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The author of the present paper "Projecting Ability of a Health Care Utilization Model" determines that the projecting ability of the model considers three factors: predisposing, need and enabling factors and their effect on health care utilization…
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Extract of sample "Projecting Ability of a Health Care Utilization Model"
The study aimed at determining the projecting ability of a health care utilization model when considering three factors: predisposing, need and enabling factors and their effect on health care utilisation. The researchers’ considered a variety of factors that were assumed to have an effect to health care utilization among Korean American immigrants. Factors such as insurance status and appropriate medical history were known to have an effect on health care utilization. Other factors such as years of residency in the United States, the patients’ priorities in life and the estimated income level did have an indirect effect on healthcare utilization. The sample for the study was 445 Korean immigrants suffering from high blood pressure. The group focused on were people aged between 40-64 years. The study tried to determine whether having access to care by immigrants affected health care utilization (Song, Han, lee, Kim, Ryu and Kim, 2010).
The researchers considered the Anderson behavioural model in carrying out this study. The researchers made some hypotheses for directing the study. According to the researchers, an individual’s use of health care is affected by enabling factors, their need for health care services and the predisposing factors. The study’s model structure utilized the assumption that, the health care utilisation model of immigrants was to be deduced in the context of relocation in an ecological sense. The explanation for this assumption is that relocation represents a stressful event that affects health care seeking behaviour. It is also assumed that factors such as health insurance coverage, socioeconomic position and the physical or mental state of an individual do affect health care utilization. Song et al also made the assumption that acculturation indicators like years of residency in the states, living arrangement and personal resources affect ones access to health care utilization (2010). The study hypothesized those factors such as high blood pressure pertinent medical histories apply a direct effect on health care utilization. Enabling factors such as health insurance and supposed income are crucial in the prediction of health care utilisation. Some of the predisposing factors considered in the study are year of residency in the United States and competing life priorities. The predisposing factors were assumed to have a direct and an indirect effect on health care utilization through enabling and need factors. The researchers hypothesized that competing life priorities of the Korean immigrants did delay health care seeking (Song et al, 2010).
The study design employed by the researchers is a descriptive study. The study is a case series study. It is a population based observation study. According to Parikh and Concato descriptive case, series involves several case reports of similar observations (2006). In the study the researchers, consider the access to care and the effects it has on health care utilization. The researchers are considering a group of Korean American immigrants suffering from high blood pressure. The study also focuses on middle aged KAIs. The study also present factors presented form previous studies that affect health care utilization either directly or indirectly. The researchers consider how these factors affect the KAI in utilising health care. The data for the study were obtained from the self-help intervention program for high blood pressure care. The sampled group filled in structured questionnaires receiving help from trained bilingual nurses. Analysis of the structured questionnaires provided information for the description of the factors considered in the study. Demographics that provide the evidence needed to make a conclusive report on the study was also from the filled in structured questionnaires.
Table 1 in the article provides the descriptive statistics for variables in the model. The computational science reference desk provided information that explains univariate data and bivariate data. Univariate data are one that has a single variable for instance age (2010). The study in the article the study carried represented immigrants whose age, limits were between 40-64 years. The total number of immigrants’ who participated in the study was 445. The blood pressure is also a univariate measure that provides a factor for descriptive analysis. The age and blood pressure of the patients was use to provide the sample require for the study. The blood pressure represents a chronic disease among the Korean American immigrants. The study focuses on predicting the importance of health care utilisation in the management of such chronic diseases. Having a sample group that suffer from a chronic disease does enable, the researchers come up with a conclusive descriptive analysis (Song et al., 2010).
Table 2 in the article does present the bivariate correlations of the main variable in the hypothesized model. Bivariate data utilises two different variables that have values that change. Bivariate data relate two variables. For example in table 2, it was evident that factors such as insurance status and the medical history of the patient did directly influence health care utilization. The main variable in the study is health care utilization. This variable is affected directly or indirectly by factors such as need factors, enabling factors and predisposing factors. The table indicates that enabling factors such as insurance status affected health care utilization directly. When we look at need factors such as life priorities, had an indirect effect on the utilisation of health care through the insurance status. Let us consider predisposing factors such as years of residency in the United States also had an indirect effect on the utilisation of health care through the insurance status. The same thing applies to the enabling factor perceived income level, which indirectly influenced health care utilisation through ones insurance status (Song et al., 2009).
The study being a descriptive case series provides a shallow explanation for the cause and effect relationship that serves to elaborate on the findings presented. For instance, the study focuses on predicting the importance of health care utilisation in the management of chronic diseases. The proposed health care model directly or indirectly relates the factors that affect the provision of care to health care utilization. However, the model does not provide information concerning the factors presented or an explanation on how they affect health care utilization. The findings presented from the study model illustrate an 18% variance in health care utilization of hypertensive KAIs. The study considered a sample of 445 from Baltimore Washington area to represent the supposed Korean American immigrants. This is a biased representation of the whole population of Korean American immigrants. For the study to be conclusive, a similar study needed to be carried out in a different region. Utilising data from a different region will provide an accurate representation of the larger population of KAI (Song et al., 2009). However, the study was successful in confirming that the health care utilization model does serve as a predictive tool when analysing the utilisation of health care. The model illustrates the importance of health care utilization in the management of chronic diseases.
References
Parikh, C.R., and Concato, J. (2006). Study design in patient-oriented research. Core Curriculum
In Nephrology, 356-364. doi:10.1053/j.ajkd.2005.09.035
Song, HJ., Han, HR., Kim, JY., Kim, KB., Ryu, JP., and Kim, M. (2010). Does access to care
Still affect health care utilization by immigrants? Testing of an empirical explanatory model of health care utilization by Koran American immigrants with high blood pressure. Journal of Immigrant and Minority Health, 4, 513-519.
The Computational Science Reference Desk. (2010). Univariate Data and Bivariate Data.
Shodor.org. Retrieved from http://www.shodor.org/interactivate/discussions/UnivariateBivariate/
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