Pneumonia among young infants in rural Southeast Asia (Bohol Island, Philippines)

Trop Med Int Health. 2009 Dec;14(12):1457-66. doi: 10.1111/j.1365-3156.2009.02398.x. Epub 2009 Oct 21.


Objective: To develop a clinical algorithm that can be used to identify pneumonia deaths in young infants in developing countries and estimate the disease burden in this population.

Patients and methods: Infants younger than 60 days hospitalized with signs of severe pneumonia who underwent clinical, microbiologic and radiological evaluation were the subjects. Stepwise logistic regression and subtractive iterative process were used to derive the algorithm.

Results: Three-hundred and one infants had either clinical or radiographic pneumonia. The case fatality rate for 185 infants with radiographic pneumonia was 21%vs. 5% for clinical pneumonia. Age below 7 days was associated with an increased risk of dying. Among 7- to 59-day-old infants, poor feeding, cyanosis and absence of crackles were predictors of death from pneumonia. Using logistic regression, an algorithm consisting of any one of three clinical signs (cyanosis, poor feeding and abnormally sleepy) was developed in infants aged 7-59 days; 80% of deaths and 50% of those with radiographic pneumonia have at least one of these signs. It performed better than both the WHO case management algorithm and the IMCI algorithm.

Conclusion: Radiographic pneumonia is a common and serious infection among infants below 2 months old in the Philippines. Cyanosis, poor feeding and abnormal sleepiness are simple signs that can be used by health workers to identify seriously ill infants who are most likely to die from pneumonia.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Cyanosis / etiology
  • Female
  • Humans
  • Infant
  • Infant, Newborn
  • Logistic Models
  • Male
  • Philippines / epidemiology
  • Pneumonia, Bacterial / complications
  • Pneumonia, Bacterial / diagnosis
  • Pneumonia, Bacterial / mortality*
  • Practice Guidelines as Topic / standards
  • Predictive Value of Tests
  • Risk Factors
  • Rural Health / standards