Last data update: May 13, 2024. (Total: 46773 publications since 2009)
Records 1-4 (of 4 Records) |
Query Trace: Readhead A[original query] |
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Association of area-based socioeconomic measures with tuberculosis incidence in California
Bakhsh Y , Readhead A , Flood J , Barry P . J Immigr Minor Health 2022 1-10 We assessed the association of area-based socio-economic status (SES) measures with tuberculosis (TB) incidence in California. We used TB disease data for 2012-2016 (n=9901), population estimates, and SES measures to calculate incidence rates, rate ratios, and 95% confidence intervals (95% CI) by SES and birth country. SES was measured by census tract and was categorized by quartiles for education, crowding, and the California Healthy Places Index (HPI)and by specific cutoffs for poverty. The lowest SES areas defined by education, crowding, poverty, and HPI had 39%, 40%, 41%, and 33% of TB cases respectively. SES level was inversely associated with TB incidence across all SES measures and birth countries. TB rates were 3.2 (95% CI 3.0-3.4), 2.1 (95% CI 1.9-2.2), 3.6 (95% CI 3.3-3.8), and 2.0 (95% CI 1.9-2.1) times higher in lowest SES areas vs. highest SES areas as defined by education, crowding, poverty and HPI respectively. Area-based SES measures are associated with TB incidence in California. This information could inform TB prevention efforts in terms of materials, partnerships, and prioritization. |
State-level prevalence estimates of latent tuberculosis infection in the United States by medical risk factors, demographic characteristics and nativity.
Mirzazadeh A , Kahn JG , Haddad MB , Hill AN , Marks SM , Readhead A , Barry PM , Flood J , Mermin JH , Shete PB . PLoS One 2021 16 (4) e0249012 INTRODUCTION: Preventing tuberculosis (TB) disease requires treatment of latent TB infection (LTBI) as well as prevention of person-to-person transmission. We estimated the LTBI prevalence for the entire United States and for each state by medical risk factors, age, and race/ethnicity, both in the total population and stratified by nativity. METHODS: We created a mathematical model using all incident TB disease cases during 2013-2017 reported to the National Tuberculosis Surveillance System that were classified using genotype-based methods or imputation as not attributed to recent TB transmission. Using the annual average number of TB cases among US-born and non-US-born persons by medical risk factor, age group, and race/ethnicity, we applied population-specific reactivation rates (and corresponding 95% confidence intervals [CI]) to back-calculate the estimated prevalence of untreated LTBI in each population for the United States and for each of the 50 states and the District of Columbia in 2015. RESULTS: We estimated that 2.7% (CI: 2.6%-2.8%) of the U.S. population, or 8.6 (CI: 8.3-8.8) million people, were living with LTBI in 2015. Estimated LTBI prevalence among US-born persons was 1.0% (CI: 1.0%-1.1%) and among non-US-born persons was 13.9% (CI: 13.5%-14.3%). Among US-born persons, the highest LTBI prevalence was in persons aged ≥65 years (2.1%) and in persons of non-Hispanic Black race/ethnicity (3.1%). Among non-US-born persons, the highest LTBI prevalence was estimated in persons aged 45-64 years (16.3%) and persons of Asian and other racial/ethnic groups (19.1%). CONCLUSIONS: Our estimations of the prevalence of LTBI by medical risk factors and demographic characteristics for each state could facilitate planning for testing and treatment interventions to eliminate TB in the United States. Our back-calculation method feasibly estimates untreated LTBI prevalence and can be updated using future TB disease case counts at the state or national level. |
Comparative modelling of tuberculosis epidemiology and policy outcomes in California
Menzies NA , Parriott A , Shrestha S , Dowdy DW , Cohen T , Salomon JA , Marks SM , Hill AN , Winston CA , Asay G , Barry P , Readhead A , Flood J , Kahn JG , Shete PB . Am J Respir Crit Care Med 2019 201 (3) 356-365 Rationale Mathematical modelling is used to understand disease dynamics, forecast trends, and inform public health prioritization. We conducted a comparative analysis of tuberculosis (TB) epidemiology and potential intervention effects in California, using three previously developed epidemiologic models of TB. Measurements and Methods We compared model results between 2005 and 2050 under a base case scenario representing current TB services, and alternative scenarios including: (i) sustained interruption of Mycobacterium tuberculosis (Mtb) transmission, (ii) sustained resolution of latent TB infection (LTBI) and TB prior to entry of new residents, and (iii) one-time targeted testing and treatment of LTBI among 25% of non-US-born individuals residing in California. Results Model estimates of TB cases and deaths in California were in close agreement over the historical period but diverged for LTBI prevalence and new Mtb infections-outcomes for which definitive data are unavailable. Between 2018 and 2050, models projected average annual declines of 0.58-1.42% in TB cases, without additional interventions. A one-time LTBI testing and treatment intervention among non-US-born residents was projected to produce sustained reductions in TB incidence. Models found prevalent Mtb infection and migration to be more significant drivers of future TB incidence than local transmission. Conclusions All models projected a stagnation in the decline of TB incidence, highlighting the need for additional interventions including greater access to LTBI diagnosis and treatment for non-US-born individuals. Differences in model results reflect gaps in historical data and uncertainty in the trends of key parameters, demonstrating the need for high-quality, up-to-date TB determinant and outcome data. |
Impact and effectiveness of state-level tuberculosis interventions in California, Florida, New York and Texas: A model-based analysis
Shrestha S , Cherng S , Hill AN , Reynolds S , Flood J , Barry PM , Readhead A , Oxtoby M , Lauzardo M , Privett T , Marks SM , Dowdy DW . Am J Epidemiol 2019 188 (9) 1733-1741 The incidence of tuberculosis (TB) disease in the United States has stabilized, and additional interventions are needed to make progress toward TB elimination. But the impact of such interventions depends on local demography and heterogeneity in populations at risk. Using state-level individual-based TB transmission models, calibrated to California, Florida, New York, and Texas, we modeled two TB interventions: (i) Increased targeted testing and treatment (TTT) of high-risk populations, including people who are non-US-born, diabetic, HIV-positive, homeless, or incarcerated; and (ii) Enhanced TB contact investigation (ECI), including higher completion of preventive therapy. For each intervention, we projected reductions in active TB incidence over 10 years (2016-2026) and numbers needed to screen and treat to avert one case. TTT delivered to half of the non-US-born adult population could lower TB incidence by 19.8%-26.7% over ten years. TTT delivered to smaller populations with higher TB risk (e.g., HIV-positive, homeless) and ECI were generally more efficient, but had less overall impact on incidence. TTT targeted to smaller, highest-risk populations, and ECI can be highly efficient; however, major reductions in incidence will only be achieved by also targeting larger, moderate-risk populations. Ultimately, to eliminate TB in the US, a combination of these approaches is necessary. |
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