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Introduction The Sustainable Development Goals framed an unprecedented commitment to achieve global convergence in child and maternal mortality rates through 2030. To meet those targets, essential health services must be scaled via integration with strengthened health systems. This is especially urgent in Madagascar, the country with the lowest level of financing for health in the world. Here, we present an interim evaluation of the first 2 years of a district-level health system strengthening (HSS) initiative in rural Madagascar, using estimates of intervention coverage and mortality rates from a district-wide longitudinal cohort. Methods We carried out a district representative household survey at baseline of the HSS intervention in over 1500 households in Ifanadiana district. The first follow-up was after the first 2 years of the initiative. For each survey, we estimated maternal, newborn and child health (MNCH) coverage, healthcare inequalities and child mortality rates both in the initial intervention catchment area and in the rest of the district. We evaluated changes between the two areas through difference-in-differences analyses. We estimated annual changes in health centre per capita utilisation from 2013 to 2016. Results The intervention was associated with 19.1% and 36.4% decreases in under-five and neonatal mortality, respectively, although these were not statistically significant. The composite coverage index (a summary measure of MNCH coverage) increased by 30.1%, with a notable 63% increase in deliveries in health facilities. Improvements in coverage were substantially larger in the HSS catchment area and led to an overall reduction in healthcare inequalities. Health centre utilisation rates in the catchment tripled for most types of care during the study period. Conclusion At the earliest stages of an HSS intervention, the rapid improvements observed for Ifanadiana add to preliminary evidence supporting the untapped and poorly understood potential of integrated HSS interventions on population health.
Ifanadiana is a rural health district of approximately 200 000 people located in the region of Vatovavy-Fitovinany in Southeastern Madagascar. As per MoH norms, Ifanadiana district has one reference hospital, one primary care health centre for each of its 13 communes (subdivision of a district with ~15 000 people) and two community health workers (CHW) for each of its 195 fokontany (subdivision of a commune with ~1000 population). In 2014, our baseline survey revealed that maternal and under-five mortality rates in Ifanadiana were 1044 per 100 000 and 145 per 1000, respectively (more than twice the national mortality estimates).31 The district population relied mostly on agriculture as the primary activity (84.8%), and nearly three-quarters lived in extreme poverty.31 Poverty, geographical barriers and unreliable health services were associated with limited access to healthcare in the district, which was substantially lower than average estimates for Madagascar.31 32 The integrated HSS intervention carried out by the PIVOT-MoH partnership was guided by existing MoH policies (summarised in table 1), covers all six building blocks and is implemented across all three levels of care in the district (community, health centre and hospital). This intervention is structured through the integration of horizontal improvements in system ‘readiness’, vertically aligned clinical programmes and information systems. Readiness includes infrastructure and sanitation, staffing and equipment to improve the quality of care; procurement systems; an ambulance network; the removal of user fees and provision of social support to patients; and trainings and frequent supervision of health staff. The clinical programmes included malnutrition and integrated management of child illness (IMCI) through strengthened community health programmes, primary healthcare centres and hospital (details can be found in ref 30). The information systems included facility-based registries, routine monitoring and evaluation systems and population-based household surveys. The core activities in the first 2 years covered approximately one-third of the population of Ifanadiana (referred to as ‘PIVOT catchment’), with some activities such as medical staff recruitments spanning the whole district (table 1). Summary of HSS interventions implemented in Ifanadiana district between 2014 and 2016, classified by building block of HSS* affected. These included the PIVOT intervention at all three levels of care in its initial catchment area, the PAUSENS project at the primary care level and the Mikolo project at the community health level. Details of each intervention are provided in the main text and online supplementary appendix S1 *Building blocks of HSS: (1) service delivery; (2) health workforce; (3) health information systems; (4) medicines and supplies; (5) financing; (6) leadership and governance. †Exceptionally, these two interventions only happened in RoD, since PIVOT substituted the medicine provision and financial incentives to CHWs. CHW, community health worker; HMIS, health management information systems; HSS, health system strengthening; IMCI, integrated management of child illness; MNCH, maternal, newborn and child health; MoH, Ministry of Health; RoD, rest of the district. bmjgh-2018-000762supp001.pdf In addition to PIVOT’s intervention, the population of Ifanadiana benefited from two other notable programmes that covered both the PIVOT catchment and the rest of the district (RoD). First, the PAUSENS project, funded by the World Bank, provided a basic package of services free of charge in all 13 major health centres for every woman attending the health centre for antenatal, delivery or postnatal care (first 6 weeks) and children under age 5 with any illness.33 The project also included trainings, support for child vaccination in remote areas and some donations of equipment and furniture to health centres. Second, the Mikolo project, funded by the US Agency for International Development, provided support to a network of 150 CHWs in the remote Fokotany (further than 5 km from a health centre) of eight communes in Ifanadiana, four of which were in the PIVOT catchment and four in RoD. The project organised annual trainings and periodic supervision, provided some equipment, supplies and an initial stock of medicines to each CHW. All the interventions are explained in detail in the online supplementary appendix S1. The main difference between PIVOT catchment and RoD (our control group) was the implementation of the PIVOT HSS intervention. A longitudinal cohort study was designed to obtain demographic, health and socioeconomic information from a representative sample of 1600 households in Ifanadiana district over time. Questionnaires were adapted from the DHS,34 with additional questions from other internationally validated surveys such as the Multiple Indicator Cluster Survey.35 Data collection, survey coordination, training and oversight were carried out by the Madagascar National Institute of Statistics (INSTAT), which implements all major national health surveys in the country. A two-stage sample stratified by PIVOT’s initial catchment area and the RoD was used to estimate indicators for each of the intervention and non-intervention areas, as well as Ifanadiana district as a whole. Eighty clusters, half from each stratum, were selected at random from enumeration areas mapped for the 2009 census. Individual households were then mapped within each cluster, and 20 households were selected at random from each cluster. Between April and May of 2014, individual face-to-face interviews were conducted with all women aged 15–49 years and men aged 15–59 years (usual residents or visitors) in 1522 of the sampled households (95.1% response rate). The original 1600 households were revisited between August and September 2016; any missing or refused households were replaced with others from the same cluster using a predefined random replacement list. Overall, 1514 households were interviewed during the follow-up survey (94.6% response rate). All residents, including children, had weight and height measured (or length in the case of infants). Data collected in the questionnaires included, among others, household composition (size, genders, ages); indicators of socioeconomic status (education, employment, household durable assets); illness in the last 30 days; preventive behaviours (bed net ownership, access to water and sanitation); women’s reproductive history and care-seeking behaviours for reproductive health; children’s health, development and care-seeking for illness; and adult, maternal and child mortality. French and Malagasy questionnaires used in the cohort, as well as data collection protocols, had been standardised and validated for Madagascar during previous national surveys carried out by INSTAT. The study was approved by the Madagascar National Ethics Committee and Harvard Medical School IRB. All adults (≥15 years) provided verbal consent for the in-person interview and anthropometric measurements. Parents or guardians provided consent for children ≤5 years of age. INSTAT provided survey data to the investigators with all individual identifiers removed and with geographical information at the cluster level. Spatial boundaries of each cluster were made available to the investigators and are stored separately; this information will not be published or shared publicly. Further details on data collection and survey design are available in ref 31. For the period of January 2013 to December 2016, we obtained data from the MoH for all 13 primary care public health centres (CSB2) in Ifanadiana district on the number of new individuals per month attending each health centre for outpatient consultations or maternal care. These data were available from the health centres’ monthly activity report to the district (“revue mensuelle d’activité”, RMA), which is aggregated from the health centres’ registries every month by MoH staff. We excluded the six basic health centres (CSB1) in the district because these lack medical doctors and provide a more limited number of health services. As a component of the HSS intervention, data quality was maintained through joint MoH-PIVOT supervision of monitoring and evaluation carried out every 3 months at a subset of health centres to compare RMA values with registry data (see ref 32 for more details). From May 2015, the MoH changed the estimation and reporting of outpatient utilisation rates. Thus, we gathered all subsequent utilisation data directly from the registries to ensure consistent estimates throughout the 2013–2016 period. Information on total catchment population was obtained from official MoH records. Consistent with MoH estimates, catchment population of children under-five, expected number of pregnant women and expected number of deliveries were set at 18%, 4.5% and 4% of the total catchment population, respectively. Since official population estimates could be inaccurate, we also estimated catchment populations using available data from WorldPop36; we run additional analyses of per capita utilisation rates based on those data (see details in the online supplementary appendix S2). Population-level under-five mortality was estimated using the synthetic life table method for DHS surveys.37 Under-five mortality was defined as the probability of death before age 60 months per 1000 children born alive. For each survey wave, we used information from the 5 years prior to the survey, which comprised a sample of 4063 children for 2014, and 4037 children for 2016. In addition to these cross-sectional estimates, the observed difference in under-five mortality for the 1446 children followed up over the 2 years was also assessed in each area. From this longitudinal analysis, incidence rates of death per person-year in under-five children were calculated using Poisson regression. Coverage indicators (see list in table 3) were estimated strictly using standard definitions for DHS surveys.37 Vaccination coverage was defined as the proportion of children aged 12–23 months who received all recommended vaccines (three doses of polio and DTP, one dose of BCG and measles). Access to treatment was estimated as the proportion of children under 5 years of age who were ill with either fever, acute respiratory infection (ARI) or diarrhoea in the 2 weeks prior to the survey and sought medical treatment (at a hospital, health centre or CHW). In order to measure the effect of PIVOT’s interventions on maternal health service coverage, indicators were estimated for the last pregnancy during the last 2 years. In addition, to track a summary indicator of maternal, newborn and child health (MNCH) intervention coverage, we estimated a modified version of the composite coverage index (CCI)38 that included all standard interventions except for family planning, which was not available in the 2014 survey (see online supplementary appendix S2 for details in the estimation of CCI). Changes in health system coverage in PIVOT initial catchment and the rest of Ifanadiana district (RoD) between 2014 and 2016 *P<0.05; **P<0.01; ***P<0.001; ‡ P<0.1 †Modified CCI, does not include family planning. ARI, acute respiratory infection; CCI, composite coverage index; MNCH, maternal, newborn and child health; RoD, rest of the district. Coverage and mortality indicators were calculated for 2014 and 2016 both within PIVOT’s initial catchment area and for the RoD. Differences in coverage between PIVOT catchment and the RoD for each year were tested through Pearson Χ2 tests, adjusted by second-order Rao & Scott approximation.39 Absolute and relative trends were estimated for the 2014–2016 period and difference-in-differences (DiD) analyses were conducted to evaluate the statistical difference in the trends between the two areas.40 A separate analysis was carried out controlling for household wealth and proximity to the main (paved) road to explore whether these changed DiD estimates (online supplementary appendix S2 and table S1). Data were entered into CSPro and all analyses were done using R statistical software V.3.1.2,41 with the exception of population-level mortality rates and associated 95% CIs, which were calculated with SAS V.9.3, and the observed under-five mortality incidence rate ratios, which were calculated with Stata V.13 (College Station, TX). Sampling weights that adjusted for unequal probability of selection due to stratification and non-response were calculated for household, women’s and men’s surveys. All estimates were done using survey commands available in R package survey and applicable sampling weights.39 To assess trends in economic inequality for these coverage indicators, wealth indices were calculated for the population of Ifanadiana following standard DHS methods.37 Briefly, household physical assets were included in a principal component analysis and then the scores from the first principal component were used. Households were classified into five wealth quintiles, with cut-off points at 20%, 40%, 60% and 80% of the cumulative wealth distribution. The first two quantiles (poorest) and the last three (wealthier) were grouped together. For each indicator, we estimated wealth-specific averages in the intervention area as well as composite indicators of inequality, such as relative concentration index (RCI) and slope index of inequality (SII).42 The RCI is a measure of relative inequality based on the concentration curve, a plot of the cumulative distribution of each coverage indicator (y-axis) in the population ranked by wealth (x-axis) and adjusted by survey weights.42 The RCI is defined as twice the area between the line of equality (45° diagonal line) and the indicator’s concentration curve, and was calculated using R package decomp.43 The SII is a measure of absolute inequality that represents the difference in coverage between the highest and the lowest values of the wealth rank (normalised between 0 and 1). It was estimated at the individual level as the slope of the health outcome on the individual wealth ranks in a logistic regression, adjusted by survey weights.42 44 Using health centre data, we calculated the annual average in per capita utilisation rates for maternal care (antenatal care, first and fourth visits, deliveries and postnatal care) and outpatient care (all patients and children under-five). We estimated annual changes during the 2013–2016 period for health centres inside and outside of PIVOT’s initial catchment area using linear regression models with an interaction term and described as: where Yij is the average per capita utilisation rate for yeari and catchmentj (inside or outside); β1 is the yearly change outside the catchment area, β2 is the baseline difference in utilisation between catchments, and β3 is the yearly change associated exclusively with the intervention (p values for β3 coefficient are reported). Equation 1 is a standard DiD formulation, equivalent to the analyses carried out for survey data.
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