Association between changes in loneliness identified through screening and changes in depression or anxiety in older adults.

BACKGROUND: Changes in loneliness are associated with corresponding changes in depression, anxiety, and general health in population surveys, but few studies have assessed these associations through repeated screening in clinical settings. METHODS: Retrospective cohort study among individuals ≥age 65 in an integrated health care system who completed loneliness screening before two annual wellness visits, separated by a mean of 12.9 (SD 2.0) months, between 2013 and 2018. Their responses identified four subgroups: individuals who were persistently lonely; not lonely; experienced an increase (recently lonely); or decrease (previously lonely) in loneliness. Loneliness was assessed with a single item. Depression was assessed with the Patient Health Questionnaire-2. Anxiety was assessed with the Generalized Anxiety Disorder-2. Fair/poor general health was assessed by a single item. Linear mixed effects models assessed changes in outcomes after covariate adjustment. RESULTS: The cohort comprised 24,666 individuals (19.2% of older adults in the system). Mean age was 73.7 years (SD 6.4); 54.6% were female, and 11.6% were members of racial and ethnic minority groups. Of these individuals, 1936 (7.8%) were persistently lonely, 1687 (6.8%) were recently lonely, 1551 (6.3%) were previously lonely, and 19,492 (79.0%) were not lonely at either time point. After adjustment for sociodemographic, clinical and social variables, recent loneliness was associated with increases in depression (adjusted odds ratio [aOR] 1.76, 95% confidence interval [CI] 1.41-2.19) and anxiety (aOR 1.67, 95% CI 1.32-2.10). Previous loneliness was associated with decreases in depression (aOR, 0.46, 95% CI 0.36-0.58) and anxiety (aOR 0.69, 95% CI 0.54-0.90). Changes in loneliness were not associated with changes in general health. CONCLUSIONS: Changes in loneliness identified through screening were associated with corresponding changes in depression and anxiety. These findings support the potential value of identifying social risk factors in clinical settings among older adults.

Testing the added value of self-reported health and well-being in understanding healthcare utilization and costs.

PURPOSE: We investigated the relationship between measures of self-reported health and well-being and concurrent and prospective healthcare utilization and costs to assess the added value of these self-reported measures in understanding utilization and cost. METHODS: Kaiser Permanente members (N = 6752) completed a 9-item survey measuring life evaluation, financial situation, social support, meaning and purpose, physical health, and mental health. Responses were linked to medical record information during the period 12 months before and after the survey. RESULTS: Correlations between health and well-being measures and healthcare utilization and cost variables were generally weak, with stronger correlations for future life evaluation and selected health measures (ρ = .20-.33, ps < .001). Better overall life evaluation had a significant but weak association with lower total cost and hospital days in the following year after controlling for age, sex, and race/ethnicity (p < .001). Full multivariate models, adjusting for age, sex, race/ethnicity, prior utilization, and relative risk models, showed weak associations between health and well-being measures and following year total healthcare cost and utilization, though the associations were relatively stronger for the health variables than the well-being variables. CONCLUSION: Overall, the health and well-being variables added little to no predictive utility for future utilization and cost beyond prior utilization and cost and the inclusion of predictive models based on clinical information. Perceptions of well-being may be associated with factors beyond healthcare utilization. When information about prior use is unavailable, self-reported health items have some predictive utility.

Using the County Health Rankings Framework to Create National Percentile Scores for Health Outcomes and Health Factors.

INTRODUCTION: As a means of conceptualizing population health, the County Health Rankings & Roadmaps program developed a methodology to rank counties within each state on Health Outcomes and Health Factors. We built on this framework by introducing an additional application that utilized national percentile scores and population size weighting to compare counties on a national, rather than a state, level. METHODS: We created national percentile scores for 3078 US counties and used population size weighting in our calculations so that values for counties with larger populations would be weighted more heavily than values for counties with smaller populations. RESULTS: We demonstrated how this application can be used to 1) compare counties nationally, 2) examine clustering and variability among counties, and 3) compare the health of states and regions. To underscore its utility, we included an example application by Kaiser Permanente. As a form of method validation, the results of this application are in line with other ranking systems (eg, US News and World Report and United Health Foundation; ρ = 0.39 to 0.91, p < 0.001). DISCUSSION: This application can be used by communities and organizations that may be interested in comparing the health of counties, service areas, and regions in which they operate. We included additional considerations and highlighted some limitations for those interested in utilizing this application. CONCLUSION: By comparing counties nationally and utilizing population size weighting, community partners can focus on areas that may be of greatest need in moving toward a national Culture of Health.

Sociodemographic Determinants of Health and Well-Being Among Adults Residing in the Combined Kaiser Permanente Regions.

CONTEXT: Kaiser Permanente commissioned a health and well-being (HWB) survey of adult members and nonmembers in its 8 Regions. OBJECTIVE: To estimate the prevalence of HWB indicators and evaluate differences in prevalence of excellent/very good (E/VG) health and thriving overall in life (thriving) by race/ethnicity, age group, sex, education, and financial situation. DESIGN: Cross-sectional survey conducted by email and phone during Winter 2016-2017 with a racial/ethnic group-stratified quota sample. Participants (N = 26,304) provided sociodemographic characteristics and ratings for 6 HWB indicators. Using population-weighted data, we estimated the prevalence of HWB indicators and used logistic regression models to test for differences in E/VG health and thriving by sociodemographic factors. MAIN OUTCOME MEASURES: Overall health and overall life evaluation. RESULTS: Of adults, 52% were in E/VG health and 63% were thriving. Blacks were less likely to be in E/VG health than whites, Hispanics, and Asian/Pacific Islanders, but there was little racial/ethnic variation in those who were thriving. E/VG health and thriving varied significantly by level of education and financial situation. Across all racial/ethnic groups, large differences in percentages were observed in E/VG health and thriving between the lowest and highest levels of education and financial situation but little racial/ethnic variation within education and financial situation strata. CONCLUSION: Differences in health status and life evaluation are associated very strongly with financial situation and educational attainment, and these social determinants partially explain racial/ethnic disparities in HWB. The lack of strong correlation of health status and life evaluation suggests these are different domains of well-being.

Measuring Population Health in a Large Integrated Health System to Guide Goal Setting and Resource Allocation: A Proof of Concept.

In integrated health care systems, techniques that identify successes and opportunities for targeted improvement are needed. The authors propose a new method for estimating population health that provides a more accurate and dynamic assessment of performance and priority setting. Member data from a large integrated health system (n = 96,246, 73.8% female, mean age = 44 ± 0.01 years) were used to develop a mechanistic mathematical simulation, representing the top causes of US mortality in 2014 and their associated risk factors. An age- and sex-matched US cohort served as comparator group. The simulation was recalibrated and retested for validity employing the outcome measure of 5-year mortality. The authors sought to estimate potential population health that could be gained by improving health risk factors in the study population. Potential gains were assessed using both average life years (LY) gained and average quality-adjusted life years (QALYs) gained. The simulation validated well compared to integrated health system data, producing an AUC (area under the curve) of 0.88 for 5-year mortality. Current population health was estimated as a life expectancy of 84.7 years or 69.2 QALYs. Comparing potential health gain in the US cohort to the Kaiser Permanente cohort, eliminating physical inactivity, unhealthy diet, smoking, and uncontrolled diabetes resulted in an increase of 1.5 vs. 1.3 LY, 1.1 vs. 0.8 LY, 0.5 vs. 0.2 LY, and 0.5 vs. 0.5 LY on average per person, respectively. Using mathematical simulations may inform efforts by integrated health systems to target resources most effectively, and may facilitate goal setting.

Food Insecurity in Older Adults in an Integrated Health Care System.

OBJECTIVES: To estimate food insecurity prevalence and develop a statistical prediction model for food insecurity. DESIGN: Retrospective cohort study. SETTING: Kaiser Permanente Colorado. PARTICIPANTS: Adult members who completed a pre-Medicare Annual Wellness Visit survey. MEASUREMENTS: Food insecurity was assessed using a single screening question. Sociodemographic and clinical characteristics from electronic health records and self-reported characteristics from the survey were used to develop the prediction model. RESULTS: Of 130,208 older adult members between January 2012 and December 2015, 50,097 (38.5%) completed food insecurity screening, 2,859 of whom (5.7% of respondents) reported food insecurity. The prevalence of food insecurity was 10.0% or greater among individuals who were black or Hispanic, had less than high school education, had Medicaid insurance, were extremely obese, had poor health status or quality of life, had depression or anxiety, had impairments in specific activities of daily living, had other nutritional risk factors, or were socially isolated (all p<.001). A multivariable model based on these and other characteristics showed moderate discrimination (c-statistic = 0.74) between individuals with food insecurity and those without and 14.3% of individuals in the highest quintile of risk reported food insecurity. CONCLUSION: Food insecurity is prevalent even in older adults with private-sector healthcare coverage. Specific individual characteristics, and a model based on those characteristics, can identify older adults at higher risk of food insecurity. System-level interventions will be necessary to connect older adults with community-based food resources.

A healthy bottom line: healthy life expectancy as an outcome measure for health improvement efforts.

CONTEXT: Good health is the most important outcome of health care, and healthy life expectancy (HLE), an intuitive and meaningful summary measure combining the length and quality of life, has become a standard in the world for measuring population health. METHODS: This article critically reviews the literature and practices around the world for measuring and improving HLE and synthesizes that information as a basis for recommendations for the adoption and adaptation of HLE as an outcome measure in the United States. FINDINGS: This article makes the case for adoption of HLE as an outcome measure at the national, state, community, and health care system levels in the United States to compare the effectiveness of alternative practices, evaluate disparities, and guide resource allocation. CONCLUSIONS: HLE is a clear, consistent, and important population health outcome measure that can enable informed judgments about value for investments in health care.