Abstract
African Americans introduce the most severe and crucial HIV burden than any other race and ethnic group represented in the USA. The problem is aggravated by the fact that the number of newly infected African American females, as well as those diagnosed AIDS-related diseases, are growing rapidly. Although the origin of HIV elicits controversy, its effect on human health is evident (Aulette-Root, Boonzaier, & Aulette, 2014). Despite available medical regimens that lower the rates of deaths among the population, HIV continues to be a threat to the social structure of the black community. Considering the fact how the virus is quickly spreading among African women in comparison with other segments of the population, South Africa appears to be the source and gateway of HIV-positive representatives of African American females. This fact results in various other challenges including social and economic aspects of their life in the United States. This quantitative study employs a correlational research design to investigate the level of treatment compliance and social support among African women with HIV in a sample of 471 subjects. The key aspects of the research process are described in this paper.
Introduction
Although the number of HIV-infected women in the US has declined since 2008, new infections remain disproportionately high among African American females. The rate of infection is estimated to be twenty times higher in this demographic than in the white female population with over 80% of new infections being attributed to sexual contact; other causes include sex trade, poverty, a lack of information, and drug addiction (Smith, 2015). Antiretroviral therapy coupled with proper social/medical support can allow HIV-positive people to lead longer healthier lives. In this view, the higher number of African American females contracting HIV is strong evidence for a lack of treatment compliance and inadequate social support for this demographic. As a result, African Americans lack access to the necessary medical information and treatment to stop the progression of the virus. A prospective study that followed a cohort of African American women living with HIV for a decade or more found that, upon learning of their status, they undergo grief that affects their health outcomes (Smith, 2015). The study also found that social support promotes recovery from the grief (Smith, 2015). Nevertheless, the actual relationship between medical adherence and social support among HIV-infected black women remains largely unknown. This quantitative study aims to determine the correlation between medical and social support and medical adherence among HIV-positive African American women.
Problem Statement
Latest statistics indicate that of all HIV-infected Americans, 44% constitute African American women (Siddiqi, Hu & Hall, 2015). The infected individuals include not only adults but also adolescents aged below 13 years. The high rate of infection in this demographic is attributed to the large number of challenges that face the community, including poverty, substance abuse, and sex trade. In addition, the tendency of African Americans to live in communities and have sexual relations amongst themselves increases the risk of HIV infection among African American women.
Globally, South Africa has one of the highest HIV prevalence and spread, making it the “gateway for the HIV to ignite and spread” (Myles, 2009, p. 18). The population of HIV-infected women in Sub-Saharan Africa is estimated to be 13.3 million (Shu-Acquaye, Mbanya & Chungong, 2008). Early HIV screening is a critical component in the prevention and management of the disease (Cannon, 2010). On the global front, treatment and social support programs have been rolled out to curb the spread of HIV in vulnerable populations in Sub-Saharan Africa (Turshen, 2000). In the US, the number of new infections among African-American women in 2011 was 6,100, constituting a third of all new infections.
The high infection rate calls for an examination of how the level of social support received affect medical non-compliance in this demographic. At present, the efficiency of the social support programs targeting HIV-positive African American women is unclear. Social support programs often involve relatives, partners, friends, and social support networks. This study will examine if social support creates positive attitudes towards medical compliance among African American women living with HIV.
Purpose Statement
This study evaluates the efficiency rate of the implemented treatment and social support programs targeting African American women with HIV. It relies on up-to-date research data and statistics obtained from HIV clinical program coordinators working with African American communities. It also utilizes background information obtained from interviews and published statistics on the outcomes of educational and medical programs rolled out in the country.
Research Questions and Hypotheses
The study investigates the various social and treatment programs currently in place to identify steps that could be taken to improve their efficiency. The key research questions guiding the study include:
- Why does the virus spread so fast? Why is the risk of HIV infection higher among African-American women than among white women?
- What steps have been taken to curb HIV infection in this demographic? Are new steps necessary?
- Which treatment or social support program is efficient in reducing HIV infection rate? Does it call for updating?
- Does the problem threaten the rest of the population? In other words, how can people protect themselves from the virus?
The researcher hypothesizes that there is a difference in medication adherence between supported and unsupported HIV-positive African American women. Thus, the null hypothesis that will be tested is there is no difference between supported and unsupported women’s adherence to treatment regimens.
Identification of Independent and Dependent Variables
The study examines whether social/medical support promotes HIV compliance in African American women with HIV. The independent variable includes social/medical support that the women enrolled in various programs receive. It determines medical compliance rate. On the other hand, medical compliance is the dependent variable, i.e., the outcome of exposure to the social and medical support programs. The study will use a correlational design to determine the relationship between the two sets of variables based on secondary data (Barnham, 2015).
Theoretical Framework
In the United States, it is estimated that up to “20 percent of the infected people” do not know their status (Patel & Rushefsky, 2014). This population includes whites, blacks, and other races and ethnicities. Research also identifies poverty as the cause of the high rate of HIV infection among African Americans (Gilbert & Wright, 2003). On the other hand, HIV is considered to occupy “a special place in the hierarchy of social stigma”, affecting access and utilization of medical care services by the black women (Kronenfeld, 2015, p. 55). This fact explains the difference between in the number of people undergoing HIV testing and those seeking care and treatment. Social and medical support programs have been launched nationally to promote safe sex and medical adherence to curb the spread of HIV. However, these programs have achieved little because of poverty and related problems of drug abuse and sex trade. Evaluating the efficiency of these programs will help devise ways to improve them to obtain the desired results.
The Appropriate Quantitative Research Design
A quantitative study utilizes a descriptive, experimental, quasi-experimental, correlational, or cross-sectional design (Cresswell, 2009). The study investigates how social/medical support (IV) affects HIV regimen compliance (DV) by HIV-infected African American women. A correlational design examines the “association/correlation existing between variables not subject to experimental manipulation” (Frankfort-Nachmias & Nachmias, 2008, p. 184). Therefore, since the research aims at determining the link between the two variables, the appropriate design recommended for the study is the correlational design.
The Reason for Selecting the Design
Barnham (2015) asserts that a correlational study can make use of primary or secondary data to infer the existing relationship or the effect of an event. Thus, this design will help determine if social and medical support improves medical adherence of the subjects. The design also allows one to measure multiple variables in their natural environment, allowing the existing interrelations to be determined.
Reasons Why the Other Designs are not Appropriate for the Research
Determining the relationship between social/medical support and medical compliance is the key focus of this study. Thus, descriptive design is inappropriate because it only describes the variables and not their relationships. Experimental and quasi-experimental designs, though appropriate for determining the causal link between the IV and DV, require manipulation of the experimental variable (Shuttleworth, 2011). Since this study does not entail experimental manipulation of variables, neither the experimental nor the quasi-experimental design is appropriate. The other quantitative design is the cross-sectional design, which is recommended for determining the strength of association between exposure and outcome (Creswell, 2009). This design is not appropriate for this study because the exposure and outcome are measured at a single point in time. This study utilizes data drawn from programs running for an extended period to test the hypothesis that there is no difference in medication adherence between supported and unsupported women subjects.
Explanation of the Independent and Dependent Variables
As aforementioned, the independent variable in this study is social/medical support. Social support is the care and assistance received by the subjects from friends, family, co-workers, partners, and social networks. It can be informational (e.g., sex education), financial, or emotional support. On the other hand, the dependent variable is medical compliance, which is the extent to which a patient adheres to “medical advice, self-care, exercises, or therapy sessions” (Gilbert & Wright, 2003, p. 97). It is the outcome of the education and awareness created by the social support programs.
The Level of Measurement for each Variable
A level of measurement or scale for a variable is the “relationship between its different values” (Creswell, 2009, p. 141). It measures the attributes of a variable on a numerical scale. Both discrete and continuous variables contain ordered categories that can be assigned values. In this study, the essential characteristics of the independent variable, i.e., medical/social support, can be measured include the perceived informational support, emotional support, and social support networks. The level of measurement for the independent variable will be the ordinal scale in which “attributes of a construct are rank-ordered” with the relative difference between the categories having no meaning (Nation, 2007, p. 17). The significance of this level is that it provides for rank order, i.e., the ordinal data can be grouped into categories based on the degree of the perceived social support received.
On the other hand, the measures of the dependent variable, i.e., medical compliance, include CD4 cell count, the level of metabolites in urine, and symptom remission. The appropriate level of measurement is the ratio scale, which measures the magnitude or intensity of the attributes. It will help specify the amount or count of CD4 cells, metabolites, and symptoms in the subjects enrolled in social support programs.
Means for Ensuring Content Validity, Empirical Validity, and Construct Validity
Each construct is evaluated to determine the content it describes and ensure that it measures the intended attribute (Blaxter, Hughes & Tight, 2006). In this view, the researcher must define the content domain of a construct. In this study, the content area of the social/medical support construct includes the perceived social, emotional, and informational support received by the subjects. On the other hand, a description of the demographics of the target population and adherence to medical advice, therapy sessions, and self-care constitute the content domain of medication compliance.
Empirical validity estimates the predictive ability of a sturdy construct. It determines the probability that a theoretical measure will predict a particular construct. In this study, a measure of social/medical support (IV) should predict medical compliance (DV). To ensure empirical validity, the researcher will test the attributes of the independent variable, i.e., informational support, emotional support, and support networks using a sample of HIV-positive patients under treatment. A positive correlation between these attributes and medical compliance rate will affirm the empirical validity of the measures.
Construct validity measures the degree to which the conclusions made resonate with the theoretical concepts of the constructs. It allows for the generalization of the actual measures to their respective concepts (Trochim, 2006). Construct validity means that a test evaluates the actual attribute it is supposed to measure. To ensure construct validity, the researcher will use t-test and ANOVA to prove that the theoretical relationship between medical/social support and medical compliance exists in reality (Frankfort-Nachmias & Nachmias, 2008). If the t-test or ANOVA scores are significant (p = 0.05), then the degree of association between the theorized constructs and the measured attributes are strong, hence, evidence for construct validity.
Means for Ensuring Reliability for the Measurement
Reliability is the “repeatability of the measures” used in quantitative studies (Shuttleworth, 2011, para. 11). Reliable measurements generate valid results. To ensure reliability in this study, the researcher will obtain data from multiple HIV program coordinators. Using multiple secondary datasets provided by different coordinators will eliminate researcher bias and measurement bias. According to Shuttleworth (2011), researcher bias is a major threat to the reliability of measurements due to adaptation effects. Thus, by using multiple datasets generated by different HIV clinical program coordinators, the researcher will enhance the reliability of the measurements.
Reliability of measurements is usually affected by researcher bias. In this study, the HIV program coordinators employed interviews to collect data on the effect of social/medical support on medical compliance. If one coordinator interviews many HIV-positive women, it can lead to researcher bias caused by adaptation effects. Researcher bias also causes systematic errors when analyzing constructs, which affects reliability. Therefore, by using datasets from multiple coordinators, the study will reduce the effects of researcher bias and enhance internal consistency of the measurements.
Besides using multiple datasets, the researcher will use correlation coefficients to ensure the reliability of the measurements. Shuttleworth (2011) identifies inter-rater reliability test as a useful method for estimating the “extent of the agreement” between measurements (para. 7). This method will be used to crosscheck the data provided by different coordinators for internal consistency.
The Recommended Sampling Strategy
This research aims at determining the relationship between HIV treatment compliance and social support among HIV-positive African American women. The study will utilize data on African American women with HIV enrolled in social support programs. It is recommended that the subjects diagnosed with HIV in 2011 be included in the study population. In this year, the number of African American women diagnosed with HIV and enrolled in social support programs was 6,100 (Smith, 2015). Given the large population size, a systematic random sampling strategy is recommended for selecting the study sample. The approach is a probability sampling method whereby individuals from a homogeneous population are selected using a regular sampling interval.
Why the Sampling Strategy is Appropriate
The systematic random sampling strategy relies on a random choice to select individuals from each sampling unit. Thus, it prevents bias in sampling, which enhances the representativeness of a systematic random sample. In addition, given the large size of the target population, i.e., 6,100 African American women with HIV, a systematic random sampling is appropriate for obtaining a representative sample from an evenly distributed population. The method employs a simple formula and an online calculator to select individuals at equal intervals.
Strengths and Weaknesses of the Sampling Strategy
Strengths
- Systematic random sampling is easy to conduct, especially when the target population is too large. Thus, the approach can allow the investigator to sample a large population at minimal resource requirements.
- With systematic random sampling, the investigator has greater control of the sampling process.
- The sampling approach has a low-risk factor, i.e., there is a minimal likelihood of contaminating the data in systematic random samples.
- Systematic random sampling helps avoid the problem of clustered selection because individuals are selected at regular intervals.
Weaknesses
- The systematic random sampling strategy is only useful when the population size is known. Both the starting point and the magnitude of the interval can only be determined when the population is available.
- The attribute being investigated must be randomly distributed in the target population. Otherwise, the sample will contain very common cases.
- The researchers may manipulate data to obtain the desired outcome. Thus, the strategy is prone to data manipulation, which subjugates the randomness of the sampling process.
The Sample Size Appropriate for the Study
Inappropriate sampling techniques used in research are likely to lead to inconsistent and one-sided samples (Shuttleworth, 2011). In clinical research, a small sample size may raise questions related to credibility while a large sample can prove senseless when the answer is obvious, or there was a mistake in the hypotheses (Creswell, 2009). Most formulae for calculating sample size take into account the α-error, the β-error, and the standard deviation or SD. The α-error considers a nonexistent difference between groups under study while the β-error does not. On the other hand, the SD is the variability of the sample data (Creswell, 2009). It entails the variables identified by the investigator that we can include in the formula of the electronic sample size calculator. If the SD is one, we set the α-error confidence at 5% and β-error at 20%, generating a desired sample size of 471 (Creswell, 2009).
As aforementioned, considering the risk of making errors in formulating the hypothesis, grounding the research upon a large population size is a waste of time and funds. For this reason, we should obtain our sample from the accessible population sharing the same characteristics (Creswell, 2009). The optimal sampling method here is the systematic random sampling approach since it ensures that every member of the population is chosen through a non-biased process (Frankfort-Nachmias & Nachmias, 2008). Defining a sample interval of systematic random sampling entails a simple formula, namely, the ratio equals the size of the whole population divided by the sample size.
African American females account for up to 24% of all newly infected people with this racial background. The actual number is 6,100. Since we cannot afford to question all of them, a sample of 471 people can be appropriate. It is calculated as follows:
K = 6100/471 ≈ 13
K represents the sample interval. Thus, it would be appropriate to choose every thirteenth woman from the evenly distributed population.
The Appropriate Statistical Tests
These tests estimate the likelihood of observing the collected data when the null hypothesis is correct (Creswell, 2009). They can be parametric or non-parametric. Parametric tests utilize samples and assume normal distribution in the data while non-parametric ones do not involve such assumptions. This study investigates the relationship between social support (IV) and medical compliance (DV) among black women living with HIV. Thus, the study seeks to determine the correlation between the two variables. The recommended statistical test for this study is Pearson correlation coefficient or R, which “tests for the strength of the association between two variables” (Creswell, 2009, p. 42).
Reasons for Selecting the Statistical Test
As aforementioned, the Pearson’s R estimates the strength of the correlation between variables. It will be necessary to determine the relationship between social support and medical compliance for one to reject or affirm the study’s null hypothesis. A positive correlation is a confirmation that social support is associated with improved medical compliance. In addition, since we rely on a sample of the population to determine the correlation between the two variables in the actual population, Pearson’s R is the most appropriate parametric test. It takes into account certain assumptions, including the normality of the data and homogeneity of within-group variances (Creswell, 2009). It also indicates the strength of the association between the variables.
Why the other Statistical Tests are not appropriate
The Student t-test (paired or unpaired) can be performed for two variables (IV and DV). However, it requires a minimum sample of 60 subjects (n < 60) (Nation, 2007). This statistic is not appropriate for the study sample, which is larger (n = 471). The other parametric test is the analysis of variance (ANOVA), which estimates the “difference between the means of two or more groups” (Nation, 2007, p. 54). Since the study seeks to find the effect of social support on compliance, a comparison of the means of the supported versus the unsupported groups is not necessary. Regression analysis determines if the IV can predict the DV. Although this test can help predict the effect of social support on compliance rate, it cannot give the strength of the association between the two variables.
The other tests fall into the category of non-parametric tests. They include the Wilcoxon rank test, Whitney-Mann-Wilcoxon (WMW) test, Kruskal-Wallis (KW) test, and Friedman’s test. Non-parametric tests are not appropriate for this study because they do not estimate the population. Thus, it is not possible to make inferences about a parameter using these tests. In addition, non-parametric tests have a “lower statistical power” than parametric ones (Nation, 2007, p. 67). Moreover, unequal variances cause “biased estimation of the significance levels” when using the KW or WMW test (Nation, 2007, p. 68). The bias creates outliers that increase type II error.
Ethical Considerations Related to the Study
Studies involving human participants must address the issues of “beneficence, justice, and respect for persons” (Blaxter, Hughes & Tight, 2006, p. 31). The researcher will seek for a written informed consent to take part in the study from the African American women. An informed consent requires that the participant understands the study’s objective, risks, and benefits before participating. The women refusing to consent to take part in the study will still receive the medical/social support. The aim is to avoid the use of coercion as a tool to force the subjects to participate. As such, the institutional review board must approve the study.
To attain beneficence and justice, the researcher will protect the subjects from stigmatization. All the women participants will be drawn from independent support groups to maintain the anonymity of the subjects. In addition, the investigator will protect the privacy of the patient data. African American women with HIV often face challenges such as substance abuse, family conflicts, and sex trade, among others that affect their treatment compliance. It is expected that the social support programs will address these prerequisites to increase the subjects’ compliance rate.
Implications of Social Change for the Study
The study’s findings on the efficiency of social support have implications for interventions for improving the medical compliance and health outcomes of the study population. Interventions to curb the spread of HIV among African American women may become more efficient by incorporating the informational element within a group context (Gilbert & Wright, 2003). Support groups act as sources of information to members, and thus, create positive attitudes towards medical compliance. The study will indicate how membership to support groups fills the informational needs of the women.
A social support network is a community of patients with experiential similarity. They provide social acceptance to the members by eliminating stigma and discrimination. A finding that social support increases medical adherence would mean that the programs improve social integration, promoting healthy lifestyles among women living HIV. Teaching the women psychosocial skills within CBT groups will help them cope with the social challenges that affect compliance. Support-seeking skills have been shown to foster emotional ties and satisfaction with the care received (Gilbert & Wright, 2003). In addition, there is a need to address the problem of homelessness, poverty, and drug abuse that affect medical compliance in this population.
Education is at the center of social support programs. Educating the women about their disease will improve their quality of life. Sex education will also help sensitize the women on risky behaviors that make them vulnerable to HIV infection. In addition, the study’s findings will provide evidence for the use of social support interventions to strengthen peer relationships and support. African American women with HIV face potential social isolation, which may affect their health outcomes. Therefore, besides reducing the risk factors, social support may encourage positive behaviors, including sexual morality, physical activity, and medical adherence.
The study also has implications for the evaluation of the effectiveness of social support programs. Effective interventions should address social barriers, such as anxiety and stigma, and meet the informational needs of the patients. They should provide social and information resources to create positive attitudes towards the use of antiretroviral drugs and the adoption of sexual morality to curb the spread of the disease.
Conclusion
This plan has highlighted the essential elements of the research process to investigate the relationship between social support and medical compliance among HIV-positive African American women. Social support is an interdisciplinary intervention that builds social and informational resources for the target population. The study hypothesizes that there will be no significant difference between supported and unsupported women in the context of medical adherence. The results will show if the support programs motivate members to adhere to their treatment regimen to improve their health outcomes. Thus, the findings have implications for support interventions targeting African American women with HIV.
References
Aulette-Root, A., Boonzaier, F., & Aulette, J. (2014). South African Women Living with HIV: Global Lessons from Local Voices. Bloomington, IN: Indiana University Press.
Barnham, C. (2015). Quantitative and Qualitative Research. International Journal of Market Research, 57(6), 837-854.
Blaxter, L., Hughes, C., & Tight, M. (2006). How to Research. Berkshire: Open University Press.
Cannon, C. (2010). Handbook of HIV and Social Work: Principles, Practice, and Populations. Hoboken, NJ: John Wiley & Sons Inc.
Creswell, J. W. (2009). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. Thousand Oaks, CA: Sage Publications.
Frankfort-Nachmias, C., & Nachmias, D. (2008). Research Methods in the Social Sciences. New York, NY: Worth Publishers.
Gilbert, D., & Wright, E. (2003). African American Women and HIV/AIDS: Critical Responses. Westport, CT: Greenwood Publishing Group, Inc.
Kronenfeld, J. (2015). Education, Social Factors, and Health Beliefs in Health and Health Care. Bingley, UK: Emerald Group Publishing Limited.
Myles, L. (2009). African-Americans and “AIDS” (The Untold Story): Why are “Black People Still “Dying” from AIDS While Other Races Are Not? Bloomington, IN: Author House.
Nation, J. R. (2007). Research Methods. New Jersey: Prentice Hall.
Patel, K., & Rushefsky, M. (2014). Healthcare Politics and Policy in America: 2014. New York, NY: Taylor & Francis.
Shu-Acquaye, F., Mbanya, D., & Chungong, S. (2008). Women, the Law, and HIV/AIDS in Africa: A Conundrum for the Legislature? Lake Mary, FL: Vandeplas Publishing.
Shuttleworth, M. (2011). Validity and Reliability. Web.
Siddiqi, A., Hu, X., & Hall, H. (2015). Mortality Among Blacks or African Americans with HIV Infection – United States, 2008-2012. Morbidity and Mortality Weekly Report, 64, 81-86.
Smith, M. K. (2015). Struggles and Resilience of African American Women Living with HIV or AIDS: A Qualitative Study. Journal of Social Work, 15(4), 409-424.
Trochim, W. M. (2006). Introduction to Validity: Social Research Methods. Web.
Turshen, M. (2000). African Women’s Health. Trenton, NJ: Africa World Press Inc.