State Median Income Research Articles

Postoperative Access and Compliance Following Medial Patellofemoral Ligament Reconstruction in Hispanic Adolescents Mirrors the Non-Hispanic Adolescent Population.

Disparity in surgical care of patellar instability patients has not been fully investigated in the adolescent Hispanic population. This demographic has been shown to have differences in their care, including a lower rate of surgical treatment for patellar instability. Socioeconomic factors have been cited as a factor that influences patient outcomes and its relationship with ethnicity in context of patellar instability has not been evaluated. Review performed of patients <19 years of age who underwent MPFL reconstruction between September 2008 and December 2015. Demographics, patient median household income data, and clinical variables were collected. Generalized linear mixed model (GLMM) with subject as random effects factor was utilized to evaluate differences between ethnicity groups due to nonindependence of data. It was then expanded to incorporate interactions between ethnicity and income. Ninety-five patellar dislocation events met criteria in 85 adolescents (mean age: 15.5y). Thirty-four (40%) adolescents identified as Hispanic. In univariate analysis no differences were found between Hispanic and non-Hispanic patients. The multivariate GLMM demonstrated a significant interaction between ethnicity and income. The Hispanic group in the >100% State median income category had the highest rate of postoperative clinic appointments attended ( P =0.019). The Hispanic group in the <100% State median income category had the lowest rate of physical therapy appointments attended ( P =0.044). No differences were observed for duration of follow-up ( P =0.57) or final Kujala score ( P =0.75). Hispanic ethnicity alone is not associated with inferior postoperative management after MPFL reconstruction in adolescents. However, when socioeconomic status is considered, Hispanic patients of lower-income backgrounds are found to have lower compliance with postoperative rehab recommendations. Level III.

The Association Between State Minimum Wage and Firearm Homicides, 2000–2020

IntroductionRecent research has indicated an association between both poverty and income inequality and firearm homicides. Increased minimum wages may serve as a strategy for reducing firearm violence by increasing economic security among workers earning low wages and reducing the number of families living in poverty. This study aimed to examine the association between state minimum wage and firearm homicides in the United States between 2000 and 2020. MethodsState minimum wage, obtained from Temple's Law Atlas and augmented by legal research, was conceptualized using the Kaitz Index. State-level homicide counts were obtained from 2000-2020 multiple-cause-of death mortality data from the National Vital Statistics System. Log-linear regressions were conducted to model the associations between state minimum wage and firearm homicides, stratifying by demographic groups. Analyses were conducted in 2023. ResultsA one percentage point increase in a state's Kaitz Index was associated with a 1.3% (95% CI: -2.1% to –0.5%) decrease in a state's firearm homicide rate. When interacted with quartile of firearm ownership, the Kaitz Index was associated with decreases in firearm homicide in all except the lowest quartile. These findings were largely consistent across stratifications. ConclusionsChanging a state's minimum wage, whereby a full-time minimum wage worker's salary is closer to a state's median income may be an option for reducing firearm homicides.

Racial/ethnic and income-based differences in the use of surgery for cervical and lumbar disorders in New York State: a retrospective analysis

BACKGROUND CONTEXTThe extent to which use of spine surgeries for patients with cervical and lumbar disorders varies by their race/ethnicity and income is currently unknown. PURPOSETo assess racial/ethnic and income-based differences in use of spine surgery in New York State (NYS) from 2016 to 2019. STUDY DESIGNRetrospective observational analysis using 2016 to 2019 New York Statewide Planning and Research Cooperative System (SPARCS) data, direct standardization, and multivariable mixed-effects linear regression models. METHODSA dataset of patients who underwent surgery for cervical and spinal disorders in NYS in the period 2016 to 2019 was used to determine county-level age- and sex-standardized annual cervical and lumbar surgery rates expressed as number of surgeries per 10,000 individuals.Further sub-analysis was performed with the key independent variables being the combination of individual-level race/ethnicity (non-Hispanic White, non-Hispanic Black, and Hispanic individuals) and income (low-/high-income residing in zip codes below/above state median income); and year. We estimated multivariable mixed-effects linear regression models which controlled county-level variables to determine the adjusted rates of spine surgeries for patients belonging to various race/ethnicity and income group combinations. RESULTSThe study included 29,650 and 42,498 patients in the cervical and lumbar cohorts, respectively. In 2019, the county-level mean cervical and lumbar surgery rates were 3.88 and 5.19 surgeries per 10,000 individuals, respectively. There was a five-fold rate variation across NYS. In 2019, the adjusted cervical rates were 4.59 (White low-income), 4.96 (White high-income), 7.20 (Black low-income), 3.01 (Black high-income), 4.37 (Hispanic low-income), and 1.17 (Hispanic high-income). The adjusted lumbar rates were 5.49 (White low-income), 6.31 (White high-income), 9.43 (Black low-income), 2.47 (Black high-income), 4.22 (Hispanic low-income), and 2.02 (Hispanic high-income). The rates for low-income Black or Hispanic patients were significantly higher than their high-income counterparts. Low-income Black patients had the highest rates. Over the study period, the gap/difference increased significantly between high-income Hispanic and White individuals by 2.19 (95% confidence interval [CI]: −4.27, −0.10, p=.04) for cervical surgery; and between low-income Black and White individuals by 2.82 (2.82, 95% CI: 0.59, 5.06, p=.01) for lumbar surgery. CONCLUSIONThere are differences in the rates of spine surgery in New York State, among identifiable groups. Black individuals from poorer zip codes experience relatively higher spine surgery rates.Understanding the drivers of surgical rate variation is key to improving the equitable delivery of spine care. A better understanding of such rate variations could inform health policy.

PD20-12 DISPARITIES IN INFERTILITY COVERAGE IN THE UNITED STATES

You have accessJournal of UrologyCME1 Apr 2023PD20-12 DISPARITIES IN INFERTILITY COVERAGE IN THE UNITED STATES Arshia Sandozi, Dhaval Jivanji, Ariel Schulman, and Jacob Khurgin Arshia SandoziArshia Sandozi More articles by this author , Dhaval JivanjiDhaval Jivanji More articles by this author , Ariel SchulmanAriel Schulman More articles by this author , and Jacob KhurginJacob Khurgin More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003286.12AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: One in five heterosexual couples in the United States experience infertility. The costs of diagnosis and treatment may not be covered by health insurance. This study aimed to evaluate disparities in infertility coverage in the United States. METHODS: State essential health benefit benchmark (EHB) insurance plans submitted to Centers for Medicare and Medicaid were reviewed. We identified provisions that detailed coverage or exclusions for infertility. A literature search was performed to identify states with legislation related to coverage for infertility. State demographic information was derived from Census data. RESULTS: Data was available for 50 states and the District of Columbia. Twenty states had legislation related to infertility coverage. Two states required employers to offer at least one such plan, and one state only prohibited exclusions for treatment based on the diagnosis of infertility. In benchmark offerings, 19 states did not provide any minimum coverage for diagnosis or treatment of infertility. Twenty-five states offered some type of treatment, of which 9 covered treatments only for underlying causes of infertility (i.e. fibroids, ductal obstruction). Ten states codified coverage for prescription medications, 10 states for artificial insemination, and six states codified coverage for assisted reproductive technologies such as in vitro fertilization (IVF). Two states codified cryopreservation of ova, but only for patients actively undergoing treatment for infertility. Higher state median income was significantly associated with a state-requirement to offer infertility coverage as well as coverage of artificial insemination (p=0.039), but not with other coverage options, including IVF. States with higher populations enrolled in Medicaid or Children’s Health Insurance Program were more likely to have requirements for some level of infertility coverage (p=0.039). CONCLUSIONS: Nineteen states do not codify any minimum level of infertility coverage in EHB benchmark plans. Higher state median income is associated with codification of coverage of artificial insemination, but most types of coverage are not significantly associated with income. Overall, health insurance coverage for the diagnosis and treatment of infertility is highly variable and continues to be a barrier faced by patients desiring to conceive. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e589 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Arshia Sandozi More articles by this author Dhaval Jivanji More articles by this author Ariel Schulman More articles by this author Jacob Khurgin More articles by this author Expand All Advertisement PDF downloadLoading ...

FUNCTIONAL DISABILITY INVENTORY AND ASSOCIATIONS WITH RACE, ETHNICITY, INCOME AND FOOD INSECURITY

Abstract PURPOSE Inflammatory bowel disease (IBD) is an autoimmune disease that consists of Crohn’s Disease (CD) and ulcerative colitis (UC). Food insecurity is defined as limited or uncertain access to enough food. It is estimated that 1 in 7 children in the United States experience food insecurity or about 13 million children. Louisiana has one of the higher rates of food insecurity in the country with over 249,000 children affected. Food insecurity has been associated with worse health outcomes in children including developmental risks, poor mental health, and poor educational outcomes. Functional Disability Inventory (FDI) is a measure of limitations in both physical and psychosocial functioning secondary to physical health. Our hypothesis is that patients in food insecure homes will have higher FDI scores compared to those in food secure homes. METHODS This study is a prospective study of newly diagnosed IBD patients seen by the Division of Pediatric Gastroenterology at Children’s Hospital of New Orleans. Patients were screened for food insecurity using the United States Department of Agriculture’s food insecurity screening questionnaire and the American Academy of Pediatrics’ Food Insecurity 2-Question Screen. Disability scores were obtained at diagnosis using the FDI self-reporting questionnaire if older than 12 years of age or a parent form was completed for patients less than 12. RESULTS Our study consisted of 20 newly diagnosed pediatric IBD patients with an average age of 14.5 years. Nineteen (95%) patients had CD. Most patients were female (70%), non-Hispanic white (50%) with Medicaid (55%) as their primary insurance. Eight (40%) patients were African American and two (10%) were listed as other. Eight patients or their caregivers identified as living in a food insecure home (40%) and these patients averaged a FDI score of 10.2 The food secure group had an average FDI of 9.3. Non-Hispanic, white patients had an average FDI score of 10.5 while the minority patients had an average FDI of 8.5. Only two patients lived in an area with a median household income below poverty with an average FDI of 15.5. Ten patients lived below the state median income and had an average FDI was 8.2. Those patients living in areas above the state median income level, 8 total, had the highest FDI scores, 10.8. CONCLUSION Food insecurity had higher rates of disability scores compared to families from food secure homes. Families in higher income areas and who were non-Hispanic white had higher FDI scores compared to minority patients and those in lower income areas. Further data will be collected with changes in FDI through the first 6 months of diagnosis. Additional larger studies are needed to determine a relationship between FDI scores and food insecurity in pediatric IBD.

CORR Insights®: Musculoskeletal Urgent Care Centers Restrict Access for Patients with Medicaid Insurance Based on Policy and Location.

CORR Insights®: Musculoskeletal Urgent Care Centers Restrict Access for Patients with Medicaid Insurance Based on Policy and Location.

Restructuring the Eligibility Policies of the Child Care and Development Fund to Address Benefits Cliffs and Affordability

This paper explores how the current eligibility policies of the federal Child Care and Development Fund (CCDF) create benefits cliffs that act as barriers to economic self-sufficiency. By examining Florida data and policies, the authors demonstrate how the program’s existing co-payment schedule affects the same hypothetical family living in two contrasting Florida counties: one with state median living costs and one with high living costs. The authors find that the CCDF income eligibility exit threshold is too low, particularly in high-cost counties. That occurs because the exit threshold is based on the state median income, as opposed to more local measures that better approximate and reflect local cost of living. The authors propose and calculate the additional family and government costs of two alternative CCDF phase-out designs, which would remove the CCDF benefits cliffs. Both proposed alternatives feature smooth phase-out schedules that align the subsidies with the local cost of childcare, thereby reducing barriers to economic mobility unintentionally created by government policies.

Abortion as a Catastrophic Health Expenditure in the United States.

Abortion is a critical reproductive health service that is difficult for many in the United States to afford owing to policies aimed at restricting insurance coverage of this basic health service. This article assesses whether the resulting high out-of-pocket cost for abortion could be considered a catastrophic health expenditure, and explores potential policies that could prevent households from experiencing financial hardship or impoverishment. We assessed if the average costs of a first and second trimester abortion procedure in 2016 were catastrophic health expenditures by applying a 40% threshold to the monthly nonsubsistence income of households earning their state's median income in all 50 states and Washington, DC. The out-of-pocket cost for a first trimester abortion procedure would have been catastrophic for households earning their state's median monthly income in 39 states. In nine of these states, the average cost was between 100% and 199% of a household's nonsubsistence income, and in another nine states, this cost was at least double a household's nonsubsistence income. The out-of-pocket cost of a second trimester abortion would have been catastrophic for households earning their state's median monthly income in all 50 states and Washington, DC. In a majority of states, the out-of-pocket cost of an abortion is financially catastrophic for households earning no more than their state's median monthly income. The United States should implement policies to create or improve health care safety nets to guarantee abortion care for all individuals, regardless of their income or insurance status.

Determining the Existence and Strength of Teen Dating Violence Policy: Testing a Comparative State Internal Determinants Model.

Research demonstrates that Teen Dating Violence (TDV) programs impact TDV knowledge, attitudes, and behaviors, decreasing the odds of TDV victimization and perpetration. Studies indicate that students who do not complete a TDV intervention have significantly higher odds of physical and emotional TDV victimization and emotional TDV perpetration. This study uses multiple logistic regression and multiple linear regression to examine predictors of the presence and the strength of state legislation addressing TDV education and school policies. Results indicate some success in predicting the existence of TDV laws but less support for forecasting the strength of the policies passed. Dominant political party and state median income were found to be potentially important determinants of TDV state school policies. A state's political culture influenced the strength of states' TDV policies. Showing that policy existence and strength are related to different processes is important for advocates to understand. Future research should look at additional variables and explore legislative histories.

Abstract A066: Inaccuracy in parental reports of adolescent human papillomavirus vaccination: A multilevel analysis of the influences of state-level and individual-level factors

Abstract Background: Accurate reporting of human papillomavirus (HPV) vaccination status is necessary for monitoring coverage and identifying under-vaccinated populations. While parental recall or reports are commonly used to identify adolescent HPV vaccination status, these self-reports may be subjected to bias. The Socioecological Framework suggests that factors at the individual (e.g., education), interpersonal (e.g., interactions with health care provider), community (e.g., state median income), and policy levels (e.g., state-level HPV vaccine policy) can influence the accuracy of parental reports. Objective: We explored potential multilevel correlates of the accuracy of parental reports of adolescent HPV vaccination status. Methods: Data from parents of 19,751 adolescents (2016 National Immunization Survey-Teen) were analyzed using multilevel modeling. We examined the adolescent's age, sex, race/ethnicity, maternal education, and household poverty status (individual-level); the number of health care providers seen (interpersonal-level); state-level median income (community-level); and state policy regarding HPV vaccine (i.e., whether a state has a policy mandating HPV vaccine for school entry or educating residents about HPV vaccine) (policy level). Outcomes included: 1) inaccuracy in reporting vaccine initiation (≥1 dose) and 2) inaccuracy in reporting completion (3 doses). Results: In the sample, 20.6% and 22.2% inaccurately reported initiation and completion, respectively. Compared to parents of White adolescents, parents of those who were Black (OR=1.6 and 1.5), Hispanic (OR=1.4 and 1.7), or of multiple races or other race (OR=1.8 and 1.8) were more likely to inaccurately report initiation and completion, respectively. Households with higher maternal education (OR=0.7 and 0.9) and nonpoverty status (OR=0.5 and 0.6) were less likely to inaccurately report. Compared to those who had seen 0 or 1 provider, those who had seen more providers were less likely to inaccurately report (OR=0.9 and 0.9 for seeing 2 providers, OR=0.8 and 0.9 for 3+ providers). Neither state-level median income nor state HPV vaccine policy was associated with either outcome. Discussion: Parents of racial/ethnic minority adolescents and households with more socioeconomic disadvantages were more likely to inaccurately report HPV vaccine initiation and completion. These results have implications for vaccine estimates drawn from self-reports. Future research can examine and target sources of inaccuracies in these groups, for example, uncertainty in provider communication, low health literacy, or social desirability in reporting. Having seen more providers was associated with higher accuracy, suggesting the importance of increased access to vaccine records. State vaccine policy does not appear to have an impact on the accuracy of parental reports, pointing to a need to further examine differential policy implementation or enforcement. Citation Format: Milkie H. N. Vu, Minh Luu, Regine Haardörfer, Carla J. Berg, Cam Escoffery, Robert A. Bednarczyk. Inaccuracy in parental reports of adolescent human papillomavirus vaccination: A multilevel analysis of the influences of state-level and individual-level factors [abstract]. In: Proceedings of the Eleventh AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2018 Nov 2-5; New Orleans, LA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(6 Suppl):Abstract nr A066.

Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity.

BackgroundStudies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research.ObjectiveThe aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors.MethodsWe utilized Twitter’s streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics.ResultsMachine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic.ConclusionsMachine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lower happiness and fewer healthy behaviors.

Does socioeconomic status impact physical activity and sleep among children with cancer?

Compared with healthy children, pediatric oncology patients have impaired sleep and engage in less physical activity (PA). Socioeconomic status (SES) may be one determinant of PA and sleep among pediatric oncology patients. Between November 12, 2009 and March 27, 2013, 50 pediatric oncology patients between the ages of 8 and 18 years were recruited from an urban children's hospital. PA and sleep were assessed by actigraphy and diaries over 7 days. Fatigue was assessed using the Fatigue Scale. SES was defined by primary payer status of insurance (state or private) and by Median Household Income (MHI) obtained from 2010 U.S. Census block data for residences. MHI was compared to Connecticut state median income ($67,000). Multivariate regression models examined the relationship between SES and PA, sleep and fatigue. PA and sleep efficiency were strongly correlated (r = 0.31, P = 0.03). Children with state insurance had higher average PA (P = 0.004) than children on private insurance. There were no significant differences in PA or sleep efficiency by block MHI. The 7-day fatigue score was lower among the participants aged 8-12 years in the group with MHI less than $67,000 (P = 0.03), although there was no significant difference among participants aged 13-18 years in the group. There was no difference in mean fatigue scores by insurance status. Participants on state insurance had higher PA than those with private insurance. Although block MHI did not influence PA or sleep efficiency among children with cancer, participants aged 8-12 years in a lower MHI block had less fatigue. Future research is needed to further understand how SES influences PA.

An Anatomy of U.S. Personal Bankruptcy Under Chapter 13

We build a structural model of Chapter 13 bankruptcy that captures salient features of personal bankruptcy under Chapter 13. We estimate our model using a novel data set we construct from bankruptcy court dockets recorded in Delaware between 2001 and 2002. Our estimation results highlight the importance of debtor’s choice of repayment plan length on Chapter 13 outcomes under the restrictions imposed by the bankruptcy law. We use the estimated model to conduct policy experiments to evaluate the impact of more stringent provisions of Chapter 13 that impose additional restrictions on the length of repayment plans. We find that these provisions would not materially affect creditor recovery rates and would not necessarily make discharge more likely for debtors with income above the state median income.

A Blunt Analysis: Marijuana Policy Liberalization and Market Prices in Colorado and Washington

The increasing popularity of marijuana has led to a recent public debate in the U.S. over its legal status. An open question remains about how the price of marijuana will change if it becomes legal. We assemble a unique dataset of marijuana prices to measure the effect of legalization. We use a difference-in-differences model to estimate this effect in the states of Colorado and Washington. Our model includes several explanatory variables such as state median income, whether a state had a medical marijuana law, and demographic characteristics to control for state heterogeneity. In addition, we control for other macroeconomic factors. In all 3 versions of our model, we find a negative effect in Colorado and a positive effect in Washington with varying levels of significance. The most significant results imply that the inflation-adjusted price of marijuana in Colorado decreased by about 8.8% after legalization while prices in Washington rose by 6.1%. We believe differences in each state’s policies explain our results.

Geographic Variation in Early MRI for Acute Work-Related Low Back Pain and Associated Factors.

Retrospective cohort study using medical claims data. To document the extent of geographic variation in utilization of magnetic resonance imaging (MRI) for working-age patients early in the course of acute, disabling low back pain (LBP); to identify potential factors associated with the most extreme variations. Although guidelines discourage MRI in acute uncomplicated LBP, this practice is highly prevalent. Geographic variation in radiologic testing is common, and may indicate problems with access or quality of care, yet this has not been studied in working-age patients with LBP (a frequent cause for acute care visits). All cases of acute, disabling LBP with onset between 1/1/2002 and 12/31/2007 were selected from a large workers' compensation data source. Detailed information from medical bills was used to identify persons who received early MRI (within 30 days of onset), classify cases by LBP severity, and exclude those with concurrent injuries or diseases, and/or prior LBP disability. Individual predictors included age, gender, job tenure, and industry. State-level predictors included economic, physician supply and practice variables, workers compensation system features, and MRI testing location. Generalized linear mixed models were constructed to evaluate within- and between-state variability, selecting the six highest and six lowest MRI utilization states. State rates of early MRI scanning varied from 6.0% to 58.4%. In the 12 selected most extreme states, non-hospital MRI sites and lower state median income were associated with higher rates of early MRIs, explaining 84% of between-state variation, and 12.5% of all observed variability. Inter-state differences in MRI rates were greatest for lower-severity cases. Higher severity diagnoses were more common in high utilization states. Between-state inappropriate early MRI variability is largely explained by rate of non-hospital MRI sites and state median income. Potential solutions include efforts to address inappropriate referral patterns based on private MRI facility ownership, and to improve quality of communication with low-income patients. 4.

The impact of state policy on teen dating violence prevalence

Teen dating violence (TDV) is a serious public health concern that is associated with many negative effects. Studies on TDV prevention most often focus on the evaluation of prevention programs in school and community settings. Much less is known about the effects of policy on TDV prevalence. This study tests a model to explain whether stronger laws regarding TDV, specifically civil protection orders, have an impact on TDV rates in states. Results show that stronger policy, Democratic party control of the governor’s office, and higher state median income are associated with lower rates of TDV. This study provides solid information regarding the role of civil protection orders as a means of TDV prevention and adds to our knowledge of the efficacy of state-level TDV policy. The information can lead to increased vigor on the part of advocates to strive for specific provisions in the law and to work for gubernatorial candidates who will support such laws.

The association between state bans on soda only and adolescent substitution with other sugar-sweetened beverages: a cross-sectional study.

BackgroundAcross the United States, many states have actively banned the sale of soda in high schools, and evidence suggests that students’ in-school access to soda has declined as a result. However, schools may be substituting soda with other sugar-sweetened beverages (SSBs), and national trends indicate that adolescents are consuming more sports drinks and energy drinks. This study examined whether students consumed more non-soda SSBs in states that banned the sale of soda in school.MethodsStudent data on consumption of various SSBs and in-school access to vending machines that sold SSBs were obtained from the National Youth Physical Activity and Nutrition Study (NYPANS), conducted in 2010. Student data were linked to state laws regarding the sale of soda in school in 2010. Students were cross-classified based on their access to vending machines and whether their state banned soda in school, creating 4 comparison groups. Zero-inflated negative binomial models were used to compare these 4 groups with respect to students’ self-reported consumption of diet soda, sports drinks, energy drinks, coffee/tea, or other SSBs. Students who had access to vending machines in a state that did not ban soda were the reference group. Models were adjusted for race/ethnicity, sex, grade, home food access, state median income, and U.S. Census region.ResultsStudents consumed more servings of sports drinks, energy drinks, coffee/tea, and other SSBs if they resided in a state that banned soda in school but attended a school with vending machines that sold other SSBs. Similar results were observed where schools did not have vending machines but the state allowed soda to be sold in school. Intake was generally not elevated where both states and schools limited SSB availability – i.e., states banned soda and schools did not have SSB vending machines.ConclusionState laws that ban soda but allow other SSBs may lead students to substitute other non-soda SSBs. Additional longitudinal research is needed to confirm this. Elevated SSB intake was not observed when both states and schools took steps to remove SSBs from school.

Financial Burden of Raising CSHCN: Association With State Policy Choices

We examined the association between state Medicaid and State Children's Health Insurance Program (SCHIP) income eligibility and the financial burden reported by low-income families raising children with special health care needs (CSHCN). Data on low-income CSHCN and their families were from the National Survey of Children With Special Health Care Needs (N = 17039), with a representative sample from each state. State Medicaid and SCHIP income-eligibility thresholds were from publicly available sources. The 3 outcomes included whether families had any out-of-pocket health care expenditures during the previous 12 months for their CSHCN, amount of expenditure, and expenditures as a percentage of family income. We used multilevel logistic regression to model the association between Medicaid and SCHIP characteristics and families' financial burden, controlling state median income and child- and family-level characteristics. Overall, 61% of low-income families reported expenditures of >$0. Among these families, 30% had expenses between $250 and $500, and 34% had expenses of more than $500. Twenty-seven percent of the families reporting any expenses had expenditures that exceeded 3% of their total household income. The percentage of low-income families with out-of-pocket expenses that exceeded 3% of their income varied considerably according to state and ranged from 5.6% to 25.8%. Families living in states with higher Medicaid and SCHIP income-eligibility guidelines were less likely to have high absolute burden and high relative burden. Beyond child and family characteristics, there is considerable state-level variability in low-income families' out-of-pocket expenditures for their CSHCN. A portion of this variability is associated with states' Medicaid and SCHIP income-eligibility thresholds. Families living in states with more generous programs report less absolute and relative financial burden than families living in states with less generous benefits.

What drives the demand for in vitro fertilization? a multivariate statistical analysis of the utilization of assisted reproductive technologies in the United States: 1995–2004

Despite a proliferation of fertility clinics and increasing public awareness of advanced reproductive technologies (ART), the annual growth of in vitro fertilization (IVF) cycles performed in the United States decreased each year from 2000–2004. This analysis examines independent variables and their correlations with the number of IVF cycles performed annually in order to assess current and future trends. Retrospective multivariate statistical analysis. Data from the 1995–2004 Centers for Disease Control ART annual reports were consolidated, including total fresh nondonor (FND) cycles and pregnancy rate per FND cycle. To allow for a significant sample size and consistent analysis with other variables, data from each reporting center were aggregated to that center's respective state. Additional state-level data were incorporated, including female population age 25–44, median income, and infertility insurance mandate status. Multiple regression analysis was conducted, using FND cycles per 100,000 females age 25–44 as the dependent variable, and all other variables as potential independent variables. Natural logarithm transformation for the dependent variable was used to improve the regression model explanatory power and residual characteristics. Median income and the presence of a mandate to cover infertility treatment were positively correlated with FND cycles per 100,000 females age 25–44. Pregnancy rate per FND cycle was negatively correlated. The resulting regression equation was ln FND cycles per 100,000 females age 25–44 = 2.56 + 0.000066 state median income + 0.442 mandate to cover – 0.0142 FND pregnancy rate (P<0.01 for each independent variable; R2 = 47.9%). Population demographics, median income, the presence of an insurance mandate to cover fertility treatment, and pregnancy rate all appear to be statistically significant variables for explaining the utilization of ART at the state level. Insurance mandates and pregnancy rate had the largest impact. The presence of a mandate to cover is associated with an increase of 1.56 FND cycles per 100,000 potential patients, and each percentage point increase in FND pregnancy rate is associated with a decrease of 1.01 FND cycles per 100,000 potential patients. This analysis can be combined with other models to develop better forecasts for the number of cycles performed in the future. This study also revealed state-level differences in ART utilization which are being analyzed further.

Whose health is affected by income inequality? A multilevel interaction analysis of contemporaneous and lagged effects of state income inequality on individual self-rated health in the United States

The empirical relationship between income inequality and health has been much debated and discussed. Recent reviews suggest that the current evidence is mixed, with the relationship between state income inequality and health in the United States (US) being perhaps the most robust. In this paper, we examine the multilevel interactions between state income inequality, individual poor self-rated health, and a range of individual demographic and socioeconomic markers in the US. We use the pooled data from the 1995 and 1997 Current Population Surveys, and the data on state income inequality (represented using Gini coefficient) from the 1990, 1980, and 1970 US Censuses. Utilizing a cross-sectional multilevel design of 201,221 adults nested within 50 US states we calibrated two-level binomial hierarchical mixed models (with states specified as a random effect). Our analyses suggest that for a 0.05 change in the state income inequality, the odds ratio (OR) of reporting poor health was 1.30 (95% CI: 1.17–1.45) in a conditional model that included individual age, sex, race, marital status, education, income, and health insurance coverage as well as state median income. With few exceptions, we did not find strong statistical support for differential effects of state income inequality across different population groups. For instance, the relationship between state income inequality and poor health was steeper for whites compared to blacks ( OR = 1.3 4 ; 95% CI: 1.20–1.48) and for individuals with incomes greater than $75,000 compared to less affluent individuals ( OR = 1.6 5 ; 95% CI: 1.26–2.15). Our findings, however, primarily suggests an overall (as opposed to differential) contextual effect of state income inequality on individual self-rated poor health. To the extent that contemporaneous state income inequality differentially affects population sub-groups, our analyses suggest that the adverse impact of inequality is somewhat stronger for the relatively advantaged socioeconomic groups. This pattern was found to be consistent regardless of whether we consider contemporaneous or lagged effects of state income inequality on health. At the same time, the contemporaneous main effect of state income inequality remained statistically significant even when conditioned for past levels of income inequality and median income of states.