Based on Table 1 data, the results of this study looks at the the distribution of per capita CHW in relation to the HIV prevalence, number of PLWH, ratio of CHW: PLWH, and poverty scores in the KZN districts. The average HIV prevalence in the KZN districts is 40.6% and for ease of reference and data aggregation, the HIV prevalence in the districts have been described as above or below the average HIV prevalence.
Table 1. Distribution of CHWs and HIV prevalence in the KZN districts
|
KZN District
|
District Population 1
|
HIV prevalence % (95% Confidence Interval) 2
|
No. of CHWs 3
|
No. of PLWH4
|
CHW:PLWH Ratio
|
MPI Score5
|
HIV prevalence >40.6%
|
uMgungundlovu
|
1 095 865
|
46.6 (43.8-49.5)
|
808
|
504098
|
1: 624
|
0.02
|
eThekwini
|
3 702 231
|
43.5 (40.7-46.3)
|
1562
|
1591959
|
1: 1019
|
0.02
|
Ugu
|
753 336
|
43.4 (40.2-46.7)
|
824
|
323934
|
1: 393
|
0.05
|
iLembe
|
657 612
|
43.1 (39.5-46.7)
|
941
|
282773
|
1: 301
|
0.04
|
uMzinyathi
|
554 882
|
41.7 (36.9-46.6)
|
495
|
227502
|
1: 460
|
0.07
|
uThukela
|
706 588
|
41.5 (38.0-45.1)
|
787
|
296767
|
1: 377
|
0.04
|
HIV prevalence <40.6%
|
Harry Gwala
|
510 865
|
39.2 (36,7-41.7)
|
811
|
199237
|
1: 246
|
0.06
|
King Cetshwayo
|
971 135
|
39.1 (34.0-44.5)
|
1035
|
378742
|
1: 366
|
0.03
|
Zululand
|
892 310
|
37.6 (34.4-41.0)
|
1008
|
339077
|
1: 336
|
0.04
|
Amajuba
|
531 327
|
36.4 (31.7-41.2)
|
485
|
191278
|
1: 394
|
0.02
|
uMkhanyakude
|
689 090
|
35 (28.7-41.9)
|
817
|
241182
|
1: 295
|
0.07
|
1. [Source: KwaZulu-Natal Community Survey, 2016 (Statistics, South Africa]
2. [Source: The 2017 National Antenatal Sentinel HIV Survey, South Africa]
3. [Source: KwaZulu-Natal Department of Health, Human Resource Electronic Persal system, 2019]
4. [Source: KwaZulu-Natal Department of Health: District Health Information System, 2019]
5. [Source: Statistics South Africa: Provincial SAMPI scores, 2016)
According to the WBPHCOT policy framework, the CHW ratio in SA is one WBPHCOT team to 6000 individuals [5]. Considering the 27% HIV prevalence in the province, we estimate that one WBPHCOT serves 1600 HIV positive people. Each WBPHCOT is supposed to have between 6 to 10 CHWs [5]. This means that one CHW may need to visit between 160-267 people living with HIV (PLWH), that is, a ratio of 1:160-267.
Overall, there was irrational and unexplained distribution of CHWs in relation to HIV prevalence and or the number of PLWH; the ratio of CHW: PLWH; and the poverty scores as indicated in Figure 2., Figure 3. and Figure 4. respectively. Considering that one CHW should be allocated to approximately 160-267 PLWH, Table 1. shows that only one district, Harry Gwala district, out of the 11 districts, met this requirement. The ratios in the other districts ranged from 1: 295 to 1:1019.
The misallocation of CHWs is worse in some districts than in others. For example, despite the HIV prevalence that is 7.4% lower than uMgungundlovu district, Harry Gwala district has almost an equal number of CHWs as uMgungundlovu district. Moreover, uMgungundlovu district has almost twice the population size of Harry Gwala district. Furthermore, as seen in Figure 3., uMgungundlovu, eThekwini and uMzinyathi districts have the most shortage of CHWs distribution indicative of their lowest ratios of 1: 1019, 1:624 and 1:460 respectively.
A common observation for, uMgungundlovu, eThekwini and uMzinyathi, is that they do not only share HIV prevalences that are more than the average of 40.6%, but they also have the lowest CHW: PLWH ratio. Equally, the two districts, namely Harry Gwala and uMkhanyakude which have HIV prevalences of less than 40.6%, have the highest ratio of CHW: PLWH. However, this pattern was not replicated in other districts where the CHW: PLWH ratio ranged between 1:301 to 1:394 regardless of the HIV prevalence being more or less than the average of 40.6%.
Human Immunodeficiency Virus has been associated with poverty and in South Africa the South African Multidimensional Poverty Index (SAMPI), an indicator to measure poverty, is made up of several factors that amount to a poor person’s experience of deprivation which include poor health, lack of education, inadequate living standards, lack of income, disempowerment, lack of decent work and threat from violence using Census data [35-36]. Figure 4. shows the poverty scores in each district with the richest and poorest districts having poverty scores of 0.02 and 0.07 respectively.
As seen in Table 1. and Figure 4, the richest districts with a SAMPI score of 0.02 had the severe shortage of CHWs indicative of the lowest CHW:PLWH ratios compared to the poorest districts with a CHW: PLWH ratio of 1:394 or more. However, one district (uMzinyathi) with a SAMPI score of 0.07 had a CHW:PLWH ratio higher than 1:394 (1:460). Overall, regardless of the poverty scores in the districts, there is apparent shortage of CHWs which is worse is the districts with the highest poverty scores.