Elsevier

Social Science & Medicine

Volume 93, September 2013, Pages 147-154
Social Science & Medicine

Travel time to maternity care and its effect on utilization in rural Ghana: A multilevel analysis

https://doi.org/10.1016/j.socscimed.2013.06.012Get rights and content

Highlights

  • We investigate the relationship between travel time and maternity care utilization.

  • Using a logistic model, we find that travel time is a predictor of maternity care.

  • Reducing travel time by one hour increases the odds of in-facility delivery by 24%.

  • Improving geographic access in Ghana could help improve maternal and child health.

Abstract

Rates of neonatal and maternal mortality are high in Ghana. In-facility delivery and other maternal services could reduce this burden, yet utilization rates of key maternal services are relatively low, especially in rural areas. We tested a theoretical implication that travel time negatively affects the use of in-facility delivery and other maternal services. Empirically, we used geospatial techniques to estimate travel times between populations and health facilities. To account for uncertainty in Ghana Demographic and Health Survey cluster locations, we adopted a novel approach of treating the location selection as an imputation problem. We estimated a multilevel random-intercept logistic regression model. For rural households, we found that travel time had a significant effect on the likelihood of in-facility delivery and antenatal care visits, holding constant education, wealth, maternal age, facility capacity, female autonomy, and the season of birth. In contrast, a facility's capacity to provide sophisticated maternity care had no detectable effect on utilization. As the Ghanaian health network expands, our results suggest that increasing the availability of basic obstetric services and improving transport infrastructure may be important interventions.

Introduction

Ghana faces high burdens from maternal and child mortality. In 2008, its maternal mortality rate was 409 deaths per 100,000 women. This rate was below the West African regional average of 629 deaths per 100,000 live births in 2008, but exceeded the global average of 251 (Rajaratnam et al., 2010). Its neonatal mortality rate was estimated at 28.1 per 1000 live births, accounting for over a third of all child deaths in the country (Hogan et al., 2010).

Ghana has made significant progress in recent years. From 1990 to 2008, child mortality decreased by 2% per year and maternal mortality decreased by 1.4% per year. Despite this progress, the burden of childbirth complications could be further reduced by increasing rates of in-facility delivery (IFD) (Campbell et al., 2006, Lawn et al., 2005). In rural Ghana, approximately 60% of births occur in the home, away from facilities equipped to deal with complications (Ghana Statistical Service, Ghana Health Service, & ICF Macro, 2009). Given the established benefits of IFD, understanding the determinants of its use (or lack thereof) is a highly relevant research question that could lead to improvements in maternal and neonatal health.

Distance has been recognized as a determinant of utilization, especially in rural areas (Gabrysch and Campbell, 2009, Gage, 2007, Hodgkin, 1996, The White Ribbon Alliance for Safe Motherhood, 2011). Descriptive statistics from the 2008 Ghana Demographic and Health Survey (GDHS) support this conclusion: 33% of rural mothers cite distance as the factor for not seeking birth services, more than any other reason (Ghana Statistical Service et al., 2009). Previous studies have analyzed the effect of distance, but few have quantified the effect of travel time on utilization. Travel time encompasses not only distance, but also the mode and difficulty of travel. It is a methodological advancement over matching facilities and their surrounding population using straight-line distance (Alegana et al., 2012, Astell-Burt et al., 2011, Lovett et al., 2002, Tanser et al., 2006). Relative to distance alone, travel time better reflects the decision-making process to utilize IFD.

Our analysis contributes to a small but growing body of work that estimates the effect of travel time on service utilization (Alegana et al., 2012, Astell-Burt et al., 2011, Lovett et al., 2002, Tanser et al., 2006). Lovett et al. (2002) calculate travel times to health facilities in East Anglia, United Kingdom, but their study is limited to a descriptive analysis. Tanser et al. (2006) calculate travel times to health facilities and estimate their effect on utilization in KwaZulu-Natal, South Africa. They find higher travel times have a significantly negative effect on utilization. Astell-Burt et al. (2011) explore whether higher travel times to primary health care centers are associated with lower rates of hepatitis C detection in France. They also find a significantly negative effect. Finally, Alegana et al. (2012) model the effect of travel time on seeking treatment for fever among children under the age of 5 years in Namibia. They find the probability of facility attendance remains relatively high for up to three hours of travel time, but decreases steadily thereafter.

Given our covariates and methodological strategy, our analysis resembles Gabrysch, Cousens, Cox, and Campbell (2011), who estimate the influence of distance and level of care on IFD in Zambia. In our analysis, we introduce two consequential improvements over their strategy. First, we employ travel time as an independent variable, rather than straight-line distance. Second, uncertainty exists in the precise location of GDHS households because the geographic coordinates are scrambled (MEASURE DHS, 2011). We address this uncertainty by treating the location selection as an imputation problem. To our knowledge, this study is the first analysis to seriously account for that uncertainty rather than ignore it.

Section snippets

Theoretical motivation

Throughout sub-Saharan Africa, pregnant women perceive that benefits exist from utilizing IFD (Kruk and Prescott, 2012, Magadi et al., 2007). This value is derived from many factors, one of which is the capacity of the facility to provide multiple types of services (Parkhurst et al., 2005). However, utilizing IFD is costly. The precise costs vary across settings, but they may include user fees and the time required to travel to the facility, among other factors (Nyonator & Kutzin, 1999).

Data

To generate estimates of travel times to facilities within Ghana, we divided the country's territory into gridded cells of 1 square kilometer. We utilized two data sources to calculate the travel times from each cell to each facility: the first dataset reports facility locations, and the second dataset reports geographic information about each cell, which we used to reflect the difficulty of traveling between cells. To generate regional-level descriptive statistics, we used disaggregated

Methods

To test our theoretical implication, we employed a multilevel random-intercept logistic regression model. This model explicitly accounted for the hierarchical and nested structure of the data (Rabe-Hesketh & Skrondal, 2012). In our data, women were nested within households, and households were nested within communities. This clustering suggested that women in the same households are very likely to have similar socioeconomic and community-level characteristics (including travel time). Therefore,

Results

Results from our population-level analysis (which is independent of the GDHS data) are reported in Fig. 1 and Table 2. Fig. 1 illustrates district-level travel times to the nearest facility. Table 2 reports regional-level population estimates and travel times to the nearest facility. Reported values are the population-weighted average of cells in that administrative unit, assuming vehicular travel on roads. In metropolitan areas such as Accra and Kumasi, average travel times were relatively low

Conclusion

Geographical features, the architecture of the road network, and road quality can substantially modify the ease of travel in many locations. Consequently, the travel times in this analysis represented a more realistic measure of health-facility accessibility than Euclidean distance. The factors influencing a woman's utilization of IFD services are complex, but our results suggest travel times affect behavior. Due to the stated effect of travel time on behavior, policy evaluations should account

Acknowledgments

We thank Andy Tatem for his help and guidance during the creation of the friction surface and cost path analysis. We thank the Ghana Health Service and UNICEF Ghana for providing the EMOC needs assessment dataset. We thank Kelsey Moore and other members of the IHME team for their insight and contributions. This research was supported by the Institute for Health Metrics and Evaluation's core funding from the Bill & Melinda Gates Foundation.

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