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In Guinea, around 17 new cases of HIV occurred each day and it was responsible for 10 deaths a day in 2022. In addition to this burden, regional disparities have emerged over the years. This study aimed to describe and explain the uneven distribution of HIV infection in Guinea using spatial analysis.
This is a retrospective cross-sectional secondary analysis using data from the 2012 and 2018 Guinea Demographic and Health Survey (DHS).
This study was conducted in Guinea.
We conducted a secondary analysis of data from 300 and 400 enumeration areas, respectively, included in the 2012 and 2018 DHS Program for participants aged 15 to 49 who underwent HIV testing. Spatial analysis methods, including Moran I, interpolation and Kulldorff’s scan statistic, were applied to examine variation and identify high-risk spatial clusters of HIV prevalence rate. The potential relationship between HIV status and socio-demographic, biological, behavioural and socio-environmental explanatory variables was explored using logistic regression at individual level.
In total, 7922 individuals in 2012 and 8539 in 2018 participated in the study. HIV prevalence rate in 2012 and 2018 was 1.9% and 1.5%, respectively. Across Guinea’s 33 prefectures, HIV prevalence rate varied from 0% to 3.9% in 2012 and from 0% to 3.5% in 2018. Spatial analysis identified four significant high-risk spatial clusters in 2012 and one high-risk cluster in 2018. The high-risk clusters in 2012 were in Kissidougou (relative risk (RR)=3.97; p value=0.037), Matam (RR=2.80; p value=0.019), Pita (RR=3.46; p value=0.035) and N’zerekore prefectures (RR=6.08; p value=0.027), the high-risk cluster in 2018 was located in Boffa prefecture (RR=3.95; p value=0.022). Factors significantly and positively associated with HIV infection in 2012 included age class 25–34 (aOR: 2.20; 95% CI 1.40 to 3.47), age class 35–49 (aOR: 2.43; 95% CI 1.51 to 3.92), number of HIV healthcare facilities>30 (aOR: 2.14; 95% CI 1.34 to 3.43). HIV infection was significantly lower in men (aOR: 0.52; 95% CI 0.35 to 0.77). In 2018, in addition to age groups 25–34 years (aOR=1.90; 95% CI 1.18 to 3.04) and 35–49 years (aOR=2.25; 95% CI 1.40 to 3.64), the Soussou ethnicity group (aOR=1.73; 95% CI 1.04 to 2.87) was also positively associated with HIV infection.
This study describes the spatial distribution of HIV prevalence rate and identified high-risk clusters in Guinea. In addition, risk factors associated with HIV status were identified. The information can help prioritise surveillance and response efforts to control HIV in Guinea.
To explore the relationship between social determinants of health and adherence to lifestyle recommendations, and how these determinants can help explain contextual and interpersonal factors contributing to adherence among individuals with prediabetes.
Explanatory sequential mixed methods study integrating a cross-sectional quantitative analysis with an ethnomethodological qualitative approach grounded in critical social paradigm.
The quantitative phase used data from the intervention arm (n = 86) of the PREDIPHONE trial, a randomised controlled study evaluating the effectiveness of a nurse-led telephone intervention for lifestyle changes in glycaemic control. Adherence was measured using a composite index, analysed as both a continuous and categorical variable. Correlation analysis examined adherence and age. Chi-square and ANOVA tests were used to analyse differences in participant characteristics across adherence quartiles. The qualitative phase included individual semi-structured interviews and a focus group with participants showing high or low adherence. Thematic content and discourse analysis were employed, ensuring validity through triangulation, reflexivity and discourse saturation.
Employment status was identified as a significant factor, with unemployed or retired participants showing better adherence. Although no statistical differences in adherence were found by social class or gender, lower social class participants reported financial barriers to healthy eating and time constraints limiting physical activity (PA). Women reported facing greater challenges due to caregiving responsibilities, whereas men benefited from household support.
Employment status emerged as a determinant of time availability for self-care, alongside social class and gender in adherence to lifestyle modifications. Women, especially those from lower social classes, experienced heightened barriers to adherence, underscoring the need for tailored, gender-sensitive and equity-focused interventions.
Addressing social determinants is essential for effective lifestyle advice among individuals with prediabetes.
The study highlights the role of social class and gender in adherence.
STROBE and COREQ guidelines.
Through interviews and focus group.
FreshRSS 