The effects of oral clefts on hospital use throughout the lifespan

BMC Health Serv Res. 2012 Mar 9;12:58. doi: 10.1186/1472-6963-12-58.


Background: Oral clefts are one of the most common birth defects worldwide. They require multiple healthcare interventions and add significant burden on the health and quality of life of affected individuals. However, not much is known about the long term effects of oral clefts on health and healthcare use of affected individuals. In this study, we evaluate the effects of oral clefts on hospital use throughout the lifespan.

Methods: We estimate two-part regression models for hospital admission and length of stay for several age groups up to 68 years of age. The study employs unique secondary population-based data from several administrative inpatient, civil registration, demographic and labor market databases for 7,670 individuals born with oral clefts between 1936 and 2002 in Denmark, and 220,113 individuals without oral clefts from a 5% random sample of the total birth population from 1936 to 2002.

Results: Oral clefts significantly increase hospital use for most ages below 60 years by up to 233% for children ages 0-10 years and 16% for middle age adults. The more severe cleft forms (cleft lip with palate) have significantly larger effects on hospitalizations than less severe forms.

Conclusions: The results suggest that individuals with oral clefts have higher hospitalization risks than the general population throughout most of the lifespan.

Publication types

  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Age Factors
  • Aged
  • Child
  • Child, Preschool
  • Chronic Disease / epidemiology
  • Cleft Lip* / complications
  • Cleft Lip* / epidemiology
  • Cleft Lip* / therapy
  • Cleft Palate* / complications
  • Cleft Palate* / epidemiology
  • Cleft Palate* / therapy
  • Comorbidity
  • Denmark / epidemiology
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Infant
  • Infant, Newborn
  • Length of Stay / statistics & numerical data*
  • Logistic Models
  • Male
  • Middle Aged
  • Outcome and Process Assessment, Health Care* / statistics & numerical data
  • Parents
  • Patient Admission / statistics & numerical data*
  • Poisson Distribution
  • Registries
  • Residence Characteristics
  • Risk Factors
  • Socioeconomic Factors