TY - JOUR
T1 - The population randomization observation process (PROP) assessment method
T2 - Using systematic habitation observations of street segments to establish household-level epidemiologic population samples
AU - Smart, Mieka
AU - Sadler, Richard
AU - Harris, Alan
AU - Buchalski, Zachary
AU - Pearson, Amber
AU - Debra Furr-Holden, C.
N1 - Funding Information:
This work was funded by National Institutes of Health National Institute on Minority Health and Health Disparities Grant U54MD011227.
Publisher Copyright:
© 2019 The Author(s).
PY - 2019/11/8
Y1 - 2019/11/8
N2 - Background: Identifying and intervening on health disparities requires representative community public health data. For cities with high vacancy and transient populations, traditional methods of population estimation for refining random samples are not feasible. The aim of this project was to develop a novel method for systematic observations to establish community epidemiologic samples. Results: We devised a four-step population randomization observation process for Flint, Michigan, USA: (1) Use recent total population data for community areas (i.e., neighborhoods) to establish the proportional sample size for each area, (2) Randomly select street segments of each community area, (3) Deploy raters to conduct observations about habitation for each randomly selected segment, and (4) Complete observations for second and third street segments, depending on vacancy levels. We implemented this systematic observation process on 400 randomly selected street segments. Of these, 130 (32.5%) required assessment of secondary segments due to high vacancy. Among the 130 primary segments, 28 (21.5%) required assessment of tertiary (or more) segments. For 71.5% of the 400 primary street segments, there was consensus among raters on whether the dwelling inhabited or uninhabited. Conclusion: Houses observed with this method could have easily been considered uninhabited via other methods. This could cause residents of ambiguous dwellings (likely to be the most marginalized residents with highest levels of unmet health needs) to be underrepresented in the resultant sample.
AB - Background: Identifying and intervening on health disparities requires representative community public health data. For cities with high vacancy and transient populations, traditional methods of population estimation for refining random samples are not feasible. The aim of this project was to develop a novel method for systematic observations to establish community epidemiologic samples. Results: We devised a four-step population randomization observation process for Flint, Michigan, USA: (1) Use recent total population data for community areas (i.e., neighborhoods) to establish the proportional sample size for each area, (2) Randomly select street segments of each community area, (3) Deploy raters to conduct observations about habitation for each randomly selected segment, and (4) Complete observations for second and third street segments, depending on vacancy levels. We implemented this systematic observation process on 400 randomly selected street segments. Of these, 130 (32.5%) required assessment of secondary segments due to high vacancy. Among the 130 primary segments, 28 (21.5%) required assessment of tertiary (or more) segments. For 71.5% of the 400 primary street segments, there was consensus among raters on whether the dwelling inhabited or uninhabited. Conclusion: Houses observed with this method could have easily been considered uninhabited via other methods. This could cause residents of ambiguous dwellings (likely to be the most marginalized residents with highest levels of unmet health needs) to be underrepresented in the resultant sample.
KW - Census block group
KW - Habitation
KW - Population sample
KW - Random sample
UR - http://www.scopus.com/inward/record.url?scp=85074731872&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85074731872&partnerID=8YFLogxK
U2 - 10.1186/s12942-019-0190-z
DO - 10.1186/s12942-019-0190-z
M3 - Article
C2 - 31703586
AN - SCOPUS:85074731872
SN - 1476-072X
VL - 18
JO - International Journal of Health Geographics
JF - International Journal of Health Geographics
IS - 1
M1 - 24
ER -