Impact of the gate-keeping policies of China's primary healthcare model on the future burden of tuberculosis in China: a protocol for a mathematical modelling study.
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ABSTRACT: Introduction
In the past three decades, China has made great strides in the prevention and treatment of tuberculosis (TB). However, the TB burden remains high. In 2019, China accounted for 8.4% of global incident cases of TB, the third highest in the world, with a higher prevalence in rural areas. The Healthy China 2030 highlights the gate-keeping role of primary healthcare (PHC). However, the impact of PHC reforms on the future TB burden is unclear. We propose to use mathematical models to project and evaluate the impacts of different gate-keeping policies.Methods and analysis
We will develop a deterministic, population-level, compartmental model to capture the dynamics of TB transmission within adult rural population. The model will incorporate seven main TB statuses, and each compartment will be subdivided by service providers. The parameters involving preference for healthcare seeking will be collected using discrete choice experiment (DCE) method. We will solve the deterministic model numerically over a 20-year (2021-2040) timeframe and predict the TB prevalence, incidence and cumulative new infections under the status quo or various policy scenarios. We will also conduct an analysis following standard protocols to calculate the average cost-effectiveness for each policy scenario relative to the status quo. A numerical calibration analysis against the available published TB prevalence data will be performed using a Bayesian approach.Ethics and dissemination
Most of the data or parameters in the model will be obtained based on secondary data (eg, published literature and an open-access data set). The DCE survey has been reviewed and approved by the Ethics Committee of the School of Public Health, Sun Yat-sen University. The approval number is SYSU [2019]140. Results of the study will be disseminated through peer-reviewed journals, media and conference presentations.
SUBMITTER: You X
PROVIDER: S-EPMC8390147 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectivesWe applied the validated Obesity Population Risk Tool (OPoRT) to estimate the future burden of obesity in Canada using baseline risk factors attained through routinely collected survey data.MethodsOPoRT was developed using logistic regression with sex-specific generalized estimating equations to predict the 10-year prevalence of obesity (outcome BMI ≥ 30.0) among adults 18 and older. The algorithm includes 17 predictive factors, including socio-demographic and health behavioural characteristics. OPoRT demonstrated excellent discrimination (C-statistic ≥ 0.89) and achieved calibration. We applied OPoRT to Canadian Community Health Survey (2013/14) data to predict the future prevalence of obesity in Canada for a variety of population subgroups.ResultsThe predicted burden of obesity grew from 261 cases per 1000 in 2013/14 to 326 cases per 1000 in 2023/24 corresponding to a total of 8.54 million individuals with obesity. The burden is expected to be higher among males (347 cases per 1000) than females (305 cases per 1000). Individuals aged 35-49 had the highest predicted burden of obesity (374 cases per 1000) and the largest number of predicted cases (2.42 million), while individuals in the ≥ 65 age group had the lowest predicted burden (236 cases per 1000). The number of individuals with obesity per 1000 is highest among those severely food insecure (452 cases per 1000), compared with food secure individuals (324 cases per 1000).ConclusionsOPoRT can be used to estimate the future population burden of obesity, to identify priority subgroups at an elevated risk. Burden estimates should be reflected in approaches to curb the future burden of obesity.
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