Estimating Spatial Inequalities of Urban Child Mortality

Overview

Journal Demogr Res

Specialty Public Health

Date 2014 Jan 31

PMID 24478594

Citations 8

Authors

Marta M Jankowska

Magdalena Benza

John R Weeks

Affiliations

Soon will be listed here.

Abstract

Background: Recent studies indicate that the traditional rural-urban dichotomy pointing to cities as places of better health in the developing world can be complicated by poverty differentials. Knowledge of spatial patterns is essential to understanding the processes that link individual demographic outcomes to characteristics of a place. A significant limitation, however, is the lack of spatial data and methods that offer flexibility in data inputs.

Objective: This paper tackles some of the issues in calculating intra-urban child mortality by combining multiple data sets in Accra, Ghana and applying a new method developed by Rajaratnam et al. (2010) that efficiently uses summary birth histories for creating local-level measures of under-five child mortality (5q0). Intra-urban 5q0 rates are then compared with characteristics of the environment that may be linked to child mortality.

Methods: Rates of child mortality are calculated for 16 urban zones within Accra for birth cohorts from 1987 to 2006. Estimates are compared to calculated 5q0 rates from full birth histories. 5q0 estimates are then related to zone measures of slum characteristics, housing quality, health facilities, and vegetation using a simple trendline R analysis.

Results: Results suggest the potential value of the Rajaratnam et al. method at the micro-spatial scale. Estimated rates indicate that there is variability in child mortality between zones, with a spread of up to 50 deaths per 1,000 births. Furthermore, there is evidence that child mortality is connected to environmental factors such as housing quality, slum-like conditions, and neighborhood levels of vegetation.

Citing Articles

Spatial spillover impact of determinants on child mortality in Pakistan: evidence from Spatial Durbin Model.

Sheikh M, Khan S, Ahmed M, Ahmad R, Abbas A, Ullah I BMC Public Health. 2023; 23(1):1612.

PMID: 37612693 PMC: 10464234. DOI: 10.1186/s12889-023-16526-6.

Quantifying within-city inequalities in child mortality across neighbourhoods in Accra, Ghana: a Bayesian spatial analysis.

Bixby H, Bennett J, Bawah A, Arku R, Annim S, Anum J BMJ Open. 2022; 12(1):e054030.

PMID: 35027422 PMC: 8762100. DOI: 10.1136/bmjopen-2021-054030.

Leave no child behind: Using data from 1.7 million children from 67 developing countries to measure inequality within and between groups of births and to identify left behind populations.

Ramos A, Flores M, Weiss R PLoS One. 2020; 15(10):e0238847.

PMID: 33052926 PMC: 7556530. DOI: 10.1371/journal.pone.0238847.

Progress in Spatial Demography.

Matthews S, Parker D Demogr Res. 2020; 28:271-312.

PMID: 32863759 PMC: 7454172. DOI: 10.4054/demres.2013.28.10.

Sub national variation and inequalities in under-five mortality in Kenya since 1965.

Macharia P, Giorgi E, Thuranira P, Joseph N, Sartorius B, Snow R BMC Public Health. 2019; 19(1):146.

PMID: 30717714 PMC: 6360661. DOI: 10.1186/s12889-019-6474-1.

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