Impact of twelve immunization-preventable infectious diseases on population health using disability-adjusted life years (DALYs) in Spain.

BACKGROUND: The objective of this study is to estimate the burden of selected immunization-preventable infectious diseases in Spain using the Burden of Communicable Diseases in Europe (BCoDE) methodology, as well as focusing on the national immunization programme and potential new inclusions. METHODS: The BCoDE methodology relies on an incidence and pathogen-based approach to calculate disease burden via disability-adjusted life year (DALY) estimates. It considers short and long-term sequelae associated to an infection via outcome trees. The BCoDE toolkit was used to populate those trees with Spanish-specific incidence estimates, and de novo outcome trees were developed for four infections (herpes zoster, rotavirus, respiratory syncytial virus [RSV], and varicella) not covered by the toolkit. Age/sex specific incidences were estimated based on data from the Spanish Network of Epidemiological Surveillance; hospitalisation and mortality rates were collected from the Minimum Basic Data Set. A literature review was performed to design the de novo models and obtain the rest of the parameters. The methodology, assumptions, data inputs and results were validated by a group of experts in epidemiology and disease modelling, immunization and public health policy. RESULTS: The total burden of disease amounted to 163.54 annual DALYs/100,000 population. Among the selected twelve diseases, respiratory infections represented around 90% of the total burden. Influenza exhibited the highest burden, with 110.00 DALYs/100,000 population, followed by invasive pneumococcal disease and RSV, with 25.20 and 10.57 DALYs/100,000 population, respectively. Herpes zoster, invasive meningococcal disease, invasive Haemophilus influenza infection and hepatitis B virus infection ranked lower with fewer than 10 DALYs/100,000 population each, while the rest of the infections had a limited burden (< 1 DALY/100,000 population). A higher burden of disease was observed in the elderly (≥ 60 years) and children < 5 years, with influenza being the main cause. In infants < 1 year, RSV represented the greatest burden. CONCLUSIONS: Aligned with the BCoDE study, the results of this analysis show a persisting high burden of immunization-preventable respiratory infections in Spain and, for the first time, highlight a high number of DALYs due to RSV. These estimates provide a basis to guide prevention strategies and make public health decisions to prioritise interventions and allocate healthcare resources in Spain.

The impact of the cost-of-living crisis on population health in the UK: rapid evidence review.

BACKGROUND: In the UK, unique and unforeseen factors, including COVID-19, Brexit, and Ukraine-Russia war, have resulted in an unprecedented cost of living crisis, creating a second health emergency. We present, one of the first rapid reviews with the aim of examining the impact of this current crisis, at a population level. We reviewed published literature, as well as grey literature, examining a broad range of physical and mental impacts on health in the short, mid, and long term, identifying those most at risk, impacts on system partners, including emergency services and the third sector, as well as mitigation strategies. METHODS: We conducted a rapid review by searching PubMed, Embase, MEDLINE, and HMIC (2020 to 2023). We searched for grey literature on Google and hand-searched the reports of relevant public health organisations. We included interventional and observational studies that reported outcomes of interventions aimed at mitigating against the impacts of cost of living at a population level. RESULTS: We found that the strongest evidence was for the impact of cold and mouldy homes on respiratory-related infections and respiratory conditions. Those at an increased risk were young children (0-4 years), the elderly (aged 75 and over), as well as those already vulnerable, including those with long-term multimorbidity. Further short-term impacts include an increased risk of physical pain including musculoskeletal and chest pain, and increased risk of enteric infections and malnutrition. In the mid-term, we could see increases in hypertension, transient ischaemic attacks, and myocardial infarctions, and respiratory illnesses. In the long term we could see an increase in mortality and morbidity rates from respiratory and cardiovascular disease, as well as increase rates of suicide and self-harm and infectious disease outcomes. Changes in behaviour are likely particularly around changes in food buying patterns and the ability to heat a home. System partners are also impacted, with voluntary sectors seeing fewer volunteers, an increase in petty crime and theft, alternative heating appliances causing fires, and an increase in burns and burn-related admissions. To mitigate against these impacts, support should be provided, to the most vulnerable, to help increase disposable income, reduce energy bills, and encourage home improvements linked with energy efficiency. Stronger links to bridge voluntary, community, charity and faith groups are needed to help provide additional aid and support. CONCLUSION: Although the CoL crisis affects the entire population, the impacts are exacerbated in those that are most vulnerable, particularly young children, single parents, multigenerational families. More can be done at a community and societal level to support the most vulnerable, and those living with long-term multimorbidity. This review consolidates the current evidence on the impacts of the cost of living crisis and may enable decision makers to target limited resources more effectively.

Undercounting diagnoses in Australian general practice: a data quality study with implications for population health reporting.

BACKGROUND: Diagnosis can often be recorded in electronic medical records (EMRs) as free-text or using a term with a diagnosis code. Researchers, governments, and agencies, including organisations that deliver incentivised primary care quality improvement programs, frequently utilise coded data only and often ignore free-text entries. Diagnosis data are reported for population healthcare planning including resource allocation for patient care. This study sought to determine if diagnosis counts based on coded diagnosis data only, led to under-reporting of disease prevalence and if so, to what extent for six common or important chronic diseases. METHODS: This cross-sectional data quality study used de-identified EMR data from 84 general practices in Victoria, Australia. Data represented 456,125 patients who attended one of the general practices three or more times in two years between January 2021 and December 2022. We reviewed the percentage and proportional difference between patient counts of coded diagnosis entries alone and patient counts of clinically validated free-text entries for asthma, chronic kidney disease, chronic obstructive pulmonary disease, dementia, type 1 diabetes and type 2 diabetes. RESULTS: Undercounts were evident in all six diagnoses when using coded diagnoses alone (2.57-36.72% undercount), of these, five were statistically significant. Overall, 26.4% of all patient diagnoses had not been coded. There was high variation between practices in recording of coded diagnoses, but coding for type 2 diabetes was well captured by most practices. CONCLUSION: In Australia clinical decision support and the reporting of aggregated patient diagnosis data to government that relies on coded diagnoses can lead to significant underreporting of diagnoses compared to counts that also incorporate clinically validated free-text diagnoses. Diagnosis underreporting can impact on population health, healthcare planning, resource allocation, and patient care. We propose the use of phenotypes derived from clinically validated text entries to enhance the accuracy of diagnosis and disease reporting. There are existing technologies and collaborations from which to build trusted mechanisms to provide greater reliability of general practice EMR data used for secondary purposes.

Cluster Analysis Methods to Support Population Health Improvement Among US Counties.

CONTEXT: Population health rankings can be a catalyst for the improvement of health by drawing attention to areas in need of relative improvement and summarizing complex information in a manner understood by almost everyone. However, ranks also have unintended consequences, such as being interpreted as "hard truths," where variations may not be significant. There is a need to improve communication about uncertainty in ranks, with accurate interpretation. The most common solutions discussed in the literature have included modeling approaches to minimize statistical noise or borrow strength from covariates. However, the use of complex models can limit communication and implementation, especially for broad audiences. OBJECTIVES: Explore data-informed grouping (cluster analysis) as an easier-to-understand, empirical technique to account for rank imprecision that can be effectively communicated both numerically and visually. DESIGN: Cluster analysis, specifically k-means clustering with Wasserstein (earth mover's) distance, was explored as an approach to identify natural and meaningful groupings and gaps in the data distribution for the County Health Rankings' (CHR) health outcomes ranks. SETTING: County-level health outcomes from the 2022 CHR. PARTICIPANTS: 3082 counties that were ranked in the 2022 CHR. MAIN OUTCOME MEASURE: Data-informed health groups. RESULTS: Cluster analysis identified 30 health groupings among counties nationwide, with cluster size ranging from 9 to 184 counties. On average, states had 16 identified clusters, ranging from 3 in Delaware and Hawaii to 27 in Virginia. Number of clusters per state was associated with number of counties per state and population of the state. The method helped address many of the issues that arise from providing rank estimates alone. CONCLUSIONS: Public health practitioners can use this information to understand uncertainty in ranks, visualize distances between county ranks, have context around which counties are not meaningfully different from one another, and compare county performance to peer counties.

On the Use of Regression Calibration in a Complex Sampling Design With Application to the Hispanic Community Health Study/Study of Latinos.

Regression calibration is the most widely used method to adjust regression parameter estimates for covariate measurement error. Yet its application in the context of a complex sampling design, for which the common bootstrap variance estimator can be less straightforward, has been less studied. We propose 2 variance estimators for a multistage probability-based sampling design, a parametric and a resampling-based multiple imputation approach, where a latent mean exposure needed for regression calibration is the target of imputation. This work was motivated by the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) data from 2008 to 2011, for which relationships between several outcomes and diet, an error-prone self-reported exposure, are of interest. We assessed the relative performance of these variance estimation strategies in an extensive simulation study built on the HCHS/SOL data. We further illustrate the proposed estimators with an analysis of the cross-sectional association of dietary sodium intake with hypertension-related outcomes in a subsample of the HCHS/SOL cohort. We have provided guidelines for the application of regression models with regression-calibrated exposures. Practical considerations for implementation of these 2 variance estimators in the setting of a large multicenter study are also discussed. Code to replicate the presented results is available online.

The Challenge of Achieving Immunity Through Multiple-Dose Vaccines in Madagascar.

Administration of many childhood vaccines requires that multiple doses be delivered within a narrow time window to provide adequate protection and reduce disease transmission. Accurately quantifying vaccination coverage is complicated by limited individual-level data and multiple vaccination mechanisms (routine and supplementary vaccination programs). We analyzed 12,541 vaccination cards from 6 districts across Madagascar for children born in 2015 and 2016. For 3 vaccines-pentavalent diphtheria-tetanus-pertussis-hepatitis B-Haemophilus influenzae type b vaccine (DTP-HB-Hib; 3 doses), 10-valent pneumococcal conjugate vaccine (PCV10; 3 doses), and rotavirus vaccine (2 doses)-we used dates of vaccination and birth to estimate coverage at 1 year of age and timeliness of delivery. Vaccination coverage at age 1 year for the first dose was consistently high, with decreases for subsequent doses (DTP-HB-Hib: 91%, 81%, and 72%; PCV10: 82%, 74%, and 64%; rotavirus: 73% and 63%). Coverage levels between urban districts and their rural counterparts did not differ consistently. For each dose of DTP-HB-Hib, the overall percentage of individuals receiving late doses was 29%, 7%, and 6%, respectively; estimates were similar for other vaccines. Supplementary vaccination weeks, held to help children who had missed routine care to catch up, did not appear to increase the likelihood of being vaccinated. Maintaining population-level immunity with multiple-dose vaccines requires a robust stand-alone routine immunization program.

Estimating the Marginal Causal Effect and Potential Impact of Waterpipe Smoking on Risk of Multiple Sclerosis Using the Targeted Maximum Likelihood Estimation Method: A Large, Population-Based Incident Case-Control Study.

There are few if any reports regarding the role of lifetime waterpipe smoking in the etiology of multiple sclerosis (MS). In a population-based incident case-control study conducted in Tehran, Iran, we investigated the association between waterpipe smoking and MS, adjusted for confounders. Cases (n = 547) were patients aged 15-50 years identified from the Iranian Multiple Sclerosis Society between 2013 and 2015. Population-based controls (n = 1,057) were persons aged 15-50 years recruited through random digit telephone dialing. A doubly robust estimation method, the targeted maximum likelihood estimator (TMLE), was used to estimate the marginal risk ratio and odds ratio for the association between waterpipe smoking and MS. The estimated risk ratio and odds ratio were both 1.70 (95% confidence interval: 1.34, 2.17). The population attributable fraction was 21.4% (95% confidence interval: 4.0, 38.8). Subject to the limitations of case-control studies in interpreting associations causally, these results suggest that waterpipe use, or strongly related but undetermined factors, increases the risk of MS. Further epidemiologic studies, including nested case-control studies, are needed to confirm these findings.

California's Mental Health Services Act and Mortality Due to Suicide, Homicide, and Acute Effects of Alcohol: A Synthetic Control Application.

California's Mental Health Services Act (MHSA) substantially expanded funding of county mental health services through a state tax, and led to broad prevention efforts and intensive services for individuals experiencing serious mental disorders. We estimated the associations between MHSA and mortality due to suicide, homicide, and acute effects of alcohol. Using annual cause-specific mortality data for each US state and the District of Columbia from 1976-2015, we used a generalization of the quasi-experimental synthetic control method to predict California's mortality rate for each outcome in the absence of MHSA using a weighted combination of comparison states. We calculated the association between MHSA and each outcome as the absolute difference and percentage difference between California's observed and predicted average annual rates over the postintervention years (2007-2015). MHSA was associated with modest decreases in average annual rates of homicide (-0.81/100,000 persons, corresponding to a 13% reduction) and mortality from acute alcohol effects (-0.35/100,000 persons, corresponding to a 12% reduction). Placebo test inference suggested that the associations were unlikely to be due to chance. MHSA was not associated with suicide. Protective associations with mortality due to homicide and acute alcohol effects provide evidence for modest health benefits of MHSA at the population level.

The social, economic, political, and genetic value of race and ethnicity in 2020.

Disparities across racial and ethnic groups are present for a range of health outcomes. In this opinion piece, we consider the origin of racial and ethnic groupings, a history that highlights the sociopolitical nature of these terms. Indeed, the terms race and ethnicity exist purely as social constructs and must not be used interchangeably with genetic ancestry. There is no scientific evidence that the groups we traditionally call "races/ethnicities" have distinct, unifying biological or genetic basis. Such a focus runs the risk of compounding equity gaps and perpetuating erroneous conclusions. That said, we suggest that the terms race and ethnicity continue to have purpose as lenses through which to quantify and then close racial and ethnic disparities. Understanding the root cause of such health disparities-namely, longstanding racism and ethnocentrism-could promote interventions and policies poised to equitably improve population health.

The Impacts of the COVID-19 Pandemic on the Medical Expenditure Panel Survey.

The COVID-19 pandemic caused substantial disruptions in the field operations of all 3 major components of the Medical Expenditure Panel Survey (MEPS). The MEPS is widely used to study how policy changes and major shocks, such as the COVID-19 pandemic, affect insurance coverage, access, and preventive and other health care utilization and how these relate to population health. We describe how the MEPS program successfully responded to these challenges by reengineering field operations, including survey modes, to complete data collection and maintain data release schedules. The impact of the pandemic on response rates varied considerably across the MEPS. Investigations to date show little effect on the quality of data collected. However, lower response rates may reduce the statistical precision of some estimates. We also describe several enhancements made to the MEPS that will allow researchers to better understand the impact of the pandemic on US residents, employers, and the US health care system. (Am J Public Health. 2021;111(12):2157-2166. https://doi.org/10.2105/AJPH.2021.306534).

Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study.

OBJECTIVES: Coronavirus disease 2019 has put unprecedented pressure on healthcare systems worldwide, leading to a reduction of the available healthcare capacity. Our objective was to develop a decision model to estimate the impact of postponing semielective surgical procedures on health, to support prioritization of care from a utilitarian perspective. METHODS: A cohort state-transition model was developed and applied to 43 semielective nonpediatric surgical procedures commonly performed in academic hospitals. Scenarios of delaying surgery from 2 weeks were compared with delaying up to 1 year and no surgery at all. Model parameters were based on registries, scientific literature, and the World Health Organization Global Burden of Disease study. For each surgical procedure, the model estimated the average expected disability-adjusted life-years (DALYs) per month of delay. RESULTS: Given the best available evidence, the 2 surgical procedures associated with most DALYs owing to delay were bypass surgery for Fontaine III/IV peripheral arterial disease (0.23 DALY/month, 95% confidence interval [CI]: 0.13-0.36) and transaortic valve implantation (0.15 DALY/month, 95% CI: 0.09-0.24). The 2 surgical procedures with the least DALYs were placing a shunt for dialysis (0.01, 95% CI: 0.005-0.01) and thyroid carcinoma resection (0.01, 95% CI: 0.01-0.02). CONCLUSION: Expected health loss owing to surgical delay can be objectively calculated with our decision model based on best available evidence, which can guide prioritization of surgical procedures to minimize population health loss in times of scarcity. The model results should be placed in the context of different ethical perspectives and combined with capacity management tools to facilitate large-scale implementation.

Effect of influenza vaccination in patients with asthma.

BACKGROUND: Although annual influenza vaccination is recommended for persons with asthma, its effectiveness in this patient population is not well described. We evaluated the effect of influenza vaccination in the current and previous seasons in preventing influenza among people with asthma. METHODS: Using population health data from the Navarre region of Spain for the 2015/16 to 2019/20 influenza seasons, we conducted a test-negative case-control study to assess the effect of influenza vaccination in the current and 5 previous seasons. From patients presenting to hospitals and primary health care centres with influenza-like illness who underwent testing for influenza, we estimated the effects of influenza vaccination among patients with asthma overall and between those presenting as inpatients or outpatients, as well as between patients with and without asthma. RESULTS: Of 1032 patients who had asthma and were tested, we confirmed that 421 had influenza and the remaining 611 were test-negative controls. We found that the average effect of influenza vaccination was 43% (adjusted odds ratio [OR] 0.57, 95% confidence interval [CI] 0.40 to 0.80) for current-season vaccination regardless of previous doses, and 38% (adjusted OR 0.62, 95% CI 0.39 to 0.96) for vaccination in previous seasons only. Effects were similar for outpatients and inpatients. Among patients with asthma and confirmed influenza, current-season vaccination did not reduce the odds of hospital admission (adjusted OR 1.05, 95% CI 0.51 to 2.18). Influenza vaccination effects were similar for patients with and without asthma. INTERPRETATION: We estimated that, on average, current or previous influenza vaccination of people with asthma prevented almost half of influenza cases. These results support recommendations that people with asthma receive influenza vaccination.

Estimating the Population Health Impact of Recently Introduced Modified Risk Tobacco Products: A Comparison of Different Approaches.

INTRODUCTION: Various approaches have been used to estimate the population health impact of introducing a Modified Risk Tobacco Product (MRTP). AIMS AND METHODS: We aimed to compare and contrast aspects of models considering effects on mortality that were known to experts attending a meeting on models in 2018. RESULTS: Thirteen models are described, some focussing on e-cigarettes, others more general. Most models are cohort-based, comparing results with or without MRTP introduction. They typically start with a population with known smoking habits and then use transition probabilities either to update smoking habits in the "null scenario" or joint smoking and MRTP habits in an "alternative scenario". The models vary in the tobacco groups and transition probabilities considered. Based on aspects of the tobacco history developed, the models compare mortality risks, and sometimes life-years lost and health costs, between scenarios. Estimating effects on population health depends on frequency of use of the MRTP and smoking, and the extent to which the products expose users to harmful constituents. Strengths and weaknesses of the approaches are summarized. CONCLUSIONS: Despite methodological differences, most modellers have assumed the increase in risk of mortality from MRTP use, relative to that from cigarette smoking, to be very low and have concluded that MRTP introduction is likely to have a beneficial impact. Further model development, supplemented by preliminary results from well-designed epidemiological studies, should enable more precise prediction of the anticipated effects of MRTP introduction. IMPLICATIONS: There is a need to estimate the population health impact of introducing modified risk nicotine-containing products for smokers unwilling or unable to quit. This paper reviews a variety of modeling methodologies proposed to do this, and discusses the implications of the different approaches. It should assist modelers in refining and improving their models, and help toward providing authorities with more reliable estimates.

Supervised mixture of experts models for population health.

We propose a machine learning driven approach to derive insights from observational healthcare data to improve public health outcomes. Our goal is to simultaneously identify patient subpopulations with differing health risks and to find those risk factors within each subpopulation. We develop two supervised mixture of experts models: a Supervised Gaussian Mixture model (SGMM) for general features and a Supervised Bernoulli Mixture model (SBMM) tailored to binary features. We demonstrate the two approaches on an analysis of high cost drivers of Medicaid expenditures for inpatient stays. We focus on the three diagnostic categories that accounted for the highest percentage of inpatient expenditures in New York State (NYS) in 2016. When compared with state-of-the-art learning methods (random forests, boosting, neural networks), our approaches provide comparable prediction performance while also extracting insightful subpopulation structure and risk factors. For problems with binary features the proposed SBMM provides as good or better performance than alternative methods while offering insightful explanations. Our results indicate the promise of such approaches for extracting population health insights from electronic health care records.

Vision for a systems architecture to integrate and transform population health.

Entities involved in population health often share a common mission while acting independently of one another and perhaps redundantly. Population health is in everybody's interest, but nobody is really in charge of promoting it. Across governments, corporations, and frontline operations, lack of coordination, lack of resources, and lack of reliable, current information have often impeded the development of situation-awareness models and thus a broad operational integration for population health. These deficiencies may also affect the technical, organizational, policy, and legal arrangements for information sharing, a desired practice of high potential value in population health. In this article, we articulate a vision for a next-generation modeling effort to create a systems architecture for broadly integrating and visualizing strategies for advancing population health. This multipurpose systems architecture would enable different views, alerts, and scenarios to better prepare for and respond to potential degradations in population health. We draw inspiration from systems engineering and visualization tools currently in other uses, including monitoring the state of the economy (market performance), security (classified intelligence), energy (power generation), transportation (global air traffic control), environment (weather monitoring), jobs (labor market dynamics), manufacturing and supply chain (tracking of components, parts, subassemblies, and products), and democratic processes (election analytics). We envision the basic ingredients for a population health systems architecture and its visualization dashboards to eventually support proactive planning and joint action among constituents. We intend our ambitious vision to encourage the work needed for progress that the population deserves.

Climate Change and Population Health: Incorporating Stages of Nursing's Political Development.

Climate change is the greatest public health threat of the 21st century and is associated with environmental degradation and deleterious health consequences. In 2019, the Lancet Commission Report on Health and Climate Change: Ensuring that the Health of a Child Born Today Is Not Defined By a Changing Climate (Watts et al., 2019) examined the critical health issues that children will face in the era of climate change. Greenhouse gas emissions (GGEs) are responsible for an alarming increase in the warming of the planet, shifts in weather patterns, loss of arable land, and exacerbations of acute health issues, chronic health problems, and disaster-related health consequences. The purpose of this paper is to provide an overview of climate change and the associated deleterious health consequences in our climate-changing world. The paper will also examine the stages of political development to advance the 21st century role of the nursing profession in climate and health advocacy and policy.

Multilevel Regression and Poststratification Versus Survey Sample Weighting for Estimating Population Quantities in Large Population Health Studies.

Multilevel regression and poststratification (MRP) is a model-based approach for estimating a population parameter of interest, generally from large-scale surveys. It has been shown to be effective in highly selected samples, which is particularly relevant to investigators of large-scale population health and epidemiologic surveys facing increasing difficulties in recruiting representative samples of participants. We aimed to further examine the accuracy and precision of MRP in a context where census data provided reasonable proxies for true population quantities of interest. We considered 2 outcomes from the baseline wave of the Ten to Men study (Australia, 2013-2014) and obtained relevant population data from the 2011 Australian Census. MRP was found to achieve generally superior performance relative to conventional survey weighting methods for the population as a whole and for population subsets of varying sizes. MRP resulted in less variability among estimates across population subsets relative to sample weighting, and there was some evidence of small gains in precision when using MRP, particularly for smaller population subsets. These findings offer further support for MRP as a promising analytical approach for addressing participation bias in the estimation of population descriptive quantities from large-scale health surveys and cohort studies.

Autonomous Vehicles and Public Health.

Autonomous vehicles (AVs) have the potential to shape urban life and significantly modify travel behaviors. "Autonomous technology" means technology that can drive a vehicle without active physical control or monitoring by a human operator. The first AV fleets are already in service in US cities. AVs offer a variety of automation, vehicle ownership, and vehicle use options. AVs could increase some health risks (such as air pollution, noise, and sedentarism); however, if proper regulated, AVs will likely reduce morbidity and mortality from motor vehicle crashes and may help reshape cities to promote healthy urban environments. Healthy models of AV use include fully electric vehicles in a system of ridesharing and ridesplitting. Public health will benefit if proper policies and regulatory frameworks are implemented before the complete introduction of AVs into the market.

Which Priorities for Health and Well-Being Stand Out After Accounting for Tangled Threats and Costs? Simulating Potential Intervention Portfolios in Large Urban Counties.

Policy Points Interventions in a regional system with intertwined threats and costs should address those threats that have the strongest, quickest, and most pervasive cross-impacts. Instead of focusing on an individual county's apparent shortcomings, a regional intervention portfolio can yield greater results when it is designed to counter those systemic threats, especially poverty and inadequate social support, that most undermine health and well-being virtually everywhere. Likewise, efforts to reduce smoking, addiction, and violent crime and to improve routine care, health insurance, and youth education are important for most counties to unlock both short- and long-term potential. CONTEXT: Counties across the United States must contend with multiple, intertwined threats and costs that defy simple solutions. Decision makers face the necessary but difficult task of prioritizing those interventions with the greatest potential to produce equitable health and well-being. METHODS: Using County Health Rankings data for a predefined peer group of 39 urban US counties, we performed statistical regressions to identify 37 cross-impacts among 15 threats to health and well-being. Adding appropriate time delays, we then developed a dynamic model of these cross-impacts and simulated each of the counties over 20 years to assess the likely impact of 12 potential interventions-individually and in a combined portfolio-for three outcomes: (1) years of potential life lost, (2) fraction of adults in fair or poor health, and (3) total spending on urgent services. FINDINGS: The combined portfolio yielded improvements by year 20 that are considerably greater than those at year 5, indicating that the time delays have a major effect. Despite the wide variation in threat levels across counties, the list of top-ranked interventions is strikingly similar. Poverty reduction and social support were the most highly ranked interventions, even in the shorter term, for all outcomes in all counties. Interventions affecting smoking, addiction, routine care, health insurance, violent crime, and youth education also were important contributors to some outcomes. CONCLUSIONS: To safeguard health and well-being in a system dominated by tangled threats and costs, the most important priorities for a county cannot be simply inferred from a profile of its relative strengths and weaknesses. Two interventions stood out as the top priorities for almost all the counties in this study, and six others also were important contributors. Interventions directed toward these priority areas are likely to yield the greatest impact, irrespective of the county's specifics. A significant concentration of resources in a regional portfolio therefore ought to go to these strongest contributors for equitable health and well-being.