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Introduction to routine data

Introduction

Learning objectives: You will learn about the benefits and limitations of routine population data, the issues to be considered when using routine data, an overview of various types of routine data, and sources of bias in population data.

Health information about a population can be at national or regional or local levels; it can be numeric (quantitative) or textual (qualitative), or it can be routine or ad hoc. Topics covered include demographic data, circumstantial data, national reference data and health event data. Links to further resources are provided.

Read the resource text below which covers some of the basic issues in population health information.

Resource text

Introduction to routine data

Values and limitations of routine data

The most valuable feature of routine data is their availability at little cost to the researcher. They may be especially helpful in establishing baseline characteristics regarding the health status of the community; in generating hypotheses as a result of sex, age, cohort or geographic variation; or in identifying potential areas requiring further research.

Value of routine data

  • readily available
  • low cost
  • useful for identifying hypotheses
  • useful for initial assessment
  • provides baseline data on expected levels of health/disease

Limitations of routine data

  • not always up-to-date (dependent on when collected)
  • lack of completeness (except census)
  • some variables of interest may not be collected
  • occasionally subject to political influences and manipulation

Key issues to be considered for any routinely collected data set providing information on population health

  • accuracy - to what extent is the data that is present accurate? What biases may be present?
  • precision - what level of uncertainty is in the data? Have appropriate measures of uncertainty been included (e.g. 95% confidence intervals)?
  • completeness - how complete is the dataset? How much missing data is there?
  • timeliness - when was the data collected? Is this sufficiently recent to still be relevant?
  • coverage - is the whole population of interest covered? If not, who is missing?
  • analysis - what analysis has been applied to the data? Have appropriate techniques been used (e.g. for direct or indirect age standardisation)?
  • accessibility - who has access to the data? Who controls this access?
  • confidentiality - can individuals be identified from the data?
  • original purpose of collection - personal data may only be used for the purposes for which it was collected. NHS data registrations generally include improvement of the health of the population and management of the NHS among their purposes, but non-NHS data may not.

To address the above issues, it is essential to have an understanding of the methods used to collect the data set in the first place. Such an understanding will allow the identification of potential biases in the data.

Four types of routine data giving population health information are:

1. demographic data
2. circumstantial data
3. national reference data
4. health event data

Related links