Methods and Applications: Estimating Costs of the HIV Comprehensive Intervention Using the Spectrum Model — China, 2015–2019

As China is striving to meet the Joint United Nations Programme on HIV/AIDS (UNAIDS) 90-90-90 targets, tracking and estimating resource needs is critical in informing financial decisions for human immunodeficiency virus (HIV) prevention and control programs.

Comprehensive HIV prevention and treatment covers various target populations, such as the general population, high-risk groups, infected persons, and a series of service providers including social organizations, community health institutions, CDCs, and hospitals. Sources of funding for prevention and treatment programs include the central government, local government, charitable funds, cooperation projects, and out-of-pocket payments from individuals. The diversity of service objectives, service providers, and funding sources leads to many uncertainties in regional HIV cost calculations.

Based on the theoretical framework of the international HIV cost-effectiveness analysis software Spectrum (1-2), our group developed a cost measurement and investigation tool for HIV (3) and calculated the cost of HIV prevention and control from the perspective of service suppliers. This was established by collecting and investigating the size of various populations, the actual coverage of various HIV interventions, and the unit cost of services. We estimated the cost of comprehensive HIV intervention in Shijiazhuang City from 2015 to 2019 (4). Then, based on this, we measured the HIV intervention costs of 6 cities in eastern China and compared the total costs and the composition change of HIV comprehensive intervention among these cities.

The eastern cities of China are economically developed areas with dense populations in the secondary and tertiary sectors, large population mobility, many groups at high risk for HIV, and have better HIV intervention coverage and epidemiological records. Overall, 5 cities in eastern China including Shijiazhuang, Yantai, Ningbo, Foshan, and Wuxi (hereafter, these cities will be abbreviated as S, Y, N, F, and W, respectively) were selected according to 2 conditions: the total population was more than 3 million and the number of newly reported cases of HIV infection was more than 200 each year. In order to increase the representativeness of urban samples, the research group added Zhenjiang City (in this paper will be abbreviated as Z; located in Jiangsu Province, the same as Wuxi City) with a population of 3 million and 50 newly reported cases of HIV infection each year.

Data was collected on population size, prevention of mother-to-child transmission (PMTCT), antiretroviral treatment (ART), population comprehensive prevention coverage and cost measurement, and program support. CDCs, designated treatment hospitals, and maternal and child health centers provided relevant quantitative data. If some unit costs were missing in one sampled city, we adopted the average value of available cities. If there were outliers (where the unit cost was drastically different from that in other cities), we used the average value of the cities with reasonable values. The coverage of each HIV intervention service in each city collected in this study was completely subject to the results reported and checked by the six cities.

There were three sub-modules, including Demographic Model (Demproj), AIDS Impact Model (AIM), and Resource Needs Model (RNM), used for comprehensive evaluation of HIV interventions under Spectrum. DemProj Module was used to perform demographic projections by age and sex according to past fertility, mortality, and migration rates. The AIM Module was used to estimate HIV prevalence. RNM was used to generate cost estimates of various interventions (5). The employed coverages and unit costs were part of our previous work.

Table 1 showed that for 2015–2019, routine HIV interventions in cities S, Y, N, Z, F, and W mainly included youth, female sex workers, and men who have sex with men (MSM) focused interventions, community mobilization, condom provision, sexually transmitted infection (STI) management, voluntary counseling and testing (VCT), PMTCT, mass media, blood safety, safe injection, universal precautions, and antiretroviral therapy (ARV). The coverage related to the interventions of condom promotion, blood safety, and safe injection was unavailable because their administration departments involved other departments, such as local Family Planning Department, blood bank, and hospital.

In order to make the costs more comparable, the gross domestic product (GDP) deflators of the six cities were obtained by referring to the regional GDP index (Supplementary Table S1). The 2019 GDP deflator was adopted to modify the projected costs. Then, the total and average annual cost of each intervention for each city was calculated.

There was an imbalance in the total costs and per-capita of HIV funds among cities (Table 2). From 2015 to 2019, the average annual costs of S, Y, N, Z, F, and W were 46.78 million Chinese yuan (CNY), 47.55 million CNY, 137.49 million CNY, 24.73 million CNY, 74.37 million CNY, and 58.30 million CNY, respectively. The per capita costs were 4.37 CNY, 6.73 CNY, 17.33 CNY, 7.77 CNY, 17.56 CNY, and 8.91 CNY, respectively. City N had the largest cost input in all service categories including priority population, service delivery, health care, treatment services and program support. City N reached 17.37 CNY per capita, city F reached 17.56 CNY per capita, and city S reached 4.37 CNY per capita. We plotted the three categories of cost results (prevention, treatment, and program support) displayed by Spectrum and calculated the cost ratio of preventive and therapeutic interventions.

Figure 1 showed that the comparable per capita cost of HIV finance increased steadily during 2015–2019 in all 6 cities. City N started at a high level and continued improving rapidly. The total cost ratio of prevention to treatment in 6 cities decreased from 0.79 in 2015 to 0.58 in 2019. The 6 cities had different manifestations in the cost changes for prevention and treatment interventions: 1) fluctuating: in city Y, the funds for prevention and treatment were in a stable fluctuating state. The ratio of prevention/treatment expenditure was around 1.8–1.4; 2) rapid increase in ARV: in city N, the investment of preventive intervention funds was stable, the cost of ARV funds increased from 24.79 million CNY in 2015 to 69.51 million CNY in 2019, and the ratio of prevention to treatment funds showed a rapid downward trend (from 1.25 in 2015 to 0.50 in 2019); 3) steady increase in ARV: in city Z, F, and W, the investment of preventive intervention funds was stable, the cost of ARV funds increased steadily, and the ratio of prevention/treatment funds showed a steady downward trend (from 0.88, 0.92, and 0.54 in 2015 to 0.59, 0.72, and 0.42 in 2019); 4) synchronous growth type: in city S, prevention and treatment funds maintained a synchronous growth trend (from 0.13 in 2015 to 0.20 in 2019).

Figure 1. 

Per capita cost and cost ratio of prevention to treatment of 6 cities from 2015 to 2019.

Note: The 6 cities include Shijiazhuang, Yantai, Ningbo, Zhenjiang, Foshan, and Wuxi, abbreviated as S, Y, N, Z, F, and W, respectively.

Abbreviation: CNY=Chinese yuan.

A mature model with standardized data collection and information processing can generate more reliable estimates. Through standardized reports, the research results are reliable for decision-makers (6-7). Particularly, it may help to reasonably estimate the resources needed to expand, maintain, or replicate successful interventions at a local or national level.

Multiple data collection sources and incomplete information greatly increased the complexity of HIV disease-tracking modeling. Data reported by different departments and institutions may often conflict with each other. Developed jointly by the United Nations Programme on HIV/AIDS and other national teams, Spectrum software can estimate the cost of HIV/AIDS based on the mathematical model established by regional HIV/AIDS epidemic data and intervention program coverage (8). This provides a tool to calculate and compare HIV funds among subnational regions in China.

Our research reflected that HIV prevention and control funds continued to grow, but there was a regional imbalance in sampled cities. Based on the data of 2019, the comparable HIV intervention total cost in 6 cities adjusted by the GDP deflator showed an upward trend, indicating that each city had continuously increased investment in HIV prevention and control. In the most economically developed provinces, Guangdong and Zhejiang, the HIV prevention and control costs of the sampled cities were significantly higher than those of other cities.

Our research suggested that it is necessary to explore the correlation among preliminary work indicators, fund allocation, and subsequent intervention coverage and quality in the future. Differentiating reasonable and unreasonable factors of regional resource allocation will help to provide evidence for equitable and efficient resource allocation (9).

The distribution structure of funds for prevention and treatment must be balanced. With the continuous scaling-up of ARV, post-exposure prophylaxis (PEP), pre-exposure prophylaxis (PrEP), and other proven effective therapeutic interventions, investment in this area has increased rapidly worldwide. From 2015 to 2019, it was determined that HIV funding in these 6 cities was mainly due to the rapid growth of ARV costs. Evidence from the past 40 years indicated that scientific innovation, research, funding, activism, and policies were all central components of HIV messaging in ending HIV (10). It is necessary to simulate the list of prioritized interventions with high cost-effectiveness combined with the local population size, characteristics and HIV epidemic trend. An optimized HIV resource reallocation model may provide a reference for future intensive HIV investments (11-14).

This study was subject to some limitations. First, in the current tool, the costs were classified according to intervention services, which was different from the actual situation. For example, the provision of condoms involved multiple departments. It was difficult to collect multi-source data and may have resulted in underestimating related costs. In future studies, cost measurement tools consistent with the implementation will be more operable and more accurate. Second, the current cost measurement tools were still difficult to distinguish between the quantity and quality of HIV interventions and quantify each dimension. For example, there are great differences between the number of people receiving standardized STI management coverage and the number of people receiving STI treatment. This study estimated the cost according to the former indicators, which may have underestimated the total expenditure of STI treatment and management. The distinction and quantification of the “quality” and “quantity” of HIV intervention will increase the accuracy of cost estimation in the next step.

No conflicts of interest.

Staff from provincial-level CDCs including Jiangsu CDC, Guangdong CDC, Zhejiang CDC, Shandong CDC, and Hebei CDC; and from city-level CDCs including Shijiazhuang CDC, Yantai CDC, Ningbo CDC, Zhenjiang CDC, Foshan CDC, and Wuxi CDC.