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This study estimated the prevalence of maternal depressive symptoms and tested associations between maternal depressive symptoms and healthcare utilization and expenditures among United States publicly insured children with chronic health conditions (CCHC). A total of 6,060 publicly insured CCHC from the 2004-2009 Medical Expenditure Panel Surveys were analyzed using negative binomial models to compare healthcare utilization for CCHC of mothers with and without depressive symptoms. Annual healthcare expenditures for both groups were compared using a two-part model with a logistic regression and generalized linear model. The prevalence of depressive symptoms among mothers with CCHC was 19 %. There were no differences in annual healthcare utilization for CCHC of mothers with and without depressive symptoms. Maternal depressive symptoms were associated with greater odds of ED expenditures [odds ratio (OR) 1.26; 95 % CI 1.03-1.54] and lesser odds of dental expenditures (OR 0.81; 95 % CI 0.66-0.98) and total expenditures (OR 0.71; 95 % CI 0.51-0.98). Children of symptomatic mothers had lower predicted outpatient expenditures and higher predicted expenditures for total health, prescription medications, dental care; and office based, inpatient and ED visits. Mothers with CCHC were more likely to report depressive symptoms than were mothers with children without chronic health conditions. There were few differences in annual healthcare utilization and expenditures between CCHC of mothers with and without depressive symptoms. However, having a mother with depressive symptoms was associated with higher ED expenditures and higher predicted healthcare expenditures in a population of children who comprise over three-fourths of the top decile of Medicaid spending., Author(s): Jada L. Brooks[sup.1] , Heather Beil[sup.1] , Linda S. Beeber[sup.1] Author Affiliations: (1) University of North Carolina at Chapel Hill, School of Nursing, Campus Box 7460, Carrington Hall, 27599-7460, [...]

There is little evidence regarding population equity in alternative payment models (APMs). We aimed to determine whether one such APM, the Maryland All-Payer Model (MDAPM), had differential effects on subpopulations of vulnerable Medicare beneficiaries. We utilized Medicare fee-for-service claims for beneficiaries living in Maryland and 48 comparison hospital market areas between 2011 and 2018. We used doubly robust difference-in-difference-in-differences regression models to estimate the differential effects of MDAPM on Medicare beneficiaries by dual eligibility for Medicare and Medicaid, disability as original reason for Medicare entitlement, presence of multiple chronic conditions (MCC), race, and rural residency status. Dual, disabled, and beneficiaries with MCC had greater reductions in expenditures and utilization than their counterparts. Hospitals may have prioritized high-cost, high-need patients as they changed their care delivery practices. The percentage of hospital discharges with 14-day follow-up was significantly lower for disadvantaged subpopulations, including duals, disabled, and non-White. [ABSTRACT FROM AUTHOR]

In 2014, Maryland incorporated global budgets into its long-running all-payer rate-setting model for hospitals in order to improve health, increase health care quality, and reduce spending. We used difference-in-differences models to estimate changes in Medicare and commercial insurance utilization and spending in Maryland relative to a hospital-based comparison group. We found slower growth in Medicare hospital spending in Maryland than in the comparison group 4.5 years after model implementation and for commercial plan members after 4 years. We identified reductions in Maryland Medicare admissions but no changes for commercial plan members, although their inpatient spending declined. Relative declines in emergency department and other hospital outpatient spending in Maryland drove slower Medicare hospital spending growth, saving $796 million. Our findings suggest global budgets reduce hospital spending and utilization but aligning incentives between hospital and nonhospital providers may be necessary to further reduce utilization and total spending. [ABSTRACT FROM AUTHOR]

Abstract Background Risk-based prioritization of dental referrals during well-child visits might improve dental access for infants and toddlers. This study identifies pediatrician-assessed risk factors for early childhood caries (ECC) and their association with the need for a dentist’s evaluation. Methods A priority oral health risk assessment and referral tool (PORRT) for children < 36 months was developed collaboratively by physicians and dentists and used by 10 pediatricians during well-child visits. PORRT documented behavioral, clinical, and child health risks for ECC. Pediatricians also assessed overall ECC risk on an 11-point scale and determined the need for a dental evaluation. Logistic regression models calculated the odds for evaluation need for each risk factor and according to a 3-level risk classification. Results In total 1,288 PORRT forms were completed; 6.8% of children were identified as needing a dentist evaluation. Behavioral risk factors were prevalent but not strong predictors of the need for an evaluation. The child’s overall caries risk was the strongest predictor of the need for an evaluation. Cavitated (OR = 17.5; 95% CI = 8.08, 37.97) and non-cavitated (OR = 6.9; 95% CI = 4.47, 10.82) lesions were the strongest predictors when the caries risk scale was excluded from the analysis. Few patients (6.3%) were classified as high risk, but their probability of needing an evaluation was only 0.36. Conclusions Low referral rates for children with disease and prior to disease onset but at elevated risk, indicate interventions are needed to help improve the dental referral rates of physicians.

Objective: To determine the accuracy of record matching using 'Link King' software that uses an ordinal score for the certainty that linked records are valid matches. Methods: We linked records in North Carolina Medicaid files to public health surveillance files using Link King matching software. We selected a stratified random sample of 230 of 45,295 linked records and 50 of 35,119 non-linked surveillance records, then manually reviewed the records. Sensitivity (Sn) and Specificity (Sp) were calculated based on each cut point of the Link King score, using manual review as the gold standard. Results: The Sn increased from 0.837 (95% CI: 0.785, 0.892) to 0.935 (0.879, 0.994) and Sp decreased from 0.893 (0.816, 0.976) to 0.865 (0.790, 0.947) as cut points were varied to widen the scope of declared matches. With a goal of both Sn and Sp being large, accuracy was best when linked record pairs from the top three levels of certainty were included in the match. Conclusions: This study found that a publicly available software program accurately merged Medicaid and surveillance data. The Link King could be useful to researchers in merging administrative databases. [ABSTRACT FROM AUTHOR]