South Africa has implemented ward-based outreach teams as part of re-engineering primary health care with teams of community health workers (CHWs). In Cape Town, such a community-orientated primary care (COPC) approach was developed at four learning sites. Community health workers registered and assessed the households they were responsible for, but a year later the data were not analysed or converted into useful information. The aim was to analyse the household data and evaluate its contribution to a community diagnosis, its quality and any implications for the performance of CHWs.
This article used descriptive secondary analysis of household data collected by CHWs at three COPC learning sites in Cape Town (Nomzamo, Eastridge and Mamre).
Data were analysed for 16 852 people from Eastridge, 1338 people from Mamre and 1008 people from Nomzamo. Data were compared in terms of household composition and demographics, type of dwelling, identification of people on treatment for chronic conditions, identification of health risks (e.g. tuberculosis symptoms, tobacco smoking, missed immunisations, missed vitamin A prophylaxis, need for human immunodeficiency virus (HIV) testing or family planning, pregnant or postnatal, and wound care) and for referrals.
Household assessment visits have great potential. Data collected is currently of poor quality, inconsistent or not captured, infrequently analysed and not comprehensive. There is a need to introduce an electronic m-health solution to assist the health information system, to revise the contents of the household assessment form and to ensure that CHWs are competent to identify risks and respond appropriately.
South Africa is committed to a health system built on primary health care (PHC) and has introduced a number of reforms to strengthen this foundation.
Cape Town has implemented ward-based outreach teams within a community-orientated primary care (COPC) approach.
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In their framework document, they explained that their intention was to:
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This same framework document identified 10 key elements to guide the implementation of COPC (
Principles of Metropolitan Health Services’ community-orientated primary care approach.
Delineation of geographic areas and alignment with primary care facilities and CHW teams |
Creating PHC teams of 10–15 CHWs led by a professional nurse, and supported by a nurse practitioner and/or medical doctor |
Forming one functional and integrated team across the facility-based and community-based members |
Partnership between the MHS and NPOs who employ the CHW teams |
Defining a generalist and comprehensive scope of practice for team members |
Supporting the teams and COPC approach with a health information system |
Engaging with communities around health needs, priorities and assets |
Engaging with stakeholders around health needs, priorities and assets |
Training the PHC team for COPC |
Changing management and communication throughout the system |
MHS, Metropolitan Health Services; CHW, community health workers; PHC, primary health care; NPO, non-profit organisation; COPC, community-orientated primary care.
The concept of COPC was originally developed in South Africa during the 1940s before being suppressed during the Apartheid era.
In Cape Town, one of the key elements in implementation was to define the scope of practice of the CHWs in a comprehensive and generalist approach. Household registration and assessment was a central part of this approach. Community health workers were expected to visit every household in their area and to gather information on the people living there, the type of dwelling and basic services available, the number of people with chronic conditions and to screen for a variety of health risks such as Tuberculosis (TB), human immunodeficiency virus (HIV), missing immunisations, vitamin A prophylaxis, pregnancy, need for family planning, tobacco smoking and wound care. People with such risks could be counselled or referred to appropriate services.
Whilst many studies have investigated factors impacting on the implementation of ward-based outreach teams,
The study performed a secondary descriptive analysis of data already collected by CHWs at COPC learning sites in Cape Town.
The Cape Town MHS is divided into four substructures and one community was selected as a COPC learning site by the MHS in each substructure. Each learning site had at least one primary care facility and one NPO that employed the CHW teams:
Northern-Tygerberg substructure: Bishop Lavis community with Bishop Lavis Community Health Centre (CHC) and Caring Network (NPO)
Klipfontein-Mitchells Plain substructure: Eastridge community with Mitchells Plain CHC and Arisen Woman (NPO)
Southern-Western substructure: Mamre community with Mamre Community Day Centre (CDC) and the TB-HIV Care Association (NPO)
Eastern-Khayelitsha sub-substructure: Nomzamo community with Nomzamo CDC, Ikwezi clinic and Masincedane (NPO).
The Bishop Lavis learning site was excluded from this study as household data had recently been analysed by a community health registrar in an unpublished report.
Eastridge is a suburb on the Cape Flats with 5953 households and 28 482 people in the 2011 census (
Location of learning sites in Cape Town.
Mamre is a small rural town on the edge of the Cape Town metropole with a population estimated at 9048 in 2226 households.
Nomzamo is situated on the edge of Cape Town in Somerset West with an estimated population of 60 528 in 19 520 households.
Data had already been collected by CHWs, who were meant to visit every household in their designated geographic area. In Mamre and Eastridge, the data were captured by the NPO on an electronic spreadsheet and all the data from a 1-year period were included in the analysis. In Nomzamo, the data were still on the paper-based household assessment forms and stored in a cupboard. The NPO had an electronic list of 1500 households that had been visited and this was used to select a random sample of forms from a 1-year period. All data were from the 2017 to 2018 period.
Data were prepared in Excel spreadsheets from each site and checked for errors or omissions. Data were then imported to the Statistical Package for Social Sciences (SPSS) version 26 for descriptive analysis. Categorical data were reported as frequencies and percentages, whilst numerical data were reported as means and standard deviations or medians and interquartile range depending on the distribution of the data.
The learning sites differed in the way that they had captured the data. For example, Eastridge captured the data per household, whereas the other sites captured the data per individual. Mamre captured age as less than 5 years or more than 5 years, whilst other sites captured the actual age. These differences made it difficult to analyse the data set as a whole.
Ethical approval to conduct the study was obtained from the Health Research Ethics Committee, Stellenbosch University on 16 November 2018 for Mamre, 03 October 2018 for Nomzamo, 01 February 2019 for Eastridge. Ethical clearance numbers were U18/10/033 for Mamre sub-study, U18/07/025 for Nomzamo and U19/01/005 for Eastridge.
In Eastridge, data were analysed from 3564 households with 16 852 individuals, in Mamre from 1338 households with 3895 individuals and from Nomzamo 321 households with 1008 individuals. In Eastridge, data were collected at the household level and only some items could be extrapolated to individuals. The data sets were otherwise very complete for the demographic and housing information.
Demographics of population and characteristics of households.
Variables | Eastridge |
Mamre |
Nomzamo |
|||
---|---|---|---|---|---|---|
% | % | % | ||||
Male | 7925 | 47.1 | 1688 | 43.3 | 432 | 45.8 |
Female | 8886 | 52.9 | 2207 | 56.7 | 512 | 54.2 |
< 5 | 1681 | 10.0 | 216 | 5.5 | 126 | 13.2 |
5–18 | 4280 | 25.6 | 3676 | 94.5 | 237 | 24.9 |
> 18 | 10 789 | 64.4 | 590 | 61.9 | ||
Social grant | 1982 | 55.6 | 883 | 67.5 | 200 | 22.4 |
Formal | 2522 | 71.3 | 3362 | 86.4 | 183 | 19.1 |
Formal in backyard | 892 | 25.2 | 437 | 11.2 | 362 | 37.9 |
Informal in backyard | 87 | 2.5 | 46 | 1.2 | 141 | 14.7 |
Informal | 38 | 1.1 | 47 | 1.2 | 270 | 28.2 |
Electricity | 3530 | 99.7 | 3805 | 99.3 | 829 | 82.2 |
In house water | 3335 | 94.4 | 3675 | 97.5 | 392 | 41.8 |
In yard | 13 | 0.4 | 66 | 1.8 | 275 | 29.3 |
Communal < 200 m | 185 | 5.2 | 27 | 0.7 | 249 | 26.5 |
Communal > 200 m | 0 | 0.0 | NA | - | 22 | 2.3 |
Flush toilet available | 3336 | 94.2 | 3792 | 99.4 | 813 | 90.0 |
In house | 3403 | 96.3 | 3726 | 99.3 | 333 | 40.8 |
In yard | 124 | 3.5 | 26 | 0.7 | 209 | 25.6 |
Outside yard | 7 | 0.2 | NA | - | 275 | 33.7 |
,
NA, not available; IQR, interquartile range.
Demographics of population and characteristics of households.
Household members | Eastridge |
Mamre |
Nomzamo |
|||
---|---|---|---|---|---|---|
Median | IQR | Median | IQR | Median | IQR | |
People per household | 4.0 | 3.0–6.0 | NA | - | 4.0 | 3.0–6.0 |
Rooms per household | 5.0 | 3.0–5.0 | 5.0 | 3.0–6.0 | 2.0 | 1.0–3.0 |
Mamre appeared to be an older population on social grants (probably pensions), with fewer children, in a community with almost all formal housing and good access to water and flush toilets in the house. Eastridge also had a substantial proportion of households receiving a social grant, mostly formal dwellings and good access to water and flush toilets. Nomzamo on the other hand was a much poorer community with a slightly younger population profile, smaller houses with more crowding, relatively fewer social grants (probably less pensions), much higher levels of informal housing and much less access to running water and flush toilets in the house.
Proportion of population identified as taking medication for chronic conditions.
Variable | Eastridge |
Mamre |
Nomzamo |
|||
---|---|---|---|---|---|---|
% | % | % | ||||
HIV | 41 | 1.1 | 10 | 0.3 | 97 | 9.6 |
Tuberculosis | 50 | 1.4 | 10 | 0.3 | 62 | 6.2 |
Hypertensive | 1399 | 39.3 | 893 | 22.9 | 40 | 4.0 |
Diabetes mellitus | 754 | 21.2 | 322 | 8.3 | 30 | 3.0 |
Asthma | 510 | 14.3 | 86 | 2.2 | 30 | 3.0 |
Psychiatric | 117 | 3.3 | 26 | 0.7 | 10 | 1.0 |
, households.
HIV, human immunodeficiency virus.
Proportion of population identified as having tuberculosis symptoms.
TB symptoms | Eastridge |
Mamre |
Nomzamo |
|||
---|---|---|---|---|---|---|
% | % | % | ||||
Cough | 14 | 0.4 | 7 | 0.2 | 18 | 1.8 |
Night sweats | 11 | 0.3 | 8 | 0.2 | 18 | 1.8 |
Weight loss | 9 | 0.3 | 4 | 0.1 | 29 | 2.9 |
Loss of appetite | 3 | 0.1 | 1 | 0.0 | 16 | 1.6 |
Fever | 31 | 0.9 | 3 | 0.1 | 21 | 2.1 |
, households.
TB, tuberculosis.
Proportion of population identified as having a health risk.
Variable | Eastridge |
Mamre |
Nomzamo |
|||
---|---|---|---|---|---|---|
% | % | % | ||||
Immunisations not up to date | 4/1288 | 0.3 | 117/216 | 54.2 | 14/126 | 11.1 |
Vitamin A not up to date | 11/1288 | 0.9 | 117/216 | 54.2 | 16/126 | 12.7 |
Post-natal women | 1 | 0.2 | 2 | 0.1 | 4 | 0.4 |
Pregnant women | 47 | 1.3 | 3 | 0.1 | 13 | 1.3 |
Needing family planning | NA | - | 3 | 0.1 | 15 | 1.5 |
Tobacco smokers | 2105 | 59.0 | 1037 | 26.6 | 92 | 9.1 |
Needing HIV test | 10 | 0.3 | 29 | 0.8 | 39 | 3.9 |
Needing wound care | 4 | 0.1 | 1 | - | 8 | 0.8 |
, denominator is households.
NA, not available; HIV, human immunodeficiency virus.
In Mamre, only 6 out of 272 (2.2%) of those identified as having a health risk had a record of referral in the household assessment form. In Eastridge, a different referral system was used, which was not captured on the electronic database. In Nomzamo, 45 out of 162 (27.8%) of those identified were recorded as referred.
At all learning sites, CHWs had visited a large number of households and completed household registrations and assessments during the first year of the COPC initiative. There was thus a huge potential for useful information. The findings demonstrated significant issues with the quality of the data and possibly with the performance of the CHWs. These issues impaired the usefulness of the data and the ability to accurately identify health needs of the communities. The findings also illustrate the strengths and weaknesses of the data in terms of giving a comprehensive view of health needs in these communities.
In all learning sites, there was a problem with missing data for many of the items. It was impossible to determine if the CHWs had asked everyone the relevant questions and only recorded positive responses or whether the questions had not been asked at all. With many items there were very few people identified (e.g. TB symptoms, missing immunisations and chronic medication), suggesting that CHWs had not consistently collected data from all household members. The household assessment form itself did not always require a definite answer and encouraged CHWs to only record a ‘yes’ answer by ticking. It is also possible that the household members who assisted the CHWs were not able to answer all of the questions for other household members who were absent. The extremely low number of referrals suggested that CHWs were using other mechanisms to record referrals and were not completing this aspect of the household assessment to save time and avoid duplication of tasks. Nevertheless, the low number of referrals raised a question as to whether CHWs were competently recognising health risks and responding to them appropriately. This question could not be answered from this data.
The need to conduct a study of this nature to analyse data that had been collected at least a year ago illustrates the flawed nature of the health information system for COPC and household assessments. None of the NPOs had the capacity to analyse the data themselves and in one case were just storing the forms in a cupboard. In addition, none of the data had been captured or analysed by the department of health. It was clear that an electronic m-health solution would potentially solve many of the problems. If data were collected via cell phone, then CHWs could be prompted to enter data more completely and accurately. Data would automatically be captured in a standardised way and analysed in terms of pre-determined indicators. Results could be immediately available to users at different parts of the system from the NPOs, primary care facilities to managers at substructure and district level. Data could be easily entered into the provincial data centre and combined with other individualised data to contribute to a personal electronic medical record. A locally developed m-health solution called Catch and Match was introduced at two of the learning centres during the implementation of COPC but has not yet delivered on its full potential.
The household assessment form does not provide a comprehensive view of the health risks or the CHWs’ scope of practice. In terms of health promotion and disease prevention, there is a focus on TB and HIV, but little attention to non-communicable diseases, other than tobacco smoking. Local research on CHWs has shown the ability to assess cardiovascular disease risk using a non-laboratory approach.
Ultimately, the purpose of the CHWs is to assist the population at risk to live a healthier life. In the household, they need to have generalist skills to enter, form a relationship, identify any health risks, and respond helpfully and appropriately. There is a risk that the household assessment becomes an administrative exercise in obtaining data or filling out a form for its own sake. Completion of the form is used to evaluate CHW performance. Community health workers must retain a person centred approach to their work and use the household assessment form as a prompt and a guide to useful action in service of the community and household members.
The study was reliant on CHWs to collect the data and, therefore, the representativeness and quality of the data were out of the researcher’s control. The analysis however shed light on issues with the type and quality of data and the performance of the CHWs. Data were also captured in slightly different ways by NPOs making inferential analysis difficult. Data have, however, been presented as far as possible in a comparable manner.
This study supports the need for:
Introduction of an electronic m-health solution to ensure household assessment data are reliably collected, captured and analysed. In addition, information derived from the data should be made accessible downstream (to CHWs, professional nurses, primary care facilities and community health forums) and upstream (to substructures, district, province and national levels).
Review of the contents of the household assessment form to align with the CHWs’ new scope of practice and the need to provide a comprehensive overview of community health needs.
Review of the competence of CHWs to identify health risks and to act appropriately. Any learning needs should be addressed through in-service training and by the professional nurses leading the CHW teams.
Household assessment visits have great potential to identify individuals at risk and to help appropriately as well as to contribute to a community diagnosis that identifies local health priorities. Data collected is currently of poor quality, inconsistent or not captured, infrequently analysed and not comprehensive. There is a need to introduce an electronic m-health solution to assist the health information system, to revise the contents of the household assessment form and to ensure that CHWs are competent to identify risks and respond appropriately.
The authors would like to thank the staff at the three non-profit organisations: Masincedane, Arisen Women and Caring Network for their assistance with accessing the data and giving feedback on the findings.
The authors have declared that no competing interests exist.
Three of the researchers completed sub-studies at the three sites as final phase medical students under the supervision of R.M. (L.P. at Eastridge, C.S. at Mamre and E.M. at Nomzamo). R.M. integrated the work and wrote the final manuscript, which was approved by all authors.
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Secondary data analysis was performed on data collected by the three non-profit organisations under contract to the Western Cape Government. Data can only be shared by approaching the authors and with permission from the third parties.
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any affiliated agency of the authors.