Improving the Rank Precision of Population Health Measures for Small Areas with Longitudinal and Joint Outcome Models

Cluster Analysis Methods to Support Population Health Improvement Among US Counties

CONTEXT

Population health rankings can be a catalyst for the improvement of health by drawing attention to areas in need of relative improvement and summarizing complex information in a manner understood by almost everyone. However, ranks also have unintended consequences, such as being interpreted as "hard truths," where variations may not be significant. There is a need to improve communication about uncertainty in ranks, with accurate interpretation. The most common solutions discussed in the literature have included modeling approaches to minimize statistical noise or borrow strength from covariates. However, the use of complex models can limit communication and implementation, especially for broad audiences.

OBJECTIVES

Explore data-informed grouping (cluster analysis) as an easier-to-understand, empirical technique to account for rank imprecision that can be effectively communicated both numerically and visually.

DESIGN

Cluster analysis, specifically k-means clustering with Wasserstein (earth mover's) distance, was explored as an approach to identify natural and meaningful groupings and gaps in the data distribution for the County Health Rankings' (CHR) health outcomes ranks.

SETTING

County-level health outcomes from the 2022 CHR.

PARTICIPANTS

3082 counties that were ranked in the 2022 CHR.

MAIN OUTCOME MEASURE

Data-informed health groups.

RESULTS

Cluster analysis identified 30 health groupings among counties nationwide, with cluster size ranging from 9 to 184 counties. On average, states had 16 identified clusters, ranging from 3 in Delaware and Hawaii to 27 in Virginia. Number of clusters per state was associated with number of counties per state and population of the state. The method helped address many of the issues that arise from providing rank estimates alone.

CONCLUSIONS

Public health practitioners can use this information to understand uncertainty in ranks, visualize distances between county ranks, have context around which counties are not meaningfully different from one another, and compare county performance to peer counties.

Disparities in years of potential life lost among racial and ethnic groups in Washington state

Background

The disproportionate mortality burden racial and ethnic groups endure compared to their non-Hispanic white (NHW) counterparts is a widely known public health issue in the United States.

Methods

We examined disparities in premature mortality through a measure of years of potential life lost (YPLL) among racial and ethnic groups after accounting for individual and place-based risk factors. Data were nearly 400,000 geocoded death records from Washington state mortality records from 2011 to 2018. Decedent records included information on marital status and educational attainment at time of death. We linked these records to census tract indicators of rurality and area deprivation based on residential longitude and latitude coordinates at time of death. We conducted censored Poisson regression to test adjusted associations between racial and ethnic identity and YPLL.

Results

Relative to non-Hispanic whites, non-Hispanic blacks, American Indian and Alaska Natives, Asian or other Pacific Islanders, multiracial, and Hispanic decedents had significantly higher rates of YPLL. Controlling for sociodemographic factors reduced but did not eliminate the disparities in YPLL between non-Hispanic whites and other racial and ethnic groups. Controlling for place-based risk factors did not further attenuate differences.

Conclusions

Racial and ethnic minorities suffer disproportionately from premature mortality. Researchers and policy makers must recognize the disproportionate risks to premature mortality and work together to alleviate them through the delivery of better and more accessible targeted services.

Value-based focused global population health management

In 2018, approximately 18 million people worldwide were diagnosed with cancer and are predicted to double by 2040. The global quality chasm in improving health care worldwide requires "systems thinking" as the key to success. Aligning the goal around person-centered care captures the total needs of care of a population and not just disease categories. The integration of the Institute of Medicine's (IOM) six aims of quality termed "value-based focused" and population health management (PHM) provides all health care leaders grappling with improving the health care of the populations a framework for the communities they serve. In this context, the question becomes finding solutions to providing high quality, compassionate and patient-centered health care delivery. Over the last two decades, three paradigms have emerged; the six aims of quality, outcome-focused population health, and the "Quadruple Aim". We have termed the intersection of these concepts as Value-based focused Population Health Management (VBPHM). This review applies VBPHM across the geographic county and community levels in the United States. Specifically, we examine VBPHM at the county or county-equivalents and community levels within the United States. Lastly, the potential role of Community-based Participatory Research and it is applicability to our framework is discussed. VBPHM can comparably be applied globally to improve population health, especially in preventing and treating cancer better.

Optimal Bayesian point estimates and credible intervals for ranking with application to county health indices

It is fairly common to rank different geographic units, e.g. counties in the USA, based on health indices. In a typical application, point estimates of the health indices are obtained for each county, and the indices are then simply ranked as if they were known constants. Several authors have considered optimal rank estimators under squared error loss on the rank scale as a default method for general purpose ranking, e.g. situations where ranking units across the full spectrum of performance (low, medium, high) is important. While computationally convenient, squared error loss on the rank scale may not represent the true inferential goals of rank consumers. We construct alternative loss functions based on three components: (1) the inferential goal (rank position or pairwise comparisons), (2) the scale (original, log-transformed or rank) and (3) the (positional or pairwise) loss function (0/1, squared error or absolute error). We can obtain optimal ranks for loss functions based on rank positions and nearly optimal ranks for loss functions based on pairwise comparisons paired with highest posterior density (HPD) credible intervals. We compare inferences produced by the various ranking methods, both optimal and heuristic, using low birth weight data for counties in the Midwestern United States, from 2006 to 2012.