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To estimate changes in public mask-wearing behavior in response to public health policies during COVID-19.Panel of observed public mask-wearing.Counts of adult behavior in Marion County, Indiana, between November 15, 2020, and May 31, 2021.(1) Removal of state masking requirement; (2) introduction of the National Strategy for the COVID-19 Response and Pandemic Preparedness; (3) the Centers for Disease Control and Prevention (CDC) recommendation that vaccinated individuals did not need to wear masks in public; and (4) COVID-19 vaccine availability.Percent observed with correct mask-wearing.Fixed-effects models estimated the association between policies and mask-wearing.Ending Indiana's mask requirement was not associated with changes in correct mask-wearing. The CDC's recommendation was associated with a decrease of 12.3 percentage points in correct mask-wearing (95% CI, -23.47 to -1.05; P = .032).Behavior encouraged by local mask requirements appeared to be resilient to changes in state policy. CDC recommendations appeared influential.

BACKGROUND Hospitals are increasingly engaging in partnerships to address population health in response to national policies, such as value-based payment models. However, little is known about how institutional factors influence hospital partnerships for population health. PURPOSE Guided by institutional theory, we examine the association between institutional pressures (coercive, normative, and mimetic isomorphism) and hospital partnerships for population health. METHODOLOGY A pooled cross-sectional analysis used an unbalanced panel of 10,777 hospital-year observations representing respondents to a supplemental question of the American Hospital Association's annual survey (2015-2017). The analysis included descriptive and bivariate statistics, and regression models that adjusted for repeated observations to examine the relationship between key independent variables and partnerships over time. FINDINGS In regression analyses, we found the most support for measures of coercive (e.g., regulatory factors) isomorphism, with nonprofit status, participation in accountable care organizations, and acceptance of bundled payments, all being consistently and significantly associated with partnerships across all organization types. Modest increases were observed from 2015 to 2017 for hospital partnerships with public health organizations (+2.8% points, p < .001), governmental organizations (+2.0% points, p = .009), schools (+4.1% points, p < .001), and businesses (+2.2% points, p = .007). PRACTICE IMPLICATIONS Our results suggest that institutional factors, particularly those related to regulatory policies and programs, may influence hospital partnerships to support population health. Findings from this study can assist hospital leaders in assessing the factors that can support or impede the creation of partnerships to support their population health efforts.

Introduction Low-income populations have higher rates of smoking and are disproportionately affected by smoking-related illnesses. This study assessed the long-term impact of increased coverage for tobacco cessation through Medicaid expansion on past-year quit attempts and prevalence of cigarette smoking. Methods Using data from CDC's annual Behavioral Risk Factor Surveillance System 2011-2019, we conducted difference-in-difference regression analyses to compare changes in smoking prevalence and past-year quit attempts in expansion states versus non-expansion states. Our sample included non-pregnant adults (18-64 years old) without dependent children with incomes at or below 100% of the Federal Poverty Level (FPL). Results Regression analyses indicate that Medicaid expansion was associated with reduced smoking prevalence in the first two years post-expansion (β=-0.019, p=0.04), but that this effect was not maintained at longer follow-up periods (β=-0.006, p=0.49). Results of regression analyses also suggest that Medicaid expansion does not significantly impact quit attempts in the short-term (β=-0.013, p=0.52) or at longer term follow-up (β=-0.026, p=0.08). Conclusions Expanded coverage for tobacco cessation services through Medicaid alone may not be enough to increase quit-attempts or sustain a reduction in overall prevalence of smoking in newly eligible populations over time. Medicaid programs should consider additional strategies, such as public education campaigns and removal of barriers, to support cessation among enrollees.

The U.S. healthcare system continues to experience high costs and suboptimal health outcomes that are largely influenced by social determinants of health. National policies such as the Affordable Care Act and value-based payment reforms incentivize healthcare systems to engage in strategies to improve population health. Healthcare systems are increasingly expanding or developing new partnerships with community-based organizations to support these efforts. We conducted a systematic review of peer-reviewed literature in the United States to identify examples of hospital–community partnerships; the main purposes or goals of partnerships; study designs used to assess partnerships; and potential outcomes (e.g., process- or health-related) associated with partnerships. Using robust keyword searches and a thorough reference review, we identified 37 articles published between January 2008 and December 2019 for inclusion. Most studies employed descriptive study designs (n = 21); health needs assessments were the most common partnership focus (n = 15); and community/social service (n = 21) and public health organizations (n = 15) were the most common partner types. Qualitative findings suggest hospital–community partnerships hold promise for breaking down silos, improving communication across sectors, and ensuring appropriate interventions for specific populations. Few studies in this review reported quantitative findings. In those that did, results were mixed, with the strongest support for improvements in measures of hospitalizations. This review provides an initial synthesis of hospital partnerships to address population health and presents valuable insights to hospital administrators, particularly those leading population health efforts.

CONTEXT: Existing hospitalization ratios for COVID-19 typically use case counts in the denominator, which problematically underestimates total infections because asymptomatic and mildly infected persons rarely get tested. As a result, surge models that rely on case counts to forecast hospital demand may be inaccurately influencing policy and decision-maker action. OBJECTIVE: Based on SARS-CoV-2 prevalence data derived from a statewide random sample (as opposed to relying on reported case counts), we determine the infection-hospitalization ratio (IHR), defined as the percentage of infected individuals who are hospitalized, for various demographic groups in Indiana. Furthermore, for comparison, we show the extent to which case-based hospitalization ratios, compared with the IHR, overestimate the probability of hospitalization by demographic group. DESIGN: Secondary analysis of statewide prevalence data from Indiana, COVID-19 hospitalization data extracted from a statewide health information exchange, and all reported COVID-19 cases to the state health department. SETTING: State of Indiana as of April 30, 2020. MAIN OUTCOME MEASURES: Demographic-stratified IHRs and case-hospitalization ratios. RESULTS: The overall IHR was 2.1% and varied more by age than by race or sex. Infection-hospitalization ratio estimates ranged from 0.4% for those younger than 40 years to 9.2% for those older than 60 years. Hospitalization rates based on case counts overestimated the IHR by a factor of 10, but this overestimation differed by demographic groups, especially age. CONCLUSIONS: In this first study of the IHR based on population prevalence, our results can improve forecasting models of hospital demand-especially in preparation for the upcoming winter period when an increase in SARS CoV-2 infections is expected.

Nonresponse bias occurs when participants in a study differ from eligible nonparticipants in ways that can distort study conclusions. The current study uses successive wave analysis, an established but underutilized approach, to assess nonresponse bias in a large-scale SARS-CoV-2 prevalence study. Such an approach makes use of reminders to induce participation among individuals. Based on the response continuum theory, those requiring several reminders to participate are more like nonrespondents than those who participate in a study upon first invitation, thus allowing for an examination of factors affecting participation.Study participants from the Indiana Population Prevalence SARS-CoV-2 Study were divided into 3 groups (eg, waves) based upon the number of reminders that were needed to induce participation. Independent variables were then used to determine whether key demographic characteristics as well as other variables hypothesized to influence study participation differed by wave using chi-square analyses. Specifically, we examined whether race, age, gender, education level, health status, tobacco behaviors, COVID-19-related symptoms, reasons for participating in the study, and SARS-CoV-2 positivity rates differed by wave.Respondents included 3658 individuals, including 1495 in wave 1 (40.9%), 1246 in wave 2 (34.1%), and 917 in wave 3 (25%), for an overall participation rate of 23.6%. No significant differences in any examined variables were observed across waves, suggesting similar characteristics among those needing additional reminders compared with early participants.Using established techniques, we found no evidence of nonresponse bias in a random sample with a relatively low response rate. A hypothetical additional wave of participants would be unlikely to change original study conclusions. Successive wave analysis is an effective and easy tool that can allow public health researchers to assess, and possibly adjust for, nonresponse in any epidemiological survey that uses reminders to encourage participation.

Encouraged by multiple federal policies, healthcare organizations are assuming greater responsibility for patients' social needs. This study describes the individual and co-occurring social needs t...

Population prevalence of persons infected with SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), varies by subpopulation and locality. U.S. studies of SARS-CoV-2 infection have examined infections in nonrandom samples (1) or seroprevalence in specific populations* (2), which are limited in their generalizability and cannot be used to accurately calculate infection-fatality rates. During April 25-29, 2020, Indiana conducted statewide random sample testing of persons aged ≥12 years to assess prevalence of active infection and presence of antibodies to SARS-CoV-2; additional nonrandom sampling was conducted in racial and ethnic minority communities to better understand the impact of the virus in certain racial and ethnic minority populations. Estimates were adjusted for nonresponse to reflect state demographics using an iterative proportional fitting method. Among 3,658 noninstitutionalized participants in the random sample survey, the estimated statewide point prevalence of active SARS-CoV-2 infection confirmed by reverse transcription-polymerase chain reaction (RT-PCR) testing was 1.74% (95% confidence interval [CI] = 1.10-2.54); 44.2% of these persons reported no symptoms during the 2 weeks before testing. The prevalence of immunoglobulin G (IgG) seropositivity, indicating past infection, was 1.09% (95% CI = 0.76-1.45). The overall prevalence of current and previous infections of SARS-CoV-2 in Indiana was 2.79% (95% CI = 2.02-3.70). In the random sample, higher overall prevalences were observed among Hispanics and those who reported having a household contact who had previously been told by a health care provider that they had COVID-19. By late April, an estimated 187,802 Indiana residents were currently or previously infected with SARS-CoV-2 (9.6 times higher than the number of confirmed cases [17,792]) (3), and 1,099 residents died (infection-fatality ratio = 0.58%). The number of reported cases represents only a fraction of the estimated total number of infections. Given the large number of persons who remain susceptible in Indiana, adherence to evidence-based public health mitigation and containment measures (e.g., social distancing, consistent and correct use of face coverings, and hand hygiene) is needed to reduce surge in hospitalizations and prevent morbidity and mortality from COVID-19.

CONTEXT State health officials (SHOs), the executive and administrative leaders of state public health, play a key role in policy development, must be versed in the relevant/current evidence, and provide expertise about health issues to the legislature and the governor. OBJECTIVE To provide an empirical examination of SHO backgrounds and qualifications over time. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional survey of current/former SHOs. MAIN OUTCOME MEASURES State health official educational backgrounds; public health experience; previous employment setting. RESULTS Two-thirds of respondents (64.6%) reported having a medical degree, approximately half (48.3%) a formal public health degree, and almost one-quarter (21.8%) a management degree. The majority had governmental public health experience at some prior point in their career (70.0%). Almost two-thirds worked in governmental public health immediately before becoming an SHO. The proportion that was female increased significantly by decade from 5.6% in the 1970s/80s to 46.4% in the 2010s (P = .02). CONCLUSIONS The main finding from this study shows that more than two-thirds of SHOs have had governmental public health experience at some point in their career. This is not a new trend as there were no statistical differences in public health experience by decade. More than half of the SHOs were appointed to the role directly from governmental public health, indicating that their public health experience is timely and likely germane to their appointment as SHO. Findings also indicate improvements in gender diversity among one of the most influential leadership roles in governmental public health whereas significant changes in racial and ethnic diversity were not identified. Women are increasingly being appointed as SHOs, indicating increasing gender diversity in this influential position. Given that governmental public health employees are predominantly women, there is still room for gender equity improvements in executive leadership roles. This is coupled with the need for further racial and ethnic diversity improvements as well.

Background Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or nicotine consumption is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. Methods Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n = 8214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in either April or June of 2020. Before testing, participants provided demographic information and self-reported health status and nicotine and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco or nicotine with SARS-CoV-2 positivity on either PCR or antibody tests. Results Compared to people with self-identified “excellent” or very good health status, those reporting “good” or “fair” health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. Conclusions In a statewide, community-based population drawn for SARS-CoV-2 testing, we find that overall health status was associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.

Objective: The objective of this paper is to provide empirical guidance by comparing the performance of six different area-level SDoH measurement approaches in predicting patient referral to a social worker and hospital admission after a primary care visit.Methods: We compared the performance of six area-level SDoH measurement approaches in predicting patient referral to a social worker and hospital admission after a primary care visit using random forest classification algorithm. Data came from 209,605 patient encounters at a federally qualified health center. Models with each area-based measurement approach were compared against the patient-level data only model using area under the curve, sensitivity, specificity, and precision.Results: Addition of area-level features to patient-level data improved the overall performance of models predicting need for a social worker referral. Entering area-level measures as individual features resulted in highest model performance.Conclusion: Researchers seeking to include area-level SDoH measures in risk prediction may be able to forego more complex measurement approaches.

Introduction Social determinants of health are critical drivers of health status and cost, but are infrequently screened or addressed in primary care settings. Systematic approaches to identifying individuals with unmet social determinants needs could better support practice workflows and linkages of patients to services. A pilot study examined the effect of a risk-stratification tool on referrals to services that address social determinants in an urban safety-net population. Methods An intervention that risk stratified patients according to the need for wraparound was evaluated in a stepped wedge design (i.e., phased implementation at the clinic level during 2017). Staff at nine federally qualified health centers received a daily report predicting patients’ needs for social worker, dietitian, behavioral health, and other wraparound services (categorized as low, rising, or high risk). Outcomes included referrals and uptake of appointments to wraparound services. Results Among 238,087 encounters, providing clinic staff with risk-stratification scores increased the odds that a patient would be referred to a social worker. For patients categorized as high risk, the odds of a social work referral was 65% higher than controls and similar patients, but lower effect sizes were observed for individuals categorized with rising and low risk. Among referred patients, the intervention was generally associated with increased odds of kept appointments. Conclusions This study provided preliminary evidence that risk-stratification interventions to identify patients in need of wraparound services to address social determinants can increase referrals and uptake of services that may address social drivers of disease burden.

Significance Infection with the novel coronovirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a worldwide pandemic of COVID-19 disease. Efforts to design local, regional, and national responses to the virus are constrained by a lack of information on the extent of the epidemic as well as inaccuracies in newly developed diagnostic tests. In this study we analyze data from testing randomly selected Indiana state residents for infection or previous exposure to SARS-CoV-2 and derive estimates of the statewide COVID-19 prevalence in an attempt to address potential biases arising from nonresponse and diagnostic testing errors., From 25 to 29 April 2020, the state of Indiana undertook testing of 3,658 randomly chosen state residents for the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, the agent causing COVID-19 disease. This was the first statewide randomized study of COVID-19 testing in the United States. Both PCR and serological tests were administered to all study participants. This paper describes statistical methods used to address nonresponse among various demographic groups and to adjust for testing errors to reduce bias in the estimates of the overall disease prevalence in Indiana. These adjustments were implemented through Bayesian methods, which incorporated all available information on disease prevalence and test performance, along with external data obtained from census of the Indiana statewide population. Both adjustments appeared to have significant impact on the unadjusted estimates, mainly due to upweighting data in study participants of non-White races and Hispanic ethnicity and anticipated false-positive and false-negative test results among both the PCR and antibody tests utilized in the study.

Objectives To estimate the prevalence of public mask-wearing in various settings through direct observation. Self-report mask-wearing is susceptible to recall bias and a desire by respondents to appear socially responsible. Direct observation of public behavior does not face such limitations and can provide differentiation between correct and incorrect mask-wearing. Methods Trained observers recorded public behavior between 11/01/2020-12/31/2020 at 37 sites across Marion County, Indiana. For each individual observed, data collection staff recorded a status of “masked,” “non-masked” or “partial mask.” “Masked” was defined as the presence of any cloth face covering or surgical mask that covered the mouth and nose, including the nostrils, and extended below the chin. “Non-masked” was defined as the complete absence of a face covering, a face covering that was worn only below the chin, a face covering held in a hand, a face covering hanging off an ear, or a face shield only. “Partial mask” was defined as the face covering only over the mouth or over the nose, but not both, or the nose being partially covered with the nostrils somewhat or fully visible. Results 80% of adults wore face masks correctly and an additional 9% were partially masked. The prevalence of mask-wearing was lowest in outdoor spaces (54%) as well as in small retail stores (58%). Higher rates of mask usage were observed at large organizations like grocery stores and “big box” retailers (84%), shopping malls (85%), civic and government sites (86%), or higher education settings (88%). Mask-wearing was more common among females (84%) than males (76%); males had a higher percentage of partial mask-wearing. Conclusions While public health professionals and agencies encourage universal mask-wearing in public settings, observed levels may still be sufficient to reduce the spread of COVID-19.

BackgroundPrior studies examining symptoms of COVID-19 are primarily descriptive and measured among hospitalized individuals. Understanding symptoms of SARS-CoV-2 infection in pre-clinical, community-based populations may improve clinical screening, particularly during flu season. We sought to identify key symptoms and symptom combinations in a community-based population using robust methods.MethodsWe pooled community-based cohorts of individuals aged 12 and older screened for SARS-CoV-2 infection in April and June 2020 for a statewide seroprevalence study. Main outcome was SARS-CoV-2 positivity. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for individual symptoms as well as symptom combinations. We further employed multivariable logistic regression and exploratory factor analysis (EFA) to examine symptoms and combinations associated with SARS-CoV-2 infection.ResultsAmong 8214 individuals screened, 368 individuals (4.5%) were RT-PCR positive for SARS-CoV-2. Although two-thirds of symptoms were highly specific (>90.0%), most symptoms individually possessed a PPV ConclusionsSymptoms can help distinguish SARS-CoV-2 infection from other respiratory viruses, especially in community or urgent care settings where rapid testing may be limited. Symptoms should further be structured in clinical documentation to support identification of new cases and mitigation of disease spread by public health. These symptoms, derived from asymptomatic as well as mildly infected individuals, can also inform vaccine and therapeutic clinical trials.Research in ContextEvidence before this studyUsing multiple journal articles queried from MEDLINE as well as a Cochrane systematic review, we examined all studies that described symptoms known to be associated with COVID-19. We further examined the guidelines from WHO and CDC on the symptoms those public health authorities consider to be associated with COVID-19. Most of the evidence comes from China, Italy, and the United States. Collectively prior research and guidance suggests there are a dozen symptoms reported by individuals who tested positive for COVID-19 in multiple countries. Symptoms include fever, cough, fatigue, anosmia, ageusia, shortness of breath, chills, myalgias, headache, sore throat, chest pain, and gastrointestinal issues. The evidence is generally of low quality as it is descriptive in nature, and it is biased towards hospitalized patients. Most studies report the proportion of patients hospitalized or testing positive for infection who report one or more symptoms within 3-14 days prior to hospitalization or infection. There has been little validation of symptoms among hospitalized or non-hospitalized patients. Furthermore, according to a Cochrane review, no studies to date assess combinations of different signs and symptoms.Added value of this studyThis study employs multiple, rigorous methods to examine the ability of specific symptoms as well as symptom combinations/groups to predict laboratory-confirmed (RT-PCR) infection of SARS-CoV-2. Furthermore, the study is unique in its large sample drawn exclusively from community-based populations rather than hospitalized patients.Implication of all the available evidenceCombining the evidence from this study with prior research suggests that anosmia and ageusia are key symptoms that differentiate COVID-19 from influenza-like symptoms. Clinical screening protocols for COVID-19 should look for these symptoms, which are not commonly asked of patients who present to urgent care or hospital with flu-like symptoms.Key pointsImportant symptoms specific to COVID-19 are fever, anosmia, ageusia, and cough. Two-thirds of symptoms were highly specific (>90.0%), yet most symptoms individually possessed a PPV

Background Much of what is known about COVID-19 risk factors comes from patients with serious symptoms who test positive. While risk factors for hospitalization or death include chronic conditions and smoking; less is known about how health status or tobacco use is associated with risk of SARS-CoV-2 infection among individuals who do not present clinically. Methods Two community-based population samples (including individuals randomly and nonrandomly selected for statewide testing, n= 8,214) underwent SARS-CoV-2 testing in nonclinical settings. Each participant was tested for current (viral PCR) and past (antibody) infection in April or June of 2020. Before testing, participants provided demographic information and self-reported health status and tobacco behaviors (smoking, chewing, vaping/e-cigarettes). Using descriptive statistics and a bivariate logistic regression model, we examined the association between health status and use of tobacco with SARS-CoV-2 positivity on either PCR or antibody tests.Results Compared to people with self-identified “excellent” or very good health status, those reporting “good” or “fair” health status had a higher risk of past or current infections. Positive smoking status was inversely associated with SARS-CoV-2 infection. Chewing tobacco was associated with infection and the use of vaping/e-cigarettes was not associated with infection. Conclusions In a statewide, community-based population drawn for seroprevalence studies, we find that overall health status is associated with infection rates. Unlike in studies of COVID-19 patients, smoking status was inversely associated with SARS-CoV-2 positivity. More research is needed to further understand the nature of this relationship.

Background Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited. Objective The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services. Methods We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models. Results Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score P Conclusions Precision health–enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance.

OBJECTIVES Previous surveys of public health graduates examine where they work; however, little is known about public health graduates' employment decisions or the factors that facilitate interest or deter interest in working in governmental public health settings. The purpose of the current pilot study was to build on the information previously collected in graduate surveys by expanding questions to undergraduates and asking about decisions and factors that influence choices of employment. METHODS A pilot survey of graduates of public health programs was conducted. Respondents provided information about their degree programs, year of graduation, and current employment. Questions asked where they applied for jobs, factors they considered, experiences with the application processes, and so forth. Descriptive statistics were calculated using frequencies and proportions. Open-ended responses were qualitatively reviewed and general themes were extracted. RESULTS Employment preferences were ranked the highest for not-for-profit organizations (ranked first among 21 of 62, 33.9%), followed by governmental public health agencies (ranked first among 18 of 62, 29.0%). Among master of public health graduates, 54.7% sought employment within this setting, although only 17.0% of those employed full time at the time of the survey were employed within a governmental public health agency. Job security (84.7%), competitive benefits (82.2%), identifying with the mission of the organization (82.2%), and opportunities for training/continuing education (80.6%) were the most influential, positive factors garnering interest in working in governmental public health. Factors that were the biggest deterrents included the ability to innovate (19.2%), competitive salary (17.8%), and autonomy/employee empowerment (15.3%). CONCLUSIONS Approximately half of the respondents applied for a job within governmental public health in anticipation of or since graduating. However, only a quarter of employed respondents are currently working within governmental public health, suggesting a missed opportunity for recruiting the other quarter who applied and were interested in governmental positions.

Introduction A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital. Materials and Methods We integrated patient clinical data and community-level data representing patients’ social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only. Results Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%. Discussion The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling.

Context: State health officials (SHOs) serve a critical role as the leaders of state public health systems. Despite their many responsibilities, there is no formal process for preparation to become an SHO, and few requirements influence the selection of an SHO. Furthermore, to date, no studies have examined SHO tenure or their experiences. Objective: This study examines SHO tenure over time and the relationship between SHO tenure and organizational and state attributes. Design: This longitudinal study employed primary data on SHOs and secondary data from the Association of State and Territorial Health Officials on organizational attributes of state public health agencies. Setting: This study examines SHOs within the United States. Participants: SHOs who served in years 1980-2017. Main Outcome Measures: Annual average SHO tenure; average SHO tenure by state. Results: In the 38 years of this study, 508 individuals served as SHOs in the 50 states and the District of Columbia. The average tenure over this period was 4.1 years, with a median tenure of 2.9 years. During the study period, almost 20% of SHOs served terms of 1 year or less. A total of 32 SHOs (32/508 or 6.3%) served for 10 years or longer. Excluding SHOs who served 10 years or longer (n = 32 SHOs who had a collective 478 years of tenure) reduces the average term in office to 3.5 years. The average number of new SHOs per year is 12.3. SHOs appointed by a board of health averaged more than 8 years in office compared with averages just under 4 years for those appointed by governors or secretaries of state agencies. Conclusions: There are notable differences in SHO tenure across states. Future research is needed to further examine SHO tenure, effectiveness, job satisfaction, transitions, and the relationship between SHOs and state health. It may be valuable to expand on opportunities for new SHOs to learn from peers who have moderate to long tenures as well as SHO alumni. Given that average SHO tenure is approximately 4 years and that an SHO could be thrust into the national spotlight at a moment's notice, governors may want to consider experience over partisanship as they appoint new SHOs.

In case conferences, health care providers work together to identify and address patients’ complex social and medical needs. Public health nurses from the local health department joined case conference teams at federally qualified health center primary care sites to foster cross-sector collaboration, integration, and mutual learning. Public health nurse participation resulted in frequent referrals to local health department services, greater awareness of public health capabilities, and potential policy interventions to address social determinants of health.

BACKGROUND Emerging interest in precision health and the increasing availability of patient- and population-level data sets present considerable potential to enable analytical approaches to identify and mitigate the negative effects of social factors on health. These issues are not satisfactorily addressed in typical medical care encounters, and thus, opportunities to improve health outcomes, reduce costs, and improve coordination of care are not realized. Furthermore, methodological expertise on the use of varied patient- and population-level data sets and machine learning to predict need for supplemental services is limited. OBJECTIVE The objective of this study was to leverage a comprehensive range of clinical, behavioral, social risk, and social determinants of health factors in order to develop decision models capable of identifying patients in need of various wraparound social services. METHODS We used comprehensive patient- and population-level data sets to build decision models capable of predicting need for behavioral health, dietitian, social work, or other social service referrals within a safety-net health system using area under the receiver operating characteristic curve (AUROC), sensitivity, precision, F1 score, and specificity. We also evaluated the value of population-level social determinants of health data sets in improving machine learning performance of the models. RESULTS Decision models for each wraparound service demonstrated performance measures ranging between 59.2%% and 99.3%. These results were statistically superior to the performance measures demonstrated by our previous models which used a limited data set and whose performance measures ranged from 38.2% to 88.3% (behavioural health: F1 score PP=.01; social work: F1 score PP=.03; dietitian: F1 score P=.001, AUROC P=.001; other: F1 score P=.01, AUROC P=.02); however, inclusion of additional population-level social determinants of health did not contribute to any performance improvements (behavioural health: F1 score P=.08, AUROC P=.09; social work: F1 score P=.16, AUROC P=.09; dietitian: F1 score P=.08, AUROC P=.14; other: F1 score P=.33, AUROC P=.21) in predicting the need for referral in our population of vulnerable patients seeking care at a safety-net provider. CONCLUSIONS Precision health–enabled decision models that leverage a wide range of patient- and population-level data sets and advanced machine learning methods are capable of predicting need for various wraparound social services with good performance.

OBJECTIVE To examine characteristics associated with tenure length of State Health Officials (SHOs) and examine reasons and consequences for SHO turnover. DESIGN Surveys of current and former SHOs linked with secondary data from the United Health Foundation. SETTING Original survey responses from SHOs in the United States. PARTICIPANTS Respondents included SHOs who served between 1973 and 2017. MAIN OUTCOME MEASURES Tenure length and consequences of SHO turnover. RESULTS Average completed tenure among SHOs was 5.3 years (median = 4) and was shorter in recent time periods compared with decades prior. Older age at appointment (β = -0.109, P = .005) and those holding a management degree (β = -1.835, P = .017) and/or a law degree (β = -3.553, P < .001) were each associated with shorter SHO tenures. State Health Officials from states in the top quartile for health rankings had significantly longer average tenures (β = 1.717, P = .036). Many former SHOs believed that their tenure was too short and reported that their departure had either a significant or very large effect on their agency's ability to fulfill its mission. CONCLUSIONS State Health Official tenures have become shorter over time and continue to be shorter than industry chief executive officers and best practice recommendations from organizational researchers. States have an opportunity to consider and address how factors within their control influence the stability of the SHO position.

State health officials (SHOs) lead state governmental public health agencies, playing an important role in their states. However, little comprehensive research has examined SHOs or characteristics of these leaders, limiting evidence about ways to improve SHO selection and subsequent performance. This brief describes the methods of the SHO-CASE study focused on current and former SHOs in state public health agencies. Methods used include qualitative components that informed the development of survey questions, survey administration, and survey response. A total of 147 SHOs responded to the SHO survey representing every state and Washington, District of Columbia. The SHO-CASE study survey database represents the most comprehensive database of its kind regarding a range of attributes of current and former SHOs. These data can be used to explore factors contributing to SHO success including valuable insights into effectively working with the states' elected officials.

CONTEXT Senior deputies work closely with state health officials (SHOs) in state public health agencies and are a valuable resource for understanding their roles, responsibilities, and characteristics. OBJECTIVE Examine senior deputies' perceptions of SHO success factors. DESIGN Qualitative study including nominal group technique focus groups, a small expert focus group, and interviews. SETTING US state public health agencies. PARTICIPANTS Senior deputies in state public health agencies 2016/2017. MAIN OUTCOME MEASURES Perceptions of SHO success factors. RESULTS The most commonly perceived professional characteristics of a successful SHO included the following: credible trusted voice with internal respect/external credibility; improves public health prominence/visibility with an evidence-based agenda; and grows the agency/leaves it stronger. Perceptions of the most common personal attributes for success included excellent listening skills; credibility/honesty/trustworthiness; and public health experience/knowledge. The most commonly perceived signs of SHO derailment included when SHOs have a visible lack of support of elected officials (eg, governor/legislators) and when the SHO is "bypassed" by elected officials. CONCLUSIONS A key finding of this study centers on the relationship between the SHO and the governor; meeting the expectations of the governor was identified as a significant professional characteristic of success. Findings highlight the expectation that SHOs have a clear understanding of the governor's priorities and how to relate to the governor's office early in their tenure. This goal should be a priority for transition teams that aid new SHOs as they begin in their new roles. Study insights can help better prepare for orientation/onboarding of new SHOs. Development of key transition documents and tools for rapid onboarding should be considered. Transition teams should assist new SHOs in establishing an understanding of the governor's priorities and how to best communicate with to the governor's office early in their tenure. Strong senior management teams should be prioritized and fostered.

Recent changes to US reimbursement policies are increasingly holding providers financially accountable for patients' health. Providing nonmedical services in conjunction with primary care-known as wraparound services-is one strategy to improve patient outcomes and reduce overall health care spending. These services leverage additional providers to address patients' social determinants of health. Eskenazi Health-an Indianapolis, Indiana, safety-net provider-introduced wraparound services at its federally qualified health center sites. Behavioral health, social work, dietetics, patient navigation, and other services that address patients' social and behavioral needs are co-located with primary care services. In an eleven-year panel of primary care patients, receipt of any wraparound service was negatively associated with subsequent hospitalizations and emergency department visits. The estimated cost savings from potentially avoided hospitalizations alone was $1.4 million annually. Under value-based payment, wraparound services may be one part of a portfolio of strategies to address the social, behavioral, and environmental factors that drive poor patient health and increase costs.

INTRODUCTION: A growing variety of diverse data sources is emerging to better inform health care delivery and health outcomes. We sought to evaluate the capacity for clinical, socioeconomic, and public health data sources to predict the need for various social service referrals among patients at a safety-net hospital. MATERIALS AND METHODS: We integrated patient clinical data and community-level data representing patients’ social determinants of health (SDH) obtained from multiple sources to build random forest decision models to predict the need for any, mental health, dietitian, social work, or other SDH service referrals. To assess the impact of SDH on improving performance, we built separate decision models using clinical and SDH determinants and clinical data only. RESULTS: Decision models predicting the need for any, mental health, and dietitian referrals yielded sensitivity, specificity, and accuracy measures ranging between 60% and 75%. Specificity and accuracy scores for social work and other SDH services ranged between 67% and 77%, while sensitivity scores were between 50% and 63%. Area under the receiver operating characteristic curve values for the decision models ranged between 70% and 78%. Models for predicting the need for any services reported positive predictive values between 65% and 73%. Positive predictive values for predicting individual outcomes were below 40%. DISCUSSION: The need for various social service referrals can be predicted with considerable accuracy using a wide range of readily available clinical and community data that measure socioeconomic and public health conditions. While the use of SDH did not result in significant performance improvements, our approach represents a novel and important application of risk predictive modeling.

The United States' decade-long transition from a paper- to technology-based information infrastructure has always been recognized as an initial step-a laying of the foundation-for future changes to the delivery of care. An increasingly important focal area for improvement is population health. Numerous policies and programs now require healthcare organizations to manage the risks, outcomes, utilization, and health of entire groups of individuals. Nonetheless, current health information technology (IT) systems are not ready to support population health improvements effectively and efficiently. Existing health IT systems were designed for organizations that are structurally, operationally, and culturally focused on individual care delivery, rather than improving health for a population. Opportunities exist to align health IT resources and population health management strategies to fill the gaps among technological capabilities, use and the emerging demands of population health. To realize this alignment, healthcare leaders must think differently about the types of data their organizations need, the types of partners with whom they share information, and how they can leverage new information and partnerships for evidence-based action.

Editors’ Note: We invited a sample of AJPM’s Editorial Board, supplement guest editors, and longtime colleagues and contributors to share their experiences and impressions of the impact of AJPM on their respective domains. We were particularly fond of Ken Powell’s analogy to being “tenders of the planted seed”—as planters, weeders, and harvesters—watching and nurturing the growth of AJPM, and have elected to lead with his commentary. In the mid-1940s my brothers and I helped our father tend our victory garden. We were suburbanites and our small garden shared a vacant lot with the concrete foundation of a home abandoned in mid-construction during the Depression. We helped plant, weed, and harvest the crops. I especially liked the tall stalks of sweet corn that grew taller than I was. Many years later, in the mid-1990s, I met a couple of other gardeners: Kevin Patrick and Doug Scutchfield visited the Centers for Disease Control (now the Centers for Disease Control and Prevention) to discuss their plans as the editors of the American Journal of Preventive Medicine. I was working in a Branch that was trying to understand the public health importance of regular physical activity, and they had the foresight to know we were planting some seeds in an important new area. I’m not sure that I had even heard of AJPM before their visit. A few years later, while the field of physical activity and public health continued to grow, I shifted my focus to violence and injury prevention. Once again, the team of Patrick and Scutchfield recognized the importance of this topic to the field of preventive medicine. By the turn of the century my work had shifted from the national to the state level. I tried to keep up with the literature by reading or scanning articles as I rode the subway to and from work. The Georgia Division of Public Health had no access to a library so I circulated my personal copy of the American Journal of Preventive Medicine to my team in the Chronic Disease and Injury Epidemiology Section because it contained by far the most articles of interest and practical application for us. Isn’t it interesting to watch things grow. Gardens, journals, and the fields of public health and preventive medicine.

Correspondence avoid surgery in rapidly progressive or chemo-insensitive disease. 4 Genotyping of pancreatic tumours via fine needle aspiration could influence the clinical management of pancreatic cancer. Fine-needle aspiration sequencing was used to identify subgroups of patients with specific actionable mutations related to resectable or locally advanced tumours. 5 In patients with radiologically resectable or borderline resectable tumours, preoperative fine-needle aspiration sequencing could distinguish between patients with a genetic pattern associated with micrometastatic tumours, who should undergo neoadjuvant therapy, and those with a truly localised disease that would be amenable to a surgery-first strategy. Michele Reni has served as a consultant for or on the advisory boards of Celgene, Boehringer-Ingelheim, Lilly, Genentech, Baxalta, Novocure, Astra-Zeneca, Pfizer, and Merck-Serono, and has received honoraria from Celgene. Massimo Falconi has received honoraria from Celgene, Ipsen and Novartis. The other authors declare no competing interests. *Stefano Crippa, Michele Reni, Gianpaolo Balzano, Claudio Doglioni, Massimo Falconi crippa1.stefano@hsr.it Division of Pancreatic Surgery, IRCCS San Raffaele Hospital, Milan, Italy (SC, GB, MF); Medical Oncology Department, IRCCS San Raffaele Hospital , Milan, Italy (MR); Department of Pathology, IRCCS San Raffaele Hospital , Milan, Italy (CD); Clinical and Translational Research Program on Pancreatic Cancer, IRCCS San Raffaele Hospital, Milan, Italy (SC, MR, GB, CD, MF) e178 Barreto SG, Windsor JA. Justifying vein resection with pancreatoduodenectomy. Lancet Oncol 2016; 17: e118–24 Giovinazzo F, Turri G, Katz MH, Heaton N, Ahmed I. Meta-analysis of benefit of portal-superior mesenteric vein resection in pancreatic resection for ductal adenocarcinoma. Br J Surg 2016; 103: 179–91. Bapat AA, Hostetter G, Von Hoff DD, Han H. Perineural invasion and associated pain in pancreatic cancer. Nat Rev Cancer 2011; Sohal DP, Walsh RM, Ramanathan RK, Khorana AA. Pancreatic adenocarcinoma: treating a systemic disease with systemic therapy. J Natl Cancer Inst 2014; 106: dju011 Valero V, Saunders TJ, He J, et al. Reliable detection of somatic mutations in fine needle aspirates of pancreatic cancer with next-generation sequencing: implications for surgical management. Ann Surg 2016; Author’s reply Stefano Crippa and colleagues, in responding to our manuscript, 1 agree that increasing the radicality of surgery for pancreatic ductal adenocarcinoma, including synchronous vein resection, is suspect. Indeed, a recent meta-analysis 2 indicates that synchronous vein resection, as reported, increases mortality and decreases survival. Crippa and colleagues put forward two interesting ideas that warrant further discussion. The first is that the surgery-first approach for pancreatic ductal adenocarcinoma might ultimately be retired, given that pancreatic ductal adenocarcinoma is usually systemic at presentation, local treatments have little effect, and neoadjuvant therapy has possible benefits. For now, the absence of high-level evidence for neoadjuvant therapy leaves largely theoretical benefits; namely that neoadjuvant therapy will reveal the biology (ie, those patients that can progress on neoadjuvant therapy will avoid futile surgery), or alter the biology (ie, those patients that are downstaged will become resectable). The preliminary results of the ALLIANCE trial 3 damages the lustre of these purported benefits with no improvement in the number of resections (10 [50%] of 20 patients who completed all preoperative therapy), and no rescue of aggressive tumour biology. This leads to the second idea, in which Crippa and colleagues suggest a biological (rather than radiological) basis for selecting patients for neoadjuvant therapy with a view to reduce the number of synchronous vein resections. Endoscopic ultrasonography- guided genotyping is a possible way to select subgroups of patients with heterogenous pancreatic ductal adenocarcinoma 4 who will benefit from neoadjuvant therapy. In support of this method, Hruban and colleagues 5 suggested that an intact SMAD4/DPC4 gene might be used to select surgery because there is lesser risk of distant metastases for this genotype. 6 In the future, we hope to more accurately select a subgroup of patients in whom a surgery-first approach, and even synchronous vein resection, is justified, but it is much more likely that precision neoadjuvant therapy will ultimately result in less radical surgery and the introduction of non-surgical techniques to support the response to neoadjuvant therapy. We declare no competing interests. Savio G Barreto, *John A Windsor j.windsor@auckland.ac.nz Department of Gastrointestinal Surgery, Gastrointestinal Oncology, and Bariatric Surgery, Medanta Institute of Digestive and Hepatobiliary Sciences, Medanta, The Medicity, Gurgaon, India (SGB); Hepatobiliary Pancreatic and Upper GI Unit, Department of General Surgery, Auckland City Hospital, Auckland, New Zealand (JAW) Barreto S, Windsor J. Justifying vein resection with pancreatoduodenectomy. Lancet Oncol 2016; 17: e118–24. Giovinazzo F, Turri G, Katz MH, Heaton N, Ahmed I. Meta-analysis of benefits of portal-superior mesenteric vein resection in pancreatic resection for ductal adenocarcinoma. Br J Surg 2016; Varadhachary G, Fleming J, Crane C, et al. Phase II study of preoperation mFOLFIRINOX and chemoradiation for high-risk resectable and borderline resectable pancreatic adenocarcinoma. Proc Am Soc Clin Oncol 2015; 33 (suppl 3): abstr 362. Killock D. Pancreatic cancer: a problem quartered—new subtypes, new solutions? Nat Rev Clin Oncol 2016; 13: 201. Hruban RH, Adsay NV. Molecular classification of neoplasms of the pancreas. Hum Pathol Iacobuzio-Donahue CA, Fu B, Yachida S, et al. DPC4 gene status of the primary carcinoma correlates with patterns of failure in patients with pancreatic cancer. J Clin Oncol 2009; The US Cancer Moonshot initiative We recently sent the following letter to Vice President of the USA, Joe Biden, to state that we, as Deans and Directors of Public Health schools and programmes around the USA, strongly support the goals of the Cancer Moonshot initiative to www.thelancet.com/oncology Vol 17 May 2016

Objectives. Although a growing body of evidence demonstrates that availability and quality of essential public health services vary widely across communities, relatively little is known about the factors that give rise to these variations. We examined the association of institutional, financial, and community characteristics of local public health delivery systems and the performance of essential services. Methods. Performance measures were collected from local public health systems in 7 states and combined with secondary data sources. Multivariate, linear, and nonlinear regression models were used to estimate associations between system characteristics and the performance of essential services. Results. Performance varied significantly with the size, financial resources, and organizational structure of local public health systems, with some public health services appearing more sensitive to these characteristics than others. Staffing levels and community characteristics also appeared to be related to the performance of selected services. Conclusions. Reconfiguring the organization and financing of public health systems in some communities—such as through consolidation and enhanced intergovernmental coordination—may hold promise for improving the performance of essential services.

Since the beginning of the 1990s, public health has struggled to measure its performance and capacity to carry out the core functions of public health practice, while facing increasing challenges within the ever-changing landscape of healthcare delivery, bioterrorism response, emerging infections, and other threats to the public's health. The article describes the development of a set of national performance standards for measuring how effectively public health systems deliver the 10 Essential Public Health Services. The standards were developed through a practice-driven approach that incorporated comprehensive field testing and iterative revisions. The standards represent a national consensus framework for measuring important aspects of public health practice.

In an attempt to stimulate development of public health finance as a field of practice, policy, and scholarship, this article proposes a working definition of the term "public health finance," embeds it in the context of the maturing literature on the public health system and its infrastructure, and proposes a four-part typology that spans both public-sector and private-sector contributions to the financing of prevention and health promotion. A developmental strategy for the field--in applied research, training and education, and performance standards--is outlined as well.

Objectives. This study examines organizational characteristics and market conditions likely to influence collaborative relationships between public health agencies and community medical care providers. Methods. Public health directors in 60 US counties were surveyed by telephone concerning their relationships with area community hospitals (n = 263) and community health centers (n=85). Multivariate models were used to estimate the effects of organizational and market characteristics on collaboration. Results. Collaboration was reported among 55% of the hospitals and 64% of the health centers. Certain forms of collaboration were more likely in markets characterized by higher HMO penetration and lower HMO competition. Conclusions. Targeted efforts to facilitate collaboration may be required in settings where institutional and market incentives are lacking. (Am J Public Health. 2000;90:1913‐1916)

Efforts are under way to develop a performance measurement monitoring system for state and local public health systems and to develop a strategic planning tool for local public health systems. The development of these measures is being based on the Essential Public Health Services. This article provides the rationale for why the Essential Services offer a good framework for identifying, analyzing, and evaluating public health activities. The article also reviews the history of local public health and the development and application of the Essential Public Health Services and their predecessor frameworks such as the core functions, the organizational practices, and the essential elements.