Latent Tuberculosis Infection Testing Practices in a Large US Integrated Healthcare System.

BACKGROUND: Tuberculosis (TB) is a public health threat, with >80% of active TB in the United States occurring due to reactivation of latent TB infection (LTBI). We may be underscreening those with high risk for LTBI and overtesting those at lower risk. A better understanding of gaps in current LTBI testing practices in relation to LTBI test positivity is needed. METHODS: This study, conducted between 1 January 2008 and 31 December 2019 at Kaiser Permanente Southern California, included individuals aged ≥18 years without a history of active TB. We examined factors associated with LTBI testing and LTBI positivity. RESULTS: Among 3 816 884 adults (52% female, 37% White, 37% Hispanic, mean age 43.5 years [standard deviation, 16.1]), 706 367 (19%) were tested for LTBI, among whom 60 393 (9%) had ≥1 positive result. Among 1 211 971 individuals who met ≥1 screening criteria for LTBI, 210 025 (17%) were tested for LTBI. Factors associated with higher adjusted odds of testing positive included male sex (1.32; 95% confidence interval, 1.30-1.35), Asian/Pacific Islander (2.78, 2.68-2.88), current smoking (1.24, 1.20-1.28), diabetes (1.13, 1.09-1.16), hepatitis B (1.45, 1.34-1.57), hepatitis C (1.54, 1.44-1.66), and birth in a country with an elevated TB rate (3.40, 3.31-3.49). Despite being risk factors for testing positive for LTBI, none of these factors were associated with higher odds of LTBI testing. CONCLUSIONS: Current LTBI testing practices may be missing individuals at high risk of LTBI. Additional work is needed to refine and implement screening guidelines that appropriately target testing for those at highest risk for LTBI.

Trends from 2008 to 2018 in Electricity-dependent Durable Medical Equipment Rentals and Sociodemographic Disparities.

BACKGROUND: Duration and number of power outages have increased over time, partly fueled by climate change, putting users of electricity-dependent durable medical equipment (hereafter, "durable medical equipment") at particular risk of adverse health outcomes. Given health disparities in the United States, we assessed trends in durable medical equipment rental prevalence and individual- and area-level sociodemographic inequalities. METHODS: Using Kaiser Permanente South California electronic health record data, we identified durable medical equipment renters. We calculated annual prevalence of equipment rental and fit hierarchical generalized linear models with ZIP code random intercepts, stratified by rental of breast pumps or other equipment. RESULTS: 243,559 KPSC members rented durable medical equipment between 2008 and 2018. Rental prevalence increased over time across age, sex, racial-ethnic, and Medicaid categories, most by >100%. In adjusted analyses, Medicaid use was associated with increased prevalence and 108 (95% confidence interval [CI] = 99, 117) additional days of equipment rental during the study period. ZIP code-level sociodemographics were associated with increased prevalence of equipment rentals, for example, a 1 SD increase in percent unemployed and

Development and validation of a prediction algorithm to identify birth in countries with high tuberculosis incidence in two large California health systems.

OBJECTIVE: Though targeted testing for latent tuberculosis infection ("LTBI") for persons born in countries with high tuberculosis incidence ("HTBIC") is recommended in health care settings, this information is not routinely recorded in the electronic health record ("EHR"). We develop and validate a prediction model for birth in a HTBIC using EHR data. MATERIALS AND METHODS: In a cohort of patients within Kaiser Permanente Southern California ("KPSC") and Kaiser Permanent Northern California ("KPNC") between January 1, 2008 and December 31, 2019, KPSC was used as the development dataset and KPNC was used for external validation using logistic regression. Model performance was evaluated using area under the receiver operator curve ("AUCROC") and area under the precision and recall curve ("AUPRC"). We explored various cut-points to improve screening for LTBI. RESULTS: KPSC had 73% and KPNC had 54% of patients missing country-of-birth information in the EHR, leaving 2,036,400 and 2,880,570 patients with EHR-documented country-of-birth at KPSC and KPNC, respectively. The final model had an AUCROC of 0.85 and 0.87 on internal and external validation datasets, respectively. It had an AUPRC of 0.69 and 0.64 (compared to a baseline HTBIC-birth prevalence of 0.24 at KPSC and 0.19 at KPNC) on internal and external validation datasets, respectively. The cut-points explored resulted in a number needed to screen from 7.1-8.5 persons/positive LTBI diagnosis, compared to 4.2 and 16.8 persons/positive LTBI diagnosis from EHR-documented birth in a HTBIC and current screening criteria, respectively. DISCUSSION: Using logistic regression with EHR data, we developed a simple yet useful model to predict birth in a HTBIC which decreased the number needed to screen compared to current LTBI screening criteria. CONCLUSION: Our model improves the ability to screen for LTBI in health care settings based on birth in a HTBIC.