Abstract
The purpose of this study is to examine the risk factors predicting asthma among adult African immigrants in California. The research problem is founded on the failure of many health studies to include African immigrants as a minority group in health studies that investigate the relationship between risk factors and asthma. From this gap in the literature, this study seeks to answer one key research question, which pivots on examining the risk factors predicting asthma among adult African immigrants in California. The dependent variable is asthma status and the independent variables are the risk factors (smoking status, alcohol use, education level, income level, employment status, and health care access). The covariates included age, gender, marital status, housing tenure, and caring neighbors.
The socioecological theory will be the main conceptual framework for this study. The research design is the quantitative correlation approach. I will analyze secondary data collected by the California Health Interview Survey. Using the SPSS software, I will conduct both descriptive and inferential statistical analyses. The inferential statistical analysis will include the multiple logistic regressions. This study is important to state-based health agencies and health professionals involved in asthma management because its findings could be used to develop health programs that target minority populations in California. It also has the potential of promoting positive social change by increasing asthma awareness and improving the health status of immigrant populations in California.
Instrumentation of Constructs
As mentioned in this study, the data will be obtained from the California Health Information Survey (CHIS). It is among the largest health interview surveys in America and is conducted on an annual basis to provide information about different health topics (UCLA, 2017). Although the CHIS publish health data relating to different years, the information used in this study is CHIS 2011-2016. Looking at the appropriateness of the CHIS data to the current study, I find that it is relevant and specific to the topic under investigation because the dataset provides health data relating to different ethnic and racial groups in California. A focus on African immigrants as one cohort in the study is ideal for my analysis because the current study focuses on this ethnic group as the target population.
The dataset is also appropriate to the current study because asthma is a health topic investigated in the research data. Other health issues surveyed in the dataset include diabetes and obesity (UCLA, 2017). The inclusion of asthma as a relevant health issue in the dataset and the provision of health data relating to immigrants bring my attention to the appropriateness of the data to the current study. The findings contained in the CHIS document are freely available to the public. Therefore, there is no special permission from the researcher needed to use the instruments. Based on the availability of the health data outlined by CHIS, different people, including policymakers, state agencies, and community organizations, find the resource useful in improving the health outcomes of their subjects.
Validity and reliability are important considerations when testing or formulating research instruments. Ensuring these measures are accurate and high quality is akin to guaranteeing a high quality of measurement instruments in a research and of the data that will emerge from the use of the same instruments (UCLA Center for Health Policy Research, 2017). The Cronbach alpha is among the most common methods used to test for reliability. It is similarly true for the CHIS data because the same technique was used to review the reliability of the tests used in the investigation through the measurement of internal consistency. According to Statistics Solutions (2017), the Cronbach’s alpha should typically give values from “0” to “1,” but it is also common to find a negative score. The internal consistency of the scales used to measure the variables in question (in the CHIS database) were found to be generally “excellent” because the Cronbach’s alpha was 0.89 for most of the research scales used, based on a sample of 100 households (California Health Interview Survey, 2014). In detail, it was 0.82 for the intrusion scale and 0.81 for the avoidance scale. Similarly, the hyper-arousal scale yielded a value of 0.67. The test-retest Pearson Correlation was also “satisfactory” because the total score and the intrusion measure were both 0.86. Comparatively, the avoidance scale was slightly higher at 0.92 (UCLA Center for Health Policy Research, 2017).
The principal components of analysis of asthma and smoking status explained 48.6% of the total variance and their Cronbach’s alphas were 0.86 and 0.89, respectively (California Health Interview Survey, 2014). The principal components of items relating to the covariates accounted for 65.9% of the total variance. Their Cronbach’s alpha ranged from 0.74 to 0.85 (UCLA Center for Health Policy Research, 2017). Using the classifications of Statistics Solutions (2017), collectively, these scores are deemed “better” than 0.70, which is perceived to be “good” and slightly lower than 0.90, which is considered the “best” score. According to this classification, the reliability of the CHIS is “above average.” Comprehensively, this scale suggests that the responses to the CHIS survey questions were reliable.
A pilot study of interviewing adults with telephones was also conducted a few months before the actual research was undertaken to assess the validity of the CHIS data. By doing so, the researchers evaluated the feasibility of sampling and interviewing the respondents using telephones. Using this technique, important information relating to the feasibility of conducting a lengthy health interview survey (via a telephone) was reviewed because many past surveys had a limited time to engage the respondents (California Health Interview Survey, 2014). The second piece of information that was obtained from the pilot study was the feasibility of using random sampling methods in a household. To get the above information, a survey plan was formulated where 100 interviews involving households that had a telephone and an adult present were completed (California Health Interview Survey, 2014). The overall results of the pilot study showed that the first 100 interviews did not show any signs of poor validity or high rates of false negative or false positive responses, especially compared to prior responses provided in other national quantitative studies. In fact, the findings of the study correlated with the Medical Expenditure Panel Survey (MEPS) by 0.70 (California Health Interview Survey, 2014). Therefore, the findings of the validity test show that the questions correctly measured the constructs.
The instrumentation of the constructs was done by carrying out a telephone survey. The constructs were measured by assigning unique codes and values to them. These indices helped the researcher to identify differences in the participant’s responses. For example, when analyzing the respondents’ housing type, homeowners were assigned a value of “1,” while renters were represented with a value of “2.” At the same time, the study’s constructs were assessed using frequencies and expressed as a percentage of the total population (UCLA Center for Health Policy Research, 2017). Again, if I use the housing example, the number of respondents who owned or rented a home was expressed as a percentage of the total respondents because the database revealed that 58.4% of them owned a home, while 41.6% of them rented one (UCLA Center for Health Policy Research, 2017). Therefore, the constructs were measures using frequencies, percentages, and codes.
The CHIS database was used to collect health data across different ethnic and racial groups in California. In the past, it has been used to collect health data from all 58 counties in the state of California (UCLA, 2017). However, there are cases where researchers have oversampled specific areas within the state that are heavily populated (such as Los Angeles and San Diego). The reliability and validity of the findings developed from the past use of CHIS have been confirmed through the involvement of large and diverse samples (UCLA, 2017). In other words, past users of the data established that the samples used in the dataset were representative of the ethnic and racial diversity of the state, particularly because the findings could be used to answer specific and important health questions pertaining to different ethnic and racial groups in the state.
The CHIS data have sufficient instrumentation to answer the research questions. For example, I have established that the dataset contains health data about different ethnic and racial groups in California. This one instrumentation is useful in answering my research questions, which focuses on one ethnic group – African immigrants. Other aspects of the instrumentation used to develop the CHIS are its consistency, flexibility, and adaptability. These attributes mean that the findings included in the report can be used to investigate new health issues and emerging trends among specific racial or ethnic groups. Lastly, the inclusion of spatial and geographic data in the CHIS dataset is another aspect of its instrumentation that would help to answer the research questions because I am able to narrow down to a specific locality (California), which is at the core of my analysis. This type of instrumentation is often unavailable in national health data (Liamputtong, 2013).
Operationalization of Constructs
The research variables identified in the CHIS database were numerous though I utilized only a few variables that are related to my research questions. As highlighted in this study, the dependent variable will be asthma and the independent variables will be the associated risk factors (smoking status, alcohol use, education level, income level, employment, and health care access). The covariates will be age, gender, marital status, housing type and caring neighbors. In the CHIS database, race emerged as one variable that helped to define the target population – African immigrants. It was denoted by the term “Self-reported African American” (SRAA). Asthma was denoted by code ASTCUR which is the current asthma status. The process of measuring the variables will be done empirically and quantitatively.
Dependent variable
Asthma status was assessed by asking participants questions about whether they had ever been diagnosed with the health condition. This variable was denoted by the code AB17 in the CHIS dataset and the exact question asked was, “Has a doctor ever told you that you have asthma?” (UCLA Center for Health Policy Research, 2017). There are two responses (1=Yes, 2=No) for this categorical variable.
Independent variables
The independent variables include smoking, alcohol use, education level, income level, employment, and health care access.
Smoking status
Smoking was assessed in the CHIS study by evaluating the current smoking status of the participants. This variable was denoted by the code SMKCUR in the CHIS dataset and the exact question asked was, “Are you a current smoker?” (UCLA Center for Health Policy Research, 2017). There are two responses (1=Yes, 2=No) for this categorical variable. In other words, the variable will be assessed by evaluating whether the respondents are currently smoking, or not.
Alcohol use
Alcohol use was assessed by evaluating participants’ alcohol consumption in the past year. This variable was denoted by the code AC32 in the CHIS dataset and the exact question asked was, “In the past 12 months, did you have any kind of alcoholic drink?” (UCLA Center for Health Policy Research, 2017). There are two responses (1=Yes, 2=No) for this categorical variable. Therefore, alcohol use was determined by two responses: “yes” or “no.”
Education level
Education level was assessed by asking participants questions about their highest level of educational attainment. This variable was denoted by the code AHEDC_P1 in the CHIS dataset and the exact question asked was, “What is your highest education level attained?” Nine education levels were outlined to choose from: grade 1-8, grade 9-11, grade 12/H.S. Diploma, some college, vocational school, associate degree, bachelor’s degree, master’s degree, or Ph.D. degree (UCLA Center for Health Policy Research, 2017). This variable will be useful in examining how asthma incidences vary across tiers of education.
Income level
Income level was assessed by asking participants to state their annual household income. This variable was denoted by the code AK22_P in the CHIS dataset and the exact question asked was, “What is your household’s total annual income?” (UCLA Center for Health Policy Research, 2017). Income level is a continuous variable and will leave it as it is.
Employment status
This variable was assessed by asking participants questions about their working/employment status. This variable was denoted by the code WRKST_P1 in the CHIS dataset and the exact question asked was, “what is your working status?” (UCLA Center for Health Policy Research, 2017). There are three main responses (1=full-time employment, 2=Part-time employment, 3=Unemployed) for this categorical variable. Evidence will be gathered to assess whether there is a pattern in the occurrence of asthma across these working statuses.
Health care access
Health care access was assessed by asking participants if they had a health care insurance plan. This variable was denoted by the code INS in the CHIS dataset and the exact question asked was, “Are you currently insured?” (UCLA Center for Health Policy Research, 2017). There are only two responses (1=Yes, 2=No) for this categorical variable. This study will explore whether there is a relationship between health care access and asthma.
Covariates
The covariates included age, gender, marital status, housing type, and caring neighbors.
Age
Age was assessed by asking participants to state their age. This variable was denoted by the code SRAGE_P1 in the CHIS dataset and the exact question asked was, “what is your self-reported age?” (California Health Interview Survey, 2016, p. 3). This covariate have the following age group responses: 18-25, 26-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85+ years. Evidence will be sought to establish if asthma incidences commonly occur among respondents within a given age range.
Gender
Gender was assessed by asking respondents about their sex. This variable was denoted by the code SRSEX in the CHIS dataset and the exact question asked was, “what is your self-reported age range? (UCLA Center for Health Policy Research, 2017). The participants could only answer in two ways: “male,” or “female.” Thus, this variable will be a dichotomous one (1=Male, 2=Female). This study will investigate whether there are any gender discrepancies in the reporting of asthma incidences.
Marital status
This variable was assessed by asking participants about their marital status. Marital status was denoted by the code MARIT in the CHIS dataset and the exact question asked was, “what is your marital status?” (California Health Interview Survey, 2016, p. 5). Marital status will be operationalized for this study by finding out whether the respondents were married, not married, or other (widowed/separated/divorced). There are only three responses (1=Married, 2=Other/widowed/separated/divorced, 3=Never married) for this categorical variable.
Housing type
Housing type was assessed by asking participants if they owned or rented a home. This variable was denoted by the code SRTENR in the CHIS dataset and the exact question asked was, “what is your self-reported household tenure?” (California Health Interview Survey, 2016, p. 6). Two options were available for this categorical variable: owning or renting a home (1=Own, 2=Rent/other). This study will examine if there is a relationship between the different types of household ownership (renting or owning a home) and the dependent variable, which is asthma.
Caring neighbors
Caring neighbors, as a covariate, was assessed by asking participants if they lived in a caring neighborhood. This variable was denoted by the code AM19 in the CHIS dataset and the exact question asked was, “are people in your neighborhood willing to help each other?” (California Health Interview Survey, 2016, p. 6). There are four possible responses to this categorical variable: strongly agree, agree, disagree, or strongly disagree.
References
California Health Interview Survey. (2014). Data collection methods. Web.
Statistics Solutions. (2017). Cronbach’s alpha. Web.
UCLA Center for Health Policy Research. (2017). CHIS data quality. Web.