Assessing the connection between health and education: identifying potential leverage points for public health to improve school attendance

Am J Public Health. 2014 Sep;104(9):e47-54. doi: 10.2105/AJPH.2014.301977. Epub 2014 Jul 17.

Abstract

Objectives: We examined multiple variables influencing school truancy to identify potential leverage points to improve school attendance.

Methods: A cross-sectional observational design was used to analyze inner-city data collected in Los Angeles County, California, during 2010 to 2011. We constructed an ordinal logistic regression model with cluster robust standard errors to examine the association between truancy and various covariates.

Results: The sample was predominantly Hispanic (84.3%). Multivariable analysis revealed greater truancy among students (1) with mild (adjusted odds ratio [AOR] = 1.57; 95% confidence interval [CI] = 1.22, 2.01) and severe (AOR = 1.80; 95% CI = 1.04, 3.13) depression (referent: no depression), (2) whose parents were neglectful (AOR = 2.21; 95% CI = 1.21, 4.03) or indulgent (AOR = 1.71; 95% CI = 1.04, 2.82; referent: authoritative parents), (3) who perceived less support from classes, teachers, and other students regarding college preparation (AOR = 0.87; 95% CI = 0.81, 0.95), (4) who had low grade point averages (AOR = 2.34; 95% CI = 1.49, 4.38), and (5) who reported using alcohol (AOR = 3.47; 95% CI = 2.34, 5.14) or marijuana (AOR = 1.59; 95% CI = 1.06, 2.38) during the past month.

Conclusions: Study findings suggest depression, substance use, and parental engagement as potential leverage points for public health to intervene to improve school attendance.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Age Factors
  • Black or African American / statistics & numerical data
  • Cross-Sectional Studies
  • Depression / epidemiology
  • Female
  • Health Behavior / ethnology
  • Hispanic or Latino / statistics & numerical data
  • Humans
  • Los Angeles
  • Male
  • Parent-Child Relations
  • Public Health*
  • Residence Characteristics / statistics & numerical data
  • Schools / statistics & numerical data*
  • Sex Factors
  • Socioeconomic Factors
  • Substance-Related Disorders / ethnology
  • Urban Population / statistics & numerical data*