Contemporary national trends and disparities for head CT use in emergency department settings: Insights from National Hospital Ambulatory Medical Care Survey (NHAMCS) 2007-2017.

BACKGROUND: The exponential growth in CT utilization in emergency department (ED) until 2008 raised concerns regarding cost and radiation exposure. Head CT was one of the commonest studies. This led to mitigating efforts such as appropriate use guidelines, policy and payment reforms. The impact of these efforts is not fully understood. In addition, disparities in outcomes of acute conditions presenting to the ED is well known however recent trends in imaging utilization patterns and disparities are not well understood. In this study, we describe nationwide trends and disparities associated with head CT in ED settings between 2007 and 2014. METHODS: We analyzed 2007-2017 National Hospital Ambulatory Medical Care Survey (NHAMCS) with the primary goal to assess the rate and patterns of head CT imaging in ED. RESULTS: There were an estimated 117 million in 2007 and 139 million ED visits in 2017. There was a 4% increase in the any CT use in 2017 compared to 2007. No significant change in head CT utilization rate was seen. The 2007 head CT rate was 6.7% (95% CI: 6.1-7.3) compared to 7.7% (95% CI: 6.8-8.6) in 2017. Trauma, Headache and Dizziness are the top three indications for head CT use in the ED respectively. On adjusted analyses, significantly higher head CT utilization was seen in elderly, (age>65 yrs) and significantly lower utilization rate was seen in Non-Hispanic Black and Medicaid patients, and patients in rural locations. CONCLUSIONS: Previously reported exponential growth of CT use in ED is no longer seen. In particular, there was no significant change in ED head CT use between 2007 and 2017. Headache and Dizziness remain commonly used indications despite limited utility in most clinical scenarios, indicating continued need for appropriate use of imaging. There is significantly lower CT utilization in Non-Hispanic Black, Medicaid patients and those in rural locations, suggesting disparities in diagnostic work-up in marginalized and rural populations. This underscores the need for standardizing care regardless of race, insurance status and location.

Social Determinants of Adherence to COVID-19 Risk Mitigation Measures Among Adults With Cardiovascular Disease.

BACKGROUND: Social determinants of health (SDOH) may limit the practice of coronavirus disease 2019 (COVID-19) risk mitigation guidelines with health implications for individuals with underlying cardiovascular disease (CVD). Population-based evidence of the association between SDOH and practicing such mitigation strategies in adults with CVD is lacking. We used the National Opinion Research Center's COVID-19 Household Impact Survey conducted between April and June 2020 to evaluate sociodemographic disparities in adherence to COVID-19 risk mitigation measures in a sample of respondents with underlying CVD representing 18 geographic areas of the United States. METHODS: CVD status was ascertained by self-reported history of receiving heart disease, heart attack, or stroke diagnosis. We built de novo, a cumulative index of SDOH burden using education, insurance, economic stability, 30-day food security, urbanicity, neighborhood quality, and integration. We described the practice of measures under the broad strategies of personal protection (mask, hand hygiene, and physical distancing), social distancing (avoiding crowds, restaurants, social activities, and high-risk contact), and work flexibility (work from home, canceling/postponing work). We reported prevalence ratios and 95% CIs for the association between SDOH burden (quartiles of cumulative indices) and practicing these measures adjusting for age, sex, race/ethnicity, comorbidity, and interview wave. RESULTS: Two thousand thirty-six of 25 269 (7.0%) adults, representing 8.69 million in 18 geographic areas of the United States, reported underlying CVD. Compared with the least SDOH burden, fewer individuals with the greatest SDOH burden practiced all personal protection (75.6% versus 89.0%) and social distancing measures (41.9% versus 58.9%) and had any flexible work schedule (26.2% versus 41.4%). These associations remained statistically significant after full adjustment: personal protection (prevalence ratio, 0.83 [95% CI, 0.73-0.96]; P=0.009), social distancing (prevalence ratio, 0.69 [95% CI, 0.51-0.94]; P=0.018), and work flexibility (prevalence ratio, 0.53 [95% CI, 0.36-0.79]; P=0.002). CONCLUSIONS: SDOH burden is associated with lower COVID-19 risk mitigation practices in the CVD population. Identifying and prioritizing individuals whose medical vulnerability is compounded by social adversity may optimize emerging preventive efforts, including vaccination guidelines.

Rapid Response to Drive COVID-19 Research in a Learning Health Care System: Rationale and Design of the Houston Methodist COVID-19 Surveillance and Outcomes Registry (CURATOR).

BACKGROUND: The COVID-19 pandemic has exacerbated the challenges of meaningful health care digitization. The need for rapid yet validated decision-making requires robust data infrastructure. Organizations with a focus on learning health care (LHC) systems tend to adapt better to rapidly evolving data needs. Few studies have demonstrated a successful implementation of data digitization principles in an LHC context across health care systems during the COVID-19 pandemic. OBJECTIVE: We share our experience and provide a framework for assembling and organizing multidisciplinary resources, structuring and regulating research needs, and developing a single source of truth (SSoT) for COVID-19 research by applying fundamental principles of health care digitization, in the context of LHC systems across a complex health care organization. METHODS: Houston Methodist (HM) comprises eight tertiary care hospitals and an expansive primary care network across Greater Houston, Texas. During the early phase of the pandemic, institutional leadership envisioned the need to streamline COVID-19 research and established the retrospective research task force (RRTF). We describe an account of the structure, functioning, and productivity of the RRTF. We further elucidate the technical and structural details of a comprehensive data repository-the HM COVID-19 Surveillance and Outcomes Registry (CURATOR). We particularly highlight how CURATOR conforms to standard health care digitization principles in the LHC context. RESULTS: The HM COVID-19 RRTF comprises expertise in epidemiology, health systems, clinical domains, data sciences, information technology, and research regulation. The RRTF initially convened in March 2020 to prioritize and streamline COVID-19 observational research; to date, it has reviewed over 60 protocols and made recommendations to the institutional review board (IRB). The RRTF also established the charter for CURATOR, which in itself was IRB-approved in April 2020. CURATOR is a relational structured query language database that is directly populated with data from electronic health records, via largely automated extract, transform, and load procedures. The CURATOR design enables longitudinal tracking of COVID-19 cases and controls before and after COVID-19 testing. CURATOR has been set up following the SSoT principle and is harmonized across other COVID-19 data sources. CURATOR eliminates data silos by leveraging unique and disparate big data sources for COVID-19 research and provides a platform to capitalize on institutional investment in cloud computing. It currently hosts deeply phenotyped sociodemographic, clinical, and outcomes data of approximately 200,000 individuals tested for COVID-19. It supports more than 30 IRB-approved protocols across several clinical domains and has generated numerous publications from its core and associated data sources. CONCLUSIONS: A data-driven decision-making strategy is paramount to the success of health care organizations. Investment in cross-disciplinary expertise, health care technology, and leadership commitment are key ingredients to foster an LHC system. Such systems can mitigate the effects of ongoing and future health care catastrophes by providing timely and validated decision support.