Hot-spots of HIV infection in Cameroon: a spatial analysis based on Demographic and Health Surveys data

Abstract

Background The Human Immunodeficiency Virus(HIV) infection prevalence in Cameroon has decreased from $$5.28\%$$ 5.28 % in 2004 to $$2.8\%$$ 2.8 % in 2018. However, this decrease in prevalence does not show disparities especially in terms of spatial or geographical pattern. Efficient control and fight against HIV infection may require targeting hotspot areas. This study aims at presenting a cartography of HIV infection situation in Cameroon using the 2004, 2011 and 2018 Demographic and Health Survey data, and investigating whether there exist spatial patterns of the disease, may help to detect hot-spots. Methods HIV biomarkers data and Global Positioning System (GPS) location data were obtained from the Cameroon 2004, 2011, and 2018 Demographic and Health Survey (DHS) after an approved request from the MEASURES Demographic and Health Survey Program. HIV prevalence was estimated for each sampled area. The Moran’s I (MI) test was used to assess spatial autocorrelation. Spatial interpolation was further performed to estimate the prevalence in all surface points. Hot-spots were identified based on Getis–Ord (Gi*) spatial statistics. Data analyses were done in the R software(version 4.1.2), while Arcgis Pro software tools’ were used for all spatial analyses. Results Generally, spatial autocorrelation of HIV infection in Cameroon was observed across the three time periods of 2004 ($$MI=0.84$$ M I = 0.84 , $$p-value < 0.001$$ p - v a l u e < 0.001 ), 2011 ($$MI=0.80$$ M I = 0.80 , $$p-value < 0.001$$ p - v a l u e < 0.001 ) and 2018 ($$MI=0.87$$ M I = 0.87 , $$p-value < 0.001$$ p - v a l u e < 0.001 ). Subdivisions in which one could find persistent hot-spots for at least two periods including the last period 2018 included: Mbéré, Lom et Djerem, Kadey, Boumba et Ngoko, Haute Sanaga, Nyong et Mfoumou, Nyong et So’o Haut Nyong, Dja et Lobo, Mvila, Vallée du Ntem, Océan, Nyong et Kellé, Sanaga Maritime, Menchum, Dounga Mantung, Boyo, Mezam and Momo. However, Faro et Déo emerged only in 2018 as a subdivision with HIV infection hot-spots. Conclusion Despite the decrease in HIV epidemiology in Cameroon, this study has shown that there are spatial patterns for HIV infection in Cameroon and possible hot-spots have been identified. In its effort to eliminate HIV infection by 2030 in Cameroon, the public health policies may consider these detected HIV hot-spots, while maintaining effective control in other parts of the country.

Distribuição espacial de travestis e mulheres transexuais com teste rápido positivo para sífilis e HIV, participantes do estudo TransOdara - Município de São Paulo, 2020.

“…Na Guatemala, Miller et al (2017) 25 compararam diferentes metodologias de captação de população trans, observando que permitem atingir subgrupos distintos. Sandie et al (2022) 26 em Camarões verificaram que, apesar de uma tendência geral de redução na prevalência de HIV no país, há um padrão espacial, com algumas áreas de marcada concentração 'hot spots', que devem ser priorizadas nas estratégias de controle. Já Wang et al (2022) 27 estudaram as características espaciais e temporais da incidência de sífilis no noroeste da China, encontrando um significativo aumento no número de casos, com clara concentração em certas áreas.…”

Spatiotemporal Analysis and Risk Assessment Model Research of Diabetes among People over 45 Years Old in China

“…A significant Moran’s I test indicates that there is a presence of spatial autocorrelation, Getis and Ord’s G* could identify the hot or cold spot areas. Identifying hot spots for diseases is important for public health authorities who should adopt them for better-targeted interventions [ 43 ]. To determine the spatial patterns of a disease, local indicators of spatial association (LISA) in the environmental GIS are very helpful.…”

Trend of new cases of Human Immunodeficiency Virus infections in two health facilities in the Northern Cameroon between 2021-2022

“…From data collected, the prevalence is not uniformly distributed across regions. This is related to socio-cultural parameters such as religion, occupation, sexual behavior, neighborhood, traditions or poverty, political security and the associated influx of refugees, or the strength of the health system [8][9][10]. According to a World Bank report, 56% of poverty in Cameroon occurs in the northern regions alone, where Islam is one of the main religions.…”