Burden of <i>Streptococcus pneumoniae</i> and <i>Haemophilus influenzae</i> type b disease in children in the era of conjugate vaccines: global, regional, and national estimates for 2000-15

Background: Pneumococcal conjugate vaccine (PCV) and Haemophilus influenzae type b (Hib) vaccine are now used in most countries. To monitor global and regional progress towards improving child health and to inform national policies for disease prevention and treatment, we prepared global, regional, and national disease burden estimates for these pathogens in children from 2000 to 2015. Methods: Using WHO and Maternal and Child Epidemiology Estimation collaboration country-specific estimates of pneumonia and meningitis mortality and pneumonia morbidity from 2000 to 2015, we applied pneumococcal and Hib cause-specific proportions to estimate pathogen-specific deaths and cases. Summary estimates of the proportion of pneumonia deaths and cases attributable to these pathogens were derived from four Hib vaccine and six PCV efficacy and effectiveness study values. The proportion of meningitis deaths due to each pathogen was derived from bacterial meningitis aetiology and adjusted pathogen-specific meningitis case–fatality data. Pneumococcal and Hib meningitis cases were inferred from modelled pathogen-specific meningitis deaths and literature-derived case–fatality estimates. Cases of pneumococcal and Hib syndromes other than pneumonia and meningitis were estimated using the ratio of pathogen-specific non-pneumonia, non-meningitis cases to pathogen-specific meningitis cases from the literature. We accounted for annual HIV infection prevalence, access to care, and vaccine use. Findings: We estimated that there were 294 000 pneumococcal deaths (uncertainty range [UR] 192 000–366 000) and 29 500 Hib deaths (18 400–40 700) in HIV-uninfected children aged 1–59 months in 2015. An additional 23 300 deaths (15 300–28 700) associated with pneumococcus and fewer than 1000 deaths associated Hib were estimated to have occurred in children infected with HIV. We estimate that pneumococcal deaths declined by 51% (7–74) and Hib deaths by 90% (78–96) from 2000 to 2015. Most children who died of pneumococcus (81%) and Hib (76%) presented with pneumonia. Less conservative assumptions result in pneumococcccal death estimates that could be as high as 515 000 deaths (302 000–609 000) in 2015. Approximately 50% of all pneumococcal deaths in 2015 occurred in four countries in Africa and Asia: India (68 700 deaths, UR 44 600–86 100), Nigeria (49 000 deaths, 32 400–59 000), the Democratic Republic of the Congo (14 500 deaths, 9300–18 700), and Pakistan (14 400 deaths, 9700–17 000]). India (15 600 deaths, 9800–21 500), Nigeria (3600 deaths, 2200–5100), China (3400 deaths, 2300–4600), and South Sudan (1000 deaths, 600–1400) had the greatest number of Hib deaths in 2015. We estimated 3·7 million episodes (UR 2·7 million–4·3 million) of severe pneumococcus and 340 000 episodes (196 000–669 000) of severe Hib globally in children in 2015. Interpretation: The widespread use of Hib vaccine and the recent introduction of PCV in countries with high child mortality is associated with reductions in Hib and pneumococcal cases and deaths. Uncertainties in the burden of pneumococcal disease are largely driven by the fraction of pneumonia deaths attributable to pneumococcus. Progress towards further reducing the global burden of Hib and pneumococcal disease burden will depend on the efforts of a few large countries in Africa and Asia

Development of a respiratory severity score for hospitalized adults in a high HIV-prevalence setting—South Africa, 2010-2011

BACKGROUND : Acute lower respiratory tract infections (LRTI) are a frequent cause of hospitalization and mortality in South Africa; however, existing respiratory severity scores may underestimate mortality risk in HIV-infected adults in resource limited settings. A simple predictive clinical score for low-resource settings could aid healthcare providers in the management of patients hospitalized with LRTI. METHODS : We analyzed 1,356 LRTI hospitalizations in adults aged ≥18 years enrolled in Severe Acute Respiratory Illness (SARI) surveillance in three South African hospitals from January 2010 to December 2011. Using demographic and clinical data at admission, we evaluated potential risk factors for in-hospital mortality. We evaluated three existing respiratory severity scores, CURB-65, CRB-65, and Classification Tree Analysis (CTA) Score assessing for discrimination and calibration. We then developed a new respiratory severity score using a multivariable logistic regression model for in-hospital mortality and assigned points to risk factors based on the coefficients in the multivariable model. Finally we evaluated the model statistically using bootstrap resampling techniques. RESULTS : Of the 1,356 patients hospitalized with LRTI, 101 (7.4%) died while hospitalized. The CURB-65, CRB-65, and CTA scores had poor calibration and demonstrated low discrimination with c-statistics of 0.594, 0.548, and 0.569 respectively. Significant risk factors for in-hospital mortality included age ≥ 45 years (A), confusion on admission (C), HIV-infection (H), and serum blood urea nitrogen >7 mmol/L (U), which were used to create the seven-point ACHU clinical predictor score. In-hospital mortality, stratified by ACHU score was: score ≤1, 2.4%, score 2, 6.4%, score 3, 11. 9%, and score ≥ 4, 29.3%. Final models showed good discrimination (c-statistic 0.789) and calibration (chi-square 1.6, Hosmer-Lemeshow goodness-of-fit p-value = 0.904) and discriminated well in the bootstrap sample (average optimism of 0.003). CONCLUSIONS : Existing clinical predictive scores underestimated mortality in a low resource setting with a high HIV burden. The ACHU score incorporates a simple set a risk factors that can accurately stratify patients ≥18 years of age with LRTI by in-hospital mortality risk. This score can quantify in-hospital mortality risk in an HIV-endemic, resource-limited setting with limited clinical information and if used to facilitate timely treatment may improve clinical outcomes.Additional file 1: BMC Pulmonary_Severity Score Data.xlsx. Severity Score Dataset. Dataset generated and used for analysis and creation of the ACHU score. Two tabs are included 1) includes the data used for the analysis 2) includes important notes related to the analytical methods and definitions for several composite variables.Additional file 2: Table S1. CURB-65, CRB-65, Classification Tree Analysis (CTA) severity scores. Table S2. Predicted and observed risk of mortality based on CURB-65, CRB-65, Classification Tree Analysis (CTA), and CURB-45 severity scores among hospitalized adults with lower respiratory tract infections, South Africa, 2010–2011. Table S3. Predicted and observed risk of mortality based by ACHU (Age, confusion, HIV, urea) respiratory severity score among hospitalized adults with lower respiratory tract infections, South Africa, 2010–2011.The Centers for Disease Control and Preventionhttp://www.biomedcentral.com/bmccom/plementalternmedam2017Medical Virolog

Programmatic Impact of QuantiFERON-TB Gold In-Tube Implementation on Latent Tuberculosis Diagnosis and Treatment in a Public Health Clinic

Background: QuantiFERON-TB Gold In-Tube (QFT-GIT) is considered an alternative to the tuberculin skin test (TST) for the diagnosis of tuberculosis (TB) infection, but the programmatic impact of QFT-GIT implementation is largely unknown. In March, 2010, the Baltimore City Health Department (BCHD) introduced routine QFT-GIT testing for individuals referred to the TB program for suspected latent TB infection (LTBI). Design: Retrospective study comparing LTBI diagnosis and treatment during the 13 months before and after QFT-GIT implementation at the BCHD TB clinic. Results: 607 and 750 individuals were referred by community-providers for suspected LTBI in the pre- and post-QFT-GIT periods, respectively. Most individuals in the pre- and post-QFT-GIT periods were referred on the basis of a positive TST (597/607 [98%] vs. 690/750 [92%], respectively) and were foreign-born (363/607[59%] vs. 507/750[68%], respectively). BCHD performed QFT-GIT testing for 375/543 (69%) eligible individuals in the post-QFT-GIT period, of which 185 (49%) were positive, 178 (47%) were negative, 1 (0.25%) was indeterminate, and 11 (3%) did not yield results. Concordance of QFT-GIT with TST was low (183/352[52%]). Foreign-born individuals had higher proportions of QFT-GIT positivity (57%) than US-born individuals (36%; AOR 3.3 [95%CI 1.7–6.2]). Significantly fewer individuals received a final diagnosis of LTBI in the post-QFT-GIT period (397/567 [70%]) compared to the pre-QFT-GIT period (445/452 [98%], p,0.001). In the post-QFT-GIT period, onl

Concordance of TST and QFT-GIT results among referred individuals that came to BCHD for LTBI evaluation and had both tests performed.

†<p>Overall, 352 individuals had a TST and interpretable QFT-GIT result available. There was an overall concordance of 52.3%.</p>*<p>8 individuals with negative TST results were referred and evaluated by BCHD. 4 individuals with B-waivers had negative TST, but were referred due to an abnormal CXR; 4 individuals had both TST and QFT-GIT performed by referral source.</p

Factors associated with QFT-GIT test positivity among those tested at BCHD.

<p>Only individuals with QFT-GIT performed by BCHD are included. 11 individuals had blood drawn for QFT-GIT but did not have interpretable results due to insufficient blood volume during venipuncture, sample transportation issues, or processing error. There was 1 indeterminate result.</p><p>°p<0.001 for both univariate and multivariate analysis comparing foreign-born to US born individuals.</p><p>°° P = 0.042 comparing HIV positive to HIV negative individuals.</p>†<p>Referral source was omitted from multivariate regression model due to collinearity with birth country.</p>††<p>p = 0.001 comparing those referred from Refugee health services to those referred from primary care providers/other; p = 0.03 comparing those referred from local health departments to those referred from primary care providers/other.</p

Differences in LTBI diagnosis among referrals to BCHD between study periods and by QFT-GIT test status.

*<p>includes individuals that had QFT-GIT performed by referral source. 11/399 individuals in the post-QFT-GIT period had QFT-GIT drawn but no results available; there was 1 indeterminate result in the post-QFT-GIT-period.</p><p>°p<.001 comparing final diagnosis of LTBI between pre-QFT-GIT and post-QFT-GIT periods.</p><p>°°p = .827 for pre-QFT-GIT period comparing LTBI diagnosis between those with and without a QFT-GIT result; p<.001 in post-QFT-GIT period comparing LTBI diagnosis between those with and without QFT-GIT performed.</p>**<p>p = .81 comparing treatment initiation among those diagnosed with LTBI between pre-QFT-GIT and post-QFT-GIT periods; p = 0.690 comparing treatment initiation between those with and without QFT-GIT performed in the pre-QFT-GIT period; p = .349 comparing treatment intiation between those with and without QFT-GIT performed in the post-QFT-GIT period.</p>†<p>Analysis restricted to those who started an INH X 9 months regimen prior to Nov 30, 2010 or Rifampin X 4 months prior to March 30, 2011 to allow time for completion. p = .606 comparing overall treatment completion between pre-QFT-GIT period and post-QFT-GIT period. p = 0.101 comparing those with and without QFT-GIT performed in the post-QFT-GIT period; p = 0.70 comparing those with and without QFT-GIT in the pre-QFT-GIT period.</p

Characteristics of individuals referred to Baltimore City Health Department TB Clinic for evaluation of suspected <i>M. tuberculosis</i> infection, by study period.

<p>Abbreviations: SD, Standard Deviation. BCHD, Baltimore City Health Department.</p>*<p>Ethnicity data was based on referral documentation and/or initial evaluation at BCHD. P-value for global comparison of equality of proportions of ethnicities by χ² test.</p>†<p>HIV test results are available only for those that came to BCHD for evaluation. HIV status not available for those who did not complete an LTBI evaluation at BCHD.</p>**<p>Includes referrals from other local health departments in Maryland and other states, as well as employment TB testing conducted through other BCHD programs.</p