The Experts below are selected from a list of 219 Experts worldwide ranked by ideXlab platform
Emilia Vynnycky - One of the best experts on this subject based on the ideXlab platform.
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re estimated rate of reactivation of latent tuberculosis infection in the united states overall and by Population Subgroup
American Journal of Epidemiology, 2014Co-Authors: Rein M G J Houben, Tom A Yates, David Moore, Timothy D Mchugh, Marc Lipman, Emilia VynnyckyAbstract:Tuberculosis (TB) disease can occur soon after new infection (or reinfection) or many years thereafter through reactivation of latent infection (1). Reliable estimates of rates of reactivation are needed to predict the impact of interventions, particularly in low-burden, high-income settings where there may be little ongoing transmission. We welcome Shea et al.'s (2) recent attempt to directly calculate this rate for the whole of the United States using empirical data. This could have permitted validation of currently used estimates, obtained from mathematical models that fit to country-level historical data (3). Shea et al. reported differential TB reactivation rates by place of birth and human immunodeficiency virus (HIV) status. If valid, these estimates would also be valuable. However, we have concerns about the assumptions used to derive these metrics from the limited data available. To calculate the rate of TB reactivation in the United States, Shea et al. divided estimates of the number of cases that they attributed to reactivation by estimates of the number of persons considered at risk of reactivation (2). For the numerator (cases of reactivation TB), they used TB isolates from 2006–2008 with a unique genotype within the national genotyping database. Here, there were substantial missing data, that is, TB cases without a genotype. Only 57% of all TB cases (73% of culture-positive cases) reported to the Centers for Disease Control and Prevention were genotyped. Results from modeling studies (4, 5) have shown that the chance that cases will be incorrectly attributed to reactivation is increased if only a proportion of all cases are genotyped. This is a particular problem when cluster sizes are small—which is likely here, given the low-burden setting. The relatively short sampling frame would also result in overestimation of the number of cases due to reactivation (4, 5), as might the authors' assumption that transmission cannot occur between persons living more than 50 km apart. Based on findings by Glynn et al. (5), the missing data might result in the proportion of cases attributable to reactivation being overestimated by as much as 60%. The use of a relatively nondiscriminatory typing method (12-locus MIRU-VNTR [mycobacterial interspersed repetitive units–variable number of tandem repeats] (6)) may have resulted in some bias in the other direction but adds further uncertainty to these estimates. The authors applied the proportion reactivated, calculated from the observed data, to persons with missing genotypes, thus amplifying any misclassification. For the denominator, Shea et al. estimated the prevalence of latent infection from 7,386 persons who received a tuberculin skin test (TST) in cross-sectional health surveys in 1999 and 2000, and then extrapolated to estimate the prevalence of latent infection for the entire US Population (over 300 million people) (2). While a 10-mm TST cutoff for all Populations is convenient, it feels simplistic given the known variation in nontuberculous mycobacteria exposure both between countries and within the United States (7) and the likely high coverage of Bacillus Calmette-Guerin vaccination in the foreign-born Population. The authors also present reactivation rates by HIV status, under the assumption that the prevalence of latent infection does not differ by HIV status (2). This is inappropriate, given the fact that in high-income countries, these infections are often co-located in disadvantaged communities (8). When presenting estimates of reactivation rates among HIV-negative persons, the authors might have attempted to quantify the impact of their assumptions with sensitivity analyses. These could have included allowing the TST cutpoint to vary (e.g., by place of birth) and adjusting the estimated number of reactivation cases to account for potential sampling bias. With regard to HIV, we agree with the authors that better understanding of the interaction between HIV infection and TB reactivation is needed, but the available data do not allow valid calculation of these rates. In summary, the questions addressed by Shea et al. (2) are important, but the conclusions drawn should be more cautious.
Shama D Ahuja - One of the best experts on this subject based on the ideXlab platform.
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re estimated rate of reactivation of latent tuberculosis infection in the united states overall and by Population Subgroup
American Journal of Epidemiology, 2014Co-Authors: Jennifer M Sanderson, Jeanne Sullivan Meissner, Shama D AhujaAbstract:In an investigation using national data sources, Shea et al. (1) estimated the rate of reactivation tuberculosis (TB) to be 0.084 cases per 100 person-years among persons with latent TB infection (LTBI) in the United States. The authors present these findings as the overall rate of reactivation TB in the United States, and they state that the groups identified as having higher rates of reactivation TB “have increased rates of progression and will receive even greater benefit from testing and treatment” (1, p. 223) for LTBI. While this study represents an important attempt to quantify the contribution of reactivation TB to the overall TB burden in the United States, this extrapolation has significant implications for TB control programs, and we urge caution in the interpretation and application of these results.
C. Robert Horsburgh - One of the best experts on this subject based on the ideXlab platform.
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Estimated Rate of Reactivation of Latent Tuberculosis Infection in the United States, Overall and by Population Subgroup
American journal of epidemiology, 2013Co-Authors: Kimberly M. Shea, J. Steve Kammerer, Carla A. Winston, Thomas R. Navin, C. Robert HorsburghAbstract:We estimated the rate of reactivation tuberculosis (TB) in the United States, overall and by Population Subgroup, using data on TB cases and Mycobacterium tuberculosis isolate genotyping reported to the Centers for Disease Control and Prevention during 2006-2008. The rate of reactivation TB was defined as the number of non-genotypically clustered TB cases divided by the number of person-years at risk for reactivation due to prevalent latent TB infection (LTBI). LTBI was ascertained from tuberculin skin tests given during the 1999-2000 National Health and Nutrition Examination Survey. Clustering of TB cases was determined using TB genotyping data collected by the Centers for Disease Control and Prevention and analyzed via spatial scan statistic. Of the 39,920 TB cases reported during 2006-2008, 79.7% were attributed to reactivation. The overall rate of reactivation TB among persons with LTBI was estimated as 0.084 (95% confidence interval (CI): 0.083, 0.085) cases per 100 person-years. Rates among persons with and without human immunodeficiency virus coinfection were 1.82 (95% CI: 1.74, 1.89) and 0.073 (95% CI: 0.070, 0.075) cases per 100 person-years, respectively. The rate of reactivation TB among persons with LTBI was higher among foreign-born persons (0.098 cases/100 person-years; 95% CI: 0.096, 0.10) than among persons born in the United States (0.082 cases/100 person-years; 95% CI: 0.080, 0.083). Differences in rates of TB reactivation across Subgroups support current recommendations for targeted testing and treatment of LTBI.
Etobssie Wako - One of the best experts on this subject based on the ideXlab platform.
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Post-disaster Health Indicators for Pregnant and Postpartum Women and Infants
Maternal and Child Health Journal, 2015Co-Authors: Marianne E. Zotti, Amy M. Williams, Etobssie WakoAbstract:United States (U.S.) pregnant and postpartum (P/PP) women and their infants may be particularly vulnerable to effects from disasters. In an effort to guide post-disaster assessment and surveillance, we initiated a collaborative process with nationwide expert partners to identify post-disaster epidemiologic indicators for these at-risk groups. This 12 month process began with conversations with partners at two national conferences to identify critical topics for P/PP women and infants affected by disaster. Next we hosted teleconferences with a 23 member Indicator Development Working Group (IDWG) to review and prioritize the topics. We then divided the IDWG into three Population Subgroups (pregnant women, postpartum women, and infants) that conducted at least three teleconferences to discuss the proposed topics and identify/develop critical indicators, measures for each indicator, and relevant questions for each measure for their respective Population Subgroup. Lastly, we hosted a full IDWG teleconference to review and approve the indicators, measures, and questions. The final 25 indicators and measures with questions (available online) are organized by Population Subgroup: pregnant women (indicators = 9; measures = 24); postpartum women (indicators = 10; measures = 36); and infants (indicators = 6; measures = 30). We encourage our partners in disaster-affected areas to test these indicators and measures for relevancy and completeness. In post-disaster surveillance, we envision that users will not use all indicators and measures but will select ones appropriate for their setting. These proposed indicators and measures promote uniformity of measurement of disaster effects among U.S. P/PP women and their infants and assist public health practitioners to identify their post-disaster needs.
Rein M G J Houben - One of the best experts on this subject based on the ideXlab platform.
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re estimated rate of reactivation of latent tuberculosis infection in the united states overall and by Population Subgroup
American Journal of Epidemiology, 2014Co-Authors: Rein M G J Houben, Tom A Yates, David Moore, Timothy D Mchugh, Marc Lipman, Emilia VynnyckyAbstract:Tuberculosis (TB) disease can occur soon after new infection (or reinfection) or many years thereafter through reactivation of latent infection (1). Reliable estimates of rates of reactivation are needed to predict the impact of interventions, particularly in low-burden, high-income settings where there may be little ongoing transmission. We welcome Shea et al.'s (2) recent attempt to directly calculate this rate for the whole of the United States using empirical data. This could have permitted validation of currently used estimates, obtained from mathematical models that fit to country-level historical data (3). Shea et al. reported differential TB reactivation rates by place of birth and human immunodeficiency virus (HIV) status. If valid, these estimates would also be valuable. However, we have concerns about the assumptions used to derive these metrics from the limited data available. To calculate the rate of TB reactivation in the United States, Shea et al. divided estimates of the number of cases that they attributed to reactivation by estimates of the number of persons considered at risk of reactivation (2). For the numerator (cases of reactivation TB), they used TB isolates from 2006–2008 with a unique genotype within the national genotyping database. Here, there were substantial missing data, that is, TB cases without a genotype. Only 57% of all TB cases (73% of culture-positive cases) reported to the Centers for Disease Control and Prevention were genotyped. Results from modeling studies (4, 5) have shown that the chance that cases will be incorrectly attributed to reactivation is increased if only a proportion of all cases are genotyped. This is a particular problem when cluster sizes are small—which is likely here, given the low-burden setting. The relatively short sampling frame would also result in overestimation of the number of cases due to reactivation (4, 5), as might the authors' assumption that transmission cannot occur between persons living more than 50 km apart. Based on findings by Glynn et al. (5), the missing data might result in the proportion of cases attributable to reactivation being overestimated by as much as 60%. The use of a relatively nondiscriminatory typing method (12-locus MIRU-VNTR [mycobacterial interspersed repetitive units–variable number of tandem repeats] (6)) may have resulted in some bias in the other direction but adds further uncertainty to these estimates. The authors applied the proportion reactivated, calculated from the observed data, to persons with missing genotypes, thus amplifying any misclassification. For the denominator, Shea et al. estimated the prevalence of latent infection from 7,386 persons who received a tuberculin skin test (TST) in cross-sectional health surveys in 1999 and 2000, and then extrapolated to estimate the prevalence of latent infection for the entire US Population (over 300 million people) (2). While a 10-mm TST cutoff for all Populations is convenient, it feels simplistic given the known variation in nontuberculous mycobacteria exposure both between countries and within the United States (7) and the likely high coverage of Bacillus Calmette-Guerin vaccination in the foreign-born Population. The authors also present reactivation rates by HIV status, under the assumption that the prevalence of latent infection does not differ by HIV status (2). This is inappropriate, given the fact that in high-income countries, these infections are often co-located in disadvantaged communities (8). When presenting estimates of reactivation rates among HIV-negative persons, the authors might have attempted to quantify the impact of their assumptions with sensitivity analyses. These could have included allowing the TST cutpoint to vary (e.g., by place of birth) and adjusting the estimated number of reactivation cases to account for potential sampling bias. With regard to HIV, we agree with the authors that better understanding of the interaction between HIV infection and TB reactivation is needed, but the available data do not allow valid calculation of these rates. In summary, the questions addressed by Shea et al. (2) are important, but the conclusions drawn should be more cautious.