Comparing Kaiser Permanente Members to the General Population: Implications for Generalizability of Research.

Introduction Insight into the characteristics of populations from which research samples are drawn is essential to understanding the generalizability of research findings. This study characterizes the membership of Kaiser Permanente and compares members to the population of the communities in which they live. Methods This study is a descriptive comparison of population distributions for Kaiser Permanente members vs the general population within counties in which Kaiser Permanente operates. Kaiser Permanente data on demographics, membership, geographically linked census data, and chronic condition prevalence were compared with community data drawn from the US Census and the Behavioral Risk Factor Surveillance System. Results Overall, Kaiser Permanente members were older (50% aged 40 or older compared to 45.8% of the general population) and more likely to be female (51.8% vs 50.5% of the general population). Distribution by race and ethnicity was similar for all Regions combined but varied somewhat within Regions. Distribution by neighborhood-linked income, education, and social vulnerability was similar between Kaiser Permanente and the community. Prevalence of 6 of 7 chronic conditions was higher in the community than in Kaiser Permanente, with differences ranging from 0.5% for depression to 7.7% for hyperlipidemia. Conclusion The demographic characteristics of Kaiser Permanente members are similar to the general population within each of the Kaiser Permanente Regions. Overall, the size and diversity of the Kaiser Permanente membership offers an effective platform for research. This approach to comparing health system members with the larger community provides valuable context for interpreting real-world evidence, including understanding the generalizability of research and of measures of system performance.

Telemedicine Familiarity and Post-Disaster Utilization of Emergency and Hospital Services for Ambulatory Care Sensitive Conditions.

INTRODUCTION: In this study, we examined the association between telemedicine use before a disaster and utilization of emergency or hospital services for ambulatory care sensitive conditions post-disaster. METHODS: Difference-in-differences analyses were conducted in 2020‒2021 to assess pre- to post-fire changes in emergency or hospital utilization for 5 ambulatory care sensitive conditions: asthma, diabetes, hypertension, coronary artery disease, and heart failure across all Kaiser Permanente Santa Rosa patients (N=108,113) based on telemedicine utilization before the 2017 Tubbs wildfire. Inverse probability of treatment weighting was employed for cohort balancing across telemedicine familiar status. RESULTS: Utilization for any ambulatory care sensitive condition increased from 9.03% pre-fire to 9.45% post-fire across the full cohort. Telemedicine familiarity (ref: not familiar) was associated with decreased absolute risk in pre- to post-fire inpatient and emergency department utilization for 4 conditions: asthma (absolute risk= -1.59%, 95% CI= -2.02%, -1.16%), diabetes (absolute risk= -0.68%, 95% CI= -0.89%, -0.47%), hypertension (absolute risk= -2.07%, 95% CI= -2.44%, -1.71%), and coronary artery disease (absolute risk= -0.43%, 95% CI= -0.61%, -0.24%). Telemedicine familiarity was associated with decreased relative change in pre- to post-fire utilization for 5 conditions: asthma (RRR=0.70, 95% CI=0.64, 0.75), diabetes (RRR=0.54, 95% CI=0.47, 0.63), hypertension (RRR=0.57, 95% CI=0.52, 0.62), heart failure (RRR=0.64, 95% CI=0.50, 0.82), and coronary artery disease (RRR=0.56, 95% CI=0.47, 0.67). Similar results were seen among patients residing in evacuation zones. CONCLUSIONS: Telemedicine familiarity pre-fire was associated with decreased inpatient and emergency department utilization for certain ambulatory care sensitive conditions for 1-year post-fire. These results suggest a role for telemedicine in preventing unnecessary emergency and hospital utilization following disasters.

Assessing Vulnerability to Urban Heat: A Study of Disproportionate Heat Exposure and Access to Refuge by Socio-Demographic Status in Portland, Oregon.

Extreme urban heat is a powerful environmental stressor which poses a significant threat to human health and well-being. Exacerbated by the urban heat island phenomenon, heat events are expected to become more intense and frequent as climate change progresses, though we have limited understanding of the impact of such events on vulnerable populations at a neighborhood or census block group level. Focusing on the City of Portland, Oregon, this study aimed to determine which socio-demographic populations experience disproportionate exposure to extreme heat, as well as the level of access to refuge in the form of public cooling centers or residential central air conditioning. During a 2014 heat wave, temperature data were recorded using a vehicle-traverse collection method, then extrapolated to determine average temperature at the census block group level. Socio-demographic factors including income, race, education, age, and English speaking ability were tested using statistical assessments to identify significant relationships with heat exposure and access to refuge from extreme heat. Results indicate that groups with limited adaptive capacity, including those in poverty and non-white populations, are at higher risk for heat exposure, suggesting an emerging concern of environmental justice as it relates to climate change. The paper concludes by emphasizing the importance of cultural sensitivity and inclusion, in combination with effectively distributing cooling centers in areas where the greatest burden befalls vulnerable populations.

Integrating High-Resolution Datasets to Target Mitigation Efforts for Improving Air Quality and Public Health in Urban Neighborhoods.

Reducing exposure to degraded air quality is essential for building healthy cities. Although air quality and population vary at fine spatial scales, current regulatory and public health frameworks assess human exposures using county- or city-scales. We build on a spatial analysis technique, dasymetric mapping, for allocating urban populations that, together with emerging fine-scale measurements of air pollution, addresses three objectives: (1) evaluate the role of spatial scale in estimating exposure; (2) identify urban communities that are disproportionately burdened by poor air quality; and (3) estimate reduction in mobile sources of pollutants due to local tree-planting efforts using nitrogen dioxide. Our results show a maximum value of 197% difference between cadastrally-informed dasymetric system (CIDS) and standard estimations of population exposure to degraded air quality for small spatial extent analyses, and a lack of substantial difference for large spatial extent analyses. These results provide the foundation for improving policies for managing air quality, and targeting mitigation efforts to address challenges of environmental justice.