scholarly journals Integrating Social Determinants of Health and Laboratory Data: A Pilot Study To Evaluate Co-Use of Opioids and Benzodiazepines

2019 ◽  
Vol 6 ◽  
pp. 237428951988487 ◽  
Author(s):  
Jill S. Warrington ◽  
Nick Lovejoy ◽  
Jamie Brandon ◽  
Keith Lavoie ◽  
Chris Powell

As the opioid crisis continues to have devastating consequences for our communities, families, and patients, innovative approaches are necessary to augment clinical care and the management of patients with opioid use disorders. As stewards of health analytic data, laboratories are uniquely poised to approach the opioid crisis differently. With this pilot study, we aimed to bridge laboratory data with social determinants of health data, which are known to influence morbidity and mortality of patients with substance use disorders. For the purpose of this pilot study, we focused on the co-use of opioids and benzodiazepines, which can lead to an increased risk of fatal opioid-related overdoses and increased utilization of acute care. Using the laboratory finding of the copresence of benzodiazepines and opioids as the primary outcome measure, we examined social determinants of health attributes that predict co-use. We found that the provider practice that ordered the laboratory result is the primary predictor of co-use. Increasing age was also predictive of co-use. Further, co-use is highly prevalent in specific geographic areas or “hotspots.” The prominent geographic distribution of co-use suggests that targeted educational initiatives may benefit the communities in which co-use is prevalent. This study exemplifies the Clinical Lab 2.0 approach by leveraging laboratory data to gain insights into the overall health of the patient.

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S151-S152
Author(s):  
Luis H Quiroga ◽  
Tomer Lagziel ◽  
Mohammed Asif ◽  
Raymond Fang ◽  
Grace F Rozycki ◽  
...  

Abstract Introduction To our knowledge, no studies have been conducted assessing the social determinants of health and the impact on the outcomes for burn patients. Such studies are needed considering burn injuries are associated with high costs, severe psychological impact, and a high burden placed on the healthcare systems. The burden is hypothesized to be aggravated by the increasing amount of diabetes and obesity seen in the general population which put patients at increased risk for developing chronic wounds. Studies have shown that several socioeconomic status (SES) factors are associated with increased risk of burns, but none have documented the outcomes of burn patients based on their social determinants of health. In our study, we will be comparing patients in the burn ICU (BICU) to patients in the surgical ICU (SICU). The purpose of this comparison is to evaluate whether the same social determinants of health have similar influences in both groups. Methods We performed a retrospective analysis of population group data from patients admitted to the BICU and SICU from January 1, 2016, to November 18, 2019. The primary outcomes were length-of-stay (LOS), mortality, 30-day-readmission, and hospital charges. Pearson’s chi-square test for categorical variables and t-test for continuous variables were used to compare population health groups. Results We analyzed a total of 487 burn and 510 surgical patients. When comparing BICU and SICU patients, we observed significantly higher mean hospital charges and LOS in burn patients with a history of mental health (mean difference: $42,756.04, p=0.013 and 7.12 days, p=0.0085), ESRD ($57,8124.7, p=0.0047 and 78.62 days, p=0.0104), sepsis ($168,825.19, p=< 0.001 and 20.68 days, p=0.0043), and VTE ($63,9924.1, p=< 0.001 and 72.9 days, p=0.002). Also, higher mortality was observed in burn patients with ESRD, STEMI, sepsis, VTE, and diabetes mellitus. Burn patients with a history of mental health, drug dependence, heart failure, and diabetes mellitus also had greater 30-day-readmissions rates. Conclusions This study sheds new knowledge on the considerable variability that exists between the different population health groups in terms of outcomes for each cohort of critically ill patients. It demonstrates the impacts of population health group on outcomes. These population groups and social determinants have different effects on BICU versus SICU patients and this study provides supporting evidence for the need to identify and develop new strategies to decrease overspending in healthcare. Further research to develop relevant and timely interventions that can improve these outcomes.


2020 ◽  
Vol 75 (11) ◽  
pp. 801
Author(s):  
Anekwe E. Onwuanyi ◽  
Diane Wirth ◽  
Faith Works-Fleming ◽  
Andrea Cafarelli ◽  
Michael Knauss ◽  
...  

Author(s):  
Monika M. Safford ◽  
Evgeniya Reshetnyak ◽  
Madeline R. Sterling ◽  
Joshua S. Richman ◽  
Paul M. Muntner ◽  
...  

Background: Social determinants of health (SDH) are individually associated with incident coronary heart disease (CHD) events. Indices reflecting social deprivation have been developed for population management, but are difficult to operationalize during clinical care. We examined whether a simple count of SDH is associated with fatal incident CHD and nonfatal myocardial infarction (MI). Methods: We used data from the prospective longitudinal REasons for Geographic And Racial Differences in Stroke cohort study, a national population-based sample of community-dwelling Black and white adults age ≥45 years recruited from 2003-7. Seven SDH from the five Healthy People 2020 domains included social context (Black race, social isolation); education (educational attainment); economic stability (annual household income); neighborhood (living in a zip code with high poverty); and healthcare (lacking health insurance, living in one of the 9 US states with the least public health infrastructure). Outcomes were expert adjudicated fatal incident CHD and nonfatal MI. Results: Of 22,152 participants free of CHD at baseline, 58.8% were women, 42.0% were Blacks, 20.6% had no SDH, 30.6% had 1, 23.0% had 2, and 25.8% had ≥3. There were 463 fatal incident CHD events and 932 nonfatal MIs over median 10.7 years [IQR 6.6-12.7]. Fewer SDH were associated with nonfatal MI than with fatal incident CHD. The age-adjusted incidence per 1000 person-years increased with the number of SDH for both fatal incident CHD (0 SDH 1.30, 1 SDH 1.44, 2 SDH 2.05, ≥3 SDH 2.86) and nonfatal MI (0 SDH 3.91, 1 SDH 4.33, ≥2 SDH 5.44). Compared to those without SDH, crude and fully adjusted hazard ratios (HR) for fatal incident CHD among those with ≥3 SDH were 3.00 (95% CI 2.17, 4.15) and 1.67 (95% CI 1.18, 2.37), respectively; and that for nonfatal MI among those with ≥2 SDH were 1.57 (95% CI 1.30, 1.90) and 1.14 (0.93, 1.41), respectively. Conclusions: A greater burden of SDH was associated with a graded increase in risk of incident CHD, with greater magnitude and independent associations for fatal incident CHD. Counting the number of SDH may be a promising approach that could be incorporated into clinical care to identify individuals at high risk of CHD.


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