Novel Methods and Data Sources for Surveillance of State-Level Diabetes and Prediabetes Prevalence
| Autor: | Frank Jenkins, Lori S. Merrill, Maurice C. Johnson, Deborah B. Rolka, Joanne R. Campione, David A. Marker, Xuanping Zhang, Sundar S. Shrestha, Linda S. Geiss, Jennifer Nooney, Russ Mardon, Sharon Saydah |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
medicine.medical_specialty
Population MEDLINE Information Storage and Retrieval Health records 01 natural sciences Laboratory testing Prediabetic State Insurance claims 010104 statistics & probability 03 medical and health sciences 0302 clinical medicine Bias Environmental health Diabetes mellitus Tools and Techniques Diabetes Mellitus Prevalence Humans Medicine 030212 general & internal medicine Prediabetes 0101 mathematics education Estimation education.field_of_study business.industry Health Policy Public Health Environmental and Occupational Health medicine.disease United States Population Surveillance Physical therapy business |
| Zdroj: | Preventing Chronic Disease |
| ISSN: | 1545-1151 |
| DOI: | 10.5888/pcd14.160572 |
| Popis: | States bear substantial responsibility for addressing the rising rates of diabetes and prediabetes in the United States. However, accurate state-level estimates of diabetes and prediabetes prevalence that include undiagnosed cases have been impossible to produce with traditional sources of state-level data. Various new and nontraditional sources for estimating state-level prevalence are now available. These include surveys with expanded samples that can support state-level estimation in some states and administrative and clinical data from insurance claims and electronic health records. These sources pose methodologic challenges because they typically cover partial, sometimes nonrandom subpopulations; they do not always use the same measurements for all individuals; and they use different and limited sets of variables for case finding and adjustment. We present an approach for adjusting new and nontraditional data sources for diabetes surveillance that addresses these limitations, and we present the results of our proposed approach for 2 states (Alabama and California) as a proof of concept. The method reweights surveys and other data sources with population undercoverage to make them more representative of state populations, and it adjusts for nonrandom use of laboratory testing in clinically generated data sets. These enhanced diabetes and prediabetes prevalence estimates can be used to better understand the total burden of diabetes and prediabetes at the state level and to guide policies and programs designed to prevent and control these chronic diseases. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|