Socioeconomic Background, Disease, and Mortality among Union Army Recruits: Implications for Economic and Demographic History

EXPLORATIONS IN ECONOMIC HISTORY ARTICLE NO.

34, 27–55 (1997)

EH960661

Socioeconomic Background, Disease, and Mortality among Union Army Recruits: Implications for Economic and Demographic History CHULHEE LEE* Department of Economics and Center for Population Economics, University of Chicago This paper examines the effects of age, occupation, population size of place of residence, nativity, and household wealth on the disease and mortality experiences of Union army recruits while in service. The pattern of the mortality differentials among the army recruits was nearly the opposite of the normal pattern found among the civilian populations. The observed features of disease-specific mortality and timing of death suggest that the different degrees of exposure to disease prior to enlistment were probably the main determinant of the wartime mortality differentials. Wealth had a significant effect only for diseases on which nutritional influence is definite. Implications of these results for some issues in economic and demographic history are discussed. r 1997 Academic Press

I. INTRODUCTION Mortality differentials have been found across individuals of different socioeconomic status or ecological environments in every society to some extent, even in highly wealthy and egalitarian nations today.1 The pattern of the association between socioeconomic status and mortality in cross section not only reveals the effects of socioeconomic factors on health at a point in time, but also provides insights into the causes of secular changes in mortality rates over time.2 This paper pursues these two objectives, exploring the pattern of mortality differentials * An earlier draft of this paper was presented at the 1995 NBER/DAE Summer Institute and the Economic History Workshop at the University of Chicago. I have benefited from the helpful criticisms of the participants. I thank Dora L. Costa, Robert W. Fogel, Susan Jones, John M. Kim, Nevin Scrimshaw, Richard Steckel, David R. Weir, and two anonymous referees for their useful comments and suggestions. Financial support from the Center for Population Economics, from NIH (P01 AG 10120), from NSF (SBR 9114981), and from the University of Chicago is gratefully acknowledged. I bear sole responsibility for any errors. 1 For patterns of inequality in health and mortality in developed countries see Kitagawa and Hauser (1973), Notkota et al. (1985), Lehmann et al. (1990), Diderichen (1990), and Lawson and Black (1993). 2 Some examples of this approach are, among others, Condran and Cheney (1982), Steckel (1983), Fogel (1986, 1991), Weir (1993), and Costa (1993b). 27 0014-4983/97 $25.00 Copyright r 1997 by Academic Press All rights of reproduction in any form reserved.

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in cross section and its implications for economic history by looking at wartime records of Union army recruits.3 The medical experiences of recruits during the Civil War provide a unique opportunity to examine the socioeconomic differentials in mortality in several aspects. The war brought together a large number of men from heterogeneous socioeconomic and ecological backgrounds in a highly unhealthy environment which caused unusually high rates of contraction of and mortality from disease. About 12% of all recruits who served in the Union army died while in service (Vinovskis, 1990); disease was far more common than wounds as a cause of death or disability.4 Moreover, recruits were confined to relatively homogeneous living conditions in terms of the quality of diet, housing, and disease environment compared to normal society. Owing to these features of army life, we are able to identify more clearly the effects of socioeconomic and ecological factors, and, in particular, the extent of previous exposure to disease, on the degree of susceptibility or resistance to disease.5 Finally, detailed descriptions of disease diagnoses, and cause and date of death while in service, which are contained in the Union army medical records, make it possible to examine the patterns of cause-specific mortality and timing of wartime death. Nineteenth-century American rural dwellers, farmers, and natives were healthier on average than urban inhabitants, nonfarmers, and nonnatives. It has also been found that wealth or economic status had little effect on mortality in the mid-nineteenth century (Steckel, 1988). As will be shown in Section III of this paper the pattern of the mortality differentials among the army recruits was nearly the opposite of this normal pattern. Recruits who were healthier prior to enlistment were more vulnerable to disease while in service. The observed features of disease-specific mortality and time structure of death indicate that the different degrees of exposure to disease prior to enlistment were probably the main determinant of the wartime mortality differentials. It was also discovered that wealth had a significant effect only for diseases on which nutritional influence is definite (Section IV). 3 This study is one of very few attempts to relate wartime mortality with civilian background. The number of cases and the number of deaths from specific diseases were computed at the national level (U.S. Surgeon General’s Office, 1870), but they were not systematically disaggregated into small cells according to different socioeconomic backgrounds. Recently, Vinovskis (1990) investigated the percentage of recruits who died, were wounded, or deserted during military service in each of the small cells of different socioeconomic factors, such as age, occupation, and education. However, this study is based on a relatively small sample composed of recruits who resided in a single town. 4 Death from disease was more than twice as frequent as death from injury (Steiner, 1968, p. 8). In the Ohio sample used in this study, death from any illness accounts for about four-fifths of the total wartime mortality. Civil War armies actually suffered comparatively less disease mortality than any previous army. The ratio of the number of deaths from disease to the number of soldiers killed in combat was 7 for the American army in the Mexican War and 8 for British soldiers in the Napoleonic Wars (McPherson, 1988, p. 487). 5 Among the civilians who did not migrate between rural and urban areas, it is hard to identify the effect of their different immunity status because they continued to live in the same environment to which they adapted.

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This study has implications for some questions related to the associations between socioeconomic factors and health among civilian populations which have been observed: Why were farmers healthier than nonfarmers even when their rural residency was held constant? Why was the effect of economic status upon health or mortality relatively weak in the nineteenth-century United States? When and how did socioeconomic conditions begin to exert a strong influence on health? It also suggests answers to questions on the secular changes in mortality and height in the nineteenth-century United States: What explains the exact timing of the decline in mortality rates in the late nineteenth century? What were the effects of urbanization and increased geographical mobility on this trend in health or mortality? What were the causes of the decrease in the urban–rural mortality gap in the late nineteenth century? These issues will be discussed in Section V. II. THE UNION ARMY MEDICAL RECORDS This study is based on a 12% sample of the several primary data sources which were collected and linked as part of the project titled Early Indicators of Later Work Levels, Disease, and Death, jointly sponsored by the National Institutes of Health, the Center for Population Economics at the University of Chicago, and Brigham Young University.6 Information on 39,616 white enlisted men in a random sample of 331 Union army infantry companies was gathered from regimental records and linked to other data sources, such as military records, carded medical records, pension records, manuscript schedules of the federal censuses of 1850, 1860, 1900, and 1910, and surgeons’ certificates. The sample used in this paper is composed of 4780 recruits who enlisted in 45 companies organized in Ohio (henceforth called the Ohio sample).7 Of these recruits, 2436 men (about 51%) were successfully linked to the 1860 census.8 The service records contain very detailed descriptions of the diseases or wounds which recruits suffered during military service. As soon as a recruit was too ill to report for duty, his condition was noted in morning reports. If his condition required medical attention, it was recorded in the regimental surgeon’s 6

For details of this project and characteristics of the data see Fogel (1993) and Costa (1993a,c). Part of these data have been deposited with the Inter-University Consortium for Political and Social Research (ICPSR) at the University of Michigan under the name ‘‘Public Use Tape on the Aging of Veterans of the Union Army First Installment: Military, Pension, and Medical Records of Ohio Regiments, 1820–1940.’’ 8 In the course of linkage, some recruits failed to be linked to particular data sources. If linkage failures occurred in a nonrandom manner, in the sense that recruits with some particular characteristic were more likely to fail to be linked to other data sources, it could cause biases in the sample. For testing the potential link failure bias I estimated the effects of various behavioral variables on the probability of being linked to particular data sources employing logistic and linear probability specifications. The regression results indicate that linkage failures were caused mostly by random factors. As another test of bias problem, wartime mortality rates were estimated for several subgroups of recruits who were linked to the same set of data sources. This control for different data sources does not change the basic pattern of wartime mortality differentials. 7

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TABLE 1 Number of Cases and Case Fatality Rates of Disease: A Comparison of the Ohio Sample and the Union Army Number of cases per one person

Number of deaths per 1000 cases Ohio sample

Ohio sample Disease

All

CENb

Union armya

Any diseases Diarrhea Typhoid Malaria Pneumonia Measles Smallpox

2.53 0.57 0.06 0.30 0.05 0.05 0.02

2.70 0.60 0.06 0.32 0.06 0.06 0.02

2.63 0.76 0.06 0.57 0.04 0.03 0.01

All

CEN

Union army

31.9 38.7 283.1 2.2 105.7 76.1 233.8

31.2 35.3 269.7 3.9 132.9 62.5 268.3

26.1 25.6 234.4 7.6 258.2 67.8 372.4

a Source for the Union army: The numbers of cases of and deaths from each disease are reported in Steiner (1968), Chap. 1. For the number of cases per person, the number of cases of each disease was divided by the total number of individuals who had served in the Union army. According to Gould (1869) the total number of white persons who served in the Union army is 2,110,000. Traditional estimate of black recruits is 179,000. Based on these figures, I assumed that 2,289,000 men had served in the Union army. b CEN refers to recruits who were linked to the 1860 census.

report; if he was hospitalized, the diagnosis of the disease was described in the case history together with the ultimate outcome, such as return to service, discharge for disabilities, or death (U.S. Surgeon General’s Office, 1870, Vol. 1). Information on disease and date and cause of death in service were gathered from these sources. Military service records provide information on demographic and socioeconomic characteristics of recruits, such as age, occupation at enlistment, place of birth, and height, among other variables, as well as on their military career including rank, military duty, company, regiment, change in military status, dates of enlistment and discharge, and so on. Additional information on socioeconomic status and household structure prior to enlistment can be drawn from manuscript schedules of the 1860 census: they contain information on age, occupation, place of birth, household wealth, place of residence, and literacy, not only for recruits but also for their family members. Among the socioeconomic variables which are needed for this study, household wealth and place of residence as of 1860 are found only in the census data. Therefore, I limit the sample to the 2436 recruits who were linked to the 1860 census whenever household wealth or place of residence is concerned. In spite of geographical concentration, the medical experiences of recruits in the sample while in service do not look significantly different from the average medical experiences of the entire Union army. Wartime mortality rates, the number of cases per person, and case fatality rate for each disease are matched fairly closely between this sample and the Union army (Table 1). For example,

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8.1% of the Ohio sample died from disease while in service, while 8.7% of all who served in the Union army were killed by disease. The number of disease cases per person is 2.53 for the Ohio sample and 2.63 for the Union army. Moreover, the occupational composition, the size of household wealth, and wealth distribution of this sample are not very different from those of the northern population at large.9 III. PATTERN OF MORTALITY DIFFERENTIALS AMONG UNION ARMY RECRUITS A. Socioeconomic Status, Health, and Mortality among Civilians: Nineteenth-Century America In the nineteenth-century United States, farmers, rural residents, and nativeborn Americans were healthier on average than nonfarmers, city dwellers, and nonnatives, respectively. Mortality rates in large cities were much higher than those in smaller cities or small towns in rural areas (United Nations, 1973, pp. 132–134). The concentration of large numbers of people in small areas accelerated the spread of communicable diseases through either direct personal contact or increased contamination of water and food (Preston and Haines, 1991, p. 31). Rapid urbanization with insufficient housing and sanitation made the living environment of large cities worse, and public sanitation measures were not effective until the last decade of the nineteenth century (McKeown, 1983; Fogel, 1986; Condran and Cheney, 1982; Higgs and Booth, 1979). Although the advantage in mortality of farmers over nonfarmers is largely attributable to the rural environment, occupation itself might have an independent effect on health and mortality. According to Preston and Haines (1991, p. 155), child mortality rates among farmers around 1900 were below average even when rural residence was held constant. The advantage of farmers could derive from access to higher quality food, lower relative price of food, or sparser density of population compared to nonfarmers living in rural areas. It could reflect selfselection in choosing occupation: since farming requires physical strength, healthier men would choose to be farmers, or healthier people would be more productive, and therefore are more likely to hold higher income occupations (Margo and Steckel, 1983; Costa, 1993b). Compared with natives, immigrants had higher mortality rates and poorer health conditions on average in nineteenth-century America.10 Possible causes of 9 The proportion of farmers in the Ohio sample (55.6%) is almost identical with that of the Midwest (55%) suggested by Atack and Bateman (1987). The mean value of household wealth among the recruits in this sample is slightly larger than that of free households in the United States (Soltow, 1992) mainly because of a different age structure of the household head. The Gini coefficient of the Ohio sample (0.65) is fairly close to the range of 0.58 to 0.62 suggested for the Midwest by Atack and Bateman (1987). 10 Haines (1977) has found, for instance, a great difference in mortality rates between the foreign and native born from state censuses of New York and Pennsylvania. Pritchett and Tunali (1995) report

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the disadvantage of immigrants are (1) they lacked certain immunities because they came from different disease environments, (2) they were disproportionately concentrated in the most unhealthy and densely populated areas, (3) the experience of immigration produced physical or emotional stress, and (4) they suffered malnutrition more frequently (Higgs, 1979, p. 387). Higher standards of living, indicated by higher levels of household wealth, might reduce mortality through better nutrition and housing. In a cross section, a father’s earnings displayed a powerful effect on infant mortality rate in the turn-of-the-century United States (Preston and Haines, 1991, pp. 43–44), and housing ownership was an important determinant of tuberculosis and pneumonia death rates in Philadelphia between 1870 and 1930 (Condran and Cheney, 1982). However, the effect of economic status upon mortality found by previous studies is weak compared to that of place of residence. Steckel (1988) has even found no systematic association between wealth and mortality of women and children during the period 1850–1860. The superiority in health condition of farmers, rural residents, and natives is reflected in their larger average stature compared to that of nonfarmers, urban dwellers, and nonnatives, respectively. The groups with lower mortality had significantly greater mean heights (Margo and Steckel, 1983; Komlos, 1987; Costa, 1993b).11 A comparison of the mean heights among the recruits of different socioeconomic backgrounds in the Ohio sample provides exactly the same implication. B. Socioeconomic Background, Disease, and Mortality among Army Recruits In this subsection, I calculate the wartime mortality from disease, the mean number of cases per person–year, and the case fatality rates of any disease for recruits in each of the small cells made according to age, occupation, population size of residential place, household wealth, and nativity.12 The mean number of cases of disease per person–year reflects how susceptible recruits of a particular socioeconomic background were to disease, while the case fatality rates indicate how robust they were in resisting the disease that they contracted (Table 2).

that the mortality risk from yellow fever was much greater for immigrants than for natives during the New Orleans epidemic of 1853. 11 A number of studies have suggested empirical evidence for the association between height and health. See, for example, Margo and Steckel (1982), Waaler (1984), Costa (1993a), and Fogel (1994). For features of height, health, and mortality of U.S. slaves and free blacks see Steckel (1979a,b, 1986) and Komlos (1992, 1994). 12 The definitions of these figures are as follows: The mean number of cases per person–year of disease k for group j is (opCASEpjk 4 Sp SERVICEpj), and the case fatality rate of disease k for group j is (DEATHjk 4 opCASEpjk ), where CASEpjk is the number of cases of disease k that a recruit p in group j suffered, SERVICEpj is the length of service in year (measured in days) for a recruit p in group j, and DEATHjk is the number of the recruits who died from disease k. Occupations of recruits, not their fathers’, and age at the time of enlistment are used. Household wealth includes the real estate and personal property.

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D

C

F

N

D

C

F

N

D

C

Age 20–24 F

N

D

C

Age 25–29 F

N

D

C

Age 30 and older

359 142 161 144 103

52.9 63.4 55.9 55.6 19.4

1.40 1.34 1.57 1.51 1.34

20.1 26.5 22.2 21.9 9.3

388 92.8 1.55 30.4 183 60.1 1.67 20.3 253 134.4 1.42 49.9 401 109.7 1.53 40.8 266 109.0 1.55 40.9 77 103.9 1.36 37.9 19 105.3 1.24 57.1 48 104.2 1.64 33.8 53 75.5 1.91 28.0 38 0.0 1.44 0.0

132 98.5 1.42 34.5 25 40.0 1.96 10.8 78 128.2 1.42 41.7 157 133.8 1.51 48.2 96 72.5 1.85 22.3 87 22 39 47 41

34.5 45.5 51.3 63.8 48.8

1.42 1.09 1.48 1.28 1.22

11.5 17.5 18.0 23.2 24.1

115 78.3 1.67 22.3 34 58.8 1.28 18.7 70 114.3 1.24 45.2 140 85.7 1.50 29.3 119 142.9 1.40 55.6

60 34 23 19 14

0.0 0.0 0.0 52.6 0.0

24.5 34.2 31.3 77.8 43.5

39.7 35.0 76.3 29.8 21.4 42.6 40.9 41.5 40.0 26.2 26.8 26.1

F

59.3 89.6 39.2 0.0 0.0

1.39 1.65 1.74 1.18 1.09

25.8 33.9 18.3 0.0 0.0

73 109.6 1.24 50.6 79 38.0 1.89 15.2 67 194.0 1.66 76.9 65 61.5 1.64 28.0 20 100.0 0.94 100.0 1.46 0.0 135 1.08 0.0 67 1.24 0.0 51 1.70 19.2 25 1.67 0.0 10

68 88.2 1.89 45 111.1 1.64 38 78.9 1.37 39 179.5 1.56 31 96.8 1.41

2658 102.0 1.50 36.8 855 112.3 1.57 37.5 870 95.4 1.38 33.8 400 102.5 1.49 15.0 533 95.7 1.62 2122 54.7 1.24 24.4 505 55.4 1.29 23.7 577 50.3 1.16 21.5 414 33.8 1.22 37.6 626 71.9 1.28 372 61.8 1.07 32.1 89 44.9 1.03 26.8 122 73.8 1.22 31.5 64 0.0 1.37 0.0 97 103.1 0.74 2031 88.1 1.57 31.1 634 105.7 1.61 34.7 594 85.9 1.47 28.7 307 81.4 1.57 29.6 496 72.6 1.70 676 59.2 1.59 21.0 187 90.9 1.71 30.1 168 59.5 1.39 19.4 104 9.6 1.52 3.8 217 55.3 1.75 235 29.8 0.93 19.0 48 41.7 0.73 36.4 69 14.5 1.19 7.3 46 0.0 1.06 0.0 72 55.6 0.74 4074 87.0 1.45 32.9 1257 94.7 1.49 34.0 1289 81.5 1.35 30.2 668 73.4 1.51 27.1 860 94.2 1.54 2510 105.6 1.52 37.8 818 114.9 1.56 38.5 830 100.0 1.40 35.3 383 104.4 1.53 37.6 479 100.2 1.68 1564 56.8 1.35 23.7 439 56.9 1.36 23.6 459 47.9 1.25 19.6 285 31.6 1.49 12.1 381 86.6 1.37 706 46.9 0.99 24.4 103 48.5 1.17 20.6 158 44.3 0.91 21.3 146 41.1 0.72 27.9 299 50.2 1.15 148 40.8 1.20 16.8 37 54.1 1.67 16.9 40 0.0 1.01 0.0 17 58.8 0.75 40.0 54 55.6 1.18 558 48.5 0.94 27.1 66 45.5 0.91 24.0 118 59.3 0.87 31.0 129 38.8 0.71 26.3 245 49.0 1.14

N

Age 19 and younger

Note. N, number of recruits; D, number of deaths per 1000 men; C, number of cases per person-year; F, number of deaths per 1000 cases.

Farmer Nonfarmer Urban county Rural county Rural nonfarmer Urban nonfarmer U.S. born Farmer Nonfarmer Foreign born Farmer Nonfarmer Farmer Wealth $0–100 Wealth $101–500 Wealth $501–1500 Wealth $1501–5000 Wealth $50011 Nonfarmer Wealth $0–100 Wealth $101–500 Wealth $501–1500 Wealth $1501–5000 Wealth $50011

Category

All ages

TABLE 2 Socioeconomic Background and Wartime Mortality from Any Disease

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Occupation, place of residence, nativity, and wartime mortality. The pattern of mortality differentials in the army was nearly the opposite of the pattern found among civilians. On average, former farmers had about a 50% higher case fatality rate and about 20% more cases of disease per one year of service than nonfarmers. As a consequence of higher susceptibility and case fatality rate combined, farmers were twice as likely as nonfarmers to be killed by disease while in service. The nativity composition and the size of household wealth were quite different between farmers and nonfarmers. Nonnatives were overrepresented among nonfarmers, and farmers were wealthier than nonfarmers on average. Hence, it is necessary to control for these factors to identify the pure effect of occupation. However, such control does not change significantly the pattern of mortality differentials between farmers and nonfarmers suggested above. Farmers had markedly higher case fatality rates in all five categories of household wealth, and greater wartime mortality from disease for four of the five wealth categories. The results for natives, who account for about 85% of the sample, are similar to that for all recruits.13 To examine the effect of population size of place of residence, I divide the sample into rural and urban residents. I include in urban areas any county in which there was a large city with 10,000 or more residents in 1860.14 Since urban diseases were frequently transmitted to the neighboring countryside through trade and the rotation of labor between urban areas and the surrounding countryside, it would be reasonable to include in urban areas the small towns that were located close to large cities. A comparison of the recruits from rural and urban areas indicates that rural residents were considerably more susceptible than urban residents to diseases, but their chances of dying from the disease they contracted were about the same as those of urban residents.15 13 The only exception is found among the foreign born, for whom the case fatality rate was remarkably higher, and the average number of cases per person–year was smaller for farmers than for nonfarmers. This exceptional pattern of the nonnatives could be attributable to the small sample size of the foreign born or some particular characteristics of the foreign-born farmers who were very rare (only 3% of the sample). The advantage of nonfarmers over farmers remains true when place of birth, place of residence, age, household wealth, and height are controlled together (see Appendix). For this analysis, recruits who were born in Ohio and lived in rural counties (67% of the sample linked to the 1860 census) are classified into 72 categories according to age, household wealth, height, and occupation. Wartime mortality from any disease was greater for farmers than for nonfarmers in 26 of the 36 age–wealth–height cells. Disease mortality was zero for both farmers and nonfarmers in four categories. 14 The cities with 10,000 or more inhabitants in Ohio as of 1860 are Cincinnati, Cleveland, Columbus, Mill Creek, Toledo, and Dayton. Urban counties in 1860 include Hamilton, Cuyahoga, Franklin, Lucas, and Montgomery. The U.S. Bureau of the Census (1864) is used as the source. Changes in the definition of urban areas, for example, inclusion in urban areas of towns with 5000 or more residents, do not change the result significantly. 15 The effect of the population density of a county on wartime mortality is also examined. Recruits are classified into five groups of an equal size according to the population density of the county measured by the number of inhabitants per acre. The mortality from disease while in service was significantly lower for persons in the group of highest population density. No clear tendency is found

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Since most of the farmers lived in rural areas, the effects of occupation and population size can be distinguished by comparing farmers, rural nonfarmers, and urban nonfarmers in terms of wartime mortality. Farmers were more than three times as likely to die from disease in service as urban nonfarmers. These differences reflect the combined effects of occupation and place of residence. The difference in the number of cases is fully accounted for by the effect of population size, while the difference in the case fatality rate is largely accounted for by the effect of occupation. This outcome seems to indicate that the adverse effects of farming and of living in rural areas could be different in nature: the former through weakened resistance to disease, and the latter through increased susceptibility to disease. Native recruits were more likely than nonnatives to die in service. Among farmers, natives suffered slightly more disease per year of service and had a more than 200% higher case fatality rate than nonnatives did. The difference between the foreign and native born is less visible for nonfarmers. Though the pattern of mortality differentials varies considerably across age groups, the above result is generally true for each age group. A notable exception is that the mortality rate was substantially lower for farmers than for nonfarmers in two age–wealth categories (household wealth 101–500 dollars; ages 20–24 or 30 and over). Why were individuals who had lived in healthier environments and who presumably had better initial health conditions prior to enlistment more vulnerable to disease while in service? A possible explanation is that farmers, rural residents, and natives had been less exposed to disease and thus had poorer immunity against disease compared to nonfarmers, urban residents, and nonnatives living in more unhealthy environments. This ‘‘immunity hypothesis’’ is not new to this study. A number of studies have noted the fragility of isolated populations once they come in contact with different disease pools (McNeill, 1976; Curtin, 1989; Pritchett and Tunali, 1995). Indeed, the difference in the development of immunity is the only potential link by which a better living environment prior to enlistment could become a source of disadvantage in fighting disease while in service. Since diseases were more prevalent in urban areas than in rural areas, those from urban populations would have better chances to develop immunity against diseases if they survived them. The advantage of farmers over rural nonfarmers could be explained by the fact that they were more isolated from other people compared to nonfarmers who were more likely to reside in towns. The advantage of nonnatives over natives could be attributable to the circumstance that they were more likely to be confined to among the other four groups. Since 40% of the urban residents in the sample lived in Hamilton County including Cincinnati, it is possible that the ‘‘urban’’ effect is an Ohio River disease effect. However, wartime mortality from any disease for those who lived in Hamilton County (5.5%) was similar to that for the urban residents in northern Ohio (6.6%). On the other hand, the disease mortality rate was much higher (12.0%) for the residents in the counties adjacent to the Ohio River, suggesting that the urban–rural difference does not reflect an Ohio River disease effect.

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unhealthy environments because of immigration and poor nutrition due to poverty. For instance, an immigrant could have suffered from the overcrowding, bad ventilation, and spoiled foods of ship cabins on the voyage from Europe to America. Moreover, most of the immigrants came to and stayed at first in large cities in the Northeast. Internal migration to Ohio could have also been a harsh experience.16 Household wealth and wartime mortality. The household wealth of recruits prior to enlistment appears to have had no clear effect on the likelihood of contracting diseases or the risk of dying from those diseases. Among farmers, recruits who belonged to the lower 40% of the household wealth distribution experienced lower case fatality rates; among nonfarmers, the recruits at the top quintile had a substantial advantage in terms of the case fatality rate. The mean number of cases per person–year varied little across occupation–wealth cells. In the mid-nineteenth century, the most important link between wealth and health should have been quality of diet or nutrition. Based on the above result, one would find it tempting to argue that the nutritional status of recruits prior to enlistment had no effect on their susceptibility to disease during military service. However, it should be noted that the effect of nutritional status does not apply to all kinds of diseases: on some diseases its effect is definite and for others, insignificant. Economic status might have a significant effect only on some diseases which are sensitive to nutritional status, not on any disease. IV. IMMUNITY, NUTRITION, AND WARTIME MORTALITY A. Patterns of Disease-Specific Mortality The preceding subsection suggests two hypotheses with regard to the effects of immunity and nutritional status. An examination of cause-specific wartime mortality differentials provides a test of these hypotheses. First, it is hypothesized that individuals who had lived in healthier environments prior to enlistment were more vulnerable to disease while in service probably because they had a poorer immunity status. According to epidemiological studies, the significance of immunity influence differs from one disease to another. For some diseases, such as measles, smallpox, and typhoid, an attack would confer immunity and thus reduce the odds of contracting or dying from those diseases in the future (these types of disease will be called immunity diseases below). For other diseases like malaria, diarrhea, dysentery, and pneumonia, a prior contraction has little influence on susceptibility to or resistance against those diseases (these types will be called nonimmunity diseases).17 If the immunity hypothesis suggested above is true, the

16 According to the ‘‘insult accumulation model,’’ each insult from illness or injury leaves the individual more susceptible to disease in the future (Alter and Riley, 1989). The wartime medical experiences of Union army recruits found in this study provide a counter-example of this hypothesis. 17 For the epidemiological characteristics of these and other diseases see May (1958), Steiner (1968, pp. 12–26), and Kunitz (1983, pp. 351–353).

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difference in mortality between recruits who had come from different environments should be larger for immunity diseases than for nonimmunity diseases. Second, it is suspected that some positive effect of the better nutritional status of wealthier individuals could be concealed under the aggregated figures because wealth might have a significant effect only for diseases which are sensitive to nutritional status. Some examples of disease for which the effect of nutritional status on case fatality is definite (nutrition-influenced diseases) are measles, diarrhea, tuberculosis, most respiratory infections, pertussis, cholera, leprosy, and herpes; the diseases on which nutritional influence is minimal (nonnutritional diseases) are smallpox, malaria, plague, typhoid, tetanus, yellow fever, encephalitis, and poliomyelitis (Journal of Interdisciplinary History, 1983, p. 506). Therefore, if the above conjecture is correct, wealth should be negatively associated with the odds of dying from nutrition-influenced diseases. I focus here on the six diseases which were most common among the Union army recruits and which can be clearly classified according to the effect of nutrition or immunization: diarrhea or dysentery, pneumonia, measles, typhoid, malaria, and smallpox or variola.18 These diseases account for about 67% of the causes of wartime death from illness and about 44% of the total number of cases of any illness in the sample. Testing the immunity hypothesis. The observed pattern of the cause-specific mortality differentials turns out to be consistent with the immunity hypothesis given above. That is, the disadvantages of farmers, rural residents, and natives were considerably larger for immunity diseases than for nonimmunity diseases (see Table 3). For example, the ratio of the wartime mortality rate of farmers to that of nonfarmers is about 1.5 for nonimmunity diseases while it is about 3.0 for immunity diseases. The results for native recruits and 12 age–wealth categories are similar. Each of the six diseases closely follows this pattern.19 A comparison between urban and rural residents, not presented here, reveals similar patterns: wartime mortality from immunity diseases was much greater for rural residents (2.9%) than for recruits from urban counties (0.3%), while no difference is found between the two groups in mortality from nonimmunity diseases (3% for both groups). The advantage of nonnatives over natives was much greater for immunity diseases than for nonimmunity diseases for the entire

18 Malaria includes intermittent and remittent fevers; typhoid includes typho-malaria and continuous fevers. 19 It is unclear why farmers were disadvantaged even in nonimmunity type diseases for which their superior nutritional status should have provided an advantage. A possibility is that people who lived in an unhealthy environment could have been more aware of how to avoid contracting disease than those with little experience of disease. According to a qualitative record, for example, German regiments in the Union army were healthier than American regiments because the Germans ate fewer sweets, cooked their food more carefully, and more actively pursued cleanliness (Hess, 1981, pp. 66–67). A number of contemporary accounts in the late nineteenth and early twentieth centuries single out rural residents and farmers as particularly unhygienic and ignorant of child health (Preston and Haines, 1991, pp. 38–39).

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A. Immunity disease Farmer Nonfarmer U.S. born Farmer Nonfarmer Foreign born Farmer Nonfarmer Wealth $0–150 Farmer Nonfarmer Wealth $151–2100 Farmer Nonfarmer Wealth $21001 Farmer Nonfarmer Typhoid Farmer Nonfarmer Smallpox or variola Farmer Nonfarmer Measles Farmer Nonfarmer

Category

C

F

N

D

C

F

N

D

C

F

N

D

C

F

0.0 0.03 4.1 0.02

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kris 73.9 855 75.5 505

9.4 0.06 7.9 0.04

79.2 870 117.6 572

6.9 0.03 98.4 400 1.7 0.01 166.7 414

2.5 0.04 0.0 0.01

35.7 533 0.0 626

0.0 0.02 0.0 0.01

3.8 0.01 0.0 0.00

0.0 0.0

6.0 0.04 2.6 0.02

2.5 0.01 111.1 533 7.5 0.01 500.0 626

2658 2122

4.6 0.01 160.0 400 1.7 0.01 83.3 414

333.0 0.0

7.0 0.01 428.6 870 2.0 0.01 200.0 572

5.2 0.01 245.0 855 2.6 0.01 200.0 505

2658 2122

69 29.0 0.02 1000.0 24 0.0 0.07 0.0 344.8 210.5

62 80.6 0.15 357.1 24 0.0 0.03 0.0

222.2 250.0

125.0 166.7

2658 25.1 0.04 366.3 855 22.2 0.03 365.4 870 25.3 0.04 297.3 400 30.0 0.04 375.0 533 18.8 0.04 2122 7.1 0.03 146.7 505 7.9 0.03 166.7 572 8.7 0.02 192.3 414 2.4 0.02 62.5 626 6.4 0.02

593 40.5 0.10 226.4 225 35.6 0.10 205.1 237 38.0 0.11 176.4 205 14.6 0.06 157.9 79 12.7 0.07 125.0 78 25.6 0.05 285.7

119 25.2 0.06 200.0 69 0.0 0.04 0.0

86 23.3 0.08 153.8 148 27.0 0.09 59 0.0 0.03 0.0 120 8.3 0.02

0.0 0.05 0.0 17 0.0 0.09 0.0 54 8.5 0.02 250.0 129 15.5 0.02 400.0 245

481 29.1 0.08 202.9 128 39.1 0.09 217.4 335 9.2 0.04 125.0 77 26.0 0.08 181.8

40 118

73 41.1 0.17 125.0 87 11.5 0.05 67 0.0 0.03 0.0 144 13.9 0.05

37 0.0 0.10 0.0 66 15.2 0.02 333.3

417 31.2 0.11 151.1 135 37.0 0.11 166.7 122 32.8 0.09 166.7 379 10.6 0.05 121.1 79 25.3 0.08 166.7 89 0.0 0.03 0.0

0.0 0.06 0.0 9.0 0.02 263.1

0.0 142.9

D

148 558

N

Age 30 and older

260.1 136.4

F

Age 25–29

2510 36.3 0.09 213.1 818 40.3 0.10 206.3 830 38.6 0.09 206.5 383 36.6 0.09 212.1 479 25.1 0.07 1564 12.1 0.05 128.4 439 18.2 0.08 133.3 459 13.1 0.04 150.0 285 7.0 0.05 76.9 381 7.9 0.04

C

Age 20–24

244.6 137.9

D

Age 19 and younger

2658 36.3 0.09 204.5 855 38.6 0.10 197.6 870 36.8 0.09 200.0 400 35.0 0.09 202.9 533 22.5 0.06 2122 12.3 0.04 143.7 505 17.8 0.07 142.9 577 12.1 0.04 159.1 414 9.7 0.04 129.0 626 6.4 0.03

N

All ages

TABLE 3 Occupation and Disease-Specific Wartime Mortality

38 CHULHEE LEE

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14.3 31.0 41.3 135 37.0 0.47 32.2 79 63.3 0.68

148 13.5 0.47 558 16.1 0.27 417 40.8 0.50 379 29.0 0.48

2658 2122

0.8 0.17 0.6 0.16

2.5 855 2.3 505

119 42.0 0.51 40.0 69 29.0 0.50 27.8

1.2 0.20 0.0 0.19

62 16.1 0.56 19.6 24 0.0 0.42 0.0

69 29.0 0.39 24 0.0 0.28

86 23.3 0.65 18.7 148 27.0 0.49 59 0.0 0.34 0.0 120 33.3 0.53

3.1 870 0.0 577

0.0 0.17 0.0 0.13

0.0 400 0.0 414

3.4 0.03 60.0 400 1.7 0.01 58.8 414

2.5 0.12 0.0 0.12

11.2 533 0.0 626

2.5 0.04 34.5 533 2.4 0.03 52.6 626

64.5 78.4

58.8 0.0

39.2 47.1

0.0 0.18 1.6 0.11

0.0 10.8

7.5 0.05 102.6 6.4 0.03 137.9

36.7 870 24.1 0.32 37.0 400 25.0 0.36 43.9 533 26.3 0.28 22.9 577 15.6 0.24 32.4 414 9.7 0.30 17.5 626 25.6 0.20

18.5 237 33.8 0.52 33.9 11.8 78 12.8 0.45 15.2

36.6 20.6

27.0 23.4

47.6 85.3

45.7 61.9

73 41.1 0.52 41.1 87 69.0 0.41 100.0 67 0.0 0.54 0.0 144 20.8 0.33 38.0

40 0.0 0.54 0.0 17 0.0 0.21 0.0 54 18.5 0.39 118 25.4 0.27 43.5 129 15.5 0.18 41.7 245 12.2 0.32

39.4 122 24.6 0.59 19.7 46.7 89 33.7 0.48 32.3

14.3 31.0

33.5 830 28.9 0.51 28.0 383 31.3 0.54 32.1 479 35.5 0.52 28.5 459 15.3 0.42 18.6 285 10.5 0.58 10.3 381 47.2 0.35

33.5 870 26.3 0.51 27.6 400 30.0 0.52 31.5 533 33.8 0.50 22.0 577 17.3 0.38 22.4 414 12.1 0.44 14.7 626 33.5 0.34

11.7 0.04 166.7 870 7.9 0.02 181.8 577

41.7 855 25.7 0.37 34.6 505 13.9 0.33

2658 25.9 0.34 2122 18.6 0.31 7.2 0.04 105.9 855 5.1 0.02 133.3 505

27.1 225 22.2 0.67 11.3 79 12.7 0.74

593 27.0 0.57 205 9.8 0.53

2658 2122

34.3 128 54.7 0.71 27.7 77 26.0 0.73

481 37.4 0.60 325 24.6 0.53

37 13.5 0.58 66 16.1 0.33

33.5 818 33.9 0.61 28.5 439 24.3 0.58

32.5 855 38.6 0.81 28.9 505 21.8 0.55

2510 33.9 0.55 1564 24.3 0.48

2658 33.9 0.54 2122 24.3 0.42

Note. N, number of recruits; D, number of deaths per 1000 men; C, number of cases per person-year; F, number of deaths per 1000 cases.

B. Nonimmunity disease Farmer Nonfarmer U.S. born Farmer Nonfarmer Foreign born Farmer Nonfarmer Wealth $0–150 Farmer Nonfarmer Wealth $151–2100 Farmer Nonfarmer Wealth $21001 Farmer Nonfarmer Diarrhea or dysentery Farmer Nonfarmer Pneumonia Farmer Nonfarmer Malaria Farmer Nonfarmer

SOCIOECONOMIC BACKGROUND AND MORTALITY

39

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sample and for farmers (Table 3). When nonfarmers are considered, however, such a pattern is not observed.20 Testing the nutrition hypothesis. The result of distinguishing the types of disease according to the degree of sensitivity to nutritional status appears to support the conjecture that wealth would have affected only nutrition-influenced diseases (Table 4). For nonnutritional diseases the effect of household wealth on the odds of dying is indefinite or positive: no clear pattern for nonfarmers and a better chance of survival for poor farmers are found. For nutrition-influenced diseases, on the other hand, the size of household wealth is negatively associated with the odds of dying while in service. For farmers, the mortality rate of individuals in the upper wealth category (over 2100 dollars) was about half of that of the lower category (150 dollars or less); for nonfarmers, it was about a third. When age is controlled for, the association between wealth and mortality from nutrition-influenced disease becomes weaker. However, the advantage of the wealthy over the poor still remains apparent. Even though it is beyond the scope of this paper, we might find a potential link between previous nutritional status and current degree of resistance to nutritioninfluenced diseases. When the size of claims against nutrition exceeds the amount of nutrition supplied, the human body starts to consume its own cells. In this sense, the human body itself is a reservoir of nutrition. Hence, a better-nourished person, owing to his higher weight, would be superior to a poorly nourished person in resisting nutrition-influenced diseases. B. Time Pattern of Wartime Mortality This subsection provides another test of the immunity and nutrition hypotheses by examining the time pattern of wartime mortality. For the purpose of this test, the hazard rate of dying from disease is calculated for each of the 4-month intervals from enlistment.21 The hazard rate for the 4th to 8th month, for example, shows what proportion of the recruits remaining alive in service at the beginning of the 4th month died from any illness or some specific type of disease within the following 4 months. If a recruit died from any cause or was discharged alive between the 4th and 8th months, he is removed from the population at risk when the hazard rate of the next time interval (the 8th to 12th month) is calculated (Table 5). Testing the immunity hypothesis. It is documented in the medical histories of the Civil War that the earlier seasoning period in the army was most critical for the survival of recruits. During this period enlistees with limited prior develop20 Wartime mortality rates from immunity and nonimmunity diseases are also calculated for 72 categories according to age, household wealth, height, and occupation for rural residents who were born in Ohio (see Appendix). The advantage of nonfarmers over farmers, as measured by the difference in the mortality rate between the two groups, is greater for immunity disease than for nonimmunity disease for 16 of 36 categories. The opposite is true for 8 cells. For the remaining 12 categories the mortality differential is the same for the two disease types. 21 Hazard rates are also calculated for 2-month and 6-month intervals. Though choosing longer time intervals makes the hazard functions more continuous, they provide the same implications.

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N

D

C

F

N

D

C

89 33.7 0.32 69 29.0 0.31 78 12.8 0.29

N

D

C

48.4 67 44.4 59 23.3 24

0.0 0.44 0.0 0.23 0.0 0.33

51.0 73 54.8 0.53 51.0 86 11.6 0.51 46.8 62 16.1 0.38

F

Age 25–29 N

D

C

F

0.0 144 20.8 0.23 0.0 120 25.0 0.32 0.0 24 0.0 0.21

54.4 57.7 0.0

53.3 87 69.0 0.25 162.1 11.9 148 27.0 0.37 59.9 28.6 69 29.0 0.25 90.9

F

Age 30 and older

379 13.2 0.19 37.9 325 12.3 0.28 26.3 205 19.5 0.22 54.1

79 25.3 0.18 77 26.0 0.45 79 25.3 0.26

71.4 33.3 66.7

89 0.0 0.24 69 0.0 0.25 78 25.6 0.21

0.0 67 0.0 59 64.5 24

0.0 0.17 0.0 0.16 0.0 0.18

0.0 144 20.8 0.16 0.0 120 16.7 0.25 0.0 24 0.0 0.16

78.9 50.5 0.0

417 21.6 0.25 44.1 135 14.8 0.24 31.3 122 32.8 0.31 87.0 73 27.4 0.17 83.3 87 11.5 0.24 27.8 481 37.4 0.22 91.4 128 46.9 0.22 103.4 119 33.6 0.21 111.1 86 46.5 0.26 95.2 148 27.0 0.22 87.0 593 47.2 0.28 95.2 225 48.9 0.32 85.9 237 42.2 0.27 322.6 62 80.6 0.34 161.3 69 29.0 0.16 142.9

79 75.9 0.63 77 26.0 0.38 79 12.7 0.58

60.6 39.2 15.2

F

379 31.7 0.37 44.8 325 21.5 0.32 40.7 205 9.8 0.38 15.5

C

81.6 122 41.0 0.38 49.4 119 42.0 0.40 25.8 237 33.8 0.38

D

Age 20–24

417 55.2 0.38 74.7 135 59.3 0.36 481 37.4 0.47 42.8 128 62.5 0.61 593 27.0 0.41 37.9 225 22.5 0.48

N

Age 19 and younger

Note. N, number of recruits; D, number of deaths per 1000 men; C, number of cases per person-year; F, number of deaths per 1000 cases.

A. Nutrition-influenced diseases Farmer Wealth $0–150 Wealth $151–2100 Wealth $21011 Nonfarmer Wealth $0–150 Wealth $151–2100 Wealth $21011 B. Nonnutritional diseases Farmer Wealth $0–150 Wealth $151–2100 Wealth $21011 Nonfarmer Wealth $0–150 Wealth $151–2100 Wealth $21011

Category

All ages

TABLE 4 Household Wealth and Disease-Specific Wartime Mortality SOCIOECONOMIC BACKGROUND AND MORTALITY

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TABLE 5 Hazard Rates of Dying from Disease: Number of Deaths per 1000 Men within 4-Month Intervals (1) Any illness

(2) Immunity

(3) Nonimmunity

(4) Nutritional

(5) Nonnutritional

Months in military service

Farm

NF

Farm

NF

Farm

NF

Rich

Poor

Rich

Poor

0–4 4–8 8–12 12–16 16–20 20–24 24–28 28–32 32–36

16.2 28.0 21.0 7.4 13.3 22.8 14.0 11.7 7.3

6.0 13.4 7.8 2.8 6.0 3.9 4.0 3.5 1.4

7.5 14.2 8.2 1.2 4.9 4.7 0.9 2.0 0.0

2.3 4.6 0.5 0.0 1.8 0.6 0.0 0.7 0.9

3.0 6.9 4.6 4.9 2.1 12.6 9.6 5.9 4.2

1.9 4.6 4.4 1.1 2.4 1.3 0.7 2.1 1.4

5.8 1.9 3.7 3.0 1.6 12.9 14.3 6.8 2.4

9.8 11.8 8.7 0.0 2.9 5.0 1.8 6.0 6.2

5.8 13.0 11.0 0.0 9.9 9.2 2.0 6.8 0.0

3.3 6.3 3.3 1.3 5.9 1.7 1.8 0.0 0.0

Note. The number of recruits who died from a particular type of disease within each 4-month interval was divided by the number of recruits who remained alive in service at the beginning of the time interval and then was multiplied by 1000. If a recruit died from any cause while in service or was discharged alive he was removed from the pool of population at risk. NF refers to nonfarmers. For the classification of disease see text.

ment of immunity were exposed to a pool of various infectious diseases in the army.22 If the differences in wartime mortality between farmers and nonfarmers and between rural and urban residents were mainly caused by the difference in immunity status, most of the differences should have occurred in the early stages of military service when the recruits were not seasoned to the severe disease environment of the army camps. This conjecture seems to be supported by the time pattern of wartime mortality. In general, hazard rates of dying from any disease or immunity disease were higher during the first 10–12 months in service than in subsequent periods, indicating that wartime deaths from disease were heavily concentrated in the early period of service for all recruits regardless of their socioeconomic backgrounds (column 1 of Table 5). This result is consistent with the remarks on the seasoning period in medical histories of the Civil War.23 Moreover, during this early period a 22 See Steiner (1968). Higher mortality rates during the seasoning period were also observed among slaves, indentured servants, and free migrants who had just arrived in the New World. 23 The actual length of time needed for seasoning could be much shorter than 10–12 months for the following reasons. First, organizing the regiment required some time. The information on the beginning and ending dates of organization contained in regimental histories suggests that it should be a month on average. Hence, recruits should have started to be exposed to risks sometime after they enlisted. Second, recruits started to die sometime after they contracted a disease: Recruits who died in service from illness died about 2 months after the initial hospitalization on average. If we consider the time lag between contraction and hospitalization, the average interval between getting a disease and death should be longer than 2 months. Finally, seasonality in the disease environment should be considered. For those who enlisted in autumn or winter, the seasoning period would have started from the next spring for some diseases which prevail during the summer. All in all, the seasoning period seems to be

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FIG. 1. Hazard functions of dying from immunity disease: Mortality from immunity diseases of those remaining alive in service within each 4-month interval.

disproportionately large part of the differences in wartime mortality from disease were observed. For instance, the difference between farmers and nonfarmers in the mortality from immunity diseases was much greater during the first year in service than later (Fig. 1). For nonimmunity diseases, on the other hand, no clear time trend in the hazard rate is found among either farmers or nonfarmers (column 3 of Table 5). A larger part of the mortality differentials between rural and urban residents is also found within the first year in service. This result seems consistent with the hypothesis that the difference in immunity status contributed a great deal to the wartime mortality differentials.24 Testing the nutrition hypothesis. The effect of different nutritional status prior to enlistment, if any, should have been stronger in the earlier period in military service. Initially better-nourished recruits would eventually lose their advantage as they continued to face a poorer diet and the fight against disease in the army. Therefore, if wealthier recruits were more robust in resisting nutrition-influenced diseases because they had initially a better nutritional status, the difference in mortality from nutrition-influenced diseases between the wealthy and the poor should be observed in the earlier period in service. Indeed, this was the case. The wealthy enjoyed a lower risk of dying from nutrition-influenced diseases only the first 6 or 7 months after a recruit started to be exposed to the disease environment in the army. 24 The time pattern of contracting diseases would also be useful in testing the immunity hypothesis. Unfortunately, the exact dates when recruits got diseases are not available in the data. However, dates of admittance to the hospital are recorded in the carded medical records. Although not all recruits suffering from disease were hospitalized and the time of contracting a disease was not identical to that of hospitalization, one would have a rough idea about the time pattern of contracting disease from the date of first hospitalization. Graphs of the survival functions of first hospitalization, which show the proportion of recruits who had not been hospitalized at each point of time, suggest that the initial hospitalizations of recruits were heavily concentrated in the first 8 months. Almost the entire difference in the hospitalization rate between farmers and nonfarmers was made during this period. This result also looks consistent with the immunity hypothesis.

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FIG. 2. Hazard functions of dying from nutrition-influenced disease: Mortality from nutritioninfluenced diseases of those remaining alive in service within each 4-month interval.

during the first year in military service, indicating that the wealthy had advantages over the poor only in the earlier period (Fig. 2). On the other hand, the wealthy were at greater risk of dying from nonnutritional diseases compared with the poor throughout the whole period in military service (column 5 of Table 5). C. Regression Analysis In comparisons of the wartime mortality among recruits from different socioeconomic backgrounds, not all factors are considered at the same time in order to avoid making the cell size too small.25 Logistic regressions are conducted to examine the effect of each of the socioeconomic factors on the probability of contracting and dying from a particular type of disease while in service with other factors held constant. The recruits in the sample are classified into three groups according to occupation and place of residence: farmers (control group), rural nonfarmers, and urban nonfarmers. They are also divided into three categories according to place of birth: natives who were born in Ohio (control group), natives who were born in other states, and nonnatives. A dummy variable of living in counties adjacent to the Ohio River is also added to examine a potential disease effect of the river. In addition, age, height, log of household wealth, and the year of enlistment are included as independent variables. The results are presented in Table 6. The outcome is well matched with the pattern of the mortality differentials reported above: Nonfarmers were significantly less likely than farmers to contract and die from disease while in service, and the advantage of nonfarmers over farmers was much greater for recruits from an urban county than for those from a rural county, controlling for other factors (columns 1–6 of Table 6). The probabil25 For example, there are only 235 urban nonfarmers in the sample. If this group is further divided according to age, wealth, and nativity, the cell size will be too small, particularly for nonnatives. In the Appendix, I control for state of birth, place of residence, age, wealth, occupation, and height together only by selecting a subsample of rural residents who were born in Ohio.

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TABLE 6 Result of Logistic Regressions: Correlates of Probability of Dying from and Contracting Disease (1) Dying from any disease (Mean 5 0.084) Independent variables Intercept Age in years (31021) Height in inches (31021) Log of wealth Dummy variables Rural nonfarmer Urban nonfarmer Born in other U.S. states Foreign born Ohio River counties Enlisted in 1862 Enlisted in 1863 Enlisted in 1864 Enlisted in 1865

Intercept Age in years (31021) Height in inches (31021) Log of wealth Dummy variables Rural nonfarmer Urban nonfarmer Born in other U.S. states Foreign born Ohio River counties Enlisted in 1862 Enlisted in 1863 Enlisted in 1864 Enlisted in 1865

(3) Dying from immunity disease (Mean 5 0.025)

Mean Parameter

≠P/≠Xi

Parameter

≠P/≠Xi

Parameter

≠P/≠Xi

22.384 2.521 20.035 6.818 0.035 5.466 0.017

24.970 20.145 20.055 0.005 0.328 0.032 20.069**

20.023 0.046 20.126

22.233 20.140 20.130 0.034

20.059 20.018 0.062

20.137 20.191 0.009 20.100 0.033 0.096 20.006 20.197 20.040

20.117 20.749** 20.212 212.672 0.025 0.095 20.055 212.075 0.045 0.209 0.116 0.319 0.006 20.619 20.094 20.890** 20.026 20.064

0.277 0.096 0.146 0.088 0.173 0.314 0.025 0.295 0.104

20.555*** 21.164*** 0.045 20.639 0.156 0.374** 20.073 20.786*** 20.235

(4) Contracting any disease (Mean 5 0.706) Independent variables

(2) Dying from nutritioninfluenced disease (Mean 5 0.036)

20.474* 21.294** 0.125 20.348 0.216 0.455 0.066 20.374 20.155

(5) Contracting nutritioninfluenced disease (Mean 5 0.363)

20.185 22.077 0.019 21.892 0.044 0.082 20.052 20.224 20.011

(6) Contracting immunity disease (Mean 5 0.111)

Mean Parameter

≠P/≠Xi

2.351* 2.521 20.054 6.818 20.085 5.466 20.024

0.794 22.417 20.023 20.314*** 20.132 20.412*** 20.174 20.012 20.030 20.004 0.276 0.039 20.044 20.020 20.037 20.022 20.041

0.277 0.096 0.146 0.088 0.173 0.314 0.025 0.295 0.104

20.212* 21.217*** 0.221 20.301* 0.075 20.003 20.343 21.059*** 20.090

20.052 20.199 0.043 20.047 0.016 20.001 20.029 20.266 20.015

Parameter

20.078 21.032*** 0.226* 20.278 20.050 0.038 20.126 20.489*** 20.101

≠P/≠Xi

20.019 20.169 0.044 20.043 20.011 0.010 20.011 20.123 20.017

Parameter

20.580*** 21.376*** 20.047 20.529 0.116 20.024 20.199 20.435*** 20.323

≠P/≠Xi

20.144 20.225 20.009 20.083 0.024 20.006 20.017 20.109 20.055

Note. The number of observations is 2359. Dependent variables are dummy variables which are one if a person died from a particular cause (columns 1–3) or contracting a particular disease (columns 4–6) while in service, and zero otherwise. Dummy variables of being a farmer, born in Ohio, and enlisted in 1861 are omitted. * Significance level 10%. ** Significance level 5%. *** Significance level 1%.

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ity of contracting any disease was significantly lower for the foreign born than for those who were born in Ohio (column 4 of Table 6). No visible effects of height and living in Ohio River counties are observed.26 The regression results for particular types of disease are generally consistent with the immunity and nutrition hypotheses: The advantages of nonfarmers and urban dwellers over farmers and rural dwellers are greater for immunity disease than for any disease. The effect of household wealth is significant only for the odds of dying from nutrition-influenced disease.27 V. WEALTH, ENVIRONMENT, AND HEALTH: IMPLICATIONS FOR ECONOMIC HISTORY Wealth, Income, and Mortality A weak association between wealth and mortality has been a puzzling phenomenon in U.S. economic and demographic history. Steckel (1988) has found that wealth conveyed no systematic advantage for the survival of women or children in households matched in the 1850 and 1860 censuses. As late as 1900, economic status appears to have been a much less important correlate of child mortality than place of residence (Preston and Haines, 1991, pp. 150–158). This has led to discussions of egalitarian patterns of death and of relatively small differences in health by social class, perhaps because the poor were better fed in the United States than in Europe. The negative association between the size of household wealth and the odds of dying from nutrition-influenced diseases found in this study seems to have a significant implication for this issue. As Condran and Crimmins have pointed out, cause-specific mortality rather than mortality from all causes needs to be investigated when one assesses the effects of a particular factor on mortality.28 In the nineteenth century, when there were practically no medical or health care services 26 It is suspected that the recruits who died within the service were a subset of all those who were at risk to die after they became symptomatic, because some veterans could have died after they were discharged from the disease they contracted while in service. For a test, I use the dummy variable of dying in 10 years following enlistment as the dependent variable. The result of this regression is very similar to that of the first regression (column 1 of Table 6), indicating that the result is not a product of a short time horizon. 27 I tried several different specifications including a model which takes into account the fixed effect of each company by controlling for the dummy variable of each company. The rationale for this model is that recruits in a company had a particular common experience in service. I also controlled for rank and duty of a recruit because socioeconomic background systematically affected the assignment of rank and duty. The basic results presented here remain unchanged for these specifications. However, the effect of wealth upon the odds of dying from nutrition-influenced disease misses statistical significance for the fixed effect model by a small margin. Even though the hazard functions of dying from disease while in service indicate that the hazard changed with time, regressions employing proportional hazard models were also performed. The results of these regressions are strikingly similar to those of the logistic regressions presented here. 28 As for the impact of the improvements in the public health system in larger cities around 1900, they have claimed it would be appropriate to study the changes in mortality from diarrheal diseases, typhoid, and tuberculosis, which would be heavily influenced by public health reforms (Condran and Crimmins-Gardner, 1978; Condran and Crimmins, 1980; Condran and Cheney, 1982).

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to be purchased, the most important link between economic status and health should have been the quality of nutrition and housing. Therefore, there could have existed a positive effect of wealth on health at least for some diseases on which nutritional influence is great. A possible reason for the weak association between wealth and mortality from all causes would be that the influence of nonnutritional infectious diseases was so strong that it dominated the effect of economic status. Another related question is ‘‘when and how did socioeconomic conditions begin to exert a strong influence on health?’’ (Steckel, 1988, p. 345). The mortality differential by socioeconomic status increased throughout the early twentieth century, while urban–rural differences in mortality rates had started to decline from the late nineteenth century.29 The advance and diffusion of medical technologies should certainly be a cause of this phenomenon. The increased residential segregation according to economic status in cities may be another factor. This study suggests that improvements in the disease environment would have also produced a stronger relationship between economic status and health by weakening the other random forces unrelated to economic factors. Indeed, a number of studies have pointed out that the positive effect of superior economic status on health or mortality is more clearly revealed under more benign disease environments. According to Kunitz (1983), for example, social class differences in life expectancy emerged for the first time in eighteenth-century Europe after the main cause of death shifted from severe crowd diseases like the plague to relatively benign childhood diseases. In addition, this study shows that the signs of the effects of socioeconomic factors on mortality were greatly different under highly severe disease conditions in the army. This result also implies that shifts in the disease environment could have changed the influence of socioeconomic status on mortality. Occupation, Nativity, and Mortality As mentioned in Section III, several different explanations, such as more isolated residence and better nutrition, have been given as the cause of lower mortality rates or better health status of farmers and natives than of nonfarmers and nonnatives. Although these factors are all plausible causes of mortality differentials, their relative importance remains unclear. The wartime experiences of recruits provide a useful clue to this issue. All recruits in service, regardless of their socioeconomic background prior to enlistment, lived under relatively homogeneous conditions in army camps: similar diet, housing, disease environment, and so on. This uniform living condition could have deprived farmers and natives of the advantageous factors (better diet and living environment) that they had prior to enlistment. However, such losses cannot account for the higher mortality while in service among farmers and 29 According to a study by Preston et al., the degree of inequality in mortality in England rose between 1921–1923 and 1970–1972. Suggested explanations for this phenomenon include the differential adoption by lower classes of deleterious personal habits such as smoking, the elimination of health disadvantages of urban areas, and the decline in agriculture (Preston et al., 1981, p. 252).

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natives found in this study. Individuals who were previously better nourished, for example, have no reason to be worse off than those who had poor diets when current diets are equalized. Therefore, neither the difference in nutrition nor self-selection in occupational choice alone can account for the sharp contrast in the pattern of mortality differentials between civilians and recruits in the army. On the other hand, the differential living environments can explain it. This evidence suggests that a more isolated and healthier place of residence should be a more important, if not the only, source of the advantages in health of farmers and natives over their counterparts. Effects of Migration on Health It is now widely accepted that life expectancy and mean adult height declined through the early nineteenth century in spite of the growth in per capita income. There is still controversy, however, over what produced the potential deterioration in health and how to interpret the finding.30 A possible explanation for the cycle in health is the epidemiological impact of increased geographical mobility. Higher rates of interregional trade and migration may have increased morbidity and mortality by spreading communicable diseases and by exposing newcomers to different disease environments. The rise of public schools and changes in labor organization could have exerted a similar effect by increasing the risk of exposure to infectious diseases (Steckel, 1995, pp. 1929–1930). The present study is relevant to the above hypothesis, particularly to the adverse effect of rural–urban migration on health. In some respect, the experiences of a newly enlisted recruit in the army could be compared to those of a new in-migrant to a large city. Both of them left the places to which they were accustomed and arrived in a much more unhealthy place where population density was very high, housing and sanitary conditions were poor, and various infectious diseases were more prevalent compared to a rural civilian society. Urbanization was not merely an extension of unhealthy and densely populated areas but a process of putting rural residents into a new environment to which they had a poor resistance.31 In light of 30 The possible causes of the rise in mortality rates and the decline in stature suggested so far are rapid urbanization and a decline in the proportion of population employed in agriculture, increased geographic mobility, more rapid increases in population than in food supply, a rise in the relative food price, an increase in inequality in the distribution of income, and the turbulence of the Civil War (Rosenberg, 1962; Steckel, 1983, 1995; Fogel, 1986, 1991; Komlos, 1987; Floud, Wachter, and Gregory, 1990; Cuff, 1992; Costa, 1993b; Gallman, 1995, 1996). 31 This point is relevant for the debate over the natural population decrease in early modern cities. The conventional belief is that early modern cities would have declined in population without in-migration because of high mortality (Wrigley, 1969). Sharlin (1978) has challenged this view, noting that in-migration itself was a primary cause of the natural decrease in the urban populations. He asserts that ‘‘the temporary migrants substantially increased the urban population subject to the risk of death, but they had little effect on the size of the population actually having children’’ (Sharlin, 1978, p. 127). A criticism of this new view is that nuptiality and fertility behavior should have been a minor factor of preindustrial urban demography in comparison to urban mortality (Vries, 1984, pp. 192–197). An urban seasoning effect on mortality could be an important addition to Sharlin’s argument that the migrants to cities contributed many more deaths than births.

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the result of this study, that recruits with poor immunity status were more vulnerable to disease despite their superior health condition, it is suspected that the adverse effect of urbanization could have been aggravated by the effect of poor immunity status of new migrants to cities during the period of rapid urbanization.32 Causes of the Decline in Urban Mortality Rates and Rural–Urban Mortality Gap Mortality rates started to decline again after the Civil War, following the upward trend through the early nineteenth century (Pope, 1992), except for the black population, whose mortality rate rose after the war. Even though a consensus has been reached that the elimination of chronic malnutrition, advances in public health, improvements in housing, sanitation, and food hygiene, and advances in medical technology were important factors which contributed to the decline in mortality, the relative importance of those factors is still under debate (Higgs, 1973, 1979; Appleby, 1975; McKeown, 1976, 1983; Condran and Cheney, 1982; Livi-Bacci, 1982; Kunitz, 1983; Fogel, 1986, 1991). Another interesting observation is that the decline in mortality rates was faster in urban areas than in rural areas. Previous studies have explained this phenomenon largely by the advances in the urban public health system (Haines, 1977; Condran and Crimmins, 1980; Preston et al., 1981). Although the explanations previously suggested for the secular decline in mortality and reduced urban–rural gap are quite plausible, they have some minor flaws, particularly in accounting for the exact timing of the change. First, the effectiveness of the advances in public health measures such as the provision of central water supplies, sewage system, and inspection of food and milk was at best limited even until the last decade of the nineteenth century (Condran and Crimmins-Gardner, 1978; Condran and Cheney, 1982). Next, no decisive evidence about when average nutritional status started to improve has been presented.33 It is still unclear when the mean height, an indicator of nutritional status, began to rise. According to pioneering regional studies, the upturn of the trend in the mean height did not occur until the 1880s or 1890s, 20 to 30 years later than the beginning of mortality improvements (Steckel and Haurin, 1994; Wu, 1994; Coclanis and Komlos, 1995). Nor does the nutrition hypothesis account for the faster decline in urban mortality rates between 1870 and 1900. This paper suggests that the degree of resistance to disease in service depended on socioeconomic background prior to the war and on the duration of service in the army. If a recruit in the Union army and a migrant to a city in the late nineteenth century shared a 32 The height decline between 1830 and 1860 is largely accounted for by that among the rural population. Steckel (1987) noted that rural-to-urban migrants often returned after short periods of time, bringing communicable diseases with them. This pattern of migration helps to explain the rural character of the height decline. 33 It is also unclear whether average levels of gross nutrition declined or stagnated during the early nineteenth century. Gallman (1995, 1996) casts doubt on the argument that average food consumption had declined during the period when mortality rates were rising and then increased again.

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common experience in the epidemiological aspect, as assumed above, new migrants and those who migrated from isolated rural areas may have been more vulnerable to the unhealthy urban environment than their counterparts.34 If this was the case, urban mortality rates could have changed with shifts in the composition of migrants to cities and the urban population. That is, if the proportion of the urban population who had resided in urban areas for a long time period increased, or if the proportion of the new migrants to urban areas who came from other urban areas or nonagricultural sectors rose, urban mortality rate could have declined, other things being equal. Though it is uncertain if the proportion of long-time residents in urban areas increased after 1870, the following trend in urbanization suggests at least a possibility: In 1830, 4.5% of the population was living in large cities with 50,000 residents or more; this percentage rose to 11.0% by 1860 and reached 18.7% by 1890 (U.S. Bureau of the Census, 1965, p. 14). Even though the growth rate of the urban population was substantially high during the late nineteenth century, the proportion of new residents could have been reduced as the rate fell over time. Moreover, as urbanization proceeded, new migrants to large cities should have been more likely to come from other small cities or towns than from isolated rural areas. The proportion of the population residing in small cities with 2500 to 50,000 inhabitants rose from 5.5% in 1830 to 9.9% in 1860 (U.S. Bureau of the Census, 1965, p. 14). The decline in the proportion of the population employed in agriculture could have also contributed to the decline in urban mortality rate, since new migrants to large cities would have been more likely to be nonagricultural workers as the agricultural sector shrank. The effect of the shifts in sectoral and geographical compositions should certainly not be a main cause of the decline in urban mortality rates. However, these structural changes could have affected the timing or speed of the mortality decline to some extent. VI. CONCLUSIONS This paper has examined the effects of socioeconomic factors such as age, occupation, population size of place of residence, household wealth, and nativity on disease and mortality experiences of the Union army recruits while in service. The pattern of the mortality differentials among the army recruits was nearly the opposite of the normal pattern. Former farmers, rural residents, and natives, who were healthier on average prior to enlistment, were more susceptible to disease and were more likely than, respectively, nonfarmers, urban dwellers, and nonnatives to die from the disease they contracted. No visible association between household wealth and overall wartime mortality was found. 34 There is some evidence indicating that this early disadvantage of urban migrants could be real. For instance, Higgs and Booth (1979) have found that the composition of young adults aged 5 to 20 among all adults in large U.S. cities in 1890 positively interacted with mortality only for blacks. They have suggested that many young blacks in large cities were recent migrants from rural areas and thus had scant immunity against urban diseases such as tuberculosis or syphilis. Longer residency in urban areas could have also provided mothers with experience and knowledge useful in protecting children from the forces of mortality. According to Preston et al. (1981), children born to women who were themselves born in London have in all cases lower mortality than the national average or children born to current London residents.

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It was hypothesized that farmers, rural residents, and natives were more vulnerable to disease while in service because they had been less exposed to disease and thus had a poorer immunity status compared to nonfarmers, urban residents, and nonnatives. It was also conjectured that wealth could have had an effect only for some diseases which are sensitive to nutritional status because nutrition should have been the most important link between economic status and health in the nineteenth century. The patterns of disease-specific mortality differentials and timing of wartime death from particular types of disease supported these hypotheses. The wartime mortality differentials between recruits from different living environments were significantly larger for immunity diseases (for which an immunity influence is greater) than for nonimmunity diseases. Recruits from wealthier households had advantages only for nutrition-influenced diseases (for which a nutritional influence is definite). The difference in the hazard rate of dying from immunity diseases between farmers and nonfarmers and between rural and urban residents was much greater in the earlier stages of military service when enlistees were not seasoned to the unhealthy environments of the army camps. The difference in mortality from nutrition-influenced diseases between the wealthy and the poor was significantly larger in the earlier period in service. These results have some implications for several issues in economic and demographic history: (1) Previous studies have found weak associations between economic status and mortality in the nineteenth-century United States. This study suggests that economic status could have exerted a positive effect for some diseases for which nutritional influence is great. This effect of wealth upon health could have been dominated by other forces not related to economic factors such as the influence of nonnutritional infectious diseases. The result also implies that improvements in the disease environment would have produced a stronger relationship between economic status and health. (2) Several factors (such as access to better foods and isolated or healthy place of residence) have been claimed as responsible for the lower mortality rates of farmers and natives compared to their counterparts. This study suggests that a more isolated and healthier place of residence should have been a more important, if not the only, source of the advantages in the health of farmers and natives over nonfarmers and nonnatives. (3) Urbanization was not merely an extension of unhealthy and densely populated areas but a process of putting rural residents into a new environment to which they had poor resistance. The wartime experiences of recruits suggest that the effects of poor immunity status of migrants from rural to urban areas could have aggravated the adverse effect of urbanization on health and mortality. (4) The proportion of the urban population who had resided in urban areas for long periods and the percentage of the new migrants to urban areas who came from other urban areas or nonagricultural sectors could have increased during the late nineteenth century as urbanization proceeded and the agricultural sector shrank. These changes could have contributed to the decline in urban mortality rates and the narrowing of the urban–rural mortality gap during the same period.

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APPENDIX Wartime Mortality among Rural Residents Born in Ohio by Age, Wealth, Height, and Occupation Number of deaths per 1000 men from disease Number of recruits in each category

Category Age

Wealth

–19

20–24

25–29

301

Height

Any disease

Immunity disease

Nonimmunity disease

Farm

NF

Farm

NF

Farm

NF

Farm

NF

$0–150

43 41 20 40 54 22 78 82 51

28 22 10 33 16 9 37 21 13

90.0 48.8 150.0 250.0 111.1 90.9 115.4 85.4 98.0

35.7 181.8 200.0 60.0 125.0 0.0 54.1 95.2 0.0

23.3 0.0 50.0 100.0 0.0 45.5 38.5 36.6 39.2

0.0 90.9 0.0 30.3 62.5 0.0 27.0 0.0 0.0

46.5 24.4 50.0 100.0 37.0 45.5 12.8 24.4 39.2

35.7 90.9 100.0 0.0 62.5 0.0 27.0 0.0 0.0

$0–150

18 39 38 26 36 44 37 91 92

11 21 24 9 25 18 18 24 24

0.0 128.2 105.3 115.4 111.1 68.2 108.1 120.9 130.4

0.0 0.0 125.0 0.0 40.0 0.0 0.0 125.0 41.7

0.0 51.3 52.6 76.9 27.8 0.0 27.0 54.9 32.6

0.0 0.0 0.0 0.0 0.0 0.0 0.0 41.7 0.0

0.0 51.3 26.3 38.5 27.8 68.2 27.0 54.9 21.7

0.0 0.0 83.3 0.0 40.0 0.0 0.0 0.0 41.7

$0–150

13 21 26 13 30 35 10 29 18

10 21 7 5 9 21 4 9 8

0.0 47.6 153.8 0.0 66.7 142.9 400.0 103.4 111.1

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 47.6 76.9 0.0 0.0 57.1 40.0 34.5 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

0.0 0.0 38.5 0.0 0.0 57.1 0.0 0.0 55.6

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

$0–150

13 27 26 18 36 46 3 22 22

11 15 20 11 20 25 3 4 5

230.8 74.1 76.9 111.1 83.3 87.0 0.0 90.9 90.0

0.0 66.7 50.0 90.9 50.0 40.0 0.0 0.0 0.0

0.0 0.0 38.5 0.0 0.0 65.2 0.0 45.5 0.0

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

230.8 0.0 38.5 55.6 27.8 0.0 0.0 45.5 45.5

0.0 66.7 0.0 90.0 50.0 0.0 0.0 0.0 0.0

Below 67 67–69 Over 69 $151–2100 Below 67 67–69 Over 69 $21011 Below 67 67–69 Over 69 Below 67 67–69 Over 69 $151–2100 Below 67 67–69 Over 69 $21011 Below 67 67–69 Over 69 Below 67 67–69 Over 69 $151–2100 Below 67 67–69 Over 69 $21011 Below 67 67–69 Over 69 Below 67 67–69 Over 69 $151–2100 Below 67 67–69 Over 69 $21011 Below 67 67–69 Over 69

Note. NF, nonfarmers. Height is measured in inches. For classification of disease see text. See footnotes 13 and 20 for a discussion of the table.

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