Causes of severe pneumonia requiring hospital admission in children without HIV infection from Africa and Asia: the PERCH multi-country case-control study

Background Pneumonia is the leading cause of death among children younger than 5 years. In this study, we estimated causes of pneumonia in young African and Asian children, using novel analytical methods applied to clinical and microbiological findings. Methods We did a multi-site, international case-control study in nine study sites in seven countries: Bangladesh, The Gambia, Kenya, Mali, South Africa, Thailand, and Zambia. All sites enrolled in the study for 24 months. Cases were children aged 1–59 months admitted to hospital with severe pneumonia. Controls were age-group-matched children randomly selected from communities surrounding study sites. Nasopharyngeal and oropharyngeal (NP-OP), urine, blood, induced sputum, lung aspirate, pleural fluid, and gastric aspirates were tested with cultures, multiplex PCR, or both. Primary analyses were restricted to cases without HIV infection and with abnormal chest x-rays and to controls without HIV infection. We applied a Bayesian, partial latent class analysis to estimate probabilities of aetiological agents at the individual and population level, incorporating case and control data. Findings Between Aug 15, 2011, and Jan 30, 2014, we enrolled 4232 cases and 5119 community controls. The primary analysis group was comprised of 1769 (41·8% of 4232) cases without HIV infection and with positive chest x-rays and 5102 (99·7% of 5119) community controls without HIV infection. Wheezing was present in 555 (31·7%) of 1752 cases (range by site 10·6–97·3%). 30-day case-fatality ratio was 6·4% (114 of 1769 cases). Blood cultures were positive in 56 (3·2%) of 1749 cases, and Streptococcus pneumoniae was the most common bacteria isolated (19 [33·9%] of 56). Almost all cases (98·9%) and controls (98·0%) had at least one pathogen detected by PCR in the NP-OP specimen. The detection of respiratory syncytial virus (RSV), parainfluenza virus, human metapneumovirus, influenza virus, S pneumoniae, Haemophilus influenzae type b (Hib), H influenzae non-type b, and Pneumocystis jirovecii in NP-OP specimens was associated with case status. The aetiology analysis estimated that viruses accounted for 61·4% (95% credible interval [CrI] 57·3–65·6) of causes, whereas bacteria accounted for 27·3% (23·3–31·6) and Mycobacterium tuberculosis for 5·9% (3·9–8·3). Viruses were less common (54·5%, 95% CrI 47·4–61·5 vs 68·0%, 62·7–72·7) and bacteria more common (33·7%, 27·2–40·8 vs 22·8%, 18·3–27·6) in very severe pneumonia cases than in severe cases. RSV had the greatest aetiological fraction (31·1%, 95% CrI 28·4–34·2) of all pathogens. Human rhinovirus, human metapneumovirus A or B, human parainfluenza virus, S pneumoniae, M tuberculosis, and H influenzae each accounted for 5% or more of the aetiological distribution. We observed differences in aetiological fraction by age for Bordetella pertussis, parainfluenza types 1 and 3, parechovirus–enterovirus, P jirovecii, RSV, rhinovirus, Staphylococcus aureus, and S pneumoniae, and differences by severity for RSV, S aureus, S pneumoniae, and parainfluenza type 3. The leading ten pathogens of each site accounted for 79% or more of the site's aetiological fraction. Interpretation In our study, a small set of pathogens accounted for most cases of pneumonia requiring hospital admission. Preventing and treating a subset of pathogens could substantially affect childhood pneumonia outcomes

Association of C-reactive protein with bacterial and respiratory syncytial virus-associated pneumonia among children aged <5 years in the PERCH study

Background. Lack of a gold standard for identifying bacterial and viral etiologies of pneumonia has limited evaluation of C-reactive protein (CRP) for identifying bacterial pneumonia. We evaluated the sensitivity and specificity of CRP for identifying bacterial vs respiratory syncytial virus (RSV) pneumonia in the Pneumonia Etiology Research for Child Health (PERCH) multicenter case-control study. Methods. We measured serum CRP levels in cases with World Health Organization-defined severe or very severe pneumonia and a subset of community controls. We evaluated the sensitivity and specificity of elevated CRP for "confirmed" bacterial pneumonia (positive blood culture or positive lung aspirate or pleural fluid culture or polymerase chain reaction [PCR]) compared to "RSV pneumonia" (nasopharyngeal/oropharyngeal or induced sputum PCR-positive without confirmed/suspected bacterial pneumonia). Receiver operating characteristic (ROC) curves were constructed to assess the performance of elevated CRP in distinguishing these cases. Results. Among 601 human immunodeficiency virus (HIV)-negative tested controls, 3% had CRP ≥40 mg/L. Among 119 HIVnegative cases with confirmed bacterial pneumonia, 77% had CRP ≥40 mg/L compared with 17% of 556 RSV pneumonia cases. The ROC analysis produced an area under the curve of 0.87, indicating very good discrimination; a cut-point of 37.1 mg/L best discriminated confirmed bacterial pneumonia (sensitivity 77%) from RSV pneumonia (specificity 82%). CRP ≥100 mg/L substantially improved specificity over CRP ≥40 mg/L, though at a loss to sensitivity. Conclusions. Elevated CRP was positively associated with confirmed bacterial pneumonia and negatively associated with RSV pneumonia in PERCH. CRP may be useful for distinguishing bacterial from RSV-associated pneumonia, although its role in discriminating against other respiratory viral-associated pneumonia needs further study

Pandemic influenza H1N1 2009 in Thailand.

BACKGROUND: Developing a quantitative understanding of pandemic influenza dynamics in South-East Asia is important for informing future pandemic planning. Hence, transmission dynamics of influenza A/H1N1 were determined across space and time in Thailand. METHODS: Dates of symptom onset were obtained for all daily laboratory-confirmed cases of influenza A/H1N1pdm in Thailand from 3 May 2009 to 26 December 2010 for four different geographic regions (Central, North, North-East, and South). These data were analysed using a probabilistic epidemic reconstruction, and estimates of the effective reproduction number, R(t), were derived by region and over time. RESULTS: Estimated R(t) values for the first wave peaked at 1.54 (95% CI: 1.42-1.71) in the Central region and 1.64 (95% CI: 1.38-1.92) in the North, whilst the corresponding values in the North-East and the South were 1.30 (95% CI: 1.17-1.46) and 1.39 (95% CI: 1.32-1.45) respectively. As the R(t) in the Central region fell below one, the value of R(t) in the rest of Thailand increased above one. R(t) was above one for 30 days continuously through the first wave in all regions of Thailand. During the second wave R(t) was only marginally above one in all regions except the South. CONCLUSIONS: In Thailand, the value of R(t) varied by region in the two pandemic waves. Higher R(t) estimates were found in Central and Northern regions in the first wave. Knowledge of regional variation in transmission potential is needed for predicting the course of future pandemics and for analysing the potential impact of control measures

Influenza A(H1N1)pdm09-associated pneumonia deaths in Thailand.

BACKGROUND: The first human infections with influenza A(H1N1)pdm09 virus were confirmed in April 2009. We describe the clinical and epidemiological characteristics of influenza A(H1N1)pdm09-associated pneumonia deaths in Thailand from May 2009-January 2010. METHODS: We identified influenza A(H1N1)pdm09-associated pneumonia deaths from a national influenza surveillance system and performed detailed reviews of a subset. RESULTS: Of 198 deaths reported, 49% were male and the median age was 37 years; 146 (73%) were 20-60 years. Among 90 deaths with records available for review, 46% had no identified risk factors for severe influenza. Eighty-eight patients (98%) received antiviral treatment, but only 16 (18%) initiated therapy within 48 hours of symptom onset. CONCLUSIONS: Most influenza A(H1N1)pdm09 pneumonia fatalities in Thailand occurred in adults aged 20-60 years. Nearly half lacked high-risk conditions. Antiviral treatment recommendations may be especially important early in a pandemic before vaccine is available. Treatment should be considered as soon as influenza is suspected

Costs and benefits of improving tuberculosis control: The case of Thailand

The study evaluates the economic costs and benefits of improving tuberculosis control interventions in Thailand. Provider costs are determined on the basis of marginal treatment costs for varying case numbers and estimates of the cost of required infrastructure changes. Indirect costs are calculated as income lost due to morbidity and premature mortality. An epidemiological model is used to calculate case numbers and mortality under current control conditions and a scenario of improved control. An improved control strategy initially leads to a higher number of detected cases. For longer projection periods, the epidemiological impact of curing a higher proportion of infectious sources results in lower case numbers than those expected without programme improvement. Model simulations show a reduction of total annual case numbers through improved control measures by an average 45% after a simulation period of 20 years. The corresponding societal savings in form of reduced indirect costs from the disease are U.S.$2.4 billion. Reductions in direct provider costs can be expected as a result of decreased numbers of detected cases for longer evaluation periods, as well as a lower proportion of multi-drug-resistant cases. The mean value of predicted savings is U.S.$8.3 million. Since this value is likely to be higher than the required investment in improved infrastructure, net savings can be expected. The result of an uncertainty analysis shows a wide range of potential additional costs or net savings with respect to direct provider costs. Indirect cost calculations show net savings for all parameter values.tuberculosis economics cost-benefit analysis Thailand

Enhanced surveillance for severe pneumonia, Thailand 2010–2015

Abstract Background The etiology of severe pneumonia is frequently not identified by routine disease surveillance in Thailand. Since 2010, the Thailand Ministry of Public Health (MOPH) and US CDC have conducted surveillance to detect known and new etiologies of severe pneumonia. Methods Surveillance for severe community-acquired pneumonia was initiated in December 2010 among 30 hospitals in 17 provinces covering all regions of Thailand. Interlinked clinical, laboratory, pathological and epidemiological components of the network were created with specialized guidelines for each to aid case investigation and notification. Severe pneumonia was defined as chest-radiograph confirmed pneumonia of unknown etiology in a patient hospitalized ≤48 h and requiring intubation with ventilator support or who died within 48 h after hospitalization; patients with underlying chronic pulmonary or neurological disease were excluded. Respiratory and pathological specimens were tested by reverse transcription polymerase chain reaction for nine viruses, including Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and 14 bacteria. Cases were reported via a secure web-based system. Results Of specimens from 972 cases available for testing during December 2010 through December 2015, 589 (60.6%) had a potential etiology identified; 399 (67.8%) were from children aged < 5 years. At least one viral agent was detected in 394 (40.5%) cases, with the most common of single vial pathogen detected being respiratory syncytial virus (RSV) (110/589, 18.7%) especially in children under 5 years. Bacterial pathogens were detected in 341 cases of which 67 cases had apparent mixed infections. The system added MERS-CoV testing in September 2012 as part of Thailand’s outbreak preparedness; no cases were identified from the 767 samples tested. Conclusions Enhanced surveillance improved the understanding of the etiology of severe pneumonia cases and improved the MOPH’s preparedness and response capacity for emerging respiratory pathogens in Thailand thereby enhanced global health security. Guidelines for investigation of severe pneumonia from this project were incorporated into surveillance and research activities within Thailand and shared for adaption by other countries

Age distribution of influenza A (H1N1)pdm09 deaths.

<p><u>Detailed legend:</u> Age distribution of influenza A (H1N1)pdm09 deaths reported to the National Avian Influenza Surveillance (NAIS) system, Bureau of Epidemiology, Ministry of Public Health, Thailand and those for whom medical charts were available for review - May 2009-January 2010. Bars represent number of deaths for each age group (Figures are in a separate file).</p

Hospital course of 90 influenza A (H1N1)pdm09 virus-associated pneumonia deaths in Thailand for whom medical record reviews were conducted, May 2009-January 2010.

*<p>Blood culture positive for <i>Acinetobacter baumannii</i> (1), <i>Salmonella</i> group D and <i>pseudomonas</i> spp. (1) and <i>Staphylococcus aureus</i> (1).</p>**<p>Records available for 89 of 90 patients.</p>***<p>Records available for 85 of 90 patients.</p>****<p>Records available for 51 of 90 patients, AST (aspatate aminotransferase), ALT (alanine transaminase); UNL (upper normal limit).</p

Laboratory-confirmed influenza A (H1N1)pdm09 cases and deaths.

<p><u>Detailed legend</u>: Laboratory-confirmed influenza A (H1N1)pdm09 cases and deaths reported to Bureau of Epidemiology, Ministry of Public Health Thailand from May 2009-March 2010. Bars represent number of deaths. Line represents number of cases.</p

Timing of antiviral therapy relative to clinical course of 90 influenza A (H1N1)pdm09 virus-associated pneumonia fatalities in Thailand for whom medical record reviews were conducted, May 2009-January 2010.

*<p>Mann-Whitney U test comparing age <18 to ≥18 years.</p