J, Espinosa M, and Baez A. Prevalencia de trastorno de estrés postraumàtico en pacientes heridos en combate hospitalizados en el Hospital Militar Central Bogota Servicio de Psiquiatria . Available online at: http://repository.unimilitar.edu.co/handle/10654/16180 (Accessed December 13, 2016).

Google Scholar

22. Goyes AB, Tuta-Quintero E, Hincapíe-Díaz G, Rodríguez AR, Piotrostanalsky A, and Contreras-Candelo S. Trastorno de estrés postraumático en una cohorte de militares con trauma de guerra. Rev Cubana Med Militar. (2021) 50:02101591.

Google Scholar

23. Jose PA, Muños FA, Hernández DM, Genaro BA, and Sergio AM. Prevalencia del tratsorno por estrés postraumático en el Ejército Mexicano. Rev Sanidad Mil Mex. (1996) 50:6.

Google Scholar

24. Laura Ccosi AK. Trastorno por estrés postraumático en militares de una institución del Ejército del Perú (2019). Universidad Peruana Unión. Available online at: https://repositorio.upeu.edu.pe/handle/20.500.12840/1794 (Accessed July 17, 2022).

Google Scholar

25. Otero-Campos I, Otero G, and Ashwell F. Prevalencia y sintomatología específica del trastorno por estrés post-traumático (TEPT) en militares expuestos a la erupción del volcán chaitén. Lima - Peru: Universidad Peruana Union (UPEU) (2017).

Google Scholar

26. Pedersen ER, Davis JP, Fitzke RE, Lee DS, and Saba S. American veterans in the era of COVID-19: reactions to the pandemic, posttraumatic stress disorder, and substance use behaviors. Int J Ment Health Addict. (2021), 1–16. doi: 10.1007/s11469-021-00620-0

PubMed Abstract | Crossref Full Text | Google Scholar

27. Pietrzak RH, Tsai J, and Southwick SM. Association of symptoms of posttraumatic stress disorder with posttraumatic psychological growth among US veterans during the COVID-19 pandemic. JAMA Netw Open. (2021) 4:e214972. doi: 10.1001/jamanetworkopen.2021.4972

PubMed Abstract | Crossref Full Text | Google Scholar

28. Letica-Crepulja M, Stevanović A, Grković J, Rončević-Gržeta I, Jovanović N, and Frančišković T. Posttraumatic stress disorder symptoms and coping with the lockdown among help-seeking veterans before and during the COVID-19 pandemic. Croat Med J. (2021) 62:241–9. doi: 10.3325/cmj.2021.62.241

PubMed Abstract | Crossref Full Text | Google Scholar

29. Zaccari B, Loftis JM, Haywood T, Hubbard K, Clark J, and Kelly UA. Synchronous telehealth yoga and cognitive processing group therapies for women veterans with posttraumatic stress disorder: A multisite randomized controlled trial adapted for COVID-19. Telemed J E Health. (2022). doi: 10.1089/tmj.2021.0612

PubMed Abstract | Crossref Full Text | Google Scholar

30. Cénat JM, Blais-Rochette C, Kokou-Kpolou CK, Noorishad PG, Mukunzi JN, McIntee SE, et al. Prevalence of symptoms of depression, anxiety, insomnia, posttraumatic stress disorder, and psychological distress among populations affected by the COVID-19 pandemic: A systematic review and meta-analysis. Psychiatry Res. (2021) 295:113599. doi: 10.1016/j.psychres.2020.113599

PubMed Abstract | Crossref Full Text | Google Scholar

31. Janiri D, Carfì A, Kotzalidis GD, et al. Posttraumatic stress disorder in patients after severe COVID-19 infection. JAMA Psychiatry. (2021) 78:567–9. doi: 10.1001/jamapsychiatry.2021.0109

PubMed Abstract | Crossref Full Text | Google Scholar

32. Singh SP and Khokhar A. Prevalence of posttraumatic stress disorder and depression in general population in India during COVID-19 pandemic home quarantine. Asia Pac J Public Health. (2021) 33:154–6. doi: 10.1177/1010539520968455

PubMed Abstract | Crossref Full Text | Google Scholar

33. Lee Y, Kim K, Park S, and Jung SJ. Associations between general perceptions of COVID-19 and posttraumatic stress disorder in korean hospital workers: effect modification by previous middle east respiratory syndrome coronavirus experience and occupational type. J Prev Med Public Health. (2021) 54:86–95. doi: 10.3961/jpmph.20.540

PubMed Abstract | Crossref Full Text | Google Scholar

34. Villarreal-Zegarra D, Copez-Lonzoy A, Vilela-Estrada AL, and Huarcaya-Victoria J. Depression, post-traumatic stress, anxiety, and fear of COVID-19 in the general population and health-care workers: prevalence, relationship, and explicative model in Peru. BMC Psychiatry. (2021) 21:455. doi: 10.1186/s12888-021-03456-z

PubMed Abstract | Crossref Full Text | Google Scholar

35. Sharif-Esfahani P, Hoteit R, El Morr C, and Tamim H. Fear of COVID-19 and depression, anxiety, stress, and PTSD among Syrian refugee parents in Canada. J Migr Health. (2022) 5:100081. doi: 10.1016/j.jmh.2022.100081

PubMed Abstract | Crossref Full Text | Google Scholar

36. Velotti P, Civilla C, and Rogier G. Beomonte zobel S. A fear of COVID-19 and PTSD symptoms in pathological personality: the mediating effect of dissociation and emotion dysregulation. Front Psychiatry. (2021) 12:590021. doi: 10.3389/fpsyt.2021.590021

PubMed Abstract | Crossref Full Text | Google Scholar

37. Letica-Crepulja M, Stevanović A, Palaić D, Vidović I, and Frančišković T. PTSD symptoms and coping with COVID-19 pandemic among treatment-seeking veterans: prospective cohort study. J Clin Med. (2022) 11:2715. doi: 10.3390/jcm11102715

PubMed Abstract | Crossref Full Text | Google Scholar

38. Na PJ, Straus E, Tsai J, SB N, SM S, and Pietrzak RH. Loneliness in U.S. military veterans during the COVID-19 pandemic: A nationally representative, prospective cohort study. J Psychiatr Res. (2022) 151:546–53. doi: 10.1016/j.jpsychires.2022.05.042

PubMed Abstract | Crossref Full Text | Google Scholar

39. Marmar CR, Schlenger W, Henn-Haase C, Qian M, Purchia E, Li M, et al. Course of posttraumatic stress disorder 40 years after the Vietnam war: findings from the national Vietnam veterans longitudinal study. JAMA Psychiatry. (2015) 72:875–81. doi: 10.1001/jamapsychiatry.2015.0803

PubMed Abstract | Crossref Full Text | Google Scholar

40. Walter KH, Levine JA, Highfill-McRoy RM, Navarro M, and Thomsen CJ. Prevalence of posttraumatic stress disorder and psychological comorbidities among U.S. Active duty service members, 2006-2013. J Trauma Stress. (2018) 31:837–44. doi: 10.1002/jts.22337

PubMed Abstract | Crossref Full Text | Google Scholar

41. Inoue C, Shawler E, Jordan CH, and Jackson CA. Veteran and military mental health issues. In: StatPearls. StatPearls Publishing (2022). Available online at: http://www.ncbi.nlm.nih.gov/books/NBK572092/.

PubMed Abstract | Google Scholar

42. Cypel YS, Katon JG, Schure MB, and Smith S. Inseguridad alimentaria en veteranos militares de EE. UU. Food Nutr Bull. (2020) 41:399–423. doi: 10.1177/0379572120963952

PubMed Abstract | Crossref Full Text | Google Scholar

43. Kamphuis W, Delahaij R, Duel J, Geuze E, and Vermetten E. The relationship between resilience resources and long-term deployment-related PTSD symptoms: A longitudinal study in dutch veterans. Military Behav Health. (2021) 9:267–74. doi: 10.1080/21635781.2020.1864528

Crossref Full Text | Google Scholar

44. Díaz-Vélez C, Failoc-Rojas VE, Valladares-Garrido MJ, Colchado J, Carrera-Acosta L, and Becerra M. SARS-CoV-2 seroprevalence study in Lambayeque, Peru. June–July 2020. PeerJ. (2021) 9:e11210. doi: 10.7717/peerj.11210

PubMed Abstract | Crossref Full Text | Google Scholar

45. Díaz-Vélez C, Urrunaga-Pastor D, Romero-Cerdán A, et al. Risk factors for mortality in hospitalized patients with COVID-19 from three hospitals in Peru: a retrospective cohort study. F1000Res. (2021) 10:224. doi: 10.12688/f1000research.51474.1

PubMed Abstract | Crossref Full Text | Google Scholar

46. Flores Morales R, Reyes Pérez V, and Reidl Martínez LM. Síntomas de estrés postraumático (Ept) en periodistas mexicanos que cubren la guerra contra el narcotráfico. Suma Psicológica. (2012) 19:7–17. doi: 10.12688/f1000research.51474.1

PubMed Abstract | Crossref Full Text | Google Scholar

47. Durón-Figueroa R, Cárdenas-López G, Castro-Calvo J, et al. Adaptación de la Lista Checable de Trastorno por Estrés Postraumático para DSM-5 en Población Mexicana. Acta investigación psicológica. (2019) 9:26–36. doi: 10.22201/fpsi.20074719e.2019.1.03

Crossref Full Text | Google Scholar

48. Zila-Velasque JP, Grados-Espinoza P, Quispe-Chura K, Valdiviezo-Morales CG, Diaz-Vélez C, and Valladares-Garrido MJ. Prevalence and factors associated with food insecurity in eight high-altitude cities in Peru during the second wave of the COVID-19 pandemic: a retrospective, cross-sectional study. BMC Public Health. (2022) 22:1962. doi: 10.1186/s12889-022-14372-6

PubMed Abstract | Crossref Full Text | Google Scholar

49. Vargas Puello V, Alvarado Orellana S, and Atalah Samur E. Inseguridad alimentaria en adultos mayores en 15 comunas del Gran Santiago: un tema pendiente. Nutrición Hospitalaria. (2013) 28:1430–7. doi: 10.3305/nh.2013.28.5.6663

PubMed Abstract | Crossref Full Text | Google Scholar

50. Hamedi-Shahraki S, Mir F, and Amirkhizi F. Food insecurity and cardiovascular risk factors among Iranian women. Ecol Food Nutr. (2021) 60:163–81. doi: 10.1080/03670244.2020.1812596

PubMed Abstract | Crossref Full Text | Google Scholar

51. Fernandez-Mendoza J, Rodriguez-Muñoz A, Vela-Bueno A, et al. The Spanish version of the Insomnia Severity Index: a confirmatory factor analysis. Sleep Med. (2012) 13:207–10. doi: 10.1016/j.sleep.2011.06.019

PubMed Abstract | Crossref Full Text | Google Scholar

52. Sierra JC, Guillén-Serrano V, and Santos-Iglesias P. Insomnia Severity Index: some indicators about its reliability and validity on an older adults sample. Rev Neurol. (2008) 47:566–70. doi: 10.33588/rn.4711.2008221

PubMed Abstract | Crossref Full Text | Google Scholar

53. Kaufmann CN, Orff HJ, Moore RC, Delano-Wood L, Depp CA, and Schiehser DM. Psychometric characteristics of the insomnia severity index in veterans with history of traumatic brain injury. Behav Sleep Med. (2019) 17:12–8. doi: 10.1080/15402002.2016.1266490

PubMed Abstract | Crossref Full Text | Google Scholar

54. Moscou-Jackson G, Allen J, Smith MT, and Haywood CJ. Psychometric validation of the insomnia severity index in adults with sickle cell disease. J Health Care Poor Underserved. (2016) 27:209–18. doi: 10.1016/j.sleep.2011.06.019

PubMed Abstract | Crossref Full Text | Google Scholar

55. Yusufov M, Zhou ES, and Recklitis CJ. Psychometric properties of the Insomnia Severity Index in cancer survivors. Psycho-Oncology. (2019) 28:540–6. doi: 10.1002/pon.4973

PubMed Abstract | Crossref Full Text | Google Scholar

56. Bastien CH, Vallières A, and Morin CM. Validation of the Insomnia Severity Index as an outcome measure for insomnia research. Sleep Med. (2001) 2:297–307. doi: 10.1016/s1389-9457(00)00065-4

PubMed Abstract | Crossref Full Text | Google Scholar

57. Pruiksma KE, Taylor DJ, Schuster Wachen J, Straud CL, Hale WJ, Mintz J, et al. Self-reported sleep problems in active-duty US Army personnel receiving posttraumatic stress disorder treatment in group or individual formats: secondary analysis of a randomized clinical trial. J Clin Sleep Med. (2023) 19:1389–98. doi: 10.5664/jcsm.10584

PubMed Abstract | Crossref Full Text | Google Scholar

58. Mantilla Toloza SC and Gómez-Conesa A. El Cuestionario Internacional de Actividad Física. Un instrumento adecuado en el seguimiento de la actividad física poblacional. In: Rev iberoam fisioter kinesiol (Ed impr). (2007). p. 12–23.

Google Scholar

59. Limb ES, Ahmad S, Cook DG, et al. Measuring change in trials of physical activity interventions: a comparison of self-report questionnaire and accelerometry within the PACE-UP trial. Int J Behav Nutr Phys Activity. (2019) 16:10. doi: 10.1186/s12966-018-0762-5

PubMed Abstract | Crossref Full Text | Google Scholar

60. García-León M, González-Gómez A, Robles-Ortega H, Padilla JL, and Peralta-Ramírez MÍ. Propiedades psicométricas de la Escala de Resiliencia de Connor y Davidson (CD-RISC) en población española. Anales Psicología. (2019) 35:33–40. doi: 10.6018/analesps.35.1.314111

Crossref Full Text | Google Scholar

61. Leiva León NF. La resilencia como factor asociado al Sindrome de Burnout, depresión y ansiedad en el personal de salud que labora en las Unidades de Cuidados Intensivos durante la pandemia COVID-19 en el Perú (2021). Available online at: https://repositorio.upch.edu.pe/handle/20.500.12866/8964 (Accessed July 14, 2022).

Google Scholar

62. Tu Z, He J, Wang Z, Song M, Tian J, Wang C, et al. Psychometric properties of the 10-item Connor-Davidson Resilience Scale in Chinese military personnel. Front Psychol. (2023) 14:1163382. doi: 10.3389/fpsyg.2023.1163382

PubMed Abstract | Crossref Full Text | Google Scholar

63. Yan X, Wang X, Xu C, et al. Psychometric properties of the 10-item Connor-Davidson Resilience Scale (CD-RISC-10) in male military personnel with and without PTSD. J Affect Disord Rep. (2023) 14:100666. doi: 10.1016/j.jadr.2023.100666

Crossref Full Text | Google Scholar

64. Seperak-Viera R, Torres-Villalobos G, Gravini-Donado M, and Dominguez-Lara S. Invarianza factorial de dos versiones breves de la Escala de Resiliencia de Connor-Davidson (CD-RISC) en estudiantes universitarios de Arequipa. Acta Colombiana Psicología. (2023) 26:95–112. doi: 10.14718/acp.2023.26.1.7

Crossref Full Text | Google Scholar

65. Vélez-Botero H and Agudelo Hernández F. Validez y confiabilidad de la Escala Breve de Resiliencia Connor-Davidson para población adulta Colombiana. Tesis psicológica: Rev la Facultad Psicología. (2023) 18:8. doi: 10.1016/j.jadr.2023.100666

Crossref Full Text | Google Scholar

66. Valladares-Garrido MJ, Morocho-Alburqueque N, Zila-Velasque JP, et al. Sleep quality and associated factors in Latin American medical students: a cross-sectional and multicenter study. BMC Public Health. (2025) 25:755. doi: 10.1186/s12889-025-21569-y

PubMed Abstract | Crossref Full Text | Google Scholar

67. Valladares-Garrido MJ, Cueva-Cañola LE, Grados-Espinoza P, et al. Prevalence and factors associated with resilience in Peruvian adolescent schoolchildren during the early post-pandemic context: a cross-sectional study. Front Psychiatry. (2025) 16:1609190. doi: 10.3389/fpsyt.2025.1609190

PubMed Abstract | Crossref Full Text | Google Scholar

68. Notario-Pacheco B, Solera-Martínez M, Serrano-Parra MD, Bartolomé-Gutiérrez R, García-Campayo J, and Martínez-Vizcaíno V. Reliability and validity of the Spanish version of the 10-item Connor-Davidson Resilience Scale (10-item CD-RISC) in young adults. Health Qual Life Outcomes. (2011) 9:63. doi: 10.1186/1477-7525-9-63

PubMed Abstract | Crossref Full Text | Google Scholar

69. Scali J, Gandubert C, Ritchie K, Soulier M, Ancelin ML, and Chaudieu I. Measuring resilience in adult women using the 10-items Connor-Davidson Resilience Scale (CD-RISC). Role of trauma exposure and anxiety disorders. PloS One. (2012) 7:e39879. doi: 10.1371/journal.pone.0039879

PubMed Abstract | Crossref Full Text | Google Scholar

70. Ahorsu DK, Lin CY, Imani V, Saffari M, Griffiths MD, and Pakpour AH. The fear of COVID-19 scale: development and initial validation. Int J Ment Health Addiction. (2022) 20:1537–45. doi: 10.1007/s11469-020-00270-8

PubMed Abstract | Crossref Full Text | Google Scholar

71. Nikopoulou VA, Holeva V, Parlapani E, Karamouzi P, Voitsidis P, and Porfyri GN. Mental health screening for COVID-19: a proposed cutoff score for the greek version of the fear of COVID-19 scale (FCV-19S). Int J Ment Health Addiction. (2022) 20:907–20. doi: 10.1007/s11469-020-00414-w

PubMed Abstract | Crossref Full Text | Google Scholar

72. Mohsen F, Bakkar B, Alsrouji SK, et al. Fear among Syrians: A proposed cutoff score for the arabic fear of COVID-19 scale. PloS One. (2022) 17:e0264257. doi: 10.1371/journal.pone.0264257

PubMed Abstract | Crossref Full Text | Google Scholar

73. Yang W, Li P, Huang Y, et al. Cross-cultural adaptation and validation of the fear of COVID-19 scale for chinese university students: A cross-sectional study. Int J Environ Res Public Health. (2022) 19:8624. doi: 10.3390/ijerph19148624

PubMed Abstract | Crossref Full Text | Google Scholar

74. Huarcaya-Victoria J, Villarreal-Zegarra D, Podestà A, and Luna-Cuadros MA. Psychometric properties of a spanish version of the fear of COVID-19 scale in general population of lima, Peru. Int J Ment Health Addict. (2022) 20:249–62. doi: 10.1007/s11469-020-00354-5

PubMed Abstract | Crossref Full Text | Google Scholar

75. Moya-Salazar J, Sevillano-Jiménez J, Cañari B, Moya-Salazar MM, and Contreras-Pulache H. Validación de la escala de miedo a la COVID-19 en una muestra de estudiantes universitarios. Rev Cubana Med Militar. (2022) 51:e02201968–e02201968. doi: 10.3390/ijerph19148624

PubMed Abstract | Crossref Full Text | Google Scholar

76. Mercado-Lara MF, Campo-Arias A, and Monterrosa-Castro A. Validity and reliability of the spanish version of fear of COVID-19 scale in Colombian physicians. Int J Ment Health Addict. (2022) 20:1122–9. doi: 10.1007/s11469-020-00430-w

PubMed Abstract | Crossref Full Text | Google Scholar

77. Corzo P PA and Bohórquez Peñaranda AP. Prevalencia del trastorno por estrés agudo y trastorno por estrés postraumático en soldados Colombianos heridos en combate. Prevalencia da reação ao stress agudo e do transtorno por stress postraumático nos soldados feridos no combate hospitalizado no Hospital Militar Central de Bogotá. (2009). Available online at: http://repository.javeriana.edu.co/handle/10554/48428 (Accessed June 6, 2022).

Google Scholar

78. Kaspersen M and Matthiesen SB. Síntomas de Estrés Postraumático entre los soldados de Naciones Unidas y el personal perteneciente al voluntariado. Eur J Psychiatry (edición en español). (2003) 17:69–77. doi: 10.1007/s11469-020-00430-w

PubMed Abstract | Crossref Full Text | Google Scholar

79. Petereit-Haack G, Bolm-Audorff U, Romero Starke K, and Seidler A. Occupational risk for post-traumatic stress disorder and trauma-related depression: A systematic review with meta-analysis. Int J Environ Res Public Health. (2020) 17:9369. doi: 10.3390/ijerph17249369

PubMed Abstract | Crossref Full Text | Google Scholar

80. Xiong LJ, Zhong BL, Cao XJ, Xiong HG, Huang M, and Ding J. Possible posttraumatic stress disorder in Chinese frontline healthcare workers who survived COVID-19–6 months after the COVID-19 outbreak: prevalence, correlates, and symptoms. Transl Psychiatry. (2021) 11:374. doi: 10.1038/s41398-021-01503-7

PubMed Abstract | Crossref Full Text | Google Scholar

81. Geng S, Zhou Y, Zhang W, et al. The influence of risk perception for COVID-19 pandemic on posttraumatic stress disorder in healthcare workers: A survey from four designated hospitals. Clin Psychol Psychother. (2021) 28:1146–59. doi: 10.1002/cpp.2564

PubMed Abstract | Crossref Full Text | Google Scholar

82. Husky MM, Pietrzak RH, Marx BP, and Mazure CM. Research on posttraumatic stress disorder in the context of the COVID-19 pandemic: A review of methods and implications in general population samples. Chronic Stress (Thousand Oaks). (2021) 5:24705470211051327. doi: 10.1177/24705470211051327

PubMed Abstract | Crossref Full Text | Google Scholar

83. Shi L, Lu ZA, Que JY, et al. Prevalence of and risk factors associated with mental health symptoms among the general population in China during the coronavirus disease 2019 pandemic. JAMA Netw Open. (2020) 3:e2014053. doi: 10.1001/jamanetworkopen.2020.14053

PubMed Abstract | Crossref Full Text | Google Scholar

84. Valladares-Garrido MJ, Picón-Reátegui CK, Zila-Velasque JP, and Grados-Espinoza P. Prevalence and factors associated with insomnia in military personnel: A retrospective study during the second COVID-19 epidemic wave in Peru. Healthcare (Switzerland). (2022) 10. doi: 10.3390/healthcare10071199

PubMed Abstract | Crossref Full Text | Google Scholar

85. Black WG. Military-induced family separation: A stress reduction intervention. Soc Work. (1993) 38:273–80. doi: 10.1177/24705470211051327

PubMed Abstract | Crossref Full Text | Google Scholar

86. Segura-Cardona AM, Moreno-Bedoya JP, González-Penagos C, and Berbesi-Fernández DY. Factores laborales asociados al riesgo de estrés postraumático en uniformados de un sector de Medellín. Rev Salud Pública. (2013) 15:373–9. doi: 10.1001/jamanetworkopen.2020.14053

PubMed Abstract | Crossref Full Text | Google Scholar

87. Grieger TA, Cozza SJ, Ursano RJ, et al. Posttraumatic stress disorder and depression in battle-injured soldiers. Am J Psychiatry. (2006) 163:1777–83. doi: 10.1176/ajp.2006.163.10.1777

PubMed Abstract | Crossref Full Text | Google Scholar

88. Greenberg J, Tsai J, Southwick SM, and Pietrzak RH. Can military trauma promote psychological growth in combat veterans? Results from the national health and resilience in veterans study. J Affect Disord. (2021) 282:732–9. doi: 10.1016/j.jad.2020.12.077

PubMed Abstract | Crossref Full Text | Google Scholar

89. Currie CL. Adult PTSD symptoms and substance use during Wave 1 of the COVID-19 pandemic. Addict Behav Rep. (2021) 13:100341. doi: 10.1016/j.abrep.2021.100341

PubMed Abstract | Crossref Full Text | Google Scholar

90. Dabbs C, Watkins EY, Fink DS, Eick-Cost A, and Millikan AM. Opiate-related dependence/abuse and PTSD exposure among the active-component U.S. Military, 2001 to 2008. Military Med. (2014) 179:885–90. doi: 10.7205/MILMED-D-14-00012

PubMed Abstract | Crossref Full Text | Google Scholar

91. Cottler LB, Compton WM, Mager D, Spitznagel EL, and Janca A. Posttraumatic stress disorder among substance users from the general population. Am J Psychiatry. (1992) 149:664–70. doi: 10.1176/ajp.149.5.664

PubMed Abstract | Crossref Full Text | Google Scholar

92. Bremner JD, Southwick SM, Darnell A, and Charney DS. Chronic PTSD in Vietnam combat veterans: course of illness and substance abuse. Am J Psychiatry. (1996) 153:369–75. doi: 10.1176/ajp.153.3.369

PubMed Abstract | Crossref Full Text | Google Scholar

93. Jacobsen LK, Southwick SM, and Kosten TR. Substance use disorders in patients with posttraumatic stress disorder: a review of the literature. Am J Psychiatry. (2001) 158:1184–90. doi: 10.1176/appi.ajp.158.8.1184

PubMed Abstract | Crossref Full Text | Google Scholar

94. Teeters JB, Lancaster CL, Brown DG, and Back SE. Substance use disorders in military veterans: prevalence and treatment challenges. Subst Abuse Rehabil. (2017) 8:69–77. doi: 10.2147/SAR.S116720

PubMed Abstract | Crossref Full Text | Google Scholar

95. Dworkin ER, Bergman HE, Walton TO, Walker DD, and Kaysen DL. Co-occurring post-traumatic stress disorder and alcohol use disorder in U.S. Military and veteran populations. Alcohol Res. (2018) 39:161–9. doi: 10.35946/arcr.v39.2.06

Crossref Full Text | Google Scholar

96. Kyzar EJ, Purpura LJ, Shah J, Cantos A, Nordvig AS, and Yin MT. Anxiety, depression, insomnia, and trauma-related symptoms following COVID-19 infection at long-term follow-up. Brain Behavior Immun - Health. (2021) 16:100315. doi: 10.1016/j.bbih.2021.100315

PubMed Abstract | Crossref Full Text | Google Scholar

97. Killgore W, Cloonan S, Taylor E, Grandner M, and Dailey N. 715 insomnia as a risk for PTSD during the COVID-19 pandemic. Sleep. (2021) 44:A279. doi: 10.1093/sleep/zsab072.713

Crossref Full Text | Google Scholar

98. McLay RN, Klam WP, and Volkert SL. Insomnia is the most commonly reported symptom and predicts other symptoms of post-traumatic stress disorder in U.S. service members returning from military deployments. Mil Med. (2010) 175:759–62. doi: 10.7205/milmed-d-10-00193

PubMed Abstract | Crossref Full Text | Google Scholar

99. Miles SR, Pruiksma KE, Slavish D, et al. Sleep disorder symptoms are associated with greater posttraumatic stress and anger symptoms in US Army service members seeking treatment for posttraumatic stress disorder. J Clin Sleep Med. (2022) 18:1617–27. doi: 10.5664/jcsm.9926

PubMed Abstract | Crossref Full Text | Google Scholar

100. Yehuda R, Hoge CW, McFarlane AC, et al. Post-traumatic stress disorder. Nat Rev Dis Primers. (2015) 1:15057. doi: 10.1038/nrdp.2015.57

PubMed Abstract | Crossref Full Text | Google Scholar

101. Asim M, van Teijlingen E, and Sathian B. Coronavirus Disease (COVID-19) and the risk of Post-Traumatic Stress Disorder: A mental health concern in Nepal. Nepal J Epidemiol. (2020) 10:841–4. doi: 10.3126/nje.v10i2.29761

PubMed Abstract | Crossref Full Text | Google Scholar

102. Rusch HL, Guardado P, Baxter T, Mysliwiec V, and Gill JM. Improved sleep quality is associated with reductions in depression and PTSD arousal symptoms and increases in IGF-1 concentrations. J Clin Sleep Med. (2015) 11:615–23. doi: 10.5664/jcsm.4770

PubMed Abstract | Crossref Full Text | Google Scholar

103. Na PJ, Tsai J, Southwick SM, and Pietrzak RH. Factors associated with post-traumatic growth in response to the COVID-19 pandemic: Results from a national sample of U.S. military veterans. Soc Sci Med. (2021) 289:114409. doi: 10.1016/j.socscimed.2021.114409

PubMed Abstract | Crossref Full Text | Google Scholar

104. Hendrikx LJ, Williamson C, Baumann J, and Murphy D. The impact of the COVID-19 pandemic on treatment-seeking veterans in the United Kingdom with preexisting mental health difficulties: A longitudinal study. J Trauma Stress. (2022) 35:330–7. doi: 10.1002/jts.22742

PubMed Abstract | Crossref Full Text | Google Scholar

105. Sekowski M, Gambin M, Hansen K, et al. Risk of developing post-traumatic stress disorder in severe COVID-19 survivors, their families and frontline healthcare workers: what should mental health specialists prepare for? Front Psychiatry. (2021) 12:562899. doi: 10.3389/fpsyt.2021.562899

PubMed Abstract | Crossref Full Text | Google Scholar

106. deRoon-Cassini TA, Mancini AD, Rusch MD, and Bonanno GA. Psychopathology and resilience following traumatic injury: a latent growth mixture model analysis. Rehabil Psychol. (2010) 55:1–11. doi: 10.1037/a0018601

PubMed Abstract | Crossref Full Text | Google Scholar

107. Ghaffarzadegan N, Ebrahimvandi A, and Jalali MS. A dynamic model of post-traumatic stress disorder for military personnel and veterans. PloS One. (2016) 11:e0161405. doi: 10.1371/journal.pone.0161405

PubMed Abstract | Crossref Full Text | Google Scholar

108. KaChadourian L, Tsai J, Na PJ, Krystal JH, Southwick SM, and Pietrzak RH. Resilience in the face of the COVID-19 pandemic: A national study of US military veterans. Prim Care Companion CNS Disord. (2021) 23:38002. doi: 10.4088/PCC.21br03075

PubMed Abstract | Crossref Full Text | Google Scholar

109. Ministerio de Salud.. Programa presupuestal 0131 control Y prevención en salud mental.chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/ (2021). Available online at: https://www.minsa.gob.pe/presupuestales/doc2021/ANEXO2_9.pdf (Accessed June 17, 2022).

Google Scholar

110. Dirección de Salud del Ejército. rección de salud del ejército. Directiva general para implementar el modelo del cuidado integral de la salud por curso de vida para persona, familia Y comunidad militar (Mci ep) en los establ ecimientos del primer nivel de atención del ejército.Chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/ (2021). Available online at: https://cdn.www.gob.pe/uploads/document/file/2146018/DIRECTIVA%20N%C2%B0001%20X-6.b15.0-Implementar%20Modelo%20de%20cuidado%20integral%20de%20salud%20por%20curso%20de%20vida%20en%20EE%201er%20nivel%20de%20atenci%C3%B3n%20%281%29.pdf.pdf.

Google Scholar

111. Estimating population prevalence of posttraumatic stress disorder: an example using the PTSD checklist. J traumatic stress. (2008). doi: 10.1002/jts.20341

PubMed Abstract | Crossref Full Text | Google Scholar

112. Valladares-Garrido MJ, Zapata-Castro LE, Domínguez-Troncos H, et al. Mental health disturbance after a major earthquake in northern Peru: A preliminary, cross-sectional study. Int J Environ Res Public Health. (2022) 19. doi: 10.3390/ijerph19148357

PubMed Abstract | Crossref Full Text | Google Scholar