Abstract
BACKGROUND: Uganda has a high, spatially heterogeneous burden of sickle cell disease (SCD) with national screening indicating sickle cell trait at 13.1 percent and disease at 0.7 percent, concentrated in the northern and eastern regions where Plasmodium falciparum transmission remains intense. Newborn sickle cell screening and follow up care programmes need a robust method to prioritize the selection of screening hubs that aligns sickle cell genetic risk and malaria transmission intensity with travel-time access to health facilities where newborn screening and early sickle cell anaemia (SCA) care can be delivered. METHODS: Sickle haemoglobin (HbS) allele frequency was interpolated from two independent district-level surveys with Empirical Bayesian Kriging (EBK). Malaria endemicity was summarised as the 2015-2024 mean Plasmodium falciparum parasite prevalence in children aged 2-10 years (PfPR(2-10)). Raster surfaces were projected to EPSG 32636, aligned on a 5 km by 5 km grid, and masked to Uganda. A co-risk siting surface was computed as HbS × PfPR(2-10) on which a deterministic greedy selection approach placed 50 candidate hub locations for newborn sickle cell screening by iteratively choosing the maximum and zeroing values within a 25 km service radius. We evaluated equity in how captured co-risk was distributed across districts with a Lorenz curve and Gini coefficient, and also implementability, defined as the extent to which candidate hub locations for newborn sickle cell screening could be hosted at existing functional public health facilities by collocating the candidate hub locations to 60-min travel time catchment areas of public health facilities. RESULTS: National HbS allele frequency had a median of 6.5 percent (IQR 4.8-8.5), and 72.2 percent of pixels exceeded 5.0 percent. The area-weighted mean PfPR(2-10) was 26.0 percent, with 94.4 percent of land in PfPR(2-10) ≥ 30.0 percent. The HbS × PfPR(2-10) co-risk surface concentrated in sub regions of Lango, Acholi and Karamoja in northern Uganda and Teso, Bukedi and Busoga sub regions in eastern Uganda. Greedy placement produced steep early gains: cumulative captured co-risk 19.7, 33.6, 45.2, 57.5, and 65.6 percent after placing 10, 20, 30, 40, and 50 screening hub locations, respectively, and an elbow between 35 and 40 screening hub locations where incremental gains fell below about 2 percentage points. Captured co-risk was highly concentrated across districts (Gini 0.81). Of the 50 candidate screening hubs, 28 (56.0 percent) fell inside 60-min catchment areas and were collocated to specific existing health facilities. CONCLUSIONS: Using a HbS-PfPR(2-10) co-risk surface and a maximal-coverage approach yielded 50 ranked candidate hub locations for newborn sickle cell screening programmes that capture 65.6 percent of baseline co-risk with 28 candidate hubs able to operationalize screening within the existing public health facility infrastructure. The ranked hub list, coverage curve, and district equity summaries offer the Uganda Ministry of Health and partners a transparent way to phase expansion of newborn sickle cell screening and to align new hubs with malaria control activities in the highest-burden districts.