scholarly journals Inequalities in health. Analytic approaches based on life expectancy and suitable for small area comparisons

2000 ◽  
Vol 54 (5) ◽  
pp. 375-380 ◽  
Author(s):  
P. J Veugelers
Keyword(s):  
Life Expectancy ◽  
Small Area ◽  
Inequalities In Health

Small area inequalities in health: Are we underestimating them?

2008 ◽  
Vol 67 (6) ◽  
pp. 891-899 ◽  
Author(s):  
Mai Stafford ◽  
Oliver Duke-Williams ◽  
Nicola Shelton
Keyword(s):  
Small Area ◽  
Inequalities In Health

Injustice and Inequalities in Health

2020 ◽  
Author(s):  
Gopal Sreenivasan
Keyword(s):  
Life Expectancy ◽  
The United States ◽  
National Average ◽  
Average Life ◽  
Average Life Expectancy ◽  
Inequalities In Health ◽  
Alternative Approach ◽  
Inequality In Health ◽  
Two Sides ◽  
Social Causes

Serious inequalities in health abound the world over. For example, there are marked differences in average life expectancy both between and within countries. Individual life expectancy varies by more than 30 years between the highest national average and the lowest. Even worldwide, average life expectancy lags more than 10 years below the highest national average. Within single countries, inequalities in life expectancy between the top and bottom groups of men, for example, have been recorded at 7 years in England and Wales and at almost 15 years in the United States, albeit using rather differently constituted groups. Intuitively, these inequalities in health will strike many observers as unjust. But why are they unjust, if they are? Are inequalities in health unjust per se? If not, what makes some inequalities in health unjust, but not others? According to an influential analysis, inequalities in health are unjust when they are avoidable, unnecessary, and unfair. Thus, if an inequality in health is inevitable, it is not unjust. Following this analysis means that answering these questions requires a combination of empirical and normative understanding. On the empirical side, some understanding of the socially controllable causes of health is required. On the normative side, various dimensions of fairness have to be understood. In addition, some appreciation of the interaction between these two sides is needed.. Each side of the question is fairly complicated. With respect to the requirements of fairness, three subsidiary controversies can be distinguished. To begin with, should a general principle of equality be applied directly to the case of health? An alternative approach traces the injustice of avoidable inequalities in health to the independent injustice of their social causes instead. Next, should inequalities be defined across social groups (such as class or race within countries or, indeed, countries themselves)? If so, which groups? An alternative is to define inequalities across individuals. Finally, should equality be defined in comparative terms (as is traditional)? An alternative is to define the requirements of fairness non-comparatively (as a matter of “priority” to the worst off). Even if a given inequality in health is avoidable, some resolution of all three controversies is needed to decide whether that inequality is unfair.

scholarly journals Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy

2019 ◽  
Author(s):  
Ikhan Kim ◽  
Hwa-Kyung Lim ◽  
Hee-Yeon Kang ◽  
Young-Ho Khang
Keyword(s):  
Population Structure ◽  
Life Expectancy ◽  
Rural Areas ◽  
Urban Areas ◽  
Small Area ◽  
Mortality Data ◽  
Standardized Mortality Ratio ◽  
Mortality Figure ◽  
Specific Mortality ◽  
Mortality Ratio

Abstract Background: This study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity.Methods: We utilized the National Health Information Database to obtain annual age-specific numbers of population and deaths for all neighborhood-level areas in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all neighborhoods. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity.Results: We found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all neighborhoods, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in neighborhoods in rural areas, particularly in those with small populations, than in metropolitan and urban areas.Conclusions: In a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each neighborhood.

scholarly journals Comparison of three small-area mortality metrics according to urbanity in Korea: the standardized mortality ratio, comparative mortality figure, and life expectancy

2020 ◽  
Author(s):  
Ikhan Kim ◽  
Hwa-Kyung Lim ◽  
Hee-Yeon Kang ◽  
Young-Ho Khang
Keyword(s):  
Population Structure ◽  
Life Expectancy ◽  
Rural Areas ◽  
Small Area ◽  
Standardized Mortality Ratio ◽  
Small Areas ◽  
Mortality Figure ◽  
Specific Mortality ◽  
Small Area Level ◽  
Mortality Ratio

Abstract Background: This study aimed to compare three small-area level mortality metrics according to urbanity in Korea: the standardized mortality ratio (SMR), comparative mortality figure (CMF), and life expectancy (LE) by urbanity.Methods: We utilized the National Health Information Database to obtain annual small-area level age-specific numbers of population and deaths in Korea between 2013 and 2017. First, differences in the SMR by urbanity were examined, assuming the same age-specific mortality rates in all small-areas. Second, we explored the differences in ranking obtained using the three metrics (SMR, CMF, and LE). Third, the ratio of CMF to SMR by population was analyzed according to urbanity.Results: We found that the age-specific population distributions in urbanized areas were similar, but rural areas had a relatively old population structure. The age-specific mortality ratio also differed by urbanity. Assuming the same rate of age-specific mortality across all small-areas, we found that comparable median values in all areas. However, areas with a high SMR showed a strong predominance of metropolitan areas. The ranking by SMR differed markedly from the rankings by CMF and LE, especially in areas of high mortality, while the latter two metrics did not differ notably. The ratio of CMF to SMR showed larger variations in small-areas in rural areas, particularly in those with small populations, than in metropolitan and urban areas.Conclusions: In a comparison of multiple SMRs, bias could exist if the study areas have large differences in population structure. The use of CMF or LE should be considered for comparisons if it is possible to acquire age-specific mortality data for each small-area.

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