Cross-Cultural Validation in Times of COVID-19: An Example Using the COVID-19 Peritraumatic Distress Index (CPDI) among Spanish and Peruvian Populations

Cross-Cultural Validation in Times of COVID-19: An Example Using the COVID-19 Peritraumatic Distress Index (CPDI) among Spanish and Peruvian Populations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cross-Cultural Validation in Times of COVID-19: An Example Using the COVID-19 Peritraumatic Distress Index (CPDI) among Spanish and Peruvian Populations Fabian Böttcher, Bruno Pedraz-Petrozzi, Eva Kathrin Lamadé, Maria Pilar Jimenez, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2891476/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 03 Nov, 2023 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background The COVID-19 pandemic has led to a significant psychological impact worldwide. The COVID-19 Peritraumatic distress index (CPDI) is widely used to assess psychological stress during the COVID-19 pandemic. Although CPDI has been validated in Peru and Spain, no cross-cultural validation studies have been conducted. As an exploratory aim, differences in CPDI factorial scores between the most prevalent medical conditions in two samples from a general population of Peru and Spain were investigated. Materials and Methods We conducted secondary data analysis with data from Peru and Spain to validate the CPDI in a cross-cultural context. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and multigroup confirmatory factor analysis (MGCFA) were performed to evaluate the factor structure and measurement invariance of the CPDI across cultural contexts. Results This study revealed a bifactorial solution (stress and rumination/information) for the CPDI, consistent with previous studies. The MGCFA demonstrated measurement invariance across cultural contexts (scalar invariance), indicating that the CPDI construct has the same meaning across both groups, regardless of cultural context and language variations of Spanish. Patients with anxious-depressive disorders showed higher CPDI factorial scores for both factors, whereas patients with respiratory diseases were only associated with the stress factor. Conclusion This study provides evidence for the cross-cultural validity of the CPDI, highlighting its utility as a reliable instrument for assessing psychological stress in the context of COVID-19 across different cultures. These findings have important implications for developing and validating measures to assess psychological distress in different cultural contexts. Biological sciences/Psychology/Human behaviour Health sciences/Medical research/Epidemiology Biological sciences/Psychology Health sciences/Health care/Quality of life Health sciences/Diseases/Psychiatric disorders/Anxiety Health sciences/Diseases/Psychiatric disorders/Depression Health sciences/Diseases/Psychiatric disorders/Post traumatic stress disorder Health sciences/Risk factors COVID-19 psychological distress validation studies measurement invariance scales Figures Figure 1 Figure 2 Figure 3 Introduction The coronavirus disease 2019 (COVID-19) represented an epidemiological issue and a significant challenge that has negatively impacted the population, leading to changes in social behavior and individual lifestyles 1 – 3 . These negative changes, including lockdowns and social restrictions, have adversely affected the population's mental health, resulting in increased cases of depression 4 , trauma 5 , anxiety 6 , and suicidal behavior 7 . In response to this phenomenon, various research groups have developed different psychometric instruments to assess the negative effects of COVID-19 on the population, with some primarily focusing on distress during the COVID-19 lockdown. In 2019, Qiu and colleagues developed the COVID-19 Peritraumatic Distress Index (CPDI) 8 . This instrument is one of the pioneering tools to assess peritraumatic stress symptoms related to COVID-19. These symptoms include negative cognitive changes, avoidance, compulsive behavior, physical symptoms related to stress, social withdrawal, loss of social functioning, anxiety, and depressive symptoms. CPDI has been validated during the COVID-19 lockdown in different languages worldwide 9 – 12 , including European 11 and Latin American Spanish 13 , 14 . Although both Spanish validation studies showed a bifactorial solution, they showed variations in the items included in each factor and the interpretation of the factors. Examples of cross-cultural validations in COVID-19 distress scales have been reported, such as the “fear of COVID-19 scale” 15 . Cross-cultural validations, assessed through measurement invariance, enable the study and validation of instrument results across different cultural groups, considering their differences 15 , 16 . Despite the frequent use of CPDI and the existence of different versions worldwide, a cross-cultural validation study using measurement invariance for the CPDI has been underreported. Therefore, this study aimed to evaluate the factorial structure and perform a cross-cultural validation using a measurement invariance analysis of the CPDI, using samples from the Spanish and Peruvian populations as examples, considering their cultural ties and shared language. As an exploratory aim, we will investigate the differences in CPDI factorial scores between the three most prevalent medical conditions in both samples after completing the cross-cultural validation. We hypothesized that both samples would exhibit metric invariance, enabling comparisons between populations and providing valuable information about the utility of the CPDI in assessing peritraumatic distress experienced by individuals in Spanish and Peruvian populations. As part of our exploratory analysis, we hypothesized that people with the most common medical conditions would have higher CPDI factorial scores in both samples, in line with existing literature 17 . Results General descriptive data The general characteristics of the Peruvian and Spanish samples, including socioeconomic variables and CPDI values, are presented in Table 1 . Concerning medical conditions, most participants had anxious-depressive disorders (13.4%), arterial hypertension (9.2%), and respiratory diseases (8.9%). There were significant statistical differences in the frequency of medical conditions in Peruvian and Spanish samples (Table 1 ). Between both samples there were no significant differences concerning uncorrected CPDI values between the two samples. Table 1 Descriptive statistics of sociodemographic and health-related variables. Abbreviations: SD = standard deviation, χ2 = chi-square test, CPDI = COVID-19 Peritraumatic distress index. Variable Level mean or n (SD or %) t or χ2 Total ( n = 2543) Spain ( n = 1074) Peru ( n = 1469) Sex Men 743 (29.2) 234 (21.8) 509 (34.6) 101.783*** Women 1801 (70.8) 840 (78.2) 962 (65.4) 8.26** Age 41.66 (15.47) 52.44 (14.10) 33.79 (13.31) 34.024*** Education Primary education 58 (2.3) 57 (5.3) 1 (1.7) 54.069*** Secondary education 419 (16.5) 239 (22.3) 180 (12.2) 8.308** Tertiary education/ vocational training 262 (10.3) 134 (12.5) 128 (8.7) 0.137 University or higher 1805 (71.0) 643 (59.9) 1162 (79.0) 149.23*** Diseases Respiratory problems 227 (8.9) 84 (7.8) 143 (9.7) 2.735 High cholesterol 182 (7.2) 170 (15.8) 12 (0.8) 210.941*** Hypertension 233 (9.2) 168 (15.7) 65 (4.4) 94.183*** Diabetes 69 (2.7) 38 (3.5) 31 (2.1) 4.836* Cardiovascular disease/s 38 (1.5) 22 (2.1) 16 (1.1) 3.907* Anxiety/depression 340 (13.4) 260 (24.2) 80 (5.4) 189.241*** Total CPDI 50.29 (14.78) 50.18 (15.32) 50.38 (14.38) -0.353 Exploratory factor analysis For the EFA, the first subsample ( N = 1271) was used, and we proceeded as follows: In the first step, the data was checked for adequacy. In the second step, the number of factors was determined using several extraction methods. Finally, the model was evaluated according to the above-mentioned aspects, and adjustments were made if these were violated. The initial exploratory factor analysis (EFA) using Bartlett's sphericity test demonstrated that the correlation matrix results were not random (χ2(276) = 12452.74, p < 0.001). Moreover, the Kaiser-Meyer-Olkin (KMO) criterion indicated that the data was well-suited for factor analysis, with a KMO value of 0.94. In addition, the multivariate normal distribution of the items was checked using the Mardia test for skewness and excess 18 , which indicated a non-normal distribution of the items. Due to the violation of standard distribution assumptions and the ordinal nature of the CPDI items, we used a polychoric correlation matrix as an input method for the EFA and a principal axis as a factor extraction method, in accordance with recommendations for the robustness of the principal axis method towards the violation of standard distribution assumptions as published elsewhere 19 . Furthermore, the number of retained factors was tested using parallel analysis 20 , Eigenvalues, visual Scree test 21 , and Velicer’s minimum average partial test (MAP) 22 . In addition, the parallel analysis scree plot for the EFA is represented in Fig. 2 . All extraction methods indicated a 2-factor solution. We conducted an exploratory principal axis analysis with oblique rotation and deleted two items (22 and 3) due to cross-loadings. With the remaining items, we ran another exploratory principal axis analysis, and the extraction methods used above were repeated, continuing to indicate a 2-factor solution. We deleted one item (item 5) with a factor loading < 0.30. The final 21 items were well-suited for factor analysis and could be well-assigned to the two factors. The two resulting factors are interpreted as follows: factor 1 or " stress in the context of COVID-19 pandemics " (e.g., " I feel tired and sometimes even exhausted "; Eigenvalue = 7.53, α = 0.91), and factor 2 or " rumination/seeking for information in the context of COVID-19 pandemics " (e.g., " I can't stop myself from imagining myself or my family being infected and feel terrified and anxious about it "; Eigenvalue = 2.41, α = 0.71). The resultant model accounted for 45% of the variation observed in the sample. Additionally, the two factors had a significant correlation (r = 0.53, p < 0.001). Multi-group confirmatory factor analysis The aim of this study was to replicate the two-factor solution obtained in the exploratory factor analysis (EFA) in a second subsample ( N = 1272) and examine metric invariance. To achieve this, a multi-group confirmatory factor analysis (MGCFA) was performed using the Diagonal Weighted Least Square (DWLS) estimation method. The hypothesized two-factor model had a good fit 23 , (χ2(376) = 775.587, CFI = 0.982, TLI = 0.980, RMSEA = 0.041, 90CI [0.037, 0.045], and SRMR = 0.057). The MGCFA yielded two important results: First, the hypothesized model was successfully replicated, and second, structural invariance was present as the items in both the Spanish and Peruvian samples loaded significantly on the latent variables as predicted. Next, we examined metric invariance by constraining the factor loadings to be equal in both samples. Also, this model showed an adequate fit to the data (χ2(395) = 1014.887, CFI = 0.972, TLI = 0.970, RMSEA = 0.05, 90CI [0.046, 0.053], and SRMR = 0.065). As described above, the measurement invariance involves nested models, which are examined using ANOVA and alternative model fit indices, revealing that the metric model had a significantly poorer fit than the configural model (χ2 difference test = 54.197, p < 0.001). It is important to note that the χ2 difference test is sensitive to large sample sizes, which can lead to the quick rejection of models even if they fit the data well. Therefore, we also considered the increase and decrease of AFI. Our findings demonstrated that the metric model was not inferior to the configural model, as shown in Table 2 , which supports the assumption of metric invariance. Additionally, we examined scalar invariance by constraining the factor loadings and intercepts to be equal across both samples. The MGCFA yielded a good model fit (χ2(414) = 1270.975, CFI = 0.961, TLI = 0.961, RMSEA = 0.057, 90CI [0.054, 0.061], and SRMR = 0.071). A comparison of both models indicated that the scalar invariance model was significantly poorer than the metric (χ2 difference test = 355.91, p < 0.001). However, the change in AFI indicated that the overall fit was not significantly different between the scalar and the metric model (see Table 2 ), thus supporting the idea of scalar invariance. Table 2 Nested model comparisons: Difference between alternative fit indices. Abbreviations: χ2 = chi-square, CFI = comparative fit index, TLI = Tucker-Lewis Index, RMSEA = root-mean-square-error of approximation, SRMR = root-mean-square-residual. Δχ2 ΔCFI ΔTLI ΔRMSEA ΔSRMR Configural vs. metric invariance 239.30 -0.010 -0.010 0.009 0.008 Metric vs. scale invariance 265.10 -0.011 -0.009 0.007 0.006 Differences in CPDI factor scores among groups and prevalent conditions Firstly, we examined the differences between the Peruvian and Spanish samples regarding stress (factor 1) and rumination/search for information (factor 2) scores. We found no significant differences in either factor 1 (U = 763562.00, p = 0.167) or factor 2 (U = 780812.00, p = 0.660). Furthermore, we investigated the differences in factorial scores among the most prevalent pathologies, including anxious-depressive disorders, respiratory diseases, and hypertension, as listed in Table 1 . To identify any differences, we used the U-Mann-Whitney test and excluded participants with multiple pathologies, only including those with the specific pathology under analysis. Factor scores were obtained by correcting items for age, sex, and education level. The analysis revealed that individuals with respiratory diseases had significantly higher Factor 1 scores than those without (U = 116697, p = 0.012). However, no significant differences were observed for Factor 2 in participants with respiratory diseases (U = 122934.50, p = 0.121). Individuals with hypertension showed no significant differences in either Factor 1 (U = 99412.50, p = 0.638) or Factor 2 (U = 101534.50, p = 0.928) compared to those without hypertension. Finally, individuals with anxious-depressive pathology had significantly higher scores in both Factor 1 and Factor 2 than those without anxious-depressive pathology (Factor 1: U = 100454.50, p < 0.001; Factor 2: U = 140850.50, p < 0.001). These results were independent of age, sex, and education level, as confirmed by the correlation analysis of residuals and represented in Fig. 3 . Discussion The results of the present study indicated a two-factor solution for both samples, resulting in a 21-item CPDI instrument. The items of factor 1 were theoretically related to stress reactions in the context of COVID-19, while the items of factor 2 were related to rumination behavior, including the excessive seeking of information in the context of COVID-19. Subsequently, the bifactorial solution for the CPDI was confirmed, and it was additionally demonstrated that the CPDI constructs were being measured consistently across both populations (i.e., Spanish and Peruvian). This demonstrates that CPDI can be used in different cultural contexts, confirming the cultural validity of our CPDI model. Finally, our exploratory results showed increased scores for factor 1 (stress reactions in the context of COVID-19) for respiratory diseases and increased scores for both factors in individuals with anxious-depressive disorders. All analyses were adjusted for participant sex, age, and education level. Previous independent studies have demonstrated a consistent two-factor solution for the COVID-19 Peritraumatic Distress Index (CPDI). In the Spanish study, a two-factor solution was identified through principal axis analysis and varimax rotation, which yielded two factors: " stress symptoms " (15 items) and " COVID-19 information " (8 items). Item 5 was eliminated due to its low factor loading 11 . Similarly, in the Peruvian study, a two-factor solution was found: " stress in the context of COVID-19 " (13 items) and " rumination in the context of COVID-19 " (8 items). However, since a correlation between the two factors was assumed, a principal component analysis with a promax rotation was performed in this study. Items 7, 8, and 11 were removed from the analysis 14 . When the datasets were combined and analyzed, the same number of factors and a similar distribution of items per factor were obtained (see Table 3 ). In contrast, other studies 24 , 25 used the original four-factor structure of the CPDI, which did not significantly fit our datasets or explain a significant percentage of the variance in our case. Table 3 CPDI items and eliminated items in the Spanish study 11 , Peruvian study 14 , and in the secondary analysis of the combined data. Items that are consistently in the factor are underlined, while the eliminated items in the different studies are written in italics. Item 12 changed factors between the studies. CPDI items Eliminated CPDI items Spanish study Factor 1 ( stress ) : 1 , 4 , 7 , 12, 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 Factor 2 ( information ) : 2 , 3 , 6 , 8 , 9 , 10 , 11 , 22 5 Peruvian study Factor 1 ( stress ) : 1 , 4 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 Factor 2 ( rumination/seeking for information ) : 2 , 3 , 5 , 6 , 9 , 10 , 12, 22 7,8 and 11 Combined data Factor 1 ( stress ) : 1 , 4 , 7 , 12, 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 23 , 24 Factor 2 ( rumination/seeking for information ) : 2 , 6 , 8 , 9 , 10 , 11 3, 5 and 22 In the second phase, the 2-factor model was successfully replicated, and scalar measurement invariance was observed across the Peruvian and Spanish samples. The measurement invariance, as previously defined 26 , demonstrated the psychometric equivalence of the CPDI construct across both groups in the context of COVID-19. This finding indicates that the construct has the same meaning, regardless of the cultural context and variations in the Spanish language (European vs. Latin-American Spanish). Measurement invariance is crucial in scale construction and validation across different cultural and ethnic contexts 27 . For instance, a Canadian study examining the cross-cultural evaluation of the Beck Depression Inventory-II (BDI-II) used MGCFA to demonstrate that BDI-II had measurement invariance across culture and gender 28 . Similar methodologies were used in other psychometric evaluations of psychiatric disorders, such as social phobia 29 , schizotypal personality 30 , and anxiety 31 . The “fear of COVID-19 scale”, which is another evaluation tool to measure psychological distress in the context of COVID-19, was demonstrated to have partial scalar invariance in seven different Latin American countries 32 as well as in 47 other countries worldwide, demonstrating a scalar invariance across groups concerning culture, gender, and education 15 . Regarding the CPDI, although descriptive and correlational data have been used to establish cross-cultural validity between German and Chinese data 10 , to date, no studies have used the MGCFA methodology to perform cross-cultural validation for CPDI similar to ours. Our exploratory analysis observed that individuals with respiratory diseases, mainly bronchial asthma, during the COVID-19 pandemic experienced higher psychological stress levels than healthy individuals. Several studies have reported a similar impact of negative emotions during COVID-19 on individuals with asthma. For example, Sheha et al. reported a high correlation between anxiety and depression symptoms and uncontrolled asthma in patients during the COVID-19 lockdown 33 . Similarly, de Boer et al. found a clinically significant increase in anxiety and depression among asthma patients during the pandemic, consistent with our findings 34 . Interestingly, Takeuchi et al. reported that participants with respiratory diseases, such as asthma, pneumonia, and COPD, experienced higher levels of psychological distress than those with cardiovascular diseases or cancer, which is also in line with our results 35 . Regarding anxious-depressive disorders, our findings suggest that participants with these conditions experience higher stress levels and engage in more rumination and information-seeking behaviors compared to healthy individuals. Specifically, rumination, a coping mechanism characterized by negative affect and self-focused attention, appears to be triggered by the repercussions and consequences of the COVID-19 pandemic 36 . This rumination manifests as a constant search for information, which can exacerbate negative and catastrophic thoughts, leading to a feedback loop of repetitive negative thinking 36 . Thus, individuals with anxious-depressive syndromes are more likely to engage in information-seeking behaviors and experience heightened levels of rumination due to the cognitive distortions associated with anxiety and depression, which are also associated with negative behaviors such as seeking out negative news and information 37 . Limitations The present study demonstrates that the Spanish version of the CPDI has consistent psychometric properties in both Peruvian and Spanish samples. While this information is a valuable contribution to the research on psychological stress during the COVID-19 pandemic, some limitations must be considered. Although the sample size of our study was large, the samples from both databases were collected using a snowball method and were not randomized at the time of sampling. Furthermore, most participants were female, well-educated, and between 35 and 50 years old. Additionally, the participants self-reported pathologies, which may introduce memory bias, and were not confirmed by a physician. According to Table 3 , the factorial structure of the CPDI does not form clear and stable factors over time, resulting in minimal variations among the excluded questions and suggesting possible heterogeneity in the factors that must be considered. Items measuring psychological distress in the CPDI were non-invariant, as shown in Table 3 , including those in the rumination/seeking for information factor. An additional limitation is the lack of testing for convergent and divergent validity. Finally, longitudinal data may provide stronger evidence for the factorial structure than a cross-sectional design. Conclusion In conclusion, this study provides evidence for the cross-cultural validity of the CPDI as a reliable instrument for assessing psychological stress in the context of COVID-19 across different Spanish-speaking cultures. These findings have important implications for developing and validating measures to assess psychological distress in other cultural contexts. Our exploratory analysis revealed that respiratory diseases and anxious-depressive disorders significantly increased psychological stress during the COVID-19 pandemic. The latter also correlated with increased rumination and seeking of information, which is consistent with the psychopathology of both disorders. Future studies should apply and validate the CPDI instrument using EFA and CFA and perform cross-cultural validations using MGCFA in languages other than Spanish. Moreover, future studies could explore whether the CPDI is theoretically related to other scales measuring stressful experiences during the COVID-19 pandemic, which would provide fruitful information. Post-COVID research could examine psychometric aspects of psychological stress after the pandemic, as health protection methods such as wearing FPP2-masks are eliminated. The social context changes drastically with the elimination of quarantine measures in most countries. Materials and Methods Participants and procedure The present study is a secondary analysis of two databases comprising online survey studies conducted in Peru and Spain during the COVID-19 pandemic. Certain findings from this survey study have already been reported elsewhere 11 , 14 . The current study incorporates certain methodological aspects of both studies, primarily pertaining to the recruitment of participants, the description of the online survey, and the CPDI. The primary objective of this study was to evaluate the psychometric properties of the Spanish version of the CPDI in two samples from Peru ( N = 1469) and Spain ( N = 1074) and to perform a cross-cultural validation using measurement invariance for both population samples (total N = 2543). As an exploratory objective, we evaluated the CPDI factorial score differences between the most common medical conditions among the two sample groups after performing the factorial and cross-cultural validation analysis. The data for this study was originally collected from voluntary participants during the COVID-19 lockdown. The collection period for the Peru study was from March 27, 2020, to September 21, 2021, while for the Spain study, it was from May 8, 2020, to June 25, 2020. Before the commencement of data collection, participants in both studies were provided with complete information regarding the study and gave written informed consent. Both studies were conducted in accordance with the Helsinki Declaration and were approved by the Ethics Committee of the Universidad Peruana Cayetano Heredia (UPCH) for the Peruvian study and the Ethics Committee Board of the Universidad Nacional de Educación a Distancia (UNED) for the Spanish study. In the Peruvian study, only residents during the COVID-19 lockdown aged 18 years or older, possessing sufficient Spanish language proficiency and providing written informed consent, were eligible for inclusion. Those who did not meet these criteria were excluded. Similarly, in the Spanish study, participants had to be Spanish residents aged 18 years or older. Both studies excluded individuals who did not fully complete the questionnaires or failed to provide socioeconomic information. Table 1 presents a comprehensive overview of the samples, encompassing socioeconomic and psychometric data. Online electronic surveys were employed in both studies to collect participant information due to the sanitary restrictions in both countries, which prevented personal contact for data collection. In the Peruvian study, Google Forms, an open-access internet-based program provided by Google Inc, USA, was used for online surveys. For the Spanish study, the surveys were recorded automatically using the Qualtrics Software (Qualtrics Research Suite: Provo, UT, USA, 2013). Both surveys were conducted anonymously, and participants were only permitted to complete the survey once. The surveys included questions regarding socio-economic status (i.e., age, gender, education), psychometric data (CPDI scale) 11 , 14 , and data of past medical history, extracting, in this case, the most frequent disorders in both populations (respiratory diseases, hypertension, hypercholesterinemia, diabetes mellitus, cardiovascular diseases, and anxious-depressive disorders). COVID-19 Peritraumatic distress index (CPDI) The COVID-19 Peritraumatic Distress Index (CPDI) is a self-report questionnaire designed to assess psychological distress during the COVID-19 pandemic. This instrument comprises 24 items, each of which is evaluated on a Likert scale ranging from 0 to 4 (i.e., never, occasionally, sometimes, often, and most of the time). The raw score is calculated by adding the value of each item, and the displayed score is obtained by adding 4 to the raw score to determine the severity degree of CPDI. The CPDI defines different categories for peritraumatic stress related to the COVID-19 pandemic: normal (0–28 display points), mild (29–52 display points), and severe (53–100 display points) 11 , 14 , 38 . In both studies, a validated version of the CPDI through expert adaptation and translation to the Spanish language was used, whose construct validation has been performed separately 11 , 14 . Data preparation and statistical analysis Table representation and text description were used to present general sample characteristics, including descriptive data on the CPDI. For numerical variables that were normally distributed, means and standard deviation were used as measures of central tendency. For non-Gaussian distributed variables, median and interquartile ranges (IQR), including 75- and 25-percentiles, were used. Descriptive information greater than one million was expressed using scientific notation, and decimal data were rounded to two decimals. Qualitative data, including count data, was characterized using absolute numerical values and percentages. These procedures were performed for both subsamples, as shown in Table 1 . Inferential statistical tests were not performed to evaluate differences between the two subsamples (i.e., first and second phase) since they corresponded to two subprojects with different objectives and hypotheses. Specifically, we investigated the factor structure of the CPDI in both populations. To achieve this, we merged the databases of both studies, randomly sorted the participants, and divided them into equal proportions (1:1 for each database) for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), as recommended in previous studies 39 . In evaluating the EFA solution, we adhered to the following literature recommendations 40 : all factors should be theoretically meaningful at least three variables should saliently load on a factor (overdetermined, i.e., factor loadings ≥ 0.30) variables should load significantly on only one factor (no cross-loadings) each factor should have an internal consistency of Cronbach's α ≥ 0.70 To determine whether cultural differences exist in the CPDI between the Spanish and Peruvian samples, it is essential to test the measurement invariance using multigroup CFA (MGCFA). This approach enables invariance evaluation by imposing cross-group restrictions and comparing models with varying degrees of constraints, as demonstrated in numerous studies 41 , 42 . ANOVA was used to compare nested models that emerged by restricting different parameters and representing different levels of measurement invariance. The hypothesized measurement model's findings were evaluated using fit indices and their cut-off points: the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root-mean-square-error of approximation (RMSEA), and the standardized root-mean-square-residual (SRMR) were used as model fit measurements. A good model fit was given if the values of the CFI and TLI were greater than or equal to 0.95 11 . Regarding RMSEA and SRMR, a good model fit was given if the values of both root-mean-square indicators were below or equal to 0.05 11 . Additionally, we calculated the 90-percent confidence intervals (90CI) for the RMSEA 43 . Differences in these alternative fit indices (AFI) were also used to compare the nested models, as the χ2-value is sensitive to sample size. In this case, following cutoffs were considered: -0.01 for ΔCFI, paired with 0.15 for ΔRMSEA and ΔSRMR of 0.030 (for metric invariance) or 0.015 (for scalar or residual invariance). Finally, exploratory data analysis was conducted by calculating the sum of the scores of the resulting factors (i.e., factors 1 and 2) and assessing the differences in each factor score among the three most prevalent medical conditions in both samples. Descriptive information was performed using JASP version 0.11.1 44 . Statistical analyses of the EFA were performed using the R-software version 4.1.2 (R Core Team, 2021, R Foundation for Statistical Computing, Vienna, Austria) 45 and for the MGCFA we utilized version 0.6–10 of the R-package lavaan 46 . Declarations Author Contributions/Conflict of Interests/Additional Information Ethics approval and consent to participate Each participant or their legally authorized representative was fully informed about the objectives and procedures of the study, as well as the potential adverse effects, and gave their written consent to participate. The study protocol and all study procedures were reviewed and approved by the ethics committee of the Universidad Peruana Cayetano Heredia (UPCH) for the Peruvian study and the Ethics Committee Board of the Universidad Nacional de Educación a Distancia (UNED) for the Spanish study. Additionally, this pilot trial was carried out according to the Helsinki Declaration. Consent for publication Not applicable Availability of data and materials The data sets generated and analyzed during the study are not publicly but they are available from the corresponding author on justified request. Declarations of interest: none Acknowledgments: none References Fegert, J. M., Vitiello, B., Plener, P. L. & Clemens, V. Challenges and burden of the Coronavirus 2019 (COVID-19) pandemic for child and adolescent mental health: a narrative review to highlight clinical and research needs in the acute phase and the long return to normality. Child Adolesc. Psychiatry Ment. Health 14, 20 (2020). 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Sozialpsychologie 71, 157–186 (2019). Krüger-Malpartida, H., Arevalo-Flores, M., Anculle-Arauco, V., Dancuart-Mendoza, M. & Pedraz-Petrozzi, B. Condiciones Médicas, Síntomas de Ansiedad y Depresión Durante la Pandemia por COVID-19 en una Muestra Poblacional de Lima, Perú. Rev. Colomb. Psiquiatr. (2022) doi: 10.1016/j.rcp.2022.04.004 . Mardia, K. V. Measures of multivariate skewness and kurtosis with applications. Biometrika 57, 519–530 (1970). Eid, M., Gollwitzer, M. & Schmitt, M. Statistik und Forschungsmethoden . (Psychologie Verlagsunion, 2015). Horn, J. L. A rationale and test for the number of factors in factor analysis. Psychometrika 30, 179–185 (1965). Cattell, R. B. The Scree Test For The Number Of Factors. Multivar. Behav. Res. 1, 245–276 (1966). Velicer, W. F. Determining the number of components from the matrix of partial correlations. Psychometrika 41, 321–327 (1976). Hu, L. & Bentler, P. M. 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Level of asthma control and mental health of asthma patients during lockdown for COVID-19: a cross-sectional survey. Egypt. J. Bronchol. 15, 12 (2021). de Boer, G. M. et al. Asthma patients experience increased symptoms of anxiety, depression and fear during the COVID-19 pandemic. Chron. Respir. Dis. 18, 14799731211029658 (2021). Takeuchi, E., Katanoda, K., Cheli, S., Goldzweig, G. & Tabuchi, T. Restrictions on healthcare utilization and psychological distress among patients with diseases potentially vulnerable to COVID-19; the JACSIS 2020 study. Health Psychol. Behav. Med. 10, 229–240 (2022). Nikolova, I., Caniëls, M. C. J. & Curseu, P. L. COVID-19 Rumination Scale (C‐19RS): Initial psychometric evidence in a sample of Dutch employees. Int. J. Health Plann. Manage. 36, 1166–1177 (2021). Michl, L. C., McLaughlin, K. A., Shepherd, K. & Nolen-Hoeksema, S. Rumination as a Mechanism Linking Stressful Life Events to Symptoms of Depression and Anxiety: Longitudinal Evidence in Early Adolescents and Adults. J. Abnorm. Psychol. 122, 339–352 (2013). Pedraz-Petrozzi, B. et al. Emotional Impact on Health Personnel, Medical Students, and General Population Samples During the COVID-19 Pandemic in Lima, Peru. Rev. Colomb. Psiquiatr. 50, 189–198 (2021). Del Rey, R., Ojeda, M. & Casas, J. A. Validation of the Sexting Behavior and Motives Questionnaire(SBM-Q). Psicothema 33, 287–295 (2021). Watkins, M. W. Exploratory Factor Analysis: A Guide to Best Practice. J. Black Psychol. 44, 219–246 (2018). Baumgartner, H. & Steenkamp, J.-B. E. M. Multi-Group Latent Variable Models for Varying Numbers of Items and Factors with Cross-National and Longitudinal Applications. Mark. Lett. 9, 21–35 (1998). Steinmetz, H., Schmidt, P., Tina-Booh, A., Wieczorek, S. & Schwartz, S. H. Testing measurement invariance using multigroup CFA: Differences between educational groups in human values measurement. Qual. Quant. Int. J. Methodol. 43, 599–616 (2009). MacCallum, R. C., Browne, M. W. & Sugawara, H. M. Power Analysis and Determination of Sample Size for Covariance Structure Modeling. 20 (1996). JASP - A Fresh Way to Do Statistics. https://jasp-stats.org/ . R: The R Project for Statistical Computing. https://www.r-project.org/ . Rosseel, Y. lavaan: An R Package for Structural Equation Modeling. J. Stat. Softw. 48, 1–36 (2012). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 03 Nov, 2023 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 19 Sep, 2023 Reviews received at journal 13 Sep, 2023 Reviewers agreed at journal 09 Sep, 2023 Reviews received at journal 23 Aug, 2023 Reviewers agreed at journal 05 Aug, 2023 Reviewers invited by journal 20 Jul, 2023 Editor assigned by journal 20 Jul, 2023 Editor invited by journal 11 May, 2023 Submission checks completed at journal 11 May, 2023 First submitted to journal 03 May, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2891476","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":199086135,"identity":"627de3f4-b34d-4960-8990-99d709b5cb58","order_by":0,"name":"Fabian Böttcher","email":"","orcid":"","institution":"Central Institute of Mental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fabian","middleName":"","lastName":"Böttcher","suffix":""},{"id":199086136,"identity":"1150fe0c-b329-4109-930e-a3527bd58d45","order_by":1,"name":"Bruno 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of Mental Health","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Eva","middleName":"Kathrin","lastName":"Lamadé","suffix":""},{"id":199086138,"identity":"67d7eea0-69c9-446d-a7c2-ff3138607bb6","order_by":3,"name":"Maria Pilar Jimenez","email":"","orcid":"","institution":"Universidad Nacional de Educación a Distancia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Pilar","lastName":"Jimenez","suffix":""},{"id":199086139,"identity":"faee3232-da34-4ef3-b330-059833513aef","order_by":4,"name":"Jennifer Rieker","email":"","orcid":"","institution":"Universidad de Nebrija","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jennifer","middleName":"","lastName":"Rieker","suffix":""},{"id":199086140,"identity":"d4a503e3-0bf6-41b4-966b-5f6ace3ddc9f","order_by":5,"name":"José Manuel Reales","email":"","orcid":"","institution":"Universidad Nacional de Educación a Distancia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"José","middleName":"Manuel","lastName":"Reales","suffix":""},{"id":199086141,"identity":"2473c55e-e795-4a86-9d67-21747346a21e","order_by":6,"name":"Martin Arevalo-Flores","email":"","orcid":"","institution":"Universidad Peruana Cayetano Heredia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Arevalo-Flores","suffix":""},{"id":199086142,"identity":"cac7e5bc-a572-48ed-adb4-2cbc705be3ca","order_by":7,"name":"Víctor Anculle-Arauco","email":"","orcid":"","institution":"Universidad Peruana Cayetano Heredia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Víctor","middleName":"","lastName":"Anculle-Arauco","suffix":""},{"id":199086143,"identity":"f2454561-03d3-42a1-aba7-c7c854360c96","order_by":8,"name":"Hever Krüger-Malpartida","email":"","orcid":"","institution":"Universidad Peruana Cayetano Heredia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hever","middleName":"","lastName":"Krüger-Malpartida","suffix":""},{"id":199086144,"identity":"f8569e9c-431d-489c-84f4-6824bdc12e5d","order_by":9,"name":"Soledad Ballesteros","email":"","orcid":"","institution":"Universidad Nacional de Educación a Distancia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Soledad","middleName":"","lastName":"Ballesteros","suffix":""}],"badges":[],"createdAt":"2023-05-03 23:44:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2891476/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2891476/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-023-46235-4","type":"published","date":"2023-11-03T15:01:06+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":37039151,"identity":"3f98c8bc-8d9a-4a92-838a-8554b8e5f08e","added_by":"auto","created_at":"2023-05-15 14:26:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":83692,"visible":true,"origin":"","legend":"\u003cp\u003eParallel analysis scree plots for the exploratory factor analysis of the COVID-19 Peritraumatic distress index (CPDI) instrument in both Peruvian and Spanish sample sizes. Of note is the 2-factor solution, which explains most of the variance for the CPDI.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-2891476/v1/28465cf88e29e496c9d45bf1.png"},{"id":37039150,"identity":"7962d604-0928-4b8d-81ac-c7a9a484b95f","added_by":"auto","created_at":"2023-05-15 14:26:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25638,"visible":true,"origin":"","legend":"\u003cp\u003eMultigroup confirmatory factor analysis, path diagram – Standardized regression coefficients of the items on factor 1 (“stress in the context of COVID-19 pandemics”) and factor 2 (“rumination in the context of COVID-19 pandemics”), for the Spanish (A) and Peruvian (B) sample sizes.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-2891476/v1/6e18b0e0816e7dde8b7af872.png"},{"id":37039152,"identity":"50038147-51ce-4726-928b-e8f9007d5980","added_by":"auto","created_at":"2023-05-15 14:26:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":351211,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot charts for CPDI factors 1 and 2 scores in the three most frequent medical conditions of both Spanish and Peruvian samples (\u003cem\u003eN\u003c/em\u003e = 2543). The median with the interquartile range (IQR) is presented. Abbreviations - w/anx-dep: participants with anxiety-depressive disorder, w/hyp: participants with arterial hypertension, w/resp: participants with respiratory diseases. The sum of factor scores 1 and 2 are corrected for age, sex, and educational level (residuals).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-2891476/v1/47f69626349765dd51f31579.png"},{"id":45942899,"identity":"edd9afb2-e330-4dfc-a3f2-e68161936f90","added_by":"auto","created_at":"2023-11-06 15:06:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":769694,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2891476/v1/a391541d-a5d6-4cff-b82a-3dea361e03b4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cross-Cultural Validation in Times of COVID-19: An Example Using the COVID-19 Peritraumatic Distress Index (CPDI) among Spanish and Peruvian Populations","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe coronavirus disease 2019 (COVID-19) represented an epidemiological issue and a significant challenge that has negatively impacted the population, leading to changes in social behavior and individual lifestyles\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. These negative changes, including lockdowns and social restrictions, have adversely affected the population's mental health, resulting in increased cases of depression\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, trauma\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, anxiety\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e, and suicidal behavior\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. In response to this phenomenon, various research groups have developed different psychometric instruments to assess the negative effects of COVID-19 on the population, with some primarily focusing on distress during the COVID-19 lockdown. In 2019, Qiu and colleagues developed the COVID-19 Peritraumatic Distress Index (CPDI)\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. This instrument is one of the pioneering tools to assess peritraumatic stress symptoms related to COVID-19. These symptoms include negative cognitive changes, avoidance, compulsive behavior, physical symptoms related to stress, social withdrawal, loss of social functioning, anxiety, and depressive symptoms. CPDI has been validated during the COVID-19 lockdown in different languages worldwide\u003csup\u003e\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, including European\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and Latin American Spanish\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Although both Spanish validation studies showed a bifactorial solution, they showed variations in the items included in each factor and the interpretation of the factors.\u003c/p\u003e \u003cp\u003eExamples of cross-cultural validations in COVID-19 distress scales have been reported, such as the \u0026ldquo;fear of COVID-19 scale\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Cross-cultural validations, assessed through measurement invariance, enable the study and validation of instrument results across different cultural groups, considering their differences\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Despite the frequent use of CPDI and the existence of different versions worldwide, a cross-cultural validation study using measurement invariance for the CPDI has been underreported. Therefore, this study aimed to evaluate the factorial structure and perform a cross-cultural validation using a measurement invariance analysis of the CPDI, using samples from the Spanish and Peruvian populations as examples, considering their cultural ties and shared language. As an exploratory aim, we will investigate the differences in CPDI factorial scores between the three most prevalent medical conditions in both samples after completing the cross-cultural validation. We hypothesized that both samples would exhibit metric invariance, enabling comparisons between populations and providing valuable information about the utility of the CPDI in assessing peritraumatic distress experienced by individuals in Spanish and Peruvian populations. As part of our exploratory analysis, we hypothesized that people with the most common medical conditions would have higher CPDI factorial scores in both samples, in line with existing literature\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGeneral descriptive data\u003c/h2\u003e \u003cp\u003eThe general characteristics of the Peruvian and Spanish samples, including socioeconomic variables and CPDI values, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Concerning medical conditions, most participants had anxious-depressive disorders (13.4%), arterial hypertension (9.2%), and respiratory diseases (8.9%). There were significant statistical differences in the frequency of medical conditions in Peruvian and Spanish samples (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Between both samples there were no significant differences concerning uncorrected CPDI values between the two samples.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics of sociodemographic and health-related variables. Abbreviations: SD\u0026thinsp;=\u0026thinsp;standard deviation, χ2\u0026thinsp;=\u0026thinsp;chi-square test, CPDI\u0026thinsp;=\u0026thinsp;COVID-19 Peritraumatic distress index.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003emean or n (SD or %)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e or χ2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2543)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpain (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1074)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePeru (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1469)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e743 (29.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e234 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e509 (34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e101.783***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWomen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1801 (70.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e840 (78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e962 (65.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.26**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.66 (15.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.44 (14.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.79 (13.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.024***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e54.069***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e419 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e180 (12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.308**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTertiary education/ vocational training\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e262 (10.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e134 (12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e128 (8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1805 (71.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e643 (59.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1162 (79.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e149.23***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRespiratory problems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e227 (8.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84 (7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e143 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e170 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12 (0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e210.941***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e233 (9.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e168 (15.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e94.183***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e69 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.836*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCardiovascular disease/s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16 (1.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.907*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnxiety/depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e340 (13.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e260 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e80 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e189.241***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal CPDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.29 (14.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50.18 (15.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.38 (14.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.353\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eExploratory factor analysis\u003c/h3\u003e\n\u003cp\u003eFor the EFA, the first subsample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1271) was used, and we proceeded as follows: In the first step, the data was checked for adequacy. In the second step, the number of factors was determined using several extraction methods. Finally, the model was evaluated according to the above-mentioned aspects, and adjustments were made if these were violated. The initial exploratory factor analysis (EFA) using Bartlett's sphericity test demonstrated that the correlation matrix results were not random (χ2(276)\u0026thinsp;=\u0026thinsp;12452.74, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Moreover, the Kaiser-Meyer-Olkin (KMO) criterion indicated that the data was well-suited for factor analysis, with a KMO value of 0.94. In addition, the multivariate normal distribution of the items was checked using the Mardia test for skewness and excess\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, which indicated a non-normal distribution of the items. Due to the violation of standard distribution assumptions and the ordinal nature of the CPDI items, we used a polychoric correlation matrix as an input method for the EFA and a principal axis as a factor extraction method, in accordance with recommendations for the robustness of the principal axis method towards the violation of standard distribution assumptions as published elsewhere\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, the number of retained factors was tested using parallel analysis\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, Eigenvalues, visual Scree test\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and Velicer\u0026rsquo;s minimum average partial test (MAP)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. In addition, the parallel analysis scree plot for the EFA is represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. All extraction methods indicated a 2-factor solution. We conducted an exploratory principal axis analysis with oblique rotation and deleted two items (22 and 3) due to cross-loadings. With the remaining items, we ran another exploratory principal axis analysis, and the extraction methods used above were repeated, continuing to indicate a 2-factor solution. We deleted one item (item 5) with a factor loading\u0026thinsp;\u0026lt;\u0026thinsp;0.30. The final 21 items were well-suited for factor analysis and could be well-assigned to the two factors. The two resulting factors are interpreted as follows: factor 1 or \"\u003cem\u003estress in the context of COVID-19 pandemics\u003c/em\u003e\" (e.g., \"\u003cem\u003eI feel tired and sometimes even exhausted\u003c/em\u003e\"; Eigenvalue\u0026thinsp;=\u0026thinsp;7.53, α\u0026thinsp;=\u0026thinsp;0.91), and factor 2 or \"\u003cem\u003erumination/seeking for information in the context of COVID-19 pandemics\u003c/em\u003e\" (e.g., \"\u003cem\u003eI can't stop myself from imagining myself or my family being infected and feel terrified and anxious about it\u003c/em\u003e\"; Eigenvalue\u0026thinsp;=\u0026thinsp;2.41, α\u0026thinsp;=\u0026thinsp;0.71). The resultant model accounted for 45% of the variation observed in the sample. Additionally, the two factors had a significant correlation (r\u0026thinsp;=\u0026thinsp;0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \n\u003ch3\u003eMulti-group confirmatory factor analysis\u003c/h3\u003e\n\u003cp\u003eThe aim of this study was to replicate the two-factor solution obtained in the exploratory factor analysis (EFA) in a second subsample (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1272) and examine metric invariance. To achieve this, a multi-group confirmatory factor analysis (MGCFA) was performed using the Diagonal Weighted Least Square (DWLS) estimation method. The hypothesized two-factor model had a good fit\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, (χ2(376)\u0026thinsp;=\u0026thinsp;775.587, CFI\u0026thinsp;=\u0026thinsp;0.982, TLI\u0026thinsp;=\u0026thinsp;0.980, RMSEA\u0026thinsp;=\u0026thinsp;0.041, 90CI [0.037, 0.045], and SRMR\u0026thinsp;=\u0026thinsp;0.057). The MGCFA yielded two important results: First, the hypothesized model was successfully replicated, and second, structural invariance was present as the items in both the Spanish and Peruvian samples loaded significantly on the latent variables as predicted.\u003c/p\u003e \u003cp\u003eNext, we examined metric invariance by constraining the factor loadings to be equal in both samples. Also, this model showed an adequate fit to the data (χ2(395)\u0026thinsp;=\u0026thinsp;1014.887, CFI\u0026thinsp;=\u0026thinsp;0.972, TLI\u0026thinsp;=\u0026thinsp;0.970, RMSEA\u0026thinsp;=\u0026thinsp;0.05, 90CI [0.046, 0.053], and SRMR\u0026thinsp;=\u0026thinsp;0.065). As described above, the measurement invariance involves nested models, which are examined using ANOVA and alternative model fit indices, revealing that the metric model had a significantly poorer fit than the configural model (χ2 difference test\u0026thinsp;=\u0026thinsp;54.197, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eIt is important to note that the χ2 difference test is sensitive to large sample sizes, which can lead to the quick rejection of models even if they fit the data well. Therefore, we also considered the increase and decrease of AFI. Our findings demonstrated that the metric model was not inferior to the configural model, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, which supports the assumption of metric invariance. Additionally, we examined scalar invariance by constraining the factor loadings and intercepts to be equal across both samples. The MGCFA yielded a good model fit (χ2(414)\u0026thinsp;=\u0026thinsp;1270.975, CFI\u0026thinsp;=\u0026thinsp;0.961, TLI\u0026thinsp;=\u0026thinsp;0.961, RMSEA\u0026thinsp;=\u0026thinsp;0.057, 90CI [0.054, 0.061], and SRMR\u0026thinsp;=\u0026thinsp;0.071). A comparison of both models indicated that the scalar invariance model was significantly poorer than the metric (χ2 difference test\u0026thinsp;=\u0026thinsp;355.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the change in AFI indicated that the overall fit was not significantly different between the scalar and the metric model (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), thus supporting the idea of scalar invariance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNested model comparisons: Difference between alternative fit indices. Abbreviations: χ2\u0026thinsp;=\u0026thinsp;chi-square, CFI\u0026thinsp;=\u0026thinsp;comparative fit index, TLI\u0026thinsp;=\u0026thinsp;Tucker-Lewis Index, RMSEA\u0026thinsp;=\u0026thinsp;root-mean-square-error of approximation, SRMR\u0026thinsp;=\u0026thinsp;root-mean-square-residual.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eΔχ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eΔCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eΔTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eΔRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eΔSRMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConfigural vs. metric invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e239.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric vs. scale invariance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e265.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDifferences in CPDI factor scores among groups and prevalent conditions\u003c/h3\u003e\n\u003cp\u003eFirstly, we examined the differences between the Peruvian and Spanish samples regarding stress (factor 1) and rumination/search for information (factor 2) scores. We found no significant differences in either factor 1 (U\u0026thinsp;=\u0026thinsp;763562.00, p\u0026thinsp;=\u0026thinsp;0.167) or factor 2 (U\u0026thinsp;=\u0026thinsp;780812.00, p\u0026thinsp;=\u0026thinsp;0.660).\u003c/p\u003e \u003cp\u003eFurthermore, we investigated the differences in factorial scores among the most prevalent pathologies, including anxious-depressive disorders, respiratory diseases, and hypertension, as listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To identify any differences, we used the U-Mann-Whitney test and excluded participants with multiple pathologies, only including those with the specific pathology under analysis. Factor scores were obtained by correcting items for age, sex, and education level.\u003c/p\u003e \u003cp\u003eThe analysis revealed that individuals with respiratory diseases had significantly higher Factor 1 scores than those without (U\u0026thinsp;=\u0026thinsp;116697, p\u0026thinsp;=\u0026thinsp;0.012). However, no significant differences were observed for Factor 2 in participants with respiratory diseases (U\u0026thinsp;=\u0026thinsp;122934.50, p\u0026thinsp;=\u0026thinsp;0.121). Individuals with hypertension showed no significant differences in either Factor 1 (U\u0026thinsp;=\u0026thinsp;99412.50, p\u0026thinsp;=\u0026thinsp;0.638) or Factor 2 (U\u0026thinsp;=\u0026thinsp;101534.50, p\u0026thinsp;=\u0026thinsp;0.928) compared to those without hypertension. Finally, individuals with anxious-depressive pathology had significantly higher scores in both Factor 1 and Factor 2 than those without anxious-depressive pathology (Factor 1: U\u0026thinsp;=\u0026thinsp;100454.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Factor 2: U\u0026thinsp;=\u0026thinsp;140850.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). These results were independent of age, sex, and education level, as confirmed by the correlation analysis of residuals and represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of the present study indicated a two-factor solution for both samples, resulting in a 21-item CPDI instrument. The items of factor 1 were theoretically related to stress reactions in the context of COVID-19, while the items of factor 2 were related to rumination behavior, including the excessive seeking of information in the context of COVID-19. Subsequently, the bifactorial solution for the CPDI was confirmed, and it was additionally demonstrated that the CPDI constructs were being measured consistently across both populations (i.e., Spanish and Peruvian). This demonstrates that CPDI can be used in different cultural contexts, confirming the cultural validity of our CPDI model. Finally, our exploratory results showed increased scores for factor 1 (stress reactions in the context of COVID-19) for respiratory diseases and increased scores for both factors in individuals with anxious-depressive disorders. All analyses were adjusted for participant sex, age, and education level.\u003c/p\u003e \u003cp\u003ePrevious independent studies have demonstrated a consistent two-factor solution for the COVID-19 Peritraumatic Distress Index (CPDI). In the Spanish study, a two-factor solution was identified through principal axis analysis and \u003cem\u003evarimax\u003c/em\u003e rotation, which yielded two factors: \"\u003cem\u003estress symptoms\u003c/em\u003e\" (15 items) and \"\u003cem\u003eCOVID-19 information\u003c/em\u003e\" (8 items). Item 5 was eliminated due to its low factor loading\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Similarly, in the Peruvian study, a two-factor solution was found: \"\u003cem\u003estress in the context of COVID-19\u003c/em\u003e\" (13 items) and \"\u003cem\u003erumination in the context of COVID-19\u003c/em\u003e\" (8 items). However, since a correlation between the two factors was assumed, a principal component analysis with a \u003cem\u003epromax\u003c/em\u003e rotation was performed in this study. Items 7, 8, and 11 were removed from the analysis\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. When the datasets were combined and analyzed, the same number of factors and a similar distribution of items per factor were obtained (see Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In contrast, other studies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e used the original four-factor structure of the CPDI, which did not significantly fit our datasets or explain a significant percentage of the variance in our case.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCPDI items and eliminated items in the Spanish study\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, Peruvian study\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and in the secondary analysis of the combined data. Items that are consistently in the factor are underlined, while the eliminated items in the different studies are written in italics. Item 12 changed factors between the studies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCPDI items\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEliminated CPDI items\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSpanish study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFactor 1 (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003estress\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4\u003c/span\u003e, \u003cem\u003e7\u003c/em\u003e, 12, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e13\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e14\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e15\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e16\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e17\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e18\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e19\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e20\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e21\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e23\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e24\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eFactor 2 (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003einformation\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2\u003c/span\u003e, \u003cem\u003e3\u003c/em\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6\u003c/span\u003e, \u003cem\u003e8\u003c/em\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e9\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10\u003c/span\u003e, \u003cem\u003e11\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePeruvian study\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFactor 1 (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003estress\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e13\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e14\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e15\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e16\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e17\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e18\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e19\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e20\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e21\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e23\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e24\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eFactor 2 (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003erumination/seeking for information\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2\u003c/span\u003e, \u003cem\u003e3\u003c/em\u003e, \u003cem\u003e5\u003c/em\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e9\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10\u003c/span\u003e, 12, \u003cem\u003e22\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,8 and 11\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCombined data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFactor 1 (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003estress\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e1\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e4\u003c/span\u003e, \u003cem\u003e7\u003c/em\u003e, 12, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e13\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e14\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e15\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e16\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e17\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e18\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e19\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e20\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e21\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e23\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e24\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eFactor 2 (\u003c/b\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003erumination/seeking for information\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e2\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e6\u003c/span\u003e, \u003cem\u003e8\u003c/em\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e9\u003c/span\u003e, \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e10\u003c/span\u003e, \u003cem\u003e11\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3, 5 and 22\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eIn the second phase, the 2-factor model was successfully replicated, and scalar measurement invariance was observed across the Peruvian and Spanish samples. The measurement invariance, as previously defined\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, demonstrated the psychometric equivalence of the CPDI construct across both groups in the context of COVID-19. This finding indicates that the construct has the same meaning, regardless of the cultural context and variations in the Spanish language (European vs. Latin-American Spanish). Measurement invariance is crucial in scale construction and validation across different cultural and ethnic contexts\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. For instance, a Canadian study examining the cross-cultural evaluation of the Beck Depression Inventory-II (BDI-II) used MGCFA to demonstrate that BDI-II had measurement invariance across culture and gender\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Similar methodologies were used in other psychometric evaluations of psychiatric disorders, such as social phobia\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, schizotypal personality\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e, and anxiety\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The “fear of COVID-19 scale”, which is another evaluation tool to measure psychological distress in the context of COVID-19, was demonstrated to have partial scalar invariance in seven different Latin American countries\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e as well as in 47 other countries worldwide, demonstrating a scalar invariance across groups concerning culture, gender, and education\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Regarding the CPDI, although descriptive and correlational data have been used to establish cross-cultural validity between German and Chinese data\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, to date, no studies have used the MGCFA methodology to perform cross-cultural validation for CPDI similar to ours.\u003c/p\u003e \u003cp\u003eOur exploratory analysis observed that individuals with respiratory diseases, mainly bronchial asthma, during the COVID-19 pandemic experienced higher psychological stress levels than healthy individuals. Several studies have reported a similar impact of negative emotions during COVID-19 on individuals with asthma. For example, Sheha et al. reported a high correlation between anxiety and depression symptoms and uncontrolled asthma in patients during the COVID-19 lockdown\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Similarly, de Boer et al. found a clinically significant increase in anxiety and depression among asthma patients during the pandemic, consistent with our findings\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Interestingly, Takeuchi et al. reported that participants with respiratory diseases, such as asthma, pneumonia, and COPD, experienced higher levels of psychological distress than those with cardiovascular diseases or cancer, which is also in line with our results\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Regarding anxious-depressive disorders, our findings suggest that participants with these conditions experience higher stress levels and engage in more rumination and information-seeking behaviors compared to healthy individuals. Specifically, rumination, a coping mechanism characterized by negative affect and self-focused attention, appears to be triggered by the repercussions and consequences of the COVID-19 pandemic\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. This rumination manifests as a constant search for information, which can exacerbate negative and catastrophic thoughts, leading to a feedback loop of repetitive negative thinking\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Thus, individuals with anxious-depressive syndromes are more likely to engage in information-seeking behaviors and experience heightened levels of rumination due to the cognitive distortions associated with anxiety and depression, which are also associated with negative behaviors such as seeking out negative news and information\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe present study demonstrates that the Spanish version of the CPDI has consistent psychometric properties in both Peruvian and Spanish samples. While this information is a valuable contribution to the research on psychological stress during the COVID-19 pandemic, some limitations must be considered. Although the sample size of our study was large, the samples from both databases were collected using a snowball method and were not randomized at the time of sampling. Furthermore, most participants were female, well-educated, and between 35 and 50 years old. Additionally, the participants self-reported pathologies, which may introduce memory bias, and were not confirmed by a physician. According to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the factorial structure of the CPDI does not form clear and stable factors over time, resulting in minimal variations among the excluded questions and suggesting possible heterogeneity in the factors that must be considered. Items measuring psychological distress in the CPDI were non-invariant, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, including those in the rumination/seeking for information factor. An additional limitation is the lack of testing for convergent and divergent validity. Finally, longitudinal data may provide stronger evidence for the factorial structure than a cross-sectional design.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study provides evidence for the cross-cultural validity of the CPDI as a reliable instrument for assessing psychological stress in the context of COVID-19 across different Spanish-speaking cultures. These findings have important implications for developing and validating measures to assess psychological distress in other cultural contexts. Our exploratory analysis revealed that respiratory diseases and anxious-depressive disorders significantly increased psychological stress during the COVID-19 pandemic. The latter also correlated with increased rumination and seeking of information, which is consistent with the psychopathology of both disorders.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFuture studies should apply and validate the CPDI instrument using EFA and CFA and perform cross-cultural validations using MGCFA in languages other than Spanish. Moreover, future studies could explore whether the CPDI is theoretically related to other scales measuring stressful experiences during the COVID-19 pandemic, which would provide fruitful information. Post-COVID research could examine psychometric aspects of psychological stress after the pandemic, as health protection methods such as wearing FPP2-masks are eliminated. The social context changes drastically with the elimination of quarantine measures in most countries.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eParticipants and procedure\u003c/h2\u003e \u003cp\u003eThe present study is a secondary analysis of two databases comprising online survey studies conducted in Peru and Spain during the COVID-19 pandemic. Certain findings from this survey study have already been reported elsewhere\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. The current study incorporates certain methodological aspects of both studies, primarily pertaining to the recruitment of participants, the description of the online survey, and the CPDI.\u003c/p\u003e \u003cp\u003eThe primary objective of this study was to evaluate the psychometric properties of the Spanish version of the CPDI in two samples from Peru (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1469) and Spain (\u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1074) and to perform a cross-cultural validation using measurement invariance for both population samples (total \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2543). As an exploratory objective, we evaluated the CPDI factorial score differences between the most common medical conditions among the two sample groups after performing the factorial and cross-cultural validation analysis.\u003c/p\u003e \u003cp\u003eThe data for this study was originally collected from voluntary participants during the COVID-19 lockdown. The collection period for the Peru study was from March 27, 2020, to September 21, 2021, while for the Spain study, it was from May 8, 2020, to June 25, 2020. Before the commencement of data collection, participants in both studies were provided with complete information regarding the study and gave written informed consent. Both studies were conducted in accordance with the Helsinki Declaration and were approved by the Ethics Committee of the Universidad Peruana Cayetano Heredia (UPCH) for the Peruvian study and the Ethics Committee Board of the Universidad Nacional de Educaci\u0026oacute;n a Distancia (UNED) for the Spanish study.\u003c/p\u003e \u003cp\u003eIn the Peruvian study, only residents during the COVID-19 lockdown aged 18 years or older, possessing sufficient Spanish language proficiency and providing written informed consent, were eligible for inclusion. Those who did not meet these criteria were excluded. Similarly, in the Spanish study, participants had to be Spanish residents aged 18 years or older. Both studies excluded individuals who did not fully complete the questionnaires or failed to provide socioeconomic information. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents a comprehensive overview of the samples, encompassing socioeconomic and psychometric data.\u003c/p\u003e \u003cp\u003eOnline electronic surveys were employed in both studies to collect participant information due to the sanitary restrictions in both countries, which prevented personal contact for data collection. In the Peruvian study, Google Forms, an open-access internet-based program provided by Google Inc, USA, was used for online surveys. For the Spanish study, the surveys were recorded automatically using the Qualtrics Software (Qualtrics Research Suite: Provo, UT, USA, 2013). Both surveys were conducted anonymously, and participants were only permitted to complete the survey once. The surveys included questions regarding socio-economic status (i.e., age, gender, education), psychometric data (CPDI scale) \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and data of past medical history, extracting, in this case, the most frequent disorders in both populations (respiratory diseases, hypertension, hypercholesterinemia, diabetes mellitus, cardiovascular diseases, and anxious-depressive disorders).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCOVID-19 Peritraumatic distress index (CPDI)\u003c/h3\u003e\n\u003cp\u003eThe COVID-19 Peritraumatic Distress Index (CPDI) is a self-report questionnaire designed to assess psychological distress during the COVID-19 pandemic. This instrument comprises 24 items, each of which is evaluated on a Likert scale ranging from 0 to 4 (i.e., never, occasionally, sometimes, often, and most of the time). The raw score is calculated by adding the value of each item, and the displayed score is obtained by adding 4 to the raw score to determine the severity degree of CPDI. The CPDI defines different categories for peritraumatic stress related to the COVID-19 pandemic: \u003cem\u003enormal\u003c/em\u003e (0\u0026ndash;28 display points), \u003cem\u003emild\u003c/em\u003e (29\u0026ndash;52 display points), and \u003cem\u003esevere\u003c/em\u003e (53\u0026ndash;100 display points)\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. In both studies, a validated version of the CPDI through expert adaptation and translation to the Spanish language was used, whose construct validation has been performed separately\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eData preparation and statistical analysis\u003c/h3\u003e\n\u003cp\u003eTable representation and text description were used to present general sample characteristics, including descriptive data on the CPDI. For numerical variables that were normally distributed, means and standard deviation were used as measures of central tendency. For non-Gaussian distributed variables, median and interquartile ranges (IQR), including 75- and 25-percentiles, were used. Descriptive information greater than one million was expressed using scientific notation, and decimal data were rounded to two decimals. Qualitative data, including count data, was characterized using absolute numerical values and percentages. These procedures were performed for both subsamples, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Inferential statistical tests were not performed to evaluate differences between the two subsamples (i.e., first and second phase) since they corresponded to two subprojects with different objectives and hypotheses.\u003c/p\u003e \u003cp\u003eSpecifically, we investigated the factor structure of the CPDI in both populations. To achieve this, we merged the databases of both studies, randomly sorted the participants, and divided them into equal proportions (1:1 for each database) for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), as recommended in previous studies\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. In evaluating the EFA solution, we adhered to the following literature recommendations\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e:\u003c/p\u003e \u003cp\u003e \u003col style=\"list-style-type: lower-alpha;\"\u003e\u003cspan\u003e \u003cli\u003e \u003cp\u003eall factors should be theoretically meaningful\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eat least three variables should saliently load on a factor (overdetermined, i.e., factor loadings\u0026thinsp;\u0026ge;\u0026thinsp;0.30)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003evariables should load significantly on only one factor (no cross-loadings)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eeach factor should have an internal consistency of Cronbach's α\u0026thinsp;\u0026ge;\u0026thinsp;0.70\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eTo determine whether cultural differences exist in the CPDI between the Spanish and Peruvian samples, it is essential to test the measurement invariance using multigroup CFA (MGCFA). This approach enables invariance evaluation by imposing cross-group restrictions and comparing models with varying degrees of constraints, as demonstrated in numerous studies \u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. ANOVA was used to compare nested models that emerged by restricting different parameters and representing different levels of measurement invariance. The hypothesized measurement model's findings were evaluated using fit indices and their cut-off points: the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root-mean-square-error of approximation (RMSEA), and the standardized root-mean-square-residual (SRMR) were used as model fit measurements. A good model fit was given if the values of the CFI and TLI were greater than or equal to 0.95\u003csup\u003e11\u003c/sup\u003e. Regarding RMSEA and SRMR, a good model fit was given if the values of both root-mean-square indicators were below or equal to 0.05\u003csup\u003e11\u003c/sup\u003e. Additionally, we calculated the 90-percent confidence intervals (90CI) for the RMSEA\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Differences in these alternative fit indices (AFI) were also used to compare the nested models, as the χ2-value is sensitive to sample size. In this case, following cutoffs were considered: -0.01 for ΔCFI, paired with 0.15 for ΔRMSEA and ΔSRMR of 0.030 (for metric invariance) or 0.015 (for scalar or residual invariance).\u003c/p\u003e \u003cp\u003eFinally, exploratory data analysis was conducted by calculating the sum of the scores of the resulting factors (i.e., factors 1 and 2) and assessing the differences in each factor score among the three most prevalent medical conditions in both samples.\u003c/p\u003e \u003cp\u003eDescriptive information was performed using JASP version 0.11.1\u003csup\u003e44\u003c/sup\u003e. Statistical analyses of the EFA were performed using the R-software version 4.1.2 (R Core Team, 2021, R Foundation for Statistical Computing, Vienna, Austria)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e and for the MGCFA we utilized version 0.6\u0026ndash;10 of the R-package \u003cem\u003elavaan\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions/Conflict of Interests/Additional Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach participant or their legally authorized representative was fully informed about the objectives and procedures of the study, as well as the potential adverse effects, and gave their written consent to participate. The study protocol and all study procedures were reviewed and approved by the ethics committee of the Universidad Peruana Cayetano Heredia (UPCH) for the Peruvian study and the Ethics Committee Board of the Universidad Nacional de Educaci\u0026oacute;n a Distancia (UNED) for the Spanish study. Additionally, this pilot trial was carried out according to the Helsinki Declaration.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets generated and analyzed during the study are not publicly but they are available from the corresponding author on justified request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of interest: \u003c/strong\u003enone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments: \u003c/strong\u003enone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFegert, J. M., Vitiello, B., Plener, P. L. \u0026amp; Clemens, V. 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Softw. 48, 1\u0026ndash;36 (2012).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"COVID-19, psychological distress, validation studies, measurement invariance, scales","lastPublishedDoi":"10.21203/rs.3.rs-2891476/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2891476/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe COVID-19 pandemic has led to a significant psychological impact worldwide. The COVID-19 Peritraumatic distress index (CPDI) is widely used to assess psychological stress during the COVID-19 pandemic. Although CPDI has been validated in Peru and Spain, no cross-cultural validation studies have been conducted. As an exploratory aim, differences in CPDI factorial scores between the most prevalent medical conditions in two samples from a general population of Peru and Spain were investigated.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMaterials and Methods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted secondary data analysis with data from Peru and Spain to validate the CPDI in a cross-cultural context. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and multigroup confirmatory factor analysis (MGCFA) were performed to evaluate the factor structure and measurement invariance of the CPDI across cultural contexts.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study revealed a bifactorial solution (stress and rumination/information) for the CPDI, consistent with previous studies. The MGCFA demonstrated measurement invariance across cultural contexts (scalar invariance), indicating that the CPDI construct has the same meaning across both groups, regardless of cultural context and language variations of Spanish. Patients with anxious-depressive disorders showed higher CPDI factorial scores for both factors, whereas patients with respiratory diseases were only associated with the stress factor.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study provides evidence for the cross-cultural validity of the CPDI, highlighting its utility as a reliable instrument for assessing psychological stress in the context of COVID-19 across different cultures. These findings have important implications for developing and validating measures to assess psychological distress in different cultural contexts.\u003c/p\u003e","manuscriptTitle":"Cross-Cultural Validation in Times of COVID-19: An Example Using the COVID-19 Peritraumatic Distress Index (CPDI) among Spanish and Peruvian Populations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-15 14:26:08","doi":"10.21203/rs.3.rs-2891476/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2023-09-19T09:08:05+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-09-13T08:09:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"9a28d971-fc66-4443-bc23-6eccad6288ba_SNPRID","date":"2023-09-09T20:07:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2023-08-23T10:08:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c9c8f3af-be3d-442f-b9fe-78766a90472d","date":"2023-08-05T10:01:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-07-20T18:21:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-07-20T17:07:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-05-11T10:13:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-05-11T10:11:02+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2023-05-03T23:28:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"da92ee14-d1fc-488b-b7f5-11ec82f13a2f","owner":[],"postedDate":"May 15th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":21309431,"name":"Biological sciences/Psychology/Human behaviour"},{"id":21309432,"name":"Health sciences/Medical research/Epidemiology"},{"id":21309433,"name":"Biological sciences/Psychology"},{"id":21309434,"name":"Health sciences/Health care/Quality of life"},{"id":21309435,"name":"Health sciences/Diseases/Psychiatric disorders/Anxiety"},{"id":21309436,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":21309437,"name":"Health sciences/Diseases/Psychiatric disorders/Post traumatic stress disorder"},{"id":21309438,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2023-11-06T15:03:00+00:00","versionOfRecord":{"articleIdentity":"rs-2891476","link":"https://doi.org/10.1038/s41598-023-46235-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-11-03 15:01:06","publishedOnDateReadable":"November 3rd, 2023"},"versionCreatedAt":"2023-05-15 14:26:08","video":"","vorDoi":"10.1038/s41598-023-46235-4","vorDoiUrl":"https://doi.org/10.1038/s41598-023-46235-4","workflowStages":[]},"version":"v1","identity":"rs-2891476","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2891476","identity":"rs-2891476","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}