State-level Data Research Articles

Exploring disparities in post-cancer treatment instructions: an analysis of rural vs. urban breast cancer survivors in Missouri using BRFSS data

BackgroundThere are more than 4 million breast cancer survivors in the United States. With continuing improvements in early detection and treatment, the number of breast cancer survivors will only continue to increase. Breast cancer survivors face a lifetime risk of long-term or late-effects from cancer treatments, thus post-cancer treatment care, referred to as survivorship care, is critical. Social determinants of health, including where a breast cancer survivor lives, may impact cancer survivorship care. Our purpose was to evaluate the relationship between rural/urban status and receipt of cancer survivorship care and instructions for Missouri breast cancer survivors using state-level data from a nationwide telephone survey—the Behavioral Risk Factor Surveillance System (BRFSS).MethodsMissouri included the BRFSS Cancer Survivorship Module in 2014, 2016, 2018, and 2020. Module items ask cancer survivors about receipt of a “post-cancer-treatment summary” and “follow-up instructions.” We hypothesized chances of receipt of both components would differ among four level Rural–Urban Commuting Area (RUCA) Code groups (Rural, Small-Town, Micropolitan, Metropolitan). Data from 430 breast cancer survivors over four survey years were combined and grouped according to RUCA status. Using a logistic model with a multilevel approach (after sampling weights calibration), the relationship between receipt of survivorship instructions and RUCA group was examined.Results46% of Missouri breast cancer survivors reported receipt of complete survivorship instructions. Compared to rural breast cancer survivors, micropolitan breast cancer survivors had 5.9 times higher odds of receiving survivorship instructions (p < 0.0001).ConclusionsLess than half of respondents reported receiving complete post-cancer treatment instructions. More urban respondents were more likely to receive instructions than their rural counterparts which raises questions about the quality of post-treatment care between rural and urban survivors. A sophisticated and purposeful approach to mitigating potential disparities is warranted. Receipt of cancer survivorship care plans may be impacted by rurality/urbanicity. Clinicians caring for cancer survivors living in rural or urban settings must continue to be diligent with providing personalized survivorship care.

Area deprivation index impact on type 2 diabetes outcomes in a regional health plan.

Rates of attainment of high-quality diabetes care have been shown to be lower for those living in more disadvantaged and rural areas. Diabetes management relies on access to care and is impacted by physical, social, and economic factors. Area deprivation index (ADI) is one way to quantify geographic disparities in aggregate. We aimed to investigate how ADI impacts outcomes in members with type 2 diabetes enrolled in a large, regional health plan. To evalute clinical and economic objectives. Clinical objectives included the percentage of members who achieved hemoglobin A1c (A1c) goal level of 7% or less, the percentage of members who received comorbidity-focused therapies, noninsulin diabetes medication adherence, and the frequency and type of health care services used. Economic outcomes included per member per month differences in total cost of care, pharmacy cost, medical cost, and diabetes-associated cost. This retrospective review of pharmacy and medical claims included 8,814 adult members with newly diagnosed type 2 diabetes enrolled in an integrated health plan during calendar year 2021. To be included, members were required to be at least 18 years of age, reside in Pennsylvania, and have continuous enrollment for 2 years prior to type 2 diabetes diagnosis. State-level ADI data were derived for each member and applied to the Census block group on file in the administrative claims data. The study population deciles were grouped into ADI quintiles for analysis. Multivariable regression models and descriptive statistics were used to evaluate the association between ADI and outcomes while controlling for confounding variables. There were no statistically significant differences between any ADI quintile for achievement of A1c goal or receipt of comorbidity-focused therapy. Significant differences were identified between ADI quintiles 1 (least deprived) and 5 (most deprived) for obtainment of at least 1 A1c test during calendar year 2021 (72% vs 56%, P < 0.01) and adherence to noninsulin diabetes medications (70% vs 62%, P < 0.01). Significant differences were also identified for all-cause inpatient, outpatient, and unplanned health care service utilization. The difference in per member per month all-cause total cost of care was on average $363.50 less for those living in ADI quintile 1 vs those in quintile 5 (P < 0.01). Significant differences were identified between ADI quintiles 1 and 5 for noninsulin diabetes medication adherence, frequency of A1c test claims, all-cause health care service utilization, and total cost of care. There were no statistically significant differences between ADI quintiles for achievement of A1c goal or receipt of comorbidity-focused therapies.

Forecasting energy poverty using different machine learning techniques for Missouri

Energy poverty in Missouri was analyzed using the four quadrant approach using both state and county level data sets for two separate definitions of the grid. Predictions used machine learning techniques including decision trees, random forest, extreme gradient boosting and support vector machines. It was determined that the extreme gradient boosting performed the best when compared to all the other models after the hyperparameters were tuned. The F1 scores for the county level data sets were higher than for the state levels thus indicating greater predictability for the National Oceanic and Atmospheric Administration (NOAA) climatological regional runs. For the county level data, the F1 score was the highest for region 1, which coincided with one of the highest expenditure risk values whilst regions 2-4 were the lowest scoring area. In grid 2, the largest class distribution changed from grid 1’s expenditure risk to the no risk category. This grid had more variability in terms of the double risk class when compared to grid 1 and, as such, its predictability in terms of its F1 scores was reduced. There were similarities in the ranking of the prediction scores for the regions for both grids as regions 1 and 6 incurred the largest F1 values. Thus energy poverty can be classified and predicted for Missouri, which in turn may aid policy makers via quantitative regional risk analysis. This data-driven informed policy making can lead to the development and implementation of laws and social programs to help ameliorate energy poverty.

Friends and partners: Estimating latent affinity networks with the graphical LASSO

The notion of affinity among countries is central in studies of international relations: it plays an important role in research as scholars use measures of affinity to study conflict and cooperation in a variety of contexts. To more effectively measure affinity, I argue that it is necessary to utilize multidimensional data and take into account the network context of international relations. In this paper, I develop the deep affinity concept and introduce a new algorithm, the three-step graphical LASSO (GLASSO), to infer and recover latent affinity networks. This technique leverages the abundance of monadic and dyadic state-level data to identify the presence or absence of affinity links between pairs of countries. Directly incorporating network effects and using a variety of multidimensional data inputs, I used the three-step GLASSO to estimate latent affinity links among countries. With these data, I examined the implications of affinity for international conflict and foreign direct investment, and found that the measure of affinity generated with the three-step GLASSO outperformed alternative affinity measures and was associated with decreased conflict and increased economic interaction.

The Effects of Big Data Regulations on Industrial Modernization in Resource-Based Cities: A Semi-Natural Study Using a State-Level Big Data Coordination Zone

As a core component of the digital economy, big data provides resource-based cities with a new impetus for upgrading their industrial structures. This paper employs a double difference method with panel data from 86 resource cities in China from 2009 to 2020 in order to investigate the effects of big data policies on resource cities' upgrading of their industrial structures objectively. Quasi-control groups are the quasi-natural trials of two batches of 19 resource cities in national big data comprehensive experimental zones. The empirical results demonstrate that the nationwide comprehensive experimental zone for big data exerts a considerable influence on the industrial upgrading of resource-based cities. This conclusion remains robust even after putting it through a series of tests, including the consolidation test and the stability test. Ultimately, this study offers a novel approach to catalyzing industrial upgrading in a big data context, shedding light on crucial issues such as economic instability in resource-based cities.

State and Local Government Expenditures and Infant Mortality.

A previous study reported that increased state and local government expenditures were associated with decreased infant mortality rates (IMRs). However, reported estimates of the association between expenditures and IMR represented the degree to which the association changed each year, not the main effect. We reproduced the original results, reporting this main effect and replicated the analysis using improved methodology and updated data. For the reproduction analysis, we used methods and data identical to the original study: A publicly-posted, state-level data set of expenditures from 2000 to 2014 US Census Bureau survey data linked to 2-year lagged IMR data with a random intercept model including an interaction between time and expenditures. For the replication analysis, we added 5 years of data and adjusted for fixed state differences and inflation. In the reproduction, the main effects of total, environmental, and educational expenditures on IMR were much larger than the interaction effects previously reported as the main effects. For example, a 1-SD increase in per-capita total expenditures was associated with a reduction of 0.35 infant deaths per 1000 live births instead of 0.02 deaths per 1000 live births originally reported. In the updated replication, the main effects were generally even larger (eg, -0.51 deaths per 1000 per SD increase in total expenditures). Increased total expenditures were associated with absolute but not relative reductions in Black-white IMR gaps. State and local government expenditures are associated with greater reductions in IMR than previously reported, underscoring the importance of continued public investment.

Economic Dynamism: Interrelationships among Entrepreneurship-Friendly Environments, Geographic Mobility, and Labor Market Freedom in both the Pre- and Post-Great Recession Periods

This empirical research study endeavors to elevate our understanding of the interrelationship of several aspects of economic dynamism at the state level in the United States. We investigate the factors influencing geographic mobility, i.e., both net in-migration and gross in-migration patterns, by simultaneously focusing on two hypotheses using state-level panel data covering the period 2005-2017, as well as Pre-Great Recession and Post-Great Recession sub-periods thereof. These two hypotheses are as follows: the pattern of geographic mobility is positively impacted by a business climate that is (a) friendly to the pursuit of entrepreneurship per se on the one hand and (b) migrants are attracted to environments having greater labor market freedom on the other hand. This paper reveals that these two hypotheses receive simultaneous empirical support (i.e., they are not incompatible with one another).

Data breach notification laws and the cost of private debt

We examine the effect of state-level data breach disclosure mandates (DBN laws) on the cost of private debt. Using the staggered passage of DBN laws across the U.S. states, we document an increase in the cost of private debt for firms affected by these laws. These findings suggest that DBN laws increase lenders’ anticipation of cash flow risk from potential future costs related to data breach disclosures. We further document that the increase in the cost of private debt is more pronounced for firms that (i) belong to industries that are susceptible to data breaches, (ii) report internal control weaknesses, or (iii) disclose cyber security risks in their risk factor disclosures. Additionally, we find that the increase in the cost of private debt is less pronounced in firms that focus on cyber security measures, such as investing in cyber security or appointing a technology officer on their board. We contribute to the debt contracting and disclosure literature by focusing on the proactive response of private lenders after the passage of DBN laws to mitigate ex-ante the risk resulting from potential future data breach disclosures while also shedding light on the cost (financing cost) associated with mandatory disclosure practices.

Examination of state-level school safety data dashboard characteristics

Choices made on data visualizations guide how users make meaning of the information presented. This research investigates design decisions made on 115 state-level dashboards reporting school safety data. Using pre-determined codes drawn from a framework of visualization rhetoric, dashboard characteristics were described and analyzed. Analysis demonstrates that school safety dashboards vary significantly in types of school safety data included as well as how such data are presented. Most dashboards lack specific interpretative text or narration, meaning the messages and stories communicated by dashboards are influenced largely by choices in data included, how it is visually represented, and the interactivity provided to users. The choices craft divergent stories about school safety for dashboard users—including, but not limited to, those that foreground student behavior as the central threat, those that present school practices as problematic, and those that center community creation—which may shape public discourse around school safety. In concert, rhetorical choices reflect different perspectives on safety, students, and schools.

US States with poor social health outcomes and more firearms have more morbidity due to mass shootings.

Mass shootings represent a persistent public health crisis. Prior studies have linked social determinants of health (SDOH) to the phenomenon of gun violence, but there remain limited analyses on mass shooting events specifically. Mass shooting events from 2014-2019 were recorded from the Gun Violence Archive. State-level data regarding population, ATF registered weapons, federal firearm licensees and several SDOHs (poverty, unemployment and educational attainment) were collected from publicly-available US governmental databases. Giffords Law Center rankings were used to assess the relative strictness of each state's gun laws. Gun ownership rates were obtained from the RAND Corporation. Bivariate analyses compared each SDOH, as well as ATF registered weapons, Giffords Center ranking and gun ownership rates, to the death rate, injury rate, and combined injury/death rate from mass shootings in each state. All associations were evaluated via Pearson's Rho. Slope and p-values were analyzed, with a threshold significance value of p less than 0.05. Unadjusted analysis revealed poor mental health, decreased educational attainment and increased unemployment to all be associated with an increased risk of mass shooting-related injury or death. Adjusted analysis revealed fewer firearm regulations, higher gun ownership, lack of handgun magazine restrictions and lack of long-gun registration requirements were associated with an increased risk of mass-shooting death. Similarly, adjusted analysis revealed lack of handgun permit requirements to be associated with both an increased risk of mass shooting-related injury and combined risk of injury/death. This study revealed associations between multiple SDOH and firearm restrictions with morbidity due to mass shooting events.

Effect of police expenditure on crime rate in India

Purpose This study aims to examine the relationship between law enforcement expenditures, economic indicators and various crime rates in India using data from the National Crime Records Bureau (NCRB) spanning from 2001 to 2021. Design/methodology/approach The study uses data sourced from the NCRB covering violent crimes, property crimes, economic crimes and total crimes under the Indian Penal Code across 27 states and 2 union territories in India. Crime rates per 100,000 people are calculated using projected population figures from the 2001 and 2011 censuses, adjusting for census periods and growth rates. Economic variables such as per capita real gross state domestic product (GSDP) and total police expenditure are obtained from state government sources, adjusted to constant prices. Pooled ordinary least squares analysis is conducted to explore the relationships between crime rates, conviction rate, economic factors and police expenditure over the three-time points: 2001, 2011 and 2021. Findings The study reveals a complex relationship between police expenditure and crime rates across different categories in India. Increased police expenditure is positively associated with higher reporting rates for violent and economic crimes, suggesting that enhanced law enforcement resources may improve crime detection and reporting. However, contrary to some expectations, higher police spending does not necessarily lead to a reduction in overall crime rates. Economic prosperity, measured by per capita GSDP, is found to reduce violent crime rates but is associated with an increase in property and economic crimes, likely due to the greater opportunities for such crimes in wealthier regions. Research limitations/implications The study’s reliance on state-level aggregate data and NCRB crime statistics, which are based on reported incidents, might not fully capture the true extent of criminal activity, particularly in regions with underreporting issues. In addition, the use of projected population data beyond 2001 and 2011 introduces some uncertainty into the analysis. Future research should explore district-level data to better understand regional variations in crime and incorporate social, cultural and innovative policing strategies to provide a more comprehensive analysis. Practical implications Policymakers should recognize the nuanced impacts of police expenditure and economic prosperity on crime rates. While increased law enforcement resources may enhance crime reporting, they do not guarantee a reduction in overall crime rates. Economic growth, while beneficial in reducing violent crimes, may inadvertently increase property and economic crimes due to greater opportunities for criminal activity. These insights suggest the need for targeted interventions that consider both economic factors and law enforcement strategies in crime prevention efforts. Originality/value This study contributes to the ongoing debate about the effectiveness of police expenditure in reducing crime by highlighting its limited impact on overall crime rates in India. The findings underscore the importance of understanding the diverse effects of economic prosperity on different crime types, offering valuable insights for policymakers aiming to design evidence-based crime prevention strategies that account for socioeconomic disparities and regional contexts.

Vehicle miles traveled induced demand, rebound effect, and price and income elasticities: A US spatial econometric analysis

The economic literature on transportation is extensive, with travel demand being a well-studied topic. However, research on light-duty vehicle travel has not fully explored spatial dependencies arising from neighboring regions' driving activities. This study uses state-level panel data from 2000 to 2019 to examine U.S. vehicle miles traveled (VMT), focusing on spatial spillover effects through a Spatial Durbin Model. Our results reveal significant spatial interactions between states. Increased VMT in one state leads to spillover effects, increasing VMT in neighboring states. We present our findings as marginal effects, distinguishing between direct and indirect impacts. The direct price elasticity is estimated at −0.257, with a positive spillover impact of 0.036, yielding a total elasticity of −0.221. Induced travel exhibits a larger indirect effect than a direct effect, resulting in an overall impact of 0.276. Additionally, direct and total income elasticities are 0.194 and 0.146, respectively, while direct and total fuel economy rebound effects are 0.546 and 0.496, respectively. Moreover, higher numbers of licensed drivers, registered vehicles, and urbanization significantly influence a state's VMT. This research highlights the importance of regional planning among states, especially considering transportation infrastructure and environmental concerns related to driving and vehicle emissions.

Are new pediatric trauma centers located close to the high-risk populations? A geolocation study.

Higher rates of injury occur in children who live in low socioeconomic areas. Since 2010, the number of verified Level I and Level II pediatric trauma centers (PTCs) has doubled. The purpose of this study is to look at the location of new verified PTC in relation to children living in high-risk areas. Historical and recent data about verified Level I and Level II PTCs were obtained from the American College of Surgeons (ACS), the American Trauma Society, and State Data where available. Census data were obtained from the US Census Bureau's American Community Survey for 2010 and 2020 for children younger than 18 years. The pediatric population was stratified by (1) poverty threshold and (2) living within 30 miles of PTC. The census data and trauma center locations were geocoded using the ArcGIS Pro program. Data analysis was performed at the census tract level. A total of 55 Level I or Level II ACS-verified PTCs were in the United States in 2010 and 116 in 2020. In 2010, there were 14.5 million children (19.6%) below the poverty threshold and 12.9 million (17.8%) in 2020. In 2010, 23 states did not have an ACS-verified PTC either in the state or in a neighboring state within 30 miles. By 2020, only nine states did not have an ACS-verified PTC or a center in a neighboring state within 30 miles. When state level data were included, this dropped to four. The 19 states who had new verified PTCs covered between 14% and 21% of children below the poverty level. The percentage of children below the poverty threshold and less than 30-mile access to a Level I or Level II PTC in 2010 was 74.56% compared with children above poverty and less than 30-mile access, which was 70.34% (p < 0.0001). In 2020, children below poverty without 30-mile access had been reduced to 52% and 45.54% for children above poverty level (p < 0.0001). Thus, there was a greater increase in 30-mile access for children above the poverty level than for those below the poverty level (24.8% vs. 22.6%, p < 0.001). On a national level, despite doubling the number of PTC, it has not improved 30-mile access for high-risk children. Alternatively, 19 states now have better access. Retrospective Cohort Study, Level III.

Is There a Stable Relationship between Unemployment and Future Inflation?

Evaluating the stability of the Phillips curve using aggregate data is challenging due to the bias that endogenous monetary policy imparts on estimated Phillips curve coefficients. We argue that regional data can be used to identify the structural relationship between unemployment and inflation. Our analysis, using city- and state-level data from 1977 to 2017, is consistent with the notion that both the reduced-form and the structural parameters of the Phillips curve are, to a substantial degree, quite stable. (JEL E12, E24, E31, E52, E58)

Data Moves as a Focusing Lens for Learning to Teach with CODAP

Innovative dynamic data tools afford opportunities for K-12 students and teachers to explore multivariate data and create linked data representations. These tools also support engagement in data moves, which are transnumerative actions to process, organize, and visualize data. The current study sought to understand how prospective K-12 mathematics teachers (PMTs) use data moves in the Common Online Data Analysis Platform (CODAP) to create and interpret visualizations and statistical measures to make sense of state-level data about education in the United States. Extending the work of Erickson et al. (2019), a framework is presented to characterize data moves and provide examples of actions within CODAP that illustrate each data move. Based on analysis of thirty screencasts created by PMTs, four examples highlight PMTs’ use of data moves to investigate data in CODAP.

Does institutional location matter? The effect of agency type on resource distribution for nonpoint water pollution

ABSTRACT With the rejuvenated emphasis on nonpoint pollution under the Water Quality Act (WQA) of 1987, states seek ways to address nonpoint pollution sources. States can use Clean Water State Revolving Funds (CWSRF) to address nonpoint needs; these funds are administered by state agencies, and funding decisions are typically made by agency personnel. While some states have aggressively funded nonpoint projects, other states have been much more reticent. Moreover, states have created different administrative structures to administer these funds. Does the nature of the structure impact the distribution of loans? Utilizing a dataset spanning state-level data from 1988 to 2016, we test a novel negative binomial cross-sectional time-series model employing the number of CWSRF loans directed to nonpoint projects as the dependent variable. We find that states where water quality management is buried within a ‘mini-EPA’ agency are the least likely to allocate CWSRF loans for nonpoint source issues, whereas states with ‘super’ agencies or combined environmental/health agencies allocate a higher number of CWSRF loans to nonpoint source projects.

Measuring the Impact of Medical Cannabis Law Adoption on Employer-Sponsored Health Insurance Costs: A Difference-in-Difference Analysis, 2003-2022.

Recent studies suggest that medical cannabis laws may contribute to arelative reduction in health insurance costs within the individual health insurance markets at the state level. We investigated the effects of adopting a medical cannabis law on the cost of employer-sponsored health insurance in the United States. We analyzed state-level data from the Medical Expenditure Panel Survey-Insurance Component (MEPS-IC) Private Sector spanning from 2003 to 2022. The outcomes included log-transformed average total premiumcosts per employee for single, employee-plus-one, and family coverage plans. We utilized the Sun and Abraham (J Econometr 225(2):175-199, 2021) difference-in-difference (DiD) method, looking at the overall DiD and event-study DiD. Models were adjusted for various state-level demographics and dichotomous policy variables, including whether a state later adopted recreational cannabis, as well as time and unit fixed effects and population weights. For states that adopted a medical cannabis law, there was a significant decrease in the log average total premium per employee for single (-0.034, standard error [SE] 0.009(-$238)) and employee-plus-one (-0.025, SE 0.009(-$348)) coverage plans per yearconsidering the first 10 years of policy change compared with states without such laws. Looking at the last 5years of policy change, we saw increases in effect size and statistical significance. In-time placebo testing suggested model robustness.Under a hypothetical scenario where all 50 states adopted medical cannabis in 2022, we estimated that employers and employees could collectively save billions on healthcare coverage, potentially reducing healthcare expenditure's contribution to GDP by 0.65% in 2022. Adoption of a medical cannabis law may contribute to decreases in healthcare costs. This phenomenon is likely a secondary effect and suggests positive externalities outside of medical cannabis patients.

The Rise of Xylazine Shows Why Local Data Must Inform Policy and Spending Decisions.

Local governments have been given direct control over a substantial amount of opioid settlement funding. While state-level data sources are valuable inputs, city and county leaders should consider local data sources when making decisions to ensure timely responses to emerging threats, as is exemplified by the rise of xylazine.

Public Maternal Health Dashboards in the United States: Descriptive Assessment.

Data dashboards have become more widely used for the public communication of health-related data, including in maternal health. We aimed to evaluate the content and features of existing publicly available maternal health dashboards in the United States. Through systematic searches, we identified 80 publicly available, interactive dashboards presenting US maternal health data. We abstracted and descriptively analyzed the technical features and content of identified dashboards across four areas: (1) scope and origins, (2) technical capabilities, (3) data sources and indicators, and (4) disaggregation capabilities. Where present, we abstracted and qualitatively analyzed dashboard text describing the purpose and intended audience. Most reviewed dashboards reported state-level data (58/80, 72%) and were hosted on a state health department website (48/80, 60%). Most dashboards reported data from only 1 (33/80, 41%) or 2 (23/80, 29%) data sources. Key indicators, such as the maternal mortality rate (10/80, 12%) and severe maternal morbidity rate (12/80, 15%), were absent from most dashboards. Included dashboards used a range of data visualizations, and most allowed some disaggregation by time (65/80, 81%), geography (65/80, 81%), and race or ethnicity (55/80, 69%). Among dashboards that identified their audience (30/80, 38%), legislators or policy makers and public health agencies or organizations were the most common audiences. While maternal health dashboards have proliferated, their designs and features are not standard. This assessment of maternal health dashboards in the United States found substantial variation among dashboards, including inconsistent data sources, health indicators, and disaggregation capabilities. Opportunities to strengthen dashboards include integrating a greater number of data sources, increasing disaggregation capabilities, and considering end-user needs in dashboard design.

Temporal Trends in the Epidemiology of Lower Back Pain in the United States.

This was an observational study. This study aims to explore sociodemographic and regional geographic variations in lower back pain (LBP) incidence, prevalence, and burden in the United States (US from 2000 to 2019). LBP is a major contributor to lost wages and disability in the United States. As LBP is associated with increasing age and sedentary lifestyle, the incidence of LBP is expected to rise. Due to LBP's multifactorial causes, US epidemiological trends lack sufficient data. Descriptive epidemiological data including disability-adjusted life years (DALYs), incidence, and prevalence per 100,000 population from 2000 to 2019 were collected from the Global Burden of Disease database. State-level data regarding poverty, insurance and employment status were obtained from the US Government Census Bureau and US Department of Labor. Statistical significance was indicated by P <0.05. From 2000 to 2019, the US demonstrated reductions in LBP incidence, prevalence, and DALYs. Regional analysis demonstrated the Midwest to have the greatest mean incidence, prevalence, and DALYs; with Midwestern females significantly more affected than females in other regions. Those aged 25 to 49 in the Midwest were impacted significantly more across all measures compared with age-matched populations in other regions. Nationally, there were no significant associations between unemployment and LBP. Poverty was inversely correlated with LBP incidence. Uninsured status was positively correlated with prevalence and DALYs. Although there has been progress in reducing the impact of LBP in the United States, the Midwest region has greater rates for all measures compared with other US regions. Further, females and those aged 25 to 49 in the Midwest were more likely to be affected by LBP compared with counterparts in other regions. Future studies should identify specific factors contributing to elevated LBP rates in the Midwest in order to guide targeted interventions to reduce the incidence and burden of LBP there.