Abstract

Diabetes mellitus is a common disease and costly public health concern and an expressive number of affected individuals have undiagnosed diabetes mellitus. In 2001, the Brazilian Ministry of Health conducted a national diabetes screening campaign. The purpose of this study was to estimate the yield and economic impact of this screening strategy. Based on positive screenees (fasting glucose >100 mg/dL or nonfasting >140 mg/dL) probable new cases of diabetes were estimated and a decision analytic model was built up. Primary and secondary data were used to estimate screening cost (in Brazilian Reais, R$) and yield (new cases of diabetes detected), assuming a single-payer-perspective. Sensitivity analyses were performed. Assuming a prevalence of undiagnosed diabetes mellitus of 4.8%, probable new cases of diabetes were 518,579 (23 new cases per 1,000 subjects screened), considering that 33% of positive-screening individuals underwent confirmatory glucose testing. The cost per new case of diabetes diagnosed would be R$89. The results were sensitive to percentage of confirmatory tests performed. The costs of nationwide community screening in Brazil were significant, however, in absolute terms lower than those described by other countries.

Highlights

  • The results were sensitive to percentage of confirmatory tests performed

  • The costs of nationwide community screening in Brazil were significant, in absolute terms lower than those described by other countries

  • O modelo usa como parâmetros: a proporção de exames de glicemia capilar positivas, o valor preditivo positivo do teste de rastreamento em jejum e fora de jejum, a prevalência de diabetes não diagnosticado, a proporção de confirmação diagnóstica, os custos associados com o rastreamento e a probabilidade de que o rastreamento resulte em diagnóstico de diabetes após exame de glicemia sérica confirmatório

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Summary

Methods

Based on positive screenees (fasting glucose ≥100 mg/dL or nonfasting ≥140 mg/dL) probable new cases of diabetes were estimated and a decision analytic model was built up.

Results
Conclusions
15 Estimado Dados reais SUS e Tabela AMB
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