title: Estimating regional centile curves from mixed data sources and countries.
abstract: Regional or national growth distributions can provide vital information on the health status of populations. In most resource poor countries, however, the required anthropometric data from purpose-designed growth surveys are not readily available. We propose a practical method for estimating regional (multi-country) age-conditional weight distributions based on existing survey data from different countries. We developed a two-step method by which one is able to model data with widely different age ranges and sample sizes. The method produces references both at the country level and at the regional (multi-country) level. The first step models country-specific centile curves by Box-Cox t and Box-Cox power exponential distributions implemented in generalized additive model for location, scale and shape through a common model. Individual countries may vary in location and spread. The second step defines the regional reference from a finite mixture of the country distributions, weighted by population size. To demonstrate the method we fitted the weight-for-age distribution of 12 countries in South East Asia and the Western Pacific, based on 273 270 observations. We modeled both the raw body weight and the corresponding Z score, and obtained a good fit between the final models and the original data for both solutions. We briefly discuss an application of the generated regional references to obtain appropriate, region specific, age-based dosing regimens of drugs used in the tropics. The method is an affordable and efficient strategy to estimate regional growth distributions where the standard costly alternatives are not an option.
authors.affiliation: TNO Quality of Life, P.O. Box 2215, 2301 CE Leiden, The Netherlands. stef.vanbuuren@tno.nl
authors.forename: Stef
authors.initials: S
authors.lastname: van Buuren
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Hayes
authors.forename: D Mikis
authors.initials: DM
authors.lastname: Stasinopoulos
authors.forename: Robert A
authors.initials: RA
authors.lastname: Rigby
authors.forename: Feiko O
authors.initials: FO
authors.lastname: ter Kuile
authors.forename: Dianne J
authors.initials: DJ
authors.lastname: Terlouw
mesh_terms: D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000962:Antimalarials / Q000008:administration & dosage; D001210:Asia, Southeastern; D056808:Biostatistics / Q000379:methods*; D001835:Body Weight; D002648:Child; D002657:Child Development; D002675:Child, Preschool; D016208:Databases, Factual; D003906:Developing Countries; D005260:Female; D006128:Growth; D006304:Health Status; D006306:Health Surveys; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D055815:Young Adult
chemical_list: D000962:Antimalarials