The Lancet. Global health · 2026
Spatial distribution of noma incidence in Nigeria, 1999-2024: a modelling study.
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Braimah Ramat Oyebunmi, Taiwo Abdurrazaq Olanrewaju, Bello Seidu, Bala Mujtaba, Butali Azeez, Ile-Ogedengbe Bruno Oludare, Bello Abubakar Abdullahi, Fatusi Olawunmi Adedoyin, Aborisade Adetayo Oluwole, Adeoye John
Аннотация
BACKGROUND: Noma (cancrum oris) is a severe, rapidly progressing and necrotising neglected tropical disease affecting the mouth and face. The risk of noma incidence and geographical distribution at small administrative divisions remain poorly characterised. This study aimed to model the spatial distribution of noma incidence in Nigeria and identify high-incidence risk areas for targeted community actions.
METHODS: This study used data from the only noma specialist centre in northern Nigeria (Noma Children's Hospital; Sokoto, Nigeria) to estimate and map areal incidence risk. We used data from 911 patients (with complete case data) presenting with acute noma (stage 2-3) at the study centre from Sept 17, 1999, to Oct 31, 2024. Incidence was calculated dynamically at the third-level administrative divisions (local government areas [LGAs]) using the WHO Oral Health Unit's method. Smoothed standardised incidence ratios (SIRs) were modelled using a zero-inflated Poisson hierarchical Bayesian approach that accounted for spatial and non-spatial effects. Choropleth maps of incidence risk were generated, and spatial autocorrelation analyses were conducted to assess clustering and variation in noma incidence.
FINDINGS: We estimated that there were 50 782 (95% credible interval [CrI] 50 341-51 226) incident noma cases among 296 LGAs across 12 states. All states had at least one LGA with a significantly higher noma incidence risk than the regional average. Notably, more areas in Sokoto (20 [87·0%] of 23), Zamfara (12 [85·7%] of 14), and Kebbi (13 [61·9%] of 21) had significantly elevated median SIRs. Illela (SIR 17·23 [95% CrI 16·56-17·93]), Wamako (13·84 [13·29-14·41]), Goronyo (11·60 [11·10-12·12]), and Tangaza (11·03 [10·43-11·68]) in Sokoto and Bade (12·19 [11·60-12·80]) in Yobe had the highest noma incidence risks. Spatial autocorrelation analysis revealed clustering of high noma incidence involving 23 LGAs in northwest Nigeria. Marked differences in areal incidence risks were also observed by sex, age group, and time period.
INTERPRETATION: Administrative units identified as having high incidence risk should be prioritised for individual and community actions that promote prevention and early detection to reduce the burden of noma in Nigeria.
FUNDING: Research Output Prize, University Research Committee, University of Hong Kong (HKU), and HKU Knowledge Exchange Impact Projects Scheme.
METHODS: This study used data from the only noma specialist centre in northern Nigeria (Noma Children's Hospital; Sokoto, Nigeria) to estimate and map areal incidence risk. We used data from 911 patients (with complete case data) presenting with acute noma (stage 2-3) at the study centre from Sept 17, 1999, to Oct 31, 2024. Incidence was calculated dynamically at the third-level administrative divisions (local government areas [LGAs]) using the WHO Oral Health Unit's method. Smoothed standardised incidence ratios (SIRs) were modelled using a zero-inflated Poisson hierarchical Bayesian approach that accounted for spatial and non-spatial effects. Choropleth maps of incidence risk were generated, and spatial autocorrelation analyses were conducted to assess clustering and variation in noma incidence.
FINDINGS: We estimated that there were 50 782 (95% credible interval [CrI] 50 341-51 226) incident noma cases among 296 LGAs across 12 states. All states had at least one LGA with a significantly higher noma incidence risk than the regional average. Notably, more areas in Sokoto (20 [87·0%] of 23), Zamfara (12 [85·7%] of 14), and Kebbi (13 [61·9%] of 21) had significantly elevated median SIRs. Illela (SIR 17·23 [95% CrI 16·56-17·93]), Wamako (13·84 [13·29-14·41]), Goronyo (11·60 [11·10-12·12]), and Tangaza (11·03 [10·43-11·68]) in Sokoto and Bade (12·19 [11·60-12·80]) in Yobe had the highest noma incidence risks. Spatial autocorrelation analysis revealed clustering of high noma incidence involving 23 LGAs in northwest Nigeria. Marked differences in areal incidence risks were also observed by sex, age group, and time period.
INTERPRETATION: Administrative units identified as having high incidence risk should be prioritised for individual and community actions that promote prevention and early detection to reduce the burden of noma in Nigeria.
FUNDING: Research Output Prize, University Research Committee, University of Hong Kong (HKU), and HKU Knowledge Exchange Impact Projects Scheme.