valentineghanem-bit/ntd-wash-poverty-nexus-ghana

Spatial epidemiology of NTDs and the WASH-poverty nexus in Ghana (261 districts, 2021-2022) # Spatial epidemiology of neglected tropical diseases and the WASH–poverty nexus in Ghana **Ecological receptivity mapping and an interpretable machine-learning model across 261 districts**  Reporting: STROBE + RECORD + TRIPOD+AI  ·  License: MIT  ·  ORCID 0009-0002-8332-0220 --- ## 1. Overview This repository contains the full, reproducible analysis behind an ecological study of the water, sanitation and hygiene (WASH)–poverty nexus underlying neglected tropical diseases (NTDs) across **all 261 districts of Ghana**. Using the 2021 Ghana Population and Housing Census (district-level poverty, literacy, insurance; 261 districts) linked to the 2022 Ghana Demographic and Health Survey (region-level WASH; 16 regions) and WHO-GHO/ESPEN national NTD data, it builds a district **NTD WASH–Poverty Receptivity Index (NWPRI)**, characterises its spatial structure, and tests whether socioeconomic determinants predict documented NTD endemicity — the socioeconomic layer absent from prior Ghana NTD risk mapping. The design is explicitly **ecological** and methodologically candid: *receptivity* denotes conditions conducive to NTD transmission, not observed incidence; WASH is region-resolved and broadcast to districts, so district poverty/literacy anchor inference. **Author:** Valentine Golden Ghanem · Ghana COCOBOD Cocoa Clinic, Accra, Ghana · ORCID 0009-0002-8332-0220. ## 2. Key findings - District deprivation was strongly spatially clustered (**Global Moran's I = 0.66, z = 15.9, p = 0.001**), with **31 High-High LISA receptivity hotspots** forming a contiguous northern savannah block. - A marked **North–South gradient**: northern-belt districts averaged 44.7% poverty, 70.6% open defecation and 49.0% literacy versus 23.6%, 9.3% and 76.5% in the south. - **100% of High-High hotspots fell within the documented onchocerciasis/trachoma poverty-belt** versus 5% of Low-Low districts — the receptivity surface recovers NTD geography from socioeconomic data …