Basis for changes in the disease burden estimates related to vitamin A and zinc deficiencies in the 2017 and 2019 Global Burden of Disease Studies

Abstract

Background: The Global Burden of Disease (GBD) Study provides estimates of death and disability from 87 risk factors, including some micronutrient deficiencies. Objectives: To review methodological changes that led to large differences in the disease burden estimates for vitamin A and zinc deficiencies between the GBD 2017 and 2019 Studies. Methods: GBD publications were reviewed; additional information was provided by GBD researchers. Results: Vitamin A deficiency prevalence is based on plasma retinol concentration, whereas the estimate for zinc deficiency prevalence uses dietary adequacy as a proxy. The estimated global prevalence of vitamin A deficiency for children aged 1-4 years in the year 2017 decreased from 0.20 (95%CI 0.17-0.24) in GBD 2017 to 0.16 (95%CI 0.15-0.19) in GBD 2019, while the global prevalence of zinc deficiency did not change between the two studies (0.09 (95%CI 0.04-0.17) and 0.09 (95%CI 0.03-0.18)). New to 2019 was that meta-analyses were performed using Meta Regression – Bayesian, Regularized, Trimmed (MR-BRT), a method developed for GBD. Due to this and multiple other methodological changes the estimated number of deaths due to vitamin A deficiency dropped from 233,000 (179,000–294,000) to 24,000 (3,000–50,000) from GBD 2017 to 2019, and for zinc deficiency from 29,000 (1,000–77,000) to 2,800 (700–6,500), respectively. Conclusion: The changes in the estimated disease burdens due to vitamin A and zinc deficiencies in the GBD reports from 2017 to 2019 are due primarily to changes in the analytical methods employed, so may not represent true changes in disease burden. Additional effort is needed to validate these results.

Burden of Ischemic and Hemorrhagic Stroke Across the US From 1990 to 2019

“…14 Bayesian meta-regression software, MR-BRT (Institute for Health Metrics and Evaluation), was used to adjust for known biases for study-level differences including studies that were purely from administrative databases such as HCUP, studies reporting first-ever and recurrent strokes together, studies that reported aggregated stroke subtypes only, and studies that excluded prehospital stroke death in measurement methods and case definitions. 8,15,16 Epidemiologic state-transition disease modeling software, DisMod-MR (Institute for Health Metrics and Evaluation), was used to estimate estimates of incidence and prevalence of stroke. Mortality for stroke and stroke subtypes was estimated using vital registration data coded to the ICD system.…”

“…Estimates of stroke incidence and prevalence were produced by using a broad range of population-representative data sources, including scientific reports of cohorts and registries, population surveys, microdata from registry and cohort studies, and health system administrative data like the Healthcare Cost and Utilization Project (HCUP) . Bayesian meta-regression software, MR-BRT (Institute for Health Metrics and Evaluation), was used to adjust for known biases for study-level differences including studies that were purely from administrative databases such as HCUP, studies reporting first-ever and recurrent strokes together, studies that reported aggregated stroke subtypes only, and studies that excluded prehospital stroke death in measurement methods and case definitions . Epidemiologic state-transition disease modeling software, DisMod-MR (Institute for Health Metrics and Evaluation), was used to estimate estimates of incidence and prevalence of stroke.…”

Cost-effectiveness of Vitamin A supplementation among children in three sub-Saharan African countries: An individual-based simulation model using estimates from Global Burden of Disease 2019

“…Changes in the modelling of VAS in GBD 2019 might have further reduced the impact of VAS. GBD 2019 used a new meta-analysis method for micronutrient modelling that was not applied in GBD 2017 [ 37 ]. The results of the new modelling approach reduced the effect of VAD on measles and diarrheal diseases by an order of magnitude [ 1 , 38 ].…”