Can packaging impact consumers perceptions of what products appear to look healthy Vs. If they actually are healthy?

Packaging is a crucial aspect in the marketing world. It can give implicit and explicit cues about the content within. Food packaging is especially crucial to examine when analyzing health perceptions about a product. Packaging is a communication vehicle for the consumer to perceive the quality of the food or beverage product and possibly infer if the product is healthy just by examining the exterior of the product. This research hopes to determine if consumers base perceived health notions on bottle shape and/or color. By manipulating the shape and color of the bottle, the study is looking to find any significant differences in perceived health ideas. Three hundred nineteen individuals in the United States took a survey that examined four stimuli. Each stimulus was an image of the fruit-juice product in a bottle that had been manipulated to be tall and slim or short and wide. Each of the manipulated bottle’s label and top was assigned the color yellow and green. The participants received one of the randomly assigned stimuli to examine and answer questions regarding product quality, nutrition, health, and confidence. The results found that there is not a significant difference in the shape of the beverage bottle influencing health perceptions about the product. However, the color yellow was seen as less attractive and less “healthy” than the color green. Although there was no major significance found between packaging shape and color impacting consumer perceived health notions in this study, that could be due to the limited sample size and time-line of the study. More research should be considered to fully understand if a product’s package shape and color can impact consumers perceived notions on health. This is important for food marketers and advertisers to examine to lessen the gap between the product’s actual health benefits and the perceived health benefits based on the exterior of the product

The Association of Stress With Anxiety and Depression: Evidence From a Community Health Needs Assessment

Background: Mental illness affects approximately 1 in 5 Americans, making mental health an important area of study for public health. Much research has been conducted on two of the most prevalent mental health disorders, anxiety and depression. However, the association of stress with these disorders, especially specific types of stress (e.g., financial, health, relationship), has been under-studied at the local level. This study aimed to gain insight into the relationship between stress, anxiety, and depression in Athens-Clarke County, Georgia. Methods: Data collected in the 2015 Athens-Clarke County Community Health Needs Assessment were analyzed using linear regression models to explore the association between stress and anxiety and depression. Results: When the data were aggregated, the presence of stress in a respondent’s household was associated with a 17.8%. Conclusions: The results demonstrated that, in Athens-Clarke County, Georgia, stress was significantly and positively associated with both anxiety and depression. Financial, home environment, and neighborhood safety stressors were the strongest predictors of household mental health disorders. These results have implications for public health policy and clinical professionals, including the possibility of tailoring treatment strategies to the types of stress present in a patient’s life. Further research is needed to explore this relationship in other communities

Improving Housing Quality to Reduce Asthma Rates and Healthcare Costs in Athens-Clarke County, GA

Background: Asthma is a chronic respiratory condition that often develops from controllable environmental factors, such as poor air quality. Our study examined the relationship between mold in the home, which compromises indoor air quality, and asthma prevalence in Athens-Clarke County (ACC), Georgia. Methods: Data from the Athens Wellbeing Project’s 2016 survey of 1,354 households in ACC was employed to examine indoor air quality and asthma prevalence in ACC. We provided descriptive statistics to examine asthma prevalence and asthma predictors, including the proportion of respondents reporting mold and pest problems, smoking prevalence, and the proportion of households reporting clinical obesity. We also examined demographic characteristics including race and ethnicity, educational attainment, home ownership status, and the percent of households in poverty at the 185% federal poverty line. We then employed a logistic regression analysis to assess the relationship between asthma and housing quality. Results: The descriptive statistics showed that seven percent of the ACC population, 17% of households, reported there was at least one individual in the home diagnosed with asthma and nine percent of the county population experienced mold problems in the home. Logistic regression analysis showed households that reported mold problems were 11% more likely to report at least one person in the household had been diagnosed with asthma. Educational attainment and obesity were also significantly associated with asthma, though not as predictive of asthma as mold. Conclusions: Strong statistical associations were found between indoor air quality and asthma prevalence in ACC. Improving housing quality by intervening on mold has the potential to reduce asthma rates, thereby increasing individual and community wellbeing. Our results have implications for the state of Georgia and the United States, as asthma prevalence is comparable throughout. This research adds to the body of literature focusing on the link between indoor air quality and asthma

Leveraging University-Community Partnerships in Rural Georgia: A Community Health Needs Assessment Template for Hospitals

Background: Under the Affordable Care Act, nonprofit hospitals are required to conduct a Community Health Needs Assessment (CHNA) every three years. Using recommendations proposed by Georgia Watch, students and faculty members from the University of Georgia (UGA) conducted a CHNA for a hospital in a rural county in Georgia. The purpose of the CHNA was to identify community health problems and needs, as well as community assets and resources. The aim of this report is to describe the process for conducting the CHNA, the findings, and the lessons learned. Methods: The CHNA team consisted of students and faculty members from UGA’s College of Public Health and a Public Service and Outreach professional who worked in the community. In completing the CHNA, the team used the following fivestep process: define community, collect secondary data on community health, gather community input and collect primary data, prioritize community health needs, and implement strategies to address community health needs. Primary and secondary data were collected. Results: By triangulating findings across data sources, the CHNA team created a community health profile for the service area of the hospital. Based on these findings, the community identified four main areas for improvement, prioritized these health issues, and developed an implementation strategy for the hospital and community. Conclusions: The process used to conduct this CHNA can serve as a model for other rural communities undergoing similar assessments. Lessons learned from completing this CHNA can be applied to future CHNA efforts

Rural Community Health Needs Assessment Findings: Access to Care and Mental Health

This article highlights the qualitative results from focus groups conducted as part of a Community Health Needs Assessments in two rural Georgia communities. Four 1-hr focus groups were facilitated with 32 community stakeholders. Sessions were audio recorded and transcribed verbatim. Thematic analysis identified two primary themes: mental health and barriers to accessing health care. Focus group participants discussed mental health challenges as they related to substance abuse and suicide. Participants acknowledged barriers to access, including no health insurance, cost, eligibility gaps for government-sponsored programs, the low availability of specialty care, and poverty. Addressing mental health and access to care in rural communities may require alternative, tailored programs

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

Background: The EMPA KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population. Methods: EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110. Findings: Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5–2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62–0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16–1·59), representing a 50% (42–58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1). Interpretation: In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease. Funding: Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council

Leveraging University-Community Partnerships in Rural Georgia: A Community Health Needs Assessment Template for Hospitals

Background: Under the Affordable Care Act, nonprofit hospitals are required to conduct a Community Health Needs Assessment (CHNA) every three years. Using recommendations proposed by Georgia Watch, students and faculty members from the University of Georgia (UGA) conducted a CHNA for a hospital in a rural county in Georgia. The purpose of the CHNA was to identify community health problems and needs, as well as community assets and resources. The aim of this report is to describe the process for conducting the CHNA, the findings, and the lessons learned. Methods: The CHNA team consisted of students and faculty members from UGA’s College of Public Health and a Public Service and Outreach professional who worked in the community. In completing the CHNA, the team used the following fivestep process: define community, collect secondary data on community health, gather community input and collect primary data, prioritize community health needs, and implement strategies to address community health needs. Primary and secondary data were collected. Results: By triangulating findings across data sources, the CHNA team created a community health profile for the service area of the hospital. Based on these findings, the community identified four main areas for improvement, prioritized these health issues, and developed an implementation strategy for the hospital and community. Conclusions: The process used to conduct this CHNA can serve as a model for other rural communities undergoing similar assessments. Lessons learned from completing this CHNA can be applied to future CHNA efforts

SNAP benefit levels and enrollment rates by race and place: evidence from Georgia, 2007–2013

<p>The U.S. Department of Agriculture’s Supplemental Nutrition Assistance Program (SNAP) decreases poverty and food insecurity for millions of Americans. Yet not all eligible households participate, and disparities in participation exist by household size, race, ethnicity, and place. We examine the county-level associations between maximum benefit levels and SNAP enrollment by household size, race, ethnicity, and metropolitan status from 2007 to 2013 in the state of Georgia. National county-level data on participation in SNAP by racial or ethnic group are not available, but Georgia features substantial variation by race and ethnicity as well as metropolitan status at the county level that make it well suited for our analysis. Maximum SNAP benefit levels were associated with increases in county-level SNAP enrollment across the board but especially for single-person households, Hispanics, and rural Whites. These findings have implications for future changes to SNAP benefits.</p