Social Work

Social Work

Section 1

           Disparity in healthcare refers to the differences in health and healthcare as they affect the different population groups in the country. In the US, disparity in healthcare can be seen with regards to how the various population groups access the same healthcare services and how these accessibility affects the health outcomes in the diverse population groups. For example, it has been found that African Americans are negatively impacted by various health conditions such as diabetes and cardiovascular disease in comparison to their White counterparts.  Equally, other minority population groups such as the Alaska Natives have been found to be affected much more negatively by the conditions in comparison to their White counterparts. Disparity in healthcare is discriminatory in nature and therefore it needs to be addressed. The disparity also creates differences in connection to the ways that the various health conditions affect the lives of the population groups and this also needs not happen. In other words, aspects such as race, ethnicity or economic backgrounds should not dictate how a person access healthcare services and how certain diseases affect their lives. Disparity in healthcare is serious issue that needs to be eliminated if there is going to be equality in the ways that the various population group members are accessing the healthcare services.

              In the US, a legislation that addresses healthcare disparities is the Patient Protection and Affordable Care Act (ACA) of 2010. This is the legislation that is meant to ensure that all the citizens living in the US are provided with affordable and quality healthcare in the various geographical regions where they are. The goal of the legislation is to help the citizens of the country access the healthcare services in ways that are not discriminatory. Through ACA, the expectation is always that the health of the general population will be improved in significant ways.

        One of the statistics related to disparity in healthcare in the US is that which concerns infant mortality. According to the CDC 2013 report on healthcare disparities in the US, infant mortality rate among the African American women more than double the numbers of non-Hispanic White women(CDC, 2017). This means that the condition negatively affects the African American women and the community in general as a population group in comparison to the members of the population groups.

        The second healthcare statistic which shows disparity in healthcare in the US is that which relates to healthcare outcomes.  The National Interview Health Survey demonstrates that 13.2 % of Native Americans report being in fair or poor health, in comparison to only 9.8%  of the entire  population(CDC, 2017).  This statistics is historical in nature and is attributed to the observation that the Native Americans have often been marginalized with regards to policies and other aspects. As such, the health outcomes in the population group have been always poorer in comparison to the health of the whole population. From these statistics, it is clear that the healthcare disparity is a serious problem in the country and one that reduces the health outcomes is some members of the population more negatively than it affects the other population groups.  Because of healthcare disparity, some members of the population groups cannot access the healthcare in the ways that are expected. Despite the fact that such groups also have a right to healthcare, issues such infrastructural developments or healthcare policies end up being barriers to the ways in which the individuals can access the healthcare services.

          Overall, health disparity is impacting negatively on some members of the population groups and since it reduces the quality of life of the individuals, it is an issue that requires urgent measures so that health of all the people living in the country can be improved regardless of their geographical locations or other factors that may account for the differences observed in their lives.

Section II Articles Analysis

Citation

Benmarhnia, T., Huang, J., Basu, R., Wu, J., & Bruckner, T. A. (2017). Decomposition Analysis of Black–White Disparities in Birth Outcomes: The Relative Contribution of Air Pollution and Social Factors in California. Environmental Health Perspectives,125(10), 1-10. doi:10.1289/ehp490

Abstract

Objective:  To use the decomposition methods to underscore and understand disparities in preterm births prevalence as it takes place between the non-Hispanic black individuals and non-Hispanic White individuals who live in California with respect to neighbourhood socio-economic environment, neighbourhood air pollution and individual demographics.

Background : Racial/ethnic disparities in pre-term births are well documented in  pre-term births but studies on the role of social and environmental determinants are few. In addition, modifiable features of  the environment are beneficial in reducing preterm births.

Methods : Live singleton births in California from 2005-2010 was used  to estimate preterm births as well as other adverse birth outcomes for the infants borne by the non-Hispanic black mothers and non-Hispanic white mothers. Non extension of the Blinder Oaxaca method was to decompose racial/ethnic disparities.

Key Results:  Predicted differences in the probability of PTB between blacks and Whites infants established to be 0.056. 37.8% White to black disparity was found to be present when all predictors were put into consideration. Individual 17.5% for PTB and neighbourhood level variables  16.1% for PTB contributed to the greater proportion of the black to white difference than air pollution at 5.7%.

Conclusion: While individual and neighbourhood factors explain the differences between white and black differences in birth outcomes, the air pollution is also an important contributor as it relates to the individual and neighbourhood factors as well.

Citation

Clegg, L. X., Reichman, M. E., Miller, B. A., Hankey, B. F., Singh, G. K., Lin, Y. D., . . . Edwards, B. K. (2009). Impact of socioeconomic status on cancer incidence and stage at diagnosis: Selected findings from the surveillance, epidemiology, and end results: National Longitudinal Mortality Study. Cancer Causes & Control,20(4), 417-435. doi:10.1007/s10552-008-9256-0

Abstract

Objective:  To show that demographic and individual level socioeconomic factors contribute to the cancer healthcare disparities observed in the populations.

Background : Population based cancer registry data obtained  from the Surveillance, Epidemiology, and End Results (SEER) Program at the National Cancer Institute (NCI) are primarily  founded on medical records and administrative information. The SEER-NLMS data offers  a new vital  research resource that is critical  for health disparity research on cancer burden.

Methods:  The records of cancer patients diagnosed in 1973-2001 while living in 1 of 11 SEER registries were connected with 26 NLMS cohorts. The number of SEER matched cancer patients that were also belonged to NLMS cohort was 26,844. Matched patients that comprised of those used in the incidence analyses and the unmatched patients were compared using aspects such as sex, age group, and ethnicity, year of diagnosis, anatomic site and residence area.

Key Results

Men and women having less than a high school education experienced high lung cancer rate ratios of 3.01 and 2.02, respectively, comparative to their college educated peers. Individuals  with family annual incomes which was less  than $12,500 experienced  incidence rates that were more than 1.7 times the lung cancer rates of the individuals with incomes $50,000 or higher.

Conclusion Socioeconomic patterns in incidence differed for particular  cancers, while such patterns in connection to stage were in general  consistent across cancers, with the  late-stage diagnoses being connected with lower SES.

Citation

Faigle, R., Ziai, W. C., Urrutia, V. C., Cooper, L. A., & Gottesman, R. F. (2017). Racial Differences in Palliative Care Use after Stroke in Majority-White, Minority-Serving, and Racially Integrated U.S. Hospitals. Critical Care Medicine,45(12), 2046-2054. doi:10.1097/ccm.0000000000002762

Abstract

Objective: To   establish  whether palliative care use after intracerebral hemorrhage and ischemic stroke differs in  hospitals serving different  proportions of the minority patients.

Background: It  has been established that there are  racial/ethnic differences in palliative care resource following the occurrences of a stroke. However, it is yet to be determined if the  unclear patient or hospital characteristics are responsible for the   disparity.

Methodology; Population-based cross-sectional study was used to establish Intracerebral hemorrhage and ischemic stroke admissions  from the Nationwide Inpatient Sample between 2007 and 2011. Hospitals were classified  with regards to the percentage of ethnic minority stroke patients in which (< 25% minorities the  [“white hospitals”], 25-50% the  minorities [“mixed hospitals”], and  50% minorities [“minority hospitals”]

Key Results: Ethnic minorities experienced  a lower likelihood of getting  palliative care in comparison to the  whites in any hospital stratum. However,  the odds of palliative care for the  white and minority intracerebral hemorrhage patients was  found to be lower in minority when compared  with white hospitals.

Conclusions: The likelihoods  of receiving palliative care for the  white and minority stroke patients is lower in minority in  comparison to   white hospitals, implying  system-level factors as primary factor to show race disparities in palliative care use  following  stroke.

Trinh, M., Agénor, M., Austin, S. B., & Jackson, C. L. (2017). Health and healthcare disparities among U.S. women and men at the intersection of sexual orientation and race/ethnicity: A nationally representative cross-sectional study. BMC Public Health,17(1), 1-11. doi:10.1186/s12889-017-4937-9

Abstract

 Objective:  To determine how the interplay between sexual orientation and race affects healthcare measures.

Background:  Sexual minorities such as gays, lesbians and bisexual people are at higher risks of adverse health outcomes and are also likely to experience reduced access to care.

Method: Poisson regression method was used to determine the association between sexual orientation identity and the healthcare outcomes in individuals within and across/ethnic racial groups.

Key Results: The sexual minority individuals were likely to experience high incidences of adverse health outcomes than there heterosexual counterparts.

Conclusion: Sexual minority groups or populations report higher prevalence of poor health outcomes in some of the health condition such as HIV/AIDs in comparison to the general population members.

Section III

Seal, K. H., Metzler, T. J., Gima, K. S., Bertenthal, D., Maguen, S., & Marmar, C. R. (2009). Trends and Risk Factors for Mental Health Diagnoses Among Iraq and Afghanistan Veterans Using Department of Veterans Affairs Health Care, 2002–2008. American Journal of Public Health,99(9), 1651-1658. doi:10.2105/ajph.2008.150284

Topic and Its Applications

In this article, the authors investigate the risk factors for mental health diagnoses among the Iraq and Afghanistan veterans using the department of Veterans Affairs Healthcare. It is well established in the literature that the veterans always have high chance of developing mental health problems such as PTSD. This study could therefore be useful in social practice and policy and this is due to the fact that the findings can be applied in formulating the mental health policies for the veterans.

Research Questions and Hypotheses

The research question that the study sought to answer was: are there specific risk factors that predisposes the veterans to mental health conditions. Equally, the hypothesis followed by the researchers was that being a veteran or serving in the military is a risk factor for the development of PTSD.  There are no evident secondary research questions for the study. Based on the research question and hypothesis used by the authors, this study is evaluative in nature. The reason for the evaluative nature of the study regards the observation that the authors are attempting to determine the risk factors for the mental health issues in the veteran population. The hypothesis is not clearly stated in the study.

Literature Review

The literature could stand to be more critical and comprehensive. Although the authors review the literature concerning the veterans and the risks for the mental health challenges, there is no specific section for the literature.  This alone is an indication that the literature is not expansive in nature. While the authors also use the relevant sources to support the review, the literature hardly uses the relevant theories to support the evidence. This means that the readers cannot easily link the issue in discussion with the theories available. Theories are always vital in explaining the different issues that are investigated by research studies. Despite the limitation, the authors articulated the purpose and assumptions in clear ways. Nevertheless, there is no sufficient coverage of the theories in the article.

Sampling

The authors used a non-probability sampling method and the procedure for recruiting and screening the participants in the study is well articulated. The informed consent procedure is not explicitly described but the authors’ talk of informed consent. In the study, the authors do not highlight a sampling frame. One of the biases in the study is that of using the veterans only enrolled in the VA healthcare service. The sample population used in the study at 424, 143 is sufficient to warrant the generalization of the results. From the methodology used, the potential pattern of attrition regarded the possibility of some veterans developing complications and dropping out of the study. This could lead to incomplete data and consequently compromise the validity of the data.

Design

The authors used a longitudinal study design. This design was used to make predictions of risks factors.

     RDI1                                                                                 Y                                               RDI2                       BP2

MED

    BP1

From the model, a key strength is that it shows how different factors interact to influence an outcome. On the other hand the weakness with the approach is that it does not highlight the causal relationships.

Measurements

For this study, the independent variable related to the risk factors associated with the mental health illnesses. On the other hand, the dependent variable was the mental health condition in the individual. The variables were operationalized through the application of the possible impacts. There were no values attached to the variables.

Ethics

The authors addressed the ethical issues by discussing the protection of the participants privacies. The authors also touched on the confidentiality of the participants information.

Statistics

The descriptive statistics are described in the results and analysis sections and they presented in tables and charts. Inferential statistics are also used in the results section of the study.

Results and Discussion

One key finding of the study is that late diagnoses are the principal cause of the mental health conditions in the veterans. The results and conclusions are clearly presented in the study. The summary of the results is helpful as it provides key information.

Main Message

From this article, the main message is that there needs to be improved interventions to promote mental health wellbeing among the veterans. This article provided  comprehensive information about the topic and this can be seen from the methodological and logical conclusions reached.

References

Benmarhnia, T., Huang, J., Basu, R., Wu, J., & Bruckner, T. A. (2017). Decomposition Analysis of Black–White Disparities in Birth Outcomes: The Relative Contribution of Air Pollution and Social Factors in California. Environmental Health Perspectives, 125(10), 1-10. doi:10.1289/ehp490

CDC.(2017) Health Disparities Report 2013 Retrieved from https://www.cdc.gov/minorityhealth/CHDIReport.html#anchor_1547838233

Clegg, L. X., Reichman, M. E., Miller, B. A., Hankey, B. F., Singh, G. K., Lin, Y. D., . . . Edwards, B. K. (2009). Impact of socioeconomic status on cancer incidence and stage at diagnosis: Selected findings from the surveillance, epidemiology, and end results: National Longitudinal Mortality Study. Cancer Causes &amp; Control, 20(4), 417-435. doi:10.1007/s10552-008-9256-0

Faigle, R., Ziai, W. C., Urrutia, V. C., Cooper, L. A., & Gottesman, R. F. (2017). Racial Differences in Palliative Care Use After Stroke in Majority-White, Minority-Serving, and Racially Integrated U.S. Hospitals. Critical Care Medicine, 45(12), 2046-2054. doi:10.1097/ccm.0000000000002762

Seal, K. H., Metzler, T. J., Gima, K. S., Bertenthal, D., Maguen, S., & Marmar, C. R. (2009). Trends and Risk Factors for Mental Health Diagnoses Among Iraq and Afghanistan Veterans Using Department of Veterans Affairs Health Care, 2002–2008. American Journal of Public Health, 99(9), 1651-1658. doi:10.2105/ajph.2008.150284

Seal, K. H., Metzler, T. J., Gima, K. S., Bertenthal, D., Maguen, S., & Marmar, C. R. (2009). Trends and Risk Factors for Mental Health Diagnoses Among Iraq and Afghanistan Veterans Using Department of Veterans Affairs Health Care, 2002–2008. American Journal of Public Health, 99(9), 1651-1658. doi:10.2105/ajph.2008.150284

Trinh, M., Agénor, M., Austin, S. B., & Jackson, C. L. (2017). Health and healthcare disparities among U.S. women and men at the intersection of sexual orientation and race/ethnicity: A nationally representative cross-sectional study. BMC Public Health, 17(1), 1-11. doi:10.1186/s12889-017-4937-9

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