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1.
J Racial Ethn Health Disparities ; 10(1): 149-159, 2023 02.
Article in English | MEDLINE | ID: mdl-35072944

ABSTRACT

COVID-19 inequities have been well-documented. We evaluated whether higher rates of severe COVID-19 in racial and ethnic minority groups were driven by higher infection rates by evaluating if disparities remained when analyses were restricted to people with infection. We conducted a retrospective cohort study of adults insured through Kaiser Permanente (Colorado, Northwest, Washington), follow-up in March-September 2020. Laboratory results and hospitalization diagnosis codes identified individuals with COVID-19. Severe COVID-19 was defined as invasive mechanical ventilation or mortality. Self-reported race and ethnicity, demographics, and medical comorbidities were extracted from health records. Modified Poisson regression estimated adjusted relative risks (aRRs) of severe COVID-19 in full cohort and among individuals with infection. Our cohort included 1,052,774 individuals, representing diverse racial and ethnic minority groups (e.g., 68,887 Asian, 41,243 Black/African American, 93,580 Hispanic or Latino/a individuals). Among 7,399 infections, 442 individuals experienced severe COVID-19. In the full cohort, severe COVID-19 aRRs for Asian, Black/African American, and Hispanic individuals were 2.09 (95% CI: 1.36, 3.21), 2.02 (1.39, 2.93), and 2.09 (1.57, 2.78), respectively, compared to non-Hispanic Whites. In analyses restricted to individuals with COVID-19, all aRRs were near 1, except among Asian Americans (aRR 1.82 [1.23, 2.68]). These results indicate increased incidence of severe COVID-19 among Black/African American and Hispanic individuals is due to higher infection rates, not increased susceptibility to progression. COVID-19 disparities most likely result from social, not biological, factors. Future work should explore reasons for increased severe COVID-19 risk among Asian Americans. Our findings highlight the importance of equity in vaccine distribution.


Subject(s)
COVID-19 , Ethnicity , Adult , Humans , Minority Groups , Retrospective Studies , White People , Asian , Black or African American , Hispanic or Latino
2.
Pharmacoepidemiol Drug Saf ; 31(9): 992-997, 2022 09.
Article in English | MEDLINE | ID: mdl-35670124

ABSTRACT

PURPOSE: To estimate the positive predictive value (PPV) of International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis codes for identifying HF subtypes. METHODS: We validated ICD-10-CM HF diagnosis codes among Kaiser Permanente Washington enrollees who were ≥18 years of age and had an ICD-10-CM HF diagnosis code during 2017-2018 and a procedure code for an echocardiogram in the 12 months before through 6 months after the HF code. Left ventricular ejection fraction (LVEF) ascertained from medical chart review was used as the gold standard for classifying patients as having reduced ejection fraction (rEF), mid-range ejection fraction (mEF), or preserved ejection fraction (pEF). RESULTS: Among 6194 eligible patients, we randomly sampled 1000 for medical chart review. A total of 974 patients had LVEF information in their chart. The ICD-10-CM HF code group with the highest PPV for rEF was I50.20-I50.23, "Systolic (congestive) heart failure," PPV = 41.4% (95% CI, 34.5-48.7%); and the highest PPV for mEF or rEF was also I50.20-I50.23, PPV = 70.2% (95% CI, 63.1-76.4%). The highest PPV for pEF was the I50.30-I50.33 group, "Diastolic (congestive) heart failure," PPV = 92.0% (95% CI, 88.1-94.7%); and the highest PPV for mEF or pEF was also I50.30-I50.33, PPV = 97.7% (95% CI, 95.1-99.0%). CONCLUSIONS: If the accuracy measure of greatest interest is PPV, our results suggest that ICD-10-CM HF codes alone may not be adequate for identifying patients with rEF but may be adequate for identifying patients with pEF. HF coding practices may vary across settings, which may impact generalizability of our findings.


Subject(s)
Heart Failure , International Classification of Diseases , Healthcare Common Procedure Coding System , Heart Failure/diagnosis , Heart Failure/epidemiology , Humans , Stroke Volume , Ventricular Function, Left
3.
J Robot Surg ; 12(1): 35-41, 2018 Mar.
Article in English | MEDLINE | ID: mdl-28247092

ABSTRACT

Equal access to novel surgical technologies remains a policy concern as hospitals adopt robotic surgery with increasing prevalence. This study sought to determine whether socioeconomic factors influence access to robotic surgery. All laparoscopic and robotic fundoplications and paraesophageal hernia repairs performed by a surgical group over 6 years at a county and two neighboring private hospitals were identified. Robotic use by hospital setting, age, gender, reported ethnicity, estimated income, insurance payer, and diagnosis were examined. Of 418 patients identified, 180 (43%) presented to the county hospital, where subjects were younger (51.1 versus 56.2 years, p < 0.001) with lower estimated income ($50,289 versus $62,959, p < 0.001). In the county setting, there was no difference in reported ethnicity (p = 0.169), estimated income (p = 0.081), or insurance payer (p = 0.535) between groups treated laparoscopically versus robotically. There was no difference in the treatment groups by estimated income in the private hospital setting (p = 0.308). Overall higher estimated income and insurance payer were associated with a higher chance of undergoing robotic procedures (p < 0.001). Presence of a paraesophageal hernia was associated with increased chance of undergoing robotic therapy in all comparisons (p < 0.001). No disparity in access to robotic surgery offered in the county hospital was observed based on age, gender, reported ethnicity, estimated income, or insurance payer. Patients with higher income and private insurers were more likely to present to the private hospital setting where robotics is utilized more often. The presence of a paraesophageal hernia was a significant factor in determining robotic therapy in both settings.


Subject(s)
Health Services Accessibility/statistics & numerical data , Hernia, Hiatal/surgery , Herniorrhaphy/statistics & numerical data , Robotic Surgical Procedures/statistics & numerical data , Age Distribution , Female , Healthcare Disparities/statistics & numerical data , Hospitals, County/statistics & numerical data , Hospitals, Private/statistics & numerical data , Humans , Income , Insurance Coverage , Insurance, Health/statistics & numerical data , Laparoscopy/statistics & numerical data , Male , Middle Aged , Sex Distribution , Socioeconomic Factors , Texas
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