This section is divided into two segments; in the first segment, we understand the percentage of drop-outs across different stages of CMHS, and in the second segment, estimated results of the MNLM is elucidated.
Table 1 shows that the percentage of women seeking complete care was only 16.82%, whereas around 73% sought partial care, and 10.6% of the respondents did not utilise any care. Around 56.05% of pregnant women married in the age group of 18–24, while 37.24% married before attaining the legal age of marriage. Nearly 70% of the women had 1–2 children. The majority (67.34%) of women had access to at least one medium of information. Among all the pregnant women, 46.58% availed of secondary education, around 28% were illiterate. The percentage of women with higher education was only 12.03%. The mean household size of a family was 6.4. Around 23.82% of pregnant women belonged to the poorest quintile, followed by poor (21.27%), middle (19.96%), rich (18.83%) and richest quintile population (16.12%), respectively. Caste-wise differentials demonstrated that most women belonged to Other Backward Caste (46.02%). The percentage of women belonging to the Hindu religion was 81%. Only 44% of Indian women had control over the household’s financial decisions. Region-wise disaggregation discerned that around 54% of women were from high-focussed states and 43% hailed from non-high focussed grouped states. Only 3.16% of the sample population belonged to north-eastern states. The percentage of women residing in rural areas was 71.60%, while only 28.40% lived in urban areas. During the first ANC visits, only 41% of pregnant women contacted an Anganwadi worker or Accredited Social Health Activist (ASHA).
Weakest and strongest link in the CMHS pathway
Figure 3 illustrates the entire mechanism of the care-seeking pathway. We tried to capture the sequential pattern of maternal healthcare- seeking behaviour by underpinning different stages of dropouts along with the determinants of CMHS.
Astonishingly, around 80% of the pregnant women availed at least one or more ANC visits, but only 20.80% of them sought adequate ANC services. The remaining 79% dropped out without availing adequate ANC services, thereby making it the weakest link in the continuum of care pathway. Of the adequate ANC users, most of the pregnant women availed SBA services (95%), making it the most vital link to the CMHS pathway. Among those who utilised adequate ANC and SBA, around 88.3% sought PNC care. We also noticed that the percentage of women dropping in the third stage was around 11.6%.
Drop-outs in the continuum of care pathway: across states
Figure 4a-d depicts the state-wise estimation of the utilisation of CMHS and dropouts at the first, second and third stages of the care seeking pathway. Figure 4a indicates that CMHS utilisation was highest in Kerala (68.09), followed by other southern states. States belonging to western ghats performed better than other states. Worst performance was recorded by the states belonging to Gangetic plains and Himalayan terrain.
The first stage of dropout indicates those women who registered for ANC in the first trimester but did not avail of adequate ANC services in India. State-wise variations are elucidated in Fig. 4b. We found the differences across states to be quite stark. For instance, in Nagaland, around 98% of women dropped out from the first stage of CMHS. Uttar Pradesh, Bihar, Jharkhand, Rajasthan, Uttarakhand and Madhya Pradesh demonstrate a worrying pattern. Similarly, the other northern and northeastern states witnessed very high dropout rates in the first stage of CMHS. The state of Kerala (29%) recorded the lowest dropout rates, the number being high at the same time.
The Second stage of dropout (Fig. 4c) indicates the percentage of women availing ANC services but forgoing SBA is relatively lower, making it the strongest linkage in the continuum of care pathway. State-wise estimation presented below indicates negligible variations.
Figure 4d depicts the third stage of dropout across the states of India. The proportion of women who undertook ANC and SBA but did not avail PNC. It ranges between 0.45 and 8.43%. It was also found that the dropout rates in this stage are comparatively higher in southern parts of India. The lowest dropout rates were witnessed in the North-eastern region.
Predictors of CMHS in India
Empirical estimation based on the Multinomial logistic regression model (MNLM) propounded by Mc Fadden (1984) revealed that a myriad of factors determines CMHS utilisation. We disaggregated these factors into household level, individual-level, community level, health-related factors and access-related barriers. We estimated two separate models to gather maximum insights on all possible alternatives. In the first model, no care was used as the base outcome, while in the second, incomplete care was chosen as the base outcome. The results of MNLM are presented in Table 2. We have used the terms – complete care/ full care and partial care/incomplete care interchangeably.
We found that women with higher education levels as compared to their illiterate counterparts have a higher likelihood to seek complete care over no care (RRR: 3.61, CI: 2.95–4.41), incomplete care over no care (RRR: 2.33, CI: 1.93–2.81) and complete care over incomplete care (RRR: 1.55, CI: 1.43–1.68). Women having access to at least one medium of information are more likely to gravitate towards complete care over no care (RRR: 2.06, CI: 1.90–2.24) and complete care over incomplete care (RRR: 1.51, CI: 1.42–1.60). Furthermore, in contrast to women of age less than 18 years at the time of marriage, women whose age was more than 18 years are more likely to seek complete maternal healthcare services over no/partial care category.
The impact of an interaction between wealth index and residence discerned that the relative risk ratio (RRR) of complete care over no care (RRR: 7.70, CI: 4.65–12.73), complete over incomplete care (RRR: 1.47, CI: 1.32–1.65) and incomplete care over no care (RRR: 5.23, CI: 3.19–8.58) is highest among the richest quintile women residing in urban areas as compared to the poorest ones. Compared to the poorest women residing in urban areas, the richest from rural areas is more likely to avail maternal health care across all three categories- highlighting the relevance of financial status of women in determining the utilisation of maternal health care services. Financial autonomy is also associated with a greater likelihood to receive complete care over incomplete care (RRR: 1.34, RRR: 1.15–1.57) and full care over no care (RRR: 1.03, CI: 1.01–1.66).
With an increase in the household size by one member, the RRR of utilising complete care over no care (RRR: 0.94, CI: 0.93–0.95), complete care over incomplete care (RRR: 0.97, CI: 0.96–0.98) and incomplete care over no care (RRR: 0.97, CI: 0.96–0.98) reduces. For all the three categories, a woman having contact with community health workers like ASHA, Anganwadi or Multipurpose Health Worker (MPW) has a higher RRR than those who did not contact CHWs. Similarly, participation of husband in care-seeking procedure increases the likelihood to seek complete care over no care (RRR: 2.33, CI: 2.16–2.51), incomplete care over no care (RRR: 1.52, CI: 1.44–1.61) and complete care over incomplete care (RRR: 1.53, CI: 1.45–1.62).
We found that a woman belonging to ST communities is more likely to go for complete care over no care (RRR: 1.15, CI: 1.04–1.28), complete care over incomplete care (RRR: 1.15, CI: 1.08–1.24) and incomplete care over no care (RRR: 1.02, CI: 0.92–1.09). Similarly, a woman belonging to the OBC community is more likely to seek complete care over no care, incomplete care over no care and complete care over incomplete care. It is found that Muslim women have a lower odds of utilising CMHS vis-à-vis the Hindu women.
A woman hailing from northeastern states and major high-focussed grouped states were found to have a lower odds of utilising complete care over no care, incomplete care over no care and complete care over incomplete care. Finally, an increase in the community’s education status is associated with a higher odds of seeking complete care over no care, complete care over incomplete care and incomplete care over no care. Affordability and availability barriers were found to reduce the likelihood to seek complete care over no care. It was also found that compared to acceptability barriers, the prevalence of affordability and availability issues are more responsible for drop-outs in the utilisation of CMHS. Finally, the influence of MPW was significant for the utilisation of complete care over no care and also for utilising complete care over incomplete care.
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