Data Source

We used the 1996-2000 series of public use files from the Medical Expenditure Panel Survey (MEPS) for this analysis. The files provide an extensive data set on the use of health services and healthcare in the United States. The MEPS data contain nationally representative estimates of healthcare use; expenditure; sources of payment; and insurance coverage of U.S. civilian nonin-stitutionalized population, including nursing homes and their residents. There are four components to the MEPS: the household component (HC), the medical provider component (MPC), insurance component (1С) and the nursing home component (NHC). The HC collects detailed data at both the person and household levels with respect to demographic characteristics, health conditions, health status, use of medical care services, charges and payments, access to care, satisfaction with care, health insurance coverage, income and employment. The MEPS MPC supplements and validates information on medical care events reported in the MEPS HC by contacting medical providers and pharmacies identified by household responders. The MEPS 1С collects data on health insurance plans obtained through employers, unions and other sources.

Description of Variables

We employed two items to construct our outcome variable for preventive care: up-to-date immunization status and dental visits. Up-to-date immunization status was collected at the person level for children ages 0-6. Only children (defined as <5 years old) were considered in this analysis. For questions about diphtheria, tetanus and pertusis (DTP) or polio immunization, there were follow-up questions that asked about the frequency of shots or drops. For questions related to immunization for measles-mumps-rubella, there were no follow-up questions. We defined up-to-date immunization as the receipt of age-appropriate vaccinations as recommended by the immunization schedule in effect during the data collection period. If at the time of the interview the child had not received the age-appropriate and recommended vaccination doses for a specific vaccine, the child was classified as being not up-to-date. For instance, if at the time of the interview, a child aged five years (60 months) had not received the 4:3:1:3 series (for DTP, polio, measles vaccines), then the child was not up-to-date since the recommended ages are 18 months for four doses of DTP, three doses of polio and three doses, and 15 months for one dose of the measles vaccine. The dental visits variable includes those for dental care to general dentists, dental technicians, dental surgeons, orthodontists, endodontists and periodontists. We qualified the child as having received preventive care when the child has received up-to-date immunization coverage and at least one dental visit during the preceding year.
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Maternal determinants that were explored as predictors of receipt of pediatric preventive care included sociodemographic characteristics and indicators of financial barriers to healthcare access. Maternal age, marital status, maternal education, family size and number of children in the household were considered. The indicators of financial barriers to healthcare access that were assessed included: total income, employment status and insurance coverage. As a secondary aim, we were interested in determining whether there was a disparity between blacks and whites with respect to preventive care use among children. We therefore, compared differences between the two racial groups in terms of maternal characteristics and indicators of financial barriers to healthcare usage as well as preventive care utilization. The race considered in the analysis was that of the mother, and children of mixed marriages were assigned the race of their mothers.

Statistical Analysis

We compared differences in proportion for univariate analysis by means of the Chi-squared test. Comparison of group means of continuous variables was performed using Student’s t test. For our multi-variable analysis to identify predictors of pediatric preventive care utilization, we employed the logistic regression model. The best model fit was determined by applying the -2 log likelihood ratio test. The Wald’s test was utilized to assess whether the estimated variable coefficients were significant. Because the MEPS utilized a complex sample design, we had to apply appropriate statistical techniques to account for this complexity. This was achieved using the proc surveyfreq and surveylogis-tic to generate weighted frequency tables and adjusted odds ratios respectively (SAS version 9.1; Cary, NC). Similarly, for computation and group comparison of means of continuous variables, we used the proc surveymeans of the same software. Since maternal income correlated strongly with employment status, only the variable coding for the latterwas retained in the adjusted model.
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All tests of hypothesis were two-tailed with a type-1 error rate set at 5%. The study was approved by the Institutional Review Board of the University of Alabama at Birmingham.