PMID- 33635758
OWN - NLM
STAT- MEDLINE
DCOM- 20211015
LR  - 20220213
IS  - 1552-5775 (Electronic)
IS  - 1552-5767 (Print)
IS  - 1552-5767 (Linking)
VI  - 25
DP  - 2020 Dec
TI  - The Application of Community-Based Information from the American Community Survey 
      in a Large Integrated Health Care Organization.
PG  - 1-3
LID - 10.7812/TPP/20.010 [doi]
LID - 20.010
AB  - BACKGROUND: The American Community Survey (ACS) is the largest household survey 
      conducted by the US Census Bureau. We sought to describe the community-level 
      characteristics derived from the ACS among enrollees of Kaiser Permanente 
      Southern California (KPSC), evaluate the associations between ACS estimates and 
      selective individual-level health outcomes, and explore how using different 
      scales of the census geography and the linearity assumption affect the 
      associations. METHODS: We examined the associations between track-level and block 
      group-level ACS 5-year estimates and 4 individual-level Healthcare Effectiveness 
      Data and Information Set (HEDIS) outcome measures (comprehensive diabetes care, 
      postpartum care, antidepressant medication management, and childhood immunization 
      status) using multilevel generalized linear models. Odds ratios and their 95% 
      confidence intervals were estimated for every 10% increase in ACS measures. 
      RESULTS: 6,357,841 addresses were successfully geocoded to at least the tract 
      level. The community-level demographic, socioeconomic, residential, and other ACS 
      measures varied among KPSC health plan enrollees. A majority of these ACS 
      measures were associated with the selected HEDIS health outcomes. The directions 
      of the effects were consistent across health outcomes; however, the magnitudes of 
      the effect sizes varied. Within each HEDIS health outcome, the relative size of 
      the effects appeared to remain similar. Differences between the census tract- and 
      block group-level estimates were minor, especially for measures related to 
      race/ethnicity, education, income, and occupation. CONCLUSION: These findings 
      support the use of many ACS measures at neighborhood levels to predict health 
      outcomes. The geographic units might have little effect on the results. The 
      linearity assumption should be made with caution.
CI  - Copyright (c) 2020 The Permanente Press. All rights reserved.
FAU - Liang, Zhi
AU  - Liang Z
AD  - Department of Research and Evaluation, Kaiser Permanente Southern California, 
      Pasadena, CA.
FAU - Nau, Claudia
AU  - Nau C
AD  - Department of Research and Evaluation, Kaiser Permanente Southern California, 
      Pasadena, CA.
FAU - Xie, Fagen
AU  - Xie F
AD  - Department of Research and Evaluation, Kaiser Permanente Southern California, 
      Pasadena, CA.
FAU - Vogel, Ralph
AU  - Vogel R
AD  - Department of Clinical Analysis, Kaiser Permanente Southern California, Pasadena, 
      CA.
FAU - Chen, Wansu
AU  - Chen W
AD  - Department of Research and Evaluation, Kaiser Permanente Southern California, 
      Pasadena, CA.
LA  - eng
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
PL  - United States
TA  - Perm J
JT  - The Permanente journal
JID - 9800474
SB  - IM
MH  - Censuses
MH  - Child
MH  - Delivery of Health Care
MH  - Female
MH  - Humans
MH  - *Income
MH  - *Residence Characteristics
MH  - Socioeconomic Factors
MH  - Surveys and Questionnaires
MH  - United States
PMC - PMC8803254
COIS- Disclosure Statement: The author(s) have no conflicts of interest to disclose.
EDAT- 2021/02/27 06:00
MHDA- 2021/10/16 06:00
PMCR- 2021/01/01
CRDT- 2021/02/26 17:08
PHST- 2021/02/26 17:08 [entrez]
PHST- 2021/02/27 06:00 [pubmed]
PHST- 2021/10/16 06:00 [medline]
PHST- 2021/01/01 00:00 [pmc-release]
AID - tpj20010 [pii]
AID - 10.7812/TPP/20.010 [doi]
PST - ppublish
SO  - Perm J. 2020 Dec;25:1-3. doi: 10.7812/TPP/20.010.