The Predictive Performance of a Pneumonia Severity Score in Human Immunodeficiency Virus-negative Children Presenting to Hospital in 7 Low- and Middle-income Countries.

BACKGROUND: In 2015, pneumonia remained the leading cause of mortality in children aged 1-59 months. METHODS: Data from 1802 human immunodeficiency virus (HIV)-negative children aged 1-59 months enrolled in the Pneumonia Etiology Research for Child Health (PERCH) study with severe or very severe pneumonia during 2011-2014 were used to build a parsimonious multivariable model predicting mortality using backwards stepwise logistic regression. The PERCH severity score, derived from model coefficients, was validated on a second, temporally discrete dataset of a further 1819 cases and compared to other available scores using the C statistic. RESULTS: Predictors of mortality, across 7 low- and middle-income countries, were age <1 year, female sex, ≥3 days of illness prior to presentation to hospital, low weight for height, unresponsiveness, deep breathing, hypoxemia, grunting, and the absence of cough. The model discriminated well between those who died and those who survived (C statistic = 0.84), but the predictive capacity of the PERCH 5-stratum score derived from the coefficients was moderate (C statistic = 0.76). The performance of the Respiratory Index of Severity in Children score was similar (C statistic = 0.76). The number of World Health Organization (WHO) danger signs demonstrated the highest discrimination (C statistic = 0.82; 1.5% died if no danger signs, 10% if 1 danger sign, and 33% if ≥2 danger signs). CONCLUSIONS: The PERCH severity score could be used to interpret geographic variations in pneumonia mortality and etiology. The number of WHO danger signs on presentation to hospital could be the most useful of the currently available tools to aid clinical management of pneumonia.

Burden of disease in adults admitted to hospital in a rural region of coastal Kenya: an analysis of data from linked clinical and demographic surveillance systems.

BACKGROUND: Estimates of the burden of disease in adults in sub-Saharan Africa largely rely on models of sparse data. We aimed to measure the burden of disease in adults living in a rural area of coastal Kenya with use of linked clinical and demographic surveillance data. METHODS: We used data from 18,712 adults admitted to Kilifi District Hospital (Kilifi, Kenya) between Jan 1, 2007, and Dec 31, 2012, linked to 790,635 person-years of observation within the Kilifi Health and Demographic Surveillance System, to establish the rates and major causes of admission to hospital. These data were also used to model disease-specific disability-adjusted life-years lost in the population. We used geographical mapping software to calculate admission rates stratified by distance from the hospital. FINDINGS: The main causes of admission to hospital in women living within 5 km of the hospital were infectious and parasitic diseases (303 per 100,000 person-years of observation), pregnancy-related disorders (239 per 100,000 person-years of observation), and circulatory illnesses (105 per 100,000 person-years of observation). Leading causes of hospital admission in men living within 5 km of the hospital were infectious and parasitic diseases (169 per 100,000 person-years of observation), injuries (135 per 100,000 person-years of observation), and digestive system disorders (112 per 100,000 person-years of observation). HIV-related diseases were the leading cause of disability-adjusted life-years lost (2050 per 100,000 person-years of observation), followed by non-communicable diseases (741 per 100,000 person-years of observation). For every 5 km increase in distance from the hospital, all-cause admission rates decreased by 11% (95% CI 7­14) in men and 20% (17­23) in women. The magnitude of this decline was highest for endocrine disorders in women (35%; 95% CI 22­46) and neoplasms in men (30%; 9­45). INTERPRETATION: Adults in rural Kenya face a combined burden of infectious diseases, pregnancy-related disorders, cardiovascular illnesses, and injuries. Disease burden estimates based on hospital data are affected by distance from the hospital, and the amount of underestimation of disease burden differs by both disease and sex. FUNDING: The Wellcome Trust, GAVI Alliance.