Validation of a neighborhood-level COVID Local Risk Index in 47 large U.S. cities

State variation in neighborhood COVID-19 burden across the United States

BACKGROUND

A lack of fine, spatially-resolute case data for the U.S. has prevented the examination of how COVID-19 infection burden has been distributed across neighborhoods, a key determinant of both risk and resilience. Without more spatially resolute data, efforts to identify and mitigate the long-term fallout from COVID-19 in vulnerable communities will remain difficult to quantify and intervene on.

METHODS

We leveraged spatially-referenced data from 21 states collated through the COVID Neighborhood Project to examine the distribution of COVID-19 cases across neighborhoods and states in the U.S. We also linked the COVID-19 case data with data on the neighborhood social environment from the National Neighborhood Data Archive. We then estimated correlations between neighborhood COVID-19 burden and features of the neighborhood social environment.

RESULTS

We find that the distribution of COVID-19 at the neighborhood-level varies within and between states. The median case count per neighborhood (coefficient of variation (CV)) in Wisconsin is 3078.52 (0.17) per 10,000 population, indicating a more homogenous distribution of COVID-19 burden, whereas in Vermont the median case count per neighborhood (CV) is 810.98 (0.84) per 10,000 population. We also find that correlations between features of the neighborhood social environment and burden vary in magnitude and direction by state.

CONCLUSIONS

Our findings underscore the importance that local contexts may play when addressing the long-term social and economic fallout communities will face from COVID-19.

Deadly places: The role of geography in Aboriginal and Torres Strait Islander COVID-19 vaccination

OBJECTIVE

The objective of this study was to investigate the geospatial distribution of COVID-19 vaccination rates for Aboriginal and Torres Strait Islander Peoples across Local Government Areas in Australia.

METHODS

We described the patterns of COVID-19 vaccination across jurisdictions, identified clusters with different levels of vaccination uptake, and assessed the relationship between contextual factors and vaccination (spatial error model, spatial lag model, and geographic weighted regression).

RESULTS

The proportion of the Aboriginal and Torres Strait Islander population that received at least two doses of a COVID-19 vaccine by the last week of June 2022 ranged from 62.9% to 97.5% across Local Government Areas. The proportion of the overall population who is Aboriginal or Torres Strait Islander (β = 0.280, standard deviation [SD] = 1.92), proportion of the total labour force employed (β =0.286, SD = 0.98), and proportion of individuals who speak an Aboriginal or Torres Strait Islander language (β =0.215, SD = 0.15) had, on average, the strongest effects on COVID-19 vaccination rates.

CONCLUSION

Findings underscore the extent to which area-level demographic influence the COVID-19 vaccination for Aboriginal and Torres Strait Islander Australians.

IMPLICATIONS FOR PUBLIC HEALTH

Findings can inform vaccination strategies that prioritise geographic areas with higher vulnerability to promote equity for Aboriginal and Torres Strait Islander Peoples.

Disparity dashboards: an evaluation of the literature and framework for health equity improvement

The growing recognition of differences in health outcomes across populations has led to a slow but increasing shift towards transparent reporting of patient outcomes. In addition, pay-for-equity initiatives, such as those proposed by the Centers for Medicare and Medicaid, will require the reporting of health outcomes across subgroups over time. Dashboards offer one means of visualising data in the health-care context that can highlight essential disparities in clinical outcomes, guide targeted quality-improvement efforts, and ultimately improve health equity. In this Viewpoint, we evaluate all studies that have reported the successful development of a disparity dashboard and share the data collected and unintended consequences reported. We propose a framework for systematic equality improvement through incentivisation of the collecting and reporting of health data and through implementation of reward systems to reduce health disparities.

Effectiveness of the COVID-19 Community Vulnerability Index in explaining COVID-19 deaths

Objectives

To explore the effectiveness of a COVID-19 specific social vulnerability index, we examined the relative importance of four COVID-19 specific themes and three general themes of the COVID-19 Community Vulnerability Index (CCVI) in explaining COVID-19 mortality rates in Cook County, Illinois.

Methods

We counted COVID-19 death records from the Cook County Medical Examiner's Office, geocoded incident addresses by census tracts, and appended census tracts' CCVI scores. Negative binomial regression and Random Forest were used to examine the relative importance of CCVI themes in explaining COVID-19 mortality rates.

Results

COVID-19 specific Themes 6 (High risk environments) and 4 (Epidemiological factors) were the most important in explaining COVID-19 mortality (incidence rate ratio (IRR) = 6.80 and 6.44, respectively), followed by a general Theme 2 (Minority status & language, IRR = 3.26).

Conclusion

The addition of disaster-specific indicators may improve the accuracy of social vulnerability indices. However, variance for Theme 6 was entirely from the long-term care resident indicator, as the other two indicators were constant at the census tract level. Thus, CCVI should be further refined to improve its effectiveness in identifying vulnerable communities. Also, building a more robust local data infrastructure is critical to understanding the vulnerabilities of local places.