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Exposure to conflicts and the continuum of maternal healthcare: Analyses of pooled cross-sectional data for 452,192 women across 49 countries and 82 surveys

['Anu Rammohan', 'Department Of Economics', 'University Of Western Australia', 'Perth', 'Astghik Mavisakalyan', 'Bankwest Curtin Economics Centre', 'Curtin University', 'Loan Vu', 'Vietnam National University Of Forestry', 'Hanoi']

Date: 2021-10

This study showed that conflict exposure is statistically significantly and negatively associated with utilization of maternal CoC services, in each component of the CoC scale. These findings have highlighted the challenges in achieving the Sustainable Development Goal 3 in conflict settings, and the need for more concerted efforts in ensuring CoC, to mitigate its negative implications on maternal and child health.

We combined data from 2 sources, the Demographic Health Surveys (DHS) and the Uppsala Conflict Data Program’s (UCDP) Georeferenced Event Dataset, for a sample of 452,192 women across 49 countries observed over the period 1997 to 2018. We utilized 2 consistent measures of conflict—incidence and intensity—and analyzed their association with maternal CoC in 4 key components: (i) at least 1 antenatal care (ANC) visit; (ii) 4 or more ANC visits; (iii) 4 or more ANC visits and institutional delivery; and (iv) 4 or more ANC visits, institutional delivery, and receipt of postnatal care (PNC) either for the mother or the child within 48 hours after birth. To identify the association between conflict exposure and components of CoC, we estimated binary logistic regressions, controlling for a large set of individual and household-level characteristics and year-of-survey and country/province fixed-effects. This empirical setup allows us to draw comparisons among observationally similar women residing in the same locality, thereby mitigating the concerns over unobserved heterogeneity. Around 39.6% (95% CI: 39.5% to 39.7%) of the sample was exposed to some form of violent conflict at the time of their pregnancy during the study period (2003 to 2018). Although access to services decreased for each additional component of CoC in maternal healthcare for all women, the dropout rate was significantly higher among women who have been exposed to conflict, relative to those who have not had such exposure. From logistic regression estimates, we observed that relative to those without exposure to conflict, the odds of utilization of each of the components of CoC was lower among those women who were exposed to at least 1 violent conflict. We estimated odds ratios of 0.86 (95% CI: 0.82 to 0.91, p < 0.001) for at least 1 ANC; 0.95 (95% CI: 0.91 to 0.98, p < 0.005) for 4 or more ANC; and 0.92 (95% CI: 0.89 to 0.96, p < 0.001) for 4 or more ANC and institutional delivery. We showed that both the incidence of exposure to conflict as well as its intensity have profound negative implications for CoC. Study limitations include the following: (1) We could not extend the CoC scale beyond PNC due to inconsistent definitions and the lack of availability of data for all 49 countries across time. (2) The measure of conflict intensity used in this study is based on the number of deaths due to the absence of information on other types of conflict-related harms.

Violent conflicts are observed in many parts of the world and have profound impacts on the lives of exposed individuals. The limited evidence available from specific country or region contexts suggest that conflict exposure may reduce health service utilization and have adverse affects on health. This study focused on identifying the association between conflict exposure and continuum of care (CoC) services that are crucial for achieving improvements in reproductive, maternal, newborn, and child health and nutrition (RMNCHN).

Data Availability: The authors confirm that all data underlying the findings are fully available without restriction. All data used in this study are owned by multiple third-party stakeholders, including Uppasala Conflict Data Program, Uppsala University, and the DHS programme, USAID. The data is made publicly available without restriction at the time of publication from below mentioned public data repositories: • Uppsala Conflict Data Program (UCDP). Uppsala Conflict Data https://ucdp.uu.se/downloads/https://www.pcr.uu.se/research/ucdp/ . • DHS M. Demographic and health surveys. Calverton: Measure DHS. 2021 Oct 4. https://dhsprogram.com/data/available-datasets.cfm

Copyright: © 2021 Rammohan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Previous research, in particular from the BRANCH consortium case studies, have demonstrated that conflict impacts negatively on maternal and child health, child birth weight, and immunization rates [ 15 – 24 ]. However, a majority of these studies are country and region specific in their scope, and their findings are context specific. A comprehensive and methodologically robust assessment of the association between conflict and a range of maternal healthcare services, particularly from a CoC perspective, is critically needed. In this paper, we empirically estimated the relationship between exposure to conflict and utilization of care through the continuum in a large sample of developing countries.

However, while these studies have examined the role of socioeconomic and demographic characteristics on CoC, to the best of our knowledge, the influence of conflict exposure on uptake of CoC services has not been previously analyzed in a multicountry context. Conflicts have the potential to adversely affect the provision of critical maternal and newborn services, both directly and indirectly. Fig 1 presents a Logic model of the potential pathways of the influence of conflicts on CoC. By disrupting health infrastructure, mobility of health workers, and availability and accessibility of healthcare provisions, conflict exposure may negatively affect the utilization of care throughout the continuum. Also, conflict-led fear and security concerns, availability of transport facilities, wage loss, and injury or death of family members or neighbors also influence health-seeking behavior [ 14 – 24 ].

Recent research has examined the CoC concept to understand maternal and healthcare usage [ 4 , 11 , 12 ] and the role of trained community health workers in improving the utilization of health services across the continuum [ 13 ]. In particular, Yeji and colleagues [ 12 ] study found that the CoC completion in Ghana was only 8% in their sample, with the greatest decline being between delivery and postnatal care (PNC) within 48 hours postpartum. Similarly, using nationally representative data from Pakistan, Iqbal and colleagues [ 4 ] showed that there was an increase in CoC completion rates from 15% to 27% over the period 2006 to 2012, and CoC was higher among women with better education, autonomy, and socioeconomic backgrounds. Agarwal and colleagues [ 13 ] found that exposure to the Accredited Social Health Activist (ASHA) program in India improved the utilization of CoC services.

In the last 15 years, the concept of continuum of care (hereafter CoC) has emerged as an important guiding principle targeting improvements in reproductive, maternal, newborn, and child health and nutrition (RMNCHN) [ 1 – 4 ]. In a systematic review of nearly 1,000 studies, Bhutta and colleagues found strong links between reproductive, maternal, newborn, and child health indicators, thus emphasizing the significance of CoC in RMNCHN for the prevention of maternal and childhood mortalities [ 5 ]. The CoC concept originally proposed in 1978 by Tanahashi [ 6 ] refers to a key package of integrated maternal, newborn, and child health services from pregnancy and delivery to the postnatal period, which are critical for gains in maternal and child survival. It has 2 components—the first is a life cycle approach, which includes adolescence, pregnancy, childbirth, postnatal period, and childhood. The second important component of CoC relates to the location of care and may include (a) household and community care; (b) outpatient and outreach services; and (c) hospital and health facilities. The CoC concept is particularly critical for newborn and maternal health in developing countries. Despite significant progress globally in maternal and newborn child health outcomes, maternal and neonatal mortality remain unacceptably high in many developing countries [ 7 – 11 ].

Methods

Data sources Our analysis combined data from 2 key sources, conflict data from the Uppsala Conflict Data Program (UCDP) and data on CoC from the Demographic Health Surveys (DHS) [25,26]. Data on conflict were obtained from UCDP’s Georeferenced Event Dataset version 19.1, which included information on the dates, locations, and the number of deaths associated with violent conflict around the world in the period from 1989 to 2018 [27]. Data on the components of CoC in maternal healthcare came from the DHS—a collection of nationally representative repeated cross-sectional surveys conducted in over 90 developing countries since 1984. The DHS interviewed women of childbearing age (15 to 49 years) using a standard questionnaire across all countries and included detailed questions on the socioeconomic and demographic characteristics of surveyed women and their households, the birth histories of all children born in the 5 years before the survey, and information relating to the use of healthcare services. The DHS provides georeferenced information on the residential location of households, including the names and GPS coordinates in more recent survey rounds [26]. We developed a detailed procedure to combine the data on violent conflicts with data on maternal healthcare utilization and other individual- and household-level socioeconomic and demographic characteristics across the spatial and temporal domains. In the spatial domain, we utilized the names of provinces at the first administrative level that are available in both datasets. These are the names of the provinces where a conflict took place in the UCDP data and the names of the provinces of residence in the DHS dataset. If there are differences in the names of provinces across the 2 datasets, we manually reconciled these differences for merging purposes. In total, there are 523 different provinces across the 49 countries in our sample. We did not utilize the data on latitude/longitude positions in the datasets for 2 reasons. Firstly, the GPS data are available for a smaller sample of DHS countries—only 33 out of 49 countries have datasets with these variables. We chose not to compromise on the number of countries represented in the sample, given that the focus of the current study is on providing large multicountry evidence on the link between conflict exposure and its influence on continuum of maternal healthcare. Secondly, to safeguard the respondent’s confidentiality, the DHS randomly display the GPS latitude/longitude positions up to 2 kilometers in urban areas and 5 kilometers in rural areas. As a result, we were not able to accurately capture the respondent’s location of residence. In the temporal domain, we utilized information on the start and end year of the conflict in UCDP, alongside information on the month of birth of the last child born in the 5 years prior to the interview date in the DHS to determine the year of their conception. We focused on the last child because complete information on the continuum of maternal healthcare within the DHS was only elicited with reference to the last-born child. We assumed that a child was conceived in the year before their birth year if they were born between January and August, otherwise the year of conception coincides with the year of birth. For each woman in each province, we assigned the information on conflict events that took place from the year of conception of the last child to the year of the interview. The oldest child in our sample was conceived in 1997, and, accordingly, our analysis covers the history of conflict dating back to 1997. This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 Table). However, this study did not have a prespecified analysis plan.

Explanatory variables The main explanatory variables in our analysis are measures of exposure to violent conflict during pregnancy in the lifetime pregnancy history of women. We have determined whether women are exposed to conflict events by matching the time of pregnancy from pregnancy history and the time of occurrence of conflict events at the provincial level as described in detail above. We used information on different types of conflict in UCDP to construct our explanatory variables of interest. UCDP defines a violent event as “an individual incident of lethal violence occurring at a given time and place” [27]. The dataset covered 3 types of violent conflict. They are (i) Type 1, which featured the involvement of government as one of the 2 parties; (ii) Type 2, which involved the use of armed force between groups either of which is the government; and (iii) Type 3, which featured the use of armed force by any party against civilians. The formal definitions of these 3 types of violence are presented in Table 1. PPT PowerPoint slide

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TIFF original image Download: Table 1. Conflict variables and definitions. https://doi.org/10.1371/journal.pmed.1003690.t001 To explore whether conflicts affected CoC in maternal healthcare, we constructed 2 binary measures of conflict exposure taking into account all 3 types of conflict. The first variable, “conflict exposure,” takes on a value of 1 if a woman has been exposed to at least 1 violent event of any type since conception of her child to the time she is observed during the interview, 0 otherwise. Based on this measure, nearly 40% of women in the sample were exposed to conflicts (Table 2). PPT PowerPoint slide

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TIFF original image Download: Table 2. Descriptive statistics of the study variables. https://doi.org/10.1371/journal.pmed.1003690.t002 Next, we captured the intensity of conflict by employing a measure of the average number of deaths per year of conflict exposure, named “exposure intensity.” This measure is estimated by dividing the total number of deaths that have occurred within the period between the earliest and the latest violent events observed in a woman’s pregnancy history by the number of years in that period. It is skewed toward 0 with 62% of the sample not been exposure to a violent event that resulted in positive deaths (Table 2). Thus, it is unlikely that the relationship between this measure and our outcome measures is linear. With this consideration in mind, as a first step, we used categorical measures of conflict intensity defined as a set of 4 dummy variables, distinguishing between women without exposure to a violent event that resulted in positive deaths (omitted category) and those across 3 different terciles of nonzero conflict-led deaths distribution (Conflict_intensity0, Conflict_intensity1, Conflict_intensity2, Conflict_intensity3). Approximately 38% of women in the sample had been exposed to a conflict that resulted in deaths throughout their pregnancy (Table 2). As a robustness check, we used an alternative measure of conflict exposure intensity, employing the square root of the number of deaths (in thousands) per year of conflict exposure. This approach to defining the conflict exposure intensity measure followed Leone and colleagues [22] and mitigates the common concerns around categorizing continuous variables such as increases of type 1 and type 2 errors. The other explanatory variables used in our analyses include the key characteristics of women, their children, and the household. These include child characteristics such as the child’s sex, birth order, whether the child was part of multiple births, and maternal characteristics including age, age at first birth, age at first cohabitation, and education level. The household’s economic status was defined using the wealth index quintiles that were available in the DHS dataset. The wealth index is a composite measure of a household’s cumulative living standard. Using a consistent methodology for all countries, the DHS calculated the wealth index using easy-to-collect data on a household’s ownership of selected assets, types of water access, and sanitation facilities. The DHS recently introduced methodology making use of household sampling weights stratified by rural/urban on a pooled sample and aggregate them for the national level [29]. Accordingly, the wealth index in all the prior datasets were updated to reflect this. We additionally controlled for household size, rural/urban residence, household head’s age and sex, and exposure to mass media (newspaper, radio, or TV). We included variables to capture the geographical heterogeneity within the sample by including the proportion of households in the poorest wealth quintile at the provincial level, as well as country and year-of-survey fixed effects.

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