Correction: The Story of #GetMePPE and GetUsPPE.org to Mobilize Health Care Response to COVID-19 : Rapidly Deploying Digital Tools for Better Health Care.

[This corrects the article DOI: 10.2196/20469.].

Medicaid Expansion Reduced Emergency Department Visits by Low-income Adults Due to Barriers to Outpatient Care.

BACKGROUND: Prior studies have found conflicting effects of Medicaid expansion on emergency department (ED) utilization but have not studied the reasons patients go to EDs. OBJECTIVES: Examine the changes in reasons for ED use associated with Medicaid expansion. RESEARCH DESIGN: Difference-in-difference analysis. SUBJECTS: We included sample adults from the 2012 to 2017 National Health Interview Survey who were US citizens and reported a total family income below 138% federal poverty level (n=30,259). MEASURES: We examined changes in the proportion of study subjects reporting: (1) any ED visits; (2) ED visits due to perceived illness severity; (3) office not open; and (4) barriers to outpatient care, comparing expansion and nonexpansion states. RESULTS: Overall, 30.6% of low-income adults reported ED use in the past year, of which 74.1% reported illness acuity, 12.4% reported office not open, 9.5% reported access barriers, and 4.0% did not report any reason. Medicaid expansion was not associated with statistically significant changes in overall ED use [-2.2% (95% confidence interval-CI), -5.5% to 1.2%), P=0.21], ED visits due to perceived illness severity [0.5% (95% CI, -2.4% to 3.5%), P=0.73], or office not open [-0.9% (95% CI, -2.3% to 0.5%); P=0.22], but was associated with significant decrease in ED visits due to access barriers [-1.4% (95% CI, -2.6% to -0.2%), P=0.022]. CONCLUSIONS: Medicaid expansion was associated with a decrease in low-income adults who reported outpatient care barriers as reasons for ED visits. There were no significant changes in overall ED utilization, likely because the majority of respondent reported ED use due to concerns with illness severity or outpatient office was closed.

The Story of #GetMePPE and GetUsPPE.org to Mobilize Health Care Response to COVID-19 : Rapidly Deploying Digital Tools for Better Health Care.

Physicians, nurses, and other health care providers initiated the #GetMePPE movement on Twitter to spread awareness of the shortage of personal protective equipment (PPE) during the coronavirus disease (COVID-19) pandemic. Dwindling supplies, such as face masks, gowns and goggles, and inadequate production to meet increasing demand have placed health care workers and patients at risk. The momentum of the #GetMePPE Twitter hashtag resulted in the creation of a petition to urge public officials to address the PPE shortage through increased funding and production. Simultaneously, the GetUsPPE.org website was launched through the collaboration of physicians and software engineers to develop a digital platform for the donation, request, and distribution of multi-modal sources of PPE. GetUsPPE.org and #GetMePPE were merged in an attempt to combine public engagement and advocacy on social media with the coordination of PPE donation and distribution. Within 10 days, over 1800 hospitals and PPE suppliers were registered in a database that enabled the rapid coordination and distribution of scarce and in-demand materials. One month after its launch, the organization had distributed hundreds of thousands of PPE items and had built a database of over 6000 PPE requesters. The call for action on social media and the rapid development of this digital tool created a productive channel for the public to contribute to the health care response to COVID-19 in meaningful ways. #GetMePPE and GetUsPPE.org were able to mobilize individuals and organizations outside of the health care system to address the unmet needs of the medical community. The success of GetUsPPE.org demonstrates the potential of digital tools as a platform for larger health care institutions to rapidly address urgent issues in health care. In this paper, we outline this process and discuss key factors determining success.

Cities as Platforms for Population Health: Past, Present, and Future.

Policy Points Cities have long driven innovation in public health in response to shifting trends in the burden of disease for populations. Today, the challenges facing municipal health departments include the persistent prevalence of chronic disease and deeply entrenched health inequities, as well as the evolving threats posed by climate change, political gridlock, and surging behavioral health needs. Surmounting these challenges will require generational investment in local public health infrastructure, drawn both from new governmental allocation and from innovative financing mechanisms that allow public health agencies to capture more of the value they create for society. Additional funding must be paired with the local development of public health data systems and the implementation of evidence-based strategies, including community health workers and the co-localization of clinical services and social resources as part of broader efforts to bridge the gap between public health and health care. Above all, advancing urban health demands transformational public policy to tackle inequality and reduce poverty, to address racism as a public health crisis, and to decarbonize infrastructure. One strategy to help achieve these ambitious goals is for cities to organize into coalitions that harness their collective power as a force to improve population health globally.

Association Between Community-Level Social Risk and Spending Among Medicare Beneficiaries: Implications for Social Risk Adjustment and Health Equity.

Importance: Payers are increasingly using approaches to risk adjustment that incorporate community-level measures of social risk with the goal of better aligning value-based payment models with improvements in health equity. Objective: To examine the association between community-level social risk and health care spending and explore how incorporating community-level social risk influences risk adjustment for Medicare beneficiaries. Design, Setting, and Participants: Using data from a Medicare Advantage plan linked with survey data on self-reported social needs, this cross-sectional study estimated health care spending health care spending was estimated as a function of demographics and clinical characteristics, with and without the inclusion of Area Deprivation Index (ADI), a measure of community-level social risk. The study period was January to December 2019. All analyses were conducted from December 2021 to August 2022. Exposures: Census block group-level ADI. Main Outcomes and Measures: Regression models estimated total health care spending in 2019 and approximated different approaches to social risk adjustment. Model performance was assessed with overall model calibration (adjusted R2) and predictive accuracy (ratio of predicted to actual spending) for subgroups of potentially vulnerable beneficiaries. Results: Among a final study population of 61 469 beneficiaries (mean [SD] age, 70.7 [8.9] years; 35 801 [58.2%] female; 48 514 [78.9%] White; 6680 [10.9%] with Medicare-Medicaid dual eligibility; median [IQR] ADI, 61 [42-79]), ADI was weakly correlated with self-reported social needs (r = 0.16) and explained only 0.02% of the observed variation in spending. Conditional on demographic and clinical characteristics, every percentile increase in the ADI (ie, more disadvantage) was associated with a $11.08 decrease in annual spending. Directly incorporating ADI into a risk-adjustment model that used demographics and clinical characteristics did not meaningfully improve model calibration (adjusted R2 = 7.90% vs 7.93%) and did not significantly reduce payment inequities for rural beneficiaries and those with a high burden of self-reported social needs. A postestimation adjustment of predicted spending for dual-eligible beneficiaries residing in high ADI areas also did not significantly reduce payment inequities for rural beneficiaries or beneficiaries with self-reported social needs. Conclusions and Relevance: In this cross-sectional study of Medicare beneficiaries, the ADI explained little variation in health care spending, was negatively correlated with spending conditional on demographic and clinical characteristics, and was poorly correlated with self-reported social risk factors. This prompts caution and nuance when using community-level measures of social risk such as the ADI for social risk adjustment within Medicare value-based payment programs.