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

Abstract

ObjectiveCountries in sub-Saharan Africa suffer the highest rates of child mortality worldwide. Urban areas tend to have lower mortality than rural areas, but these comparisons likely mask large within-city inequalities. We aimed to estimate rates of under-five mortality (U5M) at the neighbourhood level for Ghana’s Greater Accra Metropolitan Area (GAMA) and measure the extent of intraurban inequalities.MethodsWe accessed data on >700 000 women aged 25–49 years living in GAMA using the most recent Ghana census (2010). We summarised counts of child births and deaths by five-year age group of women and neighbourhood (n=406) and applied indirect demographic methods to convert the summaries to yearly probabilities of death before age five years. We fitted a Bayesian spatiotemporal model to the neighbourhood U5M probabilities to obtain estimates for the year 2010 and examined their correlations with indicators of neighbourhood living and socioeconomic conditions.ResultsU5M varied almost five-fold across neighbourhoods in GAMA in 2010, ranging from 28 (95% credible interval (CrI) 8 to 63) to 138 (95% CrI 111 to 167) deaths per 1000 live births. U5M was highest in neighbourhoods of the central urban core and industrial areas, with an average of 95 deaths per 1000 live births across these neighbourhoods. Peri-urban neighbourhoods performed better, on average, but rates varied more across neighbourhoods compared with neighbourhoods in the central urban areas. U5M was negatively correlated with multiple indicators of improved living and socioeconomic conditions among peri-urban neighbourhoods. Among urban neighbourhoods, correlations with these factors were weaker or, in some cases, reversed, including with median household consumption and women’s schooling.ConclusionReducing child mortality in high-burden urban neighbourhoods in GAMA, where a substantial portion of the urban population resides, should be prioritised as part of continued efforts to meet the Sustainable Development Goal national target of less than 25 deaths per 1000 live births.

Spatial heterogeneity in drinking water sources in the Greater Accra Metropolitan Area (GAMA), Ghana

“…GAMA is the largest urban agglomeration in the country and accounts for almost a quarter of the national GDP (Gaisie et al, 2019). It has become a laboratory for studying urban growth, resource securities, and other concomitant socio-environmental and demographic issues within the recent past (see Aliu et al, 2021;Bixby et al, 2022;Dapaah & Harris, 2017;Gaisie et al, 2019) and the current study adds to this burgeoning literature by telling a different story about the heterogeneity of GAMA's water landscape at the enumeration area level. Previous studies relied on sample survey data of selected neighborhoods of GAMA and were not comprehensive enough to identify fine-scale patterns (Ablo & Yekple, 2018;Asante-Wusu & Yeboah, 2020;Benneh et al, 1993;Songsore & McGranahan, 1993;Stoler et al, 2012b).…”

Evaluating the prerequisites for adapting a paediatric nighttime telemedicine and medication delivery service to a setting with high malarial burden: A cross‐sectional pre‐implementation study

“…Men are enrolled in greater proportions than women [26]. Jamestown and Usshertown are located in the central urban core of Accra where, despite the presence of multiple hospitals and clinics, U5M is an average of 95 deaths per 1000 live births, nearly double the national average [27]. Nkenkaasu covers an area of ~2.5 km 2 with a population of ~20,000 [24].…”

Strangers in a strange land: Mapping household and neighbourhood associations with improved wellbeing outcomes in Accra, Ghana

“…Since the GLSS does not sample the unhoused nor school-, hospital-, or prison-based populations, these are not included in this analysis. These household and area consumption metrics have been used in analyses elsewhere ( Alli et al, 2023 ; Bixby et al, 2022 ; Clark et al, 2022 ; Tetteh et al, 2022 ). For this paper, we use these consumption estimates to determine the number of households that fall into three SES categories within each EA in Accra: households in the bottom 20 th percentile (poor), 21 st -79 th percentiles (middle class), or the upper 20 th percentile (affluent).…”