Objectives This study was undertaken to determine the cost savings of prevention of adverse birth outcomes for Medicaid women participating in the CenteringPregnancy group prenatal care program at a pilot program in South Carolina. Methods A retrospective five-year cohort study of Medicaid women was assessed for differences in birth outcomes among women involved in CenteringPregnancy group prenatal care (n = 1262) and those receiving individual prenatal care (n = 5066). The study outcomes examined were premature birth and the related outcomes of low birthweight (LBW) and neonatal intensive care unit (NICU) visits. Because women were not assigned to the CenteringPregnancy group, a propensity score analysis ensured that the inference of the estimated difference in birth outcomes between the treatment groups was adjusted for nonrandom assignment based on age, race, Clinical Risk Group, and plan type. A series of generalized linear models were run to estimate the difference between the proportions of individuals with adverse birth outcomes, or the risk differences, for CenteringPregnancy group prenatal care participation. Estimated risk differences, the coefficient on the CenteringPregnancy group indicator variable from identity-link binomial variance generalized linear models, were then used to calculate potential cost savings due to participation in the CenteringPregnancy group. Results This study estimated that CenteringPregnancy participation reduced the risk of premature birth (36 %, P < 0.05). For every premature birth prevented, there was an average savings of $22,667 in health expenditures. Participation in CenteringPregnancy reduced the incidence of delivering an infant that was LBW (44 %, P < 0.05, $29,627). Additionally, infants of CenteringPregnancy participants had a reduced risk of a NICU stay (28 %, P < 0.05, $27,249). After considering the state investment of $1.7 million, there was an estimated return on investment of nearly $2.3 million. Conclusions Cost savings were achieved with better outcomes due to the participation in CenteringPregnancy among low-risk Medicaid beneficiaries.

BackgroundEfforts to stem the diabetes epidemic in the United States and other countries must take into account a complex array of individual, social, economic, and built environmental factors. Increasingly, scientists use information visualization tools to "make sense" of large multivariate data sets. Recently, ring map visualization has been explored as a means of depicting spatially referenced, multivariate data in a single information graphic. A ring map shows multiple attribute data sets as separate rings of information surrounding a base map of a particular geographic region of interest. In this study, ring maps were used to evaluate diabetes prevalence among adult South Carolina Medicaid recipients. In particular, county-level ring maps were used to evaluate disparities in diabetes prevalence among adult African Americans and Whites and to explore potential county-level associations between diabetes prevalence among adult African Americans and five measures of the socioeconomic and built environment—persistent poverty, unemployment, rurality, number of fast food restaurants per capita, and number of convenience stores per capita. Although Medicaid pays for the health care of approximately 15 percent of all diabetics, few studies have examined diabetes in adult Medicaid recipients at the county level. The present study thus addresses a critical information gap, while illustrating the utility of ring maps in multivariate investigations of population health and environmental context.ResultsRing maps showed substantial racial disparity in diabetes prevalence among adult Medicaid recipients and suggested an association between adult African American diabetes prevalence and rurality. Rurality was significantly positively associated with diabetes prevalence among adult African American Medicaid recipients in a multivariate statistical model.ConclusionsEfforts to reduce diabetes among adult African American Medicaid recipients must extend to rural African Americans. Ring maps can be used to integrate diverse data sets, explore attribute associations, and achieve insights critical to the promotion of population health.

BackgroundMeasures of small-area deprivation may be valuable in geographically targeting limited resources to prevent, diagnose, and effectively manage chronic conditions in vulnerable populations. We developed a census-based small-area socioeconomic deprivation index specifically to predict chronic disease burden among publically insured Medicaid recipients in South Carolina, a relatively poor state in the southern United States. We compared the predictive ability of the new index with that of four other small-area deprivation indicators.MethodsTo derive the ZIP Code Tabulation Area-Level Palmetto Small-Area Deprivation Index (Palmetto SADI), we evaluated ten census variables across five socioeconomic deprivation domains, identifying the combination of census indicators most highly correlated with a set of five chronic disease conditions among South Carolina Medicaid enrollees. In separate validation studies, we used both logistic and spatial regression methods to assess the ability of Palmetto SADI to predict chronic disease burden among state Medicaid recipients relative to four alternative small-area socioeconomic deprivation measures: the Townsend index of material deprivation; a single-variable poverty indicator; and two small-area designations of health care resource deprivation, Primary Care Health Professional Shortage Area and Medically Underserved Area/Medically Underserved Population.ResultsPalmetto SADI was the best predictor of chronic disease burden (presence of at least one condition and presence of two or more conditions) among state Medicaid recipients compared to all alternative deprivation measures tested.ConclusionsA low-cost, regionally optimized socioeconomic deprivation index, Palmetto SADI can be used to identify areas in South Carolina at high risk for chronic disease burden among Medicaid recipients and other low-income Medicaid-eligible populations for targeted prevention, screening, diagnosis, disease self-management, and care coordination activities.

OBJECTIVE: School absenteeism may be an underlying cause of poor school performance among overweight and obese children. We examined the associations between school absenteeism and body mass index (BMI) in a nationally representative sample. DESIGN AND SUBJECTS: We analyzed the data of 1387 children (6--11 years) and 2185 adolescents (12--18 years), who completed an interview and anthropometric measurement as a part of the National Health and Nutrition Examination Survey, 2005Survey, --2008. The CDC 2000 growth chart was used to categorize BMI status, and the number of school days missed during the past 12 months was assessed by asking the proxies or interviewees. RESULTS: The prevalence of obesity and overweight were 18.96 ± 1.44% (s.e.) and 16.41 ± 0.78%, respectively, among study populations. The means of school days missed in the last 12 months were not statistically different between the normal-weight, overweight and obese groups, 3.79±0.56, 3.86±0.38 and 4.31±0.01 days, respectively. However, when 42 days missed per school month was defined as severe absence, the prevalence of severe absence were 1.57%, 2.99% and 4.94% respectively, among 6 --11-year-old children with normal, overweight and obese. The adjusted odds of severe school absence were 2.27 (95% confidence interval ¼ 0.64 --8.03) and 3.93 (1.55 --9.95), respectively, among overweight and obese children compared with normal-weight peers (P for trend test o0.01). No significant association was found among adolescents. CONCLUSION: Increased body weight is independently associated with severe school absenteeism in children but not adolescents. Future research is needed to determine the nature, and academic and social significance of this association.

Epidemiological research often involves the visual exploration of numerous attributes to help discern patterns between health and characteristics of the physical, socioeconomic, or built environment. Unfortunately, many of the multivariate mapping techniques discussed throughout the cartographic literature can be challenging to create or interpret -particularly for individuals without a cartographic background. In this paper, we present a new style of multivariate map -the ring map -to aid in basic visualization of multivariate datasets. For purposes of example, we focus on the use of the ring map style for exploring county-level epidemiological data for the state of South Carolina to examine patterns of age, race, or gender specific mortality and morbidity within a population.