Health Indicators in International Health
Health Indicators in International Health
HeaLY and Hale
The World Health Organization (WHO) developed the Health Adjusted Life Expectancy (HALE) indicator, which is a measurement meant to derive a more accurate and comprehensive approximation of health as opposed to the basic life expectancy rates (Hyder, Puvanachandra & Morrow, 2012). At birth, the indicator approximates the number of healthy years a person is anticipated to live through subtraction of ill health years based on severity from the summative life expectancy (Hyder, Puvanachandra & Morrow, 2012). The indicator was known formerly as the Disability Adjusted Life Expectancy (DALE) and is equally used to approximate the quality of life of seniors from the age of sixty-five (Hyder, Puvanachandra & Morrow, 2012). The ability of HALE to extend beyond mortality data is what makes it an advantageous health indicator. This is because the indicator does not only measure the length of life one is to lead, but also the quality of life.
Healthy Life Years (HLY or HeaLY) is an indicator the number of healthy years an individual has left remaining to live without disability (Hyder, Puvanachandra & Morrow, 2012). The indicator perceives healthy years as an economic factor as it distinguishes years free of bodily limitations from years with at least one health impediment. Professionals across the globe employ the Sullivan method to calculate the indicator. The method is founded on age prevalence measures covering population percentages of persons with and without disabilities and mortality data (Hyder, Puvanachandra & Morrow, 2012). However, HLY is limited in application, as the indicator does not gauge the health status of a given populace in terms of health estimates during the course of a lifespan (Hyder, Puvanachandra & Morrow, 2012). An increase in HLY scores denotes that subjects are living quality and longer lives.
Criteria for Cause and Effect Relationship
An individual needs to prove three core criteria prior to asserting existence of a causal relationship. Namely, the three elements are temporal precedence, co-variation of the causal relationship and lack of plausible explanations and substitutes.
This criterion demands that one proves the cause occurred prior to the effect (Perrin, 2016). The purpose of the criterion is to ascertain one does not get lost in logic as not all causal factors result in an effect. In addition, in some rare cases such as in economic inflation and unemployment, interplaying factors can act as both causes and effects (Perrin, 2016).
Co-Variation of the Causal Relationship
In order to establish a causal relationship, the researcher needs to show existence of some of relationship (Perrin, 2016). Consider the following scenario:
If A then B
If not A then not B
Whenever A is present or absent, B is equally present or absent respectively. In such a case, the researcher has established some form of relationship between A and B. In colloquial terms, without A, there is no outcome or realization of B. However, in causal relationships, one at times needs to extend beyond the simplicity of binary relationships in order to understand the degree of interplay in a relationship (Perrin, 2016). For instance:
If more of A then less of B
If less of A more of B
No Plausible Explanations or Substitutes
The last criterion goes to ascertain that there are no other factors causing the effect in a relationship. The criterion follows the ‘missing variable’ or ‘third variable’ premise applied normally to measure internal validity (Perrin, 2016). In order to prove internal validity, one has to ascertain inexistence (rule out) other probable alternative explanations to the cause and effect. The third criterion is perceived as the hardest to prove in social sciences.
Descriptive and Analytic Epidemiology
In simple terms, descriptive epidemiology focuses on answering the questions where, when, what and who. In this branch of epidemiology, there is inclusion of a systematic method that characterizes the health problem. Therefore, the studies have the intent of defining a given disease or health status by answering the ‘4 Ws’ within the subject population (Merrill, 2012). The objective can be captured through use of case reports, cross sectional and ecological researches. Descriptive epidemiology ascertains comprehension of basic facets of a specific health problem. Therefore, it facilitates identification of persons at higher risk for the given health issue. This makes information from descriptive studies highly applicable in allocation of health resources (Merrill, 2012).
On the other hand, analytic epidemiology covers the remaining inquiries in how and why behind a given health issue. Studies under this branch of epidemiology are experimental and observational in nature as they have the objective of testing a hypothesis developed from descriptive studies (Merrill, 2012). An example of experimental studies is a clinical trial while a cohort is an example of an observational research. Therefore, analytic studies are imperative in the identification of cause or causes of a disease. Moreover, they are equally beneficial in identification of exposure risk factors and associated implications (Merrill, 2012). Essentially, descriptive epidemiology measure trends in order to develop a hypothesis, which analytic researches test.
Hyder, A. A., Puvanachandra, P., & Morrow, R. H. (2012). Measuring the health of populations: explaining composite indicators. Journal of Public Health Research, 1, 3, 222-8.
Merrill, R. M. (2012). Introduction to epidemiology. Sudbury, Mass: Jones and Bartlett Publishers.
Perrin, K. M. (2017). Principles of health navigation: Understanding roles and career options. New York: Pearson.
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