Journal of Clinical and Diagnostic Research, ISSN - 0973 - 709X

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On Sep 2018




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On Sep 2018




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On Aug 2018




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Dr. Arundhathi. S
MBBS, MD (Pathology),
Sanjay Gandhi institute of trauma and orthopedics,
Bengaluru.
On Aug 2018




Dr. Mamta Gupta,
"It gives me great pleasure to be associated with JCDR, since last 2-3 years. Since then I have authored, co-authored and reviewed about 25 articles in JCDR. I thank JCDR for giving me an opportunity to improve my own skills as an author and a reviewer.
It 's a multispecialty journal, publishing high quality articles. It gives a platform to the authors to publish their research work which can be available for everyone across the globe to read. The best thing about JCDR is that the full articles of all medical specialties are available as pdf/html for reading free of cost or without institutional subscription, which is not there for other journals. For those who have problem in writing manuscript or do statistical work, JCDR comes for their rescue.
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An yearly reward for the best article authored can also incentivize the authors. Though the process of finding the best article will be not be very easy. I do not know how reviewing process can be improved. If an article is being reviewed by two reviewers, then opinion of one can be communicated to the other or the final opinion of the editor can be communicated to the reviewer if requested for. This will help ones reviewing skills.
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Dr. Mamta Gupta
Consultant
(Ex HOD Obs &Gynae, Hindu Rao Hospital and associated NDMC Medical College, Delhi)
Aug 2018




Dr. Rajendra Kumar Ghritlaharey

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Writing is the representation of language in a textual medium i e; into the words and sentences on paper. Quality medical manuscript writing in particular, demands not only a high-quality research, but also requires accurate and concise communication of findings and conclusions, with adherence to particular journal guidelines. In medical field whether working in teaching, private, or in corporate institution, everyone wants to excel in his / her own field and get recognised by making manuscripts publication.


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Salient features of the JCDR: It is a biomedical, multidisciplinary (including all medical and dental specialities), e-journal, with wide scope and extensive author support. At the same time, a free text of manuscript is available in HTML and PDF format. There is fast growing authorship and readership with JCDR as this can be judged by the number of articles published in it i e; in Feb 2007 of its first issue, it contained 5 articles only, and now in its recent volume published in April 2011, it contained 67 manuscripts. This e-journal is fulfilling the commitments and objectives sincerely, (as stated by Editor-in-chief in his preface to first edition) i e; to encourage physicians through the internet, especially from the developing countries who witness a spectrum of disease and acquire a wealth of knowledge to publish their experiences to benefit the medical community in patients care. I also feel that many of us have work of substance, newer ideas, adequate clinical materials but poor in medical writing and hesitation to submit the work and need help. JCDR provides authors help in this regards.
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Thanking you
With sincere regards
Dr. Rajendra Kumar Ghritlaharey, M.S., M. Ch., FAIS
Associate Professor,
Department of Paediatric Surgery, Gandhi Medical College & Associated
Kamla Nehru & Hamidia Hospitals Bhopal, Madhya Pradesh 462 001 (India)
E-mail: drrajendrak1@rediffmail.com
On May 11,2011




Dr. Shankar P.R.

"On looking back through my Gmail archives after being requested by the journal to write a short editorial about my experiences of publishing with the Journal of Clinical and Diagnostic Research (JCDR), I came across an e-mail from Dr. Hemant Jain, Editor, in March 2007, which introduced the new electronic journal. The main features of the journal which were outlined in the e-mail were extensive author support, cash rewards, the peer review process, and other salient features of the journal.
Over a span of over four years, we (I and my colleagues) have published around 25 articles in the journal. In this editorial, I plan to briefly discuss my experiences of publishing with JCDR and the strengths of the journal and to finally address the areas for improvement.
My experiences of publishing with JCDR: Overall, my experiences of publishing withJCDR have been positive. The best point about the journal is that it responds to queries from the author. This may seem to be simple and not too much to ask for, but unfortunately, many journals in the subcontinent and from many developing countries do not respond or they respond with a long delay to the queries from the authors 1. The reasons could be many, including lack of optimal secretarial and other support. Another problem with many journals is the slowness of the review process. Editorial processing and peer review can take anywhere between a year to two years with some journals. Also, some journals do not keep the contributors informed about the progress of the review process. Due to the long review process, the articles can lose their relevance and topicality. A major benefit with JCDR is the timeliness and promptness of its response. In Dr Jain's e-mail which was sent to me in 2007, before the introduction of the Pre-publishing system, he had stated that he had received my submission and that he would get back to me within seven days and he did!
Most of the manuscripts are published within 3 to 4 months of their submission if they are found to be suitable after the review process. JCDR is published bimonthly and the accepted articles were usually published in the next issue. Recently, due to the increased volume of the submissions, the review process has become slower and it ?? Section can take from 4 to 6 months for the articles to be reviewed. The journal has an extensive author support system and it has recently introduced a paid expedited review process. The journal also mentions the average time for processing the manuscript under different submission systems - regular submission and expedited review.
Strengths of the journal: The journal has an online first facility in which the accepted manuscripts may be published on the website before being included in a regular issue of the journal. This cuts down the time between their acceptance and the publication. The journal is indexed in many databases, though not in PubMed. The editorial board should now take steps to index the journal in PubMed. The journal has a system of notifying readers through e-mail when a new issue is released. Also, the articles are available in both the HTML and the PDF formats. I especially like the new and colorful page format of the journal. Also, the access statistics of the articles are available. The prepublication and the manuscript tracking system are also helpful for the authors.
Areas for improvement: In certain cases, I felt that the peer review process of the manuscripts was not up to international standards and that it should be strengthened. Also, the number of manuscripts in an issue is high and it may be difficult for readers to go through all of them. The journal can consider tightening of the peer review process and increasing the quality standards for the acceptance of the manuscripts. I faced occasional problems with the online manuscript submission (Pre-publishing) system, which have to be addressed.
Overall, the publishing process with JCDR has been smooth, quick and relatively hassle free and I can recommend other authors to consider the journal as an outlet for their work."



Dr. P. Ravi Shankar
KIST Medical College, P.O. Box 14142, Kathmandu, Nepal.
E-mail: ravi.dr.shankar@gmail.com
On April 2011
Anuradha

Dear team JCDR, I would like to thank you for the very professional and polite service provided by everyone at JCDR. While i have been in the field of writing and editing for sometime, this has been my first attempt in publishing a scientific paper.Thank you for hand-holding me through the process.


Dr. Anuradha
E-mail: anuradha2nittur@gmail.com
On Jan 2020

Important Notice

Original article / research
Year : 2010 | Month : October | Volume : 4 | Issue : 5 | Page : 3045 - 3055 Full Version

Variations In Social Determinants Of Self-Rated Health And Self-Reported Illness


Published: October 1, 2010 | DOI: https://doi.org/10.7860/JCDR/2010/.964
PAUL A. BOURNE*

Dr. Paul A. Bourne, 1Socio-Medical Research Institute (Formerly of Department of Community Health and Psychiatry, Faculty of Medical Sciences, University of the West Indies, Mona, Kingston, Jamaica)

Correspondence Address :
Dr. Paul A. Bourne, 1Socio-Medical Research Institute (Formerly of Department of Community Health and Psychiatry, Faculty of Medical Sciences, University of the West Indies, Mona, Kingston, Jamaica)

Abstract

Background There are extensive empirical studies which have examined the social and/or medical determinants of the health status, but none have evaluated whether those determinants vary by the definitions of health.Aims: This research seeks to elucidate the social determinants of health, based on the definition of health and the dichotomization of health, in order to establish whether variations exist in the social determinants, based on the definition and the measurement of health, as well as the correlation between the determinants.Design and setting: By using a representative probability sampling dataset, 2007 Jamaica Survey of Living Conditions, of some 6,782 respondents, and logistic regression analyses, the determinants of health were examined in these subjects.Methods and measure: Self-rated health was a five-point ordinal scale (very poor; poor; moderate; good; very good health) measure. It was dichotomized as good-to-very good health status, and moderate-to-very good health, and not reporting an illness, to measure health to explore the effect and the determinants of each definition of health.Results: When health was measured and the cut-off was good-to-very good, eight variables emerged as statistically significant factors of the self-rated health status of Jamaicans (Model, χ2 = 1187.67, P < 0.0001; -2 Log likelihood = 3374.2, R2 = 0.367). By using a cut-off of moderate-to-very good health, six variables emerged as statistically significant factors of the self-rated health status of Jamaicans. The health status was dichotomized as moderate-to-very good self-rated health and very poor-to-poor health (Model, χ2 = 498.41, P < 0.0001; -2 Log likelihood = 1491.30, R2 = 0.295). However, when self-reported illness was used to measure health, six factors emerged from a listing of social variables as explanations of self-rated health (using not reporting an illness) (Model, χ2 = 2012.57, P < 0.0001; -2 Log likelihood = 1726.05, R2 = 0.641).Conclusion: With the importance of correct information in policy making, health assessment and health modifications, the current findings provide pertinent materials that can be used by researchers and other health professionals to make correct conclusions, the choice of the dichotomization of health and the fact that the social determinants of health vary across the different subgroups of measure and the definitions of health.
Key words: Social determinants, self-rated health status, self-reported illness, dichotomization of self-rated health status

Keywords

Social determinants, self-rated health status, self-reported illness, dichotomization of self-rated health status

Introduction
Many empirical studies have examined the social and/or medical determinants of health status (1)-(13), but none have evaluated whether those determinants vary by the definitions and the measurement of health. The majority of researches assume that there are no variations in the determinants of health, which is clearly not the case; because a study conducted by Bourne (14) revealed that differences exist between the social determinants of health by the sexes and the area of the residences. By using a cross-sectional survey, Bourne’s work highlighted that the “Length of time in a household and education were the social determinants which were synonymous with only urban areas; social class and gender were the social predictors of only rural areas, while age, self-reported illnesses and consumption were the correlates of all areas of residences”(14). Education and social class were the social determinants of health for females but not for males; and social assistance was a social determinant of health for males, but not for females (14). Clearly embedded in Bourne’s work, are the disparities which exist in the social determinants of health, based on particular variables and this has never been examined in the health literature within the context of the definitions and/or measurement of health.

Health models that have published provide a listing of the social and/or medical determinants of health (15), (16) as well as studies (1)-(14), but these fail to recognize the probability of the differences that are based on the conceptualization of health and the implications of such disparities for public policy and planning. Despite the fact that evidences show that differences do exist in the social determinants of health based on particular demographic characteristics (14), there is a paucity in health literature that have examined whether such differences are present, based on the definitions of health as well as the measurement of health.

While psychologists like Brannon and Feist (16) opined that using illness to measure health is a negative approach, they also posited that health is more than illness, which they referred to as the positive approach to health. The positive approach to health is in keeping with the broader definitions of health that include social, mental and physical wellbeing and more than the absence of illness. The aforementioned issues highlight the expanded definition of health, to include social, psychological and physical wellbeing and not merely the absence of diseases, as offered by the World Health Organization (WHO) in the preamble to its Constitution in 1946 (17). The WHO’s definition of health recognizes that health was and can be measured by using illness. Such a conceptualization required studies to operationalize it; to use it in health studies and to guide health policy formulation for the society.

Scholars like Grossman (1), Smith and Kington (2), Hambleton et al. (13) and Bourne (14) have used self-reported health status, which is keeping with the broader definition of health, to model the social and/or medical determinants of health. Studies conducted by the WHO and/or affiliated scholars (4)-(9) have utilized the health status to measure health, from which particular social or medical determinants emerged. On the other hand, by using self-reported illness, Bourne (11), (12) modelled the social determinants of health; and Hutchinson et al (3) used wellbeing, which is more in keeping with the positive approach to health as offered by the WHO.

Outside of the definition of health, be it illness, health status or wellbeing, how the health status is dichotomized, is another issue which has been omitted from the discourse of the variation in the social determinants of health. Many of the studies which have examined the social determinants of health have used multiple logistic regression analyses, which require the dichotomization of health. Self-reported (or rated, assessed and evaluated) health is measured by using a five-point ordinal scale variable (from very poor, poor, moderate, good and very good), and the question is about the cut-off point for the health dichotomization. According to Finnas et al. (18) “The dichotomization implies that some of the original information is lost”, and like Bourne (19), they found that care should be taken in interpreting the results, as well as the cut-off to use. This suggests that the dichotomization may affect the social determinants.

No studies emerged in a literature search that have examined whether the social determinants differ, based on not only the definition of health, but owing to the measurement of the health status (dichotomization). Clearly, there is a gap in health literature, which cannot be allowed to continue unresolved. Empirical findings have revealed that self-rated health is critical to understand biomedically determined health (18), (19), which emphasizes the importance of investigating the determinants of self-assessed health. With the importance of the social determinants of health to health policy formulation in a society (15),(16), the social determinants of health must be explored within the context of (1) the definition of health, (2) dichotomization of health, and (3) the variations of the determinants. The current research aims to evaluate the social determinants of health, based on the definition of health and the dichotomization of health, in order to establish whether variations exist in the social determinants, based on the definition and the measurement of health, as well as the correlation between the determinants.

Theoretical model
A model developed by Grossman (1), expanded upon by Smith and Kington (2), Hambleton et al. (13), and Bourne (11), (12), (14), was employed in this current study. This theoretical framework was used to guide this paper as it related to the measurement, the selection of variables and the appropriateness of statistical relationships. By using econometric analyses, Grossman modelled the social determinants of the self-rated health status of the people in the world. The use of the econometric analyses allowed Grossman to examine the influence of many independent variables on a single dependent variable, health status. Many scholars have used this approach since Michael Grossman (1), and Hambleton et al. (13), like Bourne (11), (12), (14), have used logistic regression analyses, which is among the econometric analytic techniques which are available for use in examining multiple variables on a single dependent variable.

Logistic regression is empirically accepted as a tool for interpreting the self-rated health status (11)-(14), (18), (19). This approach implies that health status must be dichotomized by the researcher, and this means that the choice of cut-off for the dichotomization might influence some difference in conclusion about the variable. Finnas et al. noted that “The odds ratios varied less when the cut-off point for dichotomization included a broader measure of bad health, than when it contained a less broad measure which could easily be interpreted, as the former cut-off point was more stable and reliable and thus, could be preferred in empirical analyses” (18). Bourne (19) concurred with Finnas et al, that self-rated health status is most reliable being cut-off at moderate-to-very good health than from good-to-very good health. Based on the recommendations by Bourne (19) and Finnas (18), examining the social determinants of health must take into account, not only the definition of health, but also its measurement (cut-off).

According to Smith and Kington (2), using Ht= f (Ht-1, Pm Go, Bt, MCt ED, Āt, ) to conceptualize a theoretical framework for “stock of health” noted that health in period t, Ht, is the result of health preceding this period (Ht-1), medical care is (MCt), good personal health is(Go), the price of medical care is (Pm), bad ones is (Bt), a vector of family education is(ED), and all sources of household income is(Āt).

In this paper, the researcher did not use a single definition of health or measurement (cut-off) like in other studies, but used two definitions of health (self-rated health status and self-reported illness) and two cut-offs in examining the social determinants of health. Thus, the current work used a modification of the multivariate model as utilized by aforementioned researchers to capture many independent variables on a single dependent variable, health.

Different independent variables can be used to examine self-rated health status and self-reported illness. However, the current research uses the same set of independent (explanatory) variables in the three hypotheses. This allows to ascertain the social determinants of each model and their effect and explanatory power of the measure of health. Thus, in this study, the researcher will test 3 hypotheses:

HG = f(Pmc, ED, HHt, MS, HI, SS, X, CR, Ai, HSB, Y, AR, εi) ……………….. (1)

HM = f(Pmc, ED, HHt, MS, HI, SS, X, CR, Ai, HSB, Y, AR, εi) ………..……… (2)

HI = f(Pmc, ED, HHt, MS, HI, SS, X, CR, Ai, HSB, Y, AR,εi) ……..….……… (3)

Where HG is the self-rated health status measured from good-to-very good; HM is the self-rated health status measured from moderate-to-very good; HI is the self-rated health measure by using illness; Pmc is cost of medical care; ED is educational attainment; HH is household head; MS is marital status; HI is the health insurance coverage; SS is the social class; X is the gender of the respondents; CR is crowding; Ai is the age of the respondents; HSB is health care seeking behaviour; Y is income, AR is the area of residence and εi is the residual error.

Material and Methods

Participants and questionnaire
The current research used the 2007 Jamaica Survey of Living Conditions (20). This sample was taken from a national cross-sectional survey of 6,718 respondents from Jamaica’s 14 parishes. The survey used a stratified random probability sampling technique to draw the original (20). The non-response rate for the survey was 29.7%, with 20.5% not responding to particular questions, 9% not participating in the survey, and another 0.2% being rejected due to ‘data cleaning’. The study used secondary cross-sectional data from the Jamaica Survey of Living Conditions (JSLC). The JSLC was commissioned by the PIOJ and the Statistical Institute of Jamaica (STATIN). These two organizations are responsible for the planning, data collection and the policy guidelines for Jamaica. Descriptive statistics provided background information on the demographic characteristics of the sub-sample population.
The JSLC is a self-administered questionnaire where the respondents are asked to recall detailed information on particular activities. The questionnaire covers demographic variables, health, the immunization of children between 0–59 months, education, daily expenses, non-food consumption expenditure, housing conditions, the inventory of durable goods and social assistance. The interviewers are trained to collect the data from the household members. The survey is conducted between April and July annually.
Statistical analyses used
Descriptive statistics such as mean, standard deviation (SD), frequency and percentage were used to analyze the demographic characteristics of the study population. Each metric variable (ie. age, income, medical expenditure, and crowding) was evaluated for skewness, as to where it exceeded 0.5 and whether the variable was logged to remove it (ie. lnincome, lnmedical expenditure). Bivariate analyses were carried out by using Pearson’s Product Moment Correlation, based on the definitions and the measurement of health. Four hypotheses were tested in this study, and they were based on the definitions and/or measurement of health. Stepwise logistic regression analyses which were used, examined the relationship between the dependent variable and some predisposed independent (explanatory) variables. The results were presented by using unstandardized B-coefficients, Wald statistics, Odds ratio and confidence interval (95% CI). The correlation matrix was examined in order to ascertain whether autocorrelation (or multicollinearity) existed between the variables. Wherever collinearity existed (r > 0.7), the variables were entered independently into the model to help determine which one must be retained during the final model construction (the decision was based on the variable’s contribution to the predictive power of the model and the goodness of fit).
Wald statistics were used to determine the magnitude (or contribution) of each statistically significant variable in comparison with the others, and the Odds Ratio (OR) for the interpreting of each significant variable.
Measures
Age is a continuous variable, which is the number of years alive since birth (by using the last birthday).
Self-reported illness (or self-reported dysfunction): The question was asked: “Is this a diagnosed recurring illness?” The answering options were: Yes, Cold; Yes, Diarrhoea; Yes, Asthma; Yes, Diabetes; Yes, Hypertension; Yes, Arthritis; Yes, Other; and No. A binary variable was later created from this construct (1= good health based on indicating no to all illnesses) in order to be applied in the logistic regression.
Self-rated health status: “How is your health in general?” And the options were very good; good; fair; poor and very poor. For this study, the construct was categorized into 3 groups – (i) good; (ii) fair, and (iii) poor. A binary variable was later created from this variable (1) (1=good-to-very good health, 0=otherwise), and (2) (1=moderate-to-very good health, 0=otherwise).
Social hierarchy: This variable was measured based on income quintiles: The upper classes were those in the wealthy quintiles (quintiles 4 and 5); the middle class was quintile 3 and the poor were those in the lower quintiles (quintiles 1 and 2).
Crowding was the total number of people in the household divided by the number of rooms (excluding kitchen, bathroom and verandah). Income was the measure by using the total expenditure (in Jamaican dollars).

Results

Demographic characteristics of the study population
The sample size was 6 782 respondents: 48.7% males and 51.3% females (Table 1). Poverty was substantially a rural phenomenon (29.8%) compared to the peri-urban (11.5%) and the urban areas (9.3%). Eighty percent (82.2%) of the respondents indicated at least good health status (37.0% indicated excellent health status) as compared to 0.8% who claimed very poor health status. One percent (1.1%) of the sample was injured in the 4-week period of the survey, while 14.9% reported an illness and 43.2% indicated a chronic illness (i.e. Diabetes mellitus, 13.8%; hypertension, 23.1%; and arthritis, 6.2%), as compared to 30.4% who reported acute illness (influenza, 16.7%; diarrhoea, 3.0%; and asthma, 10.7%).

(Table/Fig 1): Demographic characteristics of study population, n = 6,782 †US$ 1.00 = Ja. $ 80.47
Almost 66% (i.e. 65.5%) of the sample visited a health care practitioner (i.e. doctor, nurse, healer, pharmacist) in the 4-week period of the survey; 29.6% were the heads of households; 23.3% were married; 69.2% were never married; 1.7% were divorced; 0.9% were separated; 4.9% were widowed; and the median number of persons per room was 4 (range = 1, 17). The median annual income was USD 7 050.66 (range = USD 261.56, USD 6 523.66) and the median per capita consumption was USD 1 523.88 (range = USD 179.57, USD 20 325.55).

Multivariate analyses

Hypothesis 1: HG = f(Pmc, ED, HHt, MS, HI, SS, X, CR, Ai, HSB, Y, AR, εi)

(Table/Fig 2) presents information on the social determinants of self-rated health status. Health status is dichotomized as good-to-very good self-rated health status. Eight variables emerged as statistically significant factors of the self-rated health status of Jamaicans (Model, χ2 = 1187.67, P < 0.0001; -2 Log likelihood = 3374.2, R2 = 0.367). Overall, 84% of the data were correctly classified (95.3% of yes, and 42.0% of no).

(Table/Fig 2): Logistic regression: Explanatory variables of self-rated health status (good-to-very good health status) of Jamaicans

Model, χ2 = 1187.67, P < 0.0001; -2 Log likelihood = 3374.2, R2 = 0.367

Hypothesis 2: HM = f(Pmc, ED, HHt, MS, HI, SS, X, CR, Ai, HSB, Y, AR, εi)

Six variables emerged as being statistically significant factors of the self-rated health status of Jamaicans. Health status was dichotomized as moderate-to-very good self-rated health and very poor-to-poor health (Model, χ2 = 498.41, P < 0.0001; -2 Log likelihood = 1491.30, R2 = 0.295). Overall, 94% of the data were correctly classified (99.2% of yes, and 15.6% of no) (Table 3).

(Table/Fig 3): Logistic regression: Explanatory variables of self-rated health status (moderate-to-very good health status)

Model, χ2 = 498.41, P < 0.0001; -2 Log likelihood = 1491.30, R2 = 0.295

Hypothesis 3: HI = f(Pmc, ED, HHt, MS, HI, SS, X, CR, Ai, HSB, Y, AR, εi)

(Table/Fig 4) shows that six factors emerged from a listing of social variables as explanations of self-rated health (using not reporting an illness) (Model, χ2 = 2012.57, P < 0.0001; -2 Log likelihood = 1726.05, R2 = 0.641). Overall, 94% of the data were correctly classified (99.6% of yes, and 64.3% of no).
(Table/Fig 4): Logistic regression: Explanatory variables of self-rated health (not reporting an illness)

Model, χ2 = 2012.57, P < 0.0001; -2 Log likelihood = 1726.05, R2 = 0.641

(Table/Fig 5) summarizes at a quick glance at the social determinants of health which influence a particular definition and the measurement of self-rated health.
(Table/Fig 5): Summary of the social determinants of self-rated health status of Jamaicans by good-to-very good, moderate-to-very good health status and not reporting an illness

N represents no; Y indicates yes. NA denotes not applicable.

Hypothesis 4: The strength and magnitude of the variables that were used to ascertain the social determinants of self-rated health status or self-reported illness.

[Tables/Figs 6]-(8) present information on the correlation among the different variables that were used to ascertain the social determinants of self-rated health status or self-reported illness.

(Table/Fig 6): Correlation Matrix of Good-to-very good health


(Table/Fig 7): Correlation Matrix of Moderate-to-very good health

(Table/Fig 8): Correlation Matrix of Not reporting an illness

Conclusion

In summary, variations exist in the social determinants of health as a result of the measure of health (ill-health to measure health rather than self-rated health) and cut-off point in the dichotomization choice of self-assessed health. Owing to the fact that self-rated health is an important tool in understanding the biomedically determined health, this implies that the cut-off point for health is important and that it cannot be made arbitrarily in health statistics. As empirical evidence shows that different definitions and the measure of health correlate with the social determinants of health differently, this must be taken into consideration in the social determinants of health. The results highlight the variations in the social determinants of health across the different cut-off points in dichotomizing self-rated health and self-reported illness, which indicate that researchers and policy makers must recognize these findings in health planning, as well as in the correlation among and between the social determinants.

No significant effects of health insurance and social class were detected on the measure of health, which was different from the social determinants of health which were identified by the WHO or other researchers. These findings more than highlight the variations in the social determinants of health, but bring into focus, differences which might occur between societies. Such variations in the social determinants of health, therefore, indicate that conditionality must be established in the determinants of health because of the measures of health, the cut-off approach in dichotomization and the locality which emerged from the current work.
Disclosures
The author does not report conflict of interest with this work.
Disclaimer
The researcher would like to note that while this study used secondary data from the Jamaica Survey of Living Conditions, none of the errors in this paper should be ascribed to the National Family Planning Board, but to the researcher.

Acknowledgement

The author thanks the Data Bank in the Sir Arthur Lewis Institute of Social and Economic Studies, the University of the West Indies, Mona, Jamaica for making the dataset available for use in this study.

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