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.
Patrick, D. L., & Erickson, P. (1993). Health status and health policy. Quality of life in health care evaluation and resource.
Shi, L., & Singh, D. A. (2011). The nation’s health (8th ed.). Sudbury, MA: Jones & Bartlett Learning.
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.
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.
For 26 days in 2011, every place in Texas showed higher concentrations of lung-damaging ozone than allowed by federal air-quality standards, especially in Dallas. The federal standard set in 2008 is 75 parts per billion. The spike in ozone which is particularly a summer phenomenon is exacerbated by trucks carrying drilling materials that emit nitrogen oxides, and natural gas escaping from pipelines or storage tanks that emit volatile organic compounds, or VOCs. Known ozone “precursors” such as nitrogen oxides and VOCs can react with each other to form ozone when aided by sunlight. The most difficult environmental issue North Central Texas face today is air quality. Dallas Forth Worth (DFW) region meets the standard for five of six criteria air pollutants defined by the EPA. The six pollutants are carbon monoxide, lead, nitrogen dioxides, ozone, particulate matter, and sulfur dioxide. The only air pollutant for which DFW do not meet the National Ambient Air Quality Standard is the ozone. In hot summers, combination of nitrogen dioxides and VOCs and concentrations of traffic and industry, Dallas is an ideal incubator for the creation of ground-level ozone.
Under the Clean Air Act, ozone pollution has long been regulated because of its tremendous hazards to the public. Under the Clean Air Act, ozone poses tremendous hazards to the public health and the environment. High ozone levels lead to respiratory distress and disorders; decreased lung function; increases in the emergency room visits and sick days. To address the serious problem of ozone, the Clean Air Act provides a multi-step process for ensuring that all areas of the country achieve acceptable ozone levels. EPA establish nationwide air quality standards for ozone (called National Ambient Air Quality Standards), which are required to be strong enough to protect public health with an adequate margin of safety. The next step, EPA designate areas of the country that meet the standards, and those who do not. The last step, requiring states to submit plans for achieving and maintaining compliance with EPA’s ozone standards — with especially strict requirements for areas that currently do not meet the standards. The U.S. Environmental Protection Agency (EPA) updated its ozone air quality standards in March 2008. The EPA towards the end of 2012 promised the DFW residents for stronger protections against the harmful public health and environmental impacts of ground-level ozone. The agency announced on January 7, 2012 about its determination that Wise County, Texas contributes to high ozone levels in nearby Dallas-Fort Worth. This action required polluters in Wise County to do their fair share to reduce ozone levels in Dallas-Fort Worth. Wise County was included in the DFW ozone designation due in large part to the emissions of nitrogen oxides, and volatile organic compounds from a recent boom in oil and gas production in the area. According to the Technical Support Document (TSD), the final area designations in the Dallas-Fort Worth (DFW) area for the 2008 ozone national ambient air quality standards are based on several factors and indicators. The population density and degree of urbanization were analyzed. TSD stated: EPA evaluated the population and vehicle use characteristics and trends of the area as indicators of the probable location and magnitude of non-point source emissions. These include ozone precursor emissions from on-road and off-road vehicles and engines, consumer products, residential fuel combustion, and consumer services. Areas of dense population or commercial development are an indicator of area source and mobile source NO2 and VOC emissions that may contribute to ozone formation that contributes to nonattainment in the area. Rapid growth in population or vehicle miles traveled (VMT) in a county on the urban perimeter signifies increasing integration with the core urban area and indicates that it may be appropriate to include such perimeter area(s) as part of the nonattainment area.
It is very important to recognize the effect of ozone to a population, especially adults and children who are already had chronic respiratory diseases such as asthma. Exposure may compromise the ability of the body to fight respiratory infections. Bell et al. (2004) a multisite time-series study of 95 large US urban communities throughout a 14-year period found that widespread pollutant such as ozone adversely affects public health.
Area Designations for the 2008 Ozone National Ambient Air … (n.d.). Retrieved from http://www.epa.gov/airquality/ozonepollution/designations/2008standards/documents/R6_DFW_TSD_Final.pdf
Bell, M., McDermott, A., Zeger, S., Samet, J. & Dominici, F. (2004). Ozone and Short-term Mortality in 95 US Urban Communities, 1987-2000. JAMA;292(19):2372-2378. doi:10.1001/jama.292.19.2372.
Dallas Fort-Worth Breathes Easier Following EPA’s Decision … (n.d.). Retrieved from http://blogs.edf.org/energyexchange/2013/01/16/dallas-fort-worth-breathes-easier-following-epas-decision-on-wise-county-ozone-petitions/
Green Dallas…building a greener city! (n.d.). Retrieved from http://www.greendallas.net/air_quality.html
Labilles, U. (2013). Obstacles of Disease Surveillance Interoperability: A Challenge to Public Health. (Unpublished, PUBH-8115-1/HUMN-8115-1-Soc Behave Cultural Fact in Public Health. 2013 Spring Qtr. WK7Disc) Walden University, Minneapolis.
The true, meaningful use of personal health records (PHR), and health information exchange (HIE) between regional sites or multi-site specialty practice could amplify coordination and efficiency for higher quality and patient-centered care. PHR and HIE have been advocated as key new components in the effective delivery of modern health care. What is the impact of PHR and HIE to healthcare system? How can sharing health information between regional sites or multi-site specialty practice bridge the communication gap? What is the role of specific-disease surveillance system in enhancing the management and delivery of quality of care? The effective use of cancer-related information aggregated from evolving health communication and information technology can help identify disease cluster such as the incidence of skin cancer in a geographic area which could improve communication strategy on a population wide basis. The processes of health communication and supportive health information technology infrastructure can influence patients’ health decisions, health-related behavior, and health outcomes. These make health communication and health information technology play an increase central role in health care delivery and public health. HINTS data could help a regional manager harness the appropriate communication channel to coordinate between facilities, and to identify barriers to the use of health information across community. Gauging the target group’s attitudes, regarding perceptions of health-relevant topics such as cancer screening will help develop more effective communication strategies. For example, a marked increase in the incidence rate of non-melanoma skin cancer (NMSC) based on a comprehensive surveillance system could help Mohs Micrographic Surgery facilities coordinate with dermatologists and dermato-pathologists. HINTS data can help refine information age health communication theories, and offer unique recommendations for managers, communication planners and researchers in their common aim to reduce the population cancer burden through effective, evidence-based, and patient- or public-centered communication (Hesse et al., 2006; Hesse et al., 2005; Nelson et al., 2004). The concept that captures an interactive phenomenon such as shared decision-making (SDM) utilized in concert with HINTS data recommendations will improve clinicians and patients communication. Kasper, Légaré, Scheibler & Geiger (2012) asserted that the complexity of challenges physicians have to face in critical decision making, can be alleviated by outsourcing parts of the information and decision making process to other health or medical professionals to provide optimal conditions for communication in the physician patient dyad.
Finney Rutten, L. J., Davis, T., Beckjord, E. B., Blake, K., Moser, R. P., & Hesse, B. W. (2012). Picking up the pace: changes in method and frame for the health information National Trends Survey (2011–2014). Journal of health communication, 17(8), 979-989.
Hesse, B. W., Nelson, D. E., Kreps, G. L., Croyle, R. T., Arora, N. K., Rimer, B. K., . . . Viswanath, K. (2005). Trust and sources of health information: The impact of the Internet and its implications for health care providers: Findings from the first Health Information National Trends Survey. Archives of Internal Medicine, 165, 2618–2624.
Hesse, B. W., Moser, R. P., Rutten, L. J. F., & Kreps, G. L. (2006). The health information national trends survey: research from the baseline. Journal of Health Communication, 11(S1), vii-xvi.
Kasper, J., Légaré, F., Scheibler, F., & Geiger, F. (2012). Turning signals into meaning–‘Shared decision making’meets communication theory. Health Expectations, 15(1), 3-11.
Labilles, U. (2014). The Role of Disease-specific Surveillance and Health Information Exchange (HIE) in Managing Regional Multi-site Medical Specialty Practice. (Unpublished, RSCH-8100H-2. Research Theory, Design, and Methods. 2014 Spring Qtr. WK7Assgn) Walden University, Minneapolis.
Nelson, D. E., Kreps, G. L., Hesse, B. W., Croyle, R. T., Willis, G., Arora, N. K., . . . Alden, S.
(2004). The Health Information National Trends Survey (HINTS): Development, design,
and dissemination. Journal of Health Communication, 9, 443–460.
Office of Disease Prevention and Health Promotion. (2010). Healthy People 2020. Retrieved
Scholl, I., Loon, M. K. V., Sepucha, K., Elwyn, G., Légaré, F., Härter, M., & Dirmaier, J. (2011). Measurement of shared decision making–a review of instruments. Zeitschrift für Evidenz, Fortbildung und Qualität im Gesundheitswesen, 105(4), 313-324.
Viswanath, K. (2005). Science and society: The communications revolution and cancer control. Nature Reviews Cancer, 5, 828–835.
Wen, K. Y., Kreps, G., Zhu, F., & Miller, S. (2010). Consumers’ perceptions about and use of the internet for personal health records and health information exchange: analysis of the 2007 Health Information National Trends Survey.Journal of medical Internet research, 12(4).
Fifty years ago, President Lyndon Johnson began his quest for a more just and honorable America with the passage of the Civil Rights Act of 1964, passed the Voting Rights Act of 1965 and the Fair Housing Act of 1968. This week, President Barack Obama joined three former Presidents delivered remarks at the Civil Rights Summit at the Lyndon B. Johnson Presidential Library and Museum, and acknowledged racism has hardly been erased and that government programs have not always succeeded. Let us talk about socioeconomic and racial/ethnic disparity patterns in public health. What the patterns tell us? In Europe, the presence of detailed socioeconomic information in routine health data has facilitated the monitoring of socioeconomic patterns in diverse health indicators (Braveman, Cubbin, Egerter, Williams & Pamuk, 2010). This socioeconomic information gave Public health professionals and researchers the ability to compare health of socioeconomically disadvantaged population with health differences among middle-class subgroups and, potentially, comparisons with the wealthy. Braveman et al. (2010) gave emphasis on Europe’s data collection in contrast with routine the routine public health statistics in the United States. Health difference across groups defines by socioeconomic factors have been examined less frequently. It was further noted by the study that routine health reporting should examine socioeconomic and racial/ethnic disparity patterns, jointly and separately. According to Collins (2004) “race and ethnicity are poorly defined terms that serve as flawed surrogates for multiple environmental and genetic factors in disease causation, including ancestral geographic origins, socioeconomic status, education and access to health care. Research must move beyond these weak and imperfect proxy relationships to define the more proximate factors that influence health” (para. 1).
Health disparities in many instances will hardly to do with genetics, but more directly associated in socioeconomic status (SES), access to health care, education, social marginalization, discrimination, culture, stress, diet and other factors. SES is one of the strongest and most consistent predictors of morbidity and mortality. As a complex phenomenon, the impact of SES on disease makes its definition and measurement of vital importance. SES is typically measured by determining education, income, and occupation (Winkleby, Jatulis, Frank & Fortmann, 1992). The Farquhar et al study is the only U.S. study on the associations between separate SES dimensions and risk factors or disease outcomes (Winkleby et al., 1992). In the Farquhar et al. study (1985): Subjects aged 25 to 64 were drawn from the two control cities of the Stanford Five-City Project, a communitywide cardiovascular disease intervention study that contains data from four separate cross-sectional surveys, conducted from 1979 to 1986. Participants who were unemployed (n = 98), students (n = 130), or retirees (n = 146) were excluded because they had no occupation that could be ranked (Winkleby et al., 1992, p. 816). Associations between one measure of SES and one risk factor, morbidity, or mortality in other studies have found that education is more strongly associated with disease than income or occupation. One of the most complete studies of mortality differentials (Kitagawa et al., 1973) found “lower SES groups exhibited higher rates of all-cause mortality than did higher SES groups, irrespective of whether education, income, or occupation was used as the measure of SES” (p. 819). Lower levels of education are associated with hypertension, cigarette smoking, high cholesterol, cardiovascular disease (CVD) morbidity and mortality. According to Winkleby et al., there are no SES measure that is universally valid and suitable for all populations. The study noted “if economics and time dictate that a single parameter be chosen, and if the research hypothesis does not dictate otherwise, the conclusion is that higher education, rather than income or occupation, may be the strongest and most consistent predictor of good health” (p. 819).
Abramson, J., Gofin, R., Habib, J., Pridan, H. & Gofin, J. (1982). A comparative Appraisal of measures for use in epidemiological studies. Soc Sci Med., 16,1739-1746.
Berger, M. & Leigh, J. (1989). Schooling, self-selection and health. J Hum Res. 24, 433-455.
Dyer, A., Stamler, J., Shekelle, R. & Schoenberger, J. (1976). The relationship of education to blood pressure: findings on 40,000 employed Chicagoans. Circulation. 54, 987-992.
Helmert, U., Herman, B., Joeckel, K., Greiser, E. & Madans, J. (1989). Social class and risk factors for coronary heart disease in the Federal Republic of Germany: results of the baseline survey of the German Cardiovascular Prevention Study. J Epidemiology Community Health. 43, 37-42.
Hypertension Detection and Follow-up Program Cooperative Group. Race, education and prevalence of hypertension. (1977). Am J Epidemiology. 106, 351-361.
Jacobsen, B. & Thelle, D. (1988). Risk Factors for coronary heart disease and level of eduvation. Am J Epidemiology. 127, 923-932.
Kitagawa, E. & Hauser, P. (1973). Differential mortality in the United States: Study in socioeconomic epidemiology. Harvard University Press, Cambridge, Mass.
Labilles, U. (2013). What the Patterns Tell Us: Socioeconomic Status and Health (Unpublished, PUBH 8115-1 Wk6 Discussion, Social Behavioral and Cultural Fact in Public Health Spring Qtr.) Walden University, Minneapolis.
Millar, W. & Wigle, D. (1986). Socioeconomic disparities in risk factors for cardiovascular disease. Can Med Assoc J. 134, 127-132
Matthews, K., Kelsey, S., Meilahn, E., Kuller, L. & Wing, R.(1989). Educational attainment and behavioral and biological risk factors for coronary heart disease in middle-aged women. Am J Epidemiology, 129, 1132-1144.
Socioeconomic status and health: … preview & related info … (n.d.). Retrieved from http://www.mendeley.com/catalog/socioeconomic-status-health-education-income-occupation-contribute-risk-factors-cardiovascular-disea/
Socioeconomic disparities in health in the United States … (n.d.). Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/20147693
Pinsky, J., Leaverton, P. & Stokes, J. (1987). Predictors of good function: The Framingham study. Journal of Chronic Disease, 40, 159S-167S.
Snowden, D., Ostwald, S. & Kane, R. (1989). Education, survival and independence in elderly Catholic sisters. American Journal of Epidemiology, 130, 999-1012.
What we do and don’t know about ‘race’, ‘ethnicity’, genetics … (n.d.). Retrieved from http://www.nature.com/index.html?file=/ng/journal/v36/n11s/full/ng1436.html
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.
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 epidemiology, 39(2), 380-382.
Ndumbe-Eyoh, S., & Moffatt, H. (2013). Intersectoral action for health equity: a rapid systematic review. BMC public health, 13(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. Sustainability, 5(11), 4949-4960.
Raine, K. D. (2010). Addressing poor nutrition to promote heart health: moving upstream. Canadian Journal of Cardiology, 26, 21C-24C.
Rhona Hanning, R. D., Skinner, K., & Tsuji, L. (2011). Empowering Communities to Support School Nutrition. Population Health Intervention Research Casebook, 45.