Development and Validation of the Pediatric Medical Complexity Algorithm (PMCA) Version 2.0.

BACKGROUND AND OBJECTIVES: The Pediatric Medical Complexity Algorithm (PMCA) was developed to stratify children by level of medical complexity. We sought to refine PMCA and evaluate its performance based on the duration of eligibility and completeness of Medicaid data. METHODS: PMCA version 1.0 was applied to a cohort of 299 children insured by Washington State Medicaid with ≥1 Seattle Children's Hospital outpatient, emergency department, and/or inpatient encounter in 2012. Blinded assessment of the validation cohort's PMCA category was performed by using medical records. In-depth review of discrepant cases was performed and informed the development of PMCA version 2.0. The sensitivity and specificity of PMCA version 2.0 were assessed. RESULTS: Using Medicaid data, the sensitivity of PMCA version 2.0 was 74% for complex chronic disease (C-CD), 60% for noncomplex chronic disease (NC-CD), and 87% for those without chronic disease (CD). Specificity was 84% to 91% in Medicaid data for all 3 groups. Medicaid data were most complete for children that had primarily fee-for-service claims and were less complete for those with some managed care encounter data. PMCA version 2.0 performed optimally when children had a longer duration of coverage (25 to 36 months) with fee-for-service reimbursement, identifying children with C-CD with 85% sensitivity and 75% specificity, children with NC-CD with 55% sensitivity and 88% specificity, and children without CD with 100% sensitivity and 97% specificity. CONCLUSIONS: PMCA version 2.0 identifies children with C-CD with good sensitivity and very good specificity when applied to Medicaid data. Data quality is a critical consideration when using PMCA.

Using Medicaid and CHIP claims data to support pediatric quality measurement: lessons from 3 centers of excellence in measure development.

OBJECTIVE: We sought to explore the claims data-related issues relevant to quality measure development for Medicaid and the Children's Health Insurance Program (CHIP), illustrating the challenges encountered and solutions developed around 3 distinct performance measure topics: care coordination for children with complex needs, quality of care for high-prevalence conditions, and hospital readmissions. METHODS: Each of 3 centers of excellence presents an example that illustrates the challenges of using claims data for quality measurement. RESULTS: Our Centers of Excellence in pediatric quality measurement used innovative methods to develop algorithms that use Medicaid claims data to identify children with complex needs; overcome some shortcomings of existing data for measuring quality of care for common conditions such as otitis media; and identify readmissions after hospitalizations for lower respiratory infections. CONCLUSIONS: Our experience constructing quality measure specifications using claims data suggests that it will be challenging to measure key quality of care constructs for Medicaid-insured children at a national level in a timely and consistent way. Without better data to underpin pediatric quality measurement, Medicaid and CHIP will have difficulty using some existing measures for accountability, value-based purchasing, and quality improvement both across states and within states.

Pediatric medical complexity algorithm: a new method to stratify children by medical complexity.

OBJECTIVES: The goal of this study was to develop an algorithm based on International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM), codes for classifying children with chronic disease (CD) according to level of medical complexity and to assess the algorithm's sensitivity and specificity. METHODS: A retrospective observational study was conducted among 700 children insured by Washington State Medicaid with ≥1 Seattle Children's Hospital emergency department and/or inpatient encounter in 2010. The gold standard population included 350 children with complex chronic disease (C-CD), 100 with noncomplex chronic disease (NC-CD), and 250 without CD. An existing ICD-9-CM-based algorithm called the Chronic Disability Payment System was modified to develop a new algorithm called the Pediatric Medical Complexity Algorithm (PMCA). The sensitivity and specificity of PMCA were assessed. RESULTS: Using hospital discharge data, PMCA's sensitivity for correctly classifying children was 84% for C-CD, 41% for NC-CD, and 96% for those without CD. Using Medicaid claims data, PMCA's sensitivity was 89% for C-CD, 45% for NC-CD, and 80% for those without CD. Specificity was 90% to 92% in hospital discharge data and 85% to 91% in Medicaid claims data for all 3 groups. CONCLUSIONS: PMCA identified children with C-CD (who have accessed tertiary hospital care) with good sensitivity and good to excellent specificity when applied to hospital discharge or Medicaid claims data. PMCA may be useful for targeting resources such as care coordination to children with C-CD.

Jail incarceration and birth outcomes.

This study examined the relationships between jail incarceration during pregnancy and infant birth weight, preterm birth, and fetal growth restriction. We used multivariate regression analyses to compare outcomes for 496 births to women who were in jail for part of pregnancy with 4,960 Medicaid-funded births as matched community controls. After adjusting for potential confounding variables, the relationship between jail incarceration and birth outcomes was modified by maternal age. Relative to controls, women incarcerated during pregnancy had progressively higher odds of low birth weight and preterm birth through age 39 years; conversely, jail detainees older than 39 years were less likely than controls to experience low birth weight or preterm birth. For women in jail at all ages, postrelease maternity case management was associated with decreased odds of low birth weight, whereas prenatal care was associated with decreased odds of preterm birth. Local jails are important sites for public health intervention. Efforts to ensure that all pregnant women released from jail have access to enhanced prenatal health services may improve perinatal outcomes for this group of particularly vulnerable women and infants.

Perinatal health service use by women released from jail.

Few studies have examined health care access for the growing population of pregnant women who cycle in and out of urban jails. The present study compared use of Medicaid-funded perinatal services for births to women who were in jail during pregnancy and births to women who had been in jail, but not while pregnant. Jail contact during pregnancy increased the likelihood women would receive prenatal care (odds ratio [OR] = 5.95; 95% confidence interval [CI] 2.18-16.23) and maternity support services (OR = 1.80; 95% CI 1.12-2.88), but was associated with fewer total prenatal and support visits. Jail contact during a previous pregnancy was associated with fewer prenatal care visits, more support service visits, and longer time receiving case management. Jail settings can become a place of coordination between public health and criminal justice professionals to ensure that pregnant women receive essential services following release. Service coordination may increase women's engagement in health services during future pregnancies, with or without subsequent incarceration.

Income inequality and pregnancy spacing.

We examined the relationship between county-level income inequality and pregnancy spacing in a welfare-recipient cohort in Washington State. We identified 20,028 welfare-recipient women who had at least one birth between July 1, 1992, and December 31, 1999, and followed this cohort from the date of that first in-study birth until the occurrence of a subsequent pregnancy or the end of the study period. Income inequality was measured as the proportion of total county income earned by the wealthiest 10% of households in that county compared to that earned by the poorest 10%. To measure the relationship between income inequality and the time-dependent risk (hazard) of a subsequent pregnancy, we used Cox proportional hazards methods and adjusted for individual- and county-level covariates. Among women aged 25 and younger at the time of the index birth, the hazard ratio (HR) of subsequent pregnancy associated with income inequality was 1.24 (95% CI: 0.85, 1.80), controlling for individual-level (age, marital status, education at index birth; race, parity) and community-level variables. Among women aged 26 or older at the time of the index birth, the adjusted HR was 2.14 (95% CI: 1.09, 4.18). While income inequality is not the only community-level feature that may affect health, among women aged 26 or older at the index birth it appears to be associated with hazard of a subsequent pregnancy, even after controlling for other factors. These results support previous findings that income inequality may impact health, perhaps by influencing health-related behaviors.