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

Geographic information system‐based evaluation of spatial accessibility to maternal health facilities in Siaya County, Kenya
Geographic information system‐based evaluation of spatial accessibility to maternal health facilities in Siaya County, Kenya
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.
1745-5863
286-298
Ouko, Jacob Joseph Ochieng
63b1a640-81ce-4e2e-886f-f64229e558ce
Gachari, Moses Karoki
398d79fa-c506-4d3e-99c1-b973b9dbc9f3
Sichangi, Arthur Wafula
6f8211c3-ea4c-4cce-a142-0f1199f9760c
Alegana, Victor
f5bd6ab7-459e-4122-984f-2bdb5f906d82
Ouko, Jacob Joseph Ochieng
63b1a640-81ce-4e2e-886f-f64229e558ce
Gachari, Moses Karoki
398d79fa-c506-4d3e-99c1-b973b9dbc9f3
Sichangi, Arthur Wafula
6f8211c3-ea4c-4cce-a142-0f1199f9760c
Alegana, Victor
f5bd6ab7-459e-4122-984f-2bdb5f906d82

Ouko, Jacob Joseph Ochieng, Gachari, Moses Karoki, Sichangi, Arthur Wafula and Alegana, Victor (2019) Geographic information system‐based evaluation of spatial accessibility to maternal health facilities in Siaya County, Kenya. Geographical Research, 57 (3), 286-298. (doi:10.1111/1745-5871.12339).

Record type: Article

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.

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More information

Accepted/In Press date: 8 February 2019
e-pub ahead of print date: 3 July 2019
Published date: August 2019

Identifiers

Local EPrints ID: 434865
URI: http://eprints.soton.ac.uk/id/eprint/434865
ISSN: 1745-5863
PURE UUID: 5ad0dcb2-0050-466c-beb3-b681b5589549
ORCID for Victor Alegana: ORCID iD orcid.org/0000-0001-5177-9227

Catalogue record

Date deposited: 11 Oct 2019 16:30
Last modified: 26 Nov 2021 03:03

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Contributors

Author: Jacob Joseph Ochieng Ouko
Author: Moses Karoki Gachari
Author: Arthur Wafula Sichangi
Author: Victor Alegana ORCID iD

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