A structural equation modelling to explore the determinants of mental health disorders among reproductive-aged women in Nepal: A nation-wide cross-sectional survey

doi:10.21203/rs.3.rs-4165633/v1

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Research Article

A structural equation modelling to explore the determinants of mental health disorders among reproductive-aged women in Nepal: A nation-wide cross-sectional survey

https://doi.org/10.21203/rs.3.rs-4165633/v1

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Background

Similar to other countries, reproductive-age women in Nepal frequently encounter diverse mental disorders, yet they receive inadequate screening and attention. This research seeks to address this gap by conducting a comprehensive examination of the prevalence and factors associated with mental disorders of reproductive-age women in Nepal.

Methods

This study used the nationally representative Nepal Demographic and Health Survey 2022 dataset to examine the association between mental disorders and potential confounders. Screening tools for Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9) were employed, and descriptive and inferential statistics, including linear regression and structural equation modeling, were utilized in the analysis.

Results

The study found that 22% of Nepalese women aged 15 to 49 reported anxiety symptoms, while 5% reported depressive symptoms. The Structural Equation Modeling (SEM) reveals that anxiety had the most significant positive effect on depression (β = 0.683, p < 0.001), followed by emotional violence (β = 0.139, p < 0.001). Marital status (β = 0.072, p < 0.001) and the presence of genital discharge (β = 0.084, p < 0.001) or ulcer (β = 0.058, p < 0.001) in the last 12 months were also associated with higher levels of depression. Conversely, self-rated health status (β=-0.121, p < 0.001) was negatively associated with depression, indicating that better-perceived health was linked to lower levels of depression.

Conclusion

Although the prevalence of anxiety symptoms is higher than that of depression, there is a positive relationship between these two. Consequently, it is crucial to address the identified factors contributing to anxiety and depression in this population group through behavioral and policy interventions to enhance the mental well-being of reproductive-age women.

Women

reproductive age

anxiety

depression

Nepal

Nowadays, mental disorders have become an unavoidable crisis, seeping into every corner of our lives, from our workplaces to our sleep. It is often considered the “hidden epidemic” of the 21st century, while mental health is recognized as a universal human right for all individuals (1). In 2019, 970.1 million people worldwide were affected by mental health issues, and according to a comprehensive study, it is anticipated that half of the world's population may experience a mental disorder at some point in their lifetime (2, 3). Gender is a significant factor that influences the prevalence of mental disorders, where women are more likely to develop those conditions compared to men (4). Particularly, the impact of mental health on reproductive-age women is profound and multifaceted (5). However, in light of the growing significance of this matter, women's mental and reproductive health have been explicitly embraced as essential elements of the United Nations Sustainable Development Goal (SDG) 3 (6).

Women are noticeably at least two times more vulnerable to depression and anxiety than men, both of which negatively impact their overall well-being and functioning (5). World Health Organization (WHO) reported that the global prevalence of depression and anxiety among women is approximately 5.1% and 4.6%, respectively (7). Mental illness among women in the reproductive age group (15–49) is a severe public health concern since they have to perform multifactorial gender and social roles, from childbearing and child-rearing to managing the family (8). In addition, biological factors, specifically reproductive life events such as menstruation, pregnancy, the postpartum period, and menopause—have an adverse impact on the mental well-being of women (5, 8).

The Global Burden of Disease (GBD) study estimated that mental disorders accounted for around 11.47% of Disability-Adjusted Life Years (DALYs) among women of reproductive age worldwide (9). In parallel, a systematic review revealed that the rates of antenatal depression range from 15–65%, which often leads to serious health consequences for both the mother and child, including low birth weight and premature delivery (10). Another review involving 565 studies from 80 different countries reported that approximately 17.22% of women experience postpartum depression (PPD) (11). Besides that, women undergoing infertility, menopausal transition, and Premenstrual Syndrome (PMS) exhibit a substantially higher prevalence of depression and other mental disorders (12, 13). Many social, economic, and cultural circumstances govern women’s mental health. The factors associated with antenatal depression include poor social status, poverty, lack of control over sexual and reproductive lives, intimate partner violence, sexual or domestic violence, childhood abuse, forced marriage, weak family support systems, poor physical health including poor nutritional status, lack of access to education, lack of decision-making power including family planning to socioeconomic choices, lack of access to antenatal care (ANC), or living with sexually transmissible diseases including HIV often contribute to suicidal tendencies among women (5, 8, 11, 14).

Several studies have identified that the severity of mental disorders in women of reproductive age is more prominent in low- and middle-income countries (5, 10–13). Despite the fact, this burning issue often remains undiagnosed, unaddressed, and stigmatized in many countries, including Nepal (15). A cross-sectional study conducted in the rural Saptari district in Nepal reported a high prevalence of maternal anxiety and depression (16). Furthermore, a hospital-based study found a substantial level of depressive symptoms among post-partum mothers (17). According to another study, more than one out of four mothers has been diagnosed with depressive symptoms (18). Notably, the primary cause of death among women of reproductive age is suicide in Nepal (19). However, no study has been deep dive into the contributing factors associated with mental illness, specifically anxiety and depression among women of reproductive age. Most studies relied on small sample sizes; hence, the findings are insufficient for macro-level decision-making. Since the mental health of women is often a neglected issue, it poses a tremendous threat to the physical and psychological health and well-being of women. Remarkably, there is a need for a well-designed investigation in order to better understand the significance of this problem. Thus, this study set out to determine the prevalence and factors associated with depression among women of reproductive age in Nepal. Therefore, this study is expected to contribute to the broader public health agenda by providing evidence-based analysis and synthesizing critical factors.

Data source and sampling

The study used the Nepal Demographic and Health Survey 2022 (NDHS 2022) data, which is a periodical survey carried out across the country from Jan 05 to June 22, 2022, in order to evaluate health indicators and to offer a detailed overview of population, maternal, and child health issues along with mental health status (20). Covering 14,280 households from 476 Primary Sampling Units (PSU), this study utilized two-stage stratified sampling where all women from all the households were eligible for interviewed whereas half of the households were chosen for the males; as a result, males from every second household were qualified to be interviewed. By the end, 4,913 men and 14,845 women (representing 97% and 95% of the overall response rates, respectively) had successfully completed interviews. In this study, a total of 7,442 women’s data were analyzed who participated in the mental health questions. Details of the sampling strategy and other measurements have been found elsewhere (NDHS 2022)(20). This de-identified dataset is publicly available for academic and specific research purposes upon request. Before proceeding with the survey, all the DHS survey protocols obtained ethical approval from both the ICF Institutional Review Board and the country-specific review board.

Measurements

Outcome variables

Two widely-used instruments for screening for mental disorders were included in the 2022 NDHS's mental health module. The Generalized Anxiety Disorder (GAD-7) and Patient Health Questionnaire (PHQ-9) were employed for assessing anxiety and depression, respectively, with both scales having been previously validated in the Nepalese context and demonstrating good reliability(21). Both scales are used based on symptoms that people had in the two weeks preceding the survey. GAD-7 score of 0–5 is considered mild, while a score of 6–14 is considered moderate and 15–21 is considered severe(22) while a PHQ-9 score of 0–4 is considered minimal symptoms or no symptoms, while a score of 5–9 is considered mild, 10–14 is considered moderate, 15–19 is considered moderately severe, and 20–27 is considered severe(23). Both tools used a Likert scale to show how bad the symptoms are. Answers of "not at all" (never), "several days" (rarely), "more than half the days" (often), and "nearly every day" (always) are given numbers 1, 2 and 3. When you add up the scores of all the items, you get the total number. The sum of the scores for each individual item yields the final score. In the 2022 NDHS, using the validated Water Glass Pictorial Scale was used to rate the intensity of the symptoms. An empty glass means "never," a glass that is only 1/4 full means "rarely," a glass that is half full means "often," and a glass that is three-fourths full means "always." This picture chart helped us understand how respondents felt and put the severity of symptoms they described into context, added that, there was no diagnostic testing done. Cronbach alpha of the PHQ-9 was 0.8875, while GAD-7 was 0.8126 in this sample.

Predictor variables

To identify the frequently used common predictor factors that are linked to outcome variables, we conducted a review of the literature. A few biological and socioeconomic variables were selected based on what was available in the dataset and relevant research. Age, region, education, wealth status, residence and marital status, and self-rated health status (classified as 1 for bad or very bad and 0 for very good, good or moderate). While some biological and behavioral factors such as total lifetime number of sex partners, ever had a terminated pregnancy, genital discharge in the last 12 months, experienced emotional violence, smoked cigarettes, frequency of husband/partner being drunk, visited a health facility in last 12 months, had genital sore/ulcer in last 12 months were considered for this study purpose.

Statistical analysis

Both descriptive and inferential statistics were used for this study. Descriptive statistics were used to summarize the background characteristics of the respondents while inferential statistics were used to determine the predictors variables against outcome measurements. Since the outcome were scored/continuous data we used linear regression methods along with structural equation method (SEM). Univariate and multiple linear regression was carried out to select the predictors for final model. For the data analysis STATA MP.16 and AMOS.24 were used.

Assessing the SEM algorithms model

Structural equation modeling (SEM) is designed with the two-phase model (i) measurement model and (ii) structural model. The measurement model depicts the inner relationships between latent variables and observed variables, and the latter, along with the structural model, is used to investigate loadings and estimate indicators(24–26). The conceptual framework model was run using AMOS.24, and the structural framework used in the hypothesis testing segment is shown in Fig. 1. AMOS.24 consists of a series of standard metrics, such as loading indicators, composite reliability, average variance extracted (AVE), path coefficients, inner structural correlations, latent variable scores, t values, etc. A structural procedure has been introduced to investigate loading and eliminating indications (loadings < 0.70) was adopted(27). In the context of the study, we utilized a reflexive measurement framework. In line with the majority of other behavioral research (28, 29), a set of assessment criterion that can quantify the credibility and viability of the SEM model with recessive criterion was proposed. Which are as follows-

Justification of potential endogenous variable’s variance
Checking factor loading of Inner-model
Justifying loadings of outer model and their significance
Indicator reliability
Internal consistency reliability
Convergent validity
Discriminant validity
Assessing Structural Path Significance in Bootstrapping

Background characteristics of study participants

Table 1 presents that, majority of the respondents were in the age group of 14–24 years (36.38%), with a high percentage being married (75.84%) and residing in urban areas (54.02%). Over 90% reported having single sex partners and the ability to refuse sexual intimacy (91.14%). However, 69.30% noted that their husbands consumed alcohol. A significant portion (79.87%) never had a terminated pregnancy, and 47.33% fell into the lower quartile of wealth status. Most females (89.30%) considered their health as good, while 87.55% experienced genital discharge in the last 12 months. Emotional violence was rare (87.55% never experienced it), and a small percentage smoked cigarettes (4.60%) or had genital sores or ulcers in the last 12 months (4.01%). Despite this, a majority (69.22%) visited a health facility in the last 12 months.

Table 1

Frequency distribution of the respondents
Variables	Frequency	Percentage (%)
Age
15–24 Years	5,400	36.38
25–34 Years	4,426	29.81
35–49 Years	5,019	33.81
Marital status
Never Married	3,123	21.03
Married	11,258	75.84
Widowed/Divorced/Separated	464	3.13
Household wealth status
Poor	7,026	47.33
Middle	2,965	19.97
Rich	4,854	32.70
Place of Residence
Urban	8,019	54.02
Rural	6,826	45.98
Respondent can refuse sex
No	995	8.86
Yes	10,233	91.14
Total lifetime number of sex partner
Single	11,310	96.06
Multiple	464	3.94
Ever had a terminated pregnancy
No	11,857	79.87
Yes	2,988	20.13
Self-reported health status
Bad	1,588	10.70
Good	13,257	89.30
Had genital discharge in last 12 months
No	12,122	81.71
Yes	2,714	18.29
Experienced and emotional violence
No	3,960	87.55
Yes	563	12.45
Smoke cigarettes
No	14,162	95.40
Yes	683	4.60
Frequency of husband/partner being drunk
Never	719	30.70
Yes	1,623	69.30
Visited health facility in last 12 months
No	4,570	30.78
Yes	10,275	69.22
Had genital sore/ulcer in last 12 months
No	14,240	95.99
Yes	595	4.01

Table 2

Analysis of variance of anxiety and depression among the observed groups
	Variables	Frequency	Mean (GAD-7)	SD	Sig.	Mean (PHQ-9)	SD	Sig.
Current age
	15–24 Years	2,677	3.127	3.411	< 0.001	2.559	3.474	< 0.001
	25–34 Years	2,224	3.364	3.601		2.647	3.606
	35–49 Years	2,541	3.697	3.829		2.886	3.855
Marital status
	Never Married	1,585	2.813	3.121	< 0.001	2.239	3.111
	Married	5,619	3.472	3.666		2.745	3.698	< 0.001
	Widowed/Divorced/Separated	238	5.348	4.693		4.601	4.931
Household wealth status
	Poor	3,500	3.562	3.747	< 0.001	2.901	3.814
	Middle	1,465	3.477	3.757		2.733	3.722	< 0.001
	Rich	2,477	3.103	3.332		2.387	3.336
Place of Residence
	Urban	4,016	3.379	3.565	0.734	2.651	3.609	0.237
	Rural	3,426	3.408	3.688		2.751	3.696
Respondent can refuse sex
	No	486	4.134	4.177	< 0.001	3.327	4.291	0.0002
	Yes	5,117	3.404	3.602		2.685	3.621
Total lifetime number of sex partner
	Single	5,653	3.500	3.695	< 0.001	2.783	3.729	< 0.001
	Multiple	229	4.812	4.451		3.921	4.762
Ever had a terminated pregnancy
	No	5,986	3.167	3.492	< 0.001	2.504	3.489	< 0.001
	Yes	1,456	4.317	3.985		3.489	4.158
Self-reported health status
	Bad	757	6.087	4.672	< 0.001	5.498	5.050	< 0.001
	Good	6,685	3.087	3.350		2.380	3.310
Had genital discharge in last 12 months
	No	6,142	3.147	3.469	< 0.001	2.442	3.437	< 0.001
	Yes	1,297	4.554	4.079		3.902	4.329
Experienced and emotional violence
	No	3,960	3.164	3.428	< 0.001	2.442	3.345	< 0.001
	Yes	563	6.254	4.523		5.799	5.196
Smoke cigarettes
	No	7,091	3.321	3.579	< 0.001	2.625	3.580	< 0.001
	Yes	351	4.829	4.164		4.131	4.624
Frequency of husband/partner being drunk
	Never	719	3.168	3.509	< 0.001	2.352	3.296	< 0.001
	Yes	1,623	4.205	3.995		3.464	4.187
	Visited health facility in last 12 months
	No	2,322	3.007	3.453	< 0.001	2.272	3.349	< 0.001
	Yes	5,120	3.567	3.683		2.889	3.762
Had genital sore/ulcer in last 12 months
	No	7,147	3.316	3.564	< 0.001	2.614	3.568	< 0.001
	Yes	290	5.310	4.452		4.741	4.874

Among the respondents, it was observed in Table 2 that older respondents, specifically those aged 35–49, exhibited the highest levels of anxiety (m-GAD score = 3.697; p-value = < 0.001) and depression (m-PHQ score = 2.886; p-value = < 0.001) compared to other age groups. Additionally, individuals who were widowed, divorced, or separated reported the highest levels of anxiety (m-GAD score = 5.348; p-value = < 0.001) and depression (m-PHQ score = 4.931; p-value = < 0.001), while unmarried individuals demonstrated the lowest levels of both anxiety and depression. Economic status also played a role, with poor respondents scoring significantly higher in both anxiety (m-GAD score = 3.562; p-value = < 0.001) and depression (m-PHQ score = 2.901; p-value = < 0.001) compared to the other two economic groups, while rich individuals scored the lowest (m-GAD score = 3.103; p-value = < 0.001; m-PHQ score = 2.387; p-value = < 0.001).

Furthermore, respondents who reported an inability to refuse sex (m-GAD score = 4.134; p-value = < 0.001; m-PHQ score = 3.327; p-value = < 0.001) or those with multiple partners (m-GAD score = 4.812; p-value = < 0.001; m-PHQ score = 3.921; p-value = < 0.001) scored significantly higher in both depression and anxiety scales compared to their counterparts. Women who had terminated a pregnancy (m-GAD score = 4.317; p-value = < 0.001; m-PHQ score = 3.489; p-value = < 0.001), smoked cigarettes (m-GAD score = 4.829; p-value = < 0.001; m-PHQ score = 4.131; p-value = < 0.001), experienced emotional violence (m-GAD score = 6.254; p-value = < 0.001; m-PHQ score = 5.799; p-value = < 0.001), visited a health facility (m-GAD score = 3.567; p-value = < 0.001; m-PHQ score = 2.889; p-value = < 0.001), or had genital sores or ulcers in the last 12 months (m-GAD score = 5.310; p-value = < 0.001; m-PHQ score = 4.741; p-value = < 0.001) also scored significantly higher in both anxiety and depression scales. Additionally, females who perceived their health as bad (m-GAD score = 6.087; p-value = < 0.001; m-PHQ score = 5.498; p-value = < 0.001) or reported their husbands getting drunk (m-GAD score = 4.205; p-value = < 0.001; m-PHQ score = 3.464; p-value = < 0.001) scored higher in both scales.

In our study utilizing a Structural Equation Model (SEM) to measure depression among reproductive-aged women in Nepal, we employed standardized beta coefficients (β) to elucidate both direct and indirect associations among observed variables. The model demonstrated a good fit with the following goodness of fit indices: CMIN/df = 21.750; CFI = 0.914; TLI = 0.987; RMSEA = 0.037; PCLOSE = 0.003.

Significant variables from the linear regression table were initially incorporated into the model. However, variables exhibiting insignificant direct or indirect effects on the dependent variables were systematically removed. The resulting nine-factor model aimed to predict general depression among reproductive-aged women in Nepal. Figure 1 shows that, among the factors, anxiety exhibited the most substantial direct positive effect on respondents' depression (β = 0.65; p = < 0.001). Emotional violence (β = 0.09; p = < 0.001) demonstrated a significant direct positive effect on depression, with a significant indirect positive effect through anxiety and genital ulcer, indicating that respondents who experienced emotional violence scored higher on the depression scale and it also significantly affected the relationship of anxiety and genital ulcer to depression. Smoking (β = 0.02; p = 0.149) did not exhibit a direct significant effect on depression but had a direct significant effect (β = 0.04; p = 0.003) on anxiety, with a significant positive indirect effect on depression, suggesting that while smoking may not directly affect depression, it passively increases it. Similarly, pregnancy termination (β = 0.00; p = 0.241) did not show a significant direct effect on depression but had an indirect effect on depression and a significant positive effect (β = 0.05; p = 0.009) on anxiety, indicating it also passively increases depression among reproductive-aged women in Nepal.

Self-rated health status had a significant direct negative effect on depression (β= -0.08; p = < 0.001) and anxiety (β= -0.20; p = < 0.001). Moreover, it had an indirect negative effect on depression and anxiety, influencing respondents’ frequency of visiting health facilities and experiencing genital ulcer or discharge in the last 12 months, indicating that poor self-rated health status both directly and indirectly increases depression and anxiety. Genital ulcer and discharge both exhibited significant direct positive effects on both depression and anxiety, with an indirect effect on depression through anxiety, meaning that respondents who faced genital discharge or ulcer in the last 12 months are more likely to experience depression. Visiting a health facility in the last 12 months did not have any significant indirect effect but had a direct positive effect on both anxiety (β = 0.03; p = 0.005) and depression (β= -0.02; p = 0.026).

The objective of this study was to discern the factors associated with depression among reproductive-aged women in Nepal, constricting a comprehensive model to elucidate the pervasive contributors to depression. The analysis incorporates socioeconomic factors, anxiety, and habitual aspects to develop a holistic understanding. This research identifies a robust negative relationship between self-rated health status and depression. Structural Equation Modeling (SEM) further supports the notion that those perceiving their health as poor are more likely to experience depression. This aligns with the conclusions of Millán‐Calenti et al. (2012) and Rohrer et al. (2008), who found that reproductive-aged women with poorer self-rated health were more prone to depression (30,31). Mohit et al. (2011) highlight the interconnectedness of physical illness and depression, asserting that one in three individuals with physical ailments experiences depression (32). Furthermore, an individual's self-rated health status has an indirect influence on depression and can impact their likelihood of seeking medical care for conditions such as genital discharge or ulcers. Mulsant (1997) highlights the interconnectedness of physical health and mental well-being, suggesting that deteriorating physical health can contribute to various health complications, potentially exacerbating depression symptoms (33). Factors such as the inability to self-care, prolonged treatment regimens, persistent pain, frustration, fatigue, and the associated uncertainties and anxieties about health conditions might contribute to heightened depressive symptoms among individuals grappling with physical illnesses. Moreover, living with a physical disease can exacerbate depression, citing feelings of hopelessness, sleep disturbances, and the overarching uncertainty associated with health issues as exacerbating factors.

Genital discharge or sores in the last 12 months are identified as having a positive effect on depression, as indicated in the structural equation model (SEM). Similar findings have been reported in previous studies, with Patel (2005) and Winter & Stephenson (2013) noting that women may experience anxiety due to these conditions (34,35). Genital sores, causing continuous pain, can contribute to greater depression. Psychological factors play a significant role in this phenomenon, as these conditions can be uncomfortable, painful, and raise concerns about hygiene, infection, and sexual health.

Moreover, genital discharge or sores not only directly impact physical health but also have indirect effects on mental well-being, particularly depression. Seidman & Roose (2001) posit that sexual dysfunction resulting from such conditions can induce severe anxiety, especially among reproductive-aged women, due to concerns about further health deterioration (36). This anxiety can exacerbate depressive symptoms. Additionally, these conditions can strain relationships, causing fear of rejection, intimacy issues, and communication breakdowns, all of which contribute to heightened anxiety and subsequent depression (37). Additionally, the possibility of underlying medical conditions, such as sexually transmitted infections (STIs) or chronic skin conditions, may contribute to increased depression among women.

The study further highlights the impact of emotional violence from partners on depression, revealing it as a strong relationship in the SEM. This aligns with existing literature that establishes a robust connection between emotional violence and depression among women, which is aligned with previous studies (38–42). Dutton et al. (2006) found that the prevalence of depression in victims of emotional violence is twice that of the general population (43). Bonomi et al. (2006) further demonstrated that women who faced emotional violence are 2.06 times more likely to experience minor depression and 1.75 times more likely to experience severe depression (40). Additionally, the study underscores the impact of emotional violence on increasing anxiety levels among respondents, leading to heightened depression. Collins (2013) asserts that emotional violence, characterized by unpredictable and manipulative behavior, fosters an environment of fear and uncertainty(44). This lack of stability and control can trigger anxiety as individuals constantly anticipate potential threats. Consequently, victims of emotional violence may experience elevated levels of depression due to the ongoing emotional distress and psychological strain. Moreover, the lingering effects of emotional violence may endure longer compared to other types of violence. Emotional abuse can manifest through verbal attacks, belittlement, gaslighting, and manipulation, eroding a woman's sense of self-worth and confidence. This traumatic experience may lead to symptoms of post-traumatic stress disorder (PTSD) and contribute to a higher prevalence of depression among affected women.

Similarly, the study aligns with existing research indicating that women who smoke cigarettes are more likely to experience depression (45,46). Flensborg-Madsen et al. (2011) highlighted the association between smoking and a quick relief of stress, but the subsequent withdrawal symptoms, such as irritability and anxiety, can mimic or worsen depressive symptoms (47). Chronic smoking leading to nicotine dependence can create a cycle of heightened depression (48). This study further asserts that smoking increases anxiety and also have a passive effect on depression. Roth et al. (2001) argue that women of reproductive age, being particularly aware of the health risks associated with smoking, such as fertility issues, pregnancy complications, and increased cancer risks, experience heightened anxiety about their own health and potential impacts on future pregnancies (49). This awareness leads to anxiety, which actively and passively affects the depression levels of reproductive-aged women.

Analysis of this study, it suggests that while pregnancy termination may not directly affect depression, it does have a significant passive effect, aligning with previous research (50). However, pregnancy termination increases anxiety among reproductive-aged women, subsequently leading to depression. Marčinko et al. (2011) contends that pregnancy termination significantly raises anxiety levels among women, potentially leading to long-term depression (51). Additionally, González-Ramos (2021) notes that the decision to terminate a pregnancy, particularly for medical reasons, can evoke complex emotions like anxiety, guilt, and relief (52). Despite arguments by Andersson et al. (2014) that many women do not regret terminating pregnancies, with rational thinking often outweighing difficult emotions (53), several other studies emphasize a strong positive relationship between pregnancy termination and anxiety among reproductive-aged women (54–56). Furthermore, in the context of South Asia, personal beliefs and social environments contribute to stigma surrounding pregnancy termination, leading to feelings of shame, isolation, and judgment, thereby exacerbating anxiety and subsequent depression.

A major finding of the study is the significant relationship between anxiety and depression, with anxiety emerging as the strongest predictor of depression among reproductive-aged women in Nepal. This aligns previous studies, which reported that elevated depressive symptoms in individuals with anxiety disorders (48,57). Gdańska (2017) also supported this link, revealing that half of those with anxiety also experienced depression (58). Pawlusk et al. (2017) emphasized neurobiological factors common to both anxiety and depression, involving imbalances in neurotransmitters like serotonin, dopamine, and norepinephrine (59). These imbalances impact mood, stress response, and emotional stability, contributing to the shared features of anxiety and depression. The study also highlighted the role of negative thought patterns in anxiety, such as ruminating thoughts and catastrophizing, which can gradually erode self-esteem, fuel hopelessness, and lead to depression. Additionally, anxiety-related social withdrawal may deprive individuals of support systems, exacerbating depressive feelings.

Policy Recommendations

The findings of this study indicate the importance of prioritizing the integration of mental health services into existing reproductive health programs in Nepal. This can be achieved by ensure that healthcare providers receive training in recognizing and addressing mental health issues during routine reproductive health check-ups especially among the women have any history of having genital ulcer or discharge. This study also highlights the implication of strengthening legal frameworks against emotional violence within family and societal relationships in order to reduce the mental health symptoms in women. This may be attainable through implementing educational programs in communities to raise awareness about emotional abuse, its consequences, and available support services. Furthermore, training of healthcare professionals could be effective to identify signs of anxiety and provide timely interventions, including counseling and referral to specialized mental health services.

Strengths and limitations:

The strengths and weaknesses of this study are accredited. The strength that lies in this study is the generalizability of the findings for Nepal since this survey covered nationally representative data covering a wide geographic area. Along with appropriate statistical methods and casual modelling are the main strengths of this study. While the limitations are its cross-sectional nature limits the casual inference since outcomes and predictors variables have been collected same time frame. In addition, some biological and behavioural variables were not collected based on clinical diagnose so readers are need to aware of reading the results. Even, the outcome measurements were collected before two weeks preceding the survey based on their self-report so it might be a recall biased problem.

In terms of socioeconomic, behavioral, and biological characteristics, this study revealed a significant issue with women's mental health difficulties in Nepal. Because mental disorders are highly frequent among women in their reproductive years in Nepal, immediate, targeted efforts are required to screen and diagnose these patients in order to give care. This study may provide evidence to key stakeholders in designing programs to improve the mental health status of women in Nepal and other comparable countries.

Ethics approval and consent to participate

The current study involved analyzing secondary data, which is publicly accessible at www.dhsprogram.com and free of cost upon appropriate application. The ICF Institutional Review Board and Ethical Review Board of Nepal Health Research Council approved the data collection and survey process. Therefore, further ethical approval was not needed. However, a detailed explanation of the ethical procedure followed by the DHS program can be found elsewhere.

Consent for publication

Not applicable

Availability of data and materials

This study used publicly available Demographic and Health Surveys Program datasets from Nepal which can be freely obtained from https://dhsprogram.com/. As a third-party user, we don’t have permission to share the data publicly in any platforms.

Competing interests

None of the authors declares any conflict of interest.

Funding

This research did not receive any specific grant from any financial and non-financial sectors.

Author Contributions

MAR conceptualized the study design. MAR and MMH had all access to the data and validation of the statistical analysis and did the formal analysis and drafting the original manuscript. MAR, MMT, SZ and SK helped in drafting the original manuscript. MAI helped in the original draft and tables preparation. MSR, MHH and SK critically reviewed the manuscript. MAR supervised the whole study.

Acknowledgement

The authors declare no conflict of interest. The authors would like to thank the Demographic and health survey for providing the data freely.

WHO. World Mental Health Day 2023 - Mental health is a universal human right [Internet]. 2023 [cited 2024 Jan 18]. Available from: https://www.who.int/news-room/events/detail/2023/10/10/default-calendar/world-mental-health-day-2023---mental-health-is-a-universal-human-right
Collaborators GBD 2019 MD. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137–50.
Ferry FR. Age-of-onset and cumulative risk of mental disorders: A cross-national analysis of population surveys data based on 156,331 respondents from 29 countries: a cross-national analysis of population surveys from 29 countries. Lancet Psychiatry. 2023;10(9):668–81.
Seedat S, Scott KM, Angermeyer MC, Berglund P, Bromet EJ, Brugha TS, et al. Cross-national associations between gender and mental disorders in the World Health Organization World Mental Health Surveys. Arch Gen Psychiatry. 2009;66(7):785–95.
Organization WH, Society KC for WH in. Mental health aspects of women’s reproductive health: a global review of the literature. World Health Organization; 2009.
In focus: Women and the Sustainable Development Goals (SDGs): SDG 3: Good health and well-being | UN Women – Headquarters [Internet]. [cited 2024 Jan 18]. Available from: https://www.unwomen.org/en/news/in-focus/women-and-the-sdgs/sdg-3-good-health-well-being
Organization WH. Depression and other common mental disorders: global health estimates. World Health Organization; 2017.
Chandra PS, Raghunandan VNGP, Krishna VAS. Women’s Mental Health. International Encyclopedia of Public Health. 2008 Jan 1;608–17.
VizHub - GBD Results [Internet]. [cited 2024 Jan 19]. Available from: https://vizhub.healthdata.org/gbd-results/?params=gbd-api-2019-permalink/58dbddea28852eeae31689c0442b88d0
Dadi AF, Miller ER, Bisetegn TA, Mwanri L. Global burden of antenatal depression and its association with adverse birth outcomes: an umbrella review. BMC Public Health. 2020;20:1–16.
Wang Z, Liu J, Shuai H, Cai Z, Fu X, Liu Y, et al. Mapping global prevalence of depression among postpartum women. Transl Psychiatry. 2021;11(1):543.
Kiani Z, Simbar M, Hajian S, Zayeri F. The prevalence of depression symptoms among infertile women: a systematic review and meta-analysis. Fertil Res Pract. 2021;7:1–10.
Toffol E. Mental health and reproductive health in women. 2013;
Organization WH. World mental health report: transforming mental health for all. 2022;
Kumari Sah L, Sah P, Kumar Yadav M, B. Parajuli S, Shah R. Risk of gender-based violence and poor mental health among Nepalese women during COVID-19 pandemic: A review. Nepal Medical Journal. 2020;3(2).
Dielemans A, Mahat P, Dunn JA, Balcke E, Kumar Jha R, Ghimire S, et al. Prevalence of maternal depression and anxiety symptoms and associations with child mental health outcomes in rural Nepal. Tropical Medicine & International Health. 2023;
Giri RK, Khatri RB, Mishra SR, Khanal V, Sharma VD, Gartoula RP. Prevalence and factors associated with depressive symptoms among post-partum mothers in Nepal. BMC Res Notes. 2015;8:1–7.
Dawadi P, Bhatta AS, Shakya J. Factors Associated with Postpartum depressive symptoms in community of Central Nepal. Psychiatry J. 2020;2020.
Paudel IS, Koirala S, Pokharel D, Sharma B, Shrestha A, Panta S, et al. Pattern of deaths among women of reproductive age in major autopsy centres of capital cities of the seven provinces of Nepal. 2022;
Survey H. Nepal. 2022;
Kohrt BA, Luitel NP, Acharya P, Jordans MJD. Detection of depression in low resource settings: validation of the Patient Health Questionnaire (PHQ-9) and cultural concepts of distress in Nepal. BMC Psychiatry. 2016;16:1–14.
Spitzer RL, Kroenke K, Williams JBW, Löwe B. A brief measure for assessing generalized anxiety disorder: the GAD-7. Arch Intern Med. 2006;166(10):1092–7.
Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.
Wong KKK. Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. Marketing Bulletin. 2013;24(1):1–32.
Sarkar A, Qian L, Peau AK. Structural equation modeling for three aspects of green business practices: a case study of Bangladeshi RMG’s industry. Environmental Science and Pollution Research. 2020;27(28):35750–68.
Sarker T, Sarkar A, Rabbany MG, Barmon M, Roy R, Rahman MA, et al. Evaluation of preventive, supportive and awareness building measures among international students in China in response to COVID-19: a structural equation modeling approach. Glob Health Res Policy. 2021;6(1):1–10.
Black WC, Babin BJ, Anderson RE. Multivariate data analysis: A global perspective. Pearson; 2010.
May D, Arancibia S, Behrendt K, Adams J. Preventing young farmers from leaving the farm: Investigating the effectiveness of the young farmer payment using a behavioural approach. Land use policy. 2019;82:317–27.
Munim ZH, Noor T. Young people’s perceived service quality and environmental performance of hybrid electric bus service. Travel Behav Soc. 2020;20:133–43.
Rohrer JE, Bernard ME, Zhang Y, Rasmussen NH, Woroncow H. Marital status, feeling depressed and self-rated health in rural female primary care patients. J Eval Clin Pract. 2008;14(2).
Millán-Calenti JC, Sánchez A, Lorenzo T, Maseda A. Depressive symptoms and other factors associated with poor self-rated health in the elderly: Gender differences. Geriatr Gerontol Int. 2012;12(2).
Mohit M, Maruf M, Ahmed H, Alam M. Depression and Physical Illnesses: an Update. Bangladesh Med J. 1970;40(1).
Mulsant BH, Ganguli M, Seaberg EC. The relationship between self-rated health and depressive symptoms in an epidemiological sample of community-dwelling older adults. J Am Geriatr Soc. 1997;45(8).
Patel V, Pednekar S, Weiss H, Rodrigues M, Barros P, Nayak B, et al. Why do women complain of vaginal discharge? A population survey of infectious and pyschosocial risk factors in a South Asian community. Int J Epidemiol. 2005;34(4).
Winter A, Stephenson R. Intimate partner violence and symptoms of reproductive tract infections among married Indian women. International Journal of Gynecology and Obstetrics. 2013;121(3).
Seidman SN, Roose SP. Sexual dysfunction and depression. Vol. 3, Current psychiatry reports. 2001.
Kluger N. Manifestations cutanées et muqueuses des pratiques sexuelles. Ann Dermatol Venereol. 2020;147(10).
Carbone-López K, Kruttschnitt C, Macmillan R. Patterns of intimate partner violence and their associations with physical health, psychological distress, and substance use. Public Health Reports. 2006;121(4).
Campbell JC. Health consequences of intimate partner violence. Vol. 359, Lancet. 2002.
Bonomi AE, Thompson RS, Anderson M, Reid RJ, Carrell D, Dimer JA, et al. Intimate Partner Violence and Women’s Physical, Mental, and Social Functioning. Am J Prev Med. 2006;30(6).
Alhabib S, Nur U, Jones R. Domestic violence against women: Systematic review of prevalence studies. J Fam Violence. 2010;25(4).
Beydoun HA, Beydoun MA, Kaufman JS, Lo B, Zonderman AB. Intimate partner violence against adult women and its association with major depressive disorder, depressive symptoms and postpartum depression: A systematic review and meta-analysis. Vol. 75, Social Science and Medicine. 2012.
Dutton MA, Green BL, Kaltman SI, Roesch DM, Zeffiro TA, Krause ED. Intimate partner violence, PTSD, and adverse health outcomes. J Interpers Violence. 2006;21(7).
Collins R. Entering and leaving the tunnel of violence: Micro-sociological dynamics of emotional entrainment in violent interactions. Current Sociology. 2013;61(2).
Fluharty M, Taylor AE, Grabski M, Munafò MR. The association of cigarette smoking with depression and anxiety: A systematic review. Vol. 19, Nicotine and Tobacco Research. 2017.
Weinberger AH, Kashan RS, Shpigel DM, Esan H, Taha F, Lee CJ, et al. Depression and cigarette smoking behavior: A critical review of population-based studies. Vol. 43, American Journal of Drug and Alcohol Abuse. 2017.
Flensborg-Madsen Trine T, Bay von Scholten M, Flachs EM, Mortensen EL, Prescott E, Tolstrup JS. Tobacco smoking as a risk factor for depression. A 26-year population-based follow-up study. J Psychiatr Res. 2011;45(2).
Edwards AC, Kendler KS. A twin study of depression and nicotine dependence: Shared liability or causal relationship? J Affect Disord. 2012;142(1–3).
Roth LK, Taylor HS. Risks of smoking to reproductive health: Assessment of women’s knowledge. Am J Obstet Gynecol. 2001;184(5).
Nordal Broen A, Moum T, Sejersted Bödtker A, Ekeberg Ö. Predictors of anxiety and depression following pregnancy termination: A longitudinal five-year follow-up study. Acta Obstet Gynecol Scand. 2006;85(3).
Marcinko VM, Marcinko D, Dordević V, Oresković S. Anxiety and depression in pregnant women with previous history of spontaneous abortion. Coll Antropol. 2011;35 Suppl 1.
González-Ramos Z, Zuriguel-Pérez E, Albacar-Riobóo N, Casadó-Marín L. The emotional responses of women when terminating a pregnancy for medical reasons: A scoping review. Vol. 103, Midwifery. 2021.
Andersson IM, Christensson K, Gemzell-Danielsson K. Experiences, feelings and thoughts of women undergoing second trimester medical termination of pregnancy. PLoS One. 2014;9(12).
Geerinck-Vercammen CR, Kanhai HHH. Coping with termination of pregnancy for fetal abnormality in a supportive environment. Prenat Diagn. 2003;23(7).
Korenromp MJ, Iedema-Kuiper HR, Van Spijker HG, Christiaens GCML, Bergsma J. Termination of pregnancy on genetic grounds; coping with grieving. Journal of Psychosomatic Obstetrics and Gynecology. 1992;13(2).
Kimport K. (Mis)understanding abortion regret. Symb Interact. 2012;35(2).
Zender R, Olshansky E. Women’s Mental Health: Depression and Anxiety. Vol. 44, Nursing Clinics of North America. 2009.
Gdanska P, Drozdowicz-Jastrzebska E, Grzechocinska B, Radziwon-Zaleska M, Wegrzyn P, Wielgos M. Anxiety and depression in women undergoing infertility treatment. Ginekol Pol. 2017;88(2).
Pawluski JL, Lonstein JS, Fleming AS. The Neurobiology of Postpartum Anxiety and Depression. Vol. 40, Trends in Neurosciences. 2017.

No competing interests reported.

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A structural equation modelling to explore the determinants of mental health disorders among reproductive-aged women in Nepal: A nation-wide cross-sectional survey

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