Impacts of insurance expansion on health cost, health access, and health behaviors: evidence from the medicaid expansion in the US

2021 ◽  
Author(s):  
Prabal K. De
Keyword(s):  
Health Behaviors ◽  
Health Access ◽  
Medicaid Expansion ◽  
Health Cost ◽  
The Us

scholarly journals Sex in Late Life: Assessing Adults’ Expectations and the Role of Healthcare Professionals

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 313-313
Author(s):  
Jill Naar ◽  
Raven Weaver ◽  
Shelbie Turner
Keyword(s):  
Older Adults ◽  
Sexual Health ◽  
Health Behaviors ◽  
Healthcare Professionals ◽  
Late Life ◽  
Sexual History ◽  
Growth Curve Models ◽  
The Us ◽  
Sexual Health Behaviors ◽  
The Life Course

Abstract Sexual activity contributes to quality of life throughout the lifespan. However, stigma about sex in late life influences older adults’ perceptions and healthcare professionals’ perceptions of older adults’ sexual health/behaviors. Using a multi-methods approach, we examined attitudes and knowledge about sexual health/behaviors in late life. Using longitudinal data from the Midlife in the US Study (Wave 1-3; N=7049), we ran age-based growth curve models to analyze changes in levels of optimism about sex in their future. We also piloted a survey with healthcare professionals assessing attitudes, knowledge, and awareness of policy about sexual health/behaviors among older adults. Adults’ expectations became less optimistic with increased age (β = -0.1, SE = 0.003, p < .0001). Men were more optimistic than women at age 20 (p = 0.016), but men’s optimism decreased over the life course at a faster rate than did women’s (p < .0001), so that from ages 40-93, men were less optimistic than women. Among healthcare professionals (N=21), the majority indicated never or rarely asking their clients about sexual history or health/behaviors; however, they indicated some knowledge about issues relevant to older adults (e.g., safe-sex practices, sexual dysfunction). Few indicated awareness about policies related to sexual behavior among residents (i.e., issues of consent, STIs). Among adults, there is a need to address declining optimism for expectations about sex in late life. Health professionals are well-situated to raise awareness and normalize discussions about sexual health, thus countering negative stigma and contributing to increasing optimism for expectations to remain sexually active.

Did Medicaid Expansion Mitigate Disparities in Post-mastectomy Reconstruction Rates?

2022 ◽  
pp. 000313482110604
Author(s):  
Alison R. Goldenberg ◽  
Lauren M. Willcox ◽  
Daria M. Abolghasemi ◽  
Renjian Jiang ◽  
Zheng Z. Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Insurance Coverage ◽  
Complex Problem ◽  
Cancer Disparities ◽  
Multivariate Model ◽  
Medicaid Expansion ◽  
The Us ◽  
Caucasian Women ◽  
Breast Cancer Disparities ◽  
To Receive

Background Patient and socioeconomic factors both contribute to disparities in post-mastectomy reconstruction (PMR) rates. We sought to explore PMR patterns across the US and to determine if PMR rates were associated with Medicaid expansion. Methods The NCDB was used to identify women who underwent PMR between 2004-2016. The data was stratified by race, state Medicaid expansion status, and region. A multivariate model was fit to determine the association between Medicaid expansion and receipt of PMR. Results In comparison to Caucasian women receiving PMR in Medicaid expansion states, African American (AA) women in Medicaid expansion states were less likely to receive PMR (OR .96 [.92-1.00] P < .001). Patients in the Northeast (NE) had better PMR rates vs any other region in the US, for both Caucasian and AA women (Caucasian NE ref, Caucasian-South .80 [.77-.83] vs AA NE 1.11 [1.04-1.19], AA-South (.60 [.58-.63], P < .001). Interestingly, AA patients residing in the NE had the highest receipt of PMR 1.11 (1.04-1.19), even higher than their Caucasian counterparts residing in the same region (ref). Rural AA women had the lowest rates of PMR vs rural Caucasian women (.40 [.28-.58] vs .79 [.73-.85], P < .001]. Discussion Racial disparities in PMR rates persisted despite Medicaid expansion. When stratified by region, however, AA patients in the NE had higher rates of PMR than AA women in other regions. The largest disparities were seen in AA women in the rural US. Breast cancer disparities continue to be a complex problem that was not entirely mitigated by improved insurance coverage.

scholarly journals How to measure the economic health cost of wildfires – A systematic review of the literature for northern America

2020 ◽  
Vol 29 (11) ◽  
pp. 961
Author(s):  
Ruth Dittrich ◽  
Stuart McCallum
Keyword(s):  
Environmental Protection Agency ◽  
Economic Cost ◽  
Smoke Exposure ◽  
Health Cost ◽  
Mortality And Morbidity ◽  
Wildfire Smoke ◽  
The North ◽  
The Us ◽  
Emergent Literature ◽  
Economic Health

There has been an increasing interest in the economic health cost from smoke exposure from wildfires in the past 20 years, particularly in the north-western USA that is reflected in an emergent literature. In this review, we provide an overview and discussion of studies since 2006 on the health impacts of wildfire smoke and of approaches for the estimation of the associated economic cost. We focus on the choice of key variables such as cost estimators for determining the economic impact of mortality and morbidity effects. In addition, we provide an in-depth discussion and guidance on the functioning, advantages and challenges of BenMAP-CE, freely available software of the US Environmental Protection Agency (EPA) that has been used in a growing number of studies to assess cost from wildfire smoke. We highlight what generates differences in outcomes between relevant studies and make suggestions for increasing the comparability between studies. All studies, however, demonstrate highly significant health cost from smoke exposure, in the millions or billions of US dollars, often driven by increases in mortality. The results indicate the need to take health cost into account for a comprehensive analysis of wildfire impacts.

Models of acculturation and health behaviors among Latino immigrants to the US

2001 ◽  
Vol 53 (1) ◽  
pp. 41-53 ◽  
Author(s):  
E Arcia ◽  
M Skinner ◽  
D Bailey ◽  
V Correa
Keyword(s):  
Health Behaviors ◽  
Latino Immigrants ◽  
The Us

Challenges facing Medicaid expansion in the US

BMJ ◽  
2013 ◽  
Vol 346 (may29 4) ◽  
pp. f3261-f3261 ◽  
Author(s):  
D. Noble ◽  
N. Biller-Andorno ◽  
J. M. Sutherland ◽  
M. Anstey
Keyword(s):  
Medicaid Expansion ◽  
The Us

scholarly journals Characterizing the US Population by Patterns of Mobile Health Use for Health and Behavioral Tracking: Analysis of the National Cancer Institute's Health Information National Trends Survey Data (Preprint)

2019 ◽  
Author(s):  
Camella J Rising ◽  
Roxanne E Jensen ◽  
Richard P Moser ◽  
April Oh
Keyword(s):  
Health Behaviors ◽  
Health Information ◽  
Mobile Health ◽  
Technology Use ◽  
Older Age ◽  
Health Apps ◽  
Behavioral Tracking ◽  
The Us ◽  
Health Related ◽  
National Trends

BACKGROUND Multiple types of mobile health (mHealth) technologies are available, such as smartphone health apps, fitness trackers, and digital medical devices. However, despite their availability, some individuals do not own, do not realize they own, or own but do not use these technologies. Others may use mHealth devices, but their use varies in tracking health, behaviors, and goals. Examining patterns of mHealth use at the population level can advance our understanding of technology use for health and behavioral tracking. Moreover, investigating sociodemographic and health-related correlates of these patterns can provide direction to researchers about how to target mHealth interventions for diverse audiences. OBJECTIVE The aim of this study was to identify patterns of mHealth use for health and behavioral tracking in the US adult population and to characterize the population according to those patterns. METHODS We combined data from the 2017 and 2018 National Cancer Institute Health Information National Trends Survey (N=6789) to characterize respondents according to 5 mutually exclusive reported patterns of mHealth use for health and behavioral tracking: (1) mHealth nonowners and nonusers report not owning or using devices to track health, behaviors, or goals; (2) supertrackers track health or behaviors and goals using a smartphone or tablet plus other devices (eg, Fitbit); (3) app trackers use only a smartphone or tablet; (4) device trackers use only nonsmartphone or nontablet devices and do not track goals; and (5) nontrackers report having smartphone or tablet health apps but do not track health, behaviors, or goals. RESULTS Being in the mHealth nonowners and nonusers category (vs all mHealth owners and users) is associated with males, older age, lower income, and not being a health information seeker. Among mHealth owners and users, characteristics of device trackers and supertrackers were most distinctive. Compared with supertrackers, device trackers have higher odds of being male (odds ratio [OR] 2.22, 95% CI 1.55-3.19), older age (vs 18-34 years; 50-64 years: OR 2.83, 95% CI 1.52-5.30; 65+ years: OR 6.28, 95% CI 3.35-11.79), have an annual household income of US $20,000 to US $49,999 (vs US $75,000+: OR 2.31, 95% CI 1.36-3.91), and have a chronic condition (OR 1.69, 95% CI 1.14-2.49). Device trackers also have higher odds of not being health information seekers than supertrackers (OR 2.98, 95% CI 1.66-5.33). CONCLUSIONS Findings revealed distinctive sociodemographic and health-related characteristics of the population by pattern of mHealth use, with notable contrasts between those who do and do not use devices to track goals. Several characteristics of individuals who track health or behaviors but not goals (device trackers) are similar to those of mHealth nonowners and nonusers. Our results suggest patterns of mHealth use may inform how to target mHealth interventions to enhance reach and facilitate healthy behaviors.

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