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There have been a numerous attempts in searching for better indices which would reflect the level of community or national health. In particular, the mortality level, though an extremely negative aspect of health, has been used most commonly as a comprehensive measure for health of the people. In an effort to assess the validity of "Normal deviate for mortality" a relatively new index developed by Uematsu, the author had made an analysis of the index on the basis of mortality data of Korean population during the period of 1966-1975. Since the data on mortality, the most important one in the vital statistics, are not fully available the life table method by Brass was used in estimating the number of death by area and sex indirectly. Normal deviate for mortality which is prominent as a regional health level among regions. Spearman's rank correlation coefficient was also used for rank correlation annually. Correlation among other health indicators-crude death rate, age-corrected death rate, PMI, age-corrected PMI, 1-4 year death rate and life expectancy as birth-was shown with normal deviate for mortality were analyzed. The results obtained are as follows: 1. Normal deviate for mortality of 1966, 1970 and 1975 was calculated and ranked: Seoul was ranked the first, Busan the second and Gangwon the eleventh. 2. On the rank correlation of normal deviate for mortality by year and sex, all were significant (P<0.05). Especially rank correlations between 1966-1970 and 1970-1975 were highly significant (P<0.01) 3. Correlation coefficient between male and female of normal deviate for mortality was highly significant (0.99784, P<0.01) 4. In crude death rate, age-specific death rate, infant mortality rate, 1-4 year death rate and life expectancy at birth, coefficients of correlation with normal deviate for mortality were above 0.84 (P<0.01) 5. In PMI and age-corrected PMI, coefficients of correlation with normal deviate for mortality were 0.22163(P>0.05) and -0.46019 (P>0.05) respectively. 6. Correlation of normal diviate for mortality by sex was highly significant (r=0.99828, P<0.01). 7. Multiple correlation was 0.99952 and coefficient of determination 0.99904 so that the variation explained by regression occupied more than 99%. As the result of analysis of variance, linear regression was highly significant (F=6919.422 P<0.01). 8. Comparisons among cities and provinces on various health indicators values obtained in 1975 revealed that urbanized area enjoyed lower health level than rural areas did. In general, Seoul held the lowest value, Busan was ranked the next and Gangwon the last.
In order to compare and discuss the differences of mortality by region, 10 health indicators were chosen and their values and coefficients of correlation were also calculated respectively. Factor loadings were calculated by principal component and factor analysis, and after varimax rotating, they were calculated in order to see the association among items. The conclusions obtained are an follows: 1. Coefficients of correlation by region by sex were in general high. In male: coefficient of correlation between PMI and age-corrected PMI was -0.475 and in female: coefficient of correlation between crude death rate and PMI was the lowest (0.082) and those with life expectancy at birth were all negative except coefficient of correlation between PMI and age-corrected PMI. 2. In the principal component analysis, in male, eigenvalue more than 1.0 was the first factor and its cumulative percent of variance was 80.1%. 3. In female, eigenvalue more than 1.0 were first and the second factor and their cumulative percent of variances was 92.0%. PMI and longevity rate regard as the indicator of population composition than as that of mortality. 4. The factor loading after varimax is similar to the first factor and negative values are also similar. 5. In the graphic presentation in female, crude death rate, age-corrected death rate, corrected rate of life per person and mortality normal deviate are on the right side of the first factor axis and age-corrected PMI and life expectancy at birth are on the left side of the first factor axis and PMI and longevity rate are on the top of the second factor axis.
In order to grasp the growth of aging population, which is the population above 65 years old, population data from 1925 to 1990 were used. Tables show the relationship among age structure coefficient, aging population index and aging dependent population index by year and area, and also show aging population density by area. Results obtained are as follows: 1. The aging population of 1.2 billion as of 1975 will be over 2million after 1990. 2. Aging index of 9.14% will be 17.87% in 1990 which is almost double. 3. Aging dependent population index by area varies from 3.0% (Seoul or Pusan) to 10.1% (Jeju). 4. Aging population density of 12.21 will be 22.12 in 1990. which is almost double. 5. Compared Korea's various indices with the ones of the advanced countries, Korea will be reached to the aging of the present advanced countries after 15 or 20 years.
Between rates derived from age distribution of death, there are Lost Years of Life per Person and ?? rtional Mortality Indicator (PMI). The former ??mortality data. ??years of life per person is less influenced in ??structure and has less need to standardize ??the death rate. In general, health level is ??hen low age group death is low and high age ??death is high. ??years o flife per person is a vital indicator ??uation Community health level. And so low ??better than high in lost years of life per ?? calulated using 1975 census data as follows: ?? Lost years of life per person male: 0.1322 female:0.1107 ?? e-adjusted lost years of life per person male:0.1322 female:0.1111 there is no difference between lost years of life per person and age-Adjusted ane, But a slight difference (about 0.02) between male and female
C. Gini, the Italian demographer, defined fecundability as the monthly Probability of Concepton in the absence of contraception, outside the gestation period and the temporary sterile period following the temination of a pregnancy. Durng the past few decades demographers have estimated it by using the fitting of a theoretical model such as the Type 1 geometric model, to the observed distribution of conceptive delays of women not practising contraception. The two purposes of this study are: 1) to show that with the aid of improved measures of conceptive delays one can obtain a good fit with the Type 1 geometric model, and 2) to investigate some characteristics of rates of conception in a mixed or heterogeneous populations. Used data are 2,608 married women of Chunseong Gun Pregnancy Cohort Study between the ages 20 to 39 living with their husbands who were not premaritally pregnant, had conceived by the time of interview. The results obtained are as follows: 1) Mean fecundability computed by applying Pearson's Type 1 geometric model was 0.165. 2) The average of time required to conceive from the onset of marriage and the beginning of first conception was 7.6 months, and standard deviation was 9.7 months. 3) To test for goodness of fit, x²for the difference between observation between observation and hypothesis was 21.14,which was not significant at the 5% level. 4) The correlation between the two conception delays for Chunseong Gun was 0.13