title: Treating childhood pneumonia in hard-to-reach areas: a model-based comparison of mobile clinics and community-based care.
abstract: BACKGROUND
  Where hard-to-access populations (such as those living in insecure areas) lack access to basic health services, relief agencies, donors, and ministries of health face a dilemma in selecting the most effective intervention strategy. This paper uses a decision mathematical model to estimate the relative effectiveness of two alternative strategies, mobile clinics and fixed community-based health services, for antibiotic treatment of childhood pneumonia, the world's leading cause of child mortality.
  
  
  METHODS
  A "Markov cycle tree" cohort model was developed in Excel with Visual Basic to compare the number of deaths from pneumonia in children aged 1 to 59 months expected under three scenarios: 1) No curative services available, 2) Curative services provided by a highly-skilled but intermittent mobile clinic, and 3) Curative services provided by a low-skilled community health post. Parameter values were informed by literature and expert interviews. Probabilistic sensitivity analyses were conducted for several plausible scenarios.
  
  
  RESULTS
  We estimated median pneumonia-specific under-5 mortality rates of 0.51 (95% credible interval: 0.49 to 0.541) deaths per 10,000 child-days without treatment, 0.45 (95% CI: 0.43 to 0.48) with weekly mobile clinics, and 0.31 (95% CI: 0.29 to 0.32) with CHWs in fixed health posts. Sensitivity analyses found the fixed strategy superior, except when mobile clinics visited communities daily, where rates of care-seeking were substantially higher at mobile clinics than fixed posts, or where several variables simultaneously differed substantially from our baseline assumptions.
  
  
  CONCLUSIONS
  Current evidence does not support the hypothesis that mobile clinics are more effective than CHWs. A CHW strategy therefore warrants consideration in high-mortality, hard-to-access areas. Uncertainty remains, and parameter values may vary across contexts, but the model allows preliminary findings to be updated as new or context-specific evidence becomes available. Decision analytic modelling can guide needed field-based research efforts in hard-to-access areas and offer evidence-based insights for decision-makers.
authors.affiliation: Department of Global Health & Development, London School of Hygiene & Tropical Medicine, UK. catherine.pitt@lshtm.ac.uk
authors.forename: Catherine
authors.initials: C
authors.lastname: Pitt
authors.forename: Bayard
authors.initials: B
authors.lastname: Roberts
authors.forename: Francesco
authors.initials: F
authors.lastname: Checchi
mesh_terms: D000900:Anti-Bacterial Agents / Q000627:therapeutic use*; D002662:Child Health Services / Q000458:organization & administration*; D002675:Child, Preschool; D015331:Cohort Studies; D003661:Decision Support Techniques; D003663:Decision Trees; D006302:Health Services Research; D006801:Humans; D007223:Infant; D008507:Medically Underserved Area; D008952:Mobile Health Units / Q000458:organization & administration*; D018410:Pneumonia, Bacterial / Q000188:drug therapy* / Q000401:mortality; D016896:Treatment Outcome
chemical_list: D000900:Anti-Bacterial Agents