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Healthcare providers are in a unique position to identify barriers to healthcare for socioeconomically disadvantaged families, provide patient navigation for reducing disparities, and implement system changes for alleviating these barriers. Significant barriers to obtaining pediatric dental care exist for families with young children. This curricular resource describes an interprofessional education program that was conducted with small teams of health science students (nursing, pediatric medicine, dental medicine) at a suburban university. Five objectives were identified to assist students in realizing the barriers faced by socioeconomically disadvantaged families when seeking dental care for their young children. Students were assigned simulated child-patients, along with relevant health, demographic and insurance information and asked to use their cellular smartphones to identify timely and logistically compatible dental care for these simulated patients. Debriefing revealed that students had difficulty identifying appropriate pediatric dental care providers within a wide urban and suburban region. Post-workshop student surveys identified increased awareness of income, care access, and health literacy as factors contributing to health disparities.

Objective Though stable insurance is important to support optimal child health, the reproducibility of metrics to assess child health insurance retention at the state and county level has not been examined. We sought to determine reproducibility of public insurance retention rates for children using 3 different metrics at the state and county level. Methods Public health insurance retention for children was assessed using 3 different metrics calculated from 2006–2009 Medicaid Analytic Extract data from 12 selected states. The metrics were: 1) Duration: a prospective metric that quantifies the number of newly enrolled children continuously enrolled in public insurance 6, 12, and 18 months after initial enrollment during a selected period; (2) Infant Duration: assesses Duration only among infants born during a selected period; (3) Coverage: a prospective metric that quantifies the average percentage of time a selected population is enrolled over an 18-month interval. Reproducibility of the metrics was assessed using a range of sample sizes with resampling and determining changes in relative rankings of states/counties by retention rate. Results All 3 metrics demonstrated reproducible estimates at the state level with sample sizes of 2000, 5000, and 10,000. Reproducibility of relative rankings for child health insurance retention of counties within states were sensitive to county child population size and the amount of variability in retention rates within the county and at the state level. Conclusions As health care reform unfolds, the complete set of these 3 reproducible metrics can be used to evaluate multipronged and multilevel strategies to retain eligible children in public health insurance.

To identify parent, child, community, and health care provider characteristics associated with early intervention (EI) referral and multidisciplinary evaluation (MDE) by EI.We conducted a mixed methods secondary analysis of data from a randomized controlled trial of a developmental screening program in 4 urban primary care practices. Children30 months of age not currently enrolled in EI and their parents were included. Using logistic regression, we tested whether parent, child, community, and health care provider characteristics were associated with EI referral and MDE completion. We also conducted qualitative interviews with 9 pediatricians. Interviews were recorded, transcribed, and coded. We identified themes using modified grounded theory.Of 2083 participating children, 434 (21%) were identified with a developmental concern. A total of 253 children (58%) with a developmental concern were referred to EI. A total of 129 children (30%) received an MDE. Failure in 2 or more domains on developmental assessments was associated with EI referral (adjusted odds ratio [AOR] 3.15, 95% confidence interval [CI] 1.89-5.24) and completed MDE (AOR 2.16, 95% CI 1.19-3.93). Faxed referral to EI, as opposed to just giving families a phone number to call was associated with MDE completion (AOR 2.94, 95% CI 1.48-5.84). Pediatricians reported that office processes, family preference, and whether they thought parents understood the developmental screening tool influenced the EI referral process.In an urban setting, one third of children with a developmental concern were not referred to EI, and two thirds of children with a developmental concern were not evaluated by EI. Our results suggest that practice-based strategies that more closely connect the medical home with EI such as electronic transmission of referrals (e.g., faxing referrals) may improve completion rates of EI evaluation.

Objective To examine compliance of Medicaid-renewal applications to established state guidelines for reading level of Medicaid-related materials. Methods We assessed the reading level of the 2008 Medicaid renewal applications by using 3 readability tests: Flesch-Kincaid Grade Level Index, New Fog Count, and FORCAST. Results In 2008, 45 states and the District of Columbia had reading level guidelines for Medicaid-related materials. Of these, 24 (52.2%) states' Medicaid renewal applications failed to meet their guidelines on all 3 readability tests; in particular, 41 states (89.1%) failed the Flesch-Kincaid Grade Level Index. Only one state's application had a Flesch-Kincaid score below a 5th-grade reading level. Conclusions As health care reform unfolds, complying with established reading level guidelines for Medicaid-related materials is one simplification strategy that should be implemented to improve access for Medicaid-eligible families and prevent eligible children from losing coverage unnecessarily.

Prior research identified foster care children using Medicaid eligibility codes specific to foster care, but it is unknown whether these codes capture all foster care children.To describe the sampling bias in relying on Medicaid eligibility codes to identify foster care children.Using foster care administrative files linked to Medicaid data, we describe the proportion of children whose Medicaid eligibility was correctly encoded as foster child during a 1-year follow-up period following a new episode of foster care. Sampling bias is described by comparing claims in mental health, emergency department (ED), and other ambulatory settings among correctly and incorrectly classified foster care children.Twenty-eight percent of the 5683 sampled children were incorrectly classified in Medicaid eligibility files. In a multivariate logistic regression model, correct classification was associated with duration of foster care (9 vs2 months, odds ratio [OR] 7.67, 95% confidence interval [CI] 7.17-7.97), number of placements (3 vs 1 placement, OR 4.20, 95% CI 3.14-5.64), and placement in a group home among adjudicated dependent children (OR 1.87, 95% CI 1.33-2.63). Compared with incorrectly classified children, correctly classified foster care children were 3 times more likely to use any services, 2 times more likely to visit the ED, 3 times more likely to make ambulatory visits, and 4 times more likely to use mental health care services (P.001 for all comparisons).Identifying children in foster care using Medicaid eligibility files is prone to sampling bias that over-represents children in foster care who use more services.

To examine the relationship between childhood obesity and health care use in a large, nationally representative group of children with measured anthropometrics.Analysis of 5 combined National Health and Nutrition Survey datasets from 2001 to 2010. Unadjusted and adjusted logistic regression models assessed the relationship between health care use variables and weight status (overweight: body mass index 85th to95th percentile for age and sex; obese: body mass index ≥95th percentile for age and sex) for children 2-18 years of age.Overweight and obese children are more likely to receive their routine medical care in an emergency department than a primary care setting (overweight OR 1.95; 95% CI 1.22-3.14 and obese OR 1.88; 95% CI 1.24-2.86) than their normal-weight peers. After we adjusted for relevant covariates, this finding persisted among overweight, but not obese, children. Other health care use variables were not significantly associated with weight status.Overweight children may be more likely to use the emergency department than primary care settings for routine medical care. Interventions to establish primary care medical homes for overweight children merit consideration.

Objectives To assess the relationship between children's hospital readmission and the performance of child health systems in the states in which hospitals are located. Study design We conducted a retrospective cohort study of 197 744 patients 2 to 18 years old from 39 children's hospitals located in 24 states in the United States in 2005. Subjects were observed for a year after discharge for readmission to the same hospital. The odds of readmission were modeled on the basis of patient-level characteristics and state child health system performance as ranked by the Commonwealth Fund. Results A total of 1.8% of patients were readmitted within a week, 4.8% within a month, and 16.3% within 365 days. After adjustment for patient-level characteristics, the probability of readmission varied significantly between states ( P = .001), and the likelihood of readmission during the ensuing year increased as the states' health system performance ranking improved. States in the best ranking quartile had a 2.03% higher readmission rate than states in the lowest quartile ( P = .02); the same directional relationship was observed for readmission intervals from 1 to 365 days after discharge. Conclusions Hospital readmission rates are significantly related to the performance of the surrounding health care system.