Objective

To examine the relationship between food swamps and hospitalization rates among adults with diabetes.

Data sources

Blue Cross Blue Shield Association Community Health Management Hub^® 2014, AHRQ Health Care Cost and Utilization Project state inpatient databases 2014, and HHS Area Health Resources File 2010-2014.

Study design

Cross-sectional analysis of 784 counties across 15 states. Food swamps were measured using a ratio of fast food outlets to grocers. Multivariate linear regression estimated the association of food swamp severity and hospitalization rates. Population-weighted models were controlled for comorbidities; Medicaid; emergency room utilization; percentage of population that is female, Black, Hispanic, and over age 65; and state fixed effects. Analyses were stratified by rural-urban category.

Principal findings

Adults with diabetes residing in more severe food swamps had higher hospitalization rates. In adjusted analyses, a one unit higher food swamp score was significantly associated with 49.79 (95 percent confidence interval (CI) = 19.28, 80.29) additional all-cause hospitalizations and 19.12 (95 percent CI = 11.09, 27.15) additional ambulatory care-sensitive hospitalizations per 1000 adults with diabetes. The food swamp/all-cause hospitalization rate relationship was stronger in rural counties than urban counties.

Conclusions

Food swamps are significantly associated with higher hospitalization rates among adults with diabetes. Improving the local food environment may help reduce this disparity.

The relationship between food environments and diabetes morbidity is vastly understudied, despite the well-recognized linkage between dietary quality and diabetes complications. Further, literature demonstrates that attributes of places can have nonlinear relationships with health outcomes. This study examines the extent to which "food swamps" are associated with greater rates of hospitalizations for complications among adults with diabetes over time as well as the linearity of this relationship. We conduct a longitudinal county-level analysis of 832 counties across 16 U.S. states in 2010, 2012, and 2014 using data from the USDA Food Environment Atlas and the AHRQ Health Care Cost and Utilization Project State Inpatient Databases. Food swamp severity is measured as the percentage of food outlets in a county that sell primarily unhealthy foods. Hierarchical linear mixed models with county random intercepts are estimated, controlling for area-level covariates and state and year fixed effects. Curvilinear relationships are explored by additively incorporating quadratic terms. We find that, over the study period, mean food swamp severity remained relatively stable. Mean hospitalization rates decreased from 296.72 to 262.82 hospitalizations per 1000 diabetic adults (p < 0.001). In adjusted models, greater food swamp severity was associated with higher hospitalization rates in a curvilinear manner (severity: β = 2.181, p = 0.02; severity2: β = -0.017, p = 0.04), plateauing at approximately 64% unhealthy outlets, a saturation point observed in 17% of observations. Policies that limit saturation of the environment with unhealthy outlets may help in the prevention of diabetic complications, but more saturated counties will likely require more extensive intervention.

Background

The Centers for Medicare & Medicaid Services (CMS) State Innovation Models (SIM) Initiative funds states to accelerate delivery system and payment reforms. All SIM states focus on improving diabetes care, but SIM's effect on 30-day readmissions among adults with diabetes remains unclear.

Methods

A quasi-experimental research design estimated the impact of SIM on 30-day hospital readmissions among adults with diabetes in 3 round 1 SIM states (N=671,996) and 3 comparison states (N=2,719,603) from 2010 to 2015. Difference-in-differences multivariable logistic regression models that incorporated 4-group propensity score weighting were estimated. Heterogeneity of SIM effects by grantee state and for CMS populations were assessed.

Results

In adjusted difference-in-difference analyses, SIM was associated with an increase in odds of 30-day hospital readmission among patients in SIM states in the post-SIM versus pre-SIM period relative to the ratio in odds of readmission among patients in the comparison states post-SIM versus pre-SIM (ratio of adjusted odds ratio=1.057, P=0.01). Restricting the analyses to CMS populations (Medicare and Medicaid beneficiaries), resulted in consistent findings (ratio of adjusted odds ratio=1.057, P=0.034). SIM did not have different effects on 30-day readmissions by state.

Conclusions

We found no evidence that SIM reduced 30-day readmission rates among adults with diabetes during the first 2 years of round 1 implementation, even among CMS beneficiaries. It may be difficult to reduce readmissions statewide without greater investment in health information exchange and more intensive use of payment models that promote interorganizational coordination.

Background and aims

In June 2012, Washington state (USA) implemented Initiative 1183, privatizing liquor sales. As a result, off-premises outlets increased from 330 to over 1400 and trading hours lengthened. Increased availability of liquor may lead to increased consumption. This study examines the impact of Initiative 1183 on alcohol-related adverse health outcomes, measured by inpatient hospitalizations for alcohol-related disorders and accidental injuries. It further assesses heterogeneity by urbanicity, because outlets increased most in metropolitan-urban areas.

Design

County-by-quarter difference-in-difference linear regression models, estimated statewide and within metropolitan/rural strata.

Setting and participants

Data are from AHRQ Healthcare Cost and Utilization State Inpatient Database 2010-2014 and HHS Area Health Resource File 2010-2014. Changes in the rates of hospitalizations in the 2.5 years following Initiative 1183 in Washington (n = 39 counties) are compared with changes in Oregon (n = 36 counties).

Measurements

County rates of hospitalizations per 1000 residents, including all records with any-listed ICD-9 Clinical Classification Software code denoting an alcohol-related disorder, and all records with any-listed external cause of injury code denoting an accidental injury.

Findings

The increase in the rate of accidental injury hospitalizations in Washington's metropolitan-urban counties was on average 0.289 hospitalizations per 1000 county residents per quarter greater than the simultaneous increase observed in Oregon (P = 0.017). This result was robust to alternative specifications using a propensity score matched sample and synthetic control methods with data from other comparison states. The evidence did not suggest that Initiative 1183 was associated with differential changes in the rate of hospitalizations for alcohol-related disorders in metropolitan-urban (P = 0.941), non-metropolitan-urban (P = 0.162), or rural counties (P = 0.876).

Conclusions

Implementing Washington's Initiative 1183 (privatizing liquor sales) appears to have been associated with a significant increase in the rate of accidental injury hospitalizations in urban counties in that state but does not appear to be significantly associated with changes in the rate of hospitalizations specifically for alcohol-related disorders within 2.5 years.

Introduction

In September 2014, CVS Health ceased tobacco sales in all of its 7700 pharmacies nationwide. We investigate the impact of the CVS policy on the number of cigarettes smoked per day among metropolitan daily and non-daily smokers, who may respond to the availability of smoking cues in different manners.

Methods

Data are from the US Census Bureau Tobacco Use Supplement to the Current Population Survey 2014-2015 and the Blue Cross and Blue Shield Institute Community Health Management Hub. Adjusted difference-in-difference (DID) regressions assess changes in the number of cigarettes smoked per day among daily smokers (n=10 759) and non-daily smokers (n=3055), modelling core-based statistical area (CBSA) level CVS pharmacy market share continuously. To assess whether the policy had non-linear effects across the distribution of CVS market share, we also examine market share using tertiles.

Results

CVS's tobacco-free pharmacy policy was associated with a significant reduction in the number of cigarettes smoked by non-daily smokers in the continuous DID (rate ratio=0.985, p=0.022), with a larger reduction observed among non-daily smokers in CBSAs in the highest third of CVS market share compared with those living in CBSAs with no CVS presence (rate ratio=0.706, p=0.027). The policy, however, was not significantly associated with differential changes in the number of cigarettes by daily smokers.

Conclusion

The removal of tobacco products from CVS pharmacies was associated with a reduction in the number of cigarettes smoked per day among non-daily smokers in metropolitan CBSAs, particularly those in which CVS had a large pharmacy market share.

Introduction

Unaddressed social risks among hospitalized patients with chronic conditions contribute to costly complications and preventable hospitalizations. This study examines whether the Centers for Medicaid and Medicare Services State Innovation Models initiative, through payment and delivery system reforms, accelerates the diagnosis of social risk factors among hospitalized adults with diabetes.

Methods

Encounter-level data were from the Agency for Healthcare Research and Quality Healthcare Cost and Utilization Project State Inpatient Databases (2010-2015, N=5,040,456). Difference-in-difference logistic regression estimated the extent to which hospitalized adults with diabetes in 4 State Innovation Models states (Arkansas, Massachusetts, Oregon, and Vermont) had increased odds of having a social risk factor diagnosed with an ICD-9 V code compared with hospitalized adults with diabetes in 4 comparison states (Arizona, Georgia, New Jersey, and New Mexico) 2 years after implementation. Data were analyzed between June and December 2019.

Results

Adults with diabetes who were hospitalized in State Innovation Models states had a 30% greater increase in the odds of having a V code documented after implementation than adults with diabetes who were hospitalized in comparison states (AOR=1.29, 95% CI=1.07, 1.56). However, V code use remained infrequent, with only 2.05% of encounters, on average, having any V codes on record in State Innovation Models states after implementation.

Conclusions

The State Innovation Models initiative slightly but significantly improved the diagnosis of social risks among hospitalized adults with diabetes. State-led delivery system and payment reform may help support movement of hospitals toward better recognition and management of social determinants of health.

Objectives

An increasing number of federal initiatives allow states flexibility in selecting the strategies used to achieve initiative-specific goals. Variation in the foci and intensity of implementation may explain why federal policy initiatives succeed in some states and fail in others. The CMS State Innovation Models (SIM) initiative is a complex policy intervention implemented with substantial variation across states and may have variable impacts. This paper presents a method to characterize and account for that variation in states' implementation foci and intensity in natural policy experiments.

Study design

A combination of quantitative and qualitative measures of SIM implementation was used to characterize the foci of payment and delivery system reforms across states.

Methods

A modified Delphi expert panel process was used to prioritize the features of SIM implementation that would differentiate grantee states with respect to improved health outcomes. Three researchers then reviewed summaries of published evaluations and reports to characterize and score states on each implementation feature. Expert panelists guided the researchers on developing the criteria and weights applied to the focus areas when calculating SIM implementation intensity scores for states.

Results

Over 3 years of an expert panel process, 4 dimensions of SIM implementation that would most affect health outcomes were prioritized: 1) extent and breadth of stakeholder engagement, (2) extent that SIM implementation was focused on improving behavioral health, (3) amount of SIM funding per capita, and (4) breadth and depth of value-based payment reforms. Scoring states based on the prioritized factors resulted in composite scores that differentiated states into 3 categories: high, moderate, and low implementation intensity.

Conclusions

We developed a stakeholder-driven method to measure and account for variation in implementation foci and intensity in a federal policy initiative that was implemented heterogeneously across grantee states. Our method for characterizing state implementation variation may be useful for natural policy experiments examining the variable impact of policy initiatives.