Epidemiology of oral clefts 2012: an international perspective

Front Oral Biol. 2012:16:1-18. doi: 10.1159/000337464. Epub 2012 Jun 25.

Abstract

Classical descriptive epidemiology in the field of cleft lip and palate aims to quantify the problem, and in the higher income countries it is possible to do this with varying degrees of accuracy. This is not however possible in every country in the world, and epidemiology should seek to identify these data gaps with a view to improvement in the situation. Epidemiology must also be investigative and look for trends, associations and inter-population differences, with the aim of supporting aetiological research and advancing the translational agenda. This chapter is set out in three parts and seeks to address all three of the above areas. Birth defects in general and orofacial clefting in particular remain a relatively common and significant problem for not only the individual patients born with these defects in terms of death or disability, but also for their families and for society in general in terms of burden of care and health inequality. In high-income countries, despite very significant advances in treatment, problems in access to care and evidence base for cleft care still exist whereas in the developing world the consequences are lack of access to care and lack of infrastructure to help with quantification of the problem and consequently the ability to address it. The major questions in contemporary cleft lip and palate research surround ways of improving the evidence base for the treatment interventions used to optimise quality of care, and the ultimate scientific and humanitarian objective is primary prevention of those diseases and disorders that are preventable. Descriptive epidemiology underpins research enquiry in both of these major areas.

Publication types

  • Review

MeSH terms

  • Cleft Lip / epidemiology*
  • Cleft Palate / epidemiology*
  • Cost of Illness
  • Developed Countries / statistics & numerical data
  • Developing Countries / statistics & numerical data
  • Global Health / statistics & numerical data*
  • Healthcare Disparities / statistics & numerical data
  • Humans
  • Primary Prevention / statistics & numerical data
  • Quality of Health Care / statistics & numerical data