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Vol. 1 No. 1 (2025): Ponce Health Sciences University Scientific Journal - First Issue
Articles
Abstract
Amid the 2019 coronavirus (COVID-19) pandemic, regional disparities in outcomes were identified as crucial for an effective public health response. Regional variations in COVID-19 outcomes in the United States of America (USA) were studied in this retrospective cross-sectional analysis, with a focus on in-hospital mortality rates among COVID-19 patients with pneumonia or respiratory failure, using the 2021 National Inpatient Sample dataset. Patients aged ≥18 years with confirmed diagnoses of COVID-19 associated pneumonia or respiratory failure were included in the study. The association between hospital census regions and in-hospital mortality rates was examined using multivariate logistic regression, adjusting for demographic, clinical, and hospital characteristics. Significant disparities in regional COVID-19 outcomes in the USA were revealed by the results. Mortality rates in the COVID-19 study cohort ranged from 14% in the Midwest to 18% in the West. Adjusted significant differences were shown by regression analysis, with the West exhibiting up to 28% higher odds of death (odds ratio: 1.28, 95% confidence interval: 1.218–1.354, p<0.001) than the Midwest, the region with the lowest mortality. The importance of accounting for demographic, clinical, and contextual factors in understanding these disparities and addressing regional disparities to promote health equity during the ongoing pandemic was underscored by these substantial regional variations in COVID-19 mortality rates in the USA.
Author: José A. Acosta, MD, MBA, MPH
DOI: https://doi.org/10.71332/5rnhnz41
Keywords: COVID-19 pneumonia mortality rates, National Inpatient Sample, Regional disparities, Respiratory failure
References
Stoto, M.A.; Schlageter, S.; Kraemer, J.D. COVID-19 mortality in the United States: It's been two Americas from the start. PLOS ONE 2022, 17, e0265053, DOI:http://dx.doi.org/10.1371/journal.pone.0265053.
Bollyky, T.J.; Castro, E.; Aravkin, A.Y.; Bhangdia, K.; Dalos, J.; Hulland, E.N.; Kiernan, S.; Lastuka, A.; McHugh, T.A.; Ostroff, S.M. Assessing COVID-19 pandemic policies and behaviours and their economic and educational trade-offs across US states from Jan 1, 2020, to July 31, 2022: an observational analysis. The Lancet 2023, 401, 1341-1360, DOI:http://dx.doi.org/10.1016/S0140-6736(23)00461-0.
Eiermann, M.; Wrigley-Field, E.; Feigenbaum, J.J.; Helgertz, J.; Hernandez, E.; Boen, C.E. Racial Disparities in Mortality During the 1918 Influenza Pandemic in United States Cities. Demography 2022, 59, 1953-1979, DOI:http://dx.doi.org/10.1215/00703370-10235825.
Andrulis, D.P.; Siddiqui, N.J.; Purtle, J.; Cooper, M.R. H1N1 influenza pandemic and racially and ethnically diverse communities in the United States. 2012.
Masiano, S.P.; Martin, E.G.; Bono, R.S.; Dahman, B.; Sabik, L.M.; Belgrave, F.Z.; Adimora, A.A.; Kimmel, A.D. Suboptimal geographic accessibility to comprehensive HIV care in the US: regional and urban-rural differences. J Int AIDS Soc 2019, 22, e25286, DOI:http://dx.doi.org/10.1002/jia2.25286.
Isath, A.; Malik, A.H.; Goel, A.; Gupta, R.; Shrivastav, R.; Bandyopadhyay, D. Nationwide Analysis of the Outcomes and Mortality of Hospitalized COVID-19 Patients. Curr Probl Cardiol 2023, 48, 101440, DOI:http://dx.doi.org/10.1016/j.cpcardiol.2022.101440.
FDA Approves First COVID-19 Vaccine Approval Signifies Key Achievement for Public Health. Available online: https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine (accessed on 2 April 2024).
Bast, E.; Tang, F.; Dahn, J.; Palacio, A. Increased risk of hospitalisation and death with the delta variant in the USA. The Lancet Infectious Diseases 2021, 21, 1629-1630, DOI:http://dx.doi.org/10.1016/S1473-3099(21)00685-X.
Khera, R.; Angraal, S.; Couch, T.; Welsh, J.W.; Nallamothu, B.K.; Girotra, S.; Chan, P.S.; Krumholz, H.M. Adherence to methodological standards in research using the national inpatient sample. JAMA 2017, 318, 2011-2018, DOI:http://dx.doi.org/10.1001/jama.2017.17653.
MapChart. Available online: https://www.mapchart.net/usa.html (accessed on 4 April 2024).
Quan, H.; Sundararajan, V.; Halfon, P.; Fong, A.; Burnand, B.; Luthi, J.-C.; Saunders, L.D.; Beck, C.A.; Feasby, T.E.; Ghali, W.A. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical care 2005, 43, 1130-1139, DOI:http://dx.doi.org/10.1097/01.mlr.0000182534.19832.83.
McCormick, P.J.; Lin, H.M.; Deiner, S.G.; Levin, M.A. Validation of the All Patient Refined Diagnosis Related Group (APR-DRG) Risk of Mortality and Severity of Illness Modifiers as a Measure of Perioperative Risk. J Med Syst 2018, 42, 81, DOI:http://dx.doi.org/10.1007/s10916-018-0936-3.
HCUP. NIS Description of Data Elements - FEMALE Available online: https://hcup-us.ahrq.gov/db/vars/female/nisnote.jsp#general (accessed on 6 April 2024).
HCUP. ZIPINC_QRTL - Median household income for patient's ZIP Code (based on current year) Available online: https://hcup-us.ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp (accessed on 27 March 2024).
3M™ All Patient Refined Diagnosis Related Groups (APR DRGs). . Available online: https://www.3m.com/3M/en_US/health-information-systems-us/drive-value-based-care/patient-classification-methodologies/apr-drgs/ (accessed on April 6, 2024).
NIS Overview. Available online: https://hcup-us.ahrq.gov/nisoverview.jsp (accessed on April 6, 2024).
Wordtune AI21 Labs, 2024. Available online: https://www.wordtune.com/ (accessed on April 6, 2024).
OpenAI (2020). Chatbot GPT-4. OpenAI. . Available online: https://chat.openai.com/ (accessed on 6 June 2024).
Vandenbroucke, J.P.; von Elm, E.; Altman, D.G.; Gotzsche, P.C.; Mulrow, C.D.; Pocock, S.J.; Poole, C.; Schlesselman, J.J.; Egger, M.; Initiative, S. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Int J Surg 2014, 12, 1500-1524, DOI:http://dx.doi.org/10.1016/j.ijsu.2014.07.014.
Vardar, U.; Ilelaboye, A.; Murthi, M.; Atluri, R.; Park, D.Y.; Khamooshi, P.; Ojemolon, P.E.; Shaka, H.; Khamooshi, P. Racial Disparities in Patients With COVID-19 Infection: A National Inpatient Sample Analysis. Cureus 2023, 15, e35039, DOI:http://dx.doi.org/10.7759/cureus.35039.
Vallabhajosyula, S.; Patlolla, S.H.; Dunlay, S.M.; Prasad, A.; Bell, M.R.; Jaffe, A.S.; Gersh, B.J.; Rihal, C.S.; Holmes Jr, D.R.; Barsness, G.W. Regional variation in the management and outcomes of acute myocardial infarction with cardiogenic shock in the United States. Circulation: Heart Failure 2020, 13, e006661, DOI:http://dx.doi.org/10.1161/CIRCHEARTFAILURE.119.006661.
Mullins, R.J.; Diggs, B.S.; Hedges, J.R.; Newgard, C.D.; Arthur, M.; Adams, A.L.; Veum-Stone, J.; Lenfesty, B.; Trunkey, D.D. Regional Differences in Outcomes for Hospitalized Injured Patients. Journal of Trauma and Acute Care Surgery 2006, 60, 691-700, DOI:http://dx.doi.org/10.1097/01.ta.0000210454.92078.89.
Kolte, D.; Khera, S.; Aronow, W.S.; Palaniswamy, C.; Mujib, M.; Ahn, C.; Iwai, S.; Jain, D.; Sule, S.; Ahmed, A. Regional variation in the incidence and outcomes of in-hospital cardiac arrest in the United States. Circulation 2015, 131, 1415-1425, DOI:http://dx.doi.org/10.1161/CIRCULATIONAHA.114.014542.
Kim, S.J.; Medina, M.; Zhong, L.; Chang, J. Factors associated with in-hospital death among pneumonia patients in US hospitals from 2016~ 2019. International Journal of Health Policy and Management 2023, 12, DOI:http://dx.doi.org/10.34172/ijhpm.2023.7390.
Roth, G.A.; Emmons-Bell, S.; Alger, H.M.; Bradley, S.M.; Das, S.R.; de Lemos, J.A.; Gakidou, E.; Elkind, M.S.V.; Hay, S.; Hall, J.L.; et al. Trends in Patient Characteristics and COVID-19 In-Hospital Mortality in the United States During the COVID-19 Pandemic. JAMA Network Open 2021, 4, e218828-e218828, DOI:http://dx.doi.org/10.1001/jamanetworkopen.2021.8828.
Rosenthal, N.; Cao, Z.; Gundrum, J.; Sianis, J.; Safo, S. Risk factors associated with in-hospital mortality in a US national sample of patients with COVID-19. JAMA network open 2020, 3, e2029058-e2029058, DOI:http://dx.doi.org/10.1001/jamanetworkopen.2020.29058.
Lurie, N.; Sharfstein, J.M. State-to-state differences in US COVID-19 outcomes: searching for explanations. The Lancet 2023, 401, 1314-1315, DOI:http://dx.doi.org/10.1016/S0140-6736(23)00726-2.
Bechman, K.; Yates, M.; Mann, K.; Nagra, D.; Smith, L.-J.; Rutherford, A.I.; Patel, A.; Periselneris, J.; Walder, D.; Dobson, R.J. Inpatient COVID-19 mortality has reduced over time: results from an observational cohort. PloS one 2022, 17, e0261142, DOI:http://dx.doi.org/10.1371/journal.pone.0261142.
Ibuka, Y.; Matsuda, Y.; Shoji, K.; Ishigaki, T. Evaluation of regional variations in healthcare utilization. Japanese Journal of Statistics and Data Science 2020, 3, 349-365.
Couillard, B.K.; Foote, C.L.; Gandhi, K.; Meara, E.; Skinner, J. Rising geographic disparities in US mortality. Journal of Economic Perspectives 2021, 35, 123-146.
Duong, W.Q.; Grigorian, A.; Farzaneh, C.; Nahmias, J.; Chin, T.; Schubl, S.; Dolich, M.; Lekawa, M. Racial and Sex Disparities in Trauma Outcomes Based on Geographical Region. The American Surgeonâ„¢ 2021, 87, 988-993, DOI:http://dx.doi.org/10.1177/0003134820960063.
Hanna, D.B.; Selik, R.M.; Tang, T.; Gange, S.J. Disparities among US states in HIV-related mortality in persons with HIV infection, 2001–2007. Aids 2012, 26, 95-103.
Singh, G.K.; Azuine, R.E.; Siahpush, M.; Williams, S.D. Widening geographical disparities in cardiovascular disease mortality in the United States, 1969-2011. International Journal of MCH and AIDS 2015, 3, 134.
O’Keefe, E.B.; Meltzer, J.P.; Bethea, T.N. Health disparities and cancer: racial disparities in cancer mortality in the United States, 2000–2010. Frontiers in public health 2015, 3, 51.
Dugani, S.B.; Wood-Wentz, C.M.; Mielke, M.M.; Bailey, K.R.; Vella, A. Assessment of disparities in diabetes mortality in adults in US rural vs nonrural counties, 1999-2018. JAMA network open 2022, 5, e2232318-e2232318.
Kirby, J.B.; Taliaferro, G.; Zuvekas, S.H. Explaining racial and ethnic disparities in health care. Medical care 2006, 44, I-64-I-72.
Fiscella, K.; Sanders, M.R. Racial and ethnic disparities in the quality of health care. Annual review of public health 2016, 37, 375-394.
Lazar, H.L. Administrative databases for outcomes research-quick, easy, but dirty. J Card Surg 2017, 32, 757, DOI:http://dx.doi.org/10.1111/jocs.13380.
Introduction to the HCUP National Inpatient Sample (NIS) 2021. Available online: https://hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2021.jsp (accessed on 29 March 2024).
Abstract
Minimally invasive techniques for lumbar spinal fusions have evolved significantly to treat lumbar spinal stenosis, degenerative spondylolisthesis, and many other complex conditions. This study evaluates the clinical outcomes of patients aged 65 and older who underwent minimally invasive transforaminal lumbar interbody fusion (MIS-TLIF) with robotic assistance. In this single-surgeon single-institution retrospective cohort study, 72 patients aged 65 and older who underwent MIS-TLIF from 2018 to 2021 were analyzed. Patients had diagnoses of lumbar spinal stenosis, with or without degenerative spondylolisthesis. Clinical outcomes were assessed using the Oswestry Disability Index (ODI) and Visual Analogue Scale (VAS) for back and lower extremities at baseline, and 6, 9, or 12 months postoperatively. The data was analyzed, and outcomes were compared using paired t-tests. Significant improvements in disability were observed postoperatively, with mean ODI scores decreasing from 46.4% to 9.3% (95% CI: -41.2, -33.1). In terms of pain intensity, mean VAS scores for back pain decreased from 8.0 to 3.5 (95% CI: -5.1, -3.9) and leg pain scores also decreased from 8.2 to 2.9 (95% CI: -5.9, -4.6). These changes indicate substantial clinical improvements (p < 0.001). This study substantiates the efficacy of MIS-TLIF in significantly improving pain relief and functional mobility among seniors with lumbar conditions. The substantial reductions in ODI and VAS scores highlight its clinical benefits potential to set a new standard of care. By offering a robotically assisted, minimally invasive alternative, this approach aligns with contemporary healthcare objectives of enhancing patient recovery while minimizing procedural risks and costs.
Authors:
- Emil Varas-Rodríguez
- Gabriel González-Díaz
- Alexander Matos
- Miguel Cartagena
- Oscar Duyos
DOI: https://doi.org/10.71332/qwffpj20
Keywords: MIS-TLIF, Robotic Spine Surgery, Lumbar Stenosis, Degenerative Spondylolisthesis, Elderly Patient Care, Spinal Fusion, Postoperative Outcomes, Quality of Life, Orthopedics
References
Harms et al. The unilateral transforaminal approach for posterior lumbar inter body fusion. Orthop Traumatol 6:88-89.
Lowe et al. Unilateral transforaminal posterior lumbar inter body fusion: indications, technique, and 2-year results. Journal of Spinal Disorders and Techniques. February 2022, Volume 15, pp. 31-38.
Cloward RB. History of PLIF; forty years of personal experience. In: Lin PM, editor. Posterior lumbar inter body fusion. Springfield: Charles C Thomas; pp. 58-71.
Wang, Michael Y et al. “An analysis of the differences in the acute hospitalization charges following minimally invasive versus open posterior lumbar interbody fusion.” Journal of neurosurgery. Spine vol. 12,6 (2010): 694-9. doi:10.3171/2009.12.SPINE09621.
Dhall, Sanjay S et al. “Clinical and radiographic comparison of mini-open transforaminal lumbar interbody fusion with open transforaminal lumbar interbody fusion in 42 patients with long-term follow-up.” Journal of neurosurgery. Spine vol. 9,6 (2008): 560-5. doi:10.3171/SPI.2008.9.08142.
Administration of Aging, Department of Health and Human Services. U.S. population by age: July 1, 2010. 2010. Available online: http://www.aoa.gov/Aging_Statistics/Census_Population/census2010/Index.aspx [Accessed on July 2022]
Etame, Arnold B et al. “Clinical and radiographic outcomes after minimally invasive transforaminal lumbar interbody fusion.” SAS journal vol. 4,2 47-53. 1 Jun. 2010, doi:10.1016/j.esas.2010.03.002.
Villavicencio, Alan T et al. “Minimally invasive versus open transforaminal lumbar interbody fusion.” Surgical neurology international vol. 1,12 (2010). doi:10.4103/2152-7806.63905.
Fu, Kai-Ming G et al. “Correlation of higher preoperative American Society of Anesthesiology grade and increased morbidity and mortality rates in patients undergoing spine surgery.” Journal of neurosurgery. Spine vol. 14,4 (2011): 470-4. doi:10.3171/2010.12.SPINE10486.
Kobayashi, Kazuyoshi et al. “Postoperative Complications Associated With Spine Surgery in Patients Older Than 90 Years: A Multicenter Retrospective Study.” Global spine journal vol. 8,8 (2018): 887-891. doi:10.1177/2192568218767430.
Somani, Sulaiman et al. “ASA Classification as a Risk Stratification Tool in Adult Spinal Deformity Surgery: A Study of 5805 Patients.” Global spine journal vol. 7,8 (2017): 719-726. doi:10.1177/2192568217700106.
Lee, Low Yong et al. “Outcomes of Minimally Invasive Surgery Compared to Open Posterior Lumbar Instrumentation and Fusion.” Asian journal of neurosurgery vol. 12,4 (2017): 620-637. doi:10.4103/ajns.AJNS_331_16.
Jang, Jee-Soo, and Sang-Ho Lee. “Minimally invasive transforaminal lumbar interbody fusion with ipsilateral pedicle screw and contralateral facet screw fixation.” Journal of neurosurgery. Spine vol. 3,3 (2005): 218-23. doi:10.3171/spi.2005.3.3.0218.
Deutsch, Harel, and Michael J Musacchio Jr. “Minimally invasive transforaminal lumbar interbody fusion with unilateral pedicle screw fixation.” Neurosurgical focus vol. 20,3 E10. 15 Mar. 2006, doi:10.3171/foc.2006.20.3.11.
Masiano, S.P.; Martin, E.G.; Bono, R.S.; Dahman, B.; Sabik, L.M.; Belgrave, F.Z.; Adimora, A.A.; Kimmel, A.D. Suboptimal geographic accessibility to comprehensive HIV care in the US: regional and urban-rural differences. J Int AIDS Soc 2019, 22, e25286, DOI:http://dx.doi.org/10.1002/jia2.25286.
Isath, A.; Malik, A.H.; Goel, A.; Gupta, R.; Shrivastav, R.; Bandyopadhyay, D. Nationwide Analysis of the Outcomes and Mortality of Hospitalized COVID-19 Patients. Curr Probl Cardiol 2023, 48, 101440, DOI:http://dx.doi.org/10.1016/j.cpcardiol.2022.101440.
FDA Approves First COVID-19 Vaccine Approval Signifies Key Achievement for Public Health. Available online: https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine (accessed on 2 April 2024).
Bast, E.; Tang, F.; Dahn, J.; Palacio, A. Increased risk of hospitalisation and death with the delta variant in the USA. The Lancet Infectious Diseases 2021, 21, 1629-1630, DOI:http://dx.doi.org/10.1016/S1473-3099(21)00685-X.
Khera, R.; Angraal, S.; Couch, T.; Welsh, J.W.; Nallamothu, B.K.; Girotra, S.; Chan, P.S.; Krumholz, H.M. Adherence to methodological standards in research using the national inpatient sample. JAMA 2017, 318, 2011-2018, DOI:http://dx.doi.org/10.1001/jama.2017.17653.
MapChart. Available online: https://www.mapchart.net/usa.html (accessed on 4 April 2024).
Quan, H.; Sundararajan, V.; Halfon, P.; Fong, A.; Burnand, B.; Luthi, J.-C.; Saunders, L.D.; Beck, C.A.; Feasby, T.E.; Ghali, W.A. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical care 2005, 43, 1130-1139, DOI:http://dx.doi.org/10.1097/01.mlr.0000182534.19832.83.
McCormick, P.J.; Lin, H.M.; Deiner, S.G.; Levin, M.A. Validation of the All Patient Refined Diagnosis Related Group (APR-DRG) Risk of Mortality and Severity of Illness Modifiers as a Measure of Perioperative Risk. J Med Syst 2018, 42, 81, DOI:http://dx.doi.org/10.1007/s10916-018-0936-3.
HCUP. NIS Description of Data Elements - FEMALE Available online: https://hcup-us.ahrq.gov/db/vars/female/nisnote.jsp#general (accessed on 6 April 2024).
HCUP. ZIPINC_QRTL - Median household income for patient's ZIP Code (based on current year) Available online: https://hcup-us.ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp (accessed on 27 March 2024).
3M™ All Patient Refined Diagnosis Related Groups (APR DRGs). . Available online: https://www.3m.com/3M/en_US/health-information-systems-us/drive-value-based-care/patient-classification-methodologies/apr-drgs/ (accessed on April 6, 2024).
NIS Overview. Available online: https://hcup-us.ahrq.gov/nisoverview.jsp (accessed on April 6, 2024).
Wordtune AI21 Labs, 2024. Available online: https://www.wordtune.com/ (accessed on April 6, 2024).
OpenAI (2020). Chatbot GPT-4. OpenAI. . Available online: https://chat.openai.com/ (accessed on 6 June 2024).
Vandenbroucke, J.P.; von Elm, E.; Altman, D.G.; Gotzsche, P.C.; Mulrow, C.D.; Pocock, S.J.; Poole, C.; Schlesselman, J.J.; Egger, M.; Initiative, S. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Int J Surg 2014, 12, 1500-1524, DOI:http://dx.doi.org/10.1016/j.ijsu.2014.07.014.
Vardar, U.; Ilelaboye, A.; Murthi, M.; Atluri, R.; Park, D.Y.; Khamooshi, P.; Ojemolon, P.E.; Shaka, H.; Khamooshi, P. Racial Disparities in Patients With COVID-19 Infection: A National Inpatient Sample Analysis. Cureus 2023, 15, e35039, DOI:http://dx.doi.org/10.7759/cureus.35039.
Vallabhajosyula, S.; Patlolla, S.H.; Dunlay, S.M.; Prasad, A.; Bell, M.R.; Jaffe, A.S.; Gersh, B.J.; Rihal, C.S.; Holmes Jr, D.R.; Barsness, G.W. Regional variation in the management and outcomes of acute myocardial infarction with cardiogenic shock in the United States. Circulation: Heart Failure 2020, 13, e006661, DOI:http://dx.doi.org/10.1161/CIRCHEARTFAILURE.119.006661.
Mullins, R.J.; Diggs, B.S.; Hedges, J.R.; Newgard, C.D.; Arthur, M.; Adams, A.L.; Veum-Stone, J.; Lenfesty, B.; Trunkey, D.D. Regional Differences in Outcomes for Hospitalized Injured Patients. Journal of Trauma and Acute Care Surgery 2006, 60, 691-700, DOI:http://dx.doi.org/10.1097/01.ta.0000210454.92078.89.
Kolte, D.; Khera, S.; Aronow, W.S.; Palaniswamy, C.; Mujib, M.; Ahn, C.; Iwai, S.; Jain, D.; Sule, S.; Ahmed, A. Regional variation in the incidence and outcomes of in-hospital cardiac arrest in the United States. Circulation 2015, 131, 1415-1425, DOI:http://dx.doi.org/10.1161/CIRCULATIONAHA.114.014542.
Kim, S.J.; Medina, M.; Zhong, L.; Chang, J. Factors associated with in-hospital death among pneumonia patients in US hospitals from 2016~ 2019. International Journal of Health Policy and Management 2023, 12, DOI:http://dx.doi.org/10.34172/ijhpm.2023.7390.
Roth, G.A.; Emmons-Bell, S.; Alger, H.M.; Bradley, S.M.; Das, S.R.; de Lemos, J.A.; Gakidou, E.; Elkind, M.S.V.; Hay, S.; Hall, J.L.; et al. Trends in Patient Characteristics and COVID-19 In-Hospital Mortality in the United States During the COVID-19 Pandemic. JAMA Network Open 2021, 4, e218828-e218828, DOI:http://dx.doi.org/10.1001/jamanetworkopen.2021.8828.
Rosenthal, N.; Cao, Z.; Gundrum, J.; Sianis, J.; Safo, S. Risk factors associated with in-hospital mortality in a US national sample of patients with COVID-19. JAMA network open 2020, 3, e2029058-e2029058, DOI:http://dx.doi.org/10.1001/jamanetworkopen.2020.29058.
Lurie, N.; Sharfstein, J.M. State-to-state differences in US COVID-19 outcomes: searching for explanations. The Lancet 2023, 401, 1314-1315, DOI:http://dx.doi.org/10.1016/S0140-6736(23)00726-2.
Bechman, K.; Yates, M.; Mann, K.; Nagra, D.; Smith, L.-J.; Rutherford, A.I.; Patel, A.; Periselneris, J.; Walder, D.; Dobson, R.J. Inpatient COVID-19 mortality has reduced over time: results from an observational cohort. PloS one 2022, 17, e0261142, DOI:http://dx.doi.org/10.1371/journal.pone.0261142.
Ibuka, Y.; Matsuda, Y.; Shoji, K.; Ishigaki, T. Evaluation of regional variations in healthcare utilization. Japanese Journal of Statistics and Data Science 2020, 3, 349-365.
Couillard, B.K.; Foote, C.L.; Gandhi, K.; Meara, E.; Skinner, J. Rising geographic disparities in US mortality. Journal of Economic Perspectives 2021, 35, 123-146.
Duong, W.Q.; Grigorian, A.; Farzaneh, C.; Nahmias, J.; Chin, T.; Schubl, S.; Dolich, M.; Lekawa, M. Racial and Sex Disparities in Trauma Outcomes Based on Geographical Region. The American Surgeonâ„¢ 2021, 87, 988-993, DOI:http://dx.doi.org/10.1177/0003134820960063.
Hanna, D.B.; Selik, R.M.; Tang, T.; Gange, S.J. Disparities among US states in HIV-related mortality in persons with HIV infection, 2001–2007. Aids 2012, 26, 95-103.
Singh, G.K.; Azuine, R.E.; Siahpush, M.; Williams, S.D. Widening geographical disparities in cardiovascular disease mortality in the United States, 1969-2011. International Journal of MCH and AIDS 2015, 3, 134.
O’Keefe, E.B.; Meltzer, J.P.; Bethea, T.N. Health disparities and cancer: racial disparities in cancer mortality in the United States, 2000–2010. Frontiers in public health 2015, 3, 51.
Dugani, S.B.; Wood-Wentz, C.M.; Mielke, M.M.; Bailey, K.R.; Vella, A. Assessment of disparities in diabetes mortality in adults in US rural vs nonrural counties, 1999-2018. JAMA network open 2022, 5, e2232318-e2232318.
Kirby, J.B.; Taliaferro, G.; Zuvekas, S.H. Explaining racial and ethnic disparities in health care. Medical care 2006, 44, I-64-I-72.
Fiscella, K.; Sanders, M.R. Racial and ethnic disparities in the quality of health care. Annual review of public health 2016, 37, 375-394.
Lazar, H.L. Administrative databases for outcomes research-quick, easy, but dirty. J Card Surg 2017, 32, 757, DOI:http://dx.doi.org/10.1111/jocs.13380.
Introduction to the HCUP National Inpatient Sample (NIS) 2021. Available online: https://hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2021.jsp (accessed on 29 March 2024).
Abstract
Prostate cancer (PCa) is the leading cause of cancer in Puerto Rican men and exhibits significant racial disparities globally. Although only 8% of cases invade beyond the prostate, predicting PCa aggressiveness is challenging. This study investigated the potential of the retinoblastoma tumor suppressor protein phosphorylated in Serine 249 (Phospho-Rb S249), N-cadherin, β-catenin, and E-cadherin as biomarkers for identifying aggressive PCa in Puerto Rican men. We hypothesized that the expression of these proteins could serve as biomarkers for identifying PCa tumors with potential to becoming aggressive in Puerto Rican men. Immunohistochemistry was performed on 23 biopsies from Puerto Rican men to evaluate the biomarkers’ expression, and correlation analyses examined associations with clinicopathological parameters. Results showed that Phospho-Rb S249 expression correlated positively with tumor size and positive cores in patients with Gleason scores ≥4+3. β-catenin was positively associated with tumor size and carcinoma percentage in Gleason scores >4+3. E-cadherin expression negatively correlated with grade group, indicating a protective role. In contrast, N-cadherin and β-catenin were more prominent in Gleason scores <3+4, hinting at their involvement in early epithelial-to-mesenchymal transition (EMT). A decision tree analysis identified N-cadherin expression as a key determinant for classifying PCa aggressiveness, with an 82% likelihood. These findings suggest N-cadherin as a biomarker for identifying PCa with the potential to become aggressive. While our study provides promising results, further validation in a larger patient cohort is needed to increase the robustness and reliability of our findings. Also, combining multiple biomarkers could further enhance the specificity of aggressive PCa detection.
Authors:
- Sheila M. Valle Cortés
- Jaileene Pérez Morales
- Mariely Nieves Plaza
- Raymond Quiñones Alvarado
- Gilberto Ruiz Deyá
- Juan C. Santa Rosario
- Pedro Santiago Cardona
DOI: https://doi.org/10.71332/wdwgem70
Keywords: Prostate cancer, B-catenin, Puerto Rico, Epitheliat-toMesenchymal Transition (EMT)
References
Borregales, L.D.; DeMeo, G.; Gu, X.; et al. Grade migration of prostate cancer in the United States during the last decade. J. Natl. Cancer Inst. 2022, 114, 1012–1019. doi:10.1093/jnci/djac066.
Siegel, R.L.; Miller, K.D.; Wagle, N.S.; Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin. 2023, 73, 17–48. doi:10.3322/caac.21763.
Van de Merbel, A.F.; van der Horst, G.; Buijs, J.T.; van der Pluijm, G. Protocols for migration and invasion studies in prostate cancer. In Methods in Molecular Biology; Clifton, N.J., Ed.; Springer: Berlin, Germany, 2018; Volume 1786, pp. 67–79. doi:10.1007/978-1-4939-7845-8_4.
National Cancer Institute [NCI]. SEER Cancer Stat Facts: Prostate Cancer. Available online: https://seer.cancer.gov/statfacts/html/prost.html (accessed on 10 June 2024).
Loeb, S.; Bjurlin, M.; Nicholson, J.; Tammela, T.; Penson, D.; Carter, B.; Carroll, P.; Etzioni, R. Overdiagnosis and overtreatment of prostate cancer. Eur. Urol. 2014, 65, 1046–1055. doi:10.1016/j.eururo.2013.12.062.
Aizer, A.A.; Gu, X.; Chen, M.H.; Choueiri, T.K.; Martin, N.E.; Efstathiou, J.A.; Hyatt, A.S.; Graham, P.L.; Trinh, Q.D.; Hu, J.C.; Nguyen, P.L. Cost implications and complications of overtreatment of low-risk prostate cancer in the United States. J. Natl. Compr. Cancer Netw. 2015, 13, 61–68. doi:10.6004/jnccn.2015.0009.
Boström, P.; Bjartell, A.; Catto, J.; Eggener, S.; Lilja, H.; Loeb, S.; Schalken, J.; Schlomm, T.; Cooperberg, M. Genomic predictors of outcome in prostate cancer. Eur. Urol. 2015, 68, 1033–1044. doi:10.1016/j.eururo.2015.04.008.
Ebell, M.H. Prolaris test for prostate cancer risk assessment. Am. Fam. Physician 2019, 100, 311–312.
Health Quality Ontario. Prolaris cell cycle progression test for localized prostate cancer: A health technology assessment. Ont. Health Technol. Assess. Ser. 2017, 17, 1–75.
Dall’Era, M.A.; Maddala, T.; Polychronopoulos, L.; Gallagher, J.R.; Febbo, P.G.; Denes, B.S. Utility of the Oncotype DX® Prostate Cancer Assay in clinical practice for treatment selection in men newly diagnosed with prostate cancer: A retrospective chart review analysis. Urology Pract. 2015, 2, 343–348. doi:10.1016/j.urpr.2015.02.007.
Creed, J.H.; Berglund, A.E.; Rounbehler, R.J.; Awasthi, S.; Cleveland, J.L.; Park, J.Y.; Yamoah, K.; Gerke, T.A. Commercial gene expression tests for prostate cancer prognosis provide paradoxical estimates of race-specific risk. Cancer Epidemiol. Biomarkers Prev. 2020, 29, 246–253. doi:10.1158/1055-9965.EPI-19-0407.
Riaz, I.; Islam, M.; Ikram, W.; Naqvi, S.; Maqsood, H.; Saleem, Y.; Riaz, A.; Ravi, P.; Wang, Z.; Hussain, S.; Warner, J.; Odedina, F.; Duma, N.; Singh, P.; Kehl, K.; Kamran, S.; Murad, M.; Landman, A.; Allen, E.; Bryce, A. Disparities in the inclusion of racial and ethnic minority groups and older adults in prostate cancer clinical trials: A meta-analysis. JAMA Oncol. 2022. doi:10.1001/jamaoncol.2022.5511.
Soto-Salgado, M.; Suárez, E.; Torres-Cintrón, M.; Pettaway, C.; Colón, V.; Ortiz, A. Prostate cancer incidence and mortality among Puerto Ricans: an updated analysis comparing men in Puerto Rico with US racial/ethnic groups. P.R. Health Sci. J. 2012, 31, 107–113.
Chinea, F.; Patel, V.; Kwon, D.; Lamichhane, N.; Lopez, C.; Punnen, S.; Kobetz, E.; Abramowitz, M.; Pollack, A. Ethnic heterogeneity and prostate cancer mortality in Hispanic/Latino men: A population-based study. Oncotarget 2017, 8, 19068. doi:10.18632/oncotarget.19068.
Borrell, L. Racial identity among Hispanics: implications for health and well-being. Am. J. Public Health 2005, 95, 379–381. doi:10.2105/AJPH.2004.058172.
El-Bahrawy, M.; Pignatelli, M. E-cadherin and catenins: molecules with versatile roles in normal and neoplastic epithelial cell biology. Microsc. Res. Tech. 1998, 43, 224–235. doi:10.1002/(SICI)1097-0029(19981101)43:3<224::AID-JEMT4>3.0.CO;2-Q.
Gumbiner, B.M. Regulation of cadherin-mediated adhesion in morphogenesis. Nat. Rev. Mol. Cell Biol. 2005, 6, 622–634. doi:10.1038/nrm1699.
Mendonsa, A.M.; Na, T.Y.; Gumbiner, B.M. E-cadherin in contact inhibition and cancer. Oncogene 2018, 37, 4769–4780. doi:10.1038/s41388-018-0304-2.
Lamouille, S.; Xu, J.; Derynck, R. Molecular mechanisms of epithelial-mesenchymal transition. Nat. Rev. Mol. Cell Biol. 2014, 15, 178–196. doi:10.1038/nrm3758.
Francis, J.; Thomsen, M.; Taketo, M.; Swain, A. β-Catenin is required for prostate development and cooperates with Pten loss to drive invasive carcinoma. PLoS Genet. 2013, 9, e1003180. doi:10.1371/journal.pgen.1003180.
Yang, J.; Weinberg, R.A. Epithelial-mesenchymal transition: at the crossroads of development and tumor metastasis. Dev. Cell 2008, 14, 818–829. doi:10.1016/j.devcel.2008.05.009.
Barber, A.G.; Castillo-Martin, M.; Bonal, D.M.; Jia, A.J.; Rybicki, B.A.; Christiano, A.M.; Cordon-Cardo, C. PI3K/AKT pathway regulates E-cadherin and Desmoglein 2 in aggressive prostate cancer. Cancer Med. 2015, 4, 1258–1271. doi:10.1002/cam4.463.
Gravdal, K.; Halvorsen, O.; Haukaas, S.; Akslen, L. A switch from E-cadherin to N-cadherin expression indicates epithelial to mesenchymal transition and is of strong and independent importance for the progress of prostate cancer. Clin. Cancer Res. 2007, 13, 7003–7011. doi:10.1158/1078-0432.CCR-07-1263.
Giacinti, C.; Giordano, A. RB and cell cycle progression. Oncogene 2006, 25, 5220–5227. doi:10.1038/sj.onc.1209615.
Chen, L.; Liu, S.; Tao, Y. Regulating tumor suppressor genes: post-translational modifications. Signal Transduct. Target. Ther. 2020, 5, 196. doi:10.1038/s41392-020-0196-9.
Aparicio, A.; Den, R.B.; Knudsen, K.E. Time to stratify? The retinoblastoma protein in castrate-resistant prostate cancer. Nat. Rev. Urol. 2011, 8, 562–568. doi:10.1038/nrurol.2011.107.
Pérez-Morales, J.; Mejías-Morales, D.; Rivera-Rivera, S.; González-Flores, J.; González-Loperena, M.; Cordero-Báez, F.Y.; Pedreira-García, W.M.; Chardón-Colón, C.; Cabán-Rivera, J.; Cress, W.D.; Gordian, E.R.; Muñoz-Antonia, T.; Cabrera-Ríos, M.; Isidro, A.; Coppola, D.; Rosa, M.; Boyle, T.A.; Izumi, V.; Koomen, J.M.; Santiago-Cardona, P.G. Hyper-phosphorylation of Rb S249 together with CDK5R2/p39 overexpression are associated with impaired cell adhesion and epithelial-to-mesenchymal transition: Implications as a potential lung cancer grading and staging biomarker. PLoS One 2018, 13, e0207483. doi:10.1371/journal.pone.0207483.
Valle Cortés, S.M.; Pérez Morales, J.; Nieves Plaza, M.; Maldonado, D.; Tevenal Baez, S.M.; Negrón Blas, M.A.; Lazcano Etchebarne, C.; Feliciano, J.; Ruiz Deyá, G.; Santa Rosario, J.C.; Santiago Cardona, P. Integrating proteomic analysis and machine learning to predict prostate cancer aggressiveness. Stats 2024, 7, 875–893. doi:10.3390/stats7030053.
Pérez-Morales, J.; Núñez-Marrero, A.; Santiago-Cardona, P.G. Immunohistochemical detection of retinoblastoma protein phosphorylation in human tumor samples. In Methods in Molecular Biology; Clifton, N.J., Ed.; Springer: Berlin, Germany, 2018; Volume 1726, pp. 77–84. doi:10.1007/978-1-4939-7565-5_8.
National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®): Prostate Cancer. In NCCN Guidelines Version 1.2025; National Comprehensive Cancer Network: Plymouth Meeting, PA, USA, 2025. Available online: https://www.nccn.org/professionals/physician_gls/pdf/prostate.pdf (accessed on 14 March 2025).
Munjal, A.; Leslie, S.W. Gleason Score. [Updated 2023 May 1]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-. Available online: https://www.ncbi.nlm.nih.gov/books/NBK553178/.
Zhu, X.; Gou, X.; Zhou, M. Nomograms predict survival advantages of Gleason score 3+4 over 4+3 for prostate cancer: A SEER-based study. Front. Oncol. 2019, 9, 646. doi:10.3389/fonc.2019.00646.
Chan, T.; Partin, A.; Walsh, P.; Epstein, J. Prognostic significance of Gleason score 3+4 versus Gleason score 4+3 tumor at radical prostatectomy. Urology 2000, 56, 823–827. doi:10.1016/S0090-4295(00)00753-6.
Epstein, J. Prostate cancer grade groups correlate with prostate-specific cancer mortality: SEER data for contemporary graded specimens. Eur. Urol. 2017, 71, 764–765. doi:10.1016/j.eururo.2016.12.014.
Saadatmand, S.; de Kruijf, E.M.; Sajet, A.; Dekker-Ensink, N.G.; van Nes, J.G.H.; Putter, H.; Smit, V.T.H.B.M.; van de Velde, C.J.H.; Liefers, G.J.; Kuppen, P.J.K. Expression of cell adhesion molecules and prognosis in breast cancer. Br. J. Surg. 2013, 100, 252–260. doi:10.1002/bjs.8980.
Hui, L.; Zhang, S.; Dong, X.; Tian, D.; Cui, Z.; Qiu, X. Prognostic significance of twist and N-cadherin expression in NSCLC. PLoS One 2013, 8, e62171. doi:10.1371/journal.pone.0062171.
Seo, D.D.; Lee, H.C.; Kim, H.J.; Min, H.J.; Kim, K.M.; Lim, Y.S.; Chung, Y.H.; Lee, Y.S.; Suh, D.J.; Yu, E.; Chun, S.Y. Neural cadherin overexpression is a predictive marker for early postoperative recurrence in hepatocellular carcinoma patients. J. Gastroenterol. Hepatol. 2008, 23, 1112–1118. doi:10.1111/j.1440-1746.2007.05182.x.
Nieman, M.; Prudoff, R.; Johnson, K.; Wheelock, M. N-Cadherin promotes motility in human breast cancer cells regardless of their E-cadherin expression. J. Cell Biol. 1999, 147, 631–644. doi:10.1083/jcb.147.3.631.
Cao, Z.; Wang, Z.; Leng, P. Aberrant N-cadherin expression in cancer. Biomed. Pharmacother. 2019, 118, 109320. doi:10.1016/J.BIOPHA.2019.109320.
Li, B.; Shi, H.; Wang, F.; Hong, D.; Lv, W.; Xie, X.; Cheng, X. Expression of E-, P- and N-Cadherin and its clinical significance in cervical squamous cell carcinoma and precancerous lesions. PLoS One 2016, 11. doi:10.1371/journal.pone.0155910.
Su, Y.; Li, J.; Shi, C.; Hruban, R.H.; Radice, G.L. N-cadherin functions as a growth suppressor in a model of K-ras-induced PanIN. Oncogene 2016, 35, 3335–3341. doi:10.1038/onc.2015.382.
Loh, C.; Chai, J.; Tang, T.; Wong, W.; Sethi, G.; Shanmugam, M.; Chong, P.; Looi, C. The E-Cadherin and N-Cadherin switch in epithelial-to-mesenchymal transition: Signaling, therapeutic implications, and challenges. Cells 2019, 8. doi:10.3390/cells8101118.
Derycke, L.D.; Bracke, M.E. N-Cadherin in the spotlight of cell-cell adhesion, differentiation, embryogenesis, invasion and signalling. Int. J. Dev. Biol. 2004, 48, 463–476. doi:10.1387/ijdb.041793ld.
Sadot, E.; Simcha, I.; Shtutman, M.; Ben-Ze'ev, A.; Geiger, B. Inhibition of β-catenin-mediated transactivation by cadherin derivatives. Proc. Natl. Acad. Sci. USA 1998, 95, 15339–15344. doi:10.1073/PNAS.95.26.15339.
Kafri, P.; Hasenson, S.E.; Kanter, I.; Sheinberger, J.; Kinor, N.; Yunger, S.; Shav-Tal, Y. Quantifying β-catenin subcellular dynamics and cyclin D1 mRNA transcription during Wnt signaling in single living cells. eLife 2016, 5, e16748. doi:10.7554/eLife.16748.
Orsulic, S.; Huber, O.; Aberle, H.; Arnold, S.; Kemler, R. E-cadherin binding prevents beta-catenin nuclear localization and beta-catenin/LEF-1-mediated transactivation. J. Cell Sci. 1999, 112, 1237–1245.
Gottardi, C.J.; Wong, E.; Gumbiner, B.M. E-cadherin suppresses cellular transformation by inhibiting β-catenin signaling in an adhesion-independent manner. J. Cell Biol. 2001, 153, 1049–1060. doi:10.1083/jcb.153.5.1049.
Kawada, M.; Seno, H.; Uenoyama, Y.; Sawabu, T.; Kanda, N.; Fukui, H.; Shimahara, Y.; Chiba, T. Signal transducers and activators of transcription 3 activation is involved in nuclear accumulation of beta-catenin in colorectal cancer. Cancer Res. 2006, 66, 2913–2917. doi:10.1158/0008-5472.CAN-05-3460.
Zhi, X.; Tao, J.; Xie, K.; Zhu, Y.; Li, Z.; Tang, J.; Wang, W.; Xu, H.; Zhang, J.; Xu, Z. MUC4-induced nuclear translocation of β-catenin: a novel mechanism for growth, metastasis and angiogenesis in pancreatic cancer. Cancer Lett. 2014, 346, 104–113. doi:10.1016/j.canlet.2013.12.021.
Perumal, E.; Youn, K.; Sun, S.; Seung-Hyun, J.; Suji, M.; Liu, J.; Yeun-Jun, C. PTEN inactivation induces epithelial-mesenchymal transition and metastasis by intranuclear translocation of β-catenin and snail/slug in non-small cell lung carcinoma cells. Lung Cancer 2019, 130, 25–34. doi:10.1016/j.lungcan.2019.01.013.
Oyama, T.; Kanai, Y.; Ochiai, A.; Akimoto, S.; Oda, T.; Yanagihara, K.; Nagafuchi, A.; Tsukita, S.; Shibamoto, S.; Ito, F. A truncated beta-catenin disrupts the interaction between E-cadherin and alpha-catenin: A cause of loss of intercellular adhesiveness in human cancer cell lines. Cancer Res. 1994, 54, 6282–6287.
Sanidas, I.; Morris, R.; Fella, K.A.; Rumde, P.H.; Boukhali, M.; Tai, E.C.; Ting, D.T.; Lawrence, M.S.; Haas, W.; Dyson, N.J. A code of mono-phosphorylation modulates the function of RB. Mol. Cell 2019, 73, 985–1000.e6. doi:10.1016/j.molcel.2019.01.004.
Rubin, S. Deciphering the retinoblastoma protein phosphorylation code. Trends Biochem. Sci. 2013, 38, 12–19. doi:10.1016/j.tibs.2012.10.007.
Ren, D.; Li, W.; Zeng, R.; Liu, X.; Liang, H.; Xiong, W.; Yang, C.; Jin, X. Retinoblastoma-associated protein is important for TRIM24-mediated activation of the mTOR signaling pathway through DUSP2 action in prostate cancer. Cell Death Differ. 2024, 31, 592–604. doi:10.1038/s41418-024-01282-w.
Yu-Lee, L.Y.; Yu, G.; Lee, Y.C.; Lin, S.C.; Pan, J.; Pan, T.; Yu, K.J.; Liu, B.; Creighton, C.J.; Rodriguez-Canales, J.; Villalobos, P.A.; Wistuba, I.I.; de Nadal, E.; Posas, F.; Gallick, G.E.; Lin, S.H. Osteoblast-secreted factors mediate dormancy of metastatic prostate cancer in the bone via activation of the TGFβRIII-p38MAPK-pS249/T252RB pathway. Cancer Res. 2018, 78, 2911–2924. doi:10.1158/0008-5472.CAN-17-1051.
Janostiak, R.; Torres-Sanchez, A.; Posas, F.; de Nadal, E. Understanding retinoblastoma post-translational regulation for the design of targeted cancer therapies. Cancers 2022, 14, 1265. doi:10.3390/cancers14051265.
Kretschmer, A.; Tilki, D. Biomarkers in prostate cancer – Current clinical utility and future perspectives. Crit. Rev. Oncol. Hematol. 2017, 120, 180–193. doi:10.1016/j.critrevonc.2017.11.007.
Shore, N.M. Management of early-stage prostate cancer. In Prostate Cancer Management: Enhancing Cost-Effectiveness and Patient Outcomes; 2015; Volume 20, Issue 12.
Samaržija, I. Post-translational modifications that drive prostate cancer progression. Biomolecules 2021, 11, 247. doi:10.3390/biom11020247.
Xu, Y.Y.; Shen, H.B.; Murphy, R.F. Learning complex subcellular distribution patterns of proteins via analysis of immunohistochemistry images. Bioinformatics 2020, 36, 1908–1914. doi:10.1093/bioinformatics/btz844.
Raab, S.S. The cost-effectiveness of immunohistochemistry. Arch. Pathol. Lab. Med. 2000, 124, 1185–1191. doi:10.5858/2000-124-1185-TCEOI.
Leslie, S.W.; Shamsudeen, I.A. Prostate cancer tissue-based biomarkers. [Updated 2023 May 30]. In StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024. Available online: https://www.ncbi.nlm.nih.gov/books/NBK587345/.
Tanase, C.P.; Codrici, E.; Popescu, I.D.; Mihai, S.; Enciu, A.M.; Necula, L.G.; Preda, A.; Ismail, G.; Albulescu, R. Prostate cancer proteomics: Current trends and future perspectives for biomarker discovery. Oncotarget 2017, 8, 18497–18512. doi:10.18632/oncotarget.14501.
Assadi, M.; Jokar, N.; Ghasemi, M.; Nabipour, I.; Gholamrezanezhad, A.; Ahmadzadehfar, H. Precision Medicine Approach in Prostate Cancer. Curr. Pharm. Des. 2020, 26, 3783–3798. https://doi.org/10.2174/1381612826666200218104921.
Abstract
Physician burnout is a global concern, yet its prevalence among Puerto Rico’s surgeons remains underexplored. This study assesses burnout among orthopaedic surgeons working within the island’s strained healthcare system, marked by economic disparities, privatization, and physician exodus. A cross-sectional survey was conducted among 67 orthopaedic surgeons (over 60% of the island's workforce) and 27 romantic partners. The survey included the Maslach Burnout Inventory-Human Services Survey, General Health Questionnaire 12, and the Revised Dyadic Adjustment Scale. Burnout was identified in 28.4% of surgeons, with 58.2% reporting high emotional exhaustion and 37.3% high depersonalization. No respondents exhibited low personal accomplishment. The most frequently cited stressor was Puerto Rico's healthcare system, particularly health insurance restrictions (66.7%). Additionally, 25.4% of surgeons showed signs of mental health concerns, and 22.0% experienced relationship distress. While emotional exhaustion and depersonalization present as alarmingly high, resilience in personal accomplishment was notable. Interventions are needed to address systemic stressors while leveraging cultural factors that protect against burnout.
Author:
- Gabriel González-Díaz
- Emil Varas-Rodríguez
- Joshua Vivas
- Francis Cedeño
- Gerardo E. Rodríguez-Matias
- Jerry Cruz
- Hans Hess
- Oscar Duyos
DOI: https://doi.org/10.71332/79h63q52
Keywords: burnout, emotional exhaustion, puerto rico, healthcare system, resilience, orthopaedic surgeons
References
Rotenstein, L. S., Torre, M., Ramos, M. A., Rosales, R. C., Guille, C., Sen, S., & Mata, D. A. (2018). Prevalence of Burnout Among Physicians: A Systematic Review. JAMA, 320(11), 1131–1150. https://doi.org/10.1001/jama.2018.12777
Dimou, F. M., Eckelbarger, D., & Riall, T. S. (2016). Surgeon Burnout: A Systematic Review. Journal of the American College of Surgeons, 222(6), 1230–1239. https://doi.org/10.1016/j.jamcollsurg.2016.03.022
Shanafelt, T. D., Bradley, K. A., Wipf, J. E., & Back, A. L. (2002). Burnout and self- reported patient care in an internal medicine residency program. Annals of internal medicine, 136(5), 358–367. https://doi.org/10.7326/0003-4819-136-5-200203050-00008
Daniels, A. H., DePasse, J. M., & Kamal, R. N. (2016). Orthopaedic Surgeon Burnout: Diagnosis, Treatment, and Prevention. The Journal of the American Academy of Orthopaedic Surgeons, 24(4), 213–219. https://doi.org/10.5435/JAAOS-D-15-00148
Pearl, A., Saleh, K., & Quick, J. C. (2023). Identifying and Addressing Burnout in the Orthopaedic Surgeon. The Journal of the American Academy of Orthopaedic Surgeons, 31(5), 229–238. https://doi.org/10.5435/JAAOS-D-22-00722
Roman J. (2015). The Puerto Rico Healthcare Crisis. Annals of the American Thoracic Society, 12(12), 1760–1763. https://doi.org/10.1513/AnnalsATS.201508-531PS
American Academy of Orthopaedic Surgeons Department of Clinical Quality and Value. (2019). Orthopaedic practice in the U.S. 2018. Retrieved from https://www.aaos.org/globalassets/quality-and-practice-resources/census/2018-census.pdf
Martinez Santiago, A. (2018). Informe positivo P. DEL S. 841. Puerto Rico Senate Health Commission Report. Retrieved from https://sutra.oslpr.org/osl/sutra/anejos/124994/ps0841-inf.doc
Wilkinson, E., Killeen, D., Pérez-López, G. J., & Jabbarpour, Y. (2020). A Shrinking Primary Care Workforce in Puerto Rico. American family physician, 101(1), 13–14.
Varas-Díaz, N., Rodríguez-Madera, S., Padilla, M., Rivera-Bustelo, K., Mercado-Ríos, C., Rivera-Custodio, J., Matiz-Reyes, A., Santiago-Santiago, A., González-Font, Y., Vertovec, J., Ramos-Pibernus, A., & Grove, K. (2023). On leaving: Coloniality and physician migration in Puerto Rico. Social science & medicine (1982), 325, 115888. https://doi.org/10.1016/j.socscimed.2023.115888
Kim M. (2022). Puerto Rico's Attempts to Address a Public Health Crisis Struck Down by the United States Court of Appeals for the First Circuit. American journal of law & medicine, 48(4), 481–486. https://doi.org/10.1017/amj.2023.10
Lim, W. Y., Ong, J., Ong, S., Hao, Y., Abdullah, H. R., Koh, D. L., & Mok, U. S. M. (2019). The Abbreviated Maslach Burnout Inventory Can Overestimate Burnout: A Study of Anesthesiology Residents. Journal of clinical medicine, 9(1), 61. https://doi.org/10.3390/jcm9010061
Comotti, A., Fattori, A., Greselin, F., Bordini, L., Brambilla, P., & Bonzini, M. (2023). Psychometric Evaluation of GHQ-12 as a Screening Tool for Psychological Impairment of Healthcare Workers Facing COVID-19 Pandemic. La Medicina del lavoro, 114(1), e2023009. https://doi.org/10.23749/mdl.v114i1.13918
Goldberg, D. P., Gater, R., Sartorius, N., Ustun, T. B., Piccinelli, M., Gureje, O., & Rutter, C. (1997). The validity of two versions of the GHQ in the WHO study of mental illness in general health care. Psychological medicine, 27(1), 191–197. https://doi.org/10.1017/s0033291796004242
Sánchez-López, M.delP., & Dresch, V. (2008). The 12-Item General Health Questionnaire (GHQ-12): reliability, external validity and factor structure in the Spanish population. Psicothema, 20(4), 839–843.
Crane, R., Middleton, K., Bean, R. (2000) Establishing Criterion Scores for the Kansas Marital Satisfaction Scale and the Revised Dyadic Adjustment Scale. The American Journal of Family Therapy, 28(1), 53-60, https://doi.org/10.1080/019261800261815
Ligibel, J. A., Goularte, N., Berliner, J. I., Bird, S. B., Brazeau, C. M. L. R., Rowe, S. G., Stewart, M. T., & Trockel, M. T. (2023). Well-Being Parameters and Intention to Leave Current Institution Among Academic Physicians. JAMA network open, 6(12), e2347894. https://doi.org/10.1001/jamanetworkopen.2023.47894
Sargent, M. C., Sotile, W., Sotile, M. O., Rubash, H., & Barrack, R. L. (2009). Quality of life during orthopaedic training and academic practice. Part 1: orthopaedic surgery residents and faculty. The Journal of bone and joint surgery. American volume, 91(10), 2395–2405. https://doi.org/10.2106/JBJS.H.00665
Beaton-Comulada, D., Torres-Lugo, N. J., Serra-López, V. M., Guevara-Serra, C., Muñoz- Miro, H., Ramirez, N., & Otero-López, A. (2022). Factors Influencing Graduating Medical Students in Puerto Rico to Pursue a Primary Care Residency in the Continental United States. Family medicine, 54(8), 629–633. https://doi.org/10.22454/FamMed.2022.546991
Cabrera, N., Alonso, A., Chen, Y., Ghosh, R. (2022). Latinx Families’ Strengths and Resilience Contribute to Their Well-Being. Bethesda, MD: National Research Center on Hispanic Children & Families. Retrieved from: https://www.hispanicresearchcenter.org/research-resources/latinx-families-strengths-and-resilience-contribute-to-their-well-being
Arora, M., Diwan, A. D., & Harris, I. A. (2013). Burnout in orthopaedic surgeons: a review. ANZ journal of surgery, 83(7-8), 512–515. https://doi.org/10.1111/ans.12292
Valdivieso-Mora, E., Peet, C. L., Garnier-Villarreal, M., Salazar-Villanea, M., & Johnson, D. K. (2016). A Systematic Review of the Relationship between Familism and Mental Health Outcomes in Latino Population. Frontiers in psychology, 7(1632). https://doi.org/10.3389/fpsyg.2016.01632
Hui, R. W. H., Leung, K. C., Ge, S., Hwang, A. C., Lai, G. G. W., Leung, A. N., & Leung, J. S. L. (2019). Burnout in orthopaedic surgeons: A systematic review. Journal of clinical orthopaedics and trauma, 10(Suppl 1), S47–S52. https://doi.org/10.1016/j.jcot.2019.01.028
Lovell, L. P., Atherley, A. E. N., Watson, H. R., & King, R. D. (2022). An exploration of burnout and resilience among emergency physicians at three teaching hospitals in the English-speaking Caribbean: A cross-sectional survey. Lancet regional health. Americas, 15, 100357. https://doi.org/10.1016/j.lana.2022.100357
Wong, K. P., Kaliya-Perumal, A. K., & Oh, J. (2019). Orthopaedic Resident Burnout: A Literature Review on Vulnerability, Risk Factors, Consequences and Management Strategies. Malaysian orthopaedic journal, 13(2), 15–19. https://doi.org/10.5704/MOJ.1907.003
Abstract
Hermansky-Pudlak Syndrome (HPS), characterized by oculocutaneous albinism and a bleeding diathesis, is an autosomal recessive disorder particularly found in Puerto Ricans. The highest prevalence is noted in the northwest of Puerto Rico, where one in 1,800 individuals are affected. Due to its complex presentation and treatment regimen, health information must ideally be accessible, understandable, and of high quality. We selected three keyword phrases in English and three in Spanish that describe the disease in layman’s terms and entered them as prompts in a Google search to simulate patient-initiated searches. The first 20 websites for each of the six terms were analyzed for quality and readability utilizing a DISCERN instrument questionnaire and an online readability test tool, respectively. The results for the English terms yielded a mean general reliability of 72.8%, with the quality and reliability of treatment information at a mean of 37.4%. The average grade level (AGL) recommended for the English sites was 14, with academic and private/commercial sites scoring 15 and 13, respectively. For the Spanish keywords, the general mean reliability was 65.8%, and the treatment information mean score was 27.4%. The recommended AGL for Spanish websites was 16. Although information on HPS is readily available, our analysis suggests that the complexity of the content may be high, with extreme variations in quality. This phenomenon was observed in both languages. Our results highlight the need to provide understandable, simple, and reliable quality information in both languages for educational purposes.
Author:
- Laura I. Ortiz-López, MD
- Karla M. Santiago-Soltero, MD
- Sofia Milosavljevic, BA
- Krithika Nayudu, BA
- Mihir K. Patil, BA
- Goranit Sakunchotpanit, BS
- Rhea Malik
- TJ Hazen, BA
- Stephanie Sánchez-Meléndez, MD
- Vinod E. Nambudiri, MD, MBA
DOI: https://doi.org/10.71332/vp3egj48
Keywords: Hermansky-Pudlak Syndrome, Online Health Information, Albinism, Quality and Readability, Hispanic, Genodermatosis, Bilingual
References
Huizing M, Malicdan MCV, Gochuico BR, Gahl WA. Hermansky-Pudlak Syndrome [Internet]. Adam MP, Ardinger HH, Pagon RA, Wallace SE, Bean LJ, Mirzaa G, et al., editors. PubMed. Seattle (WA): University of Washington, Seattle; 1993 [cited 2021 Feb 17]. Available from: https://pubmed.ncbi.nlm.nih.gov/20301464/
Richmond BL, Huizing M, Knapp J, Koshoffer A, Zhao Y, Gahl WA, et al. Melanocytes Derived from Patients with Hermansky–Pudlak Syndrome Types 1, 2, and 3 Have Distinct Defects in Cargo Trafficking. Journal of Investigative Dermatology. 2005 Feb 1;124(2):420–7.
Torres-Serrant M, Ramirez SI, Cadilla CL, Ramos-Valencia G, Santiago-Borrero PJ. Newborn Screening for Hermansky-Pudlak Syndrome Type 3 in Puerto Rico. Journal of Pediatric Hematology Oncology [Internet]. 2010 Aug 1 [cited 2024 Mar 26];32(6):448–53. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3640623/
Witkop CJ, Nuñez Babcock M, Rao GH, Gaudier F, Summers CG, Shanahan F, et al. Albinism and Hermansky-Pudlak syndrome in Puerto Rico. Boletin De La Asociacion Medica De Puerto Rico [Internet]. 1990 Aug 1 [cited 2024 Mar 26];82(8):333–9. Available from: https://pubmed.ncbi.nlm.nih.gov/2261023/#:~:text=HPS%20was%20observed%20in%20five
Yu Z, Dee EC, Nambudiri VE, Ogbechie-Godec OA, Jakus J, Siegel DM. Quality and Readability of Online Health Information for Acral Lentiginous Melanoma. Dermatologic Surgery [Internet]. 2021 May 1 [cited 2022 Sep 28];47(5):697–8. Available from: https://journals.lww.com/dermatologicsurgery/Citation/2021/05000/Quality_and_Readability_of_Online_Health.36.aspx
Malik R, Chen J, Lau C, Sandoval A, Nambudiri VE. Generalized pustular psoriasis: Quality and readability of online health information. Experimental Dermatology. 2023 Feb 28;32(8):1317–21.
Charnock D, Shepperd S, Needham G, Gann R. DISCERN: an instrument for judging the quality of written consumer health information on treatment choices. Journal of Epidemiology & Community Health. 1999 Feb 1;53(2):105–11.
Walsh TM, Volsko TA. Readability assessment of internet-based consumer health information. Respiratory Care [Internet]. 2008 Oct 1;53(10):1310–5. Available from: https://pubmed.ncbi.nlm.nih.gov/18811992/
Nayudu K, Malik R, Sanchez-Melendez S, Hazen TJ, Patil MK, Nambudiri VE. Phototherapy for vitiligo: Quality and readability of online health information. Photodermatol Photoimmunol Photomed. 2024 Mar;40(2):e12958.
Meppelink CS, van Weert JCM, Brosius A, Smit EG. Dutch health websites and their ability to inform people with low health literacy. Patient Educ Couns. 2017 Jun 10.
Schluter C, Fefer M, Lee G, Alty IG, Dee EC. Investigation of the readability and reliability of online health information for cancer patients during the coronavirus pandemic. J Cancer Educ. 2023 Apr;38(2):462–6.
De Jesus Rojas W, Young LR. Hermansky-Pudlak Syndrome. Semin Respir Crit Care Med. 2020 Apr 12;41(2):238–46.
Hand, Jennifer L. “Hermansky-Pudlak Syndrome.” Edited by Moise L Levy and Rosamaria Corona, UpToDate, 16 Nov. 2023, www.uptodate.com/contents/hermansky-pudlak-syndrome#:~:text=Hermansky%2DPudlak%20syndrome%20(HPS),been%20recognized%20(table%201).
“Hermansky-Pudlak Syndrome: Medlineplus Genetics.” MedlinePlus, U.S. National Library of Medicine, 1 May 2014, medlineplus.gov/genetics/condition/hermansky-pudlak-syndrome/.
“Hermansky Pudlak Syndrome - Symptoms, Causes, Treatment: Nord.” National Organization for Rare Disorders, 20 Nov. 2023, rarediseases.org/rare-diseases/hermansky-pudlak-syndrome/.
“Home: Hermansky-Pudlak Syndrome Network.” HermanskyPudlak Syndrome Network, www.hpsnetwork.org/. Accessed 1 Sept. 2024.
“Hermansky-Pudlak.” Departamento de Salud de Puerto Rico, www.salud.pr.gov/CMS/DOWNLOAD/5793. Accessed 1 Sept. 2024.
“Síndrome de Hermansky-Pudlak.” Genetic and Rare Diseases Information Center, U.S. Department of Health and Human Services, rarediseases.info.nih.gov/espanol/13245/sindrome-de-hermansky-pudlak. Accessed 1 Sept. 2024.
¿Qué Es El Síndrome de Hermansky-Pudlak?, www.thoracic.org/patients/patient-resources/resources/spanish/hps.pdf. Accessed 1 Sept. 2024.
Masiano, S.P.; Martin, E.G.; Bono, R.S.; Dahman, B.; Sabik, L.M.; Belgrave, F.Z.; Adimora, A.A.; Kimmel, A.D. Suboptimal geographic accessibility to comprehensive HIV care in the US: regional and urban-rural differences. J Int AIDS Soc 2019, 22, e25286, DOI:http://dx.doi.org/10.1002/jia2.25286.
Isath, A.; Malik, A.H.; Goel, A.; Gupta, R.; Shrivastav, R.; Bandyopadhyay, D. Nationwide Analysis of the Outcomes and Mortality of Hospitalized COVID-19 Patients. Curr Probl Cardiol 2023, 48, 101440, DOI:http://dx.doi.org/10.1016/j.cpcardiol.2022.101440.
FDA Approves First COVID-19 Vaccine Approval Signifies Key Achievement for Public Health. Available online: https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine (accessed on 2 April 2024).
Bast, E.; Tang, F.; Dahn, J.; Palacio, A. Increased risk of hospitalisation and death with the delta variant in the USA. The Lancet Infectious Diseases 2021, 21, 1629-1630, DOI:http://dx.doi.org/10.1016/S1473-3099(21)00685-X.
Khera, R.; Angraal, S.; Couch, T.; Welsh, J.W.; Nallamothu, B.K.; Girotra, S.; Chan, P.S.; Krumholz, H.M. Adherence to methodological standards in research using the national inpatient sample. JAMA 2017, 318, 2011-2018, DOI:http://dx.doi.org/10.1001/jama.2017.17653.
MapChart. Available online: https://www.mapchart.net/usa.html (accessed on 4 April 2024).
Quan, H.; Sundararajan, V.; Halfon, P.; Fong, A.; Burnand, B.; Luthi, J.-C.; Saunders, L.D.; Beck, C.A.; Feasby, T.E.; Ghali, W.A. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical care 2005, 43, 1130-1139, DOI:http://dx.doi.org/10.1097/01.mlr.0000182534.19832.83.
McCormick, P.J.; Lin, H.M.; Deiner, S.G.; Levin, M.A. Validation of the All Patient Refined Diagnosis Related Group (APR-DRG) Risk of Mortality and Severity of Illness Modifiers as a Measure of Perioperative Risk. J Med Syst 2018, 42, 81, DOI:http://dx.doi.org/10.1007/s10916-018-0936-3.
HCUP. NIS Description of Data Elements - FEMALE Available online: https://hcup-us.ahrq.gov/db/vars/female/nisnote.jsp#general (accessed on 6 April 2024).
HCUP. ZIPINC_QRTL - Median household income for patient's ZIP Code (based on current year) Available online: https://hcup-us.ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp (accessed on 27 March 2024).
3M™ All Patient Refined Diagnosis Related Groups (APR DRGs). . Available online: https://www.3m.com/3M/en_US/health-information-systems-us/drive-value-based-care/patient-classification-methodologies/apr-drgs/ (accessed on April 6, 2024).
NIS Overview. Available online: https://hcup-us.ahrq.gov/nisoverview.jsp (accessed on April 6, 2024).
Wordtune AI21 Labs, 2024. Available online: https://www.wordtune.com/ (accessed on April 6, 2024).
OpenAI (2020). Chatbot GPT-4. OpenAI. . Available online: https://chat.openai.com/ (accessed on 6 June 2024).
Vandenbroucke, J.P.; von Elm, E.; Altman, D.G.; Gotzsche, P.C.; Mulrow, C.D.; Pocock, S.J.; Poole, C.; Schlesselman, J.J.; Egger, M.; Initiative, S. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Int J Surg 2014, 12, 1500-1524, DOI:http://dx.doi.org/10.1016/j.ijsu.2014.07.014.
Vardar, U.; Ilelaboye, A.; Murthi, M.; Atluri, R.; Park, D.Y.; Khamooshi, P.; Ojemolon, P.E.; Shaka, H.; Khamooshi, P. Racial Disparities in Patients With COVID-19 Infection: A National Inpatient Sample Analysis. Cureus 2023, 15, e35039, DOI:http://dx.doi.org/10.7759/cureus.35039.
Vallabhajosyula, S.; Patlolla, S.H.; Dunlay, S.M.; Prasad, A.; Bell, M.R.; Jaffe, A.S.; Gersh, B.J.; Rihal, C.S.; Holmes Jr, D.R.; Barsness, G.W. Regional variation in the management and outcomes of acute myocardial infarction with cardiogenic shock in the United States. Circulation: Heart Failure 2020, 13, e006661, DOI:http://dx.doi.org/10.1161/CIRCHEARTFAILURE.119.006661.
Mullins, R.J.; Diggs, B.S.; Hedges, J.R.; Newgard, C.D.; Arthur, M.; Adams, A.L.; Veum-Stone, J.; Lenfesty, B.; Trunkey, D.D. Regional Differences in Outcomes for Hospitalized Injured Patients. Journal of Trauma and Acute Care Surgery 2006, 60, 691-700, DOI:http://dx.doi.org/10.1097/01.ta.0000210454.92078.89.
Kolte, D.; Khera, S.; Aronow, W.S.; Palaniswamy, C.; Mujib, M.; Ahn, C.; Iwai, S.; Jain, D.; Sule, S.; Ahmed, A. Regional variation in the incidence and outcomes of in-hospital cardiac arrest in the United States. Circulation 2015, 131, 1415-1425, DOI:http://dx.doi.org/10.1161/CIRCULATIONAHA.114.014542.
Kim, S.J.; Medina, M.; Zhong, L.; Chang, J. Factors associated with in-hospital death among pneumonia patients in US hospitals from 2016~ 2019. International Journal of Health Policy and Management 2023, 12, DOI:http://dx.doi.org/10.34172/ijhpm.2023.7390.
Roth, G.A.; Emmons-Bell, S.; Alger, H.M.; Bradley, S.M.; Das, S.R.; de Lemos, J.A.; Gakidou, E.; Elkind, M.S.V.; Hay, S.; Hall, J.L.; et al. Trends in Patient Characteristics and COVID-19 In-Hospital Mortality in the United States During the COVID-19 Pandemic. JAMA Network Open 2021, 4, e218828-e218828, DOI:http://dx.doi.org/10.1001/jamanetworkopen.2021.8828.
Rosenthal, N.; Cao, Z.; Gundrum, J.; Sianis, J.; Safo, S. Risk factors associated with in-hospital mortality in a US national sample of patients with COVID-19. JAMA network open 2020, 3, e2029058-e2029058, DOI:http://dx.doi.org/10.1001/jamanetworkopen.2020.29058.
Lurie, N.; Sharfstein, J.M. State-to-state differences in US COVID-19 outcomes: searching for explanations. The Lancet 2023, 401, 1314-1315, DOI:http://dx.doi.org/10.1016/S0140-6736(23)00726-2.
Bechman, K.; Yates, M.; Mann, K.; Nagra, D.; Smith, L.-J.; Rutherford, A.I.; Patel, A.; Periselneris, J.; Walder, D.; Dobson, R.J. Inpatient COVID-19 mortality has reduced over time: results from an observational cohort. PloS one 2022, 17, e0261142, DOI:http://dx.doi.org/10.1371/journal.pone.0261142.
Ibuka, Y.; Matsuda, Y.; Shoji, K.; Ishigaki, T. Evaluation of regional variations in healthcare utilization. Japanese Journal of Statistics and Data Science 2020, 3, 349-365.
Couillard, B.K.; Foote, C.L.; Gandhi, K.; Meara, E.; Skinner, J. Rising geographic disparities in US mortality. Journal of Economic Perspectives 2021, 35, 123-146.
Duong, W.Q.; Grigorian, A.; Farzaneh, C.; Nahmias, J.; Chin, T.; Schubl, S.; Dolich, M.; Lekawa, M. Racial and Sex Disparities in Trauma Outcomes Based on Geographical Region. The American Surgeonâ„¢ 2021, 87, 988-993, DOI:http://dx.doi.org/10.1177/0003134820960063.
Hanna, D.B.; Selik, R.M.; Tang, T.; Gange, S.J. Disparities among US states in HIV-related mortality in persons with HIV infection, 2001–2007. Aids 2012, 26, 95-103.
Singh, G.K.; Azuine, R.E.; Siahpush, M.; Williams, S.D. Widening geographical disparities in cardiovascular disease mortality in the United States, 1969-2011. International Journal of MCH and AIDS 2015, 3, 134.
O’Keefe, E.B.; Meltzer, J.P.; Bethea, T.N. Health disparities and cancer: racial disparities in cancer mortality in the United States, 2000–2010. Frontiers in public health 2015, 3, 51.
Dugani, S.B.; Wood-Wentz, C.M.; Mielke, M.M.; Bailey, K.R.; Vella, A. Assessment of disparities in diabetes mortality in adults in US rural vs nonrural counties, 1999-2018. JAMA network open 2022, 5, e2232318-e2232318.
Kirby, J.B.; Taliaferro, G.; Zuvekas, S.H. Explaining racial and ethnic disparities in health care. Medical care 2006, 44, I-64-I-72.
Fiscella, K.; Sanders, M.R. Racial and ethnic disparities in the quality of health care. Annual review of public health 2016, 37, 375-394.
Lazar, H.L. Administrative databases for outcomes research-quick, easy, but dirty. J Card Surg 2017, 32, 757, DOI:http://dx.doi.org/10.1111/jocs.13380.
Introduction to the HCUP National Inpatient Sample (NIS) 2021. Available online: https://hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2021.jsp (accessed on 29 March 2024).
Abstract
Obesity and Type 2 Diabetes Mellitus (T2DM) represent significant global health challenges, contributing to high morbidity and mortality due to a combination of lifestyle, dietary, and genetic factors. Traditional management strategies, including lifestyle modifications, pharmacotherapy, and bariatric surgery, often fail to achieve sustained weight loss and glycemic control or are overly invasive. This review explores the potential of semaglutide (a glucagon-like peptide-1 [GLP-1] receptor agonist) and tirzepatide (a dual GLP-1/glucose-dependent insulinotropic polypeptide [GIP] receptor agonist) as transformative therapies for obesity and T2DM management. These agents act by stimulating glucose-dependent insulin secretion, suppressing glucagon release, delaying gastric emptying, and reducing appetite, resulting in significant weight reduction and improved glycemic control. Tirzepatide further enhances these benefits through dual receptor activation, promoting fat oxidation and additional metabolic benefits. Clinical trials, such as the STEP and SURPASS programs, demonstrate that semaglutide and tirzepatide achieve weight loss of up to 20.9% of initial body weight, comparable to bariatric surgery, and HbA1c reductions exceeding 2%, surpassing the 0.5% to 2% reductions typically observed with other T2DM therapies. These agents also improve cardiometabolic health and quality of life. Both drugs are generally well-tolerated, with gastrointestinal issues being the most common side effects. These therapies redefine obesity and diabetes as treatable chronic conditions, transforming metabolic health management. This review examines their mechanisms of action, efficacy, and safety, underscoring their potential to revolutionize the approach to these conditions.
Author: James P. Torres-Pirela
DOI: https://doi.org/10.71332/ekx6t266
Keywords: Semaglutide, Tirzepatide, GLP-1 receptor agonist, GIP receptor agonist, Obesity, Type 2 Diabetes Mellitus
References
Wen, J., et al., Semaglutide Versus Other Glucagon-Like Peptide-1 Agonists for Weight Loss in Type 2 Diabetes Patients: A Systematic Review and Meta-Analysis. Cureus, 2024. 16(9): p. e69008.
Majety, P., et al., Pharmacological approaches to the prevention of type 2 diabetes mellitus. Front Endocrinol (Lausanne), 2023. 14: p. 1118848.
Brown, E., et al., Weight loss variability with SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes mellitus and obesity: Mechanistic possibilities. Obesity Reviews, 2019. 20(6): p. 816-828.
Alorfi, N.M. and A.S. Algarni, Clinical Impact of Semaglutide, a Glucagon-Like Peptide-1 Receptor Agonist, on Obesity Management: A Review. Clinical Pharmacology: Advances and Applications, 2022. 14(null): p. 61-67.
Müller, T.D., et al., Glucagon-like peptide 1 (GLP-1). Mol Metab, 2019. 30: p. 72-130.
Holst, J.J. and M.M. Rosenkilde, GIP as a Therapeutic Target in Diabetes and Obesity: Insight From Incretin Co-agonists. The Journal of Clinical Endocrinology & Metabolism, 2020. 105(8): p. e2710-e2716.
Nauck, M.A. and T.D. Müller, Incretin hormones and type 2 diabetes. Diabetologia, 2023. 66(10): p. 1780-1795.
Knudsen, L.B. and J. Lau, The Discovery and Development of Liraglutide and Semaglutide. Frontiers in Endocrinology, 2019. 10.
Chao, A.M., et al., Semaglutide for the treatment of obesity. Trends Cardiovasc Med, 2023. 33(3): p. 159-166.
Wilding, J.P.H., et al., Weight regain and cardiometabolic effects after withdrawal of semaglutide: The STEP 1 trial extension. Diabetes Obes Metab, 2022. 24(8): p. 1553-1564.
Bergmann, N.C., et al., Semaglutide for the treatment of overweight and obesity: A review. Diabetes Obes Metab, 2023. 25(1): p. 18-35.
Jung, H.N. and C.H. Jung, The Upcoming Weekly Tides (Semaglutide vs. Tirzepatide) against Obesity: STEP or SURPASS? Journal of Obesity & Metabolic Syndrome, 2022. 31(1): p. 28-36.
Sinha, R., et al., Efficacy and Safety of Tirzepatide in Type 2 Diabetes and Obesity Management. J Obes Metab Syndr, 2023. 32(1): p. 25-45.
Wilding, J.P.H., et al., Once-Weekly Semaglutide in Adults with Overweight or Obesity. New England Journal of Medicine, 2021. 384(11): p. 989-1002.
Marso, S.P., et al., Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. New England Journal of Medicine, 2016. 375(19): p. 1834-1844.
Aroda, V.R., et al., Efficacy and safety of once-weekly semaglutide versus once-daily insulin glargine as add-on to metformin (with or without sulfonylureas) in insulin-naive patients with type 2 diabetes (SUSTAIN 4): a randomised, open-label, parallel-group, multicentre, multinational, phase 3a trial. The Lancet Diabetes & Endocrinology, 2017. 5(5): p. 355-366.
Smits, M.M. and D.H. Van Raalte, Safety of Semaglutide. Frontiers in Endocrinology, 2021. 12.
Zeng, Q., et al., Safety issues of tirzepatide (pancreatitis and gallbladder or biliary disease) in type 2 diabetes and obesity: a systematic review and meta-analysis. Frontiers in Endocrinology, 2023. 14.
Sodhi, M., et al., Risk of Gastrointestinal Adverse Events Associated With Glucagon-Like Peptide-1 Receptor Agonists for Weight Loss. JAMA, 2023. 330(18): p. 1795-1797.
Marx, N., et al., GLP-1 Receptor Agonists for the Reduction of Atherosclerotic Cardiovascular Risk in Patients With Type 2 Diabetes. Circulation, 2022. 146(24): p. 1882-1894.
Bloomgarden, Z., Is insulin the preferred treatment for HbA1c >9%? J Diabetes, 2017. 9(9): p. 814-816.
Takayanagi, R., et al., Evaluation of Drug Efficacy of GLP-1 Receptor Agonists and DPP-4 Inhibitors Based on Target Molecular Binding Occupancy. Biological & pharmaceutical bulletin, 2018. 41 2: p. 153-157.
Hausner, H., et al., Effect of Semaglutide on the Pharmacokinetics of Metformin, Warfarin, Atorvastatin and Digoxin in Healthy Subjects. Clin Pharmacokinet, 2017. 56(11): p. 1391-1401.
Han, S.H., et al., Public Interest in the Off-Label Use of Glucagon-like Peptide 1 Agonists (Ozempic) for Cosmetic Weight Loss: A Google Trends Analysis. Aesthetic Surgery Journal, 2023. 44(1): p. 60-67.
Peng, W., et al., Novel Insights into the Roles and Mechanisms of GLP-1 Receptor Agonists against Aging-Related Diseases. Aging Dis, 2022. 13(2): p. 468-490.
Abstract
The role of early-onset obesity-related genetic predisposition and leptin receptor variants have been previously studied. However, studies involving sleep-related disorders linked to a genetic predisposition, leading to obesity, and how leptin could play a role in sleep-related disorders have been limited. In this study, we explore a case of how leptin receptor variants could play a role in the relationship between obesity and sleep-related disorders. We present a case of a morbidly obese (BMI of 62.87 kg/m2) Puerto Rican teenage female with a past medical history of type 2 diabetes mellitus, hypothyroidism, essential primary hypertension, and obstructive sleep apnea (OSA), who was evaluated due to complications regarding sleeping difficulties, despite being on Continuous Positive Airway Pressure (CPAP) treatment. Genetic studies performed to assess the causes of obesity revealed BBS9 heterozygous gene for a sequence variant defined as c.396GC and heterozygous LEPR gene for a sequence variant defined as c.658GA, which has been associated with an increased predisposition to obesity. This case report emphasizes the value of genetic research in figuring out the root causes of obesity and its comorbidities, especially in cases of early-onset obesity and co-occurring disorders such as OSA. The discovery of genetic variations in LEPR and BBS9 genes offers crucial information on potential mechanisms underlying the clinical phenotype of the patient.
Author:
- José Colón-Soto
- Bryan Vega-Sanabria
- Roberto A. Cardona-Quiñones
- Alexandra Balsalobre Vélez
- Simón Carlo-Torres, MD
- Jesús Meléndez-Montañez, MD
- Wilfredo De Jesús-Rojas, MD, FAAP, MSc, ATSF
DOI: https://doi.org/10.71332/cv0z6q31
Keywords: leptin receptor variants, early-onset diabetes, obstructive sleep apnea
References
Apovian, C. M. (2016). Obesity: definition, comorbidities, causes, and burden. The American Journal of Managed Care, 22(7 Suppl), s176-85. PMID: 27356115.
Schwartz, M. W., Seeley, R. J., Zeltser, L. M., Drewnowski, A., Ravussin, E., Redman, L. M., & Leibel, R. L. (2017). Obesity Pathogenesis: An Endocrine Society Scientific Statement. Endocrine Reviews, 38(4), 267–296. https://doi.org/10.1210/er.2017-00111
Lin, X., & Li, H. (2021). Obesity: Epidemiology, Pathophysiology, and Therapeutics. Frontiers in Endocrinology, 12, 706978. https://doi.org/10.3389/fendo.2021.706978
Wu, Y., Duan, H., Tian, X., Xu, C., Wang, W., Jiang, W., Pang, Z., Zhang, D., & Tan, Q. (2018). Genetics of Obesity Traits: A Bivariate Genome-Wide Association Analysis. Frontiers in Genetics, 9, 179. https://doi.org/10.3389/fgene.2018.00179
Obradovic, M., Sudar-Milovanovic, E., Soskic, S., Essack, M., Arya, S., Stewart, A. J., Gojobori, T., & Isenovic, E. R. (2021). Leptin and Obesity: Role and Clinical Implication. Frontiers in Endocrinology, 12, 585887. https://doi.org/10.3389/fendo.2021.585887
Liu, X., Wu, C., Li, C., & Boerwinkle, E. (2016). dbNSFP v3.0: A One-Stop Database of Functional Predictions and Annotations for Human Nonsynonymous and Splice-Site SNVs. Human Mutation, 37(3), 235–241. https://doi.org/10.1002/humu.22932
Park, H.-K., & Ahima, R. S. (2014). Leptin signaling. F1000prime Reports, 6, 73. https://doi.org/10.12703/P6-73
Perakakis, N., Farr, O. M., & Mantzoros, C. S. (2021). Leptin in Leanness and Obesity: JACC State-of-the-Art Review. Journal of the American College of Cardiology, 77(6), 745–760. https://doi.org/10.1016/j.jacc.2020.11.069
Pomeroy, J., Krentz, A. D., Richardson, J. G., Berg, R. L., VanWormer, J. J., & Haws, R. M. (2021). Bardet-Biedl syndrome: Weight patterns and genetics in a rare obesity syndrome. Pediatric Obesity, 16(2), e12703. https://doi.org/10.1111/ijpo.12703
Nunziata, A., Funcke, J.-B., Borck, G., von Schnurbein, J., Brandt, S., Lennerz, B., Moepps, B., Gierschik, P., Fischer-Posovszky, P., & Wabitsch, M. (2019). Functional and Phenotypic Characteristics of Human Leptin Receptor Mutations. Journal of the Endocrine Society, 3(1), 27–41. https://doi.org/10.1210/js.2018-00123
Courbage, S., Poitou, C., Le Beyec-Le Bihan, J., Karsenty, A., Lemale, J., Pelloux, V., Lacorte, J.-M., Carel, J.-C., Lecomte, N., Storey, C., De Filippo, G., Coupaye, M., Oppert, J.-M., Tounian, P., Clément, K., & Dubern, B. (2021). Implication of Heterozygous Variants in Genes of the Leptin-Melanocortin Pathway in Severe Obesity. The Journal of Clinical Endocrinology and Metabolism, 106(10), 2991–3006. https://doi.org/10.1210/clinem/dgab404
Clément, K., Vaisse, C., Lahlou, N., Cabrol, S., Pelloux, V., Cassuto, D., Gourmelen, M., Dina, C., Chambaz, J., Lacorte, J. M., Basdevant, A., Bougnères, P., Lebouc, Y., Froguel, P., & Guy-Grand, B. (1998). A mutation in the human leptin receptor gene causes obesity and pituitary dysfunction. Nature, 392(6674), 398–401. https://doi.org/10.1038/32911
Farooqi, I. S., Wangensteen, T., Collins, S., Kimber, W., Matarese, G., Keogh, J. M., Lank, E., Bottomley, B., Lopez-Fernandez, J., Ferraz-Amaro, I., Dattani, M. T., Ercan, O., Myhre, A. G., Retterstol, L., Stanhope, R., Edge, J. A., McKenzie, S., Lessan, N., Ghodsi, M., … O’Rahilly, S. (2007). Clinical and molecular genetic spectrum of congenital deficiency of the leptin receptor. The New England Journal of Medicine, 356(3), 237–247. https://doi.org/10.1056/NEJMoa063988
Kleinendorst, L., van Haelst, M. M., & van den Akker, E. L. T. (2017). Young girl with severe early-onset obesity and hyperphagia. BMJ Case Reports, 2017. https://doi.org/10.1136/bcr-2017-221067
Melendez-Montañez, J. M., & De Jesus-Rojas, W. (2024). The Tip of the Iceberg: Genotype of Puerto Rican Pediatric Obesity. Genes, 15(4), 394. https://doi.org/10.3390/genes15040394
Phillips, B. G., Kato, M., Narkiewicz, K., Choe, I., & Somers, V. K. (2000). Increases in leptin levels, sympathetic drive, and weight gain in obstructive sleep apnea. American Journal of Physiology. Heart and Circulatory Physiology, 279(1), H234-7. https://doi.org/10.1152/ajpheart.2000.279.1.H234
Suárez-González, J., Seidel, V., Andrés-Zayas, C. et al. Novel biallelic variant in BBS9causative of Bardet–Biedl syndrome: expanding the spectrum of disease-causing genetic alterations. BMC Med Genomics 14, 91 (2021). https://doi.org/10.1186/s12920-021-00943-w
Forsythe, E., & Beales, P. L. (2013). Bardet-Biedl syndrome. European Journal of Human Genetics : EJHG, 21(1), 8–13. https://doi.org/10.1038/ejhg.2012.115
Chen, J., Smaoui, N., Hammer, M. B. H., Jiao, X., Riazuddin, S. A., Harper, S., Katsanis, N., Riazuddin, S., Chaabouni, H., Berson, E. L., & Hejtmancik, J. F. (2011). Molecular analysis of Bardet-Biedl syndrome families: report of 21 novel mutations in 10 genes. Investigative Ophthalmology & Visual Science, 52(8), 5317–5324. https://doi.org/10.1167/iovs.11-7554
Barton, A. R., Hujoel, M. L. A., Mukamel, R. E., Sherman, M. A., & Loh, P.-R. (2022). A spectrum of recessiveness among Mendelian disease variants in UK Biobank. American Journal of Human Genetics, 109(7), 1298–1307. https://doi.org/10.1016/j.ajhg.2022.05.008
Katsanis, N. (2004). The oligogenic properties of Bardet-Biedl syndrome. Human Molecular Genetics, 13 Spec No 1, R65-71. https://doi.org/10.1093/hmg/ddh092
Kalyta, K., Stelmaszczyk, W., Szczęśniak, D., Kotuła, L., Dobosz, P., & Mroczek, M. (2023). The Spectrum of the Heterozygous Effect in Biallelic Mendelian Diseases-The Symptomatic Heterozygote Issue. Genes, 14(8). https://doi.org/10.3390/genes14081562
Myers, M. G., Heymsfield, S. B., Haft, C., Kahn, B. B., Laughlin, M., Leibel, R. L., Tschöp, M. H., & Yanovski, J. A. (2012). Challenges and opportunities of defining clinical leptin resistance. Cell Metabolism, 15(2), 150–156. https://doi.org/10.1016/j.cmet.2012.01.002
Liu, J., Lai, F., Hou, Y., & Zheng, R. (2022). Leptin signaling and leptin resistance. Medical Review (Berlin, Germany), 2(4), 363–384. https://doi.org/10.1515/mr-2022-0017
Berger, S., & Polotsky, V. Y. (2018). Leptin and Leptin Resistance in the Pathogenesis of Obstructive Sleep Apnea: A Possible Link to Oxidative Stress and Cardiovascular Complications. Oxidative Medicine and Cellular Longevity, 2018, 5137947. https://doi.org/10.1155/2018/5137947
Abstract
This is a case describing the sequence of stercoral ulceration leading to fecal impaction and subsequent feculent peritonitis in a patient with end-stage renal disease (ESRD). Analyzing the patient’s operative course and hospitalization illustrates the complex nature of ESRD and the associated circumstances leading to this patient’s death. Utilizing the patient’s imaging and pathology results concludes that medication resins such as sevelamer caused mucosal ulceration in her colon. These results emphasize current literature which describes the potential for gastrointestinal complications with use of sevelamer.
Author:
- Shruti Rai
- Loren Bach
DOI: https://doi.org/10.71332/7z4qvf31
Keywords: sevelamer, colonic ulceration, feculent peritonitis, end-stage renal disease, hyperphosphatemia
References
Biruete, A., Hill Gallant, K. M., Lindemann, S. R., Wiese, G. N., Chen, N. X., & Moe, S. M. (2020). Phosphate binders and nonphosphate effects in the gastrointestinal tract. Journal of Renal Nutrition, 30(1), 4–10. https://doi.org/10.1053/j.jrn.2019.01.004
Madan, P., Bhayana, S., Chandra, P., & Hughes, J. I. (2008). Lower gastrointestinal bleeding: Association with sevelamer use. World Journal of Gastroenterology, 14(16), 2615–2616. https://doi.org/10.3748/wjg.14.2615
Nambiar, S., Pillai, U. K., Devasahayam, J., Oliver, T., & Karippot, A. (2018). Colonic mucosal ulceration and gastrointestinal bleeding associated with sevelamer crystal deposition in a patient with end-stage renal disease. Case Reports in Nephrology, 2018, 4708068. https://doi.org/10.1155/2018/4708068
Pant, P., LaBel, D., Hernandez Garcilazo, N., et al. (2023). Colitis associated with sevelamer carbonate: A case report. Annals of Internal Medicine Clinical Cases, 2, e220924. https://doi.org/10.7326/aimcc.2022.0924
Prlić, M. F., Jelaković, M., Brinar, M., Grgić, D., Romic, I., Marušić, Z., Ivandić, E., Jelaković, B., Vuković Brinar, I., & Krznarić, Ž. (2023). Sevelamer-associated colitis—a cause of pseudotumor formation with colon perforation and life-threatening bleeding: A case report. Frontiers in Medicine, 10. https://doi.org/10.3389/fmed.2023.1097469
Swanson, B. J., Limketkai, B. N., Liu, T. C., Montgomery, E., Nazari, K., Park, J. Y., Santangelo, W. C., Torbenson, M. S., Voltaggio, L., Yearsley, M. M., & Arnold, C. A. (2013). Sevelamer crystals in the gastrointestinal tract: A new entity associated with mucosal injury. American Journal of Surgical Pathology, 37(11), 1686–1693. https://doi.org/10.1097/PAS.0b013e3182999d8d
Todd, J., Saboori, S., Zeidan, J., Ahrens, W., Jacobs, C., & Moshiree, B. (2024). Sevelamer-induced gastrointestinal disease in 12 patients with end-stage renal disease: A case series. Clinical and Translational Gastroenterology, 15(3), e00679. https://doi.org/10.14309/ctg.0000000000000679
Yuste, C., Mérida, E., Hernández, E., García-Santiago, A., Rodríguez, Y., Muñoz, T., Gómez, G. J., Sevillano, Á., & Praga, M. (2017). Gastrointestinal complications induced by sevelamer crystals. Clinical Kidney Journal, 10(4), 539–544. https://doi.org/10.1093/ckj/sfx013
Masiano, S.P.; Martin, E.G.; Bono, R.S.; Dahman, B.; Sabik, L.M.; Belgrave, F.Z.; Adimora, A.A.; Kimmel, A.D. Suboptimal geographic accessibility to comprehensive HIV care in the US: regional and urban-rural differences. J Int AIDS Soc 2019, 22, e25286, DOI:http://dx.doi.org/10.1002/jia2.25286.
Isath, A.; Malik, A.H.; Goel, A.; Gupta, R.; Shrivastav, R.; Bandyopadhyay, D. Nationwide Analysis of the Outcomes and Mortality of Hospitalized COVID-19 Patients. Curr Probl Cardiol 2023, 48, 101440, DOI:http://dx.doi.org/10.1016/j.cpcardiol.2022.101440.
FDA Approves First COVID-19 Vaccine Approval Signifies Key Achievement for Public Health. Available online: https://www.fda.gov/news-events/press-announcements/fda-approves-first-covid-19-vaccine (accessed on 2 April 2024).
Bast, E.; Tang, F.; Dahn, J.; Palacio, A. Increased risk of hospitalisation and death with the delta variant in the USA. The Lancet Infectious Diseases 2021, 21, 1629-1630, DOI:http://dx.doi.org/10.1016/S1473-3099(21)00685-X.
Khera, R.; Angraal, S.; Couch, T.; Welsh, J.W.; Nallamothu, B.K.; Girotra, S.; Chan, P.S.; Krumholz, H.M. Adherence to methodological standards in research using the national inpatient sample. JAMA 2017, 318, 2011-2018, DOI:http://dx.doi.org/10.1001/jama.2017.17653.
MapChart. Available online: https://www.mapchart.net/usa.html (accessed on 4 April 2024).
Quan, H.; Sundararajan, V.; Halfon, P.; Fong, A.; Burnand, B.; Luthi, J.-C.; Saunders, L.D.; Beck, C.A.; Feasby, T.E.; Ghali, W.A. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Medical care 2005, 43, 1130-1139, DOI:http://dx.doi.org/10.1097/01.mlr.0000182534.19832.83.
McCormick, P.J.; Lin, H.M.; Deiner, S.G.; Levin, M.A. Validation of the All Patient Refined Diagnosis Related Group (APR-DRG) Risk of Mortality and Severity of Illness Modifiers as a Measure of Perioperative Risk. J Med Syst 2018, 42, 81, DOI:http://dx.doi.org/10.1007/s10916-018-0936-3.
HCUP. NIS Description of Data Elements - FEMALE Available online: https://hcup-us.ahrq.gov/db/vars/female/nisnote.jsp#general (accessed on 6 April 2024).
HCUP. ZIPINC_QRTL - Median household income for patient's ZIP Code (based on current year) Available online: https://hcup-us.ahrq.gov/db/vars/zipinc_qrtl/nisnote.jsp (accessed on 27 March 2024).
3M™ All Patient Refined Diagnosis Related Groups (APR DRGs). . Available online: https://www.3m.com/3M/en_US/health-information-systems-us/drive-value-based-care/patient-classification-methodologies/apr-drgs/ (accessed on April 6, 2024).
NIS Overview. Available online: https://hcup-us.ahrq.gov/nisoverview.jsp (accessed on April 6, 2024).
Wordtune AI21 Labs, 2024. Available online: https://www.wordtune.com/ (accessed on April 6, 2024).
OpenAI (2020). Chatbot GPT-4. OpenAI. . Available online: https://chat.openai.com/ (accessed on 6 June 2024).
Vandenbroucke, J.P.; von Elm, E.; Altman, D.G.; Gotzsche, P.C.; Mulrow, C.D.; Pocock, S.J.; Poole, C.; Schlesselman, J.J.; Egger, M.; Initiative, S. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration. Int J Surg 2014, 12, 1500-1524, DOI:http://dx.doi.org/10.1016/j.ijsu.2014.07.014.
Vardar, U.; Ilelaboye, A.; Murthi, M.; Atluri, R.; Park, D.Y.; Khamooshi, P.; Ojemolon, P.E.; Shaka, H.; Khamooshi, P. Racial Disparities in Patients With COVID-19 Infection: A National Inpatient Sample Analysis. Cureus 2023, 15, e35039, DOI:http://dx.doi.org/10.7759/cureus.35039.
Vallabhajosyula, S.; Patlolla, S.H.; Dunlay, S.M.; Prasad, A.; Bell, M.R.; Jaffe, A.S.; Gersh, B.J.; Rihal, C.S.; Holmes Jr, D.R.; Barsness, G.W. Regional variation in the management and outcomes of acute myocardial infarction with cardiogenic shock in the United States. Circulation: Heart Failure 2020, 13, e006661, DOI:http://dx.doi.org/10.1161/CIRCHEARTFAILURE.119.006661.
Mullins, R.J.; Diggs, B.S.; Hedges, J.R.; Newgard, C.D.; Arthur, M.; Adams, A.L.; Veum-Stone, J.; Lenfesty, B.; Trunkey, D.D. Regional Differences in Outcomes for Hospitalized Injured Patients. Journal of Trauma and Acute Care Surgery 2006, 60, 691-700, DOI:http://dx.doi.org/10.1097/01.ta.0000210454.92078.89.
Kolte, D.; Khera, S.; Aronow, W.S.; Palaniswamy, C.; Mujib, M.; Ahn, C.; Iwai, S.; Jain, D.; Sule, S.; Ahmed, A. Regional variation in the incidence and outcomes of in-hospital cardiac arrest in the United States. Circulation 2015, 131, 1415-1425, DOI:http://dx.doi.org/10.1161/CIRCULATIONAHA.114.014542.
Kim, S.J.; Medina, M.; Zhong, L.; Chang, J. Factors associated with in-hospital death among pneumonia patients in US hospitals from 2016~ 2019. International Journal of Health Policy and Management 2023, 12, DOI:http://dx.doi.org/10.34172/ijhpm.2023.7390.
Roth, G.A.; Emmons-Bell, S.; Alger, H.M.; Bradley, S.M.; Das, S.R.; de Lemos, J.A.; Gakidou, E.; Elkind, M.S.V.; Hay, S.; Hall, J.L.; et al. Trends in Patient Characteristics and COVID-19 In-Hospital Mortality in the United States During the COVID-19 Pandemic. JAMA Network Open 2021, 4, e218828-e218828, DOI:http://dx.doi.org/10.1001/jamanetworkopen.2021.8828.
Rosenthal, N.; Cao, Z.; Gundrum, J.; Sianis, J.; Safo, S. Risk factors associated with in-hospital mortality in a US national sample of patients with COVID-19. JAMA network open 2020, 3, e2029058-e2029058, DOI:http://dx.doi.org/10.1001/jamanetworkopen.2020.29058.
Lurie, N.; Sharfstein, J.M. State-to-state differences in US COVID-19 outcomes: searching for explanations. The Lancet 2023, 401, 1314-1315, DOI:http://dx.doi.org/10.1016/S0140-6736(23)00726-2.
Bechman, K.; Yates, M.; Mann, K.; Nagra, D.; Smith, L.-J.; Rutherford, A.I.; Patel, A.; Periselneris, J.; Walder, D.; Dobson, R.J. Inpatient COVID-19 mortality has reduced over time: results from an observational cohort. PloS one 2022, 17, e0261142, DOI:http://dx.doi.org/10.1371/journal.pone.0261142.
Ibuka, Y.; Matsuda, Y.; Shoji, K.; Ishigaki, T. Evaluation of regional variations in healthcare utilization. Japanese Journal of Statistics and Data Science 2020, 3, 349-365.
Couillard, B.K.; Foote, C.L.; Gandhi, K.; Meara, E.; Skinner, J. Rising geographic disparities in US mortality. Journal of Economic Perspectives 2021, 35, 123-146.
Duong, W.Q.; Grigorian, A.; Farzaneh, C.; Nahmias, J.; Chin, T.; Schubl, S.; Dolich, M.; Lekawa, M. Racial and Sex Disparities in Trauma Outcomes Based on Geographical Region. The American Surgeonâ„¢ 2021, 87, 988-993, DOI:http://dx.doi.org/10.1177/0003134820960063.
Hanna, D.B.; Selik, R.M.; Tang, T.; Gange, S.J. Disparities among US states in HIV-related mortality in persons with HIV infection, 2001–2007. Aids 2012, 26, 95-103.
Singh, G.K.; Azuine, R.E.; Siahpush, M.; Williams, S.D. Widening geographical disparities in cardiovascular disease mortality in the United States, 1969-2011. International Journal of MCH and AIDS 2015, 3, 134.
O’Keefe, E.B.; Meltzer, J.P.; Bethea, T.N. Health disparities and cancer: racial disparities in cancer mortality in the United States, 2000–2010. Frontiers in public health 2015, 3, 51.
Dugani, S.B.; Wood-Wentz, C.M.; Mielke, M.M.; Bailey, K.R.; Vella, A. Assessment of disparities in diabetes mortality in adults in US rural vs nonrural counties, 1999-2018. JAMA network open 2022, 5, e2232318-e2232318.
Kirby, J.B.; Taliaferro, G.; Zuvekas, S.H. Explaining racial and ethnic disparities in health care. Medical care 2006, 44, I-64-I-72.
Fiscella, K.; Sanders, M.R. Racial and ethnic disparities in the quality of health care. Annual review of public health 2016, 37, 375-394.
Lazar, H.L. Administrative databases for outcomes research-quick, easy, but dirty. J Card Surg 2017, 32, 757, DOI:http://dx.doi.org/10.1111/jocs.13380.
Introduction to the HCUP National Inpatient Sample (NIS) 2021. Available online: https://hcup-us.ahrq.gov/db/nation/nis/NIS_Introduction_2021.jsp (accessed on 29 March 2024).
Abstract
Founding of Clínica del Sur: Disparities in healthcare access and affordability exacerbate the constraints presented by a complex socioeconomic landscape in Puerto Rico (PR). According to the U.S. Census Bureau, the median household income in PR is $24,112, and 41.7% of people live in poverty on the island [1]. While the U.S. Census Bureau states that just 5.1% of people in PR do not have healthcare coverage, the healthcare system in PR struggles with employee shortages and lack of federal funding [2]. This makes healthcare less accessible despite Puerto Ricans having health insurance. As the elderly population continues to grow to the point that the percentage of elderly people in PR is among the top ten globally, challenges arise in accessing healthcare with family members migrating to the U.S. [1].
In 2019, medical students at Ponce Health Sciences University (PHSU) sought out to address these healthcare disparities. Despite significant earthquakes and the COVID-19 pandemic, the Student-Run Free Clinic (SRFC) Clínica del Sur was born in 2022. The clinic serves as a reliable resource for underserved populations and an opportunity for preclinical health sciences students to gain clinical experience and conduct research. Since its founding, Clínica del Sur has been run by students in medicine, psychology, nursing, and public health. Since then, the clinic has expanded to offer dermatology, dental, and neurology services. Clínica del Sur strives to provide comprehensive care in these areas to best serve the needs of its community similar to the Student-Run Clinic of University of Texas Río Grande Valley School of Medicine, which treats patients who do not have access to primary care [3]. Other clinics such as Qlinic, an LGBTQIA+ clinic at Cornell University, focus on one area of healthcare such as behavioral therapy in mental healthcare [4].
As a member of the National Association of Free and Charitable Clinics (NAFC), Clínica del Sur has been funded by the following organizations: Americares, Direct Relief, Brother’s Brother Foundation, Fundación Intellectus, and Heart to Heart Foundation. Through this membership, the clinic has also received a grant from CeraVe, from which the Sun Protection Campaign was launched to raise awareness and provide education on sun protection. Equipment has been acquired through the grant to improve the clinic’s dermatology services. For example, many patients are lost to follow-up due to the complicated structure of the island's healthcare system. To relieve patients of this burden, the clinic acquired a camera to capture photos of areas of concern on the skin so that patients have the necessary information in their file for their referral for continued cost-free care. The clinic is also a member of the Society of Student-Run Free Clinics (SSRFC), through which it hopes to gain funding and networking opportunities.
Author:
- Brittany Hahney, MPH
- Laura Domenech, MD
DOI: https://doi.org/10.71332/sv2b0m65
Keywords: student-run free clinic, preclinical, medical education, volunteers, health disparities, clinical skills, interprofessional
References
Auman-Gooding, K. Demographers identify the causes, challenges of a rapidly aging Puerto Rico. Penn State Social Science Research Institute 2023.
Roman, J. The Puerto Rico healthcare crisis. Ann Am Thorac Soc 2015, 12, 1760-1763. https://doi.org/10.1513/AnnalsATS.201508-531PS
Cauba, J. N., Callan, A., Alvarado, J., & Tapia, B. Establishing the first student-run clinic to provide free health care to a South Texas Colonia. J Stud Run Clin 2024, 10. https://doi.org/10.59586/jsrc.v10i1.415
Nghiem, J., Liu, M., Fruitman, K., Zhou, C., Zonana, J., Outram, T., Ceccolini, C. J., Spellun, J., & Hankins, D. Exploring preclinical medical students’ experience facilitating group dialectical behavioral therapy (DBT) for a student-run mental health clinic: A qualitative study. Acad Psychiatry 2024, 48, 334-338. https://doi.org/10.1007/s40596-024-01975-x
Abstract
Wilfredo De Jesus-Rojas, MD
Ponce Health Sciences University Scientific Journal
August 14, 2024
Dear Editor-in-Chief,
As former ESL (English as a Second Language) students who have pursued science, technology, engineering and mathematics (STEM) fields and current researchers and instructors, we intimately understand the challenges faced by students adapting not just to a new language, but to the complex context in which that language is used in academic settings. Our experiences have given us unique insights into the difficulties ESL students encounter in higher education, particularly in STEM disciplines.
Research aligns with our personal observations. ESL students often struggle with academic language proficiency, which can severely impact their ability to understand complex materials and engage in academic discourse [1]. These students are also more likely to face higher attrition rates [2], with studies indicating that they are more likely to experience academic probation or drop out compared to their native English-speaking peers [3]. ESL students often encounter significant obstacles in accessing and understanding academic resources that are primarily available in English, which can impede their ability to fully engage with the material.
To illustrate the challenges and potential solutions, we would like to share an experience from Nelson's academic journey that led us to appreciate the potential of generative AI in ESL students' education. During his graduate studies, Nelson was enrolled in a rigorous Real Analysis course. Despite his best efforts, he found himself struggling to grasp the material. In a meeting with his then mentor, Prof. Doug Moupasiri, Nelson confessed his difficulties. Prof. Moupasiri asked a simple yet profound question: “What other books on the subject have you read?'' When Nelson admitted that he had only been using the assigned textbook, Prof. Moupasiri encouraged him to explore other authors' works. This advice—to seek out different perspectives—was a turning point in Nelson's academic career. By finding an author whose style resonated with him, he was able to understand concepts that had previously eluded him. This experience made us realize that sometimes, the issue is not with the subject itself but with how it is presented.
This is where we see a tremendous opportunity for generative Al tools to support students, particularly ESL students, in their learning journey. ESL students often benefit from different learning approaches, and findings show that they have positive perceptions of AI-based learning tools, appreciating their personalized learning paths and time-saving advantages [4]. As evidenced in recent studies [5], AI technologies are already being successfully used in medical education to provide real-time feedback on quizzes, assist in anatomy learning, and support the recognition and diagnosis of medical images. Additionally, a study performed in a medical school in Puerto Rico showed that integrating a course aimed to bridge the gaps in AI knowledge among participants resulted in more positive perceptions of AI. However, it also revealed a lack of practical experience with AI applications, emphasizing the need for better integration of AI into educational programs [6].
Initiatives aimed specifically at Hispanic students are demonstrating the value of incorporating AI literacy into their education, helping them critically evaluate and effectively use AI technologies across different contexts [7]. By equipping students with essential AI competencies, these programs foster not only academic success but also readiness for AI-rich environments at home and in the workplace. We believe extending such support to ESL students can enhance equitable access to education by providing adaptive and personalized learning paths that cater to specific language needs. Just as different authors can present the same subject in varying ways, generative AI can offer students alternative explanations, analogies, and examples that align more closely with their individual learning styles. This ability to reshape content makes generative AI particularly powerful for students at institutions in Puerto Rico and elsewhere, allowing them to bridge gaps in understanding through tailored explanations in their native language or more accessible rephrasing, ultimately making challenging subjects more comprehensible.
Generative AI is not just a tool for information retrieval but a companion in the learning process, helping students navigate complex subjects with a personalized approach that traditional methods may not always provide. The greatest value of generative AI as a tutor lies in its ability to customize learning experiences, tailoring them to fit the context and background of each student. As we continue to integrate AI into the educational landscape, it is our hope that we can leverage these tools to not only enhance learning but also to empower students to overcome the challenges that come with language barriers and different learning styles. Just as Nelson's mentor's advice reshaped his educational path, we believe encouraging the correct use of generative AI can play a similar role for many students, directly impacting academic outcomes.
We urge educators and administrators to consider the potential of generative AI as a powerful tool for addressing the unique challenges faced by ESL students and those from diverse backgrounds. By embracing this technology, we can create a more inclusive and effective learning environment that supports all students in reaching their full potential.
Sincerely,
Nelson Colón Vargas, PhD and Marcos J. Ramos-Benitez, PhD
Author:
- Nelson Colón-Vargas, PhD
- Marcos J. Ramos Benítez, PhD
DOI: https://doi.org/10.71332/vta3fq83
Keywords: artificial intelligence, English as a Second Language, AI-learning
References
Andrade, M.S., International students in English-speaking universities: Adjustment factors. Journal of Research in International Education, 2006. 5(2): p. 131-154.
Zheng, R.X., et al., Unravelling the differences in attrition and academic performance of international and domestic nursing students with English as an additional language. Nurse Educ Today, 2014. 34(12): p. 1455-9.
Rodriguez, D., et al., Factors that challenge English learners and increase their dropout rates: recommendations from the field. International Journal of Bilingual Education and Bilingualism, 2022. 25(3): p. 878-894.
Amante-Nochefranca, G., et al. AI-Assisted English Language Learning and Teaching in a Developing Country: An Investigation of ESl Student’s Beliefs and Challenges. in Artificial Intelligence, Data Science and Applications. 2024. Cham: Springer Nature Switzerland.
Zhang, W., et al., AI in Medical Education: Global situation, effects and challenges. Education and Information Technologies, 2024. 29(4): p. 4611-4633.
Heredia-Negrón, F., et al., Assessing the Impact of AI Education on Hispanic Healthcare Professionals' Perceptions and Knowledge. Educ Sci (Basel), 2024. 14(4).
Sinha, N., Madhavarao, R., Freeman, R., Oujo, I., & Boyd, J. (2024). AI Literacy for Hispanic-Serving Institution (HSI) Students. In AAAI Spring Symposium Series (SSS-24). Palo Alto, CA: Association for the Advancement of Artificial Intelligence