Category Archives: Public Health

A Priori Tumor Grading Scale to Triage and Schedule Post COVID-19 Mohs Surgery Patients

The United States is slowly relaxing stay-at-home orders and reopening businesses to reverse the nation’s ailing economy after the spread of the coronavirus that killed millions of jobs and pummeled the global economy. The virus was initially identified as a cluster of pneumonia cases in Wuhan, Hubei Province of China. Later reclassified as a Novel Coronavirus on December 31, 2019, by the World Health Organization (WHO), and health experts in the US, Canada, Germany, Russia, China, Korea, Japan, and Nigeria declared COVID-19 as a Global Pandemic. During the peak of the COVID pandemic, the Centers for Disease and Prevention, the U.S. Centers for Medicare and Medicaid Services, and professional organizations issued countermeasures to flatten the curve, such as elective surgical procedure cancellation. The coronavirus infections are still on the upswing in Texas, Arizona, Florida, and Utah.

A system to forecast the transmission of the virus based on live and current data is critical (Petropoulos & Makridakis, 2020). Cumulative first wave data aggregated nationally and globally could provide accurate forecasting of probable second wave COVID-19 transmission. While accurate forecasting of the virus’s spread is essential, it is critical to establish guidelines to avoid a Coronavirus rebound and frivolous lawsuits that could stunt economic recovery. In the absence of liability protection that shields medical facilities, and employers, it is critical to developing a scheduling point system that controls the number of patients in the waiting area that could be exposed to the asymptomatic transmission of COVID-19. Even with robust liability protection, negligent facilities could still be punished and be sued for damages. The average wait period is 18 minutes during a medical visit. The patients have enough time to mingle, increasing the probability of transmission of infectious diseases based on the amount and nature of contacts between healthy and infected individuals (Goscé & Johansson, 2018). A priori tumor grading scale in scheduling Mohs patients in the post-COVID period as it relates to the parameters for diffusion is vital for the safety and protection of both the patients and healthcare workers. Depending on the tumor grade, the overall degree of connectivity, comorbidity in association to tumor history using the Labilles “United Paradigm of Cancer Causation” (2017), the patient needs to be seen as the earliest. The grading scale introduced on the “Manual of Frozen Section Processing for Mohs Micrographic Surgery” (2008) is an urgency-scoring system to assist Mohs surgeons, administrators, and staff in triaging surgery patients.

References

Beggs, C. B., Shepherd, S. J., & Kerr, K. G. (2010). Potential for airborne transmission of infection in the waiting areas of healthcare premises: stochastic analysis using a Monte Carlo model. BMC infectious diseases10(1), 247.

Franczyk, B. (2019, July). An Intelligent and Data-Driven Decision Support Solution for the Online Surgery Scheduling Problem. In Enterprise Information Systems: 20th International Conference, ICEIS 2018, Funchal, Madeira, Portugal, March 21-24, 2018, Revised Selected Papers (Vol. 363, p. 82). Springer.

Friedman, E., Friedman, J., Johnson, S., & Landsberg, A. (2020). Transitioning out of the coronavirus lockdown: A framework for zone-based social distancing. arXiv preprint arXiv:2004.08504.

Goscé, L., & Johansson, A. (2018). Analysing the link between public transport use and airborne transmission: mobility and contagion in the London underground. Environmental Health17(1), 84.

Labilles, U. (2017). Pathopoiesis mechanism of smoking and shared genes in pancreatic cancer (Copyright Registration No. TX0008490984) [Ph.D. dissertation, Walden University]. ProQuest Dissertations Publishing.

McCulloch, Hetzer, Geddis, McConnell, Brock, Keating, Labilles, Marino, Beck, Wade & Fisher (2008). Manual of Frozen Section Processing for Mohs Micrographic Surgery. In F. Fish III (Ed.). Labilles, U, Immunohistochemistry (p.1201). American College of Mohs Surgery.

Petropoulos, F., & Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PloS one15(3), e0231236.

IN THE CONTEXT OF DATA COLLECTION — IS BIGGER BETTER? Can big data help in our fight against COVID-19?

Color logo with backgroundThe traditional researcher concept that big data equates statistical significance should not eclipse the importance of understanding the interrelationship between the effect size, power, and sample size that could translate to both practical and statistical significance. It is critical to be guided by a working theory that gives birth to a useful solution to any given problem, such as in cancer epidemiology, most importantly, to the current COVID-19 pandemic. In 2017, the ”Unified Paradigm of Cancer Causation (UPCC),” a metatheory on cancer epidemiology, was introduced. The premise of this theory can be used in contact tracing as it relates to correlation with socio-behavioral risk factors, environmental interaction, and demographic determinants (SEDD). Understanding the trajectory of COVID-19 transmission could benefit from lessons learned from past and existing legal measures on efficient delivery of emergency and disaster relief not only by updating research reporting protocols but using a theory that bridge the gap between the federal, state, and local government. Petropoulos and Makridakis (2020) highlighted the integral significance of investigating the unknown variables associated with COVID-19 transmission. It is vital to explore the compounding factors that derail emergency relief. Should there be an improved, coordinated effort from the federal, state, and local governments? Are there other compounding factors in establishing an effective strategy in flattening the curve? In the next series, the questions on the COVID-19 epidemiology and its impact on Public Health will be explored, as well as how vital to streamline the Public Health Emergency Preparedness and Disaster Relief Systems. Months after the first case of the virus in the U.S, should we already have started establishing a seasonality pattern prediction protocol? This protocol does include not only age-specific social dynamics but also other compounding factors that complicate the public health countermeasure on social distancing?

It is crucial to adopt real-time modeling that is not only a computational algorithm but provides timely availability of relevant data, as stated by Birrell et al. (2020). Interdisciplinary collaboration both the U.S. and globally must embrace the promise of real-time modeling that provides a timely, relevant data available that could produce a novel cutting edge support tool and methodology for emergency and disaster management and public health policy.

References

Birrell, P. J., Wernisch, L., Tom, B. D., Held, L., Roberts, G. O., Pebody, R. G., & De Angelis, D. (2020). Efficient real-time monitoring of an emerging influenza pandemic: How feasible?. Annals of Applied Statistics14(1), 74-93.

Labilles, U. (2016). The New Public Health: Beyond Genetics and Social Inequalities. Unpublished manuscript, College of Health Sciences, Public Health, Epidemiology, Walden University, Minneapolis.

Labilles, U. (2017). Pathopoiesis Mechanism of Smoking and Shared Genes in Pancreatic Cancer.

Olson, D. R., Lopman, B. A., Konty, K. J., Mathes, R. W., Papadouka, V., Ternier, A., … & Pitzer, V. E. (2020). Surveillance data confirm multiyear predictions of rotavirus dynamics in New York City. Science advances6(9), eaax0586.

Petropoulos, F., & Makridakis, S. (2020). Forecasting the novel coronavirus COVID-19. PloS one15(3), e0231236.

Blast from the Past: Revisiting the Significance of Therapeutic Potential of Psilocybin

Research on the therapeutic potential of psychedelic drugs fell into a hiatus in the past decades until scientists from the United States, Switzerland, and Germany began its revival, exploring its biochemical and physiologic effects in combination with psychotherapy. In the 1950s to the mid-1960s, tens of thousands of patients are estimated to have been treated by psychedelic drugs such as psilocybin because of its unique potency, but its therapeutic promise was eclipsed by countercultural movements (Carhart-Harris, & Goodwin, 2017). Psilocybin, a naturally occurring plant alkaloid found in Psilocybe genus of mushrooms, popularly known as “magic mushrooms” may have been used for healing purposes for many years, (Carhart-Harris et al., 2016), but its pharmacodynamic significance needs high-quality clinical trials to earn medical justification. COMPASS Pathways was recently approved by the US Food and Drug Administration (FDA) for a clinical trial of psilocybin therapy for treatment-resistant depression (TRD).  A life sciences company with the primary intent of accelerating patient access to evidence-based innovation in mental health, COMPASS Pathways will springboard landmark trials across North America and Europe, including the UK and other countries, after regulatory approvals. Phase III studies will be dependent upon the success of these trials.

Globally, the burden of the high prevalence of TRD in the primary care setting justifies the need for a more clinician-led proactive approach to improving the outcome of patient management. While the 2016 study of Carhart-Harris et al. provides preliminary support for safety and efficacy of psilocybin for treatment of TRD, the study highlighted the importance of further trials with a more rigorous design. However, the lack of single definition of what constitutes ‘treatment resistance’ as noted by Thomas et al. (2013), made it critical to stratify research participants based on treatment resistance in association to non-adherence to medication, treatment resistance attributable to failure to respond after antidepressant medication to the more complex medication non-response classification systems. The FDA approval for COMPASS Pathways to conduct a Phase IIb trial will help amplify current knowledge on the effect of psilocybin on depression. Major depression has been ranked by the World Health Organization (WHO) as the fourth leading contributor to the global burden of disease and is expected to be top of the ranking by 2030 (Erritzoe et al., 2018). According to  George Goldsmith, COMPASS Pathways Chairman/Co-founder, and Ekaterina Malievskaia, Chief Medical Officer/Co-founder, the collaborative effort among scientists, clinicians, patient representatives and regulators from Europe and North America will give us evidence-based understanding of the efficacy of psilocybin therapy, as well as assess the safety and positive outcome of this new approach. The study will be the largest psychoactive care clinical trial following numerous academic early reviews on psilocybin’s pharmacodynamic and pharmacokinetic promise in improving the management outcome of TRD. Completion of COMPASS Pathways studies will raise awareness on the safety and efficacy of this novel protocol that could be benefited by patients with depression who failed to respond with either cognitive psychotherapy or medication. Removing psychological defenses as the original rationale of using psychedelic drugs in combination with psychotherapy, endorses the findings of Watts et al. (2017) on the increased amygdala (right amygdala) responses suggestive to positive mood effects and alleviation of depressive symptoms of psychedelics (Roseman et al., 2017). The continued focus on therapeutic interventions with evidence-based alternative strategies is critical in easing the socio-economic burden and improving the prognosis of TRD.

 

References

Carhart-Harris, R., Bolstridge, M., Rucker, J., Day, C., Erritzoe, D., Kaelen, M., Bloomfield, M., Rickard. J., Forbes, B., Feilding, A., Taylor, D., Pilling, S., Curran, V., Nutt, D. (2016). Psilocybin with psychological support for treatment-resistant depression: an open-label feasibility study. Lancet Psychiatry. 3, 619-27. doi: https://doi.org/10.1016/S2215-0366(16)30065-7

Carhart-Harris, R., & Goodwin, G. (2017). The Therapeutic Potential of Psychedelic Drugs: Past, Present, and Future. Neuropsychopharmacology, 1-9. doi: http://dx.doi.org/10.1038/npp.2017.84

Erritzoe, D., Roseman, L., Nour, M., MacLean, K., Kaelen, M., Nutt, D.J., Carhart-Harris, R.L. (2018). Effects of psilocybin therapy on personality structure. Acta Psychiatrica Scandinavica, 1–11. doi: 10.1111/acps.12904

Hermle L., Oepen G., Botsch H., Borchardt D., Gouzoulis E. et al. (1992). Mescaline-induced psychopathological, neuropsychological, and neurometabolic effects in normal subjects: experimental psychosis as a tool for psychiatric research. Biol Psychiatry, 32: 976–991. doi: http://dx.doi.org/10.1016/0006-3223(92)90059-9

Roseman, L., Demetriou, L., Wall, M., Nutt, D., Carhart-Harris, R. (2017). Increased amygdala responses to emotional faces after psilocybin for treatment-resistant depression. Neuropharmacology, 1-7. doi: https://doi.org/10.1016/j.neuropharm.2017.12.041

Strassman R., Qualls C. (1994). Dose-response study of N,Ndimethyltryptamine in humans. I. Neuroendocrine, autonomic, and cardiovascular effects. Arch Gen Psychiatry, 51: 85–97.

The Global Strategy Final – WHO. (n.d.). Retrieved from http://www.who.int/substance_abuse/alcstratenglishfinal.pdf?ua=1

Thomas, L., Kessler, D., Campbell, J., Morrison, J., Peters, T., Williams, C., Lewis, G., Wiles, N. (2013). Prevalence of treatment-resistant depression in primary care: a cross-sectional study. British Journal of General Practice. e852-e858. doi: http://dx.doi.org/10.3399/bjgp13X675430

Vollenweider F., Leenders K., Scharfetter C., Maguire P., Stadelmann O., Angst J. (1997). Positron emission tomography and fluorodeoxyglucose studies of metabolic hyperfrontality and psychopathology in the psilocybin model of psychosis. Neuropsychopharmacology, 16: 357–372. doi: http://dx.doi.org/10.1016/S0893-133X(96)00246-1

 

Bridging Cancer Epidemiology and Social Evolution

Research Design 2Modern epidemiology is a direct result of the paradigm shift from a population-based (upstream) to a downstream (individual) approach. The impact of modern epidemiology such as ‘molecular’ and ‘genetic’ epidemiology (Loomis & Wing, 1990; Diez-Roux, 1998) requires an explanatory power that mostly dependent upon the advances in technology and information systems. Moreover, before estimating the economic effect of a specific intervention before it is implemented, nor assess the economic and/or quality-of-life value of an ongoing or anticipated intervention (Rothermel, 2013); it is critical to recognize not only the significance of sophisticated technologies that go beyond the established genome, proteome, and gene expression platforms, but also new techniques of study design and data analysis (Pearce, 1996; Verma, Khoury & Ioannidis, 2013). Given the remarkable progress in the last decade in advanced technology and new methods for biologic measurements, the reductionist approach of modern epidemiology often ignored the significant causes of disease. Pearce (1996) argue that epidemiology must reintegrate itself into public health and must rediscover the population perspective. However, while the new paradigm could produce a lifestyle approach to social policy, the cumulative outcome of research in cancer epidemiology could equate positive implications to population health.

The key figures in the new epidemiologic model not only acknowledges the development of new techniques of study design and data analysis but also recognize the need for a multidisciplinary approach (social, biologic, statistical), and specifying the population group as the unit of study (Susser, 1985). While occupational carcinogens can be controlled with some difficulty through regulatory measures (Pearce, 1996), it is essential to acknowledge the fundamental problem of tobacco use, not by its consumption but in its production. Pearch (1996) focused on some of these fundamental changes in epidemiology over the past few decades and considered the concepts of causality involved, as well as their ideological and practical consequences. While smoking cessation could be the probable social implication, it is important to stress the epidemiologic value of a study on the apparent correlation between gender and age, modification effect of tobacco use among individuals with pancreatic cancer (PC) and cancer types with a shared-gene association (CTSG-A). The outcome of a risk factor epidemiologic study in individual terms could uplift precision medicine to meet the challenges in tailoring medical interventions based on patient’s biological profile, genetic and epigenetic traits, giving a better understanding of the environment, genetic, biodemographic interactions (EGBIs).

Embraced by both biomedical and social determinist frameworks, the interlinking of the traditional epidemiologic level of intervention (upstream or distal) and the modern epidemiologic level of intervention (downstream or proximal) put public health in the conundrum of the proximal-distal divide. Signal the importance of the argument of the 2008 study of Krieger in replacing the terms proximal and distal from the public health lexicon, supports the recommendation of Wemrell et al. (2016) on the critical need for open interdisciplinary debates on the contribution of social theory to the epidemiological inquiry. While coping with the demand of the 21st-century, global health could still be viewed and approached within the mindset of traditional epidemiology, and the purview of molecular and cancer epidemiology.

The discovery of tobacco smoking as a cause of lung cancer in the early 1950s gave the field of epidemiology its recognition (Pearce, 1996), shifting the epidemiologic paradigm in the object of study in the mid-20th century on the role of multiple causes. Establishing the correlation of age, gender a modifiable risk factor (smoking) with PC and CTSG-A requires the use of early and current epidemiologic theories, and contemporary mainstream epidemiologic concept coalescing to a United Paradigm of Cancer Causation (UPCC). The complex, integrative approach of UPCC supports the views of Loomis, and Wing (1990), Pearce (1996), and McEwen and Getz (2013) in embracing the new epidemiologic paradigm congruent to the advances in cancer genome sequencing. Theorizing the pathopoiesis mechanism of smoking, inherited genes, and association of gender and age in the etiopathogenesis of PC/CTSG-A warrants exploration of its causal footprints, conjoining both biomedical and lifestyle (Krieger, 2011).

Follow-up and future research on the role of molecular epidemiology in emphasizing individual susceptibility to PC will assess the relative contribution of modifiable risk factors to non-modifiable genetic factors. In this premise, the etiopathogenesis of the disease could be explored from the bottom up. Bridging cancer epidemiology and social evolution will be dependent upon the incorporation of the strength of the social network and social contagion theory. The testable assumption of the social network theory as its strength states that the social structure of the network itself be primarily responsible for determining individual behavior and attitudes by shaping the flow of resources which determines access to opportunities and constraints on behavior (Berkman et al., 2000). Why choose if a single theory cannot make a change? Incorporating these ideas in addition to the composite and underpinnings of UPCC could springboard a priori argument on the role of social networks in the spread of an intervention such as smoking cessation, or amplifying the promotion of the significance of early screening to improve mortality and morbidity.

While the causal nature of peer effects could be associated with tobacco use; the social contagion theory of Christakis and Fowler (2013) set an argument on human social networks exhibiting a “three degrees of separation.” Such association could support the assumption of spreading the interpersonal influence that acknowledges the significance of early screening, and the promise of a novel therapeutic approach. Like the widely discussed classic paper of Travers and Milgram (1969) on ‘six degrees of separation,’ the three degrees of separation or the three degrees of separation rule (Christakis & Fowler, 2009) agreed on the premise that telegraph phrases are meant to be evocative, and not definitive. For example, the role of interpersonal influence in spreading novel ideas such as advances in early screening to achieve a more significant therapeutic outcome. The preponderance of the evidence that points to the added significance of a passive-broadcast viral messaging to create social contagion warrants the recognition of the approach. Taking into account factors such as the promise of the outcome of a research study in the quality of life, social and economic incentives could expand the social network and amplify social support needed by individuals with PC or any deadly diseases. According to Kroenke et al. (2013), effective social support interventions need to evolve beyond social-emotional interventions and need to account for disease severity and treatment status.

References

Berkman, L. F., Glass, T., Brissette, I., & Seeman, T. E. (2000). From social integration to health: Durkheim in the new millennium. Social Science & Medicine51(6), 843-857. https://doi.org/10.1016/S0277-9536(00)00065-4

Christakis, N.A & Fowler, J.H. (2009). Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. (First ed.). New York: Little, Brown, and Company.

Christakis, N. A., & Fowler, J. H. (2013). Social contagion theory: examining dynamic social networks and human behavior. Statistics in medicine32(4), 556-577. doi: 10.1002/sim.5408

Diez-Roux, A. V. (1998). On genes, individuals, society, and epidemiology. American Journal of Epidemiology148(11), 1027-1032. http://dx.doi.org/10.1093/oxfordjournals.aje.a009578

Krieger, N. (2008). Proximal, Distal, and the Politics of Causation: What’s Level Got to Do With It?  American Journal of Public Health (AJPH), 98(2). http://dx.doi.org/10.2105/AJPH.2007.111278

Krieger, N. (2011). Epidemiology and the people’s health: theory and context (Vol. 213). New York: Oxford University Press.

Kroenke, C. H., Kwan, M. L., Neugut, A. I., Ergas, I. J., Wright, J. D., Caan, B. J., … & Kushi, L. H. (2013). Social networks, social support mechanisms, and quality of life after breast cancer diagnosis. Breast cancer research and treatment139(2), 515-527. doi:  10.1007/s10549-013-2477-2

Labilles, U. (2015a). Reevaluating the Impact of Cigarette Smoking on Pancreatic Cancer. Unpublished manuscript, College of Health Sciences, Public Health, Epidemiology, Walden University, Minneapolis.

Labilles, U. (2015b, September 27). A Promise to a Dying Brother [Web log post]. Retrieved from https://onenationsecho.com/2015/09/27/a-promised-to-a-dying-brother/.

Labilles, U. (2015c). Prospectus: Tobacco Use and Family Cancer History in the Pathopoiesis of Pancreatic Cancer. Unpublished manuscript, College of Health Sciences, Public Health, Epidemiology, Walden University, Minneapolis.

Labilles, U. (2016). The New Public Health: Beyond Genetics and Social Inequalities. Unpublished manuscript, College of Health Sciences, Public Health, Epidemiology, Walden University, Minneapolis.

Labilles, U. (2017). Pathopoiesis Mechanism of Smoking and Shared Genes in Pancreatic Cancer. ProQuest-CSA, LLC. Library of Congress, Copyright R# TX 8-490-984, Washington DC. doi: 10.13140/RG.2.2.30721.35681

Loomis, D., & Wing, S. (1990). Is molecular epidemiology a germ theory for the end of the twentieth century?. International journal of epidemiology, 19(1), 1-3. http://dx.doi.org/10.1093/ije/19.1.1

McEwen, B. S., & Getz, L. (2013). Lifetime experiences, the brain, and personalized medicine: An integrative perspective. Metabolism62, S20-S26. https://doi.org/10.1016/j.metabol.2012.08.020

Pearce, N. (1996). Traditional epidemiology, modern epidemiology, and public health. American journal of public health86(5), 678-683.

Rothermel, C. (2013). What is health economics and outcomes research? A primer for medical writers. AMWA Journal, 28(3)

Susser, M. (1985). Epidemiology in the United States after World War II: the evolution of technique. Epidemiologic reviews7(1), 147-177. http://dx.doi.org/10.1093/oxfordjournals.epirev.a036280

Travers, J., & Milgram, S. (1969). An experimental study of the small world problem. Sociometry, 425-443. doi: 10.2307/2786545

Verma, M., Khoury, M. J., & Ioannidis, J. P. (2013). Opportunities and challenges for selected emerging technologies in cancer epidemiology: mitochondrial, epigenomic, metabolomic, and telomerase profiling. Cancer Epidemiology Biomarkers & Prevention22(2), 189-200. http://dx.doi.org/10.1158/1055-9965.EPI-12-1263

Wemrell, M., Merlo, J., Mulinari, S., & Hornborg, A. C. (2016). Contemporary epidemiology: a review of critical discussions within the discipline and a call for further dialogue with social theory. Sociology Compass10(2), 153-171. doi: 10.1111/soc4.12345

 

From an Evolutionary Model to the Unified Paradigm of Cancer Causation (UPCC)

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Three essential events launched the field of cancer epidemiology during the 18th century. First, is Bernardino Ramazzini’s study on cervical cancer in 1713, the research of Percival Pott in 1775 that led the way on occupational carcinogenic exposure studies, and Thomas Venner on the danger of tobacco use in his Via Recta, published in London in 1620 (American Cancer Society, 2014). After two centuries when John Hill wrote a book entitled “Cautions Against the Immoderate Use of Snuff” in 1761; Krain (1970), along with other studies in the 1970s, Wynder, Mabuchi, Maruchi and Fortner (1973) explored the causality of tobacco use in the development of PC. Jones et al. (2008) found that PCs have an average of 63 genetic alterations that can explain the major features of pancreatic tumorigenesis. The intensive genetic studies described by Jones et al. (2008) gave way to the better understanding of the core set of pathways and processes, embracing the idea of Owens, Coffey, and Baylin (1982) that tumor heterogeneity is a fundamental facet of all solid tumors. While pancreatic cancer (PC) has few viable treatment options, Jones et al. (2008) suggested that the best hope for therapeutic development may lie in the discovery of agents that target the physiologic effects of the altered pathways and processes rather than their gene components. Above all, the significance that could not have been appreciated in the absence of global analysis is the identification of the precise genetic alterations that may be responsible for tumor pathway dysregulation (Jones et al., 2008).

The pathogenic theory of medicine or the germ theory of disease was highly controversial when first proposed as a concept that microorganisms are the cause of many diseases. After validation in the 19th century, germ theory revolutionized both medical thought and the art of surgery, becoming a fundamental part of modern medicine and clinical microbiology. My metatheory, the “Unified Paradigm of Cancer Causation (UPCC)” is as a composite of germ theory and Darwinian evolutionary system (Greaves & Maley, 2012) along with other theories will provide clarity on the narrative of the initiation of PC. Albeit the acceptance of the somatic mutation theory of carcinogenesis (SMT) as the mainstream narrative of how neoplasms develop (Soto & Sonnenschein, 2004), SMT included in the UPCC’s cocktail of theories will build on the arguments of the core principle of genetic variation and pattern of mutations (environmental and genetics) that are sufficient probable causes of the disease. UPCC could explain the behavior of PC cell in rationalizing the complex array of the possible interaction of smoking and inherited genes.

Pancreatic cancer is the fourth most prevalent cause of cancer death in Western societies and is projected to be the second leading cause within a decade (Waddell et al., 2015). While using the Darwinian methods that link human sociocultural progress to genetic evolution (Richerson & Boyd, 2000); Lynch and Rebbeck (2013) used a “Multi-level Biologic and Social Integrative Construct” (MBASIC) to integrate macro environment and individual factors with biology. Considering the limitation and information generated by single-level studies have reached a saturation point (Lynch & Rebbeck, 2013), I highlighted the significance of individual level (behaviors, carcinogenic exposures); and biologic level (inherited susceptibility variants in my dissertation “Pathopoiesis Mechanism of Smoking and Shared Genes in Pancreatic Cancer.” Germline changes associated with PC could range from slightly increased risk (low penetrance genes) to high lifetime risk (high penetrance genes). Given that PC is the antecedent of inherited (germline), and acquired (somatic) mutations in cancer-causing genes, adding the probable correlation between gender and age, modifiable risk factors to the equation that could trigger or wake up a sleeping germline mutation could position the result of a study for improved public health intervention, translation, and implementation in clinical settings to alter the expression of the disease.

References

American Cancer Society. (2014). History of cancer epidemiology. Retrieved from http://www.cancer.org/cancer/cancerbasics/thehistoryofcancer/the-history-of-cancer-cancer-epidemiology

Greaves, M., & Maley, C. C. (2012). Clonal evolution in cancer. Nature, 481(7381), 306-313. doi: 10.1038/nature10762

Hill, J. (1761). Cautions Against the Immoderate Use of Snuff: Founded on the Known Qualities of the Tobacco Plant and the Effects It Must Produce When This Way Taken into the Body. R. Baldwin and J. Jackson, London, UK. (Held now only as a self-contained pamphlet at shelfmark 1560/2918 in the British Library).

Jones, S., Hruban, R. H., Kamiyama, M., Borges, M., Zhang, X., Parsons, D. W., … & Iacobuzio-Donahue, C. A. (2009). Exomic sequencing identifies PALB2 as a pancreatic cancer susceptibility gene. Science324(5924), 217-217. doi: 10.1126/science.1171202

Krain, L. S. (1970). The rising incidence of carcinoma of the pancreas—real or apparent?. Journal of surgical oncology2(2), 115-124. doi: 10.1002/jso.2930020206

Labilles, U. (2015a). Reevaluating the Impact of Cigarette Smoking on Pancreatic Cancer. Unpublished manuscript, College of Health Sciences, Public Health, Epidemiology, Walden University, Minneapolis.

Labilles, U. (2015b, September 27). A Promise to a Dying Brother [Web log post]. Retrieved from https://onenationsecho.com/2015/09/27/a-promised-to-a-dying-brother/.

Labilles, U. (2015c). Prospectus: Tobacco Use and Family Cancer History in the Pathopoiesis of Pancreatic Cancer. Unpublished manuscript, College of Health Sciences, Public Health, Epidemiology, Walden University, Minneapolis.

Labilles, U. (2016). The New Public Health: Beyond Genetics and Social Inequalities. Unpublished manuscript, College of Health Sciences, Public Health, Epidemiology, Walden University, Minneapolis.

Labilles, U. (2017). Pathopoiesis Mechanism of Smoking and Shared Genes in Pancreatic Cancer. ProQuest-CSA, LLC. Library of Congress, Copyright R# TX 8-490-984, Washington DC. doi: 10.13140/RG.2.2.30721.35681

Lynch, S. M., & Rebbeck, T. R. (2013). Bridging the gap between biologic, individual, and macroenvironmental factors in cancer: a multilevel approach. Cancer Epidemiology Biomarkers & Prevention22(4), 485-495. doi: 10.1158/1055-9965.EPI-13-0010

Owens, A.H., Coffey, D.S. & Baylin, S.B. (1982). Tumor cell heterogeneity: Origins and Implications. (Vol 4). San Diego: Academic Press.

Richerson, P. J., & Boyd, R. (2000). Evolution: The Darwinian theory of social change: an homage to Donald T. Campbell. Paradigms of Social Change: Modernization, Development, Transformation, Evolution, pp. 1-30. http://www.des.ucdavis.edu/faculty/richerson/evolutionberlin.pdf

Richerson, P. J., Boyd, R., & Henrich, J. (2010). Gene-culture coevolution in the age of genomics. Proceedings of the National Academy of Sciences, 107(Supplement 2), 8985-8992. doi: 10.1073/pnas.0914631107

Soto, A. M., & Sonnenschein, C. (2004). The somatic mutation theory of cancer: growing problems with the paradigm?. Bioessays26(10), 1097-1107. doi: 10.1002/bies.20087

Waddell, N., Pajic, M., Patch, A. M., Chang, D. K., Kassahn, K. S., Bailey, P., … & Quinn, M. C. (2015). Whole genomes redefine the mutational landscape of pancreatic cancer. Nature518(7540), 495-501. doi: 10.1038/nature14169

Wynder, E. L., Mabuchi, K., Maruchi, N., & Fortner, J. G. (1973). Epidemiology of cancer of the pancreas. Journal of the National Cancer Institute50(3), 645-667. https://doi.org/10.1093/jnci/50.3.645

 

Continuing the Journey to Make a Difference

Innovative Management and Development (Inno MD) from Uly Labilles, DMD, Ph.D on Vimeo.

A Promise to a Dying Brother

When I was inducted into the Honor Society in winter 2013, I thought that being on top of my batch will be enough to get me through my journey as a Public Health Ph.D. candidate. Recruiting a dissertation chair is the most challenging so far, especially getting a response from them. What if I do not get a dissertation chair who will be a good match with my dissertation topic? Can I submit my premise and finish my dissertation to another university? A night before my youngest brother passed away; I was on the phone with him. He told me that he is too tired, and I responded that it is okay to let go. He asked me to promise him to go back to school and take on a graduate degree to make a difference. “Promise me that at some point to be involved in a research project that could make a difference to individuals diagnosed with pancreatic cancer.” He passed away in 2007, a few weeks before his 40th birthday, and three months before his only daughter’s first birthday.

Focusing on the impact of cigarette smoking as a factor that promotes pancreatic cancer rather than initiates it will amplify the importance of behavioral change, and enhance the quality of life. The outcome of pancreatic cancer remains dismal, even with treatment combinations of surgery, radiotherapy and chemotherapy with an estimated annual economic burden of $4.9 billion annually (Pandol, Apte, Wilson, Gukovskaya, and Edderkaoui, 2012). Advances in patient management and understanding the biology of pancreatic cancer has taken substantial progress over the years. Herman, Schulick, Hruban and Goggins (2011) found that screening first-degree relatives of individuals with family members affected by pancreatic cancer can identify non-invasive precursors of the disease. In this 2011 study shows the gradual rise in the incidence and number of deaths caused by pancreatic tumors, even with the decline in incidence and mortality of other common cancers. Furthermore, Vincent et al. found that despite developments in detection and management of pancreatic cancer, only about 4% of patients will live five years after diagnosis. Moreover, Vincent et al. (2011) found that present surgical resectioning offers the only chance of cure and improve the survival rate for those with malignant disease localized to the pancreas. Statistical analysis in 2012 study shows 80–85% of patients with advanced unresectable disease responds poorly to most chemotherapeutic agents. Therefore, it is warranted to have continued understanding of the biological mechanisms contributory to the development and progression of pancreatic tumors. On the other hand, Klein et al. (2004) emphasized the significance of quantification of the risk of individuals with a family history of pancreatic cancer as a rational basis for cancer risk screening and counseling. In a prospective registry-based approach of this 2004 study, the risk of these individuals showed an increased risk of developing the disease. Klein et al. (2004) performed standardized incidence ratios and compared the number of incident pancreatic cancers observed with those expected using Surveillance, Epidemiology and End Results (SEER) rates. It was quantified in this registry-based study the pancreatic cancer risk in kindreds with a family member who was diagnosed with the disease, supporting the hypothesis of increased risk in association with family history. While Blackford et al. (2009) failed to identify the signature tobacco-related mutation in cigarette smokers that could have strong implication to the development of pancreatic cancer; this 2009 study found the nonspecific DNA damage caused by tobacco carcinogens. Furthermore, the combined causality of non-tobacco-related mutagenic risk factors such as inherited predisposition to cancer may share mutagenic properties with the tobacco mutagens active in pancreatic tissues (Ding et al., 2008; Prokopczyk et al., 2002). The types and patterns of these mutations provide insight into the mechanisms by which cigarette smoking causes pancreatic cancer (Blackford et al., 2009). Porta et al. (2009) and Blackford et al. (2009) suggested that smoking enhances the risk for pancreatic cancer through mechanisms other than genetic mutation. The development of pancreatic cancer may have a non-significant association to pipe smoking and smokeless tobacco use, but in a large collaborative pooled analysis of non-cigarette tobacco use in 11 studies within the International Pancreatic Cancer Case-Control Consortium (PanC4) found that cigar smoking is associated with an excess risk of the disease (Bertuccio et al., 2011). Cigarette smoking was found to be an established risk factors— both exposure to environmental tobacco smoke (ETS), and active cigarette smoking (Vrieling et al., 2010). Over 40,000 individuals are diagnosed with pancreatic cancer, and less than 5% of patients diagnosed has a survival rate of five years. The component of the smoke of cigarettes that produced in the body as a metabolite of nicotine and the most abundant carcinogens in tobacco smoke is 4-(methyl nitrosamine)-1-(3-pyridyl)-1-butanone (NNK). Vary widely in nicotine content and carcinogenic nicotine metabolites, cigarettes, cigars, and other tobacco products—nicotine reaches the lungs and is quickly absorbed into the bloodstream during smoking. A cigar containing as many as 20 grams of tobacco can have nicotine between 5.9 and 335.2 mg per gram of tobacco (Henningfield, Fant, Radzius, & Frost, 1999). Prokopczyk et al. (2002) noted that the nicotine levels in pancreatic juice in smokers is seven times higher than non-smokers. Blackford et al. (2009) concluded that smokers diagnosed with pancreatic carcinomas harbors more mutations than the non-smoker, therefore, doubles the risk, accounting for 20 to 25% of pancreatic cancers.

Pandol et al. (2012) stated that the pro-carcinogenic effects of smoking on the pancreas are inadequately studied, confirming that tobacco smoking is the strongest avoidable risk and the major environmental factor for pancreatic cancer. Pandol et al. provided valuable insights into the pathogenesis of pancreatic cancer, particularly in the initiation and progression of the disease. Determining the mechanisms underlying the effect of smoking compounds on fibrosis and inflammation will improve our limited knowledge of pancreatic biology. Pancreatic cancer can be classified as genetic, environmental, or both; as well as a disease caused by inherited DNA mutation or mutation by chance. While advances in Genomics gives the promise to early pancreatic cancer detection through better understanding of pancreatic biology, it is paramount to embrace the significance of lifestyle habits that can be modified to evidence-based healthier concepts that translates to reduced cancer risk. Applying lessons learned from the outcome of my proposed study, and existing body of knowledge will prevent the emergence of pancreatic cancer, reduce cancer risk and advance population health. Early behavioral change and interventions will improve the survival rate and quality of life during the time course of pancreatic cancer progression.

References

Bayraktar, S., Bayraktar, U. D., & Rocha-Lima, C. M. (2010). Recent developments in palliative chemotherapy for locally advanced and metastatic pancreas cancer. World journal of gastroenterology: WJG16(6), 673.

Bertuccio, P., La Vecchia, C., Silverman, D. T., Petersen, G. M., Bracci, P. M., Negri, E., … & Boffetta, P. (2011). Cigar and pipe smoking, smokeless tobacco use and pancreatic cancer: an analysis from the International Pancreatic Cancer Case-Control Consortium (PanC4). Annals of Oncology, mdq613.

Blackford, A., Parmigiani, G., Kensler, T. W., Wolfgang, C., Jones, S., Zhang, X., … & Hruban, R. H. (2009). Genetic mutations associated with cigarette smoking in pancreatic cancer. Cancer research69(8), 3681-3688.

Bosetti, C., Lucenteforte, E., Silverman, D. T., Petersen, G., Bracci, P. M., Ji, B. T., … & La Vecchia, C. (2012). Cigarette smoking and pancreatic cancer: an analysis from the International Pancreatic Cancer Case-Control Consortium (Panc4). Annals of oncology23(7), 1880-1888.

Bouvier, A. M., David, M., Jooste, V., Chauvenet, M., Lepage, C., & Faivre, J. (2010). Rising incidence of pancreatic cancer in France. Pancreas39(8), 1243-1246.

Breslow, N. E., Day, N. E., & Davis, W. (1980). The analysis of case-control studies. International Agency for Research on Cancer.

Chowdhury, P., Chang, L. W., & Rayford, P. L. (1993). Tissue distribution of [3H]-nicotine in rats. Biomedical and environmental sciences: BES6(1), 59-64.

Chowdhury, P., Doi, R., Tangoku, A., & Rayford, P. L. (1995). Structural and functional changes of rat exocrine pancreas exposed to nicotine. International journal of pancreatology, 18(3), 257-264.

Colby, S. M., Clark, M. A., Rogers, M. L., Ramsey, S., Graham, A. L., Boergers, J., … & Abrams, D. B. (2012). Development and reliability of the lifetime interview on smoking trajectories. Nicotine & Tobacco Research14(3), 290-298.

Colditz, G. A., Wolin, K. Y., & Gehlert, S. (2012). Applying what we know to accelerate cancer prevention. Science translational medicine4(127), 127rv4-127rv4.

DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled clinical trials7(3), 177-188.

Ding, L., Getz, G., Wheeler, D. A., Mardis, E. R., McLellan, M. D., Cibulskis, K., … & Sawyer, C. S. (2008). Somatic mutations affect key pathways in lung adenocarcinoma. Nature, 455(7216), 1069-1075. Chicago.

Edderkaoui, M., Park, C., Lee, I., Nitsche, C., Gerloff, A., Grippo, P. J., … & Gukovskaya, A. S. (2011, November). Novel model of pancreatic neoplastic lesions induced by smoking compound NNK. In Pancreas (Vol. 40, No. 8, pp. 1321-1321). 530 WALNUT ST, PHILADELPHIA, PA 19106-3621 USA: LIPPINCOTT WILLIAMS & WILKINS.

Feuer, E. J., & Wun, L. M. DEVCAN: probability of developing or dying of cancer software, version 4.1.[internet]. Bethesda (MD): National Cancer Institute (NCI); 1999 [accessed 2002 May 28].[1 p].

Gould, G. S., Watt, K., Cadet-James, Y., & Clough, A. R. (2015). Using the risk behaviour diagnosis scale to understand Australian Aboriginal smoking—a cross-sectional validation survey in regional New South Wales. Preventive Medicine Reports2, 4-9.

Greenland, S. (1987). Quantitative methods in the review of epidemiologic literature. Epidemiologic reviews9(1), 1-30.

Henningfield, J. E., Fant, R. V., Radzius, A., & Frost, S. (1999). Nicotine concentration, smoke pH and whole tobacco aqueous pH of some cigar brands and types popular in the United States. Nicotine & Tobacco Research1(2), 163-168.

Hoffmann, D., Hoffmann, I., & El-Bayoumy, K. (2001). The less harmful cigarette: a controversial issue. A tribute to Ernst L. Wynder. Chemical research in toxicology14(7), 767-790.

Hruban, R. H., Iacobuzio-Donahue, C., Wilentz, R. E., Goggins, M., & Kern, S. E. (2000). Molecular pathology of pancreatic cancer. Cancer journal (Sudbury, Mass.)7(4), 251-258.

Institute for Health Metrics and Evaluation. (2015). US County Profile: Dallas County, Texas. 2301 Fifth Ave., Suite 600 Seattle, WA 98121 USA.

Jiao, L., Mitrou, P. N., Reedy, J., Graubard, B. I., Hollenbeck, A. R., Schatzkin, A., & Stolzenberg-Solomon, R. (2009). A combined healthy lifestyle score and risk of pancreatic cancer in a large cohort study. Archives of internal medicine, 169(8), 764-770.

Kadam, P., & Bhalerao, S. (2010). Sample size calculation. International journal of Ayurveda research1(1), 55.

Kirby, A., Gebski, V., & Keech, A. C. (2002). Determining the sample size in a clinical trial. Medical journal of Australia177(5), 256-257.

Klein, A. P., Brune, K. A., Petersen, G. M., Goggins, M., Tersmette, A. C., Offerhaus, G. J. A., … & Hruban, R. H. (2004). Prospective risk of pancreatic cancer in familial pancreatic cancer kindreds. Cancer Research, 64(7), 2634-2638. Chicago

Janghorban, R., Roudsari, R. L., & Taghipour, A. (2014). Skype interviewing: the new generation of online synchronous interview in qualitative research. International journal of qualitative studies on health and well-being9.

Labilles, U. (2015). Reevaluating the Impact of Cigarette Smoking on Pancreatic Cancer (Unpublished, Advanced Quantitative Reasoning and Analysis (RSCH – 8250H – 3), 2015 Summer Qtr. Wk11Assgn3LabillesU) Walden University, Minneapolis.

Larsson, S. C., Permert, J., Håkansson, N., Näslund, I., Bergkvist, L., & Wolk, A. (2005). Overall obesity, abdominal adiposity, diabetes and cigarette smoking in relation to the risk of pancreatic cancer in two Swedish population-based cohorts. British journal of cancer93(11), 1310-1315.

Lau, P. P., Dubick, M. A., Gloria, S. M., Morrill, P. R., & Geokas, M. C. (1990). Dynamic changes of pancreatic structure and function in rats treated chronically with nicotine. Toxicology and applied pharmacology, 104(3), 457-465.

Le Houezec, J. (2003). Role of nicotine pharmacokinetics in nicotine addiction and nicotine replacement therapy: a review. The International Journal of Tuberculosis and Lung Disease7(9), 811-819.

Lowenfels, A. B., & Maisonneuve, P. (2003). Environmental factors and risk of pancreatic cancer. Pancreatology3(1), 1-8.

Lynch, S. M., & Rebbeck, T. R. (2013). Bridging the gap between biologic, individual, and macroenvironmental factors in cancer: a multilevel approach. Cancer Epidemiology Biomarkers & Prevention22(4), 485-495.

Lynch, S. M., Vrieling, A., Lubin, J. H., Kraft, P., Mendelsohn, J. B., Hartge, P., … & Stolzenberg-Solomon, R. Z. (2009). Cigarette smoking and pancreatic cancer: a pooled analysis from the pancreatic cancer cohort consortium. American journal of epidemiology170(4), 403-413.

Miller, T. Q. (1997). Statistical methods for describing temporal order in longitudinal research. Journal of clinical epidemiology50(10), 1155-1168.

NHIS – Adult Tobacco Use – Smoking Status Recodes. (n.d.). Retrieved from http://www.cdc.gov/nchs/nhis/tobacco/tobacco_recodes.htm

Pancreatic Expression Database. (n.d.). Retrieved from http://pancreasexpression.org/

Pandol, S. J., Apte, M. V., Wilson, J. S., Gukovskaya, A. S., & Edderkaoui, M. (2012). The burning question: why is smoking a risk factor for pancreatic cancer? Pancreatology12(4), 344-349.

Philip, P. A. (2008). Targeted therapies for pancreatic cancer. Gastrointestinal cancer research: GCR2(4 Suppl 2), S16.

Prokopczyk, B., Hoffmann, D., Bologna, M., Cunningham, A. J., Trushin, N., Akerkar, S., … & El-Bayoumy, K. (2002). Identification of tobacco-derived compounds in human pancreatic juice. Chemical research in toxicology, 15(5), 677-685. Chicago.

Porta, M., Crous-Bou, M., Wark, P. A., Vineis, P., Real, F. X., Malats, N., & Kampman, E. (2009). Cigarette smoking and K-ras mutations in pancreas, lung and colorectal adenocarcinomas: etiopathogenic similarities, differences and paradoxes. Mutation Research/Reviews in Mutation Research682(2), 83-93.

Prospective Ascertainment for Late Effects among Cancer … (n.d.). Retrieved from http://www.mskcc.org/cancer-care/trial/12-143

Raimondi, S., Maisonneuve, P., Löhr, J. M., & Lowenfels, A. B. (2007). Early onset pancreatic cancer: evidence of a major role for smoking and genetic factors. Cancer Epidemiology Biomarkers & Prevention16(9), 1894-1897.

Schottenfeld, D., & Fraumeni Jr, J. F. (1982). Cancer epidemiology and prevention. Eastbourne, UK; WB Saunders Co.

Silverman, D. T., Dunn, J. A., Hoover, R. N., Schiffiman, M., Lillemoe, K. D., Schoenberg, J. B., … & Pottern, L. M. (1994). Cigarette Smoking and Pancreas Cancer: a Case—Control Study Based on Direct Interviews. Journal of the National Cancer Institute86(20), 1510-1516.

Smith-Warner, S. A., Spiegelman, D., Ritz, J., Albanes, D., Beeson, W. L., Bernstein, L., … & Hunter, D. J. (2006). Methods for Pooling Results of Epidemiologic Studies the Pooling Project of Prospective Studies of Diet and Cancer. American journal of epidemiology163(11), 1053-1064.

Special Section: Pancreatic Cancer. (n.d.). Retrieved from http://www.cancer.org/acs/groups/content/@research/documents/document/acspc-0388

Thomas, J. K., Kim, M. S., Balakrishnan, L., Nanjappa, V., Raju, R., Marimuthu, A., … & Pandey, A. (2014). Pancreatic cancer database: an integrative resource for pancreatic cancer. Cancer biology & therapy15(8), 963-967.

Validation of risk assessment scales and predictors of … (n.d.). Retrieved from http://europepmc.org/articles/PMC4054635

Vincent, A., Herman, J., Schulick, R., Hruban, R. H., & Goggins, M. (2011). Pancreatic cancer. The Lancet, 378(9791), 607-620. Chicago

Vrieling, A., Bueno‐de‐Mesquita, H. B., Boshuizen, H. C., Michaud, D. S., Severinsen, M. T., Overvad, K., … & Riboli, E. (2010). Cigarette smoking, environmental tobacco smoke exposure and pancreatic cancer risk in the European Prospective Investigation into Cancer and Nutrition. International journal of cancer126(10), 2394-2403.

Wen, K. Y., & Gustafson, D. H. (2004). Needs assessment for cancer patients and their families. Health and quality of life outcomes2(1), 11.

Wilson, L. S., & Lightwood, J. M. (1999). Pancreatic cancer: total costs and utilization of health services. Journal of surgical oncology71(3), 171-181.

Witte, K. (1996). Predicting risk behaviors: Development and validation of a diagnostic scale. Journal of health communication1(4), 317-342.

Witte, K., & Allen, M. (2000). A meta-analysis of fear appeals: Implications for effective public health campaigns. Health Education & Behavior27(5), 591-615.

Wittel, U. A., Pandey, K. K., Andrianifahanana, M., Johansson, S. L., Cullen, D. M., Akhter, M. P., … & Batra, S. K. (2006). Chronic pancreatic inflammation induced by environmental tobacco smoke inhalation in rats. The American journal of gastroenterology101(1), 148-159.