Socioeconomic Status Profiles using Household Indicators in Eastern Cape: A Latent Class Analysis
1
1 Brody School of Medicine, East Carolina University, Greenville, NC, United States
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2 Department of Public Health, Brody School of Medicine, East Carolina University, Greenville, NC, United States
3
3 The Desmond Tutu HIV Centre, University of Cape Town, Cape Town, South Africa
4
4 School of Public Health, York University, Toronto, Canada
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5 TB-HIV Pathogenesis Unit, Centre for the AIDS Programme of Research in South Africa (CAPRISA), Durban, South Africa
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6 Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
Popul. Med. 2026;8(Supplement Supplement 1):A1356
ABSTRACT
BACKGROUND:
Globally, tuberculosis (TB) is the leading cause of death due to an infectious agent. Little is known about the role of socioeconomic status (SES) in TB care engagement beyond income, education, and employment. Yet household wealth and capital have been proposed as important determinants of TB and HIV care. As part of a large, multi-level study on social determinants of TB care engagement, we sought to classify community households into meaningful SES profiles. Insights from this analysis can be applied to future analyses of TB care engagement.
METHODS:
Households in the catchment area of 18 clinics in Buffalo City Metropolitan Health District, Eastern Cape, South Africa, were selected using a geographic cluster sampling design. Surveys were administered to n=3,869 heads-of-household. Twenty-two questions covered dwelling characteristics (n=7), household items (n=9), and individual ownership (n=6). R statistical software 4.4.1, polCA package, was used for all analyses. The optimal number of classes was chosen using model fit statistics and conceptual salience. Demographic characteristics and TB/HIV prevalence were evaluated for each class using Chi-square tests.
RESULTS:
A six-class model was identified as the most appropriate, comprising two “Higher SES” classes (20% of the study population), three “Middle SES” classes (32% in the Higher-Middle SES class; 35% in the two Lower-Middle SES classes), and one “Lower SES” class (13% of the study population). classes correlated with urban/rural area, education level, employment, and income (all p<0.01), but also proved more informative of SES variability. Household TB prevalence (p=0.06) and household HIV prevalence (p<0.01) varied across classes, most notably for HIV prevalence which decreased as SES profiles moved from lower to higher.
CONCLUSIONS:
Identified classes closely align with traditional SES variables, yet provided more informative profiles that captured SES variability. Future research will evaluate association of these profiles with engagement along the TB care continuum.
Globally, tuberculosis (TB) is the leading cause of death due to an infectious agent. Little is known about the role of socioeconomic status (SES) in TB care engagement beyond income, education, and employment. Yet household wealth and capital have been proposed as important determinants of TB and HIV care. As part of a large, multi-level study on social determinants of TB care engagement, we sought to classify community households into meaningful SES profiles. Insights from this analysis can be applied to future analyses of TB care engagement.
METHODS:
Households in the catchment area of 18 clinics in Buffalo City Metropolitan Health District, Eastern Cape, South Africa, were selected using a geographic cluster sampling design. Surveys were administered to n=3,869 heads-of-household. Twenty-two questions covered dwelling characteristics (n=7), household items (n=9), and individual ownership (n=6). R statistical software 4.4.1, polCA package, was used for all analyses. The optimal number of classes was chosen using model fit statistics and conceptual salience. Demographic characteristics and TB/HIV prevalence were evaluated for each class using Chi-square tests.
RESULTS:
A six-class model was identified as the most appropriate, comprising two “Higher SES” classes (20% of the study population), three “Middle SES” classes (32% in the Higher-Middle SES class; 35% in the two Lower-Middle SES classes), and one “Lower SES” class (13% of the study population). classes correlated with urban/rural area, education level, employment, and income (all p<0.01), but also proved more informative of SES variability. Household TB prevalence (p=0.06) and household HIV prevalence (p<0.01) varied across classes, most notably for HIV prevalence which decreased as SES profiles moved from lower to higher.
CONCLUSIONS:
Identified classes closely align with traditional SES variables, yet provided more informative profiles that captured SES variability. Future research will evaluate association of these profiles with engagement along the TB care continuum.
| eISSN: | 2654-1459 |
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