Rich Kate, Burger Ronelle, Goldberg Deanne, Moultrie Harry, Rieger Matthias
Department of Economics, Stellenbosch University, Stellenbosch, South Africa.
School of Economics and Finance, University of the Witwatersrand, Johannesburg, South Africa.
BMJ Health Care Inform. 2025 May 31;32(1):e101179. doi: 10.1136/bmjhci-2024-101179.
While mobile health (mHealth) interventions are widespread, few studies assess impacts at the population level in low-income and middle-income countries. South Africa's tuberculosis (TB) burden is high, and a substantial share of cases remain undiagnosed. We evaluate the impacts of community activations of TBCheck-a WhatsApp/USSD-based chatbot that allows individuals to evaluate themselves for TB risk.
We use a quasi-experimental approach comparing treated and control subdistricts nationally before and after community activations using dashboard data from the TBCheck platform and weekly or quarterly subdistrict TB test data from the National Health Laboratory Service. Dependent variables are the number of self-screening tests on the platform, total tests and number of positive tests per subdistrict. We employ dynamic difference-in-difference models accounting for subdistrict unobservables and time trends using weekly data, and synthetic control methods matching on preintervention trends in outcomes using quarterly data.
Impact estimates suggest an increase in the number of self-screening tests on the platform (487.53, p-value<0.01) as well as TB tests (107.90, p-value=0.05) in treated relative to control subdistricts due to intervention activities in the week of the intervention. After 2 weeks, impacts on the number of self-screening tests are insignificant (-6.18, p=0.23), and after 1 week, impacts on TB tests are insignificant (36.44, p-value=0.32).
Activation activities associated with TBCheck led to short-lived and variable impacts on uptake and tests in target subdistricts. Alternative strategies are required for sustained uptake of such mHealth tools.
虽然移动健康（mHealth）干预措施广泛存在，但很少有研究在低收入和中等收入国家的人群层面评估其影响。南非的结核病负担很重，且很大一部分病例仍未得到诊断。我们评估了TBCheck社区激活的影响，TBCheck是一个基于WhatsApp/USSD的聊天机器人，可让个人评估自己的结核病风险。
我们采用准实验方法，利用TBCheck平台的仪表盘数据以及国家卫生实验室服务中心的每周或每季度分区结核病检测数据，比较全国社区激活前后的治疗分区和对照分区。因变量是平台上的自我筛查测试数量、总测试数量以及每个分区的阳性测试数量。我们使用动态差分模型，利用每周数据考虑分区不可观测因素和时间趋势，并使用季度数据通过合成控制方法匹配干预前的结果趋势。
影响估计表明，由于干预周的干预活动，与对照分区相比，治疗分区的平台自我筛查测试数量（487.53，p值<0.01）以及结核病测试数量（107.90，p值=0.05）有所增加。2周后，对自我筛查测试数量的影响不显著（-6.18，p=0.23），1周后，对结核病测试的影响不显著（36.44，p值=0.32）。
与TBCheck相关的激活活动对目标分区的使用和测试产生了短暂且多变的影响。需要采用替代策略来持续使用此类移动健康工具。
