Format

Send to

Choose Destination
Curr Diab Rep. 2018 Sep 19;18(11):108. doi: 10.1007/s11892-018-1088-z.

Advancing Measurement of Diabetes at the Population Level.

Ali MK1,2,3, Siegel KR4,5, Laxy M4,5,6, Gregg EW4,5.

Author information

1
Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA. mkali@emory.edu.
2
Hubert Department of Global Health, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA, 30322, USA. mkali@emory.edu.
3
Department of Family and Preventive Medicine, School of Medicine, Emory University, Atlanta, GA, USA. mkali@emory.edu.
4
Division of Diabetes Translation, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA.
5
Hubert Department of Global Health, Rollins School of Public Health, Emory University, 1518 Clifton Road, Atlanta, GA, 30322, USA.
6
Helmholtz Zentrum M√ľnchen, Institute of Health Economics and Health Care Management, Munich, Germany.

Abstract

PURPOSE:

The measurement and estimation of diabetes in populations guides resource allocation, health priorities, and can influence practice and future research. To provide a critical reflection on current diabetes surveillance, we provide in-depth discussion about how upstream determinants, prevalence, incidence, and downstream impacts of diabetes are measured in the USA, and the challenges in obtaining valid, accurate, and precise estimates.

FINDINGS:

Current estimates of the burden of diabetes risk are obtained through national surveys, health systems data, registries, and administrative data. Several methodological nuances influence accurate estimates of the population-level burden of diabetes, including biases in selection and response rates, representation of population subgroups, accuracy of reporting of diabetes status, variation in biochemical testing, and definitions of diabetes used by investigators. Technological innovations and analytical approaches (e.g., data linkage to outcomes data like the National Death Index) may help address some, but not all, of these concerns, and additional methodological advances and validation are still needed. Current surveillance efforts are imperfect, but measures consistently collected and analyzed over several decades enable useful comparisons over time. In addition, we proposed that focused subsampling, use of technology, data linkages, and innovative sensitivity analyses can substantially advance population-level estimation.

KEYWORDS:

Burden estimation; Diabetes; Nutrition; Quality of life; Surveillance

PMID:
30232630
DOI:
10.1007/s11892-018-1088-z

Supplemental Content

Full text links

Icon for Springer
Loading ...
Support Center