The gap in maternal health outcomes, access and utilization between the haves and have-nots continues to be a challenge globally despite improvements over the past decade. Though Ghana has experienced steady gains in maternal health access and utilization over the years, maternal out-comes, on the other hand, remain poor. In this regard, it is essential to know how various groups in the population achieved improvements and whether some women continue to be disproportionately disadvan-taged. The paper performs an analysis of cross-sectional data from the 2017 Ghana maternal health survey to examine the exis-tence of the inverse care law in maternal health services in Ghana. Using descriptive techniques and multivariate logistic regression models the study reveals a pro-rich and pro-urban gradient in the use of hospital facilities for delivery and antenatal care attendance — also, regions known for their high levels of poverty feature significantly lower rates of hospital deliveries. The paper concludes by stressing that unless policies are changed to accommodate these groups, overall gains in maternal health will continue to be incremental.
The paper conducts a quantitative crosssectional analysis. The analysis combines both descriptive charts and table with an estimation of a binary regression models using the nationally representative 2017 Ghana maternal health survey data (Figure 1). The National maternal health survey in Ghana was undertaken by the Ghana Statistical Service (GSS) and the Ghana Health Service (GHS) and is a nationally representative sample of households.3 The survey targets women of reproductive age specifically; age 15 to 49 years in the sampled household.3A previous maternal health survey had been undertaken in 2007 making this the second in a decade. The survey is designed to provide data and monitor maternal health outcomes and utilization of maternal health services. The survey is representative at the subnational levels also provides reproductive health indicators and distribution of maternal health services. The survey adopted a two-stage sample strategy leveraging an updated sampling frame from the 2010 Population and Housing Census. Out of the 27,000 households selected for sampling, interviews were conducted in 26,324 arriving at a final household response rate of 99% disaggregated at 99.1% in urban areas and 99.6% in rural areas. The response rate for eligible women was also 99%.3 The analysis examines two dichotomous variables; place of delivery coded as 1 for delivery in a hospital or clinic and 0 for otherwise, and ANC utilization variable is coded as – 1 reflecting 4 or more ANC visits and 0 – less than 4. These are selected based on theoretical and empirical evidence of their importance in ensuring positive pregnancy outcomes and improvement in their national coverage.3,30 Explanatory and control variables examined include; the highest level of education attained, wealth quintiles, mother’s level of education, region, and location of residence (rural or urban), age, total pregnancies and health insurance. The maternal health survey similar to the Demographic Health Surveys does not provide income or expenditure data as measures of socioeconomic welfare. The survey therefore uses a relative asset index to derive a wealth index as a measure for welfare.31 This index is derived using Principal Component Analysis (PCA) (Filmer & Pritchett, 2001) technique on asset variables provided in the survey. This wealth index categories households within which women live into five, from the richest to the poorest. As already indicated poverty in Ghana varies by geospatial location, rural areas record higher levels of poverty as urban areas, and specific administrative regions are known for higher levels of poverty. Table 1 presents a summary of measures used. Statistical analysis for the paper was conducted using Stata version 12.1 from StataCorp. To examine the relationship between socioeconomic variables and maternal health utilization we examine two dichotomous variables; place of delivery and use of ANC services as explained variables. Two binary logistic models are estimated as these are models of choice when the dependent variable of interest is discrete and binary variable. Inclusion criteria. Summary of variables /measures used in the analysis. A summary of the selected variables are presented after which bivariate descriptions of the explained variable with selected measures of socioeconomic status are conducted and reported. Finally, a multivariate logistic model is fitted for the explained variables and explanatory variables. To account for sample design and obtain estimates of population parameters of interest survey settings and weights were applied for all analysis.
N/A
DIMA AI Care
