A National Snapshot of Social Determinants of Health Documentation in Emergency Departments.

INTRODUCTION: Documentation and measurement of social determinants of health (SDoH) are critical to clinical care and to healthcare delivery system reforms targeting health equity. The SDoH are codified in the International Classification of Disease 10th Rev (ICD-10) Z codes. However, Z codes are listed in only 1-2% of inpatient charts. Little is known about the frequency of Z code utilization specifically among emergency department (ED) patient populations nationally. METHODS: This was a repeated cross-sectional analysis of ED visit data in the United States from the Nationwide Emergency Department Sample from 2016-2019. We characterized the use of Z codes and described associations between Z code use and patient- and hospital-level factors including the following: age; gender; race; insurance status; ED disposition; ED size; hospital urban-rural status; ownership; and clinical conditions. We calculated unadjusted odds ratios for likelihood of Z code reporting for each ED visit. RESULTS: Of approximately 140 million ED visits per year, 0.65% had an associated Z code in 2016, rising to 1.17% by 2019. Visits were more likely to have an associated Z code for adults age <65, male, Black, Medicaid or self-pay patients, and patients admitted to the hospital. Larger EDs, those in metropolitan areas, academic centers, and government-run hospitals were more likely to report Z codes. The most commonly associated clinical conditions were as follows: schizophrenia spectrum and other psychotic disorders; depressive disorder; and alcohol-related disorders. CONCLUSION: There is a paucity of Z code documentation in the health records of ED patients, although use is uptrending. Further research is warranted to better understand the drivers of clinicians' use of Z codes and to improve on their utility.

Analyzing fast and slow: Combining traditional and rapid qualitative analysis to meet multiple objectives of a complex transnational study.

Much of the methodological literature on rapid qualitative analysis describes processes used by a relatively small number of researchers focusing on one study site and using rapid analysis to replace a traditional analytical approach. In this paper, we describe the experiences of a transnational research consortium integrating both rapid and traditional qualitative analysis approaches to develop social theory while also informing program design. Research was conducted by the Innovations for Choice and Autonomy (ICAN) consortium, which seeks to understand how self-injection of the contraceptive subcutaneous depot medroxyprogesterone acetate (DMPA-SC) can be implemented in a way that best meets women's needs, as defined by women themselves. Consortium members are based in Kenya, Uganda, Malawi, Nigeria, and the United States. Data for the ICAN study was collected in all four countries in sub-Saharan Africa. In order to both illuminate social phenomena across study sites and inform the program design component of the study, researchers developed tools meant to gather both in-depth information about women's contraceptive decision-making and data targeted specifically to program design during the formative qualitative phase of the study. Using these two bodies of data, researchers then simultaneously conducted both a traditional qualitative and rapid analysis to meet multiple study objectives. To complete the traditional analysis, researchers coded interview transcripts and kept analytical memos, while also drawing on data collected by tools developed for the rapid analysis. Rapid analysis consisted of simultaneously collecting data and reviewing notes developed specifically for this analysis. We conclude that integrating traditional and rapid qualitative analysis enabled us to meet the needs of a complex transnational study with the added benefit of grounding our program design work in more robust primary data than normally is available for studies using a human-centered design approach to intervention development. However, the realities of conducting a multi-faceted study across multiple countries and contexts made truly "rapid" analysis challenging.

Adapting and Validating the G-NORM (Gender Norms Scale) in Nepal: An Examination of How Gender Norms Are Associated with Agency and Reproductive Health Outcomes.

Research calls for the sexual and reproductive rights field to prioritize gender norms to ensure that women can act on their reproductive rights. However, there is a gap in accepted measures. We addressed this by including important theoretical components of gender norms: differentiating between descriptive and injunctive norms and adding a referent group. Our team originally developed and validated the G-NORM, a gender norms scale, in India. In this paper, we describe how we subsequently adapted and validated it in Nepal. We administered items to women of reproductive age, conducted exploratory and confirmatory factor analysis, and examined associations between the subscales and reproductive health outcomes. Like the original G-NORM, our factor analyses showed that descriptive norms and injunctive norms comprise two distinct scales which fit the data well and had Cronbach alphas of 0.92 and 0.89. More equitable descriptive gender norms were associated with higher decision-making scores, increased odds of intending to use family planning, disagreeing that it is wrong to use family planning, and older ideal age at marriage. Injunctive gender norms were only associated with disagreeing that it is wrong to use family planning. Findings offer an improved measure of gender norms in Nepal and provide evidence that gender norms are critical for agency and reproductive health outcomes.

Predicting a Need for Financial Assistance in Emergency Department Care.

Identifying patients with a low likelihood of paying their bill serves the needs of patients and providers alike: aligning government programs with their target beneficiaries while minimizing patient frustration and reducing waste among emergency physicians by streamlining the billing process. The goal of this study was to predict the likelihood of patients paying the balance of their emergency department visit bill within 90 days of receipt. Three machine learning methodologies were applied to predict payment: logistic regression, decision tree, and random forest. Models were trained and performance was measured using 1,055,941 patients with non-zero balances across 27 EDs from 1 August 2015 to 31 July 2017. The decision tree accurately predicted 87% of unsuccessful payments, providing significant opportunities to identify patients in need of financial assistance.

Decreased hospital admissions through emergency departments during the COVID-19 pandemic.

STUDY OBJECTIVE: Emergency Department (ED) visits decreased significantly in the United States during the COVID-19 pandemic. A troubling proportion of this decrease was among patients who typically would have been admitted to the hospital, suggesting substantial deferment of care. We sought to describe and characterize the impact of COVID-19 on hospital admissions through EDs, with a specific focus on diagnosis group, age, gender, and insurance coverage. METHODS: We conducted a retrospective, observational study of aggregated third-party, anonymized ED patient data. This data included 501,369 patient visits from twelve EDs in Massachusetts from 1/1/2019-9/9/2019, and 1/1/2020-9/8/2020. We analyzed the total arrivals and hospital admissions and calculated confidence intervals for the change in admissions for each characteristic. We then developed a Poisson regression model to estimate the relative contribution of each characteristic to the decrease in admissions after the statewide lockdown, corresponding to weeks 11 through 36 (3/11/2020-9/8/2020). RESULTS: We observed a 32% decrease in admissions during weeks 11 to 36 in 2020, with significant decreases in admissions for chronic respiratory conditions and non-orthopedic needs. Decreases were particularly acute among women and children, as well as patients with Medicare or without insurance. The most common diagnosis during this time was SARS-CoV-2. CONCLUSION: Our findings demonstrate decreased hospital admissions through EDs during the pandemic and suggest that several patient populations may have deferred necessary care. Further research is needed to determine the clinical and operational consequences of this delay.