Evaluating Methods for Mapping Historical Redlining to Census Tracts for Health Equity Research.

Neighborhood characteristics including housing status can profoundly influence health. Recently, increasing attention has been paid to present-day impacts of "redlining," or historic area classifications that indicated less desirable (redlined) areas subject to decreased investment. Scholarship of redlining and health is emerging; limited guidance exists regarding optimal approaches to measuring historic redlining in studies of present-day health outcomes. We evaluated how different redlining approaches (map alignment methods) influence associations between redlining and health outcomes. We first identified 11 existing redlining map alignment methods and their 37 logical extensions, then merged these 48 map alignment methods with census tract life expectancy data to construct 9696 linear models of each method and life expectancy for all 202 redlined cities. We evaluated each model's statistical significance and R2 values and compared changes between historical and contemporary geographies and populations using Root Mean Squared Error (RMSE). RMSE peaked with a normal distribution at 0.175, indicating persistent difference between historical and contemporary geographies and populations. Continuous methods with low thresholds provided higher neighborhood coverage. Weighting methods had more significant associations, while high threshold methods had higher R2 values. In light of these findings, we recommend continuous methods that consider contemporary population distributions and mapping overlap for studies of redlining and health. We developed an R application {holcmapr} to enable map alignment method comparison and easier method selection.

Resource Savings Associated With Use of an Automated Symptom Monitoring Tool for COVID-19 Public Health Response, Summer 2020-Summer 2021.

CONTEXT: Active symptom monitoring is a key component of the public health response to COVID-19, but these activities are resource-intensive. Digital tools can help reduce the burden of staff time required for active symptom monitoring by automating routine outreach activities. PROGRAM: Sara Alert is an open-source, Web-based automated symptom monitoring tool launched in April 2020 to support state, tribal, local, and territorial jurisdictions in their symptom monitoring efforts. IMPLEMENTATION: As of October 2021, a total of 23 public health jurisdictions in the United States had used Sara Alert to perform daily symptom monitoring for more than 6.1 million individuals. This analysis estimates staff time and cost saved in 3 jurisdictions that used Sara Alert as part of their COVID-19 response, across 2 use cases: monitoring of close contacts exposed to COVID-19 (Arkansas; Fairfax County, Virginia), and traveler monitoring (Puerto Rico). EVALUATION: A model-based approach was used to estimate the additional staff resources that would have been required to perform the active symptom monitoring automated by Sara Alert, if monitoring instead relied on traditional methods such as telephone outreach. Arkansas monitored 283 705 individuals over a 10-month study period, generating estimated savings of 61.9 to 100.6 full-time equivalent (FTE) staff, or $2 798 922 to $4 548 249. Fairfax County monitored 63 989 individuals over a 13-month study period, for an estimated savings of 24.8 to 41.4 FTEs, or $2 826 939 to $4 711 566. In Puerto Rico, where Sara Alert was used to monitor 2 631 306 travelers over the 11-month study period, estimated resource savings were 849 to 1698 FTEs, or $26 243 161 to $52 486 322. DISCUSSION: Automated symptom monitoring helped reduce the staff time required for active symptom monitoring activities. Jurisdictions reported that this efficiency supported a rapid and comprehensive COVID-19 response even when experiencing challenges with quickly scaling up their public health workforce.

Retail Pharmacy Policy to End the Sale of Tobacco Products: What Is the Impact on Disparity in Neighborhood Density of Tobacco Outlets?

BACKGROUND: Population-level research on the implications of retail pharmacy policies to end the sale of tobacco products is scant, and the impact of such policies on racial/ethnic and socioeconomic disparities across neighborhoods in access to tobacco products remains unexplored. METHODS: We investigated the association between neighborhood sociodemographic characteristics and tobacco retail density in Rhode Island (RI; N = 240 census tracts). We also investigated whether the CVS Health (N = 60) policy to end the sale of tobacco products reduces the disparity in the density of tobacco retail across neighborhoods, and we conducted a prospective policy analysis to determine whether a similar policy change in all pharmacies in RI (N = 135) would reduce the disparity in tobacco retail density. RESULTS: The results revealed statistically significant associations between neighborhood sociodemographic characteristics and tobacco retail outlet density across RI neighborhoods. The results when excluding the CVS Health locations, as well as all pharmacies as tobacco retailers, revealed no change in the pattern for this association. CONCLUSIONS: The results of this study suggest that while a commendable tobacco control policy, the CVS Health policy appears to have no impact on the neighborhood racial/ethnic and socioeconomic disparities in the density of tobacco retailers in RI. Prospective policy analyses showed no impact on this disparity even if all other pharmacies in the state adopted a similar policy. IMPACT: Policy efforts aimed at reducing the disparity in access to tobacco products should focus on reducing the density of tobacco outlets in poor and racial/ethnic neighborhoods. Cancer Epidemiol Biomarkers Prev; 25(9); 1305-10. ©2016 AACR.