Geographic information system‐based evaluation of spatial accessibility to maternal health facilities in Siaya County, Kenya

Abstract

Maternal mortality is a major problem in middle‐income and low‐income countries, and the availability and accessibility of healthcare facilities offering safe delivery is important in averting maternal deaths. Siaya County, in Kenya, has one of the highest maternal mortality rates in the country—far more than the national average. This study aimed to evaluate geographic access to health facilities offering delivery services in Siaya County. A mixed‐methods approach incorporating geographic information system analysis and individual data from semi‐structured interviews was used to derive travel time maps to facilities using different travel scenarios: AccessMod5 and ArcGIS were used for these tasks. The derived maps were then linked to georeferenced household survey data in a multilevel logistic regression model in R to predict the probability of expectant women delivering in a health facility. Based on the derived travel times, 26 per cent (13,140) and 67 per cent (32,074) of the estimated 46,332 pregnant women could reach any facility within one and two hours, respectively, while walking with the percentage falling to seven per cent (3,415) and 20 per cent (8,845) when considering referral facilities. Motorised transport significantly increased coverage. The findings revealed that the predicted probability of a pregnant woman delivering in a health facility ranged between 0.14 and 0.86. Significant differences existed in access levels with transportation‐based interventions significantly increasing coverage. The derived maps can help health policy planners identify underserved areas and monitor future reductions in inequalities. This work has theoretical implications for conceptualising healthcare accessibility besides advancing the literature on mixed methodologies.

Too poor or too far? Partitioning the variability of hospital-based childbirth by poverty and travel time in Kenya, Malawi, Nigeria and Tanzania

“…First, the estimation of travel time from DHS cluster centroids to the nearest hospital using the MAP friction surface assumes a generalized travel speed by the type of land surface, and does not account for variabilities in temporality, seasonality and transportation used by the individuals. In particular, in rural areas characterized by a high level of poverty, walking and non-motorized vehicles remain the major means of transportation, while adoption of motorized transportation is limited by affordability issues [78][79][80][81]. In contrast, there is a wider range of transportation in urban settings.…”

Travel Time to Health Facilities as a Marker of Geographical Accessibility Across Heterogeneous Land Coverage in Peru

“…Previous studies highlighted the importance of geographical or physical accessibility using a variety of methods (14)(15)(16)(17). The emergence of "Precision Public Health" driven by estimates of a wide range of health indicators at a high spatial resolution is defined as the use of the best available data to target more effectively and efficiently interventions of all kinds to those most in need (18)(19)(20)(21).…”

Assessment of Spatial Accessibility to Residential Care Facilities in 2020 in Guangzhou by Small-Scale Residential Community Data

“…In addition, many previous studies about spatial accessibility including the assessment of the accessibility to RCFs were mainly based on the street (town)-to-district scale [3,12,14,17,18,20,21,[24][25][26][27][28], especially in cities of China. Streets (towns) are one of the administrative divisions smaller than a district in a city and the minimum statistical unit in Census in China.…”

“…To further compare and analyze the difference of the accessibilities to the RCFs between the two scales data in Guangzhou, relative and cumulative errors of the accessibilities are calculated in a similar way to the previous studies [24,25,32]. Assuming for the n residential communities in a street, the accessibilities to the RCFs based on the n residential communities and the street are x i (i = 1, 2 .…”