# **3 Methodological framework**

Extending earlier studies (Wang et al., 2017; Gage and Calixte, 2006), we use a mixed method design that combines findings from both quantitative and qualitative methods. The analysis of the quantitative data provides a general overview of factors that influence pregnant women’s decisions regarding institutional care while the qualitative data analysis explores indepth beliefs, perceptions, and attitudes. During the interpretation stage, findings from the econometric model and the behavioral sciences literature are brought together, with the same weight, for a deeper understanding of the interplay of structural and behavioral barriers that prevent women from accessing institutional care (Greene et al., 2001; Cullen et al., 2011). In addition, this design provides an opportunity for triangulation of the findings across methods.

## **3.1 Quantitative research data and analysis**

The quantitative data come from two datasets: the 2017 Demographic and Health Survey (DHS), called EMMUS ( _Enquete Mortalite, Morbidite et Utilisation des Services_ - Survey of Mortality, Morbidity and Service Utilization) in Haiti, and the 2017 Service Provision Assessment (SPA). The DHS dataset consists of a nationally representative sample of households interviewed between November 2016 and April 2017 by the Institut Ha ̈ıtien de l’Enfance with support from Institut Ha ̈ıtien de Statistiques et d’Informatique (IHSI). Besides information on household characteristics and an asset measure of wealth, typically collected in DHS-type surveys, it contains data from an in-depth survey conducted on a sample of 15- to 49-year-old women who answered questions about access to ANC, source, place, and type of assistance received during delivery for each child born within the five years prior to the survey. Surveyed women also answered questions related to decision-making in the household and agency.

In 2017, 14,371 15-49 year old women from 13,405 households were surveyed.<sup>8</sup> In addition to household- and individual-level characteristics, community characteristics can be retrieved using GPS coordinates of the cluster centroid. The DHS website provides access to topographical characteristics for all clusters.<sup>9</sup> The SPA data were collected between December 2017 and May 2018 on all 1,033 health centers across the country (IHE and ICF, 2018). This assessment made an inventory of all the facilities and equipment, surveyed the staff, observed visits for ANC and family planning services, as well as services for sick under-5-year-old children. Women who were observed attending ANC or family planning consultations and the families of sick children were also surveyed. All these survey tools allow the SPA to assess how much the services work and how satisfied service users are. In this paper we use data from facility inventory provided by the facility manager or most knowledgeable person on the infrastructure, supplies, staffing, and routine practices.<sup>10</sup> The quantitative data are analyzed using a multilevel, hierarchical model to investigate the neighborhood and individual effects, while controlling for the clustered nature of the data (Bafumi and Gelman, 2007; Gage and Calixte, 2006). Multilevel models allow one to look at

> 8The DHS uses a two-stage cluster sample design, where clusters are the enumerating areas provided by the IHSI based on a 2011 update of the 2003 population census. The sampling methods are described in IHE and ICF (2018) but in this study, weights are computed to ensure the representativeness of the evidence at the national, urban-rural, and department-levels.

> 9The cluster data are anonymized by displacing the cluster centroid by, on average, 0.8 km for urban clusters (maximum displacement of 2 km), and 2.1 km for rural clusters (maximum displacement of 5 km and of 10 km for 1 percent of rural clusters) from its original location (Wang et al., 2017). This issue was not considered when linking the cluster to all available health centers within a 10 km radius from the cluster centroid from the SPA data (Burgert and Prosnitz, 2014).

> 10Because the SPA data are collected as a census and the level of non-response is small, differences between weighted and unweighted results are minor; the results presented are unweighted.

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