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.
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.