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Mortality in Michigan: Inequalities by Age, Race, Sex, and County of ResidenceBy Nan E. Johnson |
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| Department of
Sociology Michigan State University East Lansing, Michigan 48824-1111 Michigan
State University Abstract Vital registration data for 1985 were used to compute age-specific risks of death and average years of life remaining at a series of birthdays spanning the life cycle. Regardless of race, sex, or county of residence, infancy was the age group with the greatest risk of dying before middle age. Metro black males had the shortest life expectancy at birth (63.22 years) and white females residing in nonmetro-adjacent-to-metro counties had the longest (78.88 years), at least partly because the former had the highest homicide rates and the latter had the lowest. The shorter life expectancies of all metro blacks "crossed over" and exceeded those of metro whites at age 75 for men and 80 for women, since blacks at these advanced ages became less likely to die from heart diseases, cancers, and cerebrovascular diseases. While contrasts could not be made between metro and nonmetro blacks (due to their sparse numbers in the latter counties), whites had the longest life expectancies at almost all birthdays if they lived in nonmetro counties bordering metro counties. (1)This bulletin is a research report for AES Project # No. 3283S on "Infant Mortality in Michigan." I thank Jean Kayitsinga for assistance in computer programming and Lawrence Busch for constructive commentary on an earlier draft. I obtained the enumeration of births and deaths according to age, sex, race, and county of residence in 1985 from the Michigan Department of Public Health. The mid-1985 population estimates by age, sex, race, and county were computed by Dr. Chingli Wang of the Department of Management and Budget. Introduction Most Americans will normally use what resources they can to prevent, or recover from, illness. But material and nonmaterial resources are not dispersed evenly in the population. Thus, differences in mortality reveal people's relative success in gaining those advantages that promote health or recovery. Life span should be distinguished from
longevity. Life span is the oldest age to which
the human body can possibly survive under ideal
conditions. Because ideal conditions never
prevail at all times even for the most privileged
people, life span cannot be measured directly.
The most extreme estimate of the human life span
proposed in the recent literature was the age
beyond which only 0. 1 % of the population
survived. From this definition, U.S. mortality
rates of 1985 implied that the human life Longevity is the actual rate of survival achieved by a population, given the risks of death it faces from external and internal causes. Examples of external (exogenous) causes of death are communicable diseases, homicides, and motor vehicle accidents; examples of internal (endogenous) causes are those associated with the breakdown of tissues: heart diseases, cancers, and cerebrovascular diseases. Since the vulnerability to external and internal causes of death varies with age, the actual average age at death in a population is a combined function of the separate probabilities of death at each age across the human life cycle. For newborns (at exact age 0 years), this average represents the life expectancy at birth, the most commonly used index of longevity for a population. The current study used data from death certificates filed for residents of Michigan for 1985 to calculate the conditional probabilities of surviving to each age group and then dying within it. These age-specific risks of death were then used to compute an estimate of longevity: the average number of years of life remaining (life expectancy) for someone reaching an exact age x years. To illustrate how these averages varied by sex, race, and Michigan county of residence, the life expectancy at each exact age x years was calculated separately for these factors (see Appendix for details of the calculations). The variations occurred because the incidence of poverty differs by sex, race, and county of residence, because poverty blocks access to the health-care delivery system, and because the medical system itself is imperfect. The sparseness of the population in many Michigan counties meant that some five-year age groups yielded no observations of deaths in 1985. Therefore, the 83 counties were grouped into three residential categories: the 23 metropolitan counties; the 18 nonmetropolitan counties adjacent to the metropolitan; and the 42 nonmetropolitan counties nonadjacent to the metropolitan.(2) These categorizations allocated 81 % of the 8,966,722 residents of Michigan in 1985 to the metropolitan counties. The other 19% of the population were almost evenly split between the nonmetro counties adjacent to the metro counties (9.53%) and the more remote nonmetro counties (9.47%). (2)The county designations of "metro" and "nonmetro" are those established by the U.S. Bureau of the Census on June 30,1983, to reflect the results of the 1980 Census of Michigan. Economic data from the 1980 Census of Michigan showed that these three residential groupings followed a gradient of declining per capita income and increasing percent of persons below the poverty line with remoteness from the metropolis (1979 per capita incomes were $7635 in metro counties, $6149 in adjacent nonmetro counties, and $5570 in nonadjacent nonmetro counties; the respective percentages below the poverty line were 10.13%,11.15%, and 12.52%; see U.S. Bureau of the Census, 1983: Tables 180 and 181). In 1980, the number of Doctors of Medicine (M.D.s) per 100,000 residents was greater in metro counties (159) than in the adjacent or more distant nonmetro counties (57 and 99, respectively).(3) While adjacent nonmetro counties also contained the lowest ratio of hospital beds per 100,000 residents (323), this ratio was highest in the more remote nonmetro counties (489) and intermediate in the metro counties (460). Consequently, these three classifications of Michigan counties represented meaningful and distinct clusters of economic advantage and availability of health care. (3)The number of licensed M.D.9 was 11,791 in metro counties, 470 in adjacent nonmetro counties, and 821 in nonadjacent nonmetro counties in 1980 (Michigan Department of Public Health, 1980). To compute the ratio of physicians per 100,000 residents, the number of licensed M.D.s was divided by the 1980 Census count for that county type; and the quotient was multiplied by 100,000. The 1980 Census count was 7,399,212 people in the metro counties, 831,390 people in adjacent nonmetro counties, and 826,807 people in nonadjacent nonmetro counties (U.S. Bureau of the Census, 1983). Findings Age Age had a curvilinear effect on the probability of dying in Michigan. For example, for every 100,000 white males born to metropolitan mothers in 1985, about 1046 would probably die before the first birthday (Table 1A). Their probability of death would drop with the first birthday and reach a lifetime low of about 132 deaths per 100,000 white males reaching ages 5-9 years (Table 1A). At the tenth birthday, the probability of death would again rise but not surpass its level during infancy until ages 40- 44. These relationships highlight two important facts: that the risk of death before middle age is highest during infancy (age 0 years); and that ages 5-9 years are the safest for males in metropolitan counties of Michigan. With a few important exceptions, these age patterns in the risk of death were repeated for white females, black males, and black females (Tables 1 A and 1B). One exception was that for metropolitan females. Their lowest risk of death was at ages 10-14, where 101 white females and 114 black females would die out of every 100,000 females of that racial group reaching age 1 0. The earlier "bottoming out" of the probability of dying in a particular age group for metro males (at ages 5-9) than metro females (at ages 10-14) showed that males were more likely than females to die in adolescence and young adulthood from such avoidable causes as homicide and motor vehicle accidents (National Center for Health Statistics, 1988a). Secondly, for white male residents in Michigan, the age grouping with the lowest probability of dying was younger in metro counties (ages 5-9) than in nonmetro counties (ages 10-14) (cf. Tables 1A and 1B). Adolescent males were safer in nonmetro than in metro counties, for the rate of homicidal mortality was greatest in metro counties, intermediate in nonmetro counties nonadjacent to them, and lowest in nonmetro counties adjacent to metro counties (Table 3). As a result, the expectation of life at birth for white males was shortest in metro counties (71.65 years), intermediate in nonmetro counties nonadjacent to metro counties (71.78 years), and longest in nonmetro counties adjacent to metro counties (71.81 years) (Tables 2A and 2C). These differences in life expectancy at birth are very smart but nonetheless real: they cannot be attributed to sampling error since they are not based on a population sample. Thirdly, black males had an earlier-than usual age group (ages 30-34) in which the probability of dying in adulthood surpassed the probability of dying in infancy. For every 100,000 black males born to metro Michigan mothers, 2680 would die in infancy. That risk of death would not be met or exceeded again until ages 30-34, whereat 3358 of every 100,000 black males turning 30 years old could expect to die before turning 35 years old (Table 1 A). Black males face a much higher death rate from
homicide than do white males, white females, or
black females (48.4, 8.2, 2.9, and 11.0 murders
per 100,000 people in that race/sex grouping,
respectively, in 1985; National Center for Health
Statistics, 1988a: Table 1-8) and witness its
peak at ages 30-34 (94.7 murders per 100,000
black males aged 30-34; see National Center for
Health Statistics, 1988a: Table 1-8). An analysis
of black males' mortality could not be done for
nonmetro Michigan counties due to the very small
number of blacks living there. But certainly, the
death rate from homicide is Sex After age, sex was the next most important risk factor for mortality in Michigan. Reasons given for the greater survival of females over males of the same age have been the protective effect of a female hormone, estrogen, and the lesser tendency of women to smoke tobacco (Epstein, 1965; Retherford, 1975). It is noteworthy that despite the social disadvantages of color, black females had a longer life expectancy at every age than did white males in metro Michigan counties (Table 2A). The male disadvantage in survivorship varied by race. For example, within metropolitan counties, the "gender gap" in life expectancy at birth favored white females by 6.80 years (= 78.45 - 71.65) and black females by 9.91 years (= 73.13 - 63.99) (Table 2A). Obviously, black women residents of metro counties faced a higher risk of widowhood if they married black men of their same age or an older age. One might expect that the gender gap would be narrowest in the nonmetro counties adjacent to metro counties, since these nonmetro counties have already been shown to have the longest life expectancies for males of all ages. Somewhat surprisingly, the gender gap in white mortality was widest in such counties (7.07 years = 78.88 - 71.81; see Table 2C). This relationship arose because white women benefited more than white men from the healthful advantages of fife in a nonmetro county adjacent to a metro county. As previously mentioned, life expectancies could not be computed for black residents of nonmetro counties due to their scarcity outside metro areas. Race The odds of an infant's death vary by its mother's marital status at birth and its sex (a child born to an unwed mother and a male child have greater odds of death in infancy). Yet the odds that a child will be born to an unwed mother are much greater if it is black than if it is male. Therefore, in infancy, race had a larger impact than sex on the mortality of Michigan residents in 1985. Among metro residents, black males had the greatest probability of death in infancy (2680 deaths per 100,000 live births), followed by black females (1769 deaths per 100,000 live births), white males (1046 deaths per 100,000 live births), and white females (777 deaths per 100,000 live births) (Table 1A). Nevertheless, the detrimental impact of being black on life expectancy (unlike that of being male) wore off over the life cycle. Consider the life expectancies for residents of metro counties (Table 2A). Among metro males, whites had longer Life expectancies than blacks through age 69 years; at exact age 70, their Life expectancies became nearly identical (for white men, 11.25 more years of life; for black men, 11.17). At exact age 75 years, the life expectancy for black men "crossed over" to exceed that for white men. The life expectancies became almost equal for black and white metro women at the 75th birthday (11.69 and 11.71 more years for black and white women, respectively; Table 2A); and at the 80th birthday, the life expectancy for black metro women crossed over and exceeded that for white metro women. In the U.S. at large, black superiority for either men or women in the average number of remaining years of life is not reached until a later age: the 85th birthday (National Center for Health Statistics, 1988a: Table 6- 1). The racial "crossover" from black
inferiority to black superiority in life
expectancy at advanced ages has been noted by
other researchers. Nam et al.(1978) concluded
that the greater social and economic hardships
suffered by black people in early and middle age
allow only blacks with the strongest
constitutions to survive until old age but
simultaneously permit white people with weaker
constitutions to live so long. Manton (1980)
seemed to agree when he wrote that elderly blacks
are more likely to die from single causes; and
elderly whites, from multiple chronic diseases.
Put simply, white bodies are What causes of death can best explain the switchover from white superiority to black superiority in survivorship at older ages in Michigan (75 years for Michigan men and 80 for Michigan women)? The percentage of deaths from diseases of the heart and the cerebrovascular system change from lower to higher for whites than for blacks of both sexes on the 75th birthday (Table 4). Also, the percentage of deaths from breast cancer are lower for white Michigan women than for their black counterparts before age 55 and become higher for white Michigan women thereafter (Table 4). Notably, national rates of death from identifiable diseases in 1985 were greatest for heart diseases, cancers, and cerebrovascular diseases, in that order (National Center for Health Statistics, 1988a: Table 1-7). County of Residence At all birthdays along the life cycle, life
expectancies for white males were longest in the
nonmetro counties bordering on metro counties
(Table 2C). The life expectancies for white males
in the more distant nonmetro counties were
superior to those of their metro counterparts
only until the 30th birthday, at which, with two
exceptions, the relationship was reversed (Tables
2A and 2C). The threat to survivorship of white
males of prime laborforce ages in the remote
nonmetro counties may Access to health care in the remote nonmetro counties has been hindered by the very federal laws meant to help the poor (Medicaid) and the elderly (Medicare).(4) When laws calling for Medicare and Medicaid were passed in the late 1960s, the plans were to reimburse hospitals and doctors on a fee-for-service basis. However, in 1983, Medicare started a Diagnostic Related Grouping (DRG) system in which hospitals were reimbursed a fixed payment according to the patient's diagnosis; and Medicaid shifted to a fixed reimbursement procedure in 1985. The systemic contradiction is that nonmetropolitan hospitals and physicians are now being reimbursed according to a lower pay scale than are metropolitan hospitals and physicians, although nonmetro people are more likely to be poor and less likely to have health insurance than metro people are (McManus and Newacheck, 1989). Also, the nonmetro poor are more likely than the metro poor to live in a married-couple household (Rural Nutrition and Health, 1989); and in Michigan, such households are automatically disqualified for Medicaid. Therefore, nonmetro residents in Michigan are less able to cover the fee for medical service by any means (Medicare/Medicaid reimbursement or health insurance). (4) In 1985, the concentrations of white elderly Michiganian males (aged 65+ years) rose from 9.03% in metro counties to 10.35% in adjacent nonmetro counties to 13.53% in nonadjacent nonmetro counties; and this relationship was true, also, for white Michiganian females (13.27%,13.59%, and 17.23%, respectively). An alternative to nonmetro poor and elderly
people is to forego medical service except in
emergencies. Indeed, the annual number of
emergency visits per hospital has risen more
sharply for nonmetro than metro hospitals in
Michigan since 1983. Admissions per hospital have
declined more sharply in nonmetro counties of
Michigan not only because nonmetro people have a
harder time paying for hospitalization but also
because metro hospitals have lured some of them
away (Stevens, 1989). Because these arrangements
make curative rather than preventive medicine a
higher priority for the poor and For at least two reasons, the life
expectancies of whites in metro counties fell
behind those in the adjacent nonmetro counties in
almost all age groups. The rate of homicidal
mortality was highest in metro Michigan counties
(Table 3). Likewise, Mexican Americans tend to
live in metro counties of the state and tend to
have poorer survival rates than do other white
people (Eberstein and Pol, 1982; U.S. Bureau of
the- Census, 1982: Tables 23 and 51). Because
Wayne County (of which Detroit is the county
seat) had 31 % of the Mexican Americans in
Michigan (according to the 1980 U.S. Census) and
70% of the homicides in the state in 1985,
exclusion of Wayne County from the analysis
should increase the life expectancies observed in
the remaining metropolitan counties (U.S. Bureau
of the Census, 1982: Tables 23 and 51; National
Center for Health Statistics, 1988b: Table 8-9).
Indeed, after Wayne County was removed, the life
expectancy at birth was highest for white males
if they lived in one of the other 22 metro
counties (72.20 years, Table 2B); and the life
expectancy at birth for white Motor vehicle fatalities were the only cause of death specified in Table 3 with highest rates for residents of the nonmetro counties adjacent to metro counties. A possible reason is that nonmetro residents may commute more regularly and farther to work and recreation if they five next to metro counties. Conclusions Longevity in Michigan should be judged from a national perspective. For the 81 majority of Michiganians living in metropolitan counties in 1985, the life expectancy at birth was one-quarter of a year shorter for white males and white females, 0.37 year shorter for black females, and 2.08 years shorter for black males than for the same race-sex grouping at the national level. An important reason for the longevity gap was a disproportionate exposure to inner-city poverty and homicide in Wayne County, of which Detroit is the county seat. Indeed the life expectancy at birth in the other 22 metro counties of Michigan was longer by 0.3 year for white males, 0.16 year for white females, 0.76 year for black males, and 0.61 year for black females than for the same race-sex grouping at the national level. Obviously, the reduction of crime against persons in Detroit would increase the longevity of metropolitan residents of the State of Michigan. While the data did not permit a distinction between whites with and without Mexican origins, it is likely that a disproportionate amount of premature mortality among whites in Wayne County occurred to Mexican Americans. Premature mortality among the poor residents of ghettos and barrios can be alleviated by liberalizing the qualifications for Medicaid, which is currently denied to households consisting of married couples or earning more than 60% of the income defining the poverty line. In addition, language barriers may impede communication between medical staff and those they serve, particularly when patients have very low levels of education or speak English poorly or not at all. Another disadvantaged group was white males aged 30 or more in nonmetro counties nonadjacent to metro counties. These remote nonmetro counties had the highest rate of poverty and the lowest per capita income of the three sabers in the state. Ironically, the number of hospital beds per 100,000 residents was greatest in these remote nonmetro counties; and the number of M.D.s per 100,000 residents was much greater than for the nonmetro counties contiguous to metropolitan counties. However, the presence of a healthcare delivery system is insufficient to postpone mortality when people are too poor to purchase health care. Appendix. Method of Estimating Longevity In order to measure mortality in Michigan, the author used death and birth certificates to count these vital events by age, race, sex, and county of residence in the state in 1985. For all age groups above infancy, death rates were computed as the ratio of the number of deaths in a particular age-race-sex-county subgrouping to the mid-year population estimate for that same subgrouping. The death rates were then converted into probabilities of dying in the various age groups by means of the Barclay method (Barclay, 1958). For infants, the probability of dying before the first birthday was the infant mortality rate; i.e., the ratio of the number of infant deaths to the number of live births in that race-sex-county group in 1985. The probabilities of dying in various age groups between infancy (age 0 years) and 85+ years, inclusive, were then used to compute life tables by the Barclay method (Barclay, 1958). These fife tables gave the average number of additional years that could be expected by a hypothetical infant of a certain sex born to a mother having a certain race and type of residential county in Michigan, if that child faced all of the age- specific probabilities of death that prevailed in Michigan in 1985. These averages, which shall be called the "life expectancies at exact ages x," will hold true for the death rates prevailing in Michigan in 1985 do not change. References Barclay, George W. 1958 Techniques of Population Analysis. New York: John Wiley and Sons. Eberstein, Isaac W., and Louis C. Pol 1982 "Mexican American Ethnicity, Socioeconomic Status, and Infant Mortality: A County-Level Analysis." Social Science Joumal 19: 61-71. Epstein, E. 1965 "The epidemiology of coronary heart disease." Journal of Chronic Diseases 18:735-74. Lansing State Journal 1990 "Farming: Country lifestyle far too hazardous." November 10: editorial page, Section A. McManus, Margaret A., and Paul W. Newacheck 1989 "Rural Maternal, Child, and Adolescent Health." Health Services Research 23: 807-48. Maintain, Kenneth G. 1980 "Sex and race specific mortality differentials in multiple cause of death data." The Gerontologist 20(4): 481-92. Michigan Department of Public Health 1980 Michigan Cooperative Health Information System. Licensed Health Occupations: Michigan Physicians (M.D. and D.O.), 1980, Lansing. Nam, Charles B., Norman L. Weatherby, and Kathleen A. Ockay 1978 "Causes of death which contribute to the mortality crossover effect." Social Biology 25: 306-14. National Center for Health Statistics 1988a Vital Statistics of the United States, 1985. Volume 11. Mortality. Part A. Section 1, General Mortality. Washington, DC: U.S. Government Printing Office. 1988b Vital Statistics of the United States, 1985. Volume II. Mortality. Part B. Section 8, Geographical Detail of Mortality. Washington, DC: U. S. Government Printing Office. Retherford, R. D. 1975 The Changing Sex Differentials in Mortality. Westport: Greenwood Press. Rogers, Everett M., Rabel J. Burdge, Peter F. Korsching, and Joseph F. Donnermeyer 1988 Social Change in Rural Societies. Englewood Cliffs, NJ: Prentice Hall, third edition. Rural Nutrition and Health Update 1989 "Rural Poor's Profile Differs from Urban Poor." Volume 1, no. 1, page 1. Stevens, Robert D. 1989 Shifts in Hospital Services and Resource Use to Metropolitan Areas in Michigan and East North Central States. Agricultural Economic- Report No. 526, Michigan State University. U.S. Bureau of the Census 1982 1980 Census of Population. Volume 1, Characteristics of the Population. Chapter B. General Population Characteristics. Part 24, Michigan. Washington, DC: U.S. Government Printing Office. 1983 1980 Census of Population. Volume 1, Characteristics of the Population. Chapter C. General Social and Economic Characteristics. Part 24, Michigan. Washington, DC: U.S. Government Printing Office. Wade, A. H. 1988 "United States life table functions and actuarial functions based on alternative 11 mortality probabilities used." Washington, DC: Social Security Administration. Table 1 A. Age on Last Whites Blacks Birthday (in yrs.) Males Females Males Females 0 0.01046 0.00777 0.02680 0.01769 1-4 .00261 .00131 .00298 .00299 5-9 .00132 .00111 .00192 .00116 10-14 .00185 .00101 .00259 .00114 15-19 .00511 .00234 .01307 .00304 20-24 .00695 .00241 .01682 .00537 25-29 .00617 .00238 .02172 .00762 30-34 .00749 .00313 .03358 .01288 35-39 .00919 .00509 .03641 .01351 40-44 .01382 .00762 .04713 .02024 45-49 .02247 .01315 .05908 .02903 50-54 .03820 .01961 .07221 .03783 55-59 .06112 .03637 .09775 .06081 60-64 .09958 .05905 .14190 .09029 65-69 .14726 .08201 .18585 .11556 70-74 .22095 .12429 .27070 .15140 75-79 .31726 .19100 .34096 .22617 80-84 .45082 .30551 .43816 .34853 85 + 1.00000 1.00000 1.00000 1.00000 Table 1B. Age on Last Nonmetro Counties Nonmetro Counties
Birthday Adjacent to Metro Nonadjacent to
(in yrs.) Counties Metro Counties
Males Females Males Females
0 0.01200 0.00908 0.00836 0.00959
1-4 .00252 .00221 .00380 .00144
5-9 .00201 .00120 .00127 .00186
10-14 .00110 .00134 .00105 .00084
15-19 .00356 .00261 .00630 .00300
20-24 .01176 .00277 .00675 .00197
25-29 .00822 .00263 .00434 .00289
30-34 .00515 .00380 .00742 .00320
35-39 .00925 .00378 .00959 .00452
40-44 .01275 .00643 .01111 .00738
45-49 .02144 .01199 .02348 .01196
50-54 .03654 .01935 .03929 .02166
55-59 .06545 .03767 .06391 .03118
60-64 .09034 .04957 .09413 .04999
65-69 .13531 .07778 .14000 .07645
70-74 .21581 .11306 .21740 .11778
75-79 .29246 .18370 .33306 .19788
80-84 .45232 .26995 .47905 .27450
85 + 1.00000 1.00000 1.00000 1.00000
Table 2A. Exact Age on Whites Blacks Birthday Males Females Males Females (in yrs.) 0 71.65 78.45 63.22 73.13 1 71.40 78.06 63.96 73.44 5 67.58 74.16 60.14 69.66 10 62.67 69.24 55.25 64.74 15 57.78 64.31 50.39 59.81 20 53.06 59.45 46.02 54.98 25 48.42 54.59 41.77 50.27 30 43.70 49.71 37.64 45.63 35 39.01 44.86 33.86 41.20 40 34.35 40.08 30.05 36.73 45 29.80 35.37 26.41 32.43 50 25.43 30.81 22.91 28.33 55 21.34 26.37 19.50 24.34 60 17.56 22.27 16.34 20.76 65 14.23 18.51 13.63 17.57 70 11.25 14.95 11.17 14.54 75 8.73 11.71 9.39 11.69 80 6.63 8.89 7.95 9.37 85 5.02 6.70 7.20 8.05 Table 2B. Exact Age on Whites Blacks Birthday (in yrs.) Males Females Males Females 0 72.20 78.86 66.06 74.11 1 71.95 78.48 66.68 74.63 5 68.14 74.59 62.96 70.79 10 63.22 69.67 58.10 65.87 15 58.33 64.74 53.25 60.95 20 53.61 59.89 48.59 56.05 25 48.97 55.01 44.19 51.27 30 44.24 50.11 39.85 46.55 35 39.53 45.25 35.37 41.92 40 34.82 40.45 31.07 37.35 45 30.23 35.72 27.01 32.92 50 25.82 31.15 23.19 28.62 55 21.70 26.71 19.38 24.51 60 17.87 22.57 16.01 21.07 65 14.50 18.78 13.43 17.86 70 11.43 15.17 10.85 15.01 75 8.83 11.90 9.03 12.09 80 6.77 9.01 7.19 9.04 85 5.11 6.79 5.79 7.54 Table 2C. Exact Age on Nonmetro Counties Nonmetro Counties
Birthday Adjacent to Metro Nonadjacent to
(in yrs.) Counties Metro Counties
Males Females Males Females
0 71.81 78.88 71.78 78.61
1 71.68 78.60 71.38 78.37
5 67.85 74.77 67.64 74.48
10 62.99 69.86 62.73 69.62
15 58.05 64.95 57.79 64.67
20 53.25 60.11 53.14 59.86
25 48.86 55.27 48.48 54.97
30 44.24 50.41 43.68 50.12
35 39.46 45.59 38.99 45.28
40 34.80 40.75 34.35 40.47
45 30.22 36.00 29.70 35.75
50 25.83 31.41 25.36 31.16
55 21.71 26.98 21.29 26.79
60 18.06 22.94 17.57 22.57
65 14.60 19.00 14.14 18.63
70 11.49 15.40 11.04 14.96
75 8.97 12.04 8.41 11.63
80 6.64 9.19 6.36 8.88
85 5.06 6.66 4.91 6.29
Table 3. County Homicide Heart Cancers Type Diseases Metro (N = 7,262,316 people) Deaths 1,030 24,219 13,565 Rates 14.18 333.49 186.79 Nonmetro adjacent to metro (N = 854,838 people) Deaths 15 2,818 1,647 Rates 1.75 329.65 192.67 Nonmetro nonadjacent to metro (N = 849,568 people) Deaths 22 3,613 2,094 Rates 2.59 425.27 246.48 All counties (N = 8,966,722 people) Deaths 1,067 30,650 17,306 Rates 11.90 341.82 193.00 County Cerebrovas- Motor All other Type cular Vehicle causes Metro (N = 7,262,316 people) Deaths 4,270 1,272 17,772 Rates 58.80 17.52 244.72 Nonmetro adjacent to metro (N = 854,838 people) Deaths 564 235 2132 Rates 65.98 27.49 249.40 Nonmetro nonadjacent to metro (N = 849,568 people) Deaths 702 177 2,575 Rates 82.63 20.83 303.10 All counties (N = 8,966,722 people) Deaths 5,536 1,684 22,479 Rates 61.74 18.78 250.69 Note: Frequencies of deaths by county type and cause were computed from county tabulations in Table 8-9 of Vital "Statistics of the United States. 1985." Volume D Mortality. "Part B. Section" 8 (National Center for Health Statistics, 1988). Population estimates by county type were computed from county estimates from the Department of Management and Budget, State of Michigan. Table 4. Age at Death (Yrs.) Race-Sex Total 45 45-54 55-64 65-74 75-84 85 + Group Heart Diseases White Males No. 14,041 396 895 2,464 3,950 4,008 2,328 o/o 100 2.8 6.4 17.6 28.1 28.5 16.6 White Females No. 13,024 153 247 1,010 2,447 4,375 4,792 o/o 100 1.1 1.9 7.8 18.8 33.6 36.8 Black Males No. 1,861 155 180 388 554 418 166 o/o 100 8.3 9.7 20.8 29.8 22.5 8.9 Black Females No. 1,646 71 93 265 385 484 348 o/o 100 4.3 5.7 16.1 23.4 29.4 21.1 Breast Cancer White Females No. 1,333 113 177 347 329 250 117 o/o 100.1 8.5 13.3 26.0 24.7 18.8 8.8 Black Females No. 178 30 33 46 32 28 9 o/o 100 16.9 18.5 25.8 18.0 15.7 5.1 Cerebrovascular Diseases White Males No. 1,959 53 61 188 519 735 403 o/o 100 2.7 3.1 9.6 26.5 37.5 20.6 White Females No. 2,871 50 47 184 455 953 1,182 o/o 99.9 1.7 1.6 6.4 15.8 33.2 41.2 Black Males No. 295 34 33 65 79 59 25 o/o 100 11.5 11.2 22.0 26.8 20.0 8.5 Black Females No. 391 28 29 49 96 114 75 o/o 100.1 7.2 7.4 12.5 24.6 29.2 19.2 Note: Computed by author from Table 8-6 "in Vital Statistics of the United States, 1985. Volume 11 Mortality. Part B, Section 8" (National Center for Health Statistics, 1988). Percentages may not sum to 100 due to rounding errors. |
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