Adherence, Persistence, Readmissions, and Costs in Medicaid Members with Schizophrenia or Schizoaffective Disorder Initiating Paliperidone Palmitate Versus Switching Oral Antipsychotics: A Real-World Retrospective Investigation.

INTRODUCTION: Long-acting injectable antipsychotic agents have been suggested to improve adherence and patient outcomes in schizophrenia or schizoaffective disorder. The purpose of this study was to assess medication use patterns (i.e., medication adherence, persistence), hospital and emergency department readmissions, and total direct medical costs of Oklahoma Medicaid members with schizophrenia or schizoaffective disorder switching from an oral antipsychotic (OAP) to once-monthly paliperidone palmitate (PP1M) or to another OAP (OAP-switch). METHODS: A historical cohort analysis was conducted from 1 January 2016 to 31 December 2020 among adults aged ≥ 18 and ≤ 64 years with schizophrenia or schizoaffective disorder who were previously treated with an OAP. The first claim for PP1M or a new OAP defined the study index date. Members who transitioned from PP1M to 3-month formulation (PP3M) were included (i.e., PP1M/PP3M). Proportion of days covered (PDC), 45-day treatment gaps, 30-day readmissions to hospitals or emergency department, and total direct medical costs were assessed using multivariable, machine-learning least absolute shrinkage, and selection operator (Lasso) regressions controlling for numerous demographic, clinical, mental health, and provider characteristics. RESULTS: Among 295 Medicaid members meeting full inclusion criteria, 183 involved PP1M/PP3Ms (44 PP1M cases transitioned to PP3M) and 112 involved an OAP-switch. The multivariable-adjusted odds of readmission were significantly associated with a 45-day treatment gap (p < 0.05) and non-adherence (i.e., PDC < 80%) (p < 0.05). Relative to PP1M/PP3Ms, the multivariable analyses also indicated that OAP-switch was associated with an 18.5% lower PDC, 92.3% higher number of 45-day treatment gaps, and an approximately 90% higher odds of all-cause 30-day readmission (p < 0.05). The adjusted pre- to post-index change in cost was approximately 49% lower for OAP-switches versus PP1M/PP3Ms (p < 0.001), although unadjusted post-index costs did not differ between groups (p = 0.440). CONCLUSION: This real-world investigation of adult Medicaid members with schizophrenia or schizoaffective disorder observed improved adherence and persistence with fewer readmissions with PP1M/PP3Ms versus OAP-switches.

The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study.

Physical inactivity is a primary contributor to the obesity epidemic, but may be promoted or hindered by environmental factors. To examine how cumulative environmental quality may modify the inactivity-obesity relationship, we conducted a cross-sectional study by linking county-level Behavioral Risk Factor Surveillance System data with the Environmental Quality Index (EQI), a composite measure of five environmental domains (air, water, land, built, sociodemographic) across all U.S. counties. We estimated the county-level association (N = 3,137 counties) between 2009 age-adjusted leisure-time physical inactivity (LTPIA) and 2010 age-adjusted obesity from BRFSS across EQI tertiles using multi-level linear regression, with a random intercept for state, adjusted for percent minority and rural-urban status. We modelled overall and sex-specific estimates, reporting prevalence differences (PD) and 95% confidence intervals (CI). In the overall population, the PD increased from best (PD = 0.341 (95% CI: 0.287, 0.396)) to worst (PD = 0.645 (95% CI: 0.599, 0.690)) EQI tertile. We observed similar trends in males from best (PD = 0.244 (95% CI: 0.194, 0.294)) to worst (PD = 0.601 (95% CI: 0.556, 0.647)) quality environments, and in females from best (PD = 0.446 (95% CI: 0.385, 0.507)) to worst (PD = 0.655 (95% CI: 0.607, 0.703)). We found that poor environmental quality exacerbates the LTPIA-obesity relationship. Efforts to improve obesity through LTPIA may benefit from considering this relationship.

Prevalence of primary open-angle glaucoma among patients with obstructive sleep apnea.

OBJECTIVE: Our objective was to determine primary open-angle glaucoma (POAG) prevalence among obstructive sleep apnea (OSA) patients because the perioperative environment risks further damaging the optic nerve. STUDY DESIGN: We analyzed a "convenience sample" referred by Sleep Medicine for oral appliances because of continuous positive airway pressure intolerance. We determined the aggregate prevalence of the 3 POAG subtypes-"classic" open-angle glaucoma (COAG), normal-tension glaucoma (NTG), and open-angle glaucoma suspect (OAGS)-among the index population and compared it with that of same hospital's general population. Similarly determined were associations between OSA severity levels (apnea-hypopnea index [AHI]) and POAG subtypes. RESULTS: Among the study sample of 225 patients with OSA (96.4% male; mean age 58.5 ± 12.3 years), 47 (20.9%) had POAG, with a subtype distribution of COAG: 12 (25.5%), NTG: 8 (17.0%), and OAGS: 27 (57.4%). The POAG prevalence rate among medical center's general population was 2.5%, which was significantly less (P < .00001) than among those with comorbid OSA. Severity of the breathing disorder (AHI) failed to identify a significant correlation to any POAG subtype (P > .05). CONCLUSION: The significant prevalence of POAG among OSA sufferers suggests need for preoperative consultations from an ophthalmologist to determine eye health and possibly an anesthesiologist to avoid potential vision loss.

BMI Is a Better Body Proportionality Measure than the Ponderal Index and Weight-for-Length for Preterm Infants.

BACKGROUND: Clinicians have observed preterm infants in the neonatal intensive care unit growing disproportionally; however, the only growth charts that have been available were from preterm infants born in the 1950s which utilized the ponderal index. Prior to creating the recently published BMI curves, we found only 1 reference justifying the use of the ponderal index. OBJECTIVES: To determine the best measure of body proportionality for assessing growth in US preterm infants. METHODS: Using a dataset of 391,681 infants, we determined the body proportionality measure that was most correlated with weight and least correlated with length. We examined the sex-specific overall correlations and then stratified further by gestational age (GA). We then plotted the body proportionality measures versus length to visualize apparent discrepancies in the appropriate measure. RESULTS: The overall correlations showed weight/length3 (ponderal index) was the best measure but stratification by GA indicated that BMI (weight/length2) was the best measure. This seeming inconsistency was due to negative correlations between ponderal index and length at each GA. BMI, on the other hand, had a correlation with length across GAs, but was uncorrelated with length within GAs. Both ponderal index and BMI were positively correlated with weight. CONCLUSIONS: BMI is the appropriate measure of body proportionality for preterm infants, contrary to current practice.

County-level cumulative environmental quality associated with cancer incidence.

BACKGROUND: Individual environmental exposures are associated with cancer development; however, environmental exposures occur simultaneously. The Environmental Quality Index (EQI) is a county-level measure of cumulative environmental exposures that occur in 5 domains. METHODS: The EQI was linked to county-level annual age-adjusted cancer incidence rates from the Surveillance, Epidemiology, and End Results (SEER) Program state cancer profiles. All-site cancer and the top 3 site-specific cancers for male and female subjects were considered. Incident rate differences (IRDs; annual rate difference per 100,000 persons) and 95% confidence intervals (CIs) were estimated using fixed-slope, random intercept multilevel linear regression models. Associations were assessed with domain-specific indices and analyses were stratified by rural/urban status. RESULTS: Comparing the highest quintile/poorest environmental quality with the lowest quintile/best environmental quality for overall EQI, all-site county-level cancer incidence rate was positively associated with poor environmental quality overall (IRD, 38.55; 95% CI, 29.57-47.53) and for male (IRD, 32.60; 95% CI, 16.28-48.91) and female (IRD, 30.34; 95% CI, 20.47-40.21) subjects, indicating a potential increase in cancer incidence with decreasing environmental quality. Rural/urban stratified models demonstrated positive associations comparing the highest with the lowest quintiles for all strata, except the thinly populated/rural stratum and in the metropolitan/urbanized stratum. Prostate and breast cancer demonstrated the strongest positive associations with poor environmental quality. CONCLUSION: We observed strong positive associations between the EQI and all-site cancer incidence rates, and associations differed by rural/urban status and environmental domain. Research focusing on single environmental exposures in cancer development may not address the broader environmental context in which cancers develop, and future research should address cumulative environmental exposures. Cancer 2017;123:2901-8. © 2017 American Cancer Society.

Impact of Hurricane Exposure on Reproductive Health Outcomes, Florida, 2004.

OBJECTIVE: Prenatal hurricane exposure may be an increasingly important contributor to poor reproductive health outcomes. In the current literature, mixed associations have been suggested between hurricane exposure and reproductive health outcomes. This may be due, in part, to residual confounding. We assessed the association between hurricane exposure and reproductive health outcomes by using a difference-in-difference analysis technique to control for confounding in a cohort of Florida pregnancies. METHODS: We implemented a difference-in-difference analysis to evaluate hurricane weather and reproductive health outcomes including low birth weight, fetal death, and birth rate. The study population for analysis included all Florida pregnancies conceived before or during the 2003 and 2004 hurricane season. Reproductive health data were extracted from vital statistics records from the Florida Department of Health. In 2004, 4 hurricanes (Charley, Frances, Ivan, and Jeanne) made landfall in rapid succession; whereas in 2003, no hurricanes made landfall in Florida. RESULTS: Overall models using the difference-in-difference analysis showed no association between exposure to hurricane weather and reproductive health. CONCLUSIONS: The inconsistency of the literature on hurricane exposure and reproductive health may be in part due to biases inherent in pre-post or regression-based county-level comparisons. We found no associations between hurricane exposure and reproductive health. (Disaster Med Public Health Preparedness. 2017;11:407-411).

A novel approach for measuring residential socioeconomic factors associated with cardiovascular and metabolic health.

Individual-level characteristics, including socioeconomic status, have been associated with poor metabolic and cardiovascular health; however, residential area-level characteristics may also independently contribute to health status. In the current study, we used hierarchical clustering to aggregate 444 US Census block groups in Durham, Orange, and Wake Counties, NC, USA into six homogeneous clusters of similar characteristics based on 12 demographic factors. We assigned 2254 cardiac catheterization patients to these clusters based on residence at first catheterization. After controlling for individual age, sex, smoking status, and race, there were elevated odds of patients being obese (odds ratio (OR)=1.92, 95% confidence intervals (CI)=1.39, 2.67), and having diabetes (OR=2.19, 95% CI=1.57, 3.04), congestive heart failure (OR=1.99, 95% CI=1.39, 2.83), and hypertension (OR=2.05, 95% CI=1.38, 3.11) in a cluster that was urban, impoverished, and unemployed, compared with a cluster that was urban with a low percentage of people that were impoverished or unemployed. Our findings demonstrate the feasibility of applying hierarchical clustering to an assessment of area-level characteristics and that living in impoverished, urban residential clusters may have an adverse impact on health.

Associations between Environmental Quality and Mortality in the Contiguous United States, 2000-2005.

BACKGROUND: Assessing cumulative effects of the multiple environmental factors influencing mortality remains a challenging task. OBJECTIVES: This study aimed to examine the associations between cumulative environmental quality and all-cause and leading cause-specific (heart disease, cancer, and stroke) mortality rates. METHODS: We used the overall Environmental Quality Index (EQI) and its five domain indices (air, water, land, built, and sociodemographic) to represent environmental exposure. Associations between the EQI and mortality rates (CDC WONDER) for counties in the contiguous United States (n = 3,109) were investigated using multiple linear regression models and random intercept and random slope hierarchical models. Urbanicity, climate, and a combination of the two were used to explore the spatial patterns in the associations. RESULTS: We found 1 standard deviation increase in the overall EQI (worse environment) was associated with a mean 3.22% (95% CI: 2.80%, 3.64%) increase in all-cause mortality, a 0.54% (95% CI: -0.17%, 1.25%) increase in heart disease mortality, a 2.71% (95% CI: 2.21%, 3.22%) increase in cancer mortality, and a 2.25% (95% CI: 1.11%, 3.39%) increase in stroke mortality. Among the environmental domains, the associations ranged from -1.27% (95% CI: -1.70%, -0.84%) to 3.37% (95% CI: 2.90%, 3.84%) for all-cause mortality, -2.62% (95% CI: -3.52%, -1.73%) to 4.50% (95% CI: 3.73%, 5.27%) for heart disease mortality, -0.88% (95% CI: -2.12%, 0.36%) to 3.72% (95% CI: 2.38%, 5.06%) for stroke mortality, and -0.68% (95% CI: -1.19%, -0.18%) to 3.01% (95% CI: 2.46%, 3.56%) for cancer mortality. Air had the largest associations with all-cause, heart disease, and cancer mortality, whereas the sociodemographic index had the largest association with stroke mortality. Across the urbanicity gradient, no consistent trend was found. Across climate regions, the associations ranged from 2.29% (95% CI: 1.87%, 2.72%) to 5.30% (95% CI: 4.30%, 6.30%) for overall EQI, and larger associations were generally found in dry areas for both overall EQI and domain indices. CONCLUSIONS: These results suggest that poor environmental quality, particularly poor air quality, was associated with increased mortality and that associations vary by urbanicity and climate region. Citation: Jian Y, Messer LC, Jagai JS, Rappazzo KM, Gray CL, Grabich SC, Lobdell DT. 2017. Associations between environmental quality and mortality in the contiguous United States, 2000-2005. Environ Health Perspect 125:355-362; http://dx.doi.org/10.1289/EHP119.

Additive Interaction between Heterogeneous Environmental Quality Domains (Air, Water, Land, Sociodemographic, and Built Environment) on Preterm Birth.

BACKGROUND: Environmental exposures often occur in tandem; however, epidemiological research often focuses on singular exposures. Statistical interactions among broad, well-characterized environmental domains have not yet been evaluated in association with health. We address this gap by conducting a county-level cross-sectional analysis of interactions between Environmental Quality Index (EQI) domain indices on preterm birth in the Unites States from 2000 to 2005. METHODS: The EQI, a county-level index constructed for the 2000-2005 time period, was constructed from five domain-specific indices (air, water, land, built, and sociodemographic) using principal component analyses. County-level preterm birth rates (n = 3141) were estimated using live births from the National Center for Health Statistics. Linear regression was used to estimate prevalence differences (PDs) and 95% confidence intervals (CIs) comparing worse environmental quality to the better quality for each model for (a) each individual domain main effect, (b) the interaction contrast, and (c) the two main effects plus interaction effect (i.e., the "net effect") to show departure from additivity for the all U.S. counties. Analyses were also performed for subgroupings by four urban/rural strata. RESULTS: We found the suggestion of antagonistic interactions but no synergism, along with several purely additive (i.e., no interaction) associations. In the non-stratified model, we observed antagonistic interactions, between the sociodemographic/air domains [net effect (i.e., the association, including main effects and interaction effects) PD: -0.004 (95% CI: -0.007, 0.000), interaction contrast: -0.013 (95% CI: -0.020, -0.007)] and built/air domains [net effect PD: 0.008 (95% CI 0.004, 0.011), interaction contrast: -0.008 (95% CI: -0.015, -0.002)]. Most interactions were between the air domain and other respective domains. Interactions differed by urbanicity, with more interactions observed in non-metropolitan regions. CONCLUSION: Observed antagonistic associations may indicate that those living in areas with multiple detrimental domains may have other interfering factors reducing the burden of environmental exposure. This study is the first to explore interactions across different environmental domains and demonstrates the utility of the EQI to examine the relationship between environmental domain interactions and human health. While we did observe some departures from additivity, many observed effects were additive. This study demonstrated that interactions between environmental domains should be considered in future analyses.

Hurricane Charley Exposure and Hazard of Preterm Delivery, Florida 2004.

Objective Hurricanes are powerful tropical storm systems with high winds which influence many health effects. Few studies have examined whether hurricane exposure is associated with preterm delivery. We aimed to estimate associations between maternal hurricane exposure and hazard of preterm delivery. Methods We used data on 342,942 singleton births from Florida Vital Statistics Records 2004-2005 to capture pregnancies at risk of delivery during the 2004 hurricane season. Maternal exposure to Hurricane Charley was assigned based on maximum wind speed in maternal county of residence. We estimated hazards of overall preterm delivery (<37 gestational weeks) and extremely preterm delivery (<32 gestational weeks) in Cox regression models, adjusting for maternal/pregnancy characteristics. To evaluate heterogeneity among racial/ethnic subgroups, we performed analyses stratified by race/ethnicity. Additional models investigated whether exposure to multiples hurricanes increased hazard relative to exposure to one hurricane. Results Exposure to wind speeds ≥39 mph from Hurricane Charley was associated with a 9 % (95 % CI 3, 16 %) increase in hazard of extremely preterm delivery, while exposure to wind speed ≥74 mph was associated with a 21 % (95 % CI 6, 38 %) increase. Associations appeared greater for Hispanic mothers compared to non-Hispanic white mothers. Hurricane exposure did not appear to be associated with hazard of overall preterm delivery. Exposure to multiple hurricanes did not appear more harmful than exposure to a single hurricane. Conclusions Hurricane exposure may increase hazard of extremely preterm delivery. As US coastal populations and hurricane severity increase, the associations between hurricane and preterm delivery should be further studied.

County-level hurricane exposure and birth rates: application of difference-in-differences analysis for confounding control.

BACKGROUND: Epidemiological analyses of aggregated data are often used to evaluate theoretical health effects of natural disasters. Such analyses are susceptible to confounding by unmeasured differences between the exposed and unexposed populations. To demonstrate the difference-in-difference method our population included all recorded Florida live births that reached 20 weeks gestation and conceived after the first hurricane of 2004 or in 2003 (when no hurricanes made landfall). Hurricane exposure was categorized using ≥74 mile per hour hurricane wind speed as well as a 60 km spatial buffer based on weather data from the National Oceanic and Atmospheric Administration. The effect of exposure was quantified as live birth rate differences and 95 % confidence intervals [RD (95 % CI)]. To illustrate sensitivity of the results, the difference-in-differences estimates were compared to general linear models adjusted for census-level covariates. This analysis demonstrates difference-in-differences as a method to control for time-invariant confounders investigating hurricane exposure on live birth rates. RESULTS: Difference-in-differences analysis yielded consistently null associations across exposure metrics and hurricanes for the post hurricane rate difference between exposed and unexposed areas (e.g., Hurricane Ivan for 60 km spatial buffer [-0.02 births/1000 individuals (-0.51, 0.47)]. In contrast, general linear models suggested a positive association between hurricane exposure and birth rate [Hurricane Ivan for 60 km spatial buffer (2.80 births/1000 individuals (1.94, 3.67)] but not all models. CONCLUSIONS: Ecological studies of associations between environmental exposures and health are susceptible to confounding due to unmeasured population attributes. Here we demonstrate an accessible method of control for time-invariant confounders for future research.

The associations between environmental quality and preterm birth in the United States, 2000-2005: a cross-sectional analysis.

BACKGROUND: Many environmental factors have been independently associated with preterm birth (PTB). However, exposure is not isolated to a single environmental factor, but rather to many positive and negative factors that co-occur. The environmental quality index (EQI), a measure of cumulative environmental exposure across all US counties from 2000-2005, was used to investigate associations between ambient environment and PTB. METHODS: With 2000-2005 birth data from the National Center for Health Statistics for the United States (n = 24,483,348), we estimated the association between increasing quintiles of the EQI and county-level and individual-level PTB; we also considered environmental domain-specific (air, water, land, sociodemographic and built environment) and urban-rural stratifications. RESULTS: Effect estimates for the relationship between environmental quality and PTB varied by domain and by urban-rural strata but were consistent across county- and individual-level analyses. The county-level prevalence difference (PD (95% confidence interval) for the non-stratified EQI comparing the highest quintile (poorest environmental quality) to the lowest quintile (best environmental quality) was -0.0166 (-0.0198, -0.0134). The air and sociodemographic domains had the strongest associations with PTB; PDs were 0.0196 (0.0162, 0.0229) and -0.0262 (-0.0300, -0.0224) for the air and sociodemographic domain indices, respectively. Within the most urban strata, the PD for the sociodemographic domain index was 0.0256 (0.0205, 0.0307). Odds ratios (OR) for the individual-level analysis were congruent with PDs. CONCLUSION: We observed both strong positive and negative associations between measures of broad environmental quality and preterm birth. Associations differed by rural-urban stratum and by the five environmental domains. Our study demonstrates the use of a large scale composite environment exposure metric with preterm birth, an important indicator of population health and shows potential for future research.

BMI curves for preterm infants.

BACKGROUND AND OBJECTIVES: Preterm infants experience disproportionate growth failure postnatally and may be large weight for length despite being small weight for age by hospital discharge. The objective of this study was to create and validate intrauterine weight-for-length growth curves using the contemporary, large, racially diverse US birth parameters sample used to create the Olsen weight-, length-, and head-circumference-for-age curves. METHODS: Data from 391 681 US infants (Pediatrix Medical Group) born at 22 to 42 weeks' gestational age (born in 1998-2006) included birth weight, length, and head circumference, estimated gestational age, and gender. Separate subsamples were used to create and validate curves. Established methods were used to determine the weight-for-length ratio that was most highly correlated with weight and uncorrelated with length. Final smoothed percentile curves (3rd to 97th) were created by the Lambda Mu Sigma (LMS) method. The validation sample was used to confirm results. RESULTS: The final sample included 254 454 singleton infants (57.2% male) who survived to discharge. BMI was the best overall weight-for-length ratio for both genders and a majority of gestational ages. Gender-specific BMI-for-age curves were created (n = 127 446) and successfully validated (n = 126 988). Mean z scores for the validation sample were ∼0 (∼1 SD). CONCLUSIONS: BMI was different across gender and gestational age. We provide a set of validated reference curves (gender-specific) to track changes in BMI for prematurely born infants cared for in the NICU for use with weight-, length-, and head-circumference-for-age intrauterine growth curves.