Characteristics of perinatal depression in rural central, India: a cross-sectional study

Perinatal depression is defined as depression occurring in a woman while she is pregnant or within 12 months of delivery [1]. A global systematic review by Stein et al., found evidence that perinatal depression is associated with negative effects on child behavioural, cognitive and emotional development, and mixed evidence for negative effects on birth outcomes (i.e. pre-term and birth weight) and physical growth [2]. In a subsequent systematic review by Gelaye et al. of perinatal depression among low- and middle-income country (LMIC) populations there was largely consistent evidence of negative impacts for birth outcomes and physical growth [3], though insufficient evidence is available for the other impacts which were identified in the global review.

In LMIC the prevalence of antenatal depression is estimated to be 15–20%, and 20% for postnatal depression [1, 3], though with considerable variation between countries [4–6]. Within India, prevalence estimates for antenatal or postnatal depression range between 6 and 48% [7–15]. The high variation in estimates could be due the use of screening tools; two studies from South India which used diagnostic interviews had prevalence estimates of 11% and 16% [8, 14].

From these India-based studies, a range of risk factors for perinatal depression have emerged in domains relating to sociodemographic and economic status; child gender preference; and family and marital characteristics [7–10, 13, 14, 16]. Identifying women for treatment of depression has the potential to lead to direct health benefits for women and indirect benefits for their children. The need to improve child health is urgent in India, where 30% of newborns are low birth weight [17] and 38% of children under 5 are moderately or severely stunted [18].

In India, launch of the National Mental Health Policy and Mental Healthcare Act and increased priority accorded to mental health programs mean that there is now an opportunity to reduce the population-level burden of perinatal depression. To inform planning of health promotion activities and service delivery, we have characterized community- and facility-based samples of perinatal women from rural central India who were screened for depression, and described their help-seeking behaviour.

In the Community Study, 28.1% of women were age 18–22 years, 41.5% age 23–26 years and 30.4% age 27 or more. In the Facility Study the corresponding percentages were 47.7%, 30.8% and 21.5%, and for the pooled total sample the corresponding percentages were 35.1%, 37.6% and 27.1%. In the community, 31.5% of perinatal women were pregnant, compared to 79.2% of women recruited from the facilities. In the total sample, few—if any—women were unmarried, owned land or participated in the rural employment programme (all < 5%, data not shown). For depression screening, 8.9% of women in the community were PHQ9-positive, compared to 18.5% of women in the facilities.

The pooled sample of 356 women had a median PHQ9 score of 4 (IQR 2–7), and 12.4% were PHQ9 positive. Women aged 18–22 years had a median PHQ9 score of 5 (IQR 3–8) and 16.8% were PHQ9 positive. Women aged 23–26 years had a median PHQ9 score of 3 (1–6) and 8.1% were PHQ9 positive. Woman age 27 years had a median PHQ9 score of 4 (IQR 2–7), and 12.5% were PHQ9 positive. For the continuous PHQ9 score, there was evidence of a “U” shaped association with age, and positive associations with pregnancy, disability score, suicidality and being a health facility attendee (Kruskall-Wallis P < 0.05). For the binary PHQ9 score, there was evidence of positive associations with pregnancy, disability score, suicidality and being a health facility attendee (Fisher’s exact P < 0.05). The factors associated with depression symptoms for separate samples of Facility and Community Study samples are presented in Additional file 2: Table S1 and Additional file 3: Table S2.

Only 2 of 20 (10%) of PHQ9-positive women in the Community Study reported seeking treatment for their problems, and we did not find evidence of a difference between the baseline and follow up survey rounds (Fisher’s exact P > 0.05). Only 3 of 24 (12.5%) of PHQ9-positive women in the Facility Study reported a diagnosis of depression from their clinical consultation, and again we did not find evidence of a difference between baseline and follow up survey rounds (Fisher’s exact P > 0.05).

This is the first study to describe the distribution of perinatal depression in Madhya Pradesh, India, a predominantly (72%) rural state of 72 million people. In Sehore District, we found that 8.8% of perinatal women in the community and 18.5% of women in the facility screened positive for depression. Depression symptoms were common across different strata of sociodemographic and health-related characteristics. After implementation of a mental health care plan for integrated care in three sub-district hospitals in Sehore District [22], the proportion of screen-positive women in the community who sought care did not increase, nor was there a change in clinical detection of depression among screen-positive women in facilities.

An inverse association between socioeconomic status and perinatal depression has been demonstrated in several India-based studies [8, 10, 13, 14, 31] and in a global meta-analysis of socioeconomic status and depression [32]. As housing quality and education are proxies of family- and individual-level socioeconomic status, respectively, the absence of associations with depression symptoms was unexpected. It is possible that the effect of socioeconomic status on depression is relevant in areas with greater inequality. Sehore District is a rural area which is more uniformly lower-middle class; the difference between those with lower and higher quality housing is not as extreme as in other areas of India, and employment prospects for women are modest regardless of educational attainment. Future research can consider whether the association of socioeconomic status and depression—which is usually negative [32]—is modified by community-level inequality, or is equally valid in LMIC. And alternatively, future work can determine whether housing quality and education are adequate proxies for socioeconomic status in India.

Pressure to have a male child and disappointment over having a girl child were associated positively with depression symptoms from studies conducted in North, West and South India [7, 8, 10, 13, 14, 16, 33]. Male gender preference in Sehore District is evident from data which show the under-6 sex ratio is 927 girls for every 1000 boys [19]. It is worth noting that we did not ask directly about gender preference, and instead measured a pattern of parity. Future research should directly assess women’s gender preferences prior to giving birth, screen women for depression at the time of gender reveal (at birth, legally, in India), and should establish whether any change in depression symptoms associated with unmet gender preference is persistent.

Depression ultimately affects functioning, as evidenced by the association between PHQ9 and WHODAS measures observed here. It is the loss of functioning that makes depression a major contributor to the global burden of disease [34]. An explanatory model study in Goa, India shows that the idioms of distress among women with depression are primarily somatic, and help-seeking for these complaints was common [31]. As such, health promotion programmes which focus on functioning and somatic symptoms should consider incorporating elements of psychological counselling. For women whose disability is caused by depression, counselling directly addresses the underlying cause. For women whose disability is triggering depression symptoms, counselling confers coping skills which are likely to improve the prognosis for resolving the source of the disability.

Suicide is the second leading cause of death among young women in India [35], and in this study 5.9% of women reported suicidal ideation in the past 12 months. Commentators in India have hypothesized that suicide is not strongly linked with mental health issues in the general population [36–38]. However, limited data are available about suicide or its risk factors among perinatal women in India. Though here a statistical association was present between depression and suicidality, depression would be a poor predictor of suicide: among women who expressed suicidal ideation 72% screened negative for depression. As mental health services scale up in Madhya Pradesh, service planners must recognize that treating depression is likely to have a partial effect on reducing the level of suicidal ideation. To address the 5.9% of perinatal women who express suicidal ideation, planners must engage beyond the health sector to address determinants of suicidality which are independent of mental health problems, which as noted in a study of completed suicide among young people in South India includes factors like family conflict and domestic violence [39].

In Sehore, high levels of institutional births (81%) and antenatal tetanus vaccination (90%) [19] are evidence that health care across the perinatal period is available and accessible. Thus, using the maternal health infrastructure as a foundation for provision of maternal mental health care is worth considering. However, the quality of care is highly variable across publicly-funded health clinics in India, and inappropriate care could either trigger or exacerbate mental distress. Qualitative research can be used to establish where and from whom affected women prefer to receive depression services, and economic modelling can be used to estimate the cost–benefit associated with different service models.

Strengths of this analysis include the pooling of representative samples of perinatal women recruited from the communities and facilities of Sehore District, and use of a standardized screening tool. We must note some limitations of this study. As a secondary analysis of two parent studies, we had to use proxy measures which do not necessarily perfectly capture the underlying construct (i.e. housing quality for socioeconomic status, and parity for gender preference), and may explain the absence of associations. And as a sub-group analysis from a larger sample, our statistical power to detect weaker associations was limited by sample size, particularly for the change in care seeking and for change in clinical detection. In addition, the proportion of women who screen positive on the binary PHQ9 measure should be interpreted with caution. As with any screening tool, misclassification is likely. We mitigated against this misclassification by also analysing the PHQ9 score as a continuous measure. Even for the continuous measure, the Cronbach’s alpha of 0.67 is a potential concern, highlighting the need to adapt a Hindi-specific screening tool for depression. Yet it is worth noting the strong association of the PHQ9 with WHODAS, which is evidence of concurrent validity. Another important limitation concerns the use of the voting registration list as a sampling frame for the Community Study. A 2018 report from the Election Commission of India [40] reveals that 61% of eligible voters in Madhya Pradesh state were registered, and that factors like pregnancy, lactation and disability make women less likely to be registered. As such, we certainly underestimated the proportion of depression screen positives in the Community Study. Further, some of the associations observed here may not be generalizable to women who were not registered to vote, as they were systematically different from voters.

This study highlights the key risk groups for perinatal depression—i.e. age 18–22 years, pregnant, higher disability, facility attendee—for whom mental health care should be made a priority. A recent trial demonstrated that, with training and supervision, lay health workers can deliver a manualized and adapted version of cognitive behavioural therapy to reduce depression symptoms among non-pregnant primary care attendees in India [41]. A follow up trial aims to evaluate whether peer health volunteers can do the same among pregnant women [42]; a positive result will be an impetus to promote lay counselling as a means of achieving the goals of new Mental Health Policy and Mental Healthcare Act. With adequate training and supervision, nurses and ASHA workers, who are the mainstay of national maternal and child health program in India, can be capacitated to screen vulnerable mothers and provide psychosocial interventions as indicated. In the recently published Disease Control Priorities Network, integration of mental health services into the maternal healthcare platform is recommended as being evidence-based and cost-effective [43]. Epidemiological evidence generated by this study, as well as new evidence on peer-delivered interventions for perinatal depression, must be utilized by policy-makers to prioritize mental health services for mothers along with maternal and child health services.