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Hormonal and physical changes during pregnancy affect mothers’ mental health. Because depression during pregnancy is closely associated with poor pregnancy outcomes, treatment is important for pregnant women with depression. This study aimed to identify barriers to treatment-seeking behaviors among pregnant women with depression in Indonesia.
Methods
Data from the 2018 Indonesian Basic Health Research were used, which focused on pregnant women aged 15–54 years who exhibited depressive symptoms. The Mini-International Neuropsychiatric Interview was used to assess depression. Logistic regression analysis was conducted to explore the factors affecting treatment-seeking behaviors.
Results
Among the pregnant women in Indonesia, 7.9% experienced depression; however, only 11.4% sought treatment. Higher transportation costs to the clinic were associated with 41% lower odds of seeking treatment (adjusted odds ratio [AOR], 0.59; 95% confidence interval [CI], 0.37–0.95; P=0.029). Women in their second and third trimesters had 48% (AOR, 0.52; 95% CI, 0.28–0.98; P=0.042) and 54% (AOR, 0.46; 95% CI, 0.24–0.89; P=0.022) lower odds of seeking treatment, respectively, than those in their first trimester.
Conclusion
Financial barriers and the challenges of late pregnancy hinder treatment-seeking behaviors for depression in pregnant women. Therefore, there is an urgent need for affordable and accessible mental health care for vulnerable populations.
Pregnancy constitutes a phase marked by moments of joy and hope but can also be accompanied by stress and challenges. The pregnancy and childbirth period is characterized by a multitude of physiological and psychosocial changes that may predispose mothers to mental health issues. Depression is the most prevalent mental health problem during pregnancy [1]. Globally, an estimated 15%–65% of pregnant mothers contend with mental health difficulties, with a disproportionately higher incidence in low- and middle-income countries (LMICs). In these regions, approximately 15.9% and 19.8% of antenatal and postnatal women had depression, respectively [2]. The repercussions of maternal depression are far reaching and include adverse consequences for both mothers and their offspring, including the tragic occurrence of maternal suicide [3]. Maternal suicide has been identified as a leading cause of maternal mortality globally [4].
Depression during pregnancy exerts detrimental effects on the well-being of both mothers and developing child. Pregnant women with depression frequently experience a diminished quality of life [5], disrupted sleep patterns, and a propensity for physical inactivity [6]. Such manifestations can precipitate obstetric complications, including preeclampsia and oligohydramnios [7]. Furthermore, depression during pregnancy augments the likelihood of adverse pregnancy outcomes, with women diagnosed with depression being at an elevated risk of preterm delivery (odds ratio [OR], 1.34; 95% confidence interval [CI], 1.06–1.69) [8]. Maternal depression during pregnancy poses a significant threat to fetal development. Prolonged exposure of the fetus to stress induced by maternal depression can deleteriously affect cognitive functioning and social and emotional regulation later in life [9,10].
The significance of seeking treatment for depression in pregnant women should be emphasize, as it profoundly affects both maternal and fetal well-being. Failure to address mental health concerns throughout the lifespan has led to a 21% increase in the prevalence of mental health disorders [11]. Regrettably, maternal mental health issues during pregnancy often go undiagnosed and untreated [12], and mental health care is frequently marginalized in numerous cultures. Similarly, socioeconomic factors play a significant role in the accessibility to adequate maternal health care, including mental health services, in Indonesia. Women from lower socioeconomic backgrounds often encounter financial barriers that prevent them from accessing necessary prenatal and mental health care [13]. Additionally, geographical challenges further complicate access for women in rural areas, where health-care facilities may be scarce, and specialized mental health professionals are less available [14].
Given the established association between depression and adverse maternal and infant health outcomes during pregnancy, it is imperative to gain a nuanced understanding of the factors influencing help-seeking behaviors among pregnant women. Analyzing the obstacles to the treatment of pregnant mothers with depression constitutes a pivotal step in enhancing the identification, diagnosis, and management of this critical public health issue.
Methods
Data sources
This study used secondary data from the 2018 Indonesian Basic Health Research (RISKESDAS 2018), a national cross-sectional survey conducted in 34 provinces and 514 regencies/cities. The study population included all households in districts/cities in the 2018 National Socioeconomic Survey (Badan Pusat Statistik) sample framework conducted in March 2018. Data selection used a probability proportional-to-size approach, with 30,000 census blocks and 10 households per block. All household members who intended to stay for at least six months were sampled in RISKESDAS 2018 [15]. RISKESDAS 2018 is a national health research program that is routinely conducted every 5 years to measure health indicators at the national, provincial, and district levels. One of the health indicators measured was mental health status, one of which was depression. RISKESDAS used the Mini-International Neuropsychiatric Interview (MINI) questionnaire to identify individuals with symptoms indicative of depression.
The fieldworkers visited each respondent’s home and conducted face-to-face interviews. RISKESDAS data collection used structured questionnaires administered by trained fieldworkers. Prior to the interview, the fieldworkers explained the purpose of the study and the questions to be answered. Informed consent was obtained from respondents for the interviews to proceed. Interviews were conducted with each respondent, and they were not allowed to be substituted. Particularly concerning inquiries about depressive symptoms, the questions were posed in a location that was both comfortable and confidential, ensuring that the responses remained unbiased and uninfluenced by other family members’ interventions.
Study sample
The study sample comprised pregnant women aged 15–54 years with symptoms indicative of depression. The inclusion criterion included marital status, which extended to both married and divorced individuals at the time of data acquisition. In particular, the age group of 15–54 years corresponds to the recognition of the childbearing age for women and is in line with the age limit used by RISKESDAS 2018 [15].
Depressive symptomatology was assessed using the Indonesian MINI in the Indonesian language. The MINI is a meticulously structured interview tool codeveloped by psychiatrists and medical practitioners in the United States and Europe. Its primary purpose is to evaluate psychiatric disorders, as delineated in the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition and the International Classification of Diseases-10 [16]. Depressive symptoms assessed using this instrument pertained to the respondent’s mental state over the immediate preceding 2-week period before they participated in the survey.
When employed for the diagnosis of depression in the Indonesian context, the MINI has been reported to demonstrate sensitivity levels ranging between 60% and 80%, positive predictive values ranging from 30% to 60%, and negative predictive values of approximately 90%. Furthermore, inter-rater agreement yielded a moderate level of concordance, as evidenced by a kappa value of 0.62 when applied for the diagnosis of depression [17].
Each question eliciting a “no” response was assigned a score of 0, whereas each affirmative response received a score of 1. Respondents were categorized as experiencing depressive symptoms if they endorsed a minimum of two “yes” responses to Questions 1–3, in addition to a minimum of two “yes” responses to Questions 4–10 [16].
Outcome variable
The dependent variable in this study pertained to the behavioral patterns exhibited by pregnant women who sought treatment for depression. The operational definition of “treatment-seeking” in this context denoted the conscious willingness of individuals to engage in the consumption of medicinal substances or undergo medical interventions in response to the manifestation of depressive symptoms. Treatment-seeking data were derived from responses to the following query: “Regarding the aforementioned complaints (MINI questionnaire), have you taken medication or undergone medical treatment?” The elucidation included discerning whether pregnant women with depressive symptoms opted to avail medication or sought treatment at psychiatric institutions, general health-care facilities, health centers, or through healthcare practitioners, including physicians, nurses, and midwives, as stipulated by the RISKESDAS 2018 guidelines. Respondents were dichotomously categorized as “treatment-seeking” if they responded affirmatively (“yes”) and “nontreatment-seeking” if they responded negatively (“no”).
Independent variables
The independent variables in this study comprised demographic characteristics, health service accessibility factors, and medical history indicators. The maternal demographic characteristics included marital status, age, educational attainment, employment status, type of residential region, wealth index, and cellular phone use. Marital status is dichotomized as “married” or “divorced.” Maternal age was categorized into three groups: 15–24, 25–34, and 35–54 years, signifying the mother’s age at the time of data collection. Educational attainment was stratified into three: no education or basic education (comprising individuals with no formal schooling, incomplete elementary education, or elementary school graduates), secondary education (including those who have completed junior high school or high school), and higher education (comprising graduates of diploma or university programs). Employment status is trichotomized into “not working,” “formal workers” (including civil servants, private employees, or self-employed individuals), and “informal workers” (including farmers, fishermen, laborers, drivers, helpers, and other unmentioned occupations). Residential type was classified as either “rural” or “urban.” Wealth index corresponds to the economic status of households, calculated from per capita expenditure, and is categorized into five quintiles ranging from the most economically disadvantaged (quintile 1) to the most affluent (quintile 5). The “cellphone use” variable signifies respondents who have used a cellular or wireless phone in the last 3 months.
Health service accessibility variables included insurance ownership, awareness of the proximity of health-care services, travel duration to the nearest health-care facility (in minutes), transportation expenses to access health-care facilities, and perceptions of the affordability of transportation expenses for health-care access. Health-care services included hospitals, community health centers (including auxiliary health centers, mobile health centers, and village midwives), and clinics (including doctors’ and midwives’ practices). Insurance ownership was categorized as “yes” if respondents possessed any form of insurance, such as Health BPJS Beneficiaries (PBI), non-PBI, Jamkesda, private insurance, or company/office-based insurance. Awareness of the existence of nearby health-care services is bifurcated into “unaware/unavailable” and “aware of availability.” Travel duration to the closest health-care facility quantifies the time (in minutes) required for respondents to reach the nearest health-care service location from their residence in a single journey. The “transportation cost to the nearest health-care services” variable represents the monetary expenditure (in Indonesian rupiah, IDR) associated with travelling to and from home to the closest health-care services, considering round-trip costs. Perceptions regarding the affordability of transportation expenses for health-care access are based on respondents’ subjective assessments, categorized as “not affordable” (0) or “affordable” (1).
The medical history variables included gestational age, gravida, parity, abortion history, presence of health complaints, and history of self-medication. Medical history variables pertained to pregnancy- and non-pregnancy-related conditions. Gestational age was stratified into three stages: trimester 1 (0–13 weeks), trimester 2 (14–26 weeks), and trimester 3 (27–40 weeks). Gravida denotes the total number of pregnancies experienced by a mother, including those resulting in live births, miscarriages, or ongoing pregnancies, categorized as “once” or “more than once.” Parity quantifies the number of childbirths experienced by mothers, whether live or stillborn, further categorized into “nullipara” (mothers who have never given birth), “primipara” (mothers with one childbirth), and “multipara” (mothers with more than one childbirth). Abortion history records instances where mothers gave birth to fetuses with gestational ages less than 22 weeks or 5.5 months. Health complaints included physical discomfort reported by respondents in the preceding month. The self-medication behavior variable examined whether mothers engaged in self-medication in the absence of access to health-care services for pain complaints experienced in the past month.
Statistical analysis
Data were analyzed using Stata ver. 14 (Stata Corp.). Initially, a descriptive analysis was used to examine the dataset, followed by a bivariate analysis to assess the distribution of treatment-seeking behaviors. Continuous variables are presented as mean±standard deviation, whereas categorical variables are expressed as the number (%) of subjects.
To determine the association between each predictor and treatment-seeking behaviors, a simple logistic regression analysis was conducted. Subsequently, a multivariable logistic regression model was constructed using the purposeful selection of the covariates model, as recommended by Hosmer and Lemeshow. ORs and corresponding 95% CIs were calculated, and statistical significance was defined as a two-sided P-value of less than 0.05.
Missing data were addressed using the multiple imputation method to ensure valid results and reduce potential bias caused by incomplete data. Data were assumed to be missing at random, and multiple imputations (MIs) were performed to create multiple complete datasets by generating plausible values based on the observed data distribution. The imputation process included relevant predictor variables to enhance the imputation accuracy. Imputations were performed using the MI impute procedure in Stata ver. 14.
Ethics statement
The implementation of RISKESDAS 2018 received ethical approval from the Health Research Ethics Commission of the Health Research and Development Agency of the Ministry of Health of the Republic of Indonesia (approval no., LB.02.01/3/KE024/2018). Prior to the commencement of data collection, each respondent was duly informed and voluntarily provided a written consent. This study strictly adhered to the ethical principles, including consent, voluntary participation, confidentiality, and anonymity, which were fundamental to health research. The participants were fully informed and willingly consented to participate in the study.
Results
Of the 8,889 pregnant women surveyed during the RISKESDAS 2018, 8,186 individuals (92.1%) did not exhibit signs of depression. However, 703 women (7.9%) experienced depression (Figure 1). Pregnant women affected by depression were predominantly married (99.1%), with ages 25–35 years (54.3%), possessing a middle-level education (56.2%), unemployed (63.4%), residing in rural areas (56.6%), and regular users of cell phones (75.0%). The prevalence of depression increased with higher wealth index scores (Table 1).
Among the mothers with depression, 11.4% sought treatment. Notably, the majority of those seeking treatment were married (11.5%), aged 25–35 years (12.0%), had a middle-level education (13.7%), were formally employed (13.3%), lived in rural areas (11.6%), had a medium wealth index (16.3%), and were frequent mobile phone users (12.4%) (Table 1).
Among the factors associated with health access services presented in Table 2, pregnant women with depression who sought treatment were more likely to have health insurance coverage (12.4%), were aware of the existence of the nearest health clinic (12.4%), and found transportation costs to the clinic affordable (12.7%).
Regarding medical history factors, the search for treatment for depression was notably higher among pregnant women in their first trimester (15.4%). Additionally, those who had not experienced their first pregnancy (11.6%), were multiparous (12.3%), had a history of miscarriage (12.8%), reported health complaints in the past month (13.5%), or had a habit of self-medication without consulting health-care workers in the last month (14.5%) were more likely to seek treatment for depression.
Table 3 presents the barriers to treatment seeking for depression during pregnancy among women of childbearing age, as identified through multivariate analysis. Notably, transportation costs and gestational age were found to significantly influence treatment-seeking behaviors of pregnant women with depression. As the transportation costs to clinics increase, the likelihood of seeking treatment decreases. Specifically, each additional transportation fee of IDR 10,000 per person to reach the clinic was associated with a 41% lower odds of seeking treatment. Additionally, the stage of pregnancy, as reflected by the gestational age, plays a crucial role. The higher the trimester of pregnancy, the more reluctant pregnant mothers who experience depression to seek treatment. Women in their second trimester were 48% less likely to seek treatment than those in their first trimester. Similarly, mothers in their third trimester had 54% lower odds of seeking treatment than their counterparts in the first trimester.
Discussion
This study revealed that 7.9% of pregnant women exhibited depressive symptoms, a percentage lower than that observed among expectant women in Malaysia (12.2%) and Tanzania (11.5%) as reported by Nasreen et al. [18] and Ngocho et al. [19], respectively. Depression during pregnancy in Indonesia, although at 7.9%, is far above the national depression rate of only 6.1% and the global depression rate of only 3.8%. This poses a significant threat to the public health in Indonesia. Delving deeper into our findings, only 11.6% of pregnant women with depression actively seek assistance from health-care services, including mental institutions, public hospitals, health centers, and medical practices. This implies that more than three-quarters of expectant women with depression remain untreated by health-care professionals.
The World Health Organization reports a disparity in treatment rates between LMICs, where only 15%–24% of individuals with severe mental disorders receive treatment, compared to 50%–65% in high-income countries [20]. This study highlighted that more than 80% of pregnant women displaying signs of depression did not perceive the need to access mental health services. This trend aligns with various studies indicating low intention to seek mental health services [21]. Several factors may explain this low perceived need, including educational level, awareness of the importance of mental health issues, and social stigma. Furthermore, pregnant women may normalize their mood disturbances during pregnancy, thereby contributing to their reluctance to seek help [22]. Given the severe consequences of depression in expectant women, prioritizing prompt and effective treatment is imperative.
Transportation costs
According to RISKESDAS 2018 data, more than half of pregnant women in Indonesia receive pregnancy examinations and antenatal care services at a physician’s office or midwifery clinic [15]. This aligns with recent research, indicating that pregnant women prefer these facilities because midwives are perceived as more accessible, particularly in remote or rural regions. Midwives are frequently more accessible than hospitals or other healthcare institutions [23]. Additionally, midwifery care is considered economically feasible, making it the preferred option for patients with financial constraints. Accessibility is also associated with transportation costs, which are significant factors for women from low- and middle-income backgrounds when choosing a health-care facility for delivery [24].
Indonesia’s socioeconomic context significantly influences the decision-making processes of pregnant women with depression seeking mental health services. These services are often concentrated in urban centers, imposing substantial financial burdens. Costs such as public transportation fares, fuel, or vehicle maintenance contribute to the hesitance of pregnant women with depression to seek mental health services [25]. Transportation is a prominent barrier for pregnant women with depression to access mental health services. A systematic review highlighted transportation costs as a practical barrier that limited individuals’ access to care [26]. Furthermore, a study involving low-income participants showed that financial constraints might prevent mental health providers from meeting their daily needs. Pregnant women may not prioritize mental health care because of competing expenses and may choose to ignore their mental health problems rather than incurring treatment costs [27].
Geographical and infrastructural challenges further hinder access to mental health services. Indonesia’s archipelagic nature poses significant challenges, particularly for women residing on remote islands or in isolated communities. Limited transportation options to urban centers hinder access to mental health facilities and perpetuate disparities. A qualitative study of 18–32-year-olds with mental health disorders in specific Indonesian regions revealed challenges to mental health-care facilities and high treatment costs without national health insurance deterring service use [28].
The findings of our study suggest that the constraints of travel time to health facilities, particularly clinics, as well as transportation costs to clinics, are barriers to accessing health services for pregnant women with depressive symptoms. Despite improvements in Indonesia’s infrastructure, disparities in development across provinces still affect health-care access owing to varied travel expenses and times. Studies have shown that higher travel costs and longer journeys lead to decreased usage of outpatient and inpatient services, particularly among those with lower socioeconomic status [29].
Gestational age
The findings of this study align with research conducted in Canada by Da Costa et al. [30], who concluded that women in their third trimester with higher levels of depressive symptoms reported encountering more barriers to access mental health services. The research by Da Costa et al. [30] highlights that the predominant barriers to seek treatment for perinatal depression include not perceiving the symptoms as problematic, being too occupied, not actively seeking treatment, being concerned about costs, and lacking knowledge of where to seek assistance.
A study conducted in China in 2020 also yielded analogous results, indicating that advanced gestational age was associated with reduced intention to seek mental health services (OR, 0.57 to 0.84). Pregnant women with higher gestational ages tend to view increased stress and distress as a normal part of approaching delivery, in contrast to pregnant women with lower gestational ages [21].
In Indonesia, perinatal depression services are integrated in a broader framework of maternal and mental health care. Primary health-care centers offer integrated antenatal care involving general practitioners, nurses, midwives, and pharmacists. This approach not only integrates maternal care but also includes comprehensive care that may arise during pregnancy. This comprehensive approach includes complications, infections, and mental health issues and provides holistic support to pregnant women.
Policy recommendation
This study highlights the significant barriers to treatment-seeking behaviors among pregnant women with depression, including financial constraints and limited access to health care. Thus, tailored policy interventions are required to address these challenges. An effective solution is to expand the Jaminan Kesehatan Nasional (JKN) program, which now integrates the benefits previously covered in the Jaminan Persalinan (Jampersal) program and offers free maternal health services to pregnant women, particularly those from low-income backgrounds. By incorporating mental health services such as counseling and depression screening into Jampersal, pregnant women can receive comprehensive care during antenatal visits, reducing the financial obstacles to mental health support.
Community-based initiatives, such as Dasa Wisma, focus on women’s empowerment and local development and provide a valuable platform to raise awareness of mental health. Training Dasa Wisma members to deliver mental health education and referring women to appropriate services enhance early detection of and intervention for perinatal depression, potentially in collaboration with health-care facilities or mental health professionals.
Moreover, Dasa Wisma groups can offer social support to pregnant women with depression and foster peer connections and practical assistance such as accompanying them to health-care facilities. Leveraging these networks to address transportation challenges, particularly in rural areas, by organizing community transport solutions can further improve access to mental health care.
By integrating mental health services into existing programs, such as JKN, and leveraging community-based initiatives, such as Dasa Wisma, Indonesia can develop a comprehensive strategy to overcome the financial, logistical, and societal barriers that hinder pregnant women from seeking mental health support. This approach ensures that maternal health initiatives are inclusive, accessible, and community driven.
Limitations and strengths of the study
This study has several limitations. The cross-sectional design restricted our ability to establish causal relationships between variables, allowing us to characterize only variations in prevalence and correlations among factors associated with treatment-seeking behaviors. Furthermore, this study examined data solely from the 2018 RISKESDAS survey, which might not comprehensively reflect the growing health-care landscape and socioeconomic transformations in Indonesia since that time. Elements such as the rise of universal health-care efforts, enhancements in transportation infrastructure, and effects of the COVID-19 (coronavirus disease 2019) pandemic might have influenced treatment-seeking behaviors among pregnant women and access to maternal mental health services. The possible transformations in the contextual situation represented by these changes could potentially affect the generalizability of our findings to the current situation. However, a notable strength of our study is the temporal alignment between the data on the period of treatment seeking and the experience of depressive symptoms in pregnant women, which facilitates a precise evaluation of responses.
The issue of pregnant women with depression refraining from seeking mental health treatment in Indonesia is complex and multifaceted. This necessitates a thorough acknowledgment of this barrier and the implementation of comprehensive strategies to ensure equitable access to mental health services for this vulnerable population. Collaborative efforts involving policymakers, health-care professionals, and communities are pivotal for driving positive changes and enhancing maternal mental health outcomes in Indonesia.
Conclusion
This study highlights the trend in which only a limited percentage of pregnant women with depression receive treatment. Considering the adverse effects of depression on the health and well-being of both pregnant mothers and their children, it is imperative to ensure timely and appropriate treatment of depression in pregnant women.
An effective approach to addressing this issue is to integrate treatments for depression into existing maternal health services. In Indonesia, pregnant women already have access to mental health screening programs that are seamlessly integrated into health-care services. The success of this program hinges on increasing pregnant women’s participation in mental health assessments and facilitating follow-up treatment with qualified professionals. By encouraging more pregnant women to engage in these assessments and seek the necessary follow-up care, we can take a significant step toward improving the mental health outcomes of both mothers and their children during the crucial period of pregnancy.
Notes
Conflict of interest
No potential conflict of interest relevant to this article was reported.
Acknowledgments
The authors express their gratitude to the Head of the Health Policy and Development Board at the Ministry of Health, Republic of Indonesia, for granting permission to access, analyze, present, and publish the data that were instrumental in this study. Additionally, the authors acknowledge the Indonesia Endowment Fund for Education (Lembaga Pengelola Dana Pendidikan) for supporting the doctoral studies of Hayani Anastasia and Indri Yunita Suryaputri.
Funding
None.
Data availability
Contact the corresponding author for data availability.
Author contribution
Conceptualization: TW, RM, IYS, HA, SI, AK, YFW. Data curation: TW, RM, IYS, HA. Formal analysis: TW, RM, IYS, HA. Methodology: TW, RM, IYS, HA, SI, RIA, I. Project administration: TW, RM. Visualization: TW, AK, YFW. Writing–original draft: TW, RM, IYS, HA, SI, RIA, I, AK, YFW. Writing–review and editing: TW, RM, IYS, HA, SI, RIA, I, AK, YFW. Final approval of the manuscript: all authors.
Figure. 1.
Prevalence of depression in pregnant women aged 15–54 years.
Table 1.
Frequency distribution of demographic characteristics of pregnant women with depression based on treatment-seeking behaviors
Characteristic
Total
Treatment-seeking
COR (95% CI)
P-value
No
Yes
Overall
703 (100.0)
623 (88.6)
80 (11.4)
-
-
Marital status
Married
696 (99.0)
616 (88.5)
80 (11.5)
-
-
Divorced
7 (1.0)
7 (100.0)
0 (0.0)
Age (y)
15–24
201 (28.6)
181 (90.1)
20 (9.9)
1
25–35
382 (54.3)
336 (88.0)
46 (12.0)
1.23 (0.71–2.15)
0.449
36–52
120 (17.1)
106 (88.3)
14 (11.7)
1.19 (0.57–2.46)
0.629
Education level
None/primary school
219 (31.1)
201 (91.8)
18 (8.2)
1
Middle
395 (56.2)
341 (86.3)
54 (13.7)
1.76 (1.01–3.09)
0.046
High
89 (12.7)
81 (91.0)
8 (9.0)
1.10 (0.46–2.63)
0.826
Working status
Not working
446 (63.4)
397 (89.0)
49 (11.0)
1
Formal employment
120 (17.1)
104 (86.7)
16 (13.3)
1.24 (0.68–2.28)
0.475
Informal employment
137 (19.5)
122 (89.0)
15 (11.0)
0.99 (0.53–1.83)
0.990
Residence
Urban
305 (43.4)
271 (88.8)
34 (11.2)
1
Rural
398 (56.6)
352 (88.4)
46 (11.6)
1.04 (0.65–1.66)
0.865
Wealth index
Poorest
97 (15.6)
87 (89.7)
10 (10.3)
1
Poor
106 (17.1)
96 (90.6)
10 (9.4)
0.90 (0.35–2.28)
0.834
Medium
123 (19.8)
103 (83.7)
20 (16.3)
1.68 (0.75–3.80)
0.205
Rich
132 (21.3)
118 (89.4)
14 (10.6)
1.03 (0.43–2.43)
0.942
Richest
163 (26.2)
141 (86.5)
22 (13.5)
1.35 (0.61–3.00)
0.451
Cellphone use
No
155 (25.0)
59 (90.8)
6 (9.2)
1
Yes
466 (75.0)
486 (87.4)
70 (12.6)
1.41 (0.58–3.40)
0.436
Values are presented as number (%) unless otherwise stated. Significant level 95% with simple logistic regression. Statistically significant results are marked in bold.
COR, crude odds ratio; CI, confidence interval.
Table 2.
Frequency distribution of access to health services and medical history pregnant women with depression based on treatment-seeking behaviors
Variable
Total
Treatment-seeking
COR (95% CI)
P-value
No
Yes
Overall
703 (100.0)
623 (88.6)
80 (11.4)
-
-
Health service access
Insurance ownership
No
309 (44.0)
278 (90.0)
31 (10.0)
1
Yes
394 (56.0)
345 (87.6)
49 (12.4)
1.27 (0.79–2.05)
0.320
Knowing the existence of the nearest hospital
Do not know/do not exist
95 (13.5)
84 (88.4)
11 (11.6)
1
Knowing and exist
608 (86.5)
539 (88.7)
69 (11.3)
0.97 (0.49–1.92)
0.948
Duration of travel to the hospital (min)
50.6±80.2
50.5±82.9
50.9±55.1
1.00 (0.99–1.00)
0.968
Hospital trasportation cost (thousand)
42.0±85.7
41.3±81.9
47.6±111.2
1.00 (0.99–1.00)
0.564
The affordability of hospital transportation cost
Affordable
90 (14.8)
78 (86.7)
12 (13.3)
1
Not affordable
518 (85.2)
461 (89.0)
57 (11.0)
0.80 (0.41–1.56)
0.521
Knowing the existence of the nearest community health center
Do not know/do not exist
17 (2.4)
15 (88.2)
2 (11.8)
1
Knowing and exist
686 (97.6)
608 (88.6)
78 (11.4)
0.96 (0.21–4.28)
0.960
Duration of travel to the community health center (min)
16.1±18.2
15.8±15.2
18.1±33.6
1.00 (0.99–1.01)
0.310
Community health center trasportation cost (thousand)
10.4±15.5
10.7±16.2
7.8±7.7
0.99 (0.99–1.00)
0.107
The affordability of community health center transportation cost
Not affordable
40 (5.8)
39 (97.5)
1 (2.5)
1
Affordable
646 (94.2)
569 (88.1)
77 (11.9)
5.27 (0.71–38.96)
0.103
Knowing the existence of the nearest clinic
Do not know/do not exist
187 (26.6)
171 (91.4)
16 (8.6)
1
Knowing and exist
516 (73.4)
452 (87.6)
64 (12.4)
1.51 (0.85–2.69)
0.158
Duration of travel to the health clinic (min)
16.3±43.4
17.3±46.3
9.0±5.9
0.96 (0.93–0.99)
0.046
Health clinic transportation cost (thousand)
9.8±19.6
10.4±20.8
5.6±4.7
0.99 (0.99–0.99)
0.029
The affordability of health clinic transportation cost
Not affordable
21 (4.1)
20 (95.2)
1 (4.8)
1
Affordable
495 (95.9)
432 (87.3)
63 (12.7)
2.91 (0.38–22.11)
0.300
Medical history
Gestational age
1st trimester
220 (31.3)
186 (84.6)
34 (15.4)
1
2nd trimester
256 (36.4)
232 (90.6)
24 (9.4)
0.56 (0.32–0.98)
0.045
3rd trimester
227 (32.3)
205 (90.3)
22 (9.7)
0.58 (0.33–1.04)
0.068
Gravida
1st pregnancy
256 (36.4)
228 (89.1)
28 (10.9)
1
More than one pregnancy
447 (63.6)
395 (88.4)
52 (11.6)
1.07 (0.65–1.74)
0.780
Parity
Nulliparous
178 (25.3)
158 (88.8)
20 (11.2)
1
Primiparous
225 (32.0)
202 (89.8)
23 (10.2)
0.89 (0.47–1.69)
0.743
Multiparous
300 (42.7)
263 (87.7)
37 (12.3)
1.11 (0.62–1.98)
0.721
Abortion history
Yes
179 (25.5)
156 (87.2)
23 (12.8)
1
No
524 (74.5)
467 (89.1)
57 (10.9)
0.83(0.49–1.39)
0.474
Having health complaints (last 1 mo)
Yes
223 (35.9)
193 (86.5)
30 (13.5)
1
No
398 (64.1)
352 (88.4)
46 (11.6)
1.18 (0.72–1.94)
0.490
Self-medication (last 1 mo)
No
85 (38.1)
75 (88.2)
10 (11.8)
1
Yes
138 (61.9)
118 (85.5)
20 (14.5)
1.27 (0.56–2.86)
0.563
Values are presented as number (%) or mean±standard deviation unless otherwise stated. Significant level 95% with simple logistic regression. Statistically significant results are marked in bold.
COR, crude odds ratio; CI, confidence interval.
Table 3.
Barriers to treatment seeking for depression during pregnancy
Variable
AOR (95% CI)
P-value
Health clinic transportation cost (with an increase of Rp 10,000)
0.59 (0.37–0.95)
0.029
Gestational age
1st trimester
1
2nd trimester
0.52 (0.28–0.98)
0.042
3rd trimester
0.46 (0.24–0.89)
0.022
95% Significant level with multiple logistic regression.
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Barriers to treatment-seeking behaviors among pregnant women with depression: a national cross-sectional study in Indonesia
Figure. 1. Prevalence of depression in pregnant women aged 15–54 years.
Graphical abstract
Figure. 1.
Graphical abstract
Barriers to treatment-seeking behaviors among pregnant women with depression: a national cross-sectional study in Indonesia
Characteristic
Total
Treatment-seeking
COR (95% CI)
P-value
No
Yes
Overall
703 (100.0)
623 (88.6)
80 (11.4)
-
-
Marital status
Married
696 (99.0)
616 (88.5)
80 (11.5)
-
-
Divorced
7 (1.0)
7 (100.0)
0 (0.0)
Age (y)
15–24
201 (28.6)
181 (90.1)
20 (9.9)
1
25–35
382 (54.3)
336 (88.0)
46 (12.0)
1.23 (0.71–2.15)
0.449
36–52
120 (17.1)
106 (88.3)
14 (11.7)
1.19 (0.57–2.46)
0.629
Education level
None/primary school
219 (31.1)
201 (91.8)
18 (8.2)
1
Middle
395 (56.2)
341 (86.3)
54 (13.7)
1.76 (1.01–3.09)
0.046
High
89 (12.7)
81 (91.0)
8 (9.0)
1.10 (0.46–2.63)
0.826
Working status
Not working
446 (63.4)
397 (89.0)
49 (11.0)
1
Formal employment
120 (17.1)
104 (86.7)
16 (13.3)
1.24 (0.68–2.28)
0.475
Informal employment
137 (19.5)
122 (89.0)
15 (11.0)
0.99 (0.53–1.83)
0.990
Residence
Urban
305 (43.4)
271 (88.8)
34 (11.2)
1
Rural
398 (56.6)
352 (88.4)
46 (11.6)
1.04 (0.65–1.66)
0.865
Wealth index
Poorest
97 (15.6)
87 (89.7)
10 (10.3)
1
Poor
106 (17.1)
96 (90.6)
10 (9.4)
0.90 (0.35–2.28)
0.834
Medium
123 (19.8)
103 (83.7)
20 (16.3)
1.68 (0.75–3.80)
0.205
Rich
132 (21.3)
118 (89.4)
14 (10.6)
1.03 (0.43–2.43)
0.942
Richest
163 (26.2)
141 (86.5)
22 (13.5)
1.35 (0.61–3.00)
0.451
Cellphone use
No
155 (25.0)
59 (90.8)
6 (9.2)
1
Yes
466 (75.0)
486 (87.4)
70 (12.6)
1.41 (0.58–3.40)
0.436
Variable
Total
Treatment-seeking
COR (95% CI)
P-value
No
Yes
Overall
703 (100.0)
623 (88.6)
80 (11.4)
-
-
Health service access
Insurance ownership
No
309 (44.0)
278 (90.0)
31 (10.0)
1
Yes
394 (56.0)
345 (87.6)
49 (12.4)
1.27 (0.79–2.05)
0.320
Knowing the existence of the nearest hospital
Do not know/do not exist
95 (13.5)
84 (88.4)
11 (11.6)
1
Knowing and exist
608 (86.5)
539 (88.7)
69 (11.3)
0.97 (0.49–1.92)
0.948
Duration of travel to the hospital (min)
50.6±80.2
50.5±82.9
50.9±55.1
1.00 (0.99–1.00)
0.968
Hospital trasportation cost (thousand)
42.0±85.7
41.3±81.9
47.6±111.2
1.00 (0.99–1.00)
0.564
The affordability of hospital transportation cost
Affordable
90 (14.8)
78 (86.7)
12 (13.3)
1
Not affordable
518 (85.2)
461 (89.0)
57 (11.0)
0.80 (0.41–1.56)
0.521
Knowing the existence of the nearest community health center
Do not know/do not exist
17 (2.4)
15 (88.2)
2 (11.8)
1
Knowing and exist
686 (97.6)
608 (88.6)
78 (11.4)
0.96 (0.21–4.28)
0.960
Duration of travel to the community health center (min)
16.1±18.2
15.8±15.2
18.1±33.6
1.00 (0.99–1.01)
0.310
Community health center trasportation cost (thousand)
10.4±15.5
10.7±16.2
7.8±7.7
0.99 (0.99–1.00)
0.107
The affordability of community health center transportation cost
Not affordable
40 (5.8)
39 (97.5)
1 (2.5)
1
Affordable
646 (94.2)
569 (88.1)
77 (11.9)
5.27 (0.71–38.96)
0.103
Knowing the existence of the nearest clinic
Do not know/do not exist
187 (26.6)
171 (91.4)
16 (8.6)
1
Knowing and exist
516 (73.4)
452 (87.6)
64 (12.4)
1.51 (0.85–2.69)
0.158
Duration of travel to the health clinic (min)
16.3±43.4
17.3±46.3
9.0±5.9
0.96 (0.93–0.99)
0.046
Health clinic transportation cost (thousand)
9.8±19.6
10.4±20.8
5.6±4.7
0.99 (0.99–0.99)
0.029
The affordability of health clinic transportation cost
Not affordable
21 (4.1)
20 (95.2)
1 (4.8)
1
Affordable
495 (95.9)
432 (87.3)
63 (12.7)
2.91 (0.38–22.11)
0.300
Medical history
Gestational age
1st trimester
220 (31.3)
186 (84.6)
34 (15.4)
1
2nd trimester
256 (36.4)
232 (90.6)
24 (9.4)
0.56 (0.32–0.98)
0.045
3rd trimester
227 (32.3)
205 (90.3)
22 (9.7)
0.58 (0.33–1.04)
0.068
Gravida
1st pregnancy
256 (36.4)
228 (89.1)
28 (10.9)
1
More than one pregnancy
447 (63.6)
395 (88.4)
52 (11.6)
1.07 (0.65–1.74)
0.780
Parity
Nulliparous
178 (25.3)
158 (88.8)
20 (11.2)
1
Primiparous
225 (32.0)
202 (89.8)
23 (10.2)
0.89 (0.47–1.69)
0.743
Multiparous
300 (42.7)
263 (87.7)
37 (12.3)
1.11 (0.62–1.98)
0.721
Abortion history
Yes
179 (25.5)
156 (87.2)
23 (12.8)
1
No
524 (74.5)
467 (89.1)
57 (10.9)
0.83(0.49–1.39)
0.474
Having health complaints (last 1 mo)
Yes
223 (35.9)
193 (86.5)
30 (13.5)
1
No
398 (64.1)
352 (88.4)
46 (11.6)
1.18 (0.72–1.94)
0.490
Self-medication (last 1 mo)
No
85 (38.1)
75 (88.2)
10 (11.8)
1
Yes
138 (61.9)
118 (85.5)
20 (14.5)
1.27 (0.56–2.86)
0.563
Variable
AOR (95% CI)
P-value
Health clinic transportation cost (with an increase of Rp 10,000)
0.59 (0.37–0.95)
0.029
Gestational age
1st trimester
1
2nd trimester
0.52 (0.28–0.98)
0.042
3rd trimester
0.46 (0.24–0.89)
0.022
Table 1. Frequency distribution of demographic characteristics of pregnant women with depression based on treatment-seeking behaviors
Values are presented as number (%) unless otherwise stated. Significant level 95% with simple logistic regression. Statistically significant results are marked in bold.
COR, crude odds ratio; CI, confidence interval.
Table 2. Frequency distribution of access to health services and medical history pregnant women with depression based on treatment-seeking behaviors
Values are presented as number (%) or mean±standard deviation unless otherwise stated. Significant level 95% with simple logistic regression. Statistically significant results are marked in bold.
COR, crude odds ratio; CI, confidence interval.
Table 3. Barriers to treatment seeking for depression during pregnancy
95% Significant level with multiple logistic regression.
, Rofingatul Mubasyiroh1
