What to know
Catalogued below are CDC's publications related to (1) PLACES methodology (small area estimation) and (2) epidemiology/surveillance studies using small area estimation method in deferent survey data or use cases.
PLACES Methodology
CDC uses small area estimation (SAE) to produce chronic disease related measures for areas without direct estimates from surveys (including counties, census tracts, and other geographic levels). The publications below describe SAE methods in research.
2022
Wang Y, Zhang XY, Lu H, et al. Constructing statistical intervals for small area estimates based on generalized linear mixed model in health surveys. Open J Stat. 2022;12:10.4236/ojs.2022.121005. https://doi.org/10.4236/ojs.2022.121005
Greenlund KJ, Lu H, Wang Y, et al. PLACES: Local Data for Better Health. Prev Chronic Dis. 2022;19:210459. https://doi.org/10.5888/pcd19.210459
2020
Wang Y, Zhang XY, Lu H, et al. Intercensal and postcensal estimation of population size for small geographic areas in the United States. Int J Popul Data Sci. 2020;5:1160. https://doi.org/10.23889/ijpds.v5i1.1160
2018
Wang Y, Holt JB, Xu F, et al. Using 3 health surveys to compare multilevel models for small area estimation for chronic diseases and health behaviors. Prev Chronic Dis 2018;15:E133. http://dx.doi.org/10.5888/pcd15.180313
2017
Wang Y, Holt JB, Zhang X, et al. Comparison of methods for estimating prevalence of chronic diseases and health behaviors for small geographic areas: Boston validation study, 2013. Prev Chronic Dis 2017;14:E99. http://dx.doi.org/10.5888/pcd14.170281
2015
Zhang X, Holt JB, Yun S, et al. Validation of multilevel regression and poststratification methodology for small area estimation of health indicators from the Behavioral Risk Factor Surveillance System. Am J Epidemiol 2015;182(2):127–37. https://doi.org/10.1093/aje/kwv002
2014
Zhang X, Holt JB, Lu H, et al. Multilevel regression and poststratification for small-area estimation of population health outcomes: a case study of chronic obstructive pulmonary disease prevalence using the Behavioral Risk Factor Surveillance System. Am J Epidemiol 2014;179(8):1025–33. https://doi.org/10.1093/aje/kwu018
Epidemiology & Surveillance Studies Used SAE Method
CDC has published studies using SAE to highlighting local health conditions across the United States and linking different datasets to inform public health efforts.
2023
Carlson SA, Watson KB, Rockhill S, et al. Linking local-level chronic disease and social vulnerability measures to inform planning efforts: a COPD example. Prev Chronic Dis 2023;20:230025. DOI: http://dx.doi.org/10.5888/pcd20.230025
Pankowska MM, Lu H, Wheaton AG, et al. Prevalence and geographic patterns of self-reported short sleep duration among US adults, 2020. Prev Chronic Dis 2023;20:220400. DOI: http://dx.doi.org/10.5888/pcd20.220400
Lee B, Wang Y, Carlson SA, et al. National, state-level, and county-level prevalence estimates of adults aged ≥18 years self-reporting a lifetime diagnosis of depression — United States, 2020. MMWR Morb Mortal Wkly Rep 2023;72:644–650. https://doi.org/10.15585/mmwr.mm7224a1
Lu H, Wang Y, Liu Y, et al. County-Level geographic disparities in disabilities among US adults, 2018. Prev Chronic Dis. 2023;20:230004. DOI: http://dx.doi.org/10.5888/pcd20.230004
2022
Wang Y, Tevendale H, Lu H, et al. US county-level estimation for maternal and infant health-related behavior indicators using Pregnancy Risk Assessment Monitoring System data, 2016–2018. Popul Health Metr. 2022;20:14. https://doi.org/10.1186/s12963-022-00291-6
2020
Razzaghi H, Wang Y, Lu H, et al. Estimated county-level prevalence of selected underlying medical conditions associated with increased risk for severe COVID-19 illness — United States, 2018. MMWR Morb Mortal Wkly Rep. 2020;69:945–950. https://dx.doi.org/10.15585/mmwr.mm6929a1
Samanic CM, Barbour KE, Liu Y, et al. Prevalence of self-reported hypertension and antihypertensive medication use by county and rural-urban classification — United States, 2017. MMWR Morb Mortal Wkly Rep. 2020;69(18):533–539. http://dx.doi.org/10.15585/mmwr.mm6918a1
2019
Holt JB, Matthews KA, Lu H, et al. Small area estimates of populations with chronic conditions for community preparedness for public health emergencies. Am J Public Health. 2019;109:S325–S331. https://doi.org/10.2105/ajph.2019.305241
Eke PI, Lu H, Zhang X, et al. Geospatial distribution of periodontists and US adults with severe periodontitis. J Am Dent Assoc. 2018;150(2):103–110. https://doi.org/10.1016/j.adaj.2018.09.021
2018
Barbour KE, Moss S, Croft JB, et al. Geographic variations in arthritis prevalence, health-related characteristics, and management—United States, 2015; MMWR Surveill Summ. 2018;67(4):1–28. https://doi.org/10.15585/mmwr.ss6704a1
Croft JB, Wheaton AG, Liu Y, et al. Urban-rural county and state differences in chronic obstructive pulmonary disease—United States, 2015. MMWR Morb Mortal Wkly Rep. 2018;67(7):205-211. https://doi.org/10.15585/mmwr.mm6707a1
Berkowitz Z, Zhang X, Richards TB, et al. Multilevel small–area estimation of colorectal cancer screening in the United States. Cancer Epidemiol Biomarkers Prev. 2018;27(3):245–253. https://dx.doi.org/10.1158/1055-9965.EPI-17-0488
Berkowitz Z, Zhang X, Richards TB, et al. Multilevel regression for small-area estimation of mammography use in the United States, 2014. Cancer Epidemiol Biomarkers Prev. 2018;28(1):32–40. https://dx.doi.org/10.1158/1055-9965.EPI-18-0367
Lin M, Zhang X, Holt JB, et al. Multilevel model to estimate county-level untreated dental caries among US children aged 6–9 years using the National Health and Nutrition Examination Survey. Prev Med. 2018;111:291–298. https://doi.org/10.1016/j.ypmed.2017.11.015
2016
Barbour KE, Helmick CG, Boring M, et al. Prevalence of doctor-diagnosed arthritis at state and county levels—United States, 2014. MMWR Morb Mortal Wkly Rep. 2016;65(19):489–494. http://dx.doi.org/10.15585/mmwr.mm6519a2
Berkowitz Z, Zhang X, Richards TB, et al. Multilevel small-area estimation of multiple cigarette smoking status categories using the 2012 Behavioral Risk Factor Surveillance System. Cancer Epidemiol Biomarkers Prev. 2016;25(10):1402–1410. https://dx.doi.org/10.1158/1055-9965.EPI-16-0244
Eke PI, Zhang X, Lu H, et al. Predicting periodontitis at state and local levels in the United States. J Dent Res. 2016;95(5):515–522. https://dx.doi.org/10.1177/0022034516629112
Eke PI, Wei L, Borgnakke WS, et al. Periodontitis prevalence in adults ≥ 65 years of age, in the USA. Periodontol 2000. 2016;72(1):76–95. https://dx.doi.org/10.1111/prd.12145
2013
Zhang X, Onufrak S, Holt JB, Croft JB. A multilevel approach to estimating small area childhood obesity prevalence at the census block–group level. Prev Chronic Dis. 2013;10:120252. http://dx.doi.org/10.5888/pcd10.120252