Functional Databases for Longitudinal Analyses and Tips of the Trade: The Case of the NPHS in Canada. Amélie Quesnel-Vallée McGill University Émilie Renahy University of Toronto

Data matrix structures: “wide” and “long” formats Wide format Long format Source:

Preparing data for longitudinal analyses One basic, common variable naming rule for reshaping from wide to long Marker for time of data collection (cycle, calendar year, etc) is: – a numerical stub, – at the end of the variable name – Ex: VARNAME2012

The National Population Health Survey “In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted.” – Motivated by “economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada.” In 1992, Statistics Canada received funding to carry out the NPHS It is composed of three components: the Households, the Health Institutions, and the North components. Source:

The Longitudinal Household Component of the NPHS Biennial, from 1994/ /11 (9 cycles) n=17,276 for the longitudinal household component (69.7% response rate in cycle 9) Multistage, stratified random sampling, designed to ensure adequate representation across major urban centers, smaller towns, and rural areas in all provinces. People living in Native reserves, military bases, institutions, and some remote areas of Ontario and Québec were excluded. Source:

Preparing NPHS data for longitudinal analyses NPHS variable naming rules: xxxCYCLEzzzz, where – xxx refers to the questionnaire section – CYCLE refers to the data collection cycle – zzzz refers to the specific question Two idiosyncratic challenges: – Location: cycle is positioned in the middle – Identifier: One digit, either a number or a letter, depending on the period of data collection From 1994 to 2002, numbers are used (4, 6, 8, 0, or 2 respectively) From , letters (A-D) are used because numbers would not have provided unique cycle identifiers

Solution: Development of a SAS macro Two options: – User-specific list of variables: Recommended! – Full data matrix: Time consuming and prone to errors with time-invariant variables in long format Available in both official languages To be made available to RDC users across Canada

Using the package, easy as 1, 2, 3 1.Read important comments and warning For instance, if the variable was not measured in a given cycle, the macro will create a variable with all missing values 2.Replace all XXX by the relevant info. Hint: use the 'Find' option (Ctrl+F) to find them all! 3.Run the macro in SAS: Select all (Ctrl+A) then click on the menu Run \ Submit (or F3 button). -> Three pairs of wide and long format datasets will be created, allowing the use of any statistical software: 2 SAS dataset 2 Comma Separated Values (.cvs) 2 Tab Delimited File (.txt)

WARNING! It is the researcher's responsibility to verify: 1.Whether the question was asked in all cycles 2.Whether the response categories were the same across all cycles To this end, consult the NPHS documentation.

Summarizing longitudinal information Using egen in Stata on a wide matrix – anycount: Count the number of events (e.g. poor health) experienced by a respondent over time – anymatch: Detect presence or absence of event over a time period – concat: Creates a summary “trajectory” of events for an individual over a time period. Source:

WARNING Missing values are often turned into “0” in egen Always declare missing values on created variables

Row* commands in egen rowmiss: Gives the number of missing values in varlist for each observation (row). rownonmiss: Gives the number of nonmissing values in varlist for each observation (row) -- this is the value used by rowmean() for the denominator in the mean calculation. rowmean, rowmedian, rowmax, rowmin: Respectively creates the (row) means, medians, max and min of the variables in varlist, ignoring missing values.

Acknowledgements