The protozoan parasite Trypanosoma cruzi was first described by Carlos Chagas after isolation of the organism from the blood of a Brazilian patient in 1909 (Garcia et al., 2015). An estimated 7.5 t…
The protozoan parasite Trypanosoma cruzi was first described by Carlos Chagas after isolation of the organism from the blood of a Brazilian patient in 1909 (Garcia et al., 2015). An estimated 7.5 to 10 million persons are infected with Chagas disease worldwide (Hotez et al., 2008; Hotez et al., 2014). In the United States, the disease is anecdotally referred to as a “silent killer” with a 30% chance of those infected to develop a potentially fatal cardiac disease. According to Cantey et al. (2012), Chagas disease is emerging as a significant public health concern in the United States. Given the proximity of Texas to Latin America, cases imported from highly endemic areas in Latin America would likely occur in Texas. Recent communication from the Centers for Disease Control and Prevention that the bite of blood-sucking triatomine bugs in the subfamily Triatominae also termed “kissing bugs” that transfers the parasites to humans have now been found in 28 states, including California and Pennsylvania. Garcia et al. (2015) argued that despite the numerous publications related to Chagas disease in the southern US and northern regions of Mexico, very little is known about the disease burden from imported and locally acquired T. cruzi infection.There is concern that Chagas disease might be undiagnosed in the US as a result of documented low physician awareness (Stimpert & Montgomery, 2010). While the zoonotic nature of Chagas’ life cycle implies unfeasible eradication; entomological surveillance is and will remain crucial to containing Chagas disease transmission (Tarleton et al., 2007).
While it is considered safe to breastfeed even if the mother has Chagas disease (Centers for disease control and prevention, 2013); people can also become infected through blood transfusion, congenital transmission (from a pregnant woman to her baby), organ transplantation, accidental laboratory exposure and consumption of uncooked food contaminated with feces from infected bugs. If the mother has cracked nipples or blood in the breast milk, it is warranted to pump and discard the milk until the bleeding resolves and the nipples heal (Centers for disease control and prevention, 2013). The enduring challenge of household reinfestation by locally native vectors as stated by Abad-Franch et al. (2011), horizontal strategies works better when the community takes on a protagonist role. Encouraging vector notification by residents and other simple forms of participation can substantially enhance the effectiveness of surveillance (Abad-Franch et al., 2011). Therefore, control programs in concert with community-based approaches as a strategic asset from inception that requires a timely, professional response to every notification, benefiting from a strengthened focus on community empowerment. According to Schofield (1978), when bug population density is low, vector detection failures are unavoidable. Decision-making will be dependent upon the accurate estimation of infestation rates (World Health Organization, 2002), and imperfect detection can seriously misguide Chagas disease control management program. Continued attentiveness from governmental and health organizations are warranted, as this disease continue to be a globalized public health issue. Improved diagnostic tools, expanded surveillance and increased research funding will be required in maintaining existing effective public health strategies and in preventing the spread of the disease to new areas and populations (Bonney, 2014). To improve outbreak control, and improve Chagas disease response, it is essential to discuss the gaps in the scientific knowledge of the disease. Moreover, crucial in improving the morbidity in the state of Texas and neighboring states is the recommendation of the needed steps to enhance the understanding of T. cruzi.
Abad-Franch, F., Vega, M. C., Rolón, M. S., Santos, W. S., & de Arias, A. R. (2011). Community participation in Chagas disease vector surveillance: systematic review. PLoS Negl Trop Dis, 5(6), e1207.
Bonney, K. M. (2014). Chagas disease in the 21st century: a public health success or an emerging threat?. Parasite, 21, 11.
Cantey, P. T., Stramer, S. L., Townsend, R. L., Kamel, H., Ofafa, K., Todd, C. W., … & Hall, C. (2012). The United States Trypanosoma cruzi Infection Study: evidence for vector‐borne transmission of the parasite that causes Chagas disease among United States blood donors. Transfusion, 52(9), 1922-1930.
Centers for disease control and prevention. (2013). Parasites-American Trypanosomiasis (also known as Chagas Disease). Retrieved 21 July, 2016, from http://www.cdc.gov/parasites/chagas/gen_info/detailed.html
Garcia, M. N., Woc-Colburn, L., Aguilar, D., Hotez, P. J., & Murray, K. O. (2015). Historical perspectives on the epidemiology of human chagas disease in Texas and recommendations for enhanced understanding of clinical chagas disease in the Southern United States. PLOS Negl Trop Dis, 9(11), e0003981.
Hotez, P. J., Bottazzi, M. E., Franco-Paredes, C., Ault, S. K., & Periago, M. R. (2008). The neglected tropical diseases of Latin America and the Caribbean: a review of disease burden and distribution and a roadmap for control and elimination. PLoS Negl Trop Dis, 2(9), e300.
Hotez, P. J., Alvarado, M., Basáñez, M. G., Bolliger, I., Bourne, R., Boussinesq, M., … & Carabin, H. (2014). The global burden of disease study 2010: interpretation and implications for the neglected tropical diseases. PLoS Negl Trop Dis, 8(7), e2865.
Schofield, C. J. (1978). A comparison of sampling techniques for domestic populations of Triatominae. Transactions of the Royal Society of Tropical Medicine and Hygiene, 72(5), 449-455.
Stimpert, K. K., & Montgomery, S. P. (2010). Physician awareness of Chagas disease, USA. Emerging infectious diseases, 16(5), 871.
Tarleton, R. L., Reithinger, R., Urbina, J. A., Kitron, U., & Gürtler, R. E. (2007). The challenges of Chagas disease—Grim outlook or glimmer of hope?. PLoS Med, 4(12), e332.
World Health Organization. (2002). Control of Chagas disease: second report of the WHO expert committee.
In most countries, public health approaches to address violent radicalization are already applied in street violence and bioterrorism; but leaders and stakeholders need to embrace the significance of public health interventions and research on violent radicalization (Bhui et al., 2012). While past studies (Bakker, 2006; Loza, 2007) found that overwhelming majority of people who become radicalized to violence in the West are young and male, generally aged between mid-teens and mid-20s; scarcity of research findings on the extent and nature of women’s roles in group and community radicalization (Carter, 2013). The recent acts of terrorism around the world, especially the event in San Bernardino California, it is important to note the urgent need to look at the significance of a public health approach to understanding violent radicalization. Recognizing this sense of urgency introduce the possible role of collective responsibility of leaders in epidemiology, sociology, psychology and other behavioral sciences in developing novel epidemiologic measures towards prevention strategies (Bhui, Hicks, Lashley, & Jones, 2012). While most nation’s counterterrorism approaches are grounded in inter-governmental intelligence data exchange and criminal justice systems, embracing the perceived belief that existing legal system can deal with violent radicalization effectively; it is paramount to argue that new players be included in the collection of relevant data needed in the development of public health approach to address violence such as the World Health Organization’s Violence Prevention Alliance, and the Centers for Disease Control and Prevention (CDC). The goal of CDC’s “Public Health Approach to Violence Prevention” is to decrease risk factors and increase protective factors. The logical argument for this proposed study is the need for public health research, and establish a new approach to guard against violent radicalization.
Given the current integrated surveillance system that monitors death and injuries as a direct effect of terrorism events, it is critical to recognize the risk and protective factors for violent radicalization. Bhui et al. (2102) noted “the perceived discrimination in the population as a whole or amongst distinct segments of the population; trust in authorities and their counterterrorism approaches; perceived or real economic inequalities patterned by ethnicity or religious groups; and international conflict in which the authorities appear to be biased or unfair towards a specific migrant, religious or ethnic group.” For future research, it is paramount to identify the possible independent variables that are associated with the increased probability of radicalization in certain communities such as marginalized communities, diaspora communities, and ideology. The perceived feeling of inclusion or integration in a larger, popular community was theorized to amplify the extent of susceptibility to radicalization. Baumeister and Leary (1995) asserted on the importance of adapting psychological theories on stable interpersonal relationships. It is critical to examine the perceived instability in diaspora communities that could increase the risk of marginalization. Indicators related specifically to diaspora communities are language, the size of the community, the arrival age of immigrant(s) to the community, the age structure of the population, and the spatial concentration of the community. Marret et al. (2013) asserted the importance of understanding the core of radicalization process that demands the necessity to question and debate the concept of violent radicalization at the theoretical level and the empirical level. The motivation for an individual or group to commit extremist violence or terrorism is not grounded in a single ideology, but selectively demonstrate their commitment from different clusters of belief systems. Behavioral indicators as stated by Fishman (2010) could be generated from social media, chat rooms, and involvement in public ideologically motivated legal activities might provide insights into community-based ideological sentiments.
Bakker, E. (2006). Jihadi terrorists in Europe, their characteristics and the circumstances in which they joined the jihad: an exploratory study.
Baumeister, R. F., & Leary, M. R. (1995). The need to belong: desire for interpersonal attachments as a fundamental human motivation. Psychological bulletin, 117(3), 497.
Bhui, K. S., Hicks, M. H., Lashley, M., & Jones, E. (2012). A public health approach to understanding and preventing violent radicalization. BMC medicine, 10(1), 16.
Carter, B. (2013). Women and violent extremism. GSDRC Helpdesk Research Report.
Centers for Disease Control and Prevention, National Center for Injury Prevention and Control. (2008). The public health approach to violence prevention. Atlanta, GA: CDC.
Fishman, Shira. (2010). “Community-Level Indicators of Radicalization: A Data and Methods Task Force.” Report to Human Factors / Behavioral Sciences Division, Science and Technology Directorate, U.S. Department of Homeland Security. Retrieved from http://www.start.umd.edu/pubs/START_HFD_CommRadReport.pdf
King, G., & Zeng, L. (2001). Logistic regression in rare events data. Political analysis, 9(2), 137-163.
Labilles, U. (2016). The significance of Public Health Approach on Violent Radicalization (Unpublished, Advanced Epidemiology Methods, PUBH-8520-1, 2016 Winter Qtr. Wk9Proj) Walden University, Minneapolis.
Loza, W. (2007). The psychology of extremism and terrorism: A Middle-Eastern perspective. Aggression and Violent Behavior, 12(2), 141-155.
Marret, J. L., Feddes, A. R., Mann, L., Doosje, B., & Griffioen-Young, H. (2013). An Overview of the SAFIRE Project: A Scientific Approach to Finding Indicators and Responses to Radicalization. Journal Exit-Deutschland. Zeitschrift für Deradikalisierung und demokratische Kultur, 2, 123-148.
I am currently working on two essays that I needed to submit with my fellowship application, but the event in Paris made me stop for a few minutes and reflect on the scorching reminder that terrorism has no religion, a brand of madness, not faith. Such event amplified the urgency to stress the significance of New Public Health that carries a high potential for healthy less aggressive societies. The main principles of living together in healthy communities were summarized by Laaser et al. (2002) as four ethical concepts of the New Public Health essential to violence reduction – equity, participation, subsidiarity, and sustainability. The coupling of current economic, demographic, and social issues will play a role in guiding future policy revisions. While my fellowship of interest is in epidemiology and infectious diseases, increased understanding of the interrelated dimensions of deracination or forced migration using the modern concept of public health is warranted. It is critical to understand the determinants of violence: the type of stigmatization; the process of urbanization; religious, ethnic, and racial prejudices; women’s status; the level of education; employment status; socialization of the family; availability of firearms; alcohol and drug consumption; and poverty.
Laaser, U., Donev, D., Bjegovic, V., & Sarolli, Y. (2002). Public health and peace. Croatian medical journal, 43(2), 107-113.
The traditional researcher concept that big data equates statistical significance could always eclipse the importance of understanding the interrelationship between the effect size, power, and sample size that could translate to both practical and statistical significance. In Texas, everything is bigger, everything a Texan do is bigger, but in the context of data collection—is bigger better? Current big data opportunities facing science, technology communities, and the health community is facing a tsunami of health- and healthcare-related content generated from numerous patient care points of contact, sophisticated medical instruments, and web-based health communities (Chen, Chiang & Storey, 2012). Two primary sources of health big data are payer–provider big data (electronic health records, insurance records, pharmacy prescription, patient feedback and responses), and data from my favorite field-genomics. I cannot help to imagine how many interesting research studies I could do with genomics-driven big data (genotyping, gene expression, sequencing data). Extracting knowledge from health big data poses significant research and practical challenges, especially considering the HIPAA (Health Insurance Portability and Accountability Act) and IRB (Institutional Review Board) requirements for building a privacy-preserving and trust-worthy health infrastructure and conducting ethical health-related research (Gelfand, 2011). Setting aside these challenges, can big data provide both practical and statistical significance? Just think about terabytes of expected raw sequencing data that associate variants that affect variation in two common highly heritable measures of obesity, weight and body mass index (BMI). For this discussion, let me broach the 2012 study of Hutchinson and Wilson in improving nutrition and physical activity in the workplace. The cumulative knowledge found in the meta-analysis of Hutchinson & Wilson (2012) found the extant results of 29 intervention studies examining physical activity or nutrition interventions in the workplace, published between 1999 and March 2009. The results from these 29 intervention studies were synthesized using meta-analyses in terms of the effectiveness of workplace health promotion programs to resolve inconsistent findings. The challenge of extant results that are sometimes discordant, Hutchinson & Wilson (2012) took into consideration the limitations in the methodology of some of the studies reviewed that demonstrated modest success in achieving long-term change. The importance of interventions’ association with successful outcomes that includes behavior maintenance and generalization was also considered in this study. Weighted Cohen’s d effect sizes, percentage overlap statistics, confidence intervals and failsafe Ns were calculated. The increased prevalence of obesity and its association with increased risk for chronic diseases including cancer, diabetes, cancer and cardiovascular disease warrants the needs for innovative and efficient interventions. Green (1988), stated that the workplace is a valuable intervention site for a number of reasons including the amount of time people spend at work, access to populations that may be difficult to engage in different settings and the opportunity to utilize peer networks and employer incentives. These reasons justify the practical significance of the study. Moreover, the statistical significance was established by the methodology of Hutchinson & Wilson (2012) developing inclusion criteria of the 29 identified studies. The inclusion criteria are published studies on workplace intervention; a control group, not receiving the intervention, health, and in particular diet, nutrition or physical activity as outcome measures; and statistical information for the calculation of effect sizes, (e.g. means and standard deviations, the results of t-tests or one-way F tests).Change over time (mean and standard deviation) data were requisite to calculate effect sizes for interventions. Studies that did not provide this data, the means and standard deviations at the end of the intervention of controls and interventions groups were compared. Statistical analyses was performed such as Cohen’s d to calculate effect sizes for the difference between the intervention and control groups on each outcome measure (diet measures: fruit, vegetables, fat; physical activity measures: activity, fitness; health measures: weight, cholesterol, blood pressure, heart rate or glucose). Based on outcome measures and the form of intervention, effect sizes were aggregated. Mean effect size, standard deviation and 95% confidence interval were calculated for each grouping (Zakzanis, 2001). Fail safe Ns (Nfs) were calculated to address the potential for studies with statistically significant results. The conclusion of this 2012 meta-analysis in terms of study design—randomized controlled trials were associated with larger effects; therefore, long-term maintenance of changes should be evaluated in order to determine the extent to which workplace interventions can make sustainable changes to individuals’ health.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly, 36(4), 1165-1188. Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Academic press. Ellis, P. D. (2010). The essential guide to effect sizes: Statistical power, meta-analysis, and the interpretation of research results. Cambridge University Press. Forthofer, R.N., Lee, E.S. & Hernandez, M. (2006). Biostatistics: A Guide to Design, Analysis and Discovery. 2nd Edition [Vital Source Bookshelf version]. Retrieved from http://online.vitalsource.com/books/9780123694928 Gelfand, A. (2011). Privacy and biomedical research: building a trust infrastructure: an exploration of data-driven and process-driven approaches to data privacy. Biomed Comput Rev, 2012, 23-28. Green, K. L. (1988). Issues of control and responsibility in workers’ health. Health Education & Behavior, 15(4), 473-486. Hutchinson, A. D., & Wilson, C. (2012). Improving nutrition and physical activity in the workplace: a meta-analysis of intervention studies. Health promotion international, 27(2), 238-249. Labilles, U. (2015). Big Data: Does it matter? Can it give a practical significance? Is bigger better? (Unpublished, Advanced Biostatistics (PUBH – 8500 – 1), 2015 Spring Qtr. Wk2DiscLabillesU) Walden University, Minneapolis. Thorleifsson, G., Walters, G. B., Gudbjartsson, D. F., Steinthorsdottir, V., Sulem, P., Helgadottir, A., … & Stefansson, K. (2009). Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity. Nature genetics, 41(1), 18-24. Zakzanis, K. K. (2001). Statistics to tell the truth, the whole truth, and nothing but the truth: formulae, illustrative numerical examples, and heuristic interpretation of effect size analyses for neuropsychological researchers. Archives of clinical neuropsychology, 16(7), 653-667.
Health financing is the cornerstone of strategy development based on both in terms of raising resources and of ways to manage resources. It is critical to emphasize the need for greater evaluation of the distributional impact of policies and programs. Socioeconomic status could affect public health financing such as people with insurance or money, creating higher expenditures. On the other hand, medically underserved, uninsured and underinsured create greater expenses because they enter the health system at the advanced stages of diseases and in weakened conditions (Laureate Education, Inc., 2012). In addition to socioeconomic status, other social determinants that affects both average and distribution of health includes physical environment, lifestyle or behavior, working conditions, social network, family, demographics, political, legal, institutional and cultural factors. Since funding is considered as a scarce resource, it is paramount to allocate resources based on the identified gaps in care. The significance of socioeconomic data in US public health surveillance systems should be emphasized in order to monitor socio-economic gradients in health. Socioeconomic data is important in determining the allocation of resources for public health financing. Krieger et al. (2003) stated that the use of multilevel frameworks and area-based socioeconomic measures (ABSMs) for public health monitoring can potentially overcome the absence of socioeconomic data in most US public health surveillance systems. Moreover, political will is essential to bridging public health and action that will help in the development and implementation of public health policy based on scientific evidence and community participation. Epstein, Stern and Weissman (1990) found that hospitalized patients with lower socioeconomic status have longer stays and require more resources. It was suggested in this study that supplementary payments allocated to the poor merits further consideration. Strategies for more efficient provision of care for patients with low socioeconomic status can be developed at the managerial and clinical levels.
Inequality or disparity is defined as the difference in health status, inequalities in access to and quality of health care services. Additional disparities are attributed to factors such as discrimination in relation to health care system and the regulatory climate. The Institute of Medicine (IOM) found that disparities continue to dwell even when socio-demographic factors, insurance status, and clinical needs were controlled for racial and ethnic health care. Disparities dictate funding requirements for public health initiatives for the underserved populations. Furthering social justice and maximizing individual liberties will advance traditional public health goals. Socioeconomic status of communities drives the financing needs for public health initiatives; therefore, burdens of the program must be minimized and identified to reduce pre-existing social injustices. Social benefits, public health programs that stimulate dignified employment, and strengthening of communities are important benefits that should be given high consideration. Public health professionals and health department leaders may not have the capacity to implement all programs that could be beneficial to a target population or community, but advocacy is paramount to improving health. Sufficient data is critical to justify the necessity of the program. I believe that it is our duty as healthcare and public health leaders to remove from policy debates and decision-making any discriminatory procedures or unjustified limitations on personal liberties. Public policy should be based on an ethics perspective and multiple considerations.
Bleich, S. N., Jarlenski, M. P., Bell, C. N., & LaVeist, T. A. (2012). Health inequalities: trends, progress, and policy. Annual review of public health, 33, 7.
Carter-Pokras, O. & Baquet, C. (2002). What is a” health disparity”? Public health reports, 117(5), 426.
Epstein, A. M., Stern, R. S., & Weissman, J. S. (1990). Do the poor cost more? A multihospital study of patients’ socioeconomic status and use of hospital resources. New England Journal of Medicine, 322(16), 1122-1128.
Getzen, T. E. (2013). Health economics and financing (5th ed.). Hoboken, NJ: John Wiley and Sons.
Kass, N. E. (2001). An ethics framework for public health. American Journal of Public Health, 91(11), 1776-1782.
Krieger, N., Chen, J. T., Waterman, P. D., Rehkopf, D. H., & Subramanian, S. V. (2003). Race/ethnicity, gender, and monitoring socioeconomic gradients in health: a comparison of area-based socioeconomic measures-the public health disparities geocoding project. American journal of public health, 93(10), 1655-1671.
Laureate Education, Inc. (Executive Producer). (2012). Multi-media PowerPoint: Financing public health. Baltimore, MD: Author.
Palmer, N., Mueller, D. H., Gilson, L., Mills, A., & Haines, A. (2004). Health financing to promote access in low income settings—how much do we know? The Lancet, 364(9442), 1365-1370.
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Last Christmas Eve, I received a wonderful and encouraging letter from the President. My daughter Abby opened it with excitement, and after reading his letter, she asked if she could keep it. We may all have something to say regarding what are happening around us, and we may all have our default someone to blame, but in the eyes of a growing citizen of this great country of ours, what is paramount is learning respect and love for our country. In the eyes of a child, a leader is someone who will make her/his place in this world a better place to live. They do not know about political parties, politicians at each other’s throat, but just a very simple concept “Peace.” After watching the movie “Unbroken” last night, she told me that she need to write the President a thank you letter. I told her that I already did via e-mail. Then she said, I need to thank him too since like Louis in the movie, he does not give up to something what is right. Enjoy what is left with the Holiday Season everyone.
Dallas is the seventh largest city in the United States with a population exceeding 1.1 million citizens in the year 2000. Dallas is the fourth largest park system in the United States. The second wave of the environmental justice movement is a concept concerned with urban design, public health, and availability of outdoor physical activities. The upgrade to the 21,526 acres of parkland will amplify the quality of and access to outdoor recreation. The Dallas Park and Recreation Department’s “Renaissance Plan” is a response to the increased demand of the citizens for new and expanded park facilities, recreation programs, open space areas, and unique recreational amenities. Physical activity is one of the health indicators for Healthy People 2010, and responding to these demands is a step forward of meeting its goals. Dallas’ wide spectrum of park facilities will provide physical activities that will have positive health outcome to Dallas residents including the low-income population of the Dallas County and contiguous counties. Recognition of environmental exposure affecting economically and politically disadvantaged members of the community gave birth to the first wave of environmental justice movement. In addition to health problems related to environmental exposures, environmental justice (EJ) also cover disparities in physical activity, dietary habits, and obesity among different populations. Disparities on the access of public facilities and resources for physical activity (PA) is an EJ issue that has a negative impact on health among low-income and racial/ethnic minorities (Labilles, 2013). The 2007 cross-sectional study of Taylor et al. suggest an association between disproportionate low access to parks and recreation services (PRS) and other activity-friendly environments in low-income and racial/ethnic minority communities. The prevalence of lower levels of PA and higher rates of obesity was observed in the minority population, which is a direct outcome of the prevalence of lower levels of PA. These differences violate the fair treatment principle necessary for environmental justice.
The treatment of health conditions associated with physical inactivity such as obesity poses an economic cost of at least $117 billion each year. Physical inactivity contributes to many physical and mental health problems. The reported 200,000-deaths per year in the US is attributed to physical inactivity, and data from surveillance system indicate that people from some racial/ethnic minority groups experience disproportionately higher rates of chronic diseases associated with physical inactivity. Taylor, Poston, Jones & Kraft (2006) findings, provided preliminary evidence for the hypothesis that socioeconomic status disparities in overweight and obesity are related to differences in environmental characteristics. However, most of the studies had encountered epidemiologic “black box” problem, making it impossible to determine which characteristics of the environment (e.g., density of food service outlets or physical activity resources) may be most important (Labilles, 2013). Ellaway et al. found that body-mass index (BMI), waist circumference, and prevalence of obesity, and greater obesity risk is associated with low area or neighborhood socio-economic status.
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Taylor, W., Floyd, M., Whitt-Glover, M. & Brooks, J. (2007). Environmental Justice: A Framework for Collaboration between the Public Health and Parks and Recreation Fields to Study Disparities in Physical Activity. Journal of Physical Activity & Health, 4, supp 1, s50-s63.
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