Nir Menachemi
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What is motivating parents' COVID-19 vaccination decisions? A Statewide Survey of Indiana Parents
The COVID-19 vaccine is already available to children over 12 years old, and it will soon be available for children 5- 11 years old. Professor Nir Menachemi and his research partner, Professor Katharine Head, wanted to understand Hoosier parents’ perceptions of the COVID-19 vaccine and assess their intentions to get their children vaccinated. This sort of information can assist state and local governments, public health agencies, and community organizations in crafting targeted educational campaigns and other strategies that will assure high uptake of the vaccine among children.
They worked with the Indiana Department of Health and the Indiana Department of Education to recruit parents and caregivers across Indiana to fill out a web-based survey that assessed their perceptions about the COVID-19 vaccination. Over 10,000 parents filled out the anonymous survey! Some of the key findings include that only about 45% of parents have or intend to vaccinate their children, while about 42% do not intend to vaccinate. About 13% of parents who said they would “wait and see.” Of those wait and see parents, the researchers found some interesting perceptions about the vaccine that may suggest ways they can develop targeted educational messages to hopefully encourage them to get vaccinated, such as their perceptions of safety, perceptions of what other parents are doing, and perceptions about what their healthcare provider would want them to do. Based on these findings, they have developed a set of evidence-based suggestions for designing parent and family focused COVID-19 vaccination interventions that can be implemented in communities across Indiana.
Professor Menachemi's translation of research into strategies to increase the number of vaccinated children in the fight against COVID-19 is another excellent example of how IUPUI's faculty members are TRANSLATING their RESEARCH INTO PRACTICE.
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Item Underrepresented racial minorities in biomedical informatics doctoral programs: graduation trends and academic placement (2002–2017)(Oxford University Press, 2020-11-01) Wiley, Kevin; Dixon, Brian E.; Grannis, Shaun J.; Menachemi, Nir; Health Policy and Management, School of Public HealthObjective: Biomedical informatics attracts few underrepresented racial minorities (URMs) into PhD programs. We examine graduation trends from 2002 to 2017 to determine how URM representation has changed over time. We also examine academic job placements by race and identify individual and institutional characteristics associated with URM graduates being successfully placed in academic jobs. Materials and methods: We analyze a near census of all research doctoral graduates from US-accredited institutions, surveyed at graduation by the National Science Foundation Survey of Earned Doctorates. Graduates of biomedical informatics-related programs were identified using self-reported primary and secondary disciplines. Data are analyzed using bivariate and multivariable logistic regressions. Results: During the study period, 2426 individuals earned doctoral degrees in biomedical informatics-related disciplines. URM students comprised nearly 12% of graduates, and this proportion did not change over time (2002-2017). URMs included Hispanic (5.7%), Black (3.2%), and others, including multi-racial and indigenous American populations (2.8%). Overall, 82.3% of all graduates accepted academic positions at the time of graduation with significantly more Hispanic graduates electing to go into academia (89.2%; P < .001). URM graduates were more likely to be single (OR = 1.38; P < .05), have a dependent (1.95; P < .01), and not receive full tuition remission (OR = 1.37; P = .05) as a student. URM graduates accepting an academic position were less likely to be a graduate of a private institution (OR = 0.70; P < .05). Discussion and conclusion: The proportion of URM candidates among biomedical informatics doctoral graduates has not increased over time and remains low. In order to improve URM recruitment and retention within academia, leaders in biomedical informatics should replicate strategies used to improve URM graduation rates in other fields.Item Impact of Medicaid expansion on smoking prevalence and quit attempts among those newly eligible, 2011–2019(EU European Publishing, 2021-08-05) Hilts, Katy Ellis; Blackburn, Justin; Gibson, P. Joseph; Yeager, Valerie A.; Halverson, Paul K.; Menachemi, Nir; Health Policy and Management, School of Public HealthIntroduction: 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.Item The economic burden of untreated mental illness in Indiana(2022-11-28) Taylor, Heather; Blackburn, Justin; Menachemi, NirWe use a prevalence-based approach to estimate annual costs, in a wide-range of categories, associated with untreated mental illness (MI) in Indiana. Economic burden of untreated MI in Indiana is estimated at $4.2 billion annually representing 1.2% of the state’s gross domestic product. Considering average Indiana wages, $4.2 billion is equivalent to approximately 100,000 jobs. Cost of untreated MI includes $3.3 billion for indirect costs such as premature mortality, $708.5 million for direct healthcare costs, and $116.4 million for non-health care costs including incarceration. On average, each individual experiencing untreated MI incurs $18,940 of societal costs; much of which is borne by employers in the form of premature mortality, unemployment, absenteeism and presenteeism.Item How Many SARS-CoV-2–Infected People Require Hospitalization? Using Random Sample Testing to Better Inform Preparedness Efforts(Wolters Kluwer, 2021) Menachemi, Nir; Dixon, Brian E.; Wools-Kaloustian, Kara K.; Yiannoutsos, Constantin T.; Halverson, Paul K.; Epidemiology, School of Public HealthContext: 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 Measure(s): 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.Item Does preventive dental care reduce non-preventive dental visits and expenditures among Medicaid-enrolled adults?(Wiley, 2022) Taylor, Heather L.; Sen, Bisakha; Holmes, Ann M.; Schleyer, Titus; Menachemi, Nir; Blackburn, Justin; Health Policy and Management, School of Public HealthObjective To determine whether preventive dental visits are associated with fewer subsequent non-preventive dental visits and lower dental expenditures. Data Sources Indiana Medicaid enrollment and claims data (2015–2018) and the Area Health Resource File. Study design A repeated measures design with individual and year fixed effects examining the relationship between preventive dental visits (PDVs) and non-preventive dental visits (NPVs) and dental expenditures. Data Collection/Extraction Methods Not applicable. Principal findings Of 28,152 adults (108,349 observation-years) meeting inclusion criteria, 36.0% had any dental visit, 27.8% a PDV, and 22.1% a NPV. Compared to no PDV in the prior year, at least one was associated with fewer NPVs (β = −0.13; 95% CI -0.12, −0.11), lower NPV expenditures (β = −$29.12.53; 95% CI -28.07, −21.05), and lower total dental expenditures (−$70.12; 95% -74.92, −65.31), as well as fewer PDVs (β = −0.24; 95% CI -0.26, −0.23). Conclusions Our findings suggest that prior year PDVs are associated with fewer subsequent NPVs and lower dental expenditures among Medicaid-enrolled adults. Thus, from a public insurance program standpoint, supporting preventive dental care use may translate into improved population oral health outcomes and lower dental costs among certain low-income adult populations, but barriers to consistent utilization of PDV prohibit definitive findings.Item Association of Health Status and Nicotine Consumption with SARS-CoV-2 positivity rates(BMC, 2021-10) Duszynski, Thomas J.; Fadel, William; Wools-Kaloustian, Kara K.; Dixon, Brian E.; Yiannoutsos, Constantin; Halverson, Paul K.; Menachemi, Nir; Epidemiology, School of Public HealthBACKGROUND: 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.Item The benefits of health information exchange: an updated systematic review(Oxford Academic, 2018-09) Menachemi, Nir; Rahurkar, Saurabh; Harle, Christopher A.; Vest, Joshua R.; Health Policy and Management, School of Public HealthObjective Widespread health information exchange (HIE) is a national objective motivated by the promise of improved care and a reduction in costs. Previous reviews have found little rigorous evidence that HIE positively affects these anticipated benefits. However, early studies of HIE were methodologically limited. The purpose of the current study is to review the recent literature on the impact of HIE. Methods We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to conduct our systematic review. PubMed and Scopus databases were used to identify empirical articles that evaluated HIE in the context of a health care outcome. Results Our search strategy identified 24 articles that included 63 individual analyses. The majority of the studies were from the United States representing 9 states; and about 40% of the included analyses occurred in a handful of HIEs from the state of New York. Seven of the 24 studies used designs suitable for causal inference and all reported some beneficial effect from HIE; none reported adverse effects. Conclusions The current systematic review found that studies with more rigorous designs all reported benefits from HIE. Such benefits include fewer duplicated procedures, reduced imaging, lower costs, and improved patient safety. We also found that studies evaluating community HIEs were more likely to find benefits than studies that evaluated enterprise HIEs or vendor-mediated exchanges. Overall, these finding bode well for the HIEs ability to deliver on anticipated improvements in care delivery and reduction in costs.Item Assessing the capacity of social determinants of health data to augment predictive models identifying patients in need of wraparound social services(Oxford Press, 2018-01) Kasthurirathne, Suranga N.; Vest, Joshua R.; Menachemi, Nir; Halverson, Paul K.; Grannis, Shaun J.; Health Policy and Management, School of Public HealthIntroduction 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.Item Public Health Officials and COVID-19: Leadership, Politics, and the Pandemic(Wolters Kluwer, 2021) Halverson, Paul K.; Yeager, Valerie A.; Menachemi, Nir; Fraser, Michael R.; Freeman, Lori T.; Health Policy and Management, School of Public HealthItem Bayesian estimation of SARS-CoV-2 prevalence in Indiana by random testing(NAS, 2021-02) Yiannoutsos, Constantin T.; Halverson, Paul K.; Menachemi, Nir; Biostatistics, School of Public HealthFrom 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.