In urban areas, exposure to greenspace has been found to be beneficial to human health. The biodiversity hypothesis proposed that exposure to diverse ambient microbes in greener areas may be one pathway leading to health benefits such as improved immune system functioning, reduced systemic inflammation, and ultimately reduced morbidity and mortality. Previous studies observed differences in ambient outdoor bacterial diversity between areas of high and low vegetated land cover but didn't focus on residential environments which are important to human health. This research examined the relationship between vegetated land and tree cover near residence and outdoor ambient air bacterial diversity and composition. We used a filter and pump system to collect ambient bacteria samples outside residences in the Raleigh-Durham-Chapel Hill metropolitan area and identified bacteria by 16S rRNA amplicon sequencing. Geospatial quantification of total vegetated land or tree cover was conducted within 500 m of each residence. Shannon's diversity index and weighted UniFrac distances were calculated to measure α (within-sample) and β (between-sample) diversity, respectively. Linear regression for α-diversity and permutational analysis of variance (PERMANOVA) for β-diversity were used to model relationships between vegetated land and tree cover and bacterial diversity. Data analysis included 73 ambient air samples collected near 69 residences. Analysis of β-diversity demonstrated differences in ambient air microbiome composition between areas of high and low vegetated land (p = 0.03) and tree cover (p = 0.07). These relationships remained consistent among quintiles of vegetated land (p = 0.03) and tree cover (p = 0.008) and continuous measures of vegetated land (p = 0.03) and tree cover (p = 0.03). Increased vegetated land and tree cover were also associated with increased ambient microbiome α-diversity (p = 0.06 and p = 0.03, respectively). To our knowledge, this is the first study to demonstrate associations between vegetated land and tree cover and the ambient air microbiome's diversity and composition in the residential ecosystem.

IMPORTANCE: Current estimates of productivity losses associated with heart disease and stroke in the US include income losses from premature mortality but do not include income losses from morbidity. OBJECTIVE: To estimate labor income losses associated with morbidity of heart disease and stroke in the US due to missed or lower labor force participation. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study used 2019 Panel Study of Income Dynamics data to estimate labor income losses associated with heart disease and stroke by comparing labor income between persons with and without heart disease or stroke, after controlling for sociodemographic characteristics and other chronic conditions and considering the situation of zero labor income (eg, withdrawal from the labor market). The study sample included individuals aged 18 to 64 years who were reference persons or spouses or partners. Data analysis was conducted from June 2021 to October 2022. EXPOSURE: The key exposure was heart disease or stroke. MAIN OUTCOMES AND MEASURES: The main outcome was labor income, measured for the year 2018. Covariates included sociodemographic characteristics and other chronic conditions. Labor income losses associated with heart disease and stroke were estimated using the 2-part model, in which part 1 is to model the probability that labor income is greater than zero and part 2 is to regress positive labor income, with both parts having the same set of explanatory variables. RESULTS: In the study sample consisting o 166 individuals (6721 [52.4%] females) representing a weighted mean income of $48 299 (95% CI, $45 712-$50 885), the prevalence of heart disease was 3.7% and the prevalence of stroke was 1.7%, and there were 1610 Hispanic persons (17.3%), 220 non-Hispanic Asian or Pacific Islander persons (6.0%), 3963 non-Hispanic Black persons (11.0%), and 5688 non-Hispanic White persons (60.2%). The age distribution was largely even, from 21.9% for the age 25 to 34 years group to 25.8% for the age 55 to 64 years group, except for young adults (age 18-24 years), who made up 4.4% of the sample. After adjustment for sociodemographic characteristics and other chronic conditions, persons with heart disease would receive an estimated $13 463 (95% CI, $6993-$19 933) less in annual labor income than those without heart disease (P < .001), and persons with stroke would receive an estimated $18 716 (95% CI, $10 356-$27 077) less in annual labor income than those without stroke (P < .001). Total labor income losses associated with morbidity were estimated at $203.3 billion for heart disease and $63.6 billion for stroke. CONCLUSIONS AND RELEVANCE: These findings suggest that total labor income losses associated with morbidity of heart disease and stroke were far greater than those from premature mortality. Comprehensive estimation of total costs of CVD may assist decision-makers in assessing benefits from averted premature mortality and morbidity and allocating resources to the prevention, management, and control of CVD.

INTRODUCTION: Self-measured blood pressure monitoring with support is an evidence-based intervention that helps patients control their blood pressure. This systematic economic review describes how certain intervention aspects contribute to effectiveness, intervention cost, and intervention cost per unit of the effectiveness of self-measured blood pressure monitoring with support. METHODS: Papers published between data inception and March 2021 were identified from a database search and manual searches. Papers were included if they focused on self-measured blood pressure monitoring with support and reported blood pressure change and intervention cost. Papers focused on preeclampsia, kidney disease, or drug efficacy were excluded. Quality of estimates was assessed for effectiveness, cost, and cost per unit of effectiveness. Patient characteristics and intervention features were analyzed in 2021 to determine how they impacted effectiveness, intervention cost, and intervention cost per unit of effectiveness. RESULTS: A total of 22 studies were included in this review from papers identified in the search. Type of support was not associated with differences in cost and cost per unit of effectiveness. Lower cost and cost per unit of effectiveness were achieved with simple technologies such as interactive phone systems, smartphones, and websites and where providers interacted with patients only as needed. DISCUSSION: Some of the included studies provided only limited information on key outcomes of interest to this review. However, the strength of this review is the systematic collection and synthesis of evidence that revealed the associations between the characteristics of implemented interventions and their patients and the interventions' effectiveness and cost, a useful contribution to the fields of both research and implementation.

OBJECTIVE: To describe the cost of integrating social needs activities into a health care program that works toward health equity by addressing socioeconomic barriers. DATA SOURCES/STUDY SETTING: Costs for a heart failure health care program based in a safety-net hospital were reported by program staff for the program year May 2018-April 2019. Additional data sources included hospital records, invoices, and staff survey. STUDY DESIGN: We conducted a retrospective, cross-sectional, case study of a program that includes health education, outpatient care, financial counseling and free medication; transportation and home services for those most in need; and connections to other social services. Program costs were summarized overall and for mutually exclusive categories: health care program (fixed and variable) and social needs activities. DATA COLLECTION: Program cost data were collected using a activity-based, micro-costing approach. In addition, we conducted a survey that was completed by key staff to understand time allocation. PRINCIPAL FINDINGS: Program costs were approximately $1.33 million, and the annual per patient cost was $1455. Thirty percent of the program costs was for social needs activities: 18% for 30-day supply of medications and addressing socioeconomic barriers to medication adherence, 18% for mobile health services (outpatient home visits), 53% for navigating services through a financial counselor and community health worker, and 12% for transportation to visits and addressing transportation barriers. Most of the program costs were for personnel: 92% of the health care program fixed, 95% of the health care program variable, and 78% of social needs activities. DISCUSSION: Historically, social and health care services are funded by different systems and have not been integrated. We estimate the cost of implementing social needs activities into a health care program. This work can inform implementation for hospitals attempting to address social determinants of health and social needs in their patient population.

OBJECTIVE: Significant delays in translating health care-related research into public health programs and medical practice mean that people may not get the best care when they need it. Regarding cardiovascular disease, translation delays can mean lives may be unnecessarily lost each year. To facilitate the translation of knowledge to action, we created a Best Practices Guide for Cardiovascular Disease Prevention Programs. DESIGN: Using the Rapid Synthesis Translation Process and the Best Practices Framework as guiding frameworks, we collected and rated research evidence for hypertension control and cholesterol management strategies. After identifying best practices, we gathered information about programs that were implementing the practices and about resources useful for implementation. Research evidence and supplementary information were consolidated in an informational resource and published online. Web metrics were collected and analyzed to measure use and reach of the guide. RESULTS: The Best Practices Guide was released in January 2018 and included background information and resources on 8 best practice strategies. It was published as an online resource, publicly accessible from the Centers for Disease Control and Prevention Web site in 2 different formats. Web metrics show that in the first year after publication, there were 25 589 Web page views and 2467 downloads. A query of partner use of the guide indicated that it was often shared in partners' own resources, newsletters, and online material. CONCLUSION: In following a systematic approach to creating the Best Practices Guide and documenting the steps taken in its development, we offer a replicable approach for translating research on health care practices into a resource to facilitate implementation. The success of this approach is attributed to 3 key factors: using a prescribed and documented approach to evidence translation, working closely with stakeholders throughout the process, and prioritizing the content design and accessibility of the final product.

Micro-costing data collection tools often used in literature include standardized comprehensive templates, targeted questionnaires, activity logs, on-site administrative databases, and direct observation. These tools are not mutually exclusive and are often used in combination. Each tool has unique merits and limitations, and some may be more applicable than others under different circumstances. Proper application of micro-costing tools can produce quality cost estimates and enhance the usefulness of economic evaluations to inform resource allocation decisions. A common method to derive both fixed and variable costs of an intervention involves collecting data from the bottom up for each resource consumed (micro-costing). We scanned economic evaluation literature published in 2008-2018 and identified micro-costing data collection tools used. We categorized the identified tools and discuss their practical applications in an example study of health interventions, including their potential strengths and weaknesses. Sound economic evaluations of health interventions provide valuable information for justifying resource allocation decisions, planning for implementation, and enhancing the sustainability of the interventions. However, the quality of intervention cost estimates is seldom addressed in the literature. Reliable cost data forms the foundation of economic evaluations, and without reliable estimates, evaluation results, such as cost-effectiveness measures, could be misleading. In this project, we identified data collection tools often used to obtain reliable data for estimating costs of interventions that prevent and manage chronic conditions and considered practical applications to promote their use.

PURPOSE AND OBJECTIVES: We evaluated the costs of implementing coordinated systems of stroke care by state health departments from 2012 through 2015 to help policy makers and planners gain a sense of the potential return on investments in establishing a stroke care quality improvement (QI) program. INTERVENTION APPROACH: State health departments funded by the Paul Coverdell National Acute Stroke Program (PCNASP) implemented activities to support the start and proficient use of hospital stroke registries statewide and coordinate data-driven QI efforts. These efforts were aimed at improving the treatment and transition of stroke patients from prehospital emergency medical services (EMS) to in-hospital care and postacute care facilities. Health departments provided technical assistance and data to support hospitals, EMS agencies, and posthospital care agencies to carry out small, rapid, incremental QI efforts to produce more effective and efficient stroke care practices. EVALUATION METHODS: Six of the 11 PCNASP-funded state health departments in the United States volunteered to collect and report programmatic costs associated with implementing the components of stroke systems of care. Six health departments reported costs paid directly by Centers for Disease Control and Prevention-provided funds, 5 also reported their own in-kind contributions, and 4 compiled data from a sample of their partners' estimated costs of resources, such as staff time, involved in program implementation. Costs were analyzed separately for PCNASP-funded expenditures and in-kind contributions by the health department by resource category and program activity. In-kind contributions by partners were also analyzed separately. RESULTS: PCNASP-funded expenditures ranged from $790,123 to $1,298,160 across the 6 health departments for the 3-year funding period. In-kind contributions ranged from $5,805 to $1,394,097. Partner contributions (n = 22) ranged from $3,912 to $362,868. IMPLICATIONS FOR PUBLIC HEALTH: Our evaluation reports costs for multiple state health departments and their partners for implementing components of stroke systems of care in the United States. Although there are limitations, our findings represent key estimates that can guide future program planning and efforts to achieve sustainability.

BACKGROUND: Hypertension is highly prevalent among the low-income population in the United States. This study assessed the association between Medicaid coverage and health care service use and costs among hypertensive adults following the enactment of the Patient Protection and Affordable Care Act (ACA), by income status level. METHODS: A nationally representative sample of 2,866 nonpregnant hypertensive individuals aged 18-64 years with income up to 138% of the federal poverty level (FPL) were selected from the 2014 and 2015 Medical Expenditure Panel Survey. Regression analyses were performed to examine the association of Medicaid coverage with outpatient (outpatient visits and prescription medication fills), emergency, and acute healthcare service use and costs among those potentially eligible for Medicaid by income status - the very low-income (FPL </=100%) and the moderately low-income (100%>FPL</=138%). RESULTS: Among the study population, 70.1% were very low-income and 29.9% were moderately low-income. Full-year Medicaid coverage was higher among the very low-income group (41.0%) compared with those moderately low-income (29.1%). For both income groups, having full-year Medicaid coverage was associated with increased healthcare service use and higher overall annual medical costs ($13,085 compared with $7,582 without Medicaid); costs were highest among moderately low-income patients ($17,639). CONCLUSION: Low-income individuals with hypertension, who were potentially newly eligible for Medicaid under the ACA may benefit from expanded Medicaid coverage by improving their access to outpatient services that can support chronic disease management. However, to realize decreases in medical expenditures, efforts to decrease their use of emergency and acute care services are likely needed.

In the past decade, government agencies, foundations, community- and faith-based organizations, and others have paid increasing attention to using evidence as a decision-making driver for their programs, with a focus on using evaluation and performance management data for program improvement. At the same time, converging factors have shifted perspectives about program monitoring and evaluation, from merely tolerating program monitoring and evaluation as necessary evils to embracing them as essential organizational practices. These factors can be traced to the mid-1990s and the National Partnership for Reinventing Government initiative, which advocated for an enhanced culture of accountability among government agencies.1 More recently, a series of developments further accelerated the use of program monitoring and evaluation, particularly within government agencies.

Background: The relationship between agriculture, Anopheles mosquitoes, and malaria in Africa is not fully understood, but it is important for malaria control as countries consider expanding agricultural projects to address population growth and food demand. Therefore, we aimed to assess the effect of agriculture on Anopheles biting behaviour and malaria risk in children in rural areas of the Democratic Republic of the Congo (DR Congo). Methods: We did a population-based, cross-sectional, spatial study of rural children (<5 years) in the DR Congo. We used information about the presence of malaria parasites in each child, as determined by PCR analysis of dried-blood spots from the 2013-14 DR Congo Demographic and Health Survey (DHS). We also used data from the DHS, a longitudinal entomological study, and available land cover and climate data to evaluate the relationships between agriculture, Anopheles biting behaviour, and malaria prevalence. Satellite imagery was used to measure the percentage of agricultural land cover around DHS villages and Anopheles sites. Anopheles biting behaviour was assessed by Human Landing Catch. We used probit regression to assess the relationship between agriculture and the probability of malaria infection, as well as the relationship between agriculture and the probability that a mosquito was caught biting indoors. Findings: Between Aug 13, 2013, and Feb 13, 2014, a total of 9790 dried-blood spots were obtained from the DHS, of which 4612 participants were included in this study. Falciparum malaria infection prevalence in rural children was 38.7% (95% uncertainty interval [UI] 37.3-40.0). Increasing exposure to agriculture was associated with increasing malaria risk with a high posterior probability (estimate 0.07, 95% UI -0.04 to 0.17; posterior probability [estimate >0]=0.89), with the probability of malaria infection increased between 0.2% (95% UI -0.1 to 3.4) and 2.6% (-1.5 to 6.6) given a 15% increase in agricultural cover, depending on other risk factors. The models predicted that large increases in agricultural cover (from 0% to 75%) increase the probability of infection by as much as 13.1% (95% UI -7.3 to 28.9). Increased risk might be due to Anopheles gambiae sensu lato, whose probability of biting indoors increased between 11.3% (95% UI -15.3 to 25.6) and 19.7% (-12.1 to 35.9) with a 15% increase in agriculture. Interpretation: Malaria control programmes must consider the possibility of increased risk due to expanding agriculture. Governments considering initiating large-scale agricultural projects should therefore also consider accompanying additional malaria control measures. Funding: National Institutes of Health, National Science Foundation, Bill & Melinda Gates Foundation, President's Malaria Initiative, and Royster Society of Fellows at the University of North Carolina at Chapel Hill.

INTRODUCTION: This review summarizes the current literature for the prevalence and medical costs of noncommunicable chronic diseases among adult Medicaid beneficiaries to inform future program design. METHODS: The databases MEDLINE and CINAHL were searched in August 2016 using keywords, including Medicaid, health status, and healthcare cost, to identify original studies that were published during 2000-2016, examined Medicaid as an independent population group, examined prevalence or medical costs of chronic conditions, and included adults within the age group 18-64 years. The review and data extraction was conducted in Fall 2016-Spring 2017. Disease-related costs (costs specifically to treat the disease) and total costs (all-cause medical costs for a patient with the disease) are presented separately. RESULTS: Among the 29 studies selected, prevalence estimates for enrollees aged 18-64 years were 8.8%-11.8% for heart disease, 17.2%-27.4% for hypertension, 16.8%-23.2% for hyperlipidemia, 7.5%-12.7% for diabetes, 9.5% for cancer, 7.8%-19.3% for asthma, 5.0%-22.3% for depression, and 55.7%-62.1% for one or more chronic conditions. Estimated annual per patient disease-related costs (2015 U.S. dollars) were $3,219-$4,674 for diabetes, $3,968-$6,491 for chronic obstructive pulmonary disease, and $989-$3,069 for asthma. Estimated hypertension-related costs were $687, but total costs per hypertensive beneficiary ranged much higher. Estimated total annual healthcare costs were $29,271-$51,937 per beneficiary with heart failure and $11,446-$20,585 per beneficiary with schizophrenia. Costs among beneficiaries with cancer were $29,384-$46,194 for the 6 months following diagnosis. CONCLUSIONS: These findings could help inform the evaluation of interventions to prevent and manage noncommunicable chronic diseases and their potential to control costs among the vulnerable Medicaid population.

Health equity, in the context of public health in the United States, can be characterized as action to ensure all population groups living within a targeted jurisdiction have access to the resources that promote and protect health. There appear to be several elements in program design that enhance health equity. These design elements include consideration of sociodemographic characteristics, understanding the evidence base for reducing health disparities, leveraging multisectoral collaboration, using clustered interventions, engaging communities, and conducting rigorous planning and evaluation. This article describes selected examples of public health programs the Centers for Disease Control and Prevention (CDC) has supported related to these design elements. In addition, it describes an initiative to ensure that CDC extramural grant programs incorporate program strategies to advance health equity, and examples of national reports published by the CDC related to health disparities, health equity, and social determinants of health.

To reduce obesity prevalence, public health practitioners are intervening to change health behaviors as well as the policies, systems, and environments (PSEs) that support healthy behaviors. Although the number of recommended PSE intervention strategies continues to grow, limited guidance is available on how to implement those strategies in practice. This article describes the University of North Carolina at Chapel Hill, Center for Training and Research Translation's (Center TRT's) approach to reviewing, translating, and disseminating practitioner-developed interventions, with the goal of providing more practical guidance on how to implement PSE intervention strategies in real-world practice. As of August 2014, Center TRT had disseminated 30 practice-based PSE interventions. This article provides an overview of Center TRT's process for reviewing, translating, and disseminating practice-based interventions and offers key lessons learned during the nine years that Center TRT has engaged in this work.

We read with great interest the report by Alexiev et al [1] regarding a potential role for polyoma BK virus in urinary bladder urothelial carcinoma tumorigenesis. Several centers, including our own at The University of North Carolina in Chapel Hill, have noticed expression of the SV40-T antigen in some high-grade urothelial and renal tumors arising in kidney transplant recipients. In the future, more studies are needed to evaluate the potential causative role of the polyoma BK virus strain in human oncogenesis. In this context, Alexiev et al have provided some stimulating thoughts for upcoming investigations. Their proposed hypothetical model of tumorigenesis is in part based on the assumption that polyoma BK virus can undergo a replicative cycle in tumor cells. The authors support this model by electron microscopic images interpreted as showing evidence of polyomavirus particles in the carcinoma. However, how solid is the evidence in the current report for a replicative polyomavirus infection in tumor cells?

Performance measurement is widely accepted in public health as an important management tool supporting program improvement and accountability. However, several challenges impede developing and implementing performance measurement systems at the federal level, including the complexity of public health problems that reflect multiple determinants and involve outcomes that may take years to achieve, the decentralized and networked nature of public health program implementation, and the lack of reliable and consistent data sources and other issues related to measurement. All three of these challenges hinder the ability to attribute program results to specific public health program efforts. The purpose of this paper is to explore these issues in detail and offer potential solutions that support the development of robust and practical performance measures to meet the needs for program improvement and accountability. Adapting performance measurement to public health programs is both an evolving science and art. Through the strategies presented here, appropriate systems can be developed and monitored to support the production of meaningful data that will inform effective decision making at multiple levels.