Skip to main content
Download PDF
Download ePub

Gender-related measurement invariance on the Self-Reporting Questionnaire (SRQ-20) for global mental distress with older adults in Puerto Rico

Archives of Public Health volume 82, Article number: 163 (2024) Cite this article

Abstract

Background

Common mental disorders (CMD) vary by age, gender, and culture. This study: (1) examined the factor structure of the 20-item Self Reporting Questionnaire (SRQ-20) and (2) explored gender-related measurement invariance in the SRQ’s performance with older adults in Puerto Rico, a U.S. island territory and associate member of the UN Regional Commissions.

Methods

We merged data from two cross-sectional studies on mental health status and needs of older adults in Puerto Rico (N = 367). The first study was conducted in 2019, two years after Hurricane María devastated the island (N = 154); the second study, in 2021, assessed knowledge, attitudes and practices (KAP) concerning COVID-19 (N = 213). We used chi-square and t-tests to examine gender differences in each SRQ item and assessed internal consistency reliability with Cronbach’s alpha and McDonald’s omega (values > 0.70). We ran two CFA models, then multigroup CFA to test for gender-related measurement invariance. We used weighted least square mean and variance adjusted (WLSMV) estimation to account for the binary response options in the SRQ-20 and Mplus version 8.4 for analyses. There were no missing data for any SRQ-20 items.

Results

The SRQ-20 had strong internal consistency reliability (α = 0.89; omega = 0.89). Female scores were higher than males scores (t = -2.159, p = .031). Both unidimensional and two-factor models fit the data well. We selected the more parsimonious unidimensional model, which is most widely used in practice. Standardized factor loadings were 0.548 to 0.823 and all were statistically significant (p < .001). We tested gender invariance with the one-factor model. Our findings did not support invariance.

Conclusion

We favored the unidimensional model. First, the SRQ-20 was designed to assess global distress. Also, physical symptoms have both somatic and psychological components, so their co-occurrence makes a single-factor model more meaningful. Finally, since older adults experience more physical health problems, instruments that emphasize both types of distress may provide a more accurate measure than those that exclude somatic symptoms. Using the unidimensional model, the SRQ-20 was not invariant, meaning that it performed differently for male and female participants. Future studies of common mental disorders with older adults in Puerto Rico should consider using the SRQ-20 for research and practice and should determine appropriate threshold scores for men and women.

Peer Review reports

Text box 1. Contributions to the literature

• There is very little research on the performance and psychometric properties of the Self-Reporting Questionnaire (SRQ-20) with older adults.

• Common mental disorders vary considerably by sex and our finding that the SRQ-20 was not invariant for men and women underscores the need to consider sex in assessment and establishing optimal cut-scores.

• Puerto Rico is a member of the global Alliance of Small Island States. There is an urgent need for research on the unique social, economic, and environmental challenges in these oft-overlooked locales, especially but not only as harbingers of global environmental change.

Background

An estimated 4.0% and 3.8% of the global population suffer from depressive and anxiety disorders, respectively [29]. These disorders often co-occur and both are associated with somatoform disorders, which lack an identifiable pathological basis but are commonly seen in routine clinical practice [48]. The prevalence and presentation of these common mental disorders (CMD), i.e., anxiety, depression, and somatic disorders, vary across age groups and cultures, but women are consistently more likely than men to report each disorder [31, 43, 47].

Excluding headache disorders, more than 20% of persons aged 60 years and over experience a mental or neurological disorder, and these disorders account for 6.6% of Disability Adjusted Life Years and 17.4% of Years Lived with Disability [57]. Persons in this age group also represent about a quarter of deaths from self-harm, and those aged 85 and over have the highest suicide rates of any age group. Yet, despite the high prevalence and burden of CMDs in later life, detection rates are lower than for all other age groups, and only one in three persons aged 60 and over with a mental disorder receives the treatment they need.

To determine which older adults are not reaching needed mental health services and why this is the case requires a better understanding of this treatment gap [55]. Factors that contribute to low detection rates pervade societies and care systems and include stigma and ageism, low mental health literacy, and lack of access to effective, appropriate care [9]. Another barrier to timely, accurate detection is the vast array of assessment and outcome measures that are in use [10]. The need for efficient, psychometrically sound, culturally appropriate measures of CMDs within and among populations is especially pressing in low-resource settings. To address this gap, Harding et al. [25] developed the Self-Reporting Questionnaire (SRQ) in collaboration with the WHO, which later endorsed it as a universally applicable case-finding instrument for probable CMD in primary care settings in less developed countries [8]. Studies on the performance of the SRQ in different populations and settings have since reported different factor structures and mixed findings on gender differences. There is very little research with Latin American populations, and we found only one study, set in Brazil, that reported exclusively on older adults [45].

The current study aims to: (1) examine the factor structure of the 20-item version of the SRQ (SRQ-20) with older adults in Puerto Rico two years after a calamitous hurricane and during the COVID-19 pandemic and (2) explore measurement-related gender differences in the instrument’s performance with this population. We begin with a brief overview of the study context, the data source, and the sample. We then describe the SRQ-20 and, following Boyce et al. [10], we justify our selection of this instrument as a mental health assessment and outcome measure with the study population and our focus on gender as an important source of measurement-related variance. We then present our findings and conclude with discussion and implications for using the SRQ-20 to improve the detection of CMDs among older adults in low-resource settings, notably in the Caribbean and other parts of Latin America.

Study context

Puerto Rico, an unincorporated territory of the United States, is a member of the United Nations Economic Commission for Latin America and the Caribbean (ECLAC)--one of five regional commissions established in 1948 to work with regional governments to raise standards of living and strengthen trade relations elsewhere in the world. It is the island most impacted by hurricanes in the Caribbean. Economic, political, and social contexts of natural and human-made disasters profoundly affect damage and recovery, including health and mental health outcomes of residents [7]. In the months leading up to Hurricane María in September 2017, a decade-long economic recession forced Puerto Rico into bankruptcy [11]. In July 2019, the governor was ousted for scandal and corruption and late that year and into early 2020, major earthquakes wracked the island. Within 6 months of the hurricane, an estimated 2,975 people, mostly older adults, had died [44] and nearly 200,000, mostly working-age adults and families, had migrated to the U.S. mainland. Between 2017 and 2020, the population declined from 3.16 million to 2.86 million (10%) and the median age rose from 39.2 to 44.5 years [58]; fully 23.5% of the population is now aged 65 or over [50].

In this context, the first case of COVID-19 in Puerto Rico was detected in March, 2020. The pandemic disproportionately affected Latinos, older adults, and persons with chronic health conditions [23]. But Puerto Rico’s government implemented early, aggressive public health measures and by May 2022, 83.7% of the population was fully vaccinated and 95.7% had received at least one dose of vaccine [14]. The rapid succession of these devastating events, coupled with severe U.S. restrictions on aid to the island [52] created new and worsened existing mental health risks for older adults.

The Self-Reporting Questionnaire (SRQ-20)

The full SRQ consists of 25 items derived from four psychiatric morbidity measures that are used across a wide range of cultural settings: 20 items assess neurotic symptoms, 4 measure psychotic symptoms, and 1 evaluates convulsions. The SRQ-20 comprises the neurotic items, which assess depressive symptoms, anxiety, and psychosomatic complaints during the past 30 days. Items are scored ‘yes’ (symptom present = 1) or ‘no’ (no symptom present = 0), then summed. In a systematic review of assessment instruments for CMDs in low resource settings, Ali [2] recommended the SRQ-20 because of its ease of administration, broad applications, and extensive psychometric testing. The instrument has also been used to assess CMDs in the immediate and long-term aftermath of disasters [49].

The SRQ-20 has been widely validated in primary care, community screening, and epidemiological population surveys and in multiple languages and cultural settings. It was developed as a unitary measure of CMDs, but studies report multifactor structures ranging from 2 to 7 factors, depending on context and cultural understanding of scale items [46, 54]. Consistent with the SRQ’s original intent, studies that report 3 or more factors regularly describe components that reflect depressive, anxiety and / or somatoform symptoms [15, 26]. Similarly, while a cut-off score of 7 / 8 is often used to indicate probable mental disorder (Harpham et al.), optimal clinical thresholds vary by population characteristics, particularly gender [56]. Table 1 summarizes the performance of the SRQ-20 with adults in different populations and settings, showing various factor structures and mixed findings on gender-related measurement invariance.

Table 1 Factor structure and gender-related findings on the Self-reporting Questionnaire (SRQ-20) in various countries from 1990 to 2024
Full size table

There is very little research on use of the SRQ-20 with Latin American and/or older adult populations. We identified only one validation study using the Spanish-language SRQ-20, set in Colombia [22] and one with older adults, in Brazil [45] -- a sample in Vietnam [42] did include older adults. We did not identify any psychometric studies using the Spanish version of the SRQ-20 with older adults. The current study thus aims to: (1) assess the factor structure of the 20-item SRQ (SRQ-20) with older adults in Puerto Rico, and (2) explore measurement-related gender differences in the instrument’s performance with this population.

Methods

Data source and sample

Data are from two sequential cross-sectional studies with older adults in Puerto Rico. The aim of the first study was to assess mental health status and needs 2 years after Hurricane María. From September 2019 to early January 2020, our U.S. and Puerto Rican research team conducted face-to-face interviews with a non-probability sample of 154 adults aged 60 years and over in 5 of the island’s 6 geographic regions. We could not access the south region due to earthquakes. The second study surveyed 233 same-aged adults about their knowledge, attitudes, and practices (KAP) concerning the COVID-19 pandemic in 2021. Due to pandemic restrictions, we conducted the first 62 interviews for this study by telephone in January, 2021. The same interviewers did not identify differences in response rates or quality of data for the two modes. In part, this may be due to widespread connectivity–the number of mobiles in Puerto Rico at that point was equivalent to 107.2% of the population (some users have multiple connections) [19]. Others report comparable data for telephone and face-to-face data collection, especially during pandemics [30], including with older adults [32]. To ensure accuracy, we investigated whether SRQ scores varied by method using a mimic model and found no difference.

We recruited for both studies from community and senior centers, social service agencies, primary care clinics and public spaces. Interviews lasted about one hour, and participants were compensated for their time. The study was approved by the Virginia Commonwealth University Institutional Review Board. Study participants provided written consent.

To ensure an adequate sample for psychometric testing, we merged data from the 2 studies (N = 367). The average age of the combined sample was 72.7 years (SD = 8.7, range = 60–99). Most participants were female (58.3%), unmarried (65.6%) and living alone (67.6%). Half had completed high school (50.3%), and the median annual household income was $9,552–43.5% reported incomes below the federal poverty threshold, compared to 13.1% of mainland U.S. citizens [50]. With respect to sex differences in demographic characteristics, women were more likely to be older (t=-2.702, p = .007), live alone (χ² = 5.087, p = .024), be unmarried / unpartnered (χ² = 11.093, p = .001) and report worse self-rated health (t = 2.167, p = .031) than men.

We used the WHO Spanish version of the SRQ-20 [17, 22], which performed well in our initial pilot with 10 older adults in Puerto Rico. Pilot participants reported no problems with the instrument’s clarity or its linguistic or cultural appropriateness. Interviewers uniformly agreed that it was both feasible and suitable for the study.

Data Analysis

Table 1 presents findings of previous psychometric studies of the SRQ-20. We drew on several to guide our analyses. Hanlon et al. [24] identified 2 factors with eigenvalues > 1, but they opted for a single factor solution due to significant cross-loading of items. Kootbodien et al. [33] used confirmatory factor analysis to compare one-, two-, and three-factor models, each of which fit the data well. They then tested for gender invariance using the one-factor model based on its intended use and extensive application in clinical and research settings. Following analytic decisions made by these researchers, we tested one- and two-factor models and selected the more parsimonious and widely-used one-factor model envisioned by WHO.

We used SPSS (IBM SPSS Statistics, ver. 29.0) for data management and univariate analyses. We used chi-square (item scores) and t-tests (total scale) to examine gender differences on the SRQ-20, and assessed internal consistency reliability with Cronbach’s alpha [40] and McDonald’s omega [12], with values of ≥ 0.70 deemed acceptable (Nunnally & Bernstein). We ran two CFA models with the full sample, followed by multigroup CFA to test for gender-related measurement invariance. We used weighted least-square mean and variance adjusted (WLSMV) estimation to account for the binary response options in the SRQ-20 and Mplus version 8.4 [37]. We considered standardized factor loadings of 0.40 and above to define a factor [12]. There were no missing data on any SRQ-20 items.

We tested a standard unidimensional model with all 20 items and a two-factor model, one for psychological symptoms (items 5, 6, 8–16, 20) and another for somatic symptoms (items 1–4, 7, 17–19). Our rationale for testing a two-factor model was that symptoms of depressive and anxiety disorders often overlap while somatoform disorders may vary more by age, culture, and context [5, 53]. None of the studies reviewed in Table 1 focused on older adults and the mean age of samples, where reported, were mid-life. We examined model fit with χ2 statistics, comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean squared residual (SRMR). A good fit is indicated by nonsignificant χ2 values, CFI and TLI are > 0.95, RMSEA is < 0.06, and SRMR is < 0.08 [12, 27].

We next tested for measurement invariance on the SRQ-20 with men (n = 153) and women (n = 214). To validate our measurement invariance test, we assessed the sample balance for sex. Chi-square tests were employed to determine if the number of participants was proportionally similar across groups. Additionally, we found the distribution of the SRQ-20 score to be normally distributed across sexes. Finally, we conducted confirmatory factor analysis (CFA) models by sex, confirming that all indices were acceptable for both men and women. Following Brown [12] and Muthén and Muthén [36], we tested configural invariance and then scalar invariance. Configural invariance (or equal form), which examines whether the factor structure is equal for both groups, is deemed present if the number of factors and the pattern of factor loadings are identical for men and women as evidenced by satisfactory fit indices using the same thresholds as for the CFA..

Scalar invariance (or strong factorial invariance) assesses whether the indicator intercepts for the groups are equal. Statistically nonsignificant results of a χ2 difference test between the configural and scalar models indicate that the intercepts for the two groups are invariant, i.e., do not differ. We also used a CFI change criterion of greater than 0.01 (i.e., △CFI > 0.01) for each level of invariance test to determine if the change in the fit indices was significant [13].

Results

Table 2 shows descriptive statistics for the SRQ-20, overall and by gender. The most frequently reported symptoms were feeling nervous, tense, or worried (item 6; 54.0%), feeling unhappy (item 9; 48.5%), sleeping badly (item 3; 45.0%), and feeling tired all the time (item 19; 41.4%). Five items differed by gender. Women were more likely than men to report poor appetite, χ2(1) = 12.134, p < .001, sleep badly, χ2(1) = 4.339, p = .04, poor digestion, χ2(1) = 12.201, p < .001, feeling worthless person, χ2(1) = 4.579, p = .04, and uncomfortable feelings in the stomach, χ2(1) = 16.947, p < .001. The total score for females (M = 6.53, SD = 5.03) was significantly higher than that of males (M = 5.38, SD = 4.98), t = -2.159, p = .031.

Table 2 Frequencies and chi-square results for Self-reporting Questionnaire(SRQ-20) symptom endorsement by gender
Full size table

Confirmatory factor analysis

Table 3 presents CFA results for the unidimensional and two-factor models. The unidimensional model had an acceptable fit (χ2(170) = 411.899, p < .001, CFI = .943, TLI = .936, SRMR = .088, RMSEA = .062, 90% CI [.055, .070]). The modification indices suggested that Item 7 (“Is your digestion poor?”) and Item 18 (Do you have uncomfortable feelings in your stomach?”) were highly correlated (r = .73, p < .001). As it is reasonable to expect discomfort when digestion is poor, these variables were allowed to co-vary. The revised unidimensional model had a good model fit, except for the χ2 p-value, χ2(169) = 307.137, p < .001, CFI = 0.967, TLI = 0.973, SRMR = 0.079, RMSEA = 0.047, 90% CI [0.039, 0.056]). The chi-square value may be significant when the sample size is large, as reported in many previous studies [1]. The SRQ-20 had strong internal consistency reliability (α = 0.89; omega = 0.89).

The two-factor model also fit the data well, χ2(168) = 208.033, p < .001, CFI = 0.973, TLI = 0.979, SRMR = 0.075, RMSEA = 0.043, 90% CI [0.034, 0.051]); however, the correlation between the two factors was high r = .89, p < .001), suggesting that somatic and psychological symptoms co-exist and may conceptually overlap. Based on these findings and for reasons discussed above, we selected the unidimensional model. Figure 1 shows the model structure and its standardized factor loadings, which ranged from 0.55 to 0.82; all were statistically significant (p < .001).

Table 3 also presents the results of measurement invariance testing by gender for the unidimensional model. Before conducting the measurement invariance test, we ensured that the one-factor model was acceptable in both groups. Configural invariance testing revealed no difference in factor structures for males and females. This finding was supported by fit statistics t χ2(338) = 448.402, p < .001, CFI = 0.973, TLI = 0.970, SRMR = 0.096, RMSEA = 0.042 [0.031, 0.052]). Scalar invariance was not supported by results of the χ2 difference test between the configural and scalar models, χ2(18) = 41.733, p = .001, which differed significantly. These models suggest that the factor loadings and intercepts for males and females were not equivalent. Additionally, we examined changes in the fit indices and found no significant changes in the fit indices (ΔCFI = 0.005) when comparing the configural model and the scalar model. Due to the inconsistency between the two tests, we further examined differential item functioning (DIF). There were statistically significant gender differences in five items. Women were more likely to report poor appetite than men (b = 0.563, p < .001), poor sleep (b = 0.279, p = .037), poor digestion (b = 0.520, p < .001), and uncomfortable feelings in their stomach (b = 0.602, p < .001). Conversely, women were less likely to feel they were a worthless person (b = -0.378, p = .034).

Table 3 Confirmatory factor analysis and measurement invariance testing for Self-reporting Questionnaire (SRQ-20) by gender
Full size table
Fig. 1
figure 1

Confirmatory factor analysis and standardized factor loadings for the unidimensional model of the Self-reporting Questionnaire (SRQ-20) with older adults in Puerto Rico

Full size image

Discussion

The purpose of this study was to examine the factor structure and gender-related measurement invariance of the SRQ-20 with older adults in Puerto Rico. Factors are not always clear cut and multiple models may provide an equally good fit. Both the unidimensional and two-factor models fit the data well, but we favored the former, more parsimonious, model for several reasons. First, the SRQ-20 was designed to assess overall distress. Also, since physical symptoms involve both psychological and somatic components [6], particularly among older adults [18] and in Hispanic cultures [21], their coexistence may make a one-factor model more meaningful [24]. Lastly, since older adults have more physical health problems, instruments that emphasize both types of distress may provide a more accurate measure than those that exclude somatic symptoms [20]. Using the unidimensional model, internal consistency reliability of the SRQ-20 was strong, and the instrument was not invariant, meaning that it performed differently for men and women.

Because gender and age act and interact to influence the experience and expression of mental disorders, it is important to test for measurement invariance to determine whether the same construct is being measured across groups and whether different groups ascribe the same meanings to scale items [35]. To our knowledge, only two studies have used CFA to examine gender invariance on the SRQ-20. Kootbodien et al. [33] found that unidimensional and multidimensional models provided a good fit in a sample of younger adults in South Africa, and measurement between genders was not invariant. Stratton et al. [49] used a latent variable modeling approach to examine psychometric properties of the instrument in a large community survey in Vietnam. They found that a bifactor model and a correlated three factor model fit the data equally well. Regarding measurement invariance, they reported gender differences on factor loadings and thresholds of a single factor construct. On average, females and older persons reported more distress than males and younger individuals, respectively.

Consistent with these previous studies, our findings suggest that assessment of common mental disorders may differ for men and women. Our sample comprised adults aged 60 and over. Since older men and women have more physical health problems than younger adults, they may be more inclined to conflate their experience and reporting of psychological and somatic symptoms. This may be especially the case for women, who were more likely to report higher somatic symptoms in our data.

There may also be age-related cohort effects. The current cohort of older adults in Puerto Rico have experienced multiple political, economic, and environmental ordeals, including social, economic, and health losses associated with Hurricane María and the COVID-19 pandemic. The impact of cumulative stressors and social and psychological coping strategies may vary for men and women who came of age with different sociocultural scripts for males and females. The concept of machismo, for example, includes both positive and negative aspects of masculinity, e.g., courage, honor, dominance, aggression, sexism, and reserved emotions. Women, on the other hand may embrace values and behaviors associated with marianismo, honoring family- and home-centeredness and encouraging passivity, self-sacrifice, and chastity. Nuñez et al. [39] provide a thorough review of the influence of these traditional gender roles on negative cognitions and emotions and help-seeking behaviors in Hispanic cultures.

Clinical somatoform disorders are widely neglected in research with older adults, yet as Azoulay and Gilboa-Schechtman [4] note, they are prevalent and highly impairing in this age group, especially after heightened stress. Noting that women report greater post-traumatic distress than men after a physically threatening event, they suggest that gender differences in stress reactions may be related to loss of social status among men. This hypothesis warrants further examination, especially in more traditionally patriarchal cultures, as it is likely to be associated with distribution, assessment, and intervention in mental disorders.

This study has several limitations. First, the sample size was relatively small. However, adequacy depends on features such as study design, the strength of the relationships among the indicators, and the reliability of indicators and missing data patterns [12]. The overall sample size and number of groups may not be related to level of invariance, and group differences are most problematic in invariance testing in cases of more severe imbalance of groups [59]. All absolute, parsimony, and comparative fit indices were acceptable in our data. And although our sample was purposive, the proportion of males and females was the same as the distribution of persons aged 60 + in the 2022 American Community Survey [51].

Our cross-sectional design negates our ability to evaluate psychometric properties of the SRQ-20 or to assess gender invariance over time. There is also potential for self-report bias due to factors such as cultural beliefs and behaviors, stigma, and social desirability, which lead to under-reporting of mental health conditions in community surveys [28]. Finally, Puerto Rico’s status as a U.S. territory may distinguish the experiences of its older adults from those in other countries in the region. Since the mid-twentieth century, for example, Puerto Ricans have engaged in extensive circulatory migration between the island and the mainland.

With respect to theory on psychosocial distress and its measurement, the co-occurrence of psychological and somatic symptoms observed in our data may be due to cultural context; this overlap of symptoms should be examined within and among other Latin American populations. Likewise, when assessing point prevalence and trajectories of symptom reporting for CMD, it will be important to consider the potential role of intersectional identities such as ethnicity, age, and gender [4]. Due to lack of research on the SRQ-20 in Latin America, we could not compare our data with other studies in the region.

Conclusions

We conclude that the SRQ-20 is well suited for use with older adults in Puerto Rico. It is among the most widely used and rigorously tested instruments for measuring CMDs, especially in low-resource settings. Our findings of gender variance in the SRQ-20 are consistent with a large body of evidence on gender differences in common mental disorders across the life course (See [3]). Optimal cutoff thresholds should thus be established for older men and women for both clinical and research purposes.

As the first study to examine the factor structure and gender invariance among older Puerto Ricans, we also conclude that a unidimensional structure is appropriate. We recognize that the internal structure of the SRQ-20 is influenced by the methodology employed. Following recommendations of Merino-Soto et al. [34], future explorations of bifactor or second-order models could be a valuable direction to further validate and extend our findings. Finally, future research should re-evaluate our findings with a larger sample of older adults in Puerto Rico.

Most future global population aging will occur in low- and middle-income countries (LMIC), where CMDs are highly prevalent and burdensome to individuals and societies. This study joins a small but growing body of evidence that the SRQ-20 performs well with older adults. Future research should extend the scope of inquiry on this measure, including gender-related invariance testing, to this age group in other LMIC, including those in Latin America and to other similarly situated Small Island Developing States (SIDS) and Associate Members of U.N. regional commissions.

Availability of data and materials

Data are available from the first author upon request.

References

  1. Alavi M, Visentin DC, Thapa DK, Hunt GE, Watson R, Cleary M. Chi-square for model fit in confirmatory factor analysis. J Adv Nurs. 2020;76(9):2209–11.

    Article  PubMed  Google Scholar 

  2. Ali GC, Ryan G, De Silva MJ. Validated screening tools for Common Mental Disorders in low and middle income countries: A systematic review. PLoS ONE. 2016;11(6):e0156939. https://doi.org/10.1371/journal.pone.0156939.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Åhs A, Nummi T, Hammarström A. Gender inequalities in common mental disorders over the life course. Eur J Pub Health. 2017;27(Issue suppl_3):ckx189.186. https://doi.org/10.1093/eurpub/ckx189.186.

    Article  Google Scholar 

  4. Azoulay R, Gilboa-Schechtman E. Social construction and evolutionary perspectives on Gender differences in post-traumatic distress: The case of status loss events. Front Psychiatr. 2022;13:858304. https://doi.org/10.3389/fpsyt.2022.858304.

    Article  Google Scholar 

  5. Bagayogo IP, Interian A, Escobar JI. Transcultural aspects of somatic symptoms in the context of depressive disorders. Adv Psychosom Med. 2013;33:64–74. https://doi.org/10.1159/000350057.

    Article  PubMed  Google Scholar 

  6. Barsky AJ, Peekna HM, Borus JF. Somatic symptom reporting in women and men. J Gen Intern Med. 2001;16(4):266–75. https://doi.org/10.1046/j.1525-1497.2001.016004266.x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Benedek DM, Fullerton C, Ursano RJ. First responders: Mental health consequences of natural and human-made disasters for public health and public safety workers. Annu Rev Public Health. 2007;28(1):55–68. https://doi.org/10.1146/annurev.publhealth.28.021406.144037.

    Article  PubMed  Google Scholar 

  8. Beusenberg M, Orley J. A user's guide to the Self-Reporting Questionnaire (SRQ). World Health Organization, Division of Mental Health. 1994. https://apps.who.int/iris/handle/10665/61113.

  9. Bor JS. Among the elderly, many mental illnesses go undiagnosed. Health Aff. 2015;34(5):727–31. https://doi.org/10.1377/hlthaff.2015.0314.

    Article  Google Scholar 

  10. Boyce N, Graham D, Marsh J. Choice of outcome measures in mental health research. The Lancet. 2021;8(6):455. https://doi.org/10.1016/S2215-0366(21)00123-1.

    Article  PubMed  Google Scholar 

  11. Brown N. Puerto Rico files for biggest ever U.S. local government bankruptcy. Reuters. 2017  https://www.reuters.com/article/us-puertorico-debt-bankruptcy/puerto-rico-files-for-biggest-ever-u-s-local-government-bankruptcy-idUSKBN17Z1UC

  12. Brown TA. Confirmatory factor analysis for applied research (2nd ed.). The Guilford Press. 2015.

  13. Cheung GW, Rensvold RB. Evaluating goodness-of-fit indexes for testing measurement invariance. Struct Equ Modeling. 2002;9(2):233–55. https://doi.org/10.1207/S15328007SEM0902_5.

  14. Centers for Disease Control & Prevention (CDC). Vaccinating people in Puerto Rico. 2021.https://www.cdc.gov/vaccines/covid-19/health-departments/features/puerto-rico.html

  15. Chen S, Zhao G, Li L, Wang Y, Chiu H, Caine E. Psychometric properties of the Chinese version of the Self-Reporting Questionnaire 20 (SRQ-20) in community settings. Int J Soc Psychiatr. 2009;55(6):538–47. https://doi.org/10.1177/0020764008095116.

    Article  Google Scholar 

  16. Chipimo PJ, Fylkesnes K. Comparative validity of screening instruments for mental distress in Zambia. Clin Prac Epidemiol Mental Health. 2010;6:4–15. https://doi.org/10.2174/1745017901006010004.

    Article  Google Scholar 

  17. Climent CE, De Arango MV. Manual de Psyquiatria para Trabajadores de Atencion Primaria (Psychiatric Manual for Primary Care Workers). Organización Panamericana de la Salud (Pan American Health Education Foundation) (PAHEF)). 1983 https://iris.paho.org/bitstream/handle/10665.2/3287/Manual%20de%20psiquiatria%20para%20trabajadores%20de%20atencion%20primaria%201.pdf?sequence=1

  18. Dehoust MC, Schulz H, Härter M, Volkert J, Sehner S, Drabik A, Wegscheider K, Canuto A, et al. Prevalence and correlates of somatoform disorders in the elderly: Results of a European study. Int J Methods Psychiatr Res. 2017;26(1):e1550. https://doi.org/10.1002/mpr.1550.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Digital 2021, Puerto Rico. https://datareportal.com/reports/digital-2021-puerto-rico. Retrieved July 12, 2024

  20. Drayer RA, Mulsant BH, Lenze EJ, Rollman BL, Dew MA, Kelleher K, Karp JF, Begley A, Schulberg HC, Reynolds CF 3rd. Somatic symptoms of depression in elderly patients with medical comorbidities. Int J Geriatr Psychiatry. 2005;20(10):973–82. https://doi.org/10.1002/gps.1389. PMID: 16163749.

    Article  PubMed  Google Scholar 

  21. Dunlop BW, Still S, LoParo D, Aponte-Rivera V, Johnson BN, Schneider RL, Nemeroff CB, Mayberg HS, Craighead WE. Somatic symptoms in treatment-naïve Hispanic and non-Hispanic patients with major depression. Depress Anxiety. 2020;37(2):156–65. https://doi.org/10.1002/da.22984.

    Article  PubMed  Google Scholar 

  22. Fischer J, Jansen B, Rivera A, Gómez LJ, Barbosa MC, Bilbao JL, González JM, Restrepo L, Vidal Y, Peters RMH, van Brakel WH. Validations of a cross-NTD toolkit for assessment of NTG-related morbidity and disability. A cross-cultural qualitative validation of study instruments in Colombia. PLoS ONE. 2019;14(12):e0223042. https://doi.org/10.1371/journal.pone.0223042.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. García C, Rivera FI, Garcia MA, Burgos G, Aranda MP. Contextualizing the COVID-19 Era in Puerto Rico: Compounding disasters and parallel pandemics. Journals of Gerontology, B Psychological and Social Sciences. 2021;76(7):e263–7. https://doi.org/10.1093/geronb/gbaa186.

    Article  Google Scholar 

  24. Hanlon C, Medhin G, Selamu M, Breuer E, Worku B, Hailemariam M, Lund C, Prince M, Fekadu A. Validity of brief screening questionnaires to detect depression in primary care in Ethiopia. J Affect Disord. 2015;186:32–9. https://doi.org/10.1016/j.jad.2015.07.015.

    Article  PubMed  Google Scholar 

  25. Harding TW, de Arango V, Baltazar J, Climent CE, Ibrahim HHA, Ladrido-Ignacio L, Wig NN. Mental disorders in primary health care: A study of the frequency and diagnosis in four developing countries. Psychol Med. 1980;10(2):231–41. https://doi.org/10.1017/s0033291700043993.

    Article  CAS  PubMed  Google Scholar 

  26. Harpham T, Reichenheim M, Oser R, Thomas E, Hamid N, Jaswal S, Ludermir A, Aidoo M. Measuring mental health in a cost-effective manner. Health Policy Plan. 2003;18(3):344–9. https://doi.org/10.1093/heapol/czg041.

    Article  PubMed  Google Scholar 

  27. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Struct Equ Model. 1999;6(1):1–55. https://doi.org/10.1080/10705519909540118.

    Article  Google Scholar 

  28. Hunt M, Auriemma J, Cashaw ACA. Self-report bias and underreporting of depression on the BDI-II. J Pers Assess. 2003;80(1):26–30. https://doi.org/10.1207/S15327752JPA8001_10unde.

    Article  PubMed  Google Scholar 

  29. Institute for Health Metrics and Evaluation. GBD Results Tool. In: Global Health Data Exchange. Seattle. 2019. https://vizhub.healthdata.org/gbd-results?params=gbd-api-2019-permalink/716f37e05d94046d6a06c1194a8eb0c9 Accessed 5 September 2023.

  30. Khaled SM, Amro I, Bader L, Lee HJ, Diop Aa, Le Trung K. Feasibility ofreplacing face-to-face with telephone interviews for the World Mental Health Qatar survey during the COVID-19 pandemic. Int J Methods Psychiatr Res. 2024;33(1):e2009. https://doi.org/10.1002/mpr.2009.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kiely KM, Brady B, Byles J. Gender, mental health and ageing. Maturitas. 2019;129:76–84. https://doi.org/10.1016/j.maturitas.2019.09.004.

    Article  CAS  PubMed  Google Scholar 

  32. Klaus F, Peek E, Quynh A, Sutherland AN, Selvam D, Moore RC, Depp CA, Eyler LT. Mobile survey engagement by older adults is high during multiple phases of the COVID-19 pandemic and is predicted by baseline and structural factors. Frontiers in Digital Health. 2022;4:920706. https://doi.org/10.3389/fdgth.2022.920706.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Kootbodien T, Becker P, Naicker N, Mathee A. Gender invariance of the Self- Reporting Questionnaire (SRQ-20). South African Journal of Psychology. 2015;45(3):318–31. https://doi.org/10.1177/0081246315572500.

    Article  Google Scholar 

  34. Merino-Soto C, Boluarte Carbajal A, Toledano-Toledano F, Nabors LA, Núñez-Benítez MÁ. A new story on the multidimensionality of the MSPSS: Validity of the internal structure through bifactor ESEM. Int J Environ Res Public Health. 2022;19(2):935. https://doi.org/10.3390/ijerph19020935.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Milfont TL, Fischer R. Testing measurement invariance across groups: Applications in cross-cultural research. Int J Psychol Res. 2010;3(1):111–30. https://doi.org/10.21500/20112084.857.

    Article  Google Scholar 

  36. Muthén LK, Muthén BO. Mplus User’s Guide. 8th ed. Los Angeles, CA: Muthén & Muthén; 2017. https://www.statmodel.com/download/usersguide/MplusUserGuideVer_8.pdf.

    Google Scholar 

  37. Muthén LK, Muthén BO. Mplus (Version 8.4). Author. 2019.

  38. Netsereab TB, Kifle MM, Tesfagiorgis RB, Habteab SG, Weldeabzgi YK, Tesfamariam OZ. Validation of the WHO Self-Reporting Questionnaire-20 (SRQ-20) item in primary health care settings in Eritrea. Int J Ment Heal Syst. 2018;12(1):1–9. https://doi.org/10.1186/s13033-018-0242-y.

    Article  Google Scholar 

  39. Nuñez A, González P, Talavera GA, Sanchez-Johnsen L, Roesch SC, Davis SM, et al. Machismo, marianismo, and negative cognitive-emotional factors: Findings from the Hispanic Community Health Study/Study of Latinos Sociocultural Ancillary Study. J Latino Psychol. 2016;4(4):202–17. https://doi.org/10.1037/lat0000050.

    Article  Google Scholar 

  40. Nunnally JC, Bernstein IH. Psychometric theory (3rd ed.). McGraw–Hill. 1994.

  41. Rasmussen A, Ventevogel P, Sancilio A, Eggerman M, Panter-Brick C. Comparing the validity of the Self Reporting Questionnaire and the Afghan Symptom Checklist: Dysphoria, aggression, and gender in transcultural assessment of mental health. BMC Psychiatry. 2014;14(1):1–12. https://doi.org/10.1186/1471-244X-14-206.

    Article  Google Scholar 

  42. Richardson LK, Amstadter AB, Kilpatrick DG, Gaboury MT, Tran TL, Trung LT, Tam NT, Tuan T, Buoi LT, Ha TT, Thach TD, Acierno R. Estimating mental distress in Vietnam: the use of the SRQ-20. Int J Soc Psychiatr. 2010;56(2):133–42. https://doi.org/10.1177/0020764008099554.

    Article  Google Scholar 

  43. Riecher-Rössler A. Sex and gender differences in mental health. Lancet Psychiatry. 2017;4(1):8–9. https://doi.org/10.1016/S2215-0366(16)30348-0.

    Article  PubMed  Google Scholar 

  44. Santos-Burgoa C, Goldman A, Andrade E, Barrett N, Colon-Ramos U, Edberg M, Garcia-Meza A, Goldman L, Roess A, Sandberg J, Zeger S. Ascertainment of the estimated excess mortality from Hurricane Maria in Puerto Rico. George Washington University. 2018.  https://hsrc.himmelfarb.gwu.edu/sphhs_global_facpubs/288. 

  45. Scazufca M, Menezes PR, Vallada H, Araya R. Validity of the Self Reporting Questionnaire-20 in epidemiological studies with older adults. Soc Psychiatry Psychiatr Epidemiol. 2009;44(3):247–54. https://doi.org/10.1007/s00127-008-0425-y.

    Article  PubMed  Google Scholar 

  46. Scholte WF, Verduin F, Kamperman AM, Rutayisire T, Zwinderman AH, Stronk K. The effect on mental health of a large-scale psychosocial intervention for survivors of mass violence: A quasi-experimental study in Rwanda. PLoS ONE. 2011;6(8):e21819. https://doi.org/10.1371/journal.pone.0021819.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Seedat S, Scott KM, Angermeyer MC, Berglund P, Kessler RC. Cross-national associations between gender and mental disorders in the World Health Organization World Mental Health Surveys. Archives Gen Psychiatr. 2009;66(7):785–95. https://doi.org/10.1001/archgenpsychiatry.2009.36.

    Article  Google Scholar 

  48. Shidhaye R, Mendenhall E, Sumathipala K, Sumathipala A, Patel V. Association of somatoform disorders with anxiety and depression in women in low- and middle-income countries: A systematic review. Int Rev Psychiatry. 2013;25(1):65–76. https://doi.org/10.3109/09540261.2012.748651.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Stratton KJ, Richardson LK, Trinh Tran TL, Tam NT, Aggen SH, Berenz EC, Trung LT, Tuan T, Buoi LT, Ha TT, Thach TD, Amstadter AB. Using the SRQ–20 factor structure to examine changes in mental distress following typhoon exposure. Psychol Assess. 2014;26(2):528–38. https://doi.org/10.1037/a0035871.

    Article  PubMed  PubMed Central  Google Scholar 

  50. U.S. Census Bureau. QuickFacts Puerto Rico; 2023. https://www.census.gov/quickfacts/PR Accessed 22 Jan 2024.

  51. U.S. Census Bureau. Puerto Rico 2022 American Community Survey 1-Year Estimates; 2022. https://data.census.gov/profile/Puerto_Rico?g=040XX00US72.

  52. U.S. Government Accounting Office. Puerto Rico Electricity: FEMA and HUD have not approved long-term projects and need to implement recommendations to address uncertainties and enhance resilience; 2020. https://www.gao.gov/products/gao-21-54.

  53. van der Westhuizen C, Wyatt G, Williams JK, Stein DJ, Sorsdahl K. Validation of the self reporting questionnaire 20-item (SRQ-20) for use in a low-and middle-income country emergency centre setting. Int J Ment Heal Addict. 2016;14(1):37–48. https://doi.org/10.1007/s11469-015-9566-x.

    Article  Google Scholar 

  54. Ventevogel P, De Vries G, Scholte WF, Shinwari NR, Faiz H, Nassery R, van den Brink W, Olff M. Properties of the Hopkins Symptom Checklist-25 (HSCL-25) and the Self-Reporting Questionnaire (SRQ-20) as screening instruments used in primary care in Afghanistan. Soc Psychiatry Psychiatr Epidemiol. 2007;42:328–35. https://doi.org/10.1007/s00127-007-0161-8.

    Article  PubMed  Google Scholar 

  55. Werlen L, Puhan MA, Landolt MA, Mohler-Kuo M. Mind the treatment gap: the prevalence of common mental disorder symptoms, risky substance use and service utilization among young Swiss adults. BMC Public Health. 2020;20(1):1470. https://doi.org/10.1186/s12889-020-09577-6.

    Article  PubMed  PubMed Central  Google Scholar 

  56. World Health Organization (WHO). Gender and mental health. 2002. https://www.google.com/search?q=gender+differences+common+mental+disorders&oq=&aqs=chrome.0.69i59i450l2.398732161j0j15&sourceid=chrome&ie=UTF-8.

  57. World Health Organization (WHO). Mental health of older adults. 2017. https://www.who.int/news-room/fact-sheets/detail/mental-health-of-older-adults.

  58. Worldometer. Puerto Rico population (live). 2021. https://www.worldometers.info/world-population/puerto-rico-population/.

  59. Yoon M, Lai MHC. Testing factorial invariance with unbalanced samples. Struct Equ Modeling. 2018;25(2):201–13. https://doi.org/10.1080/10705511.2017.1387859.

    Article  Google Scholar 

Download references

Acknowledgements

None.

Funding

The study was supported by internal funding to the first author.

Author information

Authors and Affiliations

  1. School of Social Work, Virginia Commonwealth University-Richmond, Richmond, VA, USA

    Denise Burnette, Kyeongmo Kim & Seon Kim

Authors
  1. Denise Burnette
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kyeongmo Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seon Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.B. conducted the literature review and wrote all of the manuscript except the methods and results. K.K. conducted the data analysis and supervised S.K. on data analysis. K.K. and S.K. wrote the data analysis plan and results sections. S.K. compiled the references.

Corresponding author

Correspondence to Denise Burnette.

Ethics declarations

Ethics approval and consent to participate

The Virginia Commonwealth University Institutional Review Board approved this study. All participants provided signed informed consent.

Consent for publication

No individual personal data.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Burnette, D., Kim, K. & Kim, S. Gender-related measurement invariance on the Self-Reporting Questionnaire (SRQ-20) for global mental distress with older adults in Puerto Rico. Arch Public Health 82, 163 (2024). https://doi.org/10.1186/s13690-024-01396-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13690-024-01396-0

Keywords

Archives of Public Health

ISSN: 2049-3258