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OBJECTIVE We studied the utilization of home health care (HHC) among acute myocardial infarction (AMI) patients, impact of HHC on and predictors of 30-day readmission. METHODS We queried the National Readmission Database (NRD) from 2012 to 2014identify patients with AMI discharged home with (HHC+) and without HHC (HHC-). Linkage provided in the data identified patients who had 30-day readmission, our primary end-point. The probability for each patient to receive HHC was calculated by a multivariable logistic regression. Average treatment of treated weights were derived from propensity scores. Weight-adjusted logistic regression was used to determine impact of HHC on readmission. RESULTS A total of 406 237 patients with AMI were discharged home. Patients in the HHC+ cohort (38 215 patients, 9.4%) were older (mean age 77 vs. 60 years P < 0.001), more likely women (53 vs. 26%, P < 0.001), have heart failure (5 vs. 0.5%, P < 0.001), chronic kidney disease (26 vs. 6%, P < 0.001) and diabetes (35 vs. 26%, P < 0.001). Patients readmitted within 30-days were older with higher rates of diabetes (RR = 1.4, 95% CI: 1.37-1.48) and heart failure (RR = 5.8, 95% CI: 5.5-6.2). Unadjusted 30-day readmission rates were 21 and 8% for HHC+ and HHC- patients, respectively. After adjustment, readmission was lower with HHC (21 vs. 24%, RR = 0.89, 95% CI: 0.82-0.96; P < 0.001). CONCLUSION In the United States, AMI patients receiving HHC are older and have more comorbidities; however, HHC was associated with a lower 30-day readmission rate.

Background/objectives Severity classification systems of acute pancreatitis (AP) assess inpatient morbidity and mortality without predicting outpatient course of AP. To provide appropriate outpatient care, determinants of long-term prognosis must also be identified. The aim of this study was to define clinical groups that carry long-term prognostic significance in AP. Methods A retrospective study that included patients admitted with AP was conducted. Determinants of long-term prognosis were extracted: These included Revised Atlanta and Determinant Based Classification (RAC), Charlson Comorbidity Index (CCI), Modified CT Severity Index (MCTSI), etiology, and local complications (LCs). Seven surrogates of morbidity up to 1 year after discharge were also collected and subsequently imputed into a clustering algorithm. The algorithm was set to produce three categories and multinomial regression analysis was performed. Results 281 patients were included. The incidences of morbidity endpoints were similar among the 3 RAC categories. Three clusters were identified that carried long-term prognostic significance. Each cluster was given a name to reflect prognosis. The limited AP had the best prognosis and included patients without LCs with a low co-morbidity burden. The brittle AP had a low co-morbidity burden and high MCTSI (LCs 94%). It ran a very morbid course but had excellent survival. The high-risk AP had the worst prognosis with the highest mortality rate (28%). They had a high co-morbidity burden without local complications. Conclusion Categories that carry long-term prognostic significance in AP have been developed. This study could help formulate appropriate follow-up and ultimately improve AP outcomes.

Introduction The Supplemental Nutrition Assistance Program was designed to prevent food insecurity among low-income Americans and has been linked to improvements in pregnancy health, long-term child development, and criminal recidivism. However, the pursuit of food security does not ensure nutritional sufficiency, and the program has not improved diet quality or cardiometabolic mortality (i.e., heart disease, stroke, diabetes). In this study, longitudinal cohort data are used to identify by Supplemental Nutrition Assistance Program status the proinflammatory characteristics that predispose to chronic disease. Methods Between 2015 and 2018, annual 24-hour dietary recalls were conducted with 409 residents from low-income, urban neighborhoods in Columbus and Cleveland, Ohio (statistical analysis started in 2019). The Dietary Inflammatory Index was calculated. It provides empirically validated estimates of the internal inflammation that each diet should produce; higher Dietary Inflammatory Index scores have been associated with elevated inflammatory biomarkers. Finally, associations between Supplemental Nutrition Assistance Program and Dietary Inflammatory Index were evaluated, and dietary components that differed by Supplemental Nutrition Assistance Program status were identified. Results Supplemental Nutrition Assistance Program recipients had higher Dietary Inflammatory Index scores (+0.40, 95% CI=0.09, 0.70) and a consistently lower intake of 4 anti-inflammatory nutrients (dietary fiber, β-carotene, magnesium, vitamin E) than nonrecipients. Vitamin D intake did not differ by Supplemental Nutrition Assistance Program status but was well below the Recommended Daily Allowance in this sample. Conclusions Supplemental Nutrition Assistance Program recipients had elevated Dietary Inflammatory Index scores, implying higher diet-driven inflammation. This was due, in part, to low intake of 4 anti-inflammatory food components, which were higher yet still nutritionally insufficient among nonrecipients. Findings highlight specific nutritional targets for improving public health through dietary change.

We studied the utilization of home health care (HHC) among acute myocardial infarction (AMI) patients, impact of HHC on and predictors of 30-day readmission.We queried the National Readmission Database (NRD) from 2012 to 2014identify patients with AMI discharged home with (HHC+) and without HHC (HHC-). Linkage provided in the data identified patients who had 30-day readmission, our primary end-point. The probability for each patient to receive HHC was calculated by a multivariable logistic regression. Average treatment of treated weights were derived from propensity scores. Weight-adjusted logistic regression was used to determine impact of HHC on readmission.A total of 406 237 patients with AMI were discharged home. Patients in the HHC+ cohort (38 215 patients, 9.4%) were older (mean age 77 vs. 60 years P0.001), more likely women (53 vs. 26%, P0.001), have heart failure (5 vs. 0.5%, P0.001), chronic kidney disease (26 vs. 6%, P0.001) and diabetes (35 vs. 26%, P0.001). Patients readmitted within 30-days were older with higher rates of diabetes (RR = 1.4, 95% CI: 1.37-1.48) and heart failure (RR = 5.8, 95% CI: 5.5-6.2). Unadjusted 30-day readmission rates were 21 and 8% for HHC+ and HHC- patients, respectively. After adjustment, readmission was lower with HHC (21 vs. 24%, RR = 0.89, 95% CI: 0.82-0.96; P0.001).In the United States, AMI patients receiving HHC are older and have more comorbidities; however, HHC was associated with a lower 30-day readmission rate.

Objective: Home health care (HHC) is a support tool to transition patients after discharge and acute myocardial infarction (AMI) is a significant cause of morbidity and mortality in the U.S. However, little is known regarding the impact of HHC on AMI patients. We sought to identify predictors of readmissions among AMI patients, characteristics of those who receive HHC and investigate the association of HHC with readmission. Methods: We queried the National Readmission Database (NRD) (January 2012 - December 2014), to identify patients discharged after AMI and selected patients who were discharged home with (HHC+) and without HHC (HHC-). We reported national estimates with survey methods with weights provided in our data. After univariate exploratory analyses, we developed a regression model to identify the probability of each patient to receive HHC. From the propensity score, we calculated average treatment on the treated (ATT) weights. These ATT weights were included in the logistic regression model to determine the impact of HHC on readmission after adjusting for available clinical confounders. We considered post-weighting standardized differences Results: Between January 2012 to December 2014, 406,237 patients were treated for AMI and discharged home with or without HHC. Among these 9.4% (38,215) received HHC. HHC+ patients were older (mean age 77 ± 11 vs 60 ±12 years p Conclusion: In the United States, a small proportion of patients receive home health care after discharge post-AMI. Older, females and those with diabetes or heart failure are more likely to receive home health care. Use of home health care may be associated with lower 30-day readmission rates after AMI.

Living in a low-income neighborhood with low access to healthy food retailers is associated with increased risk for chronic disease. The U.S. Healthy Food Financing Initiative (HFFI) provides resources to support the development of infrastructure to improve neighborhood food environments. This natural experiment examined a HFFI funded food hub that was designed to be implemented by a community development corporation in an urban neighborhood in Cleveland, Ohio. It was intended to increase access to affordable, local, and healthy foods; establish programs to increase social connections and support for healthy eating; and create job opportunities for residents. We used a quasi-experimental, longitudinal design to externally evaluate food hub implementation and its impact on changes to the built and social environment and dietary patterns among residents living in the intervention neighborhood (n = 179) versus those in a comparison (n = 150) neighborhood. Overall, many of the food hub components were not implemented fully, and dose and reach of the executed food hub components was low. There were statistically significant improvements in observed availability of healthy foods in the intervention neighborhood versus the comparison neighborhood. There were no changes over time in diet quality scores, total caloric intake, or fruit and vegetable intake in the intervention neighborhood. In conclusion, low dose implementation of a food hub led to small improvements in availability of healthy foods but not in dietary patterns. Findings highlight challenges to implementing a food hub in neighborhoods with low access to healthy food retailers.

ObjectiveTo describe trends of childhood stunting among under-5s in Uganda and to assess the impact of maternal education, wealth and residence on stunting.DesignSerial and pooled cross-sectional analyses of data from Uganda Demographic and Health Surveys (UDHS) of 1995, 2001, 2006 and 2011. Prevalence of stunting and mean height-for-age Z-score were computed by maternal education, wealth index, region and other sociodemographic characteristics. Multivariable logistic and linear regression models were fitted to survey-specific and pooled data to estimate independent associations between covariates and stunting or Z-score. Sampling weights were applied in all analyses.SettingUganda.SubjectsChildren aged ResultsWeighted sample size was 14 747 children. Stunting prevalence decreased from 44·8% in 1995 to 33·2% in 2011. UDHS reported stunting as 38% in 1995, underestimating the decline because of transitioning from National Center for Health Statistics/Centers for Disease Control and Prevention standards to WHO standards. Nevertheless, one in three Ugandan children was still stunted by 2011. South Western, Mid Western, Kampala and East Central regions had highest odds of stunting. Being born in a poor or middle-income household, of a teen mother, without secondary education were associated with stunting. Other persistent stunting predictors included small birth size, male gender and age 2–3 years.ConclusionsSustained decrease in stunting suggests that child nutrition interventions have been successful; however, current prevalence does not meet Millennium Development Goals. Stunting remains a public health concern and must be addressed. Customizing established measures such as female education and wealth creation while targeting the most vulnerable groups may further reduce childhood stunting.

Nutrition-related policy, system, and environmental (PSE) interventions such as farmers' markets have been recommended as effective strategies for promoting healthy diet for chronic disease prevention. Tools are needed to assess community readiness and capacity factors influencing successful farmers' market implementation among diverse practitioners in different community contexts. We describe a multiphase consensus modeling approach used to develop a diagnostic tool for assessing readiness and capacity to implement farmers' market interventions among public health and community nutrition practitioners working with low-income populations in diverse contexts. Modeling methods included the following: phase 1, qualitative study with community stakeholders to explore facilitators and barriers influencing successful implementation of farmers' market interventions in low-income communities; phase 2, development of indicators based on operationalization of qualitative findings; phase 3, assessment of relevance and importance of indicators and themes through consensus conference with expert panel; phase 4, refinement of indicators based on consensus conference; and phase 5, pilot test of the assessment tool. Findings illuminate a range of implementation factors influencing farmers' market PSE interventions and offer guidance for tailoring intervention delivery based on levels of community, practitioner, and organizational readiness and capacity.

Objective Promote practitioners’ confidence to use and disseminate the PSE Readiness Assessment and Decision Instrument (PSE READI) to tailor PSE strategies to local contexts. Use of Theory or Research There is increasing attention within the Supplemental Nutrition Assistance Program-Education (SNAP-Ed) toward implementation of PSE strategies to improve dietary outcomes among low-income populations. Tools to adapt PSEs to local levels of readiness and capacity are needed. Target Audience SNAP-Ed and public health practitioners in Ohio. Program Description The PSE READI was designed to adapt implementation of four community nutrition PSEs: farmers’ markets, farm-to-school, healthy eating in childcare, healthy food retail. After completing the assessment, a tailored report is provided with three recommendations to guide local PSE implementation, which are linked to a resource library with > 1,000 tools/guides. The tool was tested for usability and first disseminated via a day-long interactive training focused on supporting others use of the tool to assess, plan, and act to optimize PSE implementation in targeted areas. Evaluation Methods Pre- and post-survey design without randomization was used to test confidence in practitioners’ ability to support and train others to use the PSE READI. Results Practitioners (N = 23) doing work in 60 Ohio counties participated in the PSE READI training. One-third reported PSE implementation is related to their current work plan; 41% supported others conducting PSEs. Confidence to support colleagues’ implementation of PSEs (extremely/very confident: pre-35%, post-70%) and train colleagues to use the PSE READI tool (pre-10%, Post-70%) increased substantially after the training. The majority (87%) planned to use the PSE READI tool in the next three months. Post-training, practitioners reported they were “better equipped,” “more informed,” and “inspired.” Conclusions The PSE READI tool provides a feasible and efficient method to tailor PSE implementation to the realities of diverse communities. The tool and training were well-received by SNAP-Ed and public health practitioners resulting in an increase in practitioner confidence to support implementation of community nutrition PSEs.

Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples' responses, the change in haze (%) depended on individual samples' responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R2 values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction.