title: Assessing socioeconomic effects on different sized populations: to weight or not to weight?
abstract: OBJECTIVE
  Researchers in health care often use ecological data from population aggregates of different sizes. This paper deals with a fundamental methodological issue relating to the use of such data. This study investigates the question of whether, in doing analyses involving different areas, the estimating equations should be weighted by the populations of those areas. It is argued that the correct answer to that question turns on some deep epistemological issues that have been little considered in the public health literature.
  
  
  METHODS
  To illustrate the issue, an example is presented that estimates entitlements to primary physician visits in Manitoba, Canada based on age/gender and socioeconomic status using both population weighted and unweighted regression analyses.
  
  
  METHODS
  The entire population of the province furnish the data. Primary care visits to physicians based on administrative data, demographics and a measure of socioeconomic status (SERI), based on census data, constitute the measures.
  
  
  RESULTS
  Significant differences between weighted and unweighted analyses are shown to emerge, with the weighted analyses biasing entitlements towards the more populous and advantaged population.
  
  
  CONCLUSIONS
  The authors endorse the position that, in certain problems, data analyses involving population aggregates unweighted by population size are more appropriate and normatively justifiable than are analyses weighted by population. In particular, when the aggregated units make sense, theoretically, as units, it is more appropriate to carry out the analyses without weighting by the size of the units. Unweighted analyses yield more valid estimations.
authors.affiliation: Manitoba Centre for Health Policy and Evaluation, University of Manitoba, Canada. Norman_Frohlich@Umanitoba.ca
authors.forename: N
authors.initials: N
authors.lastname: Frohlich
authors.forename: K C
authors.initials: KC
authors.lastname: Carriere
authors.forename: L
authors.initials: L
authors.lastname: Potvin
authors.forename: C
authors.initials: C
authors.lastname: Black
mesh_terms: D000367:Age Factors; D015982:Bias; D004463:Ecology; D005260:Female; D006301:Health Services Needs and Demand / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D008350:Manitoba; D011156:Population Density; D011320:Primary Health Care / Q000706:statistics & numerical data*; D012044:Regression Analysis; D012737:Sex Factors; D012959:Socioeconomic Factors