Jose Pagan
Jose Pagan
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Chair and Professor of the Department of Public Health Policy and Management
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Professional overview
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Dr. Pagán received his PhD in economics from the University of New Mexico and is a former Robert Wood Johnson Foundation Health & Society Scholar with expertise in health economics and population health. He has led research, implementation, and evaluation projects on the redesign of health care delivery and payment systems. He is interested in population health management, health care payment and delivery system reform, and the social determinants of health. Over the years his research has been funded through grants and contracts from the Department of Defense, the Agency for Healthcare Research and Quality, the National Institutes of Health, the Centers for Medicare & Medicaid Services, the European Commission, and the Robert Wood Johnson Foundation, among others.
Dr. Pagán is Chair of the Board of Directors of NYC Health + Hospitals, the largest public healthcare system in the United States. He also served as Chair of the National Advisory Committee of the Robert Wood Johnson Foundation’s Health Policy Research Scholars and was a member of the Board of Directors of the Interdisciplinary Association for Population Health Science and the American Society of Health Economists.
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Areas of research and study
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Applied EconomicsHealth EconomicsPopulation HealthPublic Health Policy
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Publications
Publications
Trends in Reported Health Care Affordability for Men and Women with Employer-Sponsored Health Insurance Coverage in the US, 2000 to 2020
A decision-making model to optimize the impact of community-based health programs
AbstractPérez, E., Li, Y., & Pagán, J. A. (n.d.).Publication year
2021Journal title
Preventive MedicineVolume
149AbstractHospitals and clinics are increasingly interested in building partnerships with community-based organizations to address the social determinants of health. Choosing among community-based health programs can be complex given that programs may have different effectiveness levels and implementation costs. This study develops a decision-making model that can be used to evaluate multiple key factors that would be relevant in resource allocation decisions related to a set of community-based health programs. The decision-making model compares community-based health programs by considering funding limitations, program duration, and participant retention until program completion. Specifically, the model allows decision makers to select the optimal mix of community-based health programs based on the profiles of the population given the above constraints. The model can be used to improve resource allocation in communities, ultimately contributing to the long-term goal of strengthening cross-sector partnerships and the integration of services to improve health outcomes.Aligning Health Care and Social Services to Reduce Hospitalizations and Emergency Department Visits: An Evaluation of the Community Care Connections Program
AbstractAkiya, K., Fisher, E., Wells, A., Li, Y., Peck, C., & Pagán, J. A. (n.d.).Publication year
2021Journal title
Medical careVolume
59Issue
8Page(s)
671-678AbstractBackground: Integration of social services in health care delivery is increasingly recognized as a potential strategy for improving health and reducing the use of acute care services. Collaborative models that provide older adults with case management, linkages to social services, and assistance with health care navigation have emerged as promising strategies. Objective: The objective of this study was to evaluate the Community Care Connections (CCC) program, a cross-sector collaboration designed to align social and health care services for older adults. Research Design: We compared hospitalizations and emergency department (ED) visits 90 days after enrollment with a propensity score-matched group of non-CCC patients. Subgroup analyses were also conducted for adults with hypertension, diabetes, and high cholesterol. Subjects: A total of 1004 patients enrolled in CCC between June 1, 2016, and November 15, 2018, and 1004 matched patients from the same metropolitan area. Measures: Mean hospitalizations and ED visits per patient 90 days after CCC enrollment. Results: Mean hospitalizations were lower among CCC patients 90 days after enrollment than among non-CCC adults [difference=-0.039, 95% confidence interval (CI): -0.077 to -0.001, P=0.044]. They were also lower among CCC patients with hypertension (difference=-0.057, 95% CI: -0.103 to -0.010, P=0.017). However, 90 days after enrollment mean ED visits were higher among CCC patients relative to non-CCC adults (difference=0.238, 95% CI: 0.195-0.281, P<0.001). Conclusions: Connecting older adults to social services while being served by the health care system may lead to decreases in hospitalizations. Cross-sector partnerships that address social and economic needs may reduce the use of costly health care services.Aligning social and health care services: The case of Community Care Connections
AbstractFisher, E. M., Akiya, K., Wells, A., Li, Y., Peck, C., & Pagán, J. A. (n.d.).Publication year
2021Journal title
Preventive MedicineVolume
143AbstractThe Community Care Connections (CCC) program aims to align social and healthcare services to improve health outcomes in older adults with complex medical and social needs. This study assessed changes in healthcare utilization before and after CCC program participation. Between June 2016 and March 2019, 1214 adults with complete data who provided informed consent participated in the CCC program. CCC client data were linked with data on hospitalizations, emergency department (ED) visits, and observation stays 90 days before and after program start. Data analysis examined changes in health care utilization 90 days after program start, compared to 90 days before. Hospitalizations decreased by 30% (Change = −0.029, 95% Confidence Interval (CI) = −0.053, −0.005), ED visits decreased by 29% (Change = −0.114, 95% CI = -0.163, −0.066), and observation stays decreased by 23% (Change = −0.041, 95% CI = -0.073, −0.009) during the post period. ED visits decreased by 37% (Change = −0.140, 95% CI = -0.209, −0.070) for those with hypertension and by 30% (Change = −0.109, 95% CI = -0.199, −0.020) for those with high cholesterol, while observation stays decreased by 46% (Change = −0.118, 95% CI = -0.185, −0.052) for those with diabetes and by 44% (Change = −0.082, 95% CI = -0.150, −0.014) for those with high cholesterol during the post period. Connecting older adults with social services through the healthcare delivery system may lead to decreases in hospitalizations, ED visits, and observation stays. Implementation of cross-sector partnerships that address non-clinical factors that impact the health of older adults may reduce the use of costly healthcare services.Bridging hospital quality leadership to patient care quality
AbstractChakraborty, S., Kaynak, H., & Pagán, J. A. (n.d.).Publication year
2021Journal title
International Journal of Production EconomicsVolume
233AbstractUnderstanding what drives quality in the delivery of healthcare services is critical to improve the patient care experience. In a hospital, the integration of technology platforms and effective teamwork promote quality care, but this outcome requires that hospital leadership prioritizes technology integration and commits resources to sustain effective healthcare delivery teams. Some of these concepts have been investigated with a limited focus or in very narrow research contexts. Because these concepts do not interact in isolation, an empirical study that examines the relationships between them simultaneously is important to explore the links between hospital quality leadership (QL), technology integration (TI), healthcare team effectiveness (HTE) and patient care quality (PCQ). An online survey of 300 middle and senior-level U.S. hospital executives and quality heads completed during a four-month period is used to test the research hypotheses drawn primarily from quality management, information processing, and team effectiveness theories. The results suggest that hospital leaders should emphasize the integration of all technology systems in their hospitals and continuously encourage their healthcare teams to work effectively thereby improving the quality of patient care delivered. Based on the post-hoc results, we suggest that hospital quality leaders should recognize the difference in magnitude of the effects of HTE and TI on the four PCQ facets.Community Health Needs Predict Population Health Partnerships Among U.S. Children’s Hospitals
Do State Opioid Policies Influence Nonprofit Hospitals’ Decisions to Address Substance Abuse in Their Communities?
AbstractFranz, B., Cronin, C. E., Skinner, D., & Pagán, J. A. (n.d.).Publication year
2021Journal title
Medical Care Research and ReviewVolume
78Issue
4Page(s)
371-380AbstractThe U.S. epidemic of opioid abuse calls for broad collaboration between a wide range of health care institutions and the various levels of government. Through the community benefit programs they provide, nonprofit hospitals are well positioned to be key partners in local efforts. Although substance abuse appears on approximately 90% of the most recent community health needs assessments completed by hospitals, many hospitals are not addressing substance abuse in their programmatic efforts. Given wide state variation in policies to combat opioid abuse, we assess whether state leadership to address the opioid crisis influences hospital decisions to invest in substance abuse programs. Our findings suggest that several key state policies are related to hospital investments in substance abuse initiatives. To capitalize on the community benefit responsibilities of local hospitals, policies that provide specific direction for and engagement with local hospitals may increase cooperation and investments to address substance abuse.Effects of New York’s Executive Order on Face Mask Use on COVID-19 Infections and Mortality: A Modeling Study
AbstractShen, M., Zu, J., Fairley, C. K., Pagán, J. A., Ferket, B., Liu, B., Yi, S. S., Chambers, E., Li, G., Guo, Y., Rong, L., Xiao, Y., Zhuang, G., Zebrowski, A., Carr, B. G., Li, Y., & Zhang, L. (n.d.).Publication year
2021Journal title
Journal of Urban HealthVolume
98Issue
2Page(s)
197-204AbstractThere is growing evidence on the effect of face mask use in controlling the spread of COVID-19. However, few studies have examined the effect of local face mask policies on the pandemic. In this study, we developed a dynamic compartmental model of COVID-19 transmission in New York City (NYC), which was the epicenter of the COVID-19 pandemic in the USA. We used data on daily and cumulative COVID-19 infections and deaths from the NYC Department of Health and Mental Hygiene to calibrate and validate our model. We then used the model to assess the effect of the executive order on face mask use on infections and deaths due to COVID-19 in NYC. Our results showed that the executive order on face mask use was estimated to avert 99,517 (95% CIs 72,723–126,312) COVID-19 infections and 7978 (5692–10,265) deaths in NYC. If the executive order was implemented 1 week earlier (on April 10), the averted infections and deaths would be 111,475 (81,593–141,356) and 9017 (6446–11,589), respectively. If the executive order was implemented 2 weeks earlier (on April 3 when the Centers for Disease Control and Prevention recommended face mask use), the averted infections and deaths would be 128,598 (94,373–162,824) and 10,515 (7540–13,489), respectively. Our study provides public health practitioners and policymakers with evidence on the importance of implementing face mask policies in local areas as early as possible to control the spread of COVID-19 and reduce mortality.Excess Deaths During the COVID-19 Economic Downturn
Genetic Variant Reinterpretation: Economic and Population Health Management Challenges
Projected COVID-19 epidemic in the United States in the context of the effectiveness of a potential vaccine and implications for social distancing and face mask use
AbstractShen, M., Zu, J., Fairley, C. K., Pagán, J. A., An, L., Du, Z., Guo, Y., Rong, L., Xiao, Y., Zhuang, G., Li, Y., & Zhang, L. (n.d.).Publication year
2021Journal title
VaccineVolume
39Issue
16Page(s)
2295-2302AbstractBackground: Multiple candidates of COVID-19 vaccines have entered Phase III clinical trials in the United States (US). There is growing optimism that social distancing restrictions and face mask requirements could be eased with widespread vaccine adoption soon. Methods: We developed a dynamic compartmental model of COVID-19 transmission for the four most severely affected states (New York, Texas, Florida, and California). We evaluated the vaccine effectiveness and coverage required to suppress the COVID-19 epidemic in scenarios when social contact was to return to pre-pandemic levels and face mask use was reduced. Daily and cumulative COVID-19 infection and death cases from 26th January to 15th September 2020 were obtained from the Johns Hopkins University Coronavirus resource center and used for model calibration. Results: Without a vaccine (scenario 1), the spread of COVID-19 could be suppressed in these states by maintaining strict social distancing measures and face mask use levels. But relaxing social distancing restrictions to the pre-pandemic level without changing the current face mask use would lead to a new COVID-19 outbreak, resulting in 0.8–4 million infections and 15,000–240,000 deaths across these four states over the next 12 months. Under this circumstance, introducing a vaccine (scenario 2) would partially offset this negative impact even if the vaccine effectiveness and coverage are relatively low. However, if face mask use is reduced by 50% (scenario 3), a vaccine that is only 50% effective (weak vaccine) would require coverage of 55–94% to suppress the epidemic in these states. A vaccine that is 80% effective (moderate vaccine) would only require 32–57% coverage to suppress the epidemic. In contrast, if face mask usage stops completely (scenario 4), a weak vaccine would not suppress the epidemic, and further major outbreaks would occur. A moderate vaccine with coverage of 48–78% or a strong vaccine (100% effective) with coverage of 33–58% would be required to suppress the epidemic. Delaying vaccination rollout for 1–2 months would not substantially alter the epidemic trend if the current non-pharmaceutical interventions are maintained. Conclusions: The degree to which the US population can relax social distancing restrictions and face mask use will depend greatly on the effectiveness and coverage of a potential COVID-19 vaccine if future epidemics are to be prevented. Only a highly effective vaccine will enable the US population to return to life as it was before the pandemic.Rurality and origin–destination trajectories of medical school application and matriculation in the united states
AbstractMu, L., Liu, Y., Zhang, D., Gao, Y., Nuss, M., Rajbhandari-Thapa, J., Chen, Z., Pagán, J. A., Li, Y., Li, G., & Son, H. (n.d.).Publication year
2021Journal title
ISPRS International Journal of Geo-InformationVolume
10Issue
6AbstractPhysician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin–destination (O–D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural–urban gap in medical school applications, and reduce physician shortages in rural areas.The role of good governance in the race for global vaccination during the COVID-19 pandemic
AbstractTatar, M., Faraji, M. R., Montazeri Shoorekchali, J., Pagán, J. A., & Wilson, F. A. (n.d.).Publication year
2021Journal title
Scientific reportsVolume
11Issue
1AbstractGovernments have developed and implemented various policies and interventions to fight the COVID-19 pandemic. COVID-19 vaccines are now being produced and distributed globally. This study investigated the role of good governance and government effectiveness indicators in the acquisition and administration of COVID-19 vaccines at the population level. Data on six World Bank good governance indicators for 172 countries for 2019 and machine-learning methods (K-Means Method and Principal Component Analysis) were used to cluster countries based on these indicators and COVID-19 vaccination rates. XGBoost was used to classify countries based on their vaccination status and identify the relative contribution of each governance indicator to the vaccination rollout in each country. Countries with the highest COVID-19 vaccination rates (e.g., Israel, United Arab Emirates, United States) also have higher effective governance indicators. Regulatory Quality is the most important indicator in predicting COVID-19 vaccination status in a country, followed by Voice and Accountability, and Government Effectiveness. Our findings suggest that coordinated global efforts led by the World Health Organization and wealthier nations may be necessary to assist in the supply and distribution of vaccines to those countries that have less effective governance.Trends in Medical School Application and Matriculation Rates Across the United States from 2001 to 2015: Implications for Health Disparities
AbstractZhang, D., Li, G., Mu, L., Thapa, J., Li, Y., Chen, Z., Shi, L., Su, D., Son, H., & Pagan, J. A. (n.d.).Publication year
2021Journal title
Academic MedicineVolume
96Issue
6Page(s)
885-893AbstractPurpose Socioeconomic and geographic determinants of medical school application and matriculation may help explain the unequal distribution of physicians in the United States. This study describes trends in MD-granting medical school application and matriculation rates and explores the relationship between county median family income, proximity to a medical school, and medical school application and matriculation rates. Method Data were obtained from the Association of American Medical Colleges, including the age, gender, and Federal Information Processing Standards code for county of legal residence for each applicant and matriculant to U.S. MD-granting medical schools from 2001 through 2015. The application and matriculation rates in each county were calculated using the number of applicants and matriculants per 100,000 residents. Counties were classified into 4 groups according to the county median family income (high-income, middle-income, middle-low-income, low-income). The authors performed chi-square tests to assess trends across the study period and the association of county median family income with application and matriculation rates. Results There were 581,833 applicants and 262,730 (45.2%) matriculants to MD-granting medical schools between 2001 and 2015. The application rates per 100,000 residents during 2001-2005, 2006-2010, and 2011-2015 were 57.2, 62.7, and 69.0, respectively, and the corresponding matriculation rates were 27.5, 28.1, and 29.8. The ratios of the application rate in high-income counties to that in low-income counties during the 3 time periods were 1.9, 2.4, and 2.8, respectively. Conclusions The application and matriculation rates to MD-granting medical schools increased steadily from 2001 to 2015. Yet, applicants and matriculants disproportionately came from high-income counties. The differences in the application and matriculation rates between low-income and high-income counties grew during this period. Exploring these differences can lead to better understanding of the factors that drive geographic differences in physician access and the associated health disparities across the United States.Views on the need to implement restriction policies to be able to address COVID-19 in the United States
AbstractWang, V. H. C., & Pagán, J. A. (n.d.).Publication year
2021Journal title
Preventive MedicineVolume
143AbstractSeveral restriction policies implemented in many states in the United States have demonstrated their effectiveness in mitigating the spread of the coronavirus disease (COVID-19), but less is known about the differences in views on the restriction policies among different population segments. This study aimed to understand which different population groups of adults in the United States consider several key restriction policies as necessary to combat COVID-19. Survey data from Wave 64 (March 19–24, 2020) of the Pew Research Center's American Trends Panel (n=10,609) and logistic regression were used to evaluate the association between socioeconomic and demographic characteristics, employment status, political party affiliation, news exposure, census region, and opinions about COVID-19 restriction policies. The policies included restricting international travel, imposing business closures, banning large group gatherings, cancelling entertainment events, closing schools, limiting restaurants to carry-out only, and postponing state primary elections. Most survey respondents viewed COVID-19 restriction policies as necessary. Views on each restriction policy varied substantially across some population segments such as age, race, and ethnicity. Regardless of population segments, those who followed news closely or considered themselves Democrat/lean Democrat were more likely to consider all the policies as necessary than those not following the news closely or those who considered themselves Republican/lean Republican. The effectiveness of key COVID-19 restriction policies is likely to vary substantially across population groups given that views on the need to implement these policies vary widely. Tailored health messages may be needed for some population segments given divergent views on COVID-19 restriction policies.What Strategies Are Hospitals Adopting to Address the Opioid Epidemic? Evidence From a National Sample of Nonprofit Hospitals
AbstractFranz, B., Cronin, C. E., & Pagan, J. A. (n.d.).Publication year
2021Journal title
Public Health ReportsVolume
136Issue
2Page(s)
228-238AbstractObjectives: Hospitals are on the front lines of the opioid epidemic, seeing patients who overdose or have complicated infections, but the extent of services offered or whether services are evidence-based is not known. The objective of our study was to assess the extent to which nonprofit hospitals are addressing opioid abuse, a critical public health issue, through their community benefit work and to identify which evidence-based strategies they adopt. Methods: We reviewed community benefit documents from January 1, 2015, through December 31, 2018, for a sample (N = 446) of all nonprofit hospitals in the United States. We classified hospital opioid-related strategies into 9 categories. Using logistic regression, we predicted the likelihood of hospitals adopting various strategies to address opioid abuse. Results: Of the 446 nonprofit hospitals in our sample, 49.1% (n = 219) adopted ≥1 clinical strategy to address opioid use disorder in their community. Approximately one-quarter (26.5%; n = 118) of hospitals adopted a strategy related to treatment services for substance use disorder; 28.2% (n = 126) had ≥1 program focused on connecting patients to a primary care medical home, and 14.6% (n = 65) focused on caring for patients with opioid-related overdoses in the emergency department. We also identified factors that predicted involvement in programs that were less common than clinical strategies, but potentially effective, such as harm reduction and prescriber initiatives (both 6.3% of hospitals). Conclusions: Evidence-based prevention and treatment require strong collaboration between health care and community institutions at all levels. Effective policy interventions may exist to encourage various types and sizes of nonprofit hospitals to adopt evidence-based interventions to address opioid abuse in their communities.“Hey, We Can Do This Together”: Findings from an Evaluation of a Multi-sectoral Community Coalition
AbstractRealmuto, L., Weiss, L., Masseo, P., Madondo, K., Kumar, R., Beane, S., & Pagán, J. A. (n.d.).Publication year
2021Journal title
Journal of Urban HealthVolume
98Issue
5Page(s)
687-694AbstractMulti-sectoral coalitions focused on systemic health inequities are commonly promoted as important mechanisms to facilitate changes with lasting impacts on population health. However, the development and implementation of such initiatives present significant challenges, and evaluation results are commonly inconclusive. In an effort to add to the evidence base, we conducted a mixed-methods evaluation of the Claremont Healthy Village Initiative, a multi-sectoral partnership based in the Bronx, New York City. At an organizational level, there were positive outcomes with respect to expanded services, increased access to resources for programs, improved linkages, better coordination, and empowerment of local leaders—all consistent with a systemic, community building approach to change. Direct impacts on community members were more difficult to assess: perceived access to health and other services improved, while community violence and poor sanitation, which were also priorities for community members, remained important challenges. Findings suggest significant progress, as well as continued need.Addressing practical issues of predictive models translation into everyday practice and public health management: A combined model to predict the risk of type 2 diabetes improves incidence prediction and reduces the prevalence of missing risk predictions
AbstractVettoretti, M., Longato, E., Zandonà, A., Li, Y., Pagán, J. A., Siscovick, D., Carnethon, M. R., Bertoni, A. G., Facchinetti, A., & Di Camillo, B. (n.d.).Publication year
2020Journal title
BMJ Open Diabetes Research and CareVolume
8Issue
1AbstractIntroduction Many predictive models for incident type 2 diabetes (T2D) exist, but these models are not used frequently for public health management. Barriers to their application include (1) the problem of model choice (some models are applicable only to certain ethnic groups), (2) missing input variables, and (3) the lack of calibration. While (1) and (2) drives to missing predictions, (3) causes inaccurate incidence predictions. In this paper, a combined T2D risk model for public health management that addresses these three issues is developed. Research design and methods The combined T2D risk model combines eight existing predictive models by weighted average to overcome the problem of missing incidence predictions. Moreover, the combined model implements a simple recalibration strategy in which the risk scores are rescaled based on the T2D incidence in the target population. The performance of the combined model was compared with that of the eight existing models using data from two test datasets extracted from the Multi-Ethnic Study of Atherosclerosis (MESA; n=1031) and the English Longitudinal Study of Ageing (ELSA; n=4820). Metrics of discrimination, calibration, and missing incidence predictions were used for the assessment. Results The combined T2D model performed well in terms of both discrimination (concordance index: 0.83 on MESA; 0.77 on ELSA) and calibration (expected to observed event ratio: 1.00 on MESA; 1.17 on ELSA), similarly to the best-performing existing models. However, while the existing models yielded a large percentage of missing predictions (17%-45% on MESA; 63%-64% on ELSA), this was negligible with the combined model (0% on MESA, 4% on ELSA). Conclusions Leveraging on existing literature T2D predictive models, a simple approach based on risk score rescaling and averaging was shown to provide accurate and robust incidence predictions, overcoming the problem of recalibration and missing predictions in practical application of predictive models.Assessment of Changes in Rural and Urban Primary Care Workforce in the United States from 2009 to 2017
AbstractZhang, D., Son, H., Shen, Y., Chen, Z., Rajbhandari-Thapa, J., Li, Y., Eom, H., Bu, D., Mu, L., Li, G., & Pagán, J. A. (n.d.).Publication year
2020Journal title
JAMA network openVolume
3Issue
10AbstractImportance: Access to primary care clinicians, including primary care physicians and nonphysician clinicians (nurse practitioners and physician assistants) is necessary to improving population health. However, rural-urban trends in primary care access in the US are not well studied. Objective: To assess the rural-urban trends in the primary care workforce from 2009 to 2017 across all counties in the US. Design, Setting, and Participants: In this cross-sectional study of US counties, county rural-urban status was defined according to the national rural-urban classification scheme for counties used by the National Center for Health Statistics at the Centers for Disease Control and Prevention. Trends in the county-level distribution of primary care clinicians from 2009 to 2017 were examined. Data were analyzed from November 12, 2019, to February 10, 2020. Main Outcomes and Measures: Density of primary care clinicians measured as the number of primary care physicians, nurse practitioners, and physician assistants per 3500 population in each county. The average annual percentage change (APC) of the means of the density of primary care clinicians over time was calculated, and generalized estimating equations were used to adjust for county-level sociodemographic variables obtained from the American Community Survey. Results: The study included data from 3143 US counties (1167 [37%] urban and 1976 [63%] rural). The number of primary care clinicians per 3500 people increased significantly in rural counties (2009 median density: 2.04; interquartile range [IQR], 1.43-2.76; and 2017 median density: 2.29; IQR, 1.57-3.23; P <.001) and urban counties (2009 median density: 2.26; IQR. 1.52-3.23; and 2017 median density: 2.66; IQR, 1.72-4.02; P <.001). The APC of the mean density of primary care physicians in rural counties was 1.70% (95% CI, 0.84%-2.57%), nurse practitioners was 8.37% (95% CI, 7.11%-9.63%), and physician assistants was 5.14% (95% CI, 3.91%-6.37%); the APC of the mean density of primary care physicians in urban counties was 2.40% (95% CI, 1.19%-3.61%), nurse practitioners was 8.64% (95% CI, 7.72%-9.55%), and physician assistants was 6.42% (95% CI, 5.34%-7.50%). Results from the generalized estimating equations model showed that the density of primary care clinicians in urban counties increased faster than in rural counties (β = 0.04; 95% CI, 0.03 to 0.05; P <.001). Conclusions and Relevance: Although the density of primary care clinicians increased in both rural and urban counties during the 2009-2017 period, the increase was more pronounced in urban than in rural counties. Closing rural-urban gaps in access to primary care clinicians may require increasingly intensive efforts targeting rural areas.Comparison of Use of Health Care Services and Spending for Unauthorized Immigrants vs Authorized Immigrants or US Citizens Using a Machine Learning Model
AbstractWilson, F. A., Zallman, L., Pagán, J. A., Ortega, A. N., Wang, Y., Tatar, M., & Stimpson, J. P. (n.d.).Publication year
2020Journal title
JAMA network openVolume
3Issue
12AbstractImportance: Knowledge about use of health care services (health care utilization) and expenditures among unauthorized immigrant populations is uncertain because of limitations in ascertaining legal status in population data. Objective: To examine health care utilization and expenditures that are attributable to unauthorized and authorized immigrants vs US-born individuals. Design, Setting, and Participants: This cross-sectional study used the data on documentation status from the Los Angeles Family and Neighborhood Survey (LAFANS) to develop a random forest classifier machine learning model. K-fold cross-validation was used to test model performance. The LAFANS is a randomized, multilevel, in-person survey of households residing in Los Angeles County, California, consisting of 2 waves. Wave 1 began in April 2000 and ended in January 2002, and wave 2 began in August 2006 and ended in December 2008. The machine learning model was then applied to a nationally representative database, the 2016-2017 Medical Expenditure Panel Survey (MEPS), to predict health care expenditures and utilization among unauthorized and authorized immigrants and US-born individuals. A generalized linear model analyzed health care expenditures. Logistic regression modeling estimated dichotomous use of emergency department (ED), inpatient, outpatient, and office-based physician visits by immigrant groups with adjusting for confounding factors. Data were analyzed from May 1, 2019, to October 14, 2020. Exposures: Self-reported immigration status (US-born, authorized, and unauthorized status). Main Outcomes and Measures: Annual health care expenditures per capita and use of ED, outpatient, inpatient, and office-based physician care. Results: Of 47199 MEPS respondents with nonmissing data, 35079 (74.3%) were US born, 10816 (22.9%) were authorized immigrants, and 1304 (2.8%) were unauthorized immigrants (51.7% female; mean age, 47.6 [95% CI, 47.4-47.8] years). Compared with authorized immigrants and US-born individuals, unauthorized immigrants were more likely to be aged 18 to 44 years (80.8%), Latino (96.3%), and Spanish speaking (95.2%) and to have less than 12 years of education (53.7%). Half of unauthorized immigrants (47.1%) were uninsured compared with 15.9% of authorized immigrants and 6.0% of US-born individuals. Mean annual health care expenditures per person were $1629 (95% CI, $1330-$1928) for unauthorized immigrants, $3795 (95% CI, $3555-$4035) for authorized immigrants, and $6088 (95% CI, $5935-$6242) for US-born individuals. Conclusions and Relevance: Contrary to much political discourse in the US, this cross-sectional study found no evidence that unauthorized immigrants are a substantial economic burden on safety net facilities such as EDs. This study illustrates the value of machine learning in the study of unauthorized immigrants using large-scale, secondary databases..Connecting healthcare professionals in Central America through management and leadership development: A social network analysis
AbstractPrado, A. M., Pearson, A. A., Bertelsen, N. S., & Pagán, J. A. (n.d.).Publication year
2020Journal title
Globalization and HealthVolume
16Issue
1AbstractBackground: Leadership and management training has become increasingly important in the education of health care professionals. Previous research has shown the benefits that a network provides to its members, such as access to resources and information, but ideas for creating these networks vary. This study used social network analysis to explore the interactions among Central American Healthcare Initiative (CAHI) Fellowship alumni and learn more about information sharing, mentoring, and project development activities among alumni. The CAHI Fellowship provides leadership and management training for multidisciplinary healthcare professionals to reduce health inequities in the region. Access to a network was previously reported as one of the top benefits of the program. Results: Information shared from the work of 100 CAHI fellows from six countries, especially within the same country, was analyzed. Mentoring relationships clustered around professions and project types, and networks of joint projects clustered by country. Mentorship, which CAHI management promoted, and joint project networks, in which members voluntarily engaged, had similar inclusiveness ratios. Conclusion: Social networks are strategic tools for health care leadership development programs to increase their impact by promoting interactions among participants. These programs can amplify intergenerational and intercountry ties by organizing events, provide opportunities for alumni to meet, assign mentors, and support collaborative action groups. Collaborative networks have great value to potentiate health professionals' leadership and management capabilities in a resource-constrained setting, such as the Global South.Sleep duration and health care expenditures in the United States
AbstractJasani, F. S., Seixas, A. A., Madondo, K., Li, Y., Jean-Louis, G., & Pagán, J. A. (n.d.).Publication year
2020Journal title
Medical careVolume
58Issue
9Page(s)
770-777AbstractObjective:To estimate the average incremental health care expenditures associated with habitual long and short duration of sleep as compared with healthy/average sleep duration.Data Source:Medical Expenditure Panel Survey data (2012; N=6476) linked to the 2010-2011 National Health Interview Survey.Study Design:Annual differences in health care expenditures are estimated for habitual long and short duration sleepers as compared with average duration sleepers using 2-part logit generalized linear regression models.Principal Findings:Habitual short duration sleepers reported an additional $1400 in total unadjusted health care expenditures compared to people with average sleep duration (P<0.01). After adjusting for demographics, socioeconomic factors, and health behavior factors, this difference remained significant with an additional $1278 in total health care expenditures over average duration sleepers (P<0.05). Long duration sleepers reported even higher, $2994 additional health care expenditures over average duration sleepers. This difference in health care expenditures remained significantly high ($1500, P<0.01) in the adjusted model. Expenditure differences are more pronounced for inpatient hospitalization, office expenses, prescription expenses, and home health care expenditures.Conclusions:Habitual short and long sleep duration is associated with higher health care expenditures, which is consistent with the association between unhealthy sleep duration and poorer health outcomes.Timely postpartum visits for low-income women: A health system and medicaid payer partnership
Trends and sociodemographic disparities in sugary drink consumption among adults in New York City, 2009–2017
AbstractJiang, N., Yi, S. S., Russo, R., Bu, D. D., Zhang, D., Ferket, B., Zhang, F. F., Pagán, J. A., Wang, Y. C., & Li, Y. (n.d.).Publication year
2020Journal title
Preventive Medicine ReportsVolume
19AbstractDespite efforts to decrease sugary drink consumption, sugary drinks remain the largest single source of added sugars in diets in the United States. This study aimed to examine trends in sugary drink consumption among adults in New York City (NYC) over the past decade by key sociodemographic factors. We used data from the 2009–2017 NYC Community Health Survey to examine trends in sugary drink consumption overall, and across different age, gender, and racial/ethnic subgroups. We conducted a test of trend to examine the significance of change in mean sugary drink consumption over time. We also conducted multiple zero-inflated negative binomial regression to identify the association between different sociodemographic and neighborhood factors and sugary drink consumption. Sugary drink consumption decreased from 2009 to 2014 from 0.97 to 0.69 servings per day (p < 0.001), but then plateaued from 2014 to 2017 (p = 0.01). Although decreases were observed across all age, gender and racial/ethnic subgroups, the largest decreases over this time period were observed among 18–24 year old (1.75 to 1.22 servings per day, p < 0.001); men (1.12 to 0.86 servings per day, p < 0.001); Blacks (1.45 to 1.14 servings per day, p < 0.001); and Hispanics (1.26 to 0.86 servings per day, p < 0.001). Despite these decreases, actual mean consumption remains highest in these same sociodemographic subgroups. Although overall sugary drink consumption has been declining, the decline has slowed in more recent years. Further, certain age, gender and racial/ethnic groups still consume disproportionately more sugary drinks than others. More research is needed to understand and address the root causes of disparities in sugary drink consumption.Why Are Some US Nonprofit Hospitals Not Addressing Opioid Misuse in Their Communities?
AbstractCronin, C. E., Franz, B., & Pagán, J. A. (n.d.).Publication year
2020Journal title
Population Health ManagementVolume
23Issue
6Page(s)
407-413AbstractThe US opioid epidemic is national in scope, but many local solutions have been shown to have efficacy. Many nonprofit hospitals have the resources and infrastructure to lead these community-based efforts, but there is evidence that some organizations are not adopting opioid services as part of their community benefit requirements to assess and address critical community health needs. This paper assesses why hospitals do not address opioid abuse after completing a community health needs assessment. For a 20% random sample of nonprofit hospitals, a unique data set was constructed of hospital efforts to address opioid abuse using the most recent publicly available community health needs assessments and implementation strategies adopted by hospitals (calendar years 2015, 2016, 2017, or 2018). Multinomial logistic regression was used to assess the relationship between 5 different reasons hospitals cited for not addressing opioid abuse and both hospital and community characteristics. Results indicate that opioid abuse was not addressed by 32% (143) of hospitals in their formal implementation strategies. State community benefit laws, county overdose level, county poverty rate, hospital region, and hospital system membership all were significantly related to the reasons hospitals cited for not addressing opioid abuse as part of their community health engagement. Hospitals in communities with significant substance abuse needs and few institutional resources may need support to address opioid misuse and adopt treatment and harm reduction initiatives. Policies that support hospital-public health partnerships may be especially important to assist hospitals to address nonmedical or behavioral health needs in their communities.