Predicting Risk of Tuberculosis (TB) Disease in People Who Migrate to a Low-TB Incidence Country: Development and Validation of a Multivariable, Dynamic Risk-Prediction Model Using Health Administrative Data.
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BACKGROUND: Tuberculosis (TB) incidence remains disproportionately high in people who migrate to Canada and other countries with low TB incidence, but systematic TB screening and prevention in migrants are often cost-prohibitive for TB programs. We aimed to develop and validate a TB risk-prediction model to inform TB screening decisions in foreign-born permanent residents of Canada. METHODS: We developed and validated a proportional baselines landmark supermodel for TB risk prediction using health administrative data from British Columbia and Ontario, 2 distinct provincial healthcare systems in Canada. Demographic (age, sex, refugee status, year of entry, TB incidence in country of origin), TB exposure, and medical (human immunodeficiency virus, kidney disease, diabetes, solid organ transplantation, cancer) covariates were used to derive and test models in British Columbia; 1 model was chosen for external validation in the Ontario cohort. The model's ability to predict 2- and 5-year TB risk in the Ontario cohort was assessed using discrimination and calibration statistics. RESULTS: The study included 715 423 individuals (including 1407 people with TB disease) in the British Columbia derivation cohort and 958 131 individuals (including 1361 people with TB disease) in the Ontario validation cohort. The 2- and 5-year concordance statistic in the validation cohort was 0.77 (95% confidence interval [CI]: .75 to .78) and 0.77 (95% CI: .76 to .78), respectively. Calibration-in-the-large values were 0.14 (95% CI: .08 to .21) and -0.05 (95% CI: -.12 to .02) in 2- and 5-year prediction windows. CONCLUSIONS: This prediction model, available online at https://tb-migrate.com, may improve TB risk stratification in people who migrate to low-incidence countries and may help inform TB screening policy and guidelines.