Tag Archives: determinants of health

Comparing Hospital Care in My Area

Living in northeastern Connecticut, I find myself equidistant from two area hospitals. As a health care provider and consumer, I feel that it is important to choose the professionals who will provide my care based on fact. Websites created by the Joint Commission (2011) and the U.S. Department of Health and Human Services (HHS; 2011) prove to be a helpful repository of information regarding the safety and quality of care delivered by hospitals and practitioners across the country.

Using these two websites, I will compare the three closest hospitals to my zip code: 1) Day Kimball Hospital (10.3 mi), 2) Harrington Memorial Hospital (10.0 mi), and 3) Windham Community Memorial Hospital (21.7 mi). The mean distance from my home to these hospitals is 15.85 mi. with all three being acceptable by me in distance and time in the case of an emergency. Day Kimball Hospital (DKH; 2011) is a 104-bed acute care facility located in Putnam, Connecticut. Harrington Memorial Hospital (HMH; 2009) is a 114-bed acute care facility located in Southbridge, Massachusetts. Windham Community Memorial Hospital (WCMH; n.d.) is a 130-bed acute care facility located in Windham, Connecticut.

General process of care measures account for best practices in medicine and health care. The Surgical Care Improvement Project has set goals preventing untoward cardiac effects during certain surgical procedures along with infection control measures. According to Health Compare (HHS, 2011), cumulative scores for each hospital based on general process of care measures in the Surgical Care Improvement Project are as follows: DKH=0.954, HMH=0.901, WCMH=0.935. Another general process measure aimed at providing the standard of care of heart attack victims is the Heart Attack or Chest Pain Process of Care. The cumulative scores for these reported measures are: DKH=0.967, HMH=0.973, WCMH=0.956. Another cardiac related measure is the heart failure process of care measure. The cumulative results are: DKH=0.950, HMH=0.873, WCMH=0.893. Pneumonia process of care measures are important to gauge the appropriateness of treatments provided to stave off further development of respiratory failure and sepsis, two highly conditions with increase mortality. The cumulative scores for the pneumonia process of care measures are: DKH=0.932, HMH=0.860, WCMH=0.955. The last general process of care measure reflects the adherence to best practices in treating and managing children’s asthma; however, none of the three hospitals provided data for any of the process measures of this category.

Along with process of care measures, outcome of care measures are also important as they reflect the ability of each hospital to manage the risks of mortality and morbidity in caring for their patients. Outcome measures are based on both death and readmission of heart attack, heart failure, and pneumonia patients. For all three hospitals, DKH, HMH, and WCMH, the cumulative results for outcome of care measures were not statistically different from than the national rates in all categories. Health Compare (HHS, 2011) reports these measures as such.

One final measure that I find important in choosing a hospital is the patient satisfaction scores. Cumulative scores of the Survey of Patients’ Hospital Experience allow us to compare the three hospitals: DKH=0.695, HMH=0.701, WCMH=0.677.

In ranking each of the three hospitals, I used an average of the cumulative scores for each hospital’s measure discussed above. The final score, according to the averages of the Hospital Compare (HHS, 2011) scores, is: DKH=0.900, HMH=0.862, WCMH=0.883; therefore, my first choice of hospitals, according to the data presented in Hospital Compare is DKH with WCMH being second and HMH third. According to this data, though, each of the three hospitals appears to be equitable with the others striving in some measures and faltering in others. This is also evidenced by Quality Check (The Joint Commission, 2011), which shows a graphic representation of the same overall data, National Quality Improvement Goals and the Surgical Care Improvement Project, used by HHS (2011). Quality Check (The Joint Commission, 2011) compares quality data with the target ranges of other hospitals.

According to Quality Check (The Joint Commission, 2011), DKH met all the target goals while exceeding the goals set for infection prevention. HMH failed to meet the pneumonia care goal, but met all other goals. HMH did not exceed any of the goals. WCMH failed to meet the heart failure care goal, but met all other goals. WCMH did not exceed any of the goals.

In considering the data from Hospital Compare (HHS, 2011) and Quality Check (The Joint Commission, 2011), it is clear that this data can be used by consumers to make more informed decisions regarding their health care. Though the methods in this paper might be questionable and simple, consumers may disregard some measures while favoring others, depending on their perception of what measures are important in judging the provision of the care that they might receive. Additionally, the data used for the comparisons, many times, accounted for a small patient population; however, each hospital serves comparable communities with comparable levels of service. This may be a consideration when performing scientific statistical analyses, but that would be beyond the scope of this paper.

The provision of health care must be ethical, just, and equitable. Allowing consumers access to data regarding the performance of hospitals in their area can provide additional insight to patients when choosing their health care provider.


Day Kimball Hospital. (2011). Sevices and locations: Day Kimball Hospital. Retrieved from http://www.daykimball.org/services-and-locations/day-kimball-hospital/

Harrington Memorial Hospital. (2009). About us: Harrington at a glance. Retrieved from http://www.harringtonhospital.org/about_us/harrington_at_a_glance

The Joint Commission. (2011). Quality check. Retrieved from http://www.qualitycheck.org/ consumer/searchQCR.aspx

U.S. Department of Health and Human Services. (2011). Hospital compare. Retrieved from http://www.hospitalcompare.hhs.gov/

Windham Community Memorial Hospital. (n.d.). CEO’s message. Retrieved from http://www.windhamhospital.org/wh.nsf/View/CEOsMessage

A Novel Approach to Combat Heart Disease

According to Hansson (2005), cardiovascular disease is fast becoming the number one killer in the world among in developing countries and the Western world, due mainly to the correlation of increased rates of obesity and diabetes (Haffner, Lehto, Rönnemaa, Pyörälä, & Laakso, 1998; Miller, 2011; Willer et al., 2008). The goal of eradicating heart disease by the end of the twentieth century has been missed as cardiovascular disease is still responsible for 38% of deaths in North America. There has been much research over the last three decades regarding correlations between cardiovascular disease, obesity, and diabetes. Miller et al. (2011) identifies, based on the current literature, a number of metabolic syndromes in which elevated triglyceride levels are responsible for significantly increasing the risk of cardiovascular disease and the risk of death from a cardiac event.

Risk factors for cardiovascular disease, including smoking, hypercholesterolemia, and diabetes, which have positive predictive value for CVD, include a positive family history, hypertension, male gender, and age (Haffner, Lehto, Rönnemaa, Pyörälä, & Laakso, 1998; Hansson, 2005; Koliaki, 2011).

Demographically, according to NHANES 1999-2008 (as cited in Miller, 2011), Mexican American men (50 to 59 years old, 58.8%) are at the greatest risk with the highest prevalence of elevated triglyceride levels ( 150 mg/dL) followed by (in order of decreasing prevalence) Mexican American women ( 70 years old, 50.5%), non-Hispanic White men (60 to 69 years old, 43.6%), non-Hispanic White women (60 to 69 years old, 42.2%), non-Hispanic Black men (40 to 49 years old, 30.4%), and non-Hispanic Black women (60 to 69 years old, 25.3%).

Haffner et al. (1998) describe the importance of lowering cholesterol levels in those with diabetes mellitus type II as they both contribute to increases in mortality and morbidity from cardiovascular disease; therefore, efforts should be focused on identifying risks to heart health starting at age 30 with concomitant risk factors of diabetes or dyslipidemia, or any combination of two or more identified risk factors. More specific screening should begin at age 40 with Mexican American males and all other demographics suffering from any one of the secondary risk-factors, and at age 50 with all other ethnic demographics, regardless of the presence of risk-factors.

Specific screening for the at-risk population should include diagnostic percutaneous transthoracic coronary angiography (PTCA) and angioplasty, if needed. PTCA is a method of introducing a catheter through an artery to the coronary arteries of the heart, guided by radiology, to diagnose specific narrowing of these vessels, at which time a repair (angioplasty) can proceed immediately. PTCA, according to Koliaki et al. (2011), is the gold standard of diagnosing the presence and degree of atherosclerotic CVD. Currently, the standard for initiating PTCA requires a more acute presentation, typically complaints of chest pain or some other cardiac related illness. However, the proven safety and efficacy of PTCA may allow it to be used more as a screening tool as well as a primary coronary intervention in acute cases.

Utilizing the diffusions of innovations model of behavior change, public health entities can provide specific information to encourage interventional cardiologists to employ this technique as a focused CVD screening tool for at-risk populations (“Culture and health,” 2012). Adoption, however, is conditional on remuneration; therefore, a public health task force at the national level should investigate the potential for spending versus savings, and if significant, should disseminate the information to third-party payors (heath insurance providers, etc.) to ensure coverage when required. Additionally, grassroots efforts should be two-pronged, focusing on both the affected communities and the physicians most likely to contact the at-risk community. For the at-risk community, using mass-media, the message should simply be to discuss your risk with your physician, stop smoking, eat healthy, and exercise. The message, itself, needs to be conveyed in an effective manner, however. For the physicians, using mass-mailing and professional development campaigns, the message needs to more complex outlining risk versus reward, cost-effectiveness, and the potential for impacting a growing trend of heart-related death and disability. The American Heart Association has a proven track record of effective mass-media campaigns as well as professional development programs. So long as PTCA can be considered as an effective and cost-saving screening tool, the American Heart Association should certainly be involved in sending the message out.

Like with the proliferation of television advertisement of pharmaceuticals, using diffusions of innovations, we can get the heart-healthy message to the communities that would most benefit and the providers who can facilitate appropriate and novel screening and treatment techniques. We have already failed to eradicate CVD by the turn of the century, but if we think outside the box and develop novel approaches to consider, we may still have a chance at effectively lowering the incidence and prevalence of CVD in the years to come.


Culture and health. (2012). Public health and global essentials (Custom ed.; pp. 213-226). Sudbury, MA: Jones & Bartlett.

Haffner, S. M., Lehto, S., Rönnemaa, T., Pyörälä, K., & Laakso, M. (1998). Mortality from coronary heart disease in subjects with type 2 diabetes and in nondiabetic subjects with and without prior myocardial infarction. New England Journal of Medicine, 339(4), 229-234. doi:10.1056/NEJM199807233390404

Hansson, G. K. (2005). Inflammation, atherosclerosis, and coronary artery disease. New England Journal of Medicine, 352(16), 1685-1695. doi:10.1056/NEJMra043430

Koliaki, C., Sanidas, E., Dalianis, N., Panagiotakos, D., Papadopoulos, D., Votteas, V., & Katsilambros, N. (2011). Relationship between established cardiovascular risk factors and specific coronary angiographic findings in a large cohort of Greek catheterized patients. Angiology, 62(1), 74-80. doi:10.1177/0003319710370960

Miller, M., Stone, N. J., Ballantyne, C., Bittner, V., Criqui, M. H., Henry N. Ginsberg, H. N., … Council on the Kidney in Cardiovascular Disease (2011). Triglycerides and cardiovascular disease: A scientific statement from the American Heart Association. Circulation, 123(20), 2292-2333. doi:10.1161/CIR.0b013e3182160726

Willer, C. J., Sanna, S., Jackson, A. U., Scuteri, A., Bonnycastle, L. L., Clarke, R., … Abecasis, G. R. (2008). Newly identified loci that influence lipid concentrations and risk of coronary artery disease. Nature, 40(2), 161-169. doi:10.1038/ng.76


P.E.R.I. Problem Identification

The health problem I have identified is cardiovascular disease (CVD). According to Hansson (2005), CVD was expected to be significantly reduced or eliminated by the turn of the century; however, cardiovascular disease remains one of the leading cause of death globally with a rise in obesity and diabetes incidence (Willer et al., 2008). The two primary factors contributing to CVD are thought to be hypercholesterolemia, or high cholesterol levels in the blood, and hypertension, or high blood pressure, and although Koliaki et al. (2011) shows no predictive value between obesity and CVD, there remains a strong correlation between obesity and diabetes (Haffner, Lehto, Rönnemaa, Pyörälä, & Laakso, 1998; Hansson, 2005). A better look at the emerging literature might provide insight as to why attempts to control cholesterol and blood pressure have largely failed to eradicate CVD.

Koliaki et al. (2011) contend that smoking, hypercholesterolemia, and diabetes have positive predictive value for CVD while a positive family history, hypertension, male gender, and age, though predictive, are significantly less specific. Considering the causative risk factors and admitting the difficulty in changing age, family history, and gender, altering smoking status, cholesterol levels, and severity of diabetes and blood pressure have all been shown to decrease the risk of CVD. However, like genetic factors such as family history and gender, researchers are finding difficulty in controlling cholesterol levels effectively in many patients, especially those with concommitant diabetes mellitus (Haffner et al., 1998; Willer, 2008). However, statin-type cholesterol-lowering medications appear to have other protective effects than merely lowering cholesterol (Hansson, 2005).

In order to combat the growing concern of cardiovascular disease and, ultimately, the increasing mortality from the same, the American Heart Association (AHA) has published a scientific statement paper regarding the latest literature and research (Miller et al., 2011). AHA has taken the lead in cardiovascular health and strives to promote best practices based on the available evidence. By promoting AHA’s position using mass-mailing campaigns to physicians practicing in primary care, emergency, cardiology, and endocrinology, we can be assured that the right message is being disseminated rapidly to those most inclined to intervene. As more physicans in the identified roles adopt the latest evidence-based practice, more at-risk patients can be screened for CVD and the contributing factors. As screening paradigms become more focused, more of the at-risk population will be identified sooner which will allow for earlier intervention decreasing overall mortality and morbidity from CVD.

P cardiovascular disease
E Causes: DM, type II; dyslipidemia (hypercholesterolemia); smoking; diet; exercise; gender; age
Burden: increasing mortality and morbidity globally
R Diabetes mellitus screening and control, HTN screening and control, statin-type medication prescription, PTCA screening recommendations, smoking cessation
I AHA position, public health mailing campaign, cadre of physician groups

Determinants of Health – Mental Illness

When attempting to solve many of the issues relevant to public health, it is essential to understand the factors that contribute to disparities across various ethnic, racial, cultural and socioeconomic boundaries (Satcher & Higginbotham, 2008). In northeastern Connecticut, however, health disparities are primarily related to the socioeconomic strata, as much of the population is Caucasian and there are identifiable health disparities within this group (U.S. Census Bureau, 2002, 2008; U.S. Department of Health and Human Services, 2009). The disparity that I will focus on in this paper is mental illness.

According to Adler and Rehkopf (2008), unjust social disparity leads to greater health disparity, but what is unjust about social disparity? Adler and Rehkopf continue to describe efforts of researchers to evaluate how socioeconomic status, both, in conjunction with and independent of race or ethnicity, contribute to health disparities. There exists a significant difference in the manner in which different cultures approach mental health needs (Hatzenbuehler, Keyes, Narrow, Grant, & Hasin, 2008). Whites, who are more prone to suffering mental health issues, according to McGuire and Miranda (2008), preferring to seek professional care while Blacks are more likely to opt for self-directed care. Though Wang, Burglund, and Kessler (2001) tell of mental health treatment disparities between Whites and Blacks, in their study, 14 times more Whites responded than Blacks which may suggest that Whites are more apt to discuss mental health issues and Blacks might not unless they are motivated by extrinsic factors, such as poor care or the impression thereof. As long as Blacks are not prevented or discouraged from seeking care, there is no injustice in choosing self-care; however, it may not be the most effective option. Cultural awareness on the part of health care providers who may have an opportunity to provide health education to Blacks may alone increase the utilization of mental health services among the Black demographic.

More importantly, mental illness often exists in the presence of poverty and the lack of education. Much of the literature, such as Schwartz and Meyer (2010), seems to make the implication that low socioeconomic status is a causative risk-factor for mental illness, yet the literature also makes the distinction that one of the lowest groups on the socioeconomic ladder, Blacks, have a lower incidence, overall, of mental illness. This may be true in some instances; however, it is more likely that mental illness may be the proximal cause for an afflicted person’s socioeconomic status, especially if the illness manifested early enough to interfere with the person’s education.

More research needs to be undertaken to identify effective programs that aim to mitigate bias of mental health conditions within the community. As mental health disorders lose their stigma, more people who suffer from mental health issues will be able to seek care comfortably and unafraid, leading to increased treatment rates and increased synthesis within the community. This synthesis alone would alleviate much of the socioeconomic burden. Additionally, we need to shift our focus and strive to fix health issues locally, not nationally or globally. The world is comprised of a network of communities of individuals. Impacting the individual is the first step to affecting positive social change. Focusing on individual health will ultimately impact community, national, and global health.

The U.S. Health care system is overtaxed in caring for people with mental illness. According to Insel (2008), we need to refocus our efforts on providing care for mental illness to reduce the enormous indirect costs estimated at $193.2-billion per year. A viable solution in addressing mental illness as a health disparity, I feel, lies in understanding the manner that mental illness causes lower socioeconomic status which, in turn, causes risk of disparate care. Programs designed to aim for situational mitigation instead of mental health recovery will be less costly, more effective and, overall, more ideal. There will still be an obvious and great need for treatment and recovery programs, but with mitigation, I posit that they will be more effective, also.


Adler, N. E. & Rehkopf, D. H. (2008). U.S. disparities in health: descriptions, causes, and mechanisms. Annual Review of Public Health, 29(1), 235-252. doi:10.1146/annurev.publhealth.29.020907.090852

Hatzenbuehler, M. L., Keyes, K. M., Narrow, W. E., Grant, B. F., & Hasin, D. S. (2008). Racial/ethnic disparities in service utilization for individuals with co-occurring mental health and substance use disorders in the general population. Journal of Clinical Psychology, 69(7), 1112-1121. doi:10.4088/JCP.v69n0711

Insel, T. R. (2008). Assessing the economic costs of serious mental illness. American Journal of Psychiatry, 165, 663-665. doi:10.1176/appi.ajp.2008.08030366

McGuire, T. G. & Miranda, J. (2008). New evidence regarding racial and ethnic disparities in mental health: policy implications. Health Affairs, 27(2), 393-403. doi:10.1377/hlthaff.27.2.393

Newport, F. & Mendes, E. (2009, July 22). About one in six U.S. adults are without health insurance: Highest uninsured rates among Hispanics, the young, and those with low incomes. Gallup-Heathways Well-Being Index. Retrieved from http://www.gallup.com/poll/121820/one-six-adults-without-health-insurance.aspx

Satcher, D. & Higginbotham, E. J. (2008). The public health approach to eliminating health disparities. American Journal of Public Health, 98(3), 400–403. doi:10.2105/AJPH.2007.123919

Schwartz, S. & Meyer, I. H. (2010). Mental health disparities research: The impact of within and between group analyses on tests of social stress hypotheses. Social Science and Medicine, 70, 1111-1118. doi:10.1016/j.socscimed.2009.11.032

U.S. Census Bureau. (2002). Census 2000. Retrieved from http://www.ct.gov/ecd/cwp/view.asp?a=1106&q=250616

U.S. Census Bureau. (2008). Population estimates: Annual estimates of the resident population by age, sex, race, and Hispanic origin for counties in Connecticut: April 1, 2000 to July 1, 2008 [Data]. Retrieved from http://www.census.gov/popest/counties/asrh/files/cc-est2008-alldata-09.csv

U.S. Department of Health and Human Services. (2009). Community health status indicators report. Retrieved from http://communityhealth.hhs.gov/

Community Health: How Healthy is My Community?

I currently reside in Windham County, Connecticut. Windham County is primarily rural with one community, Willimantic, comprising most of the urban demographic. Windham County is functionally divided in half (north to south) in regards to health and hospital services. Primarily, Windham Community Memorial Hospital serves the west and Day Kimball Hospital serves the east. Accordingly, the eastern and western portions of the county may not be representative of each other, yet both are represented as a singular group when considering county-based statistics. This is a shortcoming of county-based statistics. In this instance, Willimantic, in the western portion of Windham County, may negatively affect the statistics of towns like Killingly, Pomfret, and Putnam, in the eastern portion of the county, due primarily to an increase in impoverished populations residing in Willimantic (U.S. Census Bureau, 2002). Additionally, data is lacking for a number of measures, according to the Community Health Status Indicators Project Working Group (2009), but continuing efforts will be made to increase reporting over time.

According to the U.S. Census Bureau (2008) and the U.S. Department of Health and Human Services (2009), the population of Windham County is 117,345 and is predominantly white (94.3%) with the remaining (5.7%) divided among, in order of predominance, Hispanics, Blacks, Asians and Pacific Islanders, and American Indians. The particularly vulnerable populations identified are adults age 25 and older who do not hold a high school diploma, are unemployed, are severely disabled and unable to work, suffer major depression, or have recently used illicit drugs. The uninsured rate in Windham County is well below the 16% national average at 9.5% (Newport & Mendes, 2009; U.S. Department of Health and Human Services, 2009).

Windham County fares equal or better in most measures, at least within the margin of error; therefore, I feel that Windham County, though not exceptionally healthy, is better than most and striving to meet the national standards (U.S. Department of Health and Human Services, 2009). For example, though the incidence of cancer and subsequent death resulting remains higher than peer counties, Windham County falls well within the expected range of death measures and exceeds peer counties in homicide, stroke, suicide, and unintentional injuries. Windham County also falls below the national standardized target for both stroke and coronary heart disease deaths. Infant mortality and birth measures seem representative of peer counties. Windham County also meets or exceeds environmental standards in all cases except for two reports of E. coli infections. There were also reports of five cases of Haemophilus influenzae B, two cases of Hepatitis A, and three cases of Hepatitis B — the only unexpected cases of infectious diseases reported. Pertussis incidence was limited to 25% of expected cases.

Windham County is not exceptional, but living here gives me the sense that the focus is on preventative care rather than acute care, which might explain how the health goals are being achieved overall. The report from the U.S. Department of Health and Human Services (2009) is in agreement.


Community Health Status Indicators Project Working Group. (2009). Data sources, definitions, and notes for CHSI2009. Retrieved from http://communityhealth.hhs.gov/

Newport, F. & Mendes, E. (2009, July 22). About one in six U.S. adults are without health insurance: Highest uninsured rates among Hispanics, the young, and those with low incomes. Gallup-Heathways Well-Being Index. Retrieved from http://www.gallup.com/poll/121820/one-six-adults-without-health-insurance.aspx

U.S. Census Bureau. (2002). Census 2000. Retrieved from http://www.ct.gov/ecd/cwp/view.asp?a=1106&q=250616

U.S. Census Bureau. (2008). Population estimates: Annual estimates of the resident population by age, sex, race, and Hispanic origin for counties in Connecticut: April 1, 2000 to July 1, 2008 [Data]. Retrieved from http://www.census.gov/popest/counties/asrh/files/cc-est2008-alldata-09.csv

U.S. Department of Health and Human Services. (2009). Community health status indicators report. Retrieved from http://communityhealth.hhs.gov/

Addressing Health Disparities

It is troubling to many people to see any person suffering in our society. It is even more troubling to see inequality extend to whole ethnic and racial groups within our society. We certainly do not want to be an unjust society, and we certainly want every member of our society to benefit from the technological gains made in the last century.

One of the more troublesome areas that many view as unjust is health and health care. It is unfortunate that some members of our society suffer from disparities in health. For instance, immunizations and vaccines for most of the common deadly pathogens are readily available, yet many people fail to immunize themselves or their family.

Immunization and vaccination programs have eradicated smallpox and polio and have all but eliminated the threat of measles in the United States (U. S. Department of Health and Human Services [DHHS], 2000). With influenza and pneumonia causing 30,000 to 41,000 deaths in the U. S., annually, the importance of vaccinating against these diseases is quite evident. Obviously, lacking immunity to a deadly pathogen is a disparate condition of health status, and Hispanic and African American populations are vaccinated with less frequency than Whites. How are these issues being addressed?

On the international level, the United Nations (2009) is addressing health disparities by attempting to eradicate poverty on a global scale. Unfortunately, many of these global initiatives have created an environment rife with economic turmoil that we are just now starting to see and understand. Though the premise of helping people out of poverty is very noble, the reality seems to be that we can only offer means for people to help themselves. Otherwise, we risk thrusting whole populations into a world they know nothing about, setting them up for failure. Poverty is based on local economy, and I believe that these interrelated problems are best addressed on the local levels with assistance from states, nations, and global endeavors. The people must direct their own path for a successful transition. They must take responsibility for their own successes and failures.

The United States addresses these concerns on a federal level, offering guidance to states and municipalities in ways to address them. One of these methods is a report from the U. S. Department of Health and Human Services. Healthy People 2010 (DHHS, 2000) has two stated major goals: 1) to increase quality and years of healthy life, and 2) to eliminate major health disparities. There are also 467 objectives in 28 focus areas designed to further these two major goals. Immunization is one of these focus areas.

According to the CDC’s National Center for Disease Statistics (2010), the goal of achieving a 90% immunization rate for children 19-35 months of age is close to being reached. The combination diphtheria, tetanus, and pertussis (DTP) vaccine (85%) and pneumococcal conjugate vaccine (75%) are the only two recommended childhood vaccines that are not being administered at least 90% of the time. According to DHHS (2000), the goal for DTP vaccination was 80% in 2000. It appears that this goal has been reached and exceeded.

Conversely, older adults, age 65 and greater, are at an increased risk of contracting illnesses that could be prevented by vaccination. “In 1999 approximately 90 percent of all influenza and pneumonia-related deaths occurred in individuals aged 65 and older” (Centers for Disease Control and Prevention, Office of Minority Health and Health Disparities, 2007, para. 2). DHHS (2000) does not state a quantitative goal for vaccinating noninstitutionalized older adults, though it does mention a need to “increase the proportion of noninstitutionalized adults who are vaccinated annually against influenza and ever vaccinated against pneumococcal disease” (p. 42). In 2000, 46% of the population in the U. S. were vaccinated against pneumococcal disease, and 64% were vaccinated against influenza (DHHS, 2000). In 2009, pneumococcal disease vaccinations increased by 15%, whereas influenza vaccinations increased by only 3% (Centers for Disease Control and Prevention, National Center for Health Statistics, 2010).

Striving to eliminating health disparities is a noble endeavor; however, the mere fact of attaining this goal contributes to the increase of health care disparity. By increasing the health care delivery model for one at-risk population, we must accept negative gains in the delivery of health care for all other populations. This is an example of the law of conservation describing the divisional nature of finite resources: when an isolated system undergoes change, its change in entropy will be zero or greater than zero (Negi & Anand, 1985). This concept is better stated as it applies to the zero-sum game of our economics today. Kathleen Madigan (2010), in a Wall Street Journal blog post, stated, “More spending in one area has to be financed by less purchases elsewhere” (para. 5).

Two conclusions can be drawn from observing this phenomena in health care. First, if people are spending their health care dollars on other staples, such as food, clothing, and shelter, then we should see a decline in the health of individuals that are making these choices. Second, within health care, in order to increase a focus on one population, an equal negative effect will be seen in all other population groups.

In all aspects of health care delivery, care should be taken to ensure just and equitable delivery of care regardless of socioeconomic factors, race, gender, religion, or creed. All people should have access to the minimum required care in order to maintain a healthy and productive life. We can counsel and educate our patients and clients to best health practices, but we cannot, however, force people to choose health over other facets of their lives.


Centers for Disease Control and Prevention, National Center for Health Statistics. (2010). Immunization. FastStats. Retrieved from http://www.cdc.gov/nchs/fastats/immunize.htm

Centers for Disease Control and Prevention, Office of Minority Health and Health Disparities. (2007). Eliminate disparities in adult & child immunization rates. Retrieved from http://www.cdc.gov/omhd/AMH/factsheets/immunization.htm

Madigan, K. (2010, August 3). With wallets thin, consumers face zero-sum game. Real time economics: Economic insight and analysis from the Wall Street Journal. Retrieved from http://blogs.wsj.com/economics/2010/08/03/with-wallets-thin-consumers-face-zero-sum-game/

Negi, A. S. & Anand, S. C. (1985). The second law of thermodynamics. A textbook of physical chemistry (pp. 241-289). Retrieved from http://books.google.com/

United Nations. (2009). The millenium development goals report: 2009. Retrieved from http://www.un.org/millenniumgoals/pdf/MDG_Report_2009_ENG.pdf

U.S. Department of Health and Human Services. (2000, November). Healthy People 2010: Understanding and improving health (2nd ed.). Washington, DC: U. S. Government Printing Office.

The Socio-economics and Certain Illnesses or Injuries

Kovner and Knickman (2008) describe health disparities as health problems common to specific populations, and they differentiate health care disparities as a “[reflection of] the interaction of health care access and utilization with broader societal issues related to racial and ethnic, socioeconomic, and gender differences” (p. 421). Many social groups take part in risky behaviors. If these social groups are drawn along certain socio-economic lines, then it would appear that there is a causal relationship between socio-economics and certain illnesses or injuries when the correlation is truly the risk-taking behavior. Blacks having a ten-fold incidence of AIDS over whites may be related to preliminary health education with no causal relationship to the access of health care (Kovner et al., 2008). Additionally, Kovner et al. point out a higher incidence of Blacks leaving emergency departments before being cared for. Could this be a result of Blacks seeking emergent care for non-emergent problems? Certainly, there are health problems and health care problems common to specific populations.

Initially, when considering racial and ethnic differences, my views revolve around socioeconomic determinants where causal relationships are not what many would consider. Most, I imagine, would consider the cause of poor care to be uncaring health care professionals, but I would venture that the attitudes of some health professionals are the end-result that correlates to poor care. If a health care provider treats patients who continually dismiss their poor health or take part in risky health behavior without considering the long-term effects, the health care professional becomes dispassionate and disconnected, mistrusting patients, and delivering care that is substandard, but presumed to be aligned with the responsibilities taken by the patients, generally speaking. Ergo, if they don’t care, why should I? This generalization creates a common distrust between patient and provider. Aside from the patient-provider relationship, there seems to be a more daunting issue of access to health insurance, which obviates the correlation to a lack of health care access. What are the causes of these disparities?

How do we address the disparities in health care? First, we need to identify if there are truly disparities, what they are exactly, and what is causing them. Recent research suggests a need to find the methods most appropriate to tackle these questions (Kirby, Taliaferro, & Zuvekas, 2006; Lê Cook, McGuire, Meara, & Zaslavsky, 2009; Lê Cook, McGuire, & Zuvekas, 2009). Do we need to understand the problem? Educating both the providers and patients effectively in how to approach each other as well as instituting quality improvement strategies within each health care practice should assure, at least retrospectively, that all patients within a practice would get the same care as any other patient treated by that practice. Additionally, providing patient education about how to access health care appropriately and effectively would help to avoid some of the pitfalls common in our health care system. Some of which may be attributable causes to many of the health care disparities of today.

In conclusion, I feel that many of the health care disparities are not caused by the health care system, though the relationship is noticeable. There are many other factors that need to be considered, and as Kirby et al. point out, “Researchers and policymakers may need to broaden the scope of factors they consider as barriers to access if the goal of eliminating disparities in health care is to be achieved” (p. I64).


Kirby, J. B., Taliaferro, G., & Zuvekas, S. H. (2006) Explaining racial and ethnic disparities in health care. Official Journal of Medical Care, 44(5), I64-I72. doi:10.1097/01.mlr.0000208195.83749.c3

Kovner, A. R., Knickman, J. R., & Jonas, S. (Eds.). (2008). Jonas & Kovner’s health care delivery in the United States (9th ed.). New York, NY: Springer.

Lê Cook, B., McGuire, T. G., Meara, E., & Zaslavsky, A. M. (2009). Adjusting for Health Status in Non-Linear Models of Health Care Disparities [Manuscript]. Health Service Outcomes Research Methodologies, 9(1), 1–21. doi:10.1007/s10742-008-0039-6

Lê Cook, B., McGuire, T. G., & Zuvekas, S. H. (2009). Measuring Trends in Racial/Ethnic Health Care Disparities [Manuscript]. Medical Care Research Review, 66(1), 23-48. doi:10.1177/1077558708323607