Geo-analysis: the distribution of community health workers in relation to the HIV prevalence in KwaZulu-Natal province, South Africa

G E Khumalo; S Ntuli; E Lutge; T P Mashamba-Thompson

doi:10.1186/s12913-022-07707-x

Geo-analysis: the distribution of community health workers in relation to the HIV prevalence in KwaZulu-Natal province, South Africa

BMC Health Serv Res. 2022 Mar 11;22(1):326. doi: 10.1186/s12913-022-07707-x.

Authors

G E Khumalo^{1

2}, S Ntuli³, E Lutge^{4

5}, T P Mashamba-Thompson^{4

6}

Affiliations

¹ University of KwaZulu-Natal, Discipline of Public Health Medicine, School of Nursing & Public Health, Howard College Campus, Durban, South Africa. gugu.khumalo@kznhealth.gov.za.
² Health Research & Knowledge Management Unit; KwaZulu-Natal Department of Health, 330 Langalibalele Street, Pietermaritzburg, South Africa. gugu.khumalo@kznhealth.gov.za.
³ Geographical Information System Unit, African Health Research Institute, Somkhele, Mtubatuba, South Africa.
⁴ University of KwaZulu-Natal, Discipline of Public Health Medicine, School of Nursing & Public Health, Howard College Campus, Durban, South Africa.
⁵ Health Research & Knowledge Management Unit; KwaZulu-Natal Department of Health, 330 Langalibalele Street, Pietermaritzburg, South Africa.
⁶ Faculty of Health Sciences, University of Pretoria, Prinshof Campus, Bophelo Rd, Prinshof 349-Jr, Pretoria, 0084a, South Africa.

Abstract

Background: The South African Ward Based Primary Health Care Outreach Team (WBPHCOT) policy framework states that the distribution of community health workers (CHWs) should be proportional to levels of poverty and disease within the population. We aimed to describe the spatial distribution of CHWs in relation to the prevalence of the Human Immunodeficiency Virus (HIV) which has itself been associated with poverty in previous studies.

Methods: This was a descriptive, cross-sectional study in which secondary data was used for geospatial analysis. Based on the extrapolation from the norm of one WBPHCOT per 6000 individuals, we utilized geographic information system (GIS) methods to visualize the distribution of CHWs in relation to the prevalence of HIV in KwaZulu-Natal (KZN). Dot density mapping was used to visualize the random distribution of CHWs in relation to HIV prevalence and population in the districts. The districts' HIV prevalence, number of PLWH, ratio of CHW: people living with HIV (PLWH), ratio of CHW: population and poverty scores were mapped using choropleth mapping. MapInfo Pro 17.0 was used to map geospatial presentation of the data.

Results: Overall, KZN province showed under allocation of CHWs with a CHW: people ratio of 1: 1156 compared to the estimated norm of 1: 600-1000. At district level, only two of 11 districts met the suggested norm of CHW: PLWH (1: 109-181). This indicates shortages and misallocation of CHWs in the nine remaining districts. Furthermore, our findings showed extensive geospatial heterogeneity with no clear pattern in the distribution of CHWs. There was no relationship between CHW distribution and HIV prevalence or poverty scores in the districts.

Conclusion: This study shows inequality in the distribution of CHWs which may be associated with inequalities in the provision of HIV related services. It is critical to strengthen the response to the HIV epidemic through the appropriate distribution of CHWs especially in those districts with high levels of HIV prevalence and poverty.

Keywords: Community health worker distribution; Geospatial; HIV prevalence; KwaZulu-Natal.

MeSH terms

Community Health Workers*
Cross-Sectional Studies
HIV Infections* / epidemiology
Humans
Prevalence
South Africa / epidemiology