Background: Globally, rural populations have poorer health and considerably lower levels of access to healthcare compared with urban populations. Although the drive to ensure universal coverage through community healthcare worker programmes has shown significant results elsewhere, their value has yet to be realised in South Africa. Aim: The aim of this study was to determine the potential impact, cost-effectiveness and benefit-to-cost ratio (BCR) of information and communications technology (ICT)-enabled community-oriented primary care (COPC) for rural and remote populations. Setting: The Waterberg district of Limpopo province in South Africa is a rural mining area. The majority of 745 000 population are poor and in poor health. Methods: The modelling considers condition-specific effectiveness, population age and characteristics, health-determined service demand, and costs of delivery and resources. Results: Modelling showed 122 teams can deliver a full ICT-enabled COPC service package to 630 565 eligible people. Annually, at scale, it could yield 35 877 unadjusted life years saved and 994 deaths avoided at an average per capita service cost of R170.37, and R2668 per life year saved. There could be net annual savings of R120 million (R63.4m for Waterberg district) from reduced clinic (110.7m) and hospital outpatient (23 646) attendance and admissions. The service would inject R51.6m into community health worker (CHW) households and approximately R492m into district poverty reduction and economic growth. Conclusion: With a BCR of 3.4, ICT-enabled COPC is an affordable systemic investment in universal, pro-poor, integrated healthcare and makes community-based healthcare delivery particularly compelling in rural and remote areas.
The study was designed to articulate the additional resources necessary to integrate a full ICT-enabled COPC service into the current district health service. It assumed that the current staffing and infrastructure would continue to deliver in-facility care but all CHWs, team leaders (TLs), clinical supervisors and care coordination staff would be added. The analysis was population-based, and the service demand was derived from risk-adjusted workload using the government’s primary care re-engineering best practice profile of contacts by condition. In addition to condition-specific contacts, the CHW workload included ensuring every household is visited annually, and routine visits involve health promotion and disease prevention, treatment-adherence support, rehabilitation and palliative care support through home-based care (HBC). The estimates of lives saved were based on condition-specific outcomes in the literature available but without adjustment for the gain of utility from years of avoided illness or disability. Staff cost was based on current government policy and occupation-specific dispensations for each of the staff cadres and was discounted at the net of growth and inflation. Equipment, vehicle and material costs were at current market values. The following assumptions were used to inform the modelling: condition-specific effectiveness, population size and characteristics, health-determined service demand, resources and cost of delivery, cost-effectiveness and cost benefits. Based on the evidence of the impact of well-managed community-based CHW support and care, the following ranges of impact were applied to the model (Table 2). These are considered conservative given available, albeit limited, evidence of the extent to which community-based healthcare could impact on outcomes (Table 3). Anticipated potential impact effectiveness (lives saved) for primary conditions. NCD, non-communicable disease. Community-based healthcare effectiveness for primary conditions. HIV, human immunodeficiency virus; TB, tuberculosis; CHW, community health worker; ART, antiretroviral therapy. The iHSP model uses Statistics South Africa’s (StatsSA) small area layer (SAL) data set. Each SAL is linked to the nearest PHC facility using a geographical information system (GIS). This ‘nearest neighbour’ approach was considered to be robust because 92.8% of households used the nearest health facility.13 The catchment population and its characteristics (age, gender, households and income profile) for each PHC facility are thus defined by the sum of the SAL population profiles. Statistics South Africa’s full national data17 were used to calculate income thresholds for each of the income quintiles. These were used to determine the number of households in each of the income quintiles in each small area. The analysis focussed on quintiles 1 to 4, assuming quintile 5 population was insured privately. Population growth was applied by district, based on StatsSA mid-year estimates (2011–2018).18 Income was discounted using the net value of growth versus inflation, as increase in wealth and wages was eroded by inflation. National mortality and morbidity rates hide a significant variation in rates by income level, with the risk of under-5 mortality in low- and middle-income countries being twice as high for children in the poorest households compared with those in richest.19 The mortality ratio between the poorest and the richest households is 2.78 for communicable diseases in general, 5.9 for maternal deaths, 4.9 for child deaths, 3.3 for diabetes mellitus, 9.1 for TB and 2.3 for cardiovascular disease.20 Service demand, as reflected in the number of visits required, is adjusted for relative burden of disease using income and mortality rates. Baseline mortality is calculated in the tool from mortality rates for each health condition. Because mortality rates vary with income, the mortality rates for the selected quintiles were further adjusted in the analysis. The costing is based on the annual full operational cost of the community-based teams. Costing of CHWs and HBCs was performed in line with the government’s minimum wages (2019 – R20.00 per hour or R3500 per month) for full-time employment (a 40-h working week) with paid and sick leave benefits. Community health workers and TLs (staff nurses and equivalent) were managed by a coordinator (professional nurse or equivalent) at the health facility and each sub-district had a contracting unit manager (clinician). Equipment costs were annualised and included maintenance and replacement. Community health worker kit included replacement of equipment and replenishment of consumables. Transport costs were included for TL and a clinical supervisor to carry out twice-weekly in-field supervision and twice-monthly field team visits, respectively. Community health workers were provided with uniforms and patient information leaflets for each of the seven conditions. Each team member was given two cycles of 16-day in-service training for a year. Marginal costs of space and other resource utilisation incurred at facility level were not included in the modelling. For communication and data management, each CHW and TL had either a smart phone or tablet. Each clinic was provided with general consumables as well as a computer, a printer, Wi-Fi and uninterruptible power supply (UPS). From experience, data collected on the AitaHealthTM platform lead to a multiplicity of savings and service efficiencies when compared with paper-based systems. Information and communications technology removes the need for data clerks, as well as filing cabinets and space for storage. It allows CHWs to work remotely. This means that they can clock in and out without incurring out-of-pocket expenses on transport or system loss to work time costs. Through real-time data, TLs have visibility of individual and team performance, creating efficiency in planning and in-field service support. Because healthcare workers are able to update, send, store and retrieve existing records, ICT supports information continuity, streamlines household visits and contributes to quality of care. Thus, whilst the time spent by a CHW visiting a household (visit time) in both systems depended on individual multi-morbidity and the number of people requiring healthcare, ICT-related savings reduced the actual time allocated per service for a single condition. Covering all hardware and software costs and overheads, ICT yields an overall 15% savings on paper-based systems. To see whether the benefits accruing from that investment warrant expenses, for this study the entire community-based service was considered to be an additional cost, irrespective of the source of funding. We assumed a steady state where if the interventions and investments to save lives were not sustained, then morbidity and mortality would revert to pre-intervention levels. Conservatively, we ignored secondary positive knock-on effects of reduced morbidity and the future lives saved through early detection and treatment, reduction in infections and improved health literacy in the population. The costs of CHW interventions in relation to each condition were proportionally based on the proposed number of visits related to that condition. Annual revisits to all households were critical for the expected outcomes as they enabled healthcare services to update and respond to demographic, social and healthcare changes. Life years saved are measured as the difference between average age of the affected population for each condition, namely, people living with HIV, TB and adults (32 years, 15–49); pregnant women (25 years, 15–35), stillbirths and infants (0 years); children (3 years, 0–6); NCD (45 years, 40–50) and national life expectancy (60 years, 56–64). Cost-effectiveness is calculated as the cost per life year saved for each condition. The calculation of cost-effectiveness was based on the full incremental cost of adding community-based services to facility services as described above. We also explored the minimum impact thresholds that would make the interventions not very cost-effective (using both gross domestic product [GDP] per capita and average income). The benefits of intervention were measured in terms of the discounted earnings for the remainder of the lives saved by population group and condition as well as the earnings of the CHWs employed. Both individual benefits and gains to the economy were included. As the population served was relatively poor, it was assumed that they would not accrue savings but would substantially return all of their earnings to the economy through the purchase of goods and services. Adjustments were made for tax (18% standard rate), underlying level of unemployment, children’s schooling21 and child support grants.22 For poverty reduction, tax was deducted but not the cost of schooling and support grants. For GDP growth, tax was not deducted (because it was transferred to the state’s benefit) but the cost of schooling and support grants were deducted (as costs to the state). Ethics clearance was obtained from the Faculty of Health Sciences Research Committee (University of Pretoria) (Reference No.: 102/2011).
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DIMA AI Care
