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. 2023 Apr 19;23(1):270.
doi: 10.1186/s12884-023-05586-6.

Spatial heterogeneity of low-birthweight deliveries on the Kenyan coast

Moses M Musau  1 , Stella Mwakio  2 , David Amadi  2 , Amek Nyaguara  2 , Philip Bejon  2   3 , James A Berkley  2   4 , Robert W Snow  1   3 , Alice Kamau  5
Affiliations

Spatial heterogeneity of low-birthweight deliveries on the Kenyan coast

Moses M Musau et al. BMC Pregnancy Childbirth. .

Abstract

Background: Understanding spatial variations in health outcomes is a fundamental component in the design of effective, efficient public health strategies. Here we analyse the spatial heterogeneity of low birthweight (LBW) hospital deliveries from a demographic surveillance site on the Kenyan coast.

Methods: A secondary data analysis on singleton livebirths that occurred between 2011 and 2021 within the rural areas of the Kilifi Health and demographic surveillance system (KHDSS) was undertaken. Individual-level data was aggregated at enumeration zone (EZ) and sub-location level to estimate the incidence of LBW adjusted for accessibility index using the Gravity model. Finally, spatial variations in LBW were assessed using Martin Kulldorf's spatial scan statistic under Discrete Poisson distribution.

Results: Access adjusted LBW incidence was estimated as 87 per 1,000 person years in the under 1 population (95% CI: 80, 97) at the sub-location level similar to EZ. The adjusted incidence ranged from 35 to 159 per 1,000 person years in the under 1 population at sub-location level. There were six significant clusters identified at sub-location level and 17 at EZ level using the spatial scan statistic.

Conclusions: LBW is a significant health risk on the Kenya coast, possibly under-estimated from previous health information systems, and the risk of LBW is not homogenously distributed across areas served by the County hospital.

Keywords: Accessibility; Kenya; Kilifi; Low birthweight; Spatial heterogeneity.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The inclusion and exclusion criteria for deliveries as KCH. The population in the Kilifi Urban area is characterised by temporary (transient) residents thus presenting a challenge in establishing whether the permanent residence of this population is within the KHDSS and was thus excluded. In comparison to singleton births, multiple births are associated with increased odds of LBW [32]. Therefore, multiple births were excluded to remove bias towards LBW outcome. Still born babies have increased chances of being born with LBW [33]. Therefore, their inclusion would introduce bias in the data. Health workers’ strikes occurred in Mar 2012, Dec 2012, Dec-2016 to Feb-2017, Jun-2017 to Nov-2017, Dec-2020 to Mar-2021
Fig. 2
Fig. 2
The distribution of the unadjusted and adjusted LBW incidence with the red shade showing higher incidence and yellow shade showing lower incidence. The unadjusted incidence represents the LBW events that occurred at KCH while the adjusted incidence represents LBW events that have been corrected for underestimation due to variable geographical access to health care. A the distribution of unadjusted LBW incidence at sub-location level. B distribution of adjusted LBW incidence at sub-location level. C distribution of unadjusted LBW incidence at EZ level. D distribution of adjusted LBW incidence at EZ level
Fig. 3
Fig. 3
The distribution of significant high incidence clusters (hotspots) identified using the SaTScan software. A shows clusters obtained at sub-location level with 1 representing the primary clusters and 2 – 6 are the secondary clusters. B shows clusters obtained at EZ level where 1 indicates the primary clusters and 2 – 17 are the secondary clusters
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