Journal of public health management and practice : JPHMP最新文献

Context: The 2010 Patient Protection and Affordable Care Act (ACA) eliminated the restrictions on preexisting conditions for health care coverage. Little is known about the effects of the ACA on health care access among individuals with chronic health conditions.

Objective: To determine how the implementations of the ACA affected health care access for adults with chronic health conditions.

Design, setting, and participants: Data from respondents aged 18 to 64 years to the 2011-2017 nationally representative Behavioral Risk Factor Surveillance System (BFRSS) who reported preexisting chronic health conditions (n = 1 133 609). Multivariable logistic regression models were used to examine the changes in health care access from 2011-2013 (before the ACA) to 2015-2017 (after the ACA), overall and by sociodemographic groups.

Main outcomes measures: Self-reported access to health care coverage, skipped doctor visits because of cost issues, and having a routine checkup in the past 12 months.

Results: The percentage of adults with chronic health conditions having no health care coverage declined from 19.7% before the ACA to 11.9% after the ACA (adjusted odds ratio [AOR] = 0.5], P < .001), the percentage of skipped doctor visits because of cost declined from 24.6% to 20.0% (AOR = 0.8, P < .001), and the percentage with an annual routine checkup increased from 69.6% to 72.5% (AOR = 1.1, P < .001). The improvements in health care access were pronounced across sociodemographic groups after the ACA, especially among some disadvantaged groups (ie, young adults, non-Hispanic Blacks and Hispanics, and those with low income and low education). However, substantial disparities in health care access persisted, especially among individuals with low socioeconomic status.

Conclusions: This study identifies substantial improvements in health care access among adults with chronic health conditions after ACA implementation, especially among disadvantaged populations.

Continuous Quality Improvement (CQI) is the use of a deliberate and defined improvement process to advance organizational systems. Quality improvement in public health is increasingly widespread, but there are still limited examples of success or descriptions of developmental trajectories for building CQI capacity. The goal of this article is to add to the extant knowledge on the topic by describing one state's implementation of evidence-based CQI in the Maternal, Infant, and Early Childhood Home Visiting program between 2014 and 2019. On the basis of a systematic review of Annual Yearly Progress reports and semistructured key informant interviews, analysis yielded 3 themes that facilitated successful implementation of CQI in Alabama: starting small and building capacity; engaging in continuous and supported learning; and establishing and maintaining a culture of quality. This project demonstrates that CQI can help public health practitioners refine processes and grow capacity to best serve clients' diverse needs.

Objectives: To compare city and census tract-level diabetes and hypertension prevalence using 500 Cities Project modeled estimates from the Centers for Disease Control and Prevention (CDC) and insurance claims data.

Methods: Insurance claims by census tract were collected from 3 local health plans for the city of Pittsburgh, Pennsylvania, for 2015-2016; conditions were defined using International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) codes. Crude prevalence estimates with 95% confidence intervals were downloaded from the CDC 500 Cities Web site to obtain modeled estimates by census tract. Confidence intervals were calculated for claims and compared with modeled estimates; nonoverlapping intervals were considered significant. Pearson correlation coefficients were generated for census tract-level comparison.

Results: City-level model-based and claims estimates were 9% versus 10% for diabetes and 31% versus 21% for hypertension. At the census tract level, model-based and insurance claims estimates were more concordant for diabetes (r = 0.366) than for hypertension (r = 0.220). Modeled estimates were significantly higher than claims estimates for 89% of census tracts for hypertension and 35% for diabetes.

Conclusions: Modeled estimates from the 500 Cites Project were significantly higher than insurance claims estimates for hypertension but were more consistent for diabetes. Utilization of multiple data sources to understand local-level chronic disease burden requires consideration of the strengths and limitations of each.

Context: Income and health insurance are important social determinants of cardiovascular disease (CVD) and may explain much racial/ethnic variation in CVD burden. However, racial/ethnic disparities in cumulative cardiometabolic (CMB) risk profile by insurance type and income level have not been studied on a national scale.

Objectives: To test the hypothesis that racial/ethnic minorities experience greater CMB burden at each income level and insurance type than non-Hispanic Whites (NHW).

Setting: This study used nationally representative data from the National Health Interview Survey (NHIS).

Design: Observational (cross-sectional).

Participants: In total, 134661 (weighted N = 197780611) adults, 18 years or older, from the 2013-2017 NHIS.

Primary outcome: CMB risk profile.

Intervention/analysis: Age-adjusted prevalence of optimal, average, and poor CMB risk profile-defined respectively as self-report of 0, 1-2, and 3 or more risk factors of diabetes, hypertension, obesity, or hypercholesterolemia-was examined for NHW, non-Hispanic Blacks (NHB), and Hispanics. Multivariable ordinal logistic regression models were used to test the association between race and ethnicity and CMB profile overall and separately by household income level and insurance type.

Results: Overall, 15% of NHB and 11% of Hispanics experienced poor CMB risk profile, compared with 9% for NHW. In fully adjusted models, NHB and Hispanics, respectively had nearly 25%-90% and 10%-30% increased odds of poor CMB profile across insurance types and 45%-60% and 15%-30% increased odds of poor CMB profile across income levels, relative to NHW. The observed disparities were widest for the Medicare group (NHB: OR = 1.90; Hispanics: OR = 1.31) and highest-income level (NHB: OR = 1.62).

Conclusions: Racial/ethnic minorities experience poor CMB profile at each level of income and insurance. These findings point to the need for greater investigation of unmeasured determinants of minority cardiovascular (CV) health, including structural racism and implicit bias in CV care.

Objective: To establish a model for Public Health 3.0 in order to define and measure community resilience (CR) as a method to measure equity, address structural racism, and improve population health.

Design: To develop the CR model, we conducted a literature review in medicine, psychology, early childhood development, neurobiology, and disaster preparedness and response and applied system dynamics modeling to analyze the complex interactions between public systems, policies, and community.

Main outcome measures: The CR model focuses on community and population health outcomes associated with the policies and practices of the housing, public education, law enforcement, and criminal justice sectors as CR measures. The model demonstrates how behaviors of these systems interact and produce outcome measures such as employment, homelessness, educational attainment, incarceration, and mental and physical health.

Results: The policies and practices within housing, public schools, law enforcement, and criminal justice can suppress resilience for families and communities because they are shaped by structural racism and influence the character and nature of resources that promote optimal community health and well-being.

Conclusions: Community resilience is relational and place-based and varies depending on the demographic makeup of residents, historical patterns of place-based racism and discrimination, jurisdictional policy, and investment priorities-all influenced by structural racism.

Implications for policy and practice: Using system dynamics modeling and the CR approach, chief health strategists can convene partners from multiple sectors to systematically identify, measure, and address inequities produced by structural racism that result in and contribute to adverse childhood and community experiences.

Context: The New York City (NYC) Test & Trace Corps (Test & Trace), under New York City Health + Hospitals (NYC H+H), set out to provide universal access to COVID-19 testing. Test & Trace partnered with numerous organizations to direct mobile COVID-19 testing from concept through implementation to reduce COVID-19-related health inequities.

Program: Test & Trace employs a community-informed mobile COVID-19 testing model to deliver testing to the hardest-hit, underserved communities. Community partners, uniquely knowledgeable of the residents they serve, are engaged as decision makers and operational partners in mobile COVID-19 testing delivery.

Implementation: Through several mobile testing methods, community partners choose testing locations and tailor outreach to their community. Test & Trace assumes logistical responsibility for mobile testing but defers critical programmatic decisions and community engagement to partners. Integral to the success of this program is responsive, bidirectional communication.

Evaluation: During the reporting period of December 1, 2020, to April 30, 2021, Test & Trace's community-informed mobile COVID-19 testing model provided testing to 150351 unique patients and processed 274083 tests in total. The available outcomes data and qualitative feedback provided by community partners illustrate that this intervention, combined with robust governmental investment, successfully ensured that NYC-identified, low-resource neighborhoods had greater access to COVID-19 testing.

Discussion: Making community partners decision makers reduced inequities in access to testing for communities of color. In addition, the model has served as the framework for Test & Trace's community-informed mobile COVID-19 vaccination program, operated in concert with NYC's Vaccine Command Center, and is a foundation for addressing health inequities at scale, including during public health crises.

There are no evidence-based findings to assist professionals with advanced public health and social science degrees in choosing the appropriate academic location. A cross-sectional case study in 2019 was conducted using publicly available online data of full-time, nonclinical, doctoral-level academic faculty in schools of public health (SOPHs) and schools of medicine (SOMs), within one large university system. Analyses included descriptive statistics and generalized linear regression models comparing salaries between school types by academic rank, after gender and race/ethnicity adjustment. The study included 181 faculty members, 35.8% assistant, 34.1% associate, and 30.1% full professors. After accounting for race/ethnicity and gender, SOM assistant and associate professors had 9% (P = .03) and 14% (P = .008) higher mean salaries than SOPH counterparts. Findings suggest slight salary advantages for SOM faculty for early- to mid-career PhDs in one university system. Factors such as start-up packages, time to promotion, and grant funding need further exploration.

Laws and policies are critical determinants of health and well-being. They can encourage positive behaviors and discourage harmful behaviors, and they can enhance or worsen health, health equity, health disparities, and health literacy. Recognizing their contribution to conditions in the environments in which people are born, live, learn, work, play, worship, and age, and people's experiences of these conditions, the US Department of Health and Human Services considered the roles of law and policy throughout its development of Healthy People 2030. Laws and policies often interrelate, but they have different purposes. A law is an established procedure, standard, or system of rules that members of a society must follow. A policy is a decision or set of decisions meant to address a long-term purpose or problem. Healthy People 2030 offers an opportunity for users in diverse sectors and at all levels to use laws and policies to support or inform the initiative's implementation, address health disparities and health inequities, and improve health and well-being in this decade. Introducing new laws and policies or rescinding existing ones to achieve Healthy People 2030 goals offers a chance to rigorously assess outcomes and weigh the balance of good outcomes against unintended consequences.