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Forecasting risk-factor distributions by sex and age, 1990-2050: a parametric estimation framework using GBD summary exposure values, demonstrated for Australia.
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Dhungel Bibha, Wilson Tim, Khuu Stephanie, Blakely Tony
Аннотация
BACKGROUND: This paper presents a method to convert existing Global Burden of Disease (GBD) forecasts of summary exposure values (SEVs) into business-as-usual forecasts of risk-factor distributions.
METHOD: SEVs from the GBD are a relative-risk-weighted summary of risk-factor exposure, ranging from 0 (everyone below the theoretical minimum risk exposure level) to 1 (everyone above the 99th percentile of global exposure). Given the known inputs to calculate SEVs, with one additional assumption of the coefficient of variation of the risk factor, it is possible to generate parametric distributions (e.g. log-normal, gamma) of risk-factor exposures. We demonstrate this method by forecasting SEVs to 2050 by sex, age, and year for body mass index (BMI), systolic blood pressure (SBP), low-density lipoprotein (LDL) cholesterol, and fasting plasma glucose in Australia.
RESULTS: Our method generates plausible age- and sex-specific risk-factor distribution forecasts for Australia. Compared with the 2017/18 National Health Survey, fasting plasma glucose and LDL cholesterol agreed reasonably well in younger age groups, with wider discrepancies at older ages. SEV-derived distributions overestimated the mean SBP by ∼3-10 mmHg and mean BMI by ∼1 kg/m2 relative to survey estimates. The method was sensitive to values of the 99th global percentile of risk factors.
CONCLUSION: This method is extensible to any country or demographic group. To facilitate broader use of this method, we recommend further cross-validation studies for other countries and making key GBD data (e.g. 95th percentiles of risk factors) more publicly available.
METHOD: SEVs from the GBD are a relative-risk-weighted summary of risk-factor exposure, ranging from 0 (everyone below the theoretical minimum risk exposure level) to 1 (everyone above the 99th percentile of global exposure). Given the known inputs to calculate SEVs, with one additional assumption of the coefficient of variation of the risk factor, it is possible to generate parametric distributions (e.g. log-normal, gamma) of risk-factor exposures. We demonstrate this method by forecasting SEVs to 2050 by sex, age, and year for body mass index (BMI), systolic blood pressure (SBP), low-density lipoprotein (LDL) cholesterol, and fasting plasma glucose in Australia.
RESULTS: Our method generates plausible age- and sex-specific risk-factor distribution forecasts for Australia. Compared with the 2017/18 National Health Survey, fasting plasma glucose and LDL cholesterol agreed reasonably well in younger age groups, with wider discrepancies at older ages. SEV-derived distributions overestimated the mean SBP by ∼3-10 mmHg and mean BMI by ∼1 kg/m2 relative to survey estimates. The method was sensitive to values of the 99th global percentile of risk factors.
CONCLUSION: This method is extensible to any country or demographic group. To facilitate broader use of this method, we recommend further cross-validation studies for other countries and making key GBD data (e.g. 95th percentiles of risk factors) more publicly available.