Tag Archives: Obesity

In Texas, Everything is Bigger: In the context of data collection—is bigger better?

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.

References

Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big Data to Big Impact. MIS quarterly36(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 Rev2012, 23-28. Green, K. L. (1988). Issues of control and responsibility in workers’ health. Health Education & Behavior15(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 international27(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 genetics41(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 neuropsychology16(7), 653-667.

Dallas’ Renaissance Plan: A Response to the Second Wave of Environmental Justice

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.

References

Behavioral Risk Factor Surveillance System (BRFSS). Atlanta: Centers for Disease Control and Prevention; 2000.Centers for Disease Control and Prevention; 2000.

Ellaway A, Anderson A, Macintyre S. Does area of residence affect body size and shape? Int J Obes Relat Metab Disord. 1997; 21:304-308.

Labilles, U. (2013). Environment Matters: The Disproportionate Burden of Environmental Challenges. PUBH 8115-1 Environmental Health Spring Qtr. Minneapolis: Walden University.

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.

US Dept of Health and Human Services. Physical activity and health: A report of the Surgeon General. Atlanta: Centers for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion; 1996.

US Dept of Health and Human Services. Healthy People 2010: With understanding and improving health and objectives for improving health (2nd ed). Washington: US Govt Printing Office; 2000.

Wolf AM, Manson JE, Colditz GA. The economic impact of overweight, obesity, and weight loss. In: Eckel R, ed. Obesity Mechanisms and Clinical Management. Philadelphia: Lippincott, Williams, & Wilkins; 2002.

 

 

Parental Obesity and New Mentality: Raising the Risk of Child Obesity

Our nation’s most urgent health problem is the disparities in health care. There are stark disparities in health by gender and socioeconomic status. According to Davis et al. (2005), “the social and community environments affect health directly as well as indirectly by influencing behavior”(p. 2168). Which group do we put parents who have a distorted perception of their child’s body size? This phenomenon is most prevalent among low-income women and Hispanic mothers. But regardless of race or socioeconomic background, the obesity epidemic is eroding the general impression of what healthy looks like. What if obese is the new normal? If obese is the new normal, then it will be our failure as Public Health professionals to emphasize the importance of the role of parents and family to combat child obesity. Parents should play a crucial role in influencing children’s food habits and physical activity. Parental obesity may increase the risk of a child becoming obese. Wrotniak et al. (2004) is the first study to examine the incremental effects of parental weight change on child weight change while controlling for variables that influence child weight loss. The study stated that youth benefit the most from parents who lose the most weight in family-based behavioral treatments (Wrotniak et al., 2004, p. 342).

The prevalence of obesity is increasing in all pediatric age groups according to the Health and Nutrition Examination Survey (NHANES). Genetics, environment, metabolism, lifestyle, and eating habits are among the factors believed to play a role in the development of obesity. More than 90% of cases are idiopathic; less than 10% are associated with hormonal or genetic causes. Hirschler et al. (2008) found an association between mothers’ distorted perception of their children’s shape and eating habits and mothers’ obesity and their children’s overweight. The study provides clues for obesity prevention programs. There is a multitude of health problems that are associated with obesity. Without dealing with the new trend of maternally distorted perception of their child’s body size, health problems faced by family care physicians will continue to rise. There will be continued prevalence of obesity associated diseases such as type 2 diabetes and heart disease to hyperlipidemia, asthma, and obstructive sleep apnea. According to Friedman & Schwartz (2008), “A key concept in developing obesity-related policies is creating ‘optimal defaults’17. When there is an optimal default, the health promoting behaviors are those that come most easily, require the least effort or thought, and offer a more healthful option” (p.718).

References

JAMA Network | JAMA Pediatrics | Parent Weight Change as a … (n.d.). Retrieved from http://archpedi.jamanetwork.com/article.aspx?articleid=485676

Hirschler, V., Calcagno M., Clemente A., Aranda C., Gonzalez, C. (2008, July 21). Association between school children’s overweight and maternal obesity and perception of      their children’s weight status. Journal Pediatric Endocrinololgy & Metabolism. 7:641-9.

Cohen, L., Chavez, V., Chehimi, S. (2010). Achieving Health Equity and Social Justice. L. Liburd & W. Giles, Prevention is Primary (pp. 33-53). San Francisco: Jossey-            Bass.

Friedman, R., & Schwartz, M. (2008). Public Policy to Prevent Childhood Obesity, and the Role of Pediatric Endocrinologists.Journal of Pediatric Endocrinology &                    Metabolism, 21, 717-725.

Preventive and Curative Health Care Services: Integrating Cultural Health Capital

Critical to improving the health of the US population is expanding the role of primary care in the prevention and treatment of childhood obesity. Providers can improve prevention and treatment through efforts in clinical and community setting, healthy lifestyle promotion, community health education, policy advocacy, weight status assessment and monitoring, clinic infrastructure development, and multi sector community initiatives. Coordinated and collective efforts in multiple sectors and settings are needed to address high prevalence of childhood obesity. There is a recognized need to expand the role of primary care to include advocacy in addition to traditional measurement of patients’ heights and weights to assess growth. It is important to identify successful models that integrate primary care, public health, and community-based efforts to accelerate progress in childhood obesity prevention. Vine, Hargreaves, Briefel & Orfield (2013) stated that based on 96 peer-reviewed articles published between 2005 to 2012, primary care providers (PCPs) are increasingly being included in childhood obesity interventions which is consistent with current recommendations from scientific and professional organizations. Being the critical stages of growth and healthy lifestyle development, prenatal and childhood periods need new strategies that encompass more than individual-level behavior change and post-assessment treatment. Well-child visits is the best timing to counsel parents about healthy lifestyle, mold healthy behaviors and refer families to community resources. It is necessary to stress the importance of PCPs to take on the role as educators, promoters of healthy lifestyle practices, and advocates in the broader community on treatment and intervention initiatives. Incorporating curative health services into broader population health is in essence within the scope of universal health coverage (UHC). Built on the 1978 Declaration of Alma Ata, Rodin (2013) stated that UHC movement reaffirmed that health is a human right and identified primary healthcare as the means for attaining “health for all” (p. 710). Transitioning towards UHC, it is necessary for government leaders and policy makers to take into consideration the unique health needs of women. It is important for policy makers to understand the biological and gender-based differences to successfully incorporate women’s needs into UHC schemes. The social protection schemes that cover women’s preventive services and curative services should seek to eliminate or at least reduce out-of-pocket spending on health and to remove the formidable financial barriers that prevent more women than men from accessing needed services (Rodin, 2013). The success or the efficient performance of UHC systems will be dependent upon the stakeholders’ focus on persistent differences between men and women’s health risks, health status, and access to service. Systematically including women’s health needs during the planning process of UHC will not only improve women’s empowerment, but also economic development.

Linguistic facility is a cultural health capital element that could be improved in order to understand, recognized and increase access to care for cultural and linguistic minorities utilizing ethnicity-specific subsystems of care. To create an organizational development model for ethnicity-specific health care organizations and infrastructures, it is useful to consider the historical experiences of the Chinese community in San Francisco. This model includes the development and recruitment of bicultural and bilingual healthcare workforce which will induce satisfying engagements between the target population and health professionals. The other stages in the development of this model are structuring health care resources for maximum accessibility, expanding health care organizations, and integrating ethnicity-specific health care resources into the mainstream health care system (Yang & Kagawa-Singer, 2007). This study further stated: “moving forward from the documentation of racial and ethnic disparities in health care toward long-term solutions that ameliorate disparities, ethnicity-specific health care organizations have untapped potential as a source for a strategy that addresses the structure of health systems that inhibit full access to quality health care for cultural and linguistic minorities” (p. 546). Ethnicity-specific health care systems can contribute to greater equity, comprehensive, and accessible quality care by greater expansion and integration of this health system into the mainstream. Delivering quality health care in culturally appropriate way, and opening the access which was impeded by cultural and linguistic characteristics could be efficiently implemented by matching of patients and providers. Integration of this system to the mainstream will need monitoring of discriminatory practices, and appropriate action to ensure fair competition among provider groups.

References

Baum, F. E., Legge, D. G., Freeman, T., Lawless, A., Labonté, R., & Jolley, G. M. (2013). The potential for multi-disciplinary primary health care services to take action on the social determinants of health: actions and constraints. BMC public health13(1), 460.

Rodin, J. (2013). Accelerating action towards universal health coverage by applying a gender lens. Bulletin of the World Health Organization91(9), 710-711.

Shim, J. K. (2010). Cultural Health Capital A Theoretical Approach to Understanding Health Care Interactions and the Dynamics of Unequal Treatment. Journal of Health and Social Behavior51(1), 1-15.

Vine, M., Hargreaves, M. B., Briefel, R. R., & Orfield, C. (2013). Expanding the Role of Primary Care in the Prevention and Treatment of Childhood Obesity: A Review of Clinic-and Community-Based Recommendations and Interventions. Journal of obesity2013.

Yang, J. S., & Kagawa-Singer, M. (2007). Increasing access to care for cultural and linguistic minorities: ethnicity-specific health care organizations and infrastructure. Journal of Health Care for the Poor and Underserved18(3), 532-549.

Canadian Strategies to Help Reverse U.S. obesity Epidemic

Health within the looking glass of a population health perspective is defined in broad terms which address questions such as what are the most important factors affecting the target population’s health; why some people are sicker than others; and what initiatives can be implemented to improve the health of all people and communities. The less talk, more action approach of Canadian health professionals and policy makers recognized the emerging field of research that is critical in advancing initiatives to reduce health inequities. Canadian Institutes of Health Research-Institute of Population and Public Health, (2011) stated that population health intervention research built on several decades of research in important areas such as health promotion, health education and community interventions. The Institute of Population and Public Health (IPPH) in partnership with the Canadian Population Health Initiative (CPHI) shared the vision to develop the program that will promote, advance and support population and public health research, infrastructure development, capacity building and knowledge exchange to improve the health of individuals, communities and global populations. The partnership developed a strong pan-Canadian population health research network to implement proactive and ongoing external relations with research organizations, population and public researchers, and research funders across disciplines and sectors. The synthesis on lessons learned from research to address population health issues of vulnerable populations is applied to the real-world situation as continuance of research that involves complex interventions in non-health sectors or multi-level interventions that cut across the socioecological systems. This strategy will give way to the proper application of intervention programs, use evidence to make decisions on how to use scarce resources in ways that will equitably improve the target population’s health status.

The Population Health Intervention Research Initiative for Canada (PHIRIC) aims to increase the quantity, quality and use of population health intervention research through a strategic and deliberate alignment of initiatives by key organizations responsible for public health research, policy and practice. Public Health Agency of Canada has invested in six National Collaborating Centers for Public Health which have a knowledge synthesis, translation and exchange mandate that can promote population health intervention research. In addition, Di Ruggiero, Rose & Gaudreau (2009) noted CIHI, CIHR, the Heart and Stroke Foundation of Canada, and the Public Health Agency of Canada are examples of national organizations that have made strategic funding investments in population health intervention research. Population health intervention research supported through a variety of implementation processes such as intersectoral collaboration, knowledge synthesis and the development of decision-making tools could empower communities to support school nutrition. School nutrition programs in remote First Nations communities of the western James Bay region implement school-based nutrition interventions and improve access to quality healthy diets. In collaboration between academics and First Nations communities, this project highlights the factors that support sustainable change in remote settings which include comprehensive program design and provision, supportive infrastructure such as modified school curricula and policies, greenhouse gardens, funding, and local champions and volunteers. Stakeholders such students, teachers and parents valued this program regardless of the challenges and barriers. It continue to address the significance of school-based nutrition programs for the continuum of equal access to healthy foods, systematic action to address inequities. I believe that such a program should be replicated to United States school system to help reverse US obesity epidemic. Canadian initiatives to improve children’s access to healthy foods could act as the basis for US frameworks to make societal changes to enhance the target population’s well-being, and address socially-determined health conditions while preventing new incidence from emerging.

References

Canadian Institutes of Health Research – Institute of Population and Public Health, (2011). Canadian Institute for Health Information – Canadian Population Health Initiative. Population Health Intervention Research Casebook, 2011.

Di Ruggiero, E., Rose, A., & Gaudreau, K. (2009). Canadian Institutes of Health Research Support for Population Health Intervention Research in Canada. CJPH 100 (Suppl. 1) I15-I19.

Hawe, P., & Potvin, L. (2009). What is population health intervention research? CJPH 100 (Suppl. 1) I8-I14.

Manuel, D. G., & Rosella, L. C. (2010). Commentary: Assessing population (baseline) risk is a cornerstone of population health planning—looking forward to address new challenges. International journal of epidemiology39(2), 380-382.

Ndumbe-Eyoh, S., & Moffatt, H. (2013). Intersectoral action for health equity: a rapid systematic review. BMC public health13(1), 1056.

Ostry, A., & Morrision, K. (2013). A Method for Estimating the Extent of Regional Food Self-Sufficiency and Dietary Ill Health in the Province of British Columbia, Canada. Sustainability5(11), 4949-4960.

Raine, K. D. (2010). Addressing poor nutrition to promote heart health: moving upstream. Canadian Journal of Cardiology26, 21C-24C.

Rhona Hanning, R. D., Skinner, K., & Tsuji, L. (2011). Empowering Communities to Support School Nutrition. Population Health Intervention Research Casebook, 45.