[Spatial-temporal analysis on the human immunodeficiency virus/acquired immunodeficiency syndrome among permanent residence and migrants in Shanghai, 2005-2015]

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

Background

The Human Immunodeficiency Virus(HIV) infection prevalence in Cameroon has decreased from \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$5.28\%$$\end{document}5.28% in 2004 to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2.8\%$$\end{document}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 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MI=0.84$$\end{document}MI=0.84, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p-value < 0.001$$\end{document}p-value<0.001), 2011 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MI=0.80$$\end{document}MI=0.80, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p-value < 0.001$$\end{document}p-value<0.001) and 2018 (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MI=0.87$$\end{document}MI=0.87, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p-value < 0.001$$\end{document}p-value<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.

The associations of population mobility in HIV disease severity and mortality rate in China

Background

Human immunodeficiency virus (HIV) infection has become a chronic disease and attracted public attention globally. Population migration was considered hindering the control and management of HIV infection, but limited studies have explored how population mobility could influence the development of HIV-related complications and overall prognosis.

Methods

We enrolled hospitalized HIV patients in this cross-sectional study between January 1, 2006, and December 31, 2016. We extracted demographic, hospitalization, and patient diagnosis data. Patients were divided into three groups according to the population type: (I) resident of Guangzhou City (local resident); (II) migrant outside of Guangzhou City but within Guangdong Province (migrant within the province); and (III) migrant outside of Guangdong Province (migrant outside the province). To represent the prognosis of HIV, in-hospital death was defined as the worst outcome, whereas vision-related events were considered moderate-to-severe complications. Logistic regression models were used to analyze factors influencing the prognosis of HIV.

Results

Eight thousand and six hundred thirty-one inpatients (14,954 cases) were enrolled. The overall mortality was 7.9%, decreasing from 21.5% in 2006 to 3.8% in 2016. The prevalence of vision-related events was 14.4% between 2015 and 2016. Compared to local patients, migrant patients (within and outside the province) were younger, had significantly less access to health insurance, fewer hospitalization admissions, longer hospital stays, and a higher proportion of physical work (P<0.01). Furthermore, they had a higher prevalence of vision-related events (16.2% and 17.4% in migrant patients within the province and outside the province, respectively vs. 9.5%) and infectious diseases, but lower in-hospital mortality (5.9% and 7.0% vs. 12.3%) than local patients. Migrants correlated negatively with in-hospital death [odds ratio (OR) 95% CI, 0.37 (0.29–0.48) and 0.52 (0.40–0.68)] but correlated positively with vision-related events [OR (95% CI), 2.08 (1.54–2.80) and 2.03 (1.47–2.80)].

Conclusions

Migrant patients have significantly poorer access to health insurance, with an increased risk of developing moderate-to-severe HIV infection but a decreased risk of in-hospital death, indicating a trend toward withdrawing treatment in migrant patients when they have advanced diseases. Managements such as optimizing access to health insurance and improving follow-up visits for HIV infection should be considered in the context of the population mobility of HIV patients.