Puac-Polanco, Victor

Puac-Polanco, Victor

Biography

Dr. Puac-Polanco is an Assistant Professor in the Departments of Health Policy and Management & Epidemiology and Biostatistics. Dr. Puac-Polanco holds a DrPH in epidemiology from Columbia University Mailman School of Public Health, a master’s in clinical epidemiology from the Perelman School of Medicine at the University of Pennsylvania, and an MD from Universidad de San Carlos de Guatemala. Most recently, he completed a two-year postdoctoral appointment at Harvard Medical School in the Department of Health Care Policy. Dr. Puac-Polanco has experience in population-based survey designs, implementation, data analysis, and interpretation. He has applied this knowledge to address public health issues in the areas of mental health, injury prevention, global health, and social inequalities. His clinical experiences, combined with training in clinical epidemiology, have allowed him to use different advanced methods to study complex data. His current focus is precision medicine, estimating individual differences in the effects of alternative intervention strategies by developing personalized treatment rules. These rules can aid treatment decisions for individual patients when multiple therapeutic options exist. His dissertation examined how drink special laws, which are implemented at the state level, affected mortality rates of motor vehicle crashes related to the use of alcohol. Dr. Puac-Polanco is a member of the Diversity & Inclusion Committee of the Society for Epidemiologic Research and an active collaborator on research projects in Latin America, demonstrating his commitment to participating in initiatives to serve disadvantaged populations and underrepresented groups in academia.

Publication Search Results

Now showing 1 - 10 of 10

Open Access
A Systematic Review of Drink Specials, Drink Special Laws, and Alcohol-Related Outcomes.
(2020-10-31) Puac-Polanco, Victor; Keyes, Katherine M; Mauro, Pia M; Branas, Charles C
The adverse health and safety consequences of heavy alcohol consumption are a leading problem around the world. While many risk factors have been extensively studied and presented in comprehensive summaries, not all questions regarding risk factors for problematic drinking behaviors have been answered and presented in systematic reviews. As of March 2020, no review has summarized studies assessing the role of promotional price practices at on-premises alcohol outlets, known as drink specials. Also missing was systematic information of policies that regulated these promotional practices. We aimed to synthesize the available research evidence of the effects that drink specials and drink special laws have on different alcohol-related outcomes.
Open Access
Development of a model to predict antidepressant treatment response for depression among Veterans
(Cambridge University Press (CUP), 2022-07-15) Puac-Polanco, Victor; Ziobrowski, Hannah N.; Ross, Eric L.; Liu, Howard; Turner, Brett; Cui, Ruifeng; Leung, Lucinda B.; Bossarte, Robert M.; Bryant, Corey; Joormann, Jutta; Nierenberg, Andrew A.; Oslin, David W.; Pigeon, Wilfred R.; Post, Edward P.; Zainal, Nur Hani; Zaslavsky, Alan M.; Zubizarreta, Jose R.; Luedtke, Alex; Kennedy, Chris J.; Cipriani, Andrea; Furukawa, Toshiaki A.; Kessler, Ronald C.
Background Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). Methods A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. Results In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (S.E.) of 0.66 (0.04) in the test sample. A strong gradient in probability (S.E.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. Conclusions Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.
Open Access
Mortality from motorcycle crashes: the baby-boomer cohort effect.
(2016-08-09) Puac-Polanco, Victor; Keyes, Katherine M; Li, Guohua
Motorcyclists are known to be at substantially higher risk per mile traveled of dying from crashes than car occupants. In 2014, motorcycling made up less than 1 % of person-miles traveled but 13 % of the total mortality from motor-vehicle crashes in the United States. We assessed the cohort effect of the baby-boomers (i.e., those born between 1946 and 1964) in motorcycle crash mortality from 1975 to 2014 in the United States.
Open Access
Previous violent events and mental health outcomes in Guatemala.
(2015-02-25) Puac-Polanco, Victor D; Lopez-Soto, Victor A; Kohn, Robert; Xie, Dawei; Richmond, Therese S; Branas, Charles C
We analyzed a probability sample of Guatemalans to determine if a relationship exists between previous violent events and development of mental health outcomes in various sociodemographic groups, as well as during and after the Guatemalan Civil War.
Open Access
A Diverse and Inclusive Academic Membership for All.
Puac-Polanco, Victor; Morabia, Alfredo
With the growing recognition that diversity and inclusion are essential for the improvement of science and innovation, we provide some perspectives on 3 findings of DeVilbiss et al. (Am J Epidemiol. 2020;189(10):998-1010). We provide points of discussion on factors and strategies to consider when drafting diversity and inclusion programs for the Society for Epidemiologic Research.
Open Access
"In Spanish": advancing public health.
González, Mila C; Santaella, Julian; Puac-Polanco, Victor
Open Access
Mental health in the Americas: an overview of the treatment gap.
(2018-10-10) Kohn, Robert; Ali, Ali Ahsan; Puac-Polanco, Victor; Figueroa, Chantal; López-Soto, Victor; Morgan, Kristen; Saldivia, Sandra; Vicente, Benjamín
To understand the mental health treatment gap in the Region of the Americas by examining the prevalence of mental health disorders, use of mental health services, and the global burden of disease.
Open Access
Mental Health of Guatemalan Health Care Workers During the COVID-19 Pandemic: Baseline Findings From the HEROES Cohort Study.
Paniagua-Avila, Alejandra; Ramírez, Dorian E; Barrera-Pérez, Aida; Calgua, Erwin; Castro, Claudia; Peralta-García, Ana; Mascayano, Franco; Susser, Ezra; Alvarado, Rubén; Puac-Polanco, Victor
To assess the baseline prevalence of mental health conditions and associated exposures in a cohort of health care workers (HCWs) in Guatemala. We analyzed baseline information from the 2020 Web-based COVID-19 Health Care Workers Study (HEROES)-Guatemala. Outcomes included mental distress and depressive symptoms. Exposures included COVID-19 experiences, sociodemographic characteristics, and job characteristics. We used crude and adjusted Poisson regression models in our analyses. Of the 1801 HCWs who accepted to participate, 1522 (84.5%) completed the questionnaire; 1014 (66.8%) were women. Among the participants, 59.1% (95% confidence interval [CI] = 56.6, 61.5) screened positive for mental distress and 23% (95% CI = 20.9, 25.2) for moderate to severe depressive symptoms. COVID-19 experiences, sociodemographic characteristics, and job characteristics were associated with the study outcomes. Participants who were worried about COVID-19 infection were at higher risk of mental distress (relative risk [RR] = 1.47; 95% CI = 1.30, 1.66) and depressive symptoms (RR = 1.51; 95% CI = 1.17, 1.96). Similarly, the youngest participants were at elevated risk of mental distress (RR = 1.80; 95% CI = 1.24, 2.63) and depressive symptoms (OR = 4.58; 95% CI = 1.51, 13.87). Mental health conditions are highly prevalent among Guatemalan HCWs. (. 2022;112(S6):S602-S614. https://doi.org/10.2105/AJPH.2021.306648).
Open Access
Treatment Differences in Primary and Specialty Settings in Veterans with Major Depression.
Puac-Polanco, Victor; Leung, Lucinda B; Bossarte, Robert M; Bryant, Corey; Keusch, Janelle N; Liu, Howard; Ziobrowski, Hannah N; Pigeon, Wilfred R; Oslin, David W; Post, Edward P; Kessler, Ronald C
The Veterans Health Administration (VHA) supports the nation's largest primary care-mental health integration (PC-MHI) collaborative care model to increase treatment of mild to moderate common mental disorders in primary care (PC) and refer more severe-complex cases to specialty mental health (SMH) settings. It is unclear how this treatment assignment works in practice.
Open Access
Prescription Drug Monitoring Programs and Prescription Opioid-Related Outcomes in the United States.
Puac-Polanco, Victor; Chihuri, Stanford; Fink, David S; Cerdá, Magdalena; Keyes, Katherine M; Li, Guohua
Prescription drug monitoring programs (PDMPs) are a crucial component of federal and state governments' response to the opioid epidemic. Evidence about the effectiveness of PDMPs in reducing prescription opioid-related adverse outcomes is mixed. We conducted a systematic review to examine whether PDMP implementation within the United States is associated with changes in 4 prescription opioid-related outcome domains: opioid prescribing behaviors, opioid diversion and supply, opioid-related morbidity and substance-use disorders, and opioid-related deaths. We searched for eligible publications in Embase, Google Scholar, MEDLINE, and Web of Science. A total of 29 studies, published between 2009 and 2019, met the inclusion criteria. Of the 16 studies examining PDMPs and prescribing behaviors, 11 found that implementing PDMPs reduced prescribing behaviors. All 3 studies on opioid diversion and supply reported reductions in the examined outcomes. In the opioid-related morbidity and substance-use disorders domain, 7 of 8 studies found associations with prescription opioid-related outcomes. Four of 8 studies in the opioid-related deaths domain reported reduced mortality rates. Despite the mixed findings, emerging evidence supports that the implementation of state PDMPs reduces opioid prescriptions, opioid diversion and supply, and opioid-related morbidity and substance-use disorder outcomes. When PDMP characteristics were examined, mandatory access provisions were associated with reductions in prescribing behaviors, diversion outcomes, hospital admissions, substance-use disorders, and mortality rates. Inconsistencies in the evidence base across outcome domains are due to analytical approaches across studies and, to some extent, heterogeneities in PDMP policies implemented across states and over time.