Centers for Disease Control, Atlanta, Georgia. Blood for the ...... Co., 1962, p 328. 31. ... Iron fortification of flour and bread: proposed statement of reasons,.
Prevalences of Anemia and Iron Deficiency Anemia In Black and White Women in the United States Estimated by Two Methods LINDA D. MEYERS, PHD, JEAN-PIERRE HABICHT, MD, CLIFFORD L. JOHNSON, MSPH. AND CAVELL BROWNIE, PHD Abstract: Prevalences of anemia were estimated by two methods for 742 Black and 3,074 White nonpregnant women of childbearing age drawn from a large probability sample of the United States civilian noninstitutionalized population (NHANES I). One method defines the prevalence of anemia as the proportion of women with hemoglobin levels below a 12 g/dl "cut-off'. The second method defines the prevalence of anemia as the proportion of women whose hemoglobin values are shifted downwards relative to a distribution of hemoglobin values of non-anemic women. Estimates produced by both methods suggest a higher prevalence of anemia in Black than in White women. Estimates produced by the "cut-off' method, how-
ever, are higher than those from the "distribution" method for both racial groups, probably because the "cut-off' method results in large overestimates in populations where anemia prevalence is low. The "distribution" method is further used to estimate the contribution of iron deficiency to anemia. Essentially all anemia in White women and a high proportion of anemia in Black women is associated with iron deficiency in the US civilian noninstitutionalized population. Iron supplementation trials are needed in order to define the magnitude of the problem accurately and plan appropriate public health programs. (Am J Public Health 1983; 73:1042-1049.)
Introduction Iron deficiency anemia in women of childbearing age is a recognized public health concern in the United States. Estimates of the prevalence of iron deficiency anemia for selected groups of American women range from 5 to 15 per cent.'4 These estimates have usually been made using "cut-off points"5 of indicators of iron deficiency anemia which were derived from a different population. Such a method, however, has been criticized for failing to adequately enumerate anemic and non-anemic populations because the hemoglobin values of the two populations probably overlap.''2 Another approach, referred to here as mixed-distribution analysis, has been used by Cook, et al'3 to estimate the prevalence of anemia from the frequency distribution of hemoglobin values in a population. In this paper we compare prevalences of anemia calculated by using a mixed-distribution analysis with those obtained by using conventional cut-off points for samples of 742 Black and of 3,074 White women ages 18 through 44 years drawn from a probability sample of the United States population studied in the NHANES I survey.'4 The paper then examines the contribution of iron deficiency to anemia and, lastly, it extrapolates these findings to estimate the prevalence of iron deficiency anemia in noninstitutionalized women of reproductive age in the USA so as to be able to compare the findings in this survey with those in other US surveys.
lactating and for whom values for both hemoglobin and per cent transferrin saturation (TS) were available. The data were obtained during the first National Health and Nutrition Examination Survey (NHANES I) conducted by the National Center for Health Statistics from April 1971 through June
Methods and Materials Sampling and Data Collection
The analysis included 3,074 White and 742 Black women, 18 through 44 years old, who were neither pregnant nor From the Cornell University Agricultural Experiment Station. Division of Nutritional Sciences, Ithaca, NY, and the National Center for Health Statistics, USPHS, Hyattsville, MD. Address reprint requests to Dr. Jean-Pierre Habicht, Division of Nutritional Sciences, Savage Hall, Cornell University, Ithaca, NY 14853. Dr. Brownie's present address is Department of Statistics, North Carolina State University, Raleigh. This paper, submitted to the Journal July 1. 1982, was revised and accepted for publication November 2, 1982. © 1983 American Journal of Public Health 0090-0036/83 $1.50
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1974. 14 Race was observed by the interviewer and recorded as "White," "Negro," or "Other". 14 The small number of women in the last category are not included in this analysis because of insufficient sample size. The biochemical analyses were conducted under the direction of the Nutritional Biochemistry Section of the Centers for Disease Control, Atlanta, Georgia. Blood for the determinations was obtained by venipuncture with stasis. Hemoglobin determinations were made at the NHANES I mobile centers by the cyanmethemoglobin method."' Samples were reanalyzed if a difference between duplicates of more than 0.2 g/dl was found, or if the mean hemoglobin value was less than 11.0 g/dl or greater than 18.5 g/dl.'6 Serum iron and total iron binding capacity determinations were made in the Nutritional Biochemistry Section at the Centers for Disease Control using a modification'6 of methods originally developed by Giovanniello, et al, ' 7and Ramsey.'8 The analyses were repeated if calculated serum iron values were less than 50 p.g/dl or greater than 250 ,ug/dl or if serum total binding capacity values were less than 500 .Lg/ dl. 16
Per cent transferrin saturation was selected as an indicator of iron nutrition for these analyses since it is generally considered a better indicator of those levels of iron depletion which affect hemoglobin synthesis than either serum iron or total iron binding capacity alone,' '9-20 the other two indicators of iron status available in NHANES I. Per cent transferrin saturation was calculated by multiplying the ratio of serum iron to total iron binding capacity by 100. Logarithmic transformations of TS values conformed more closely to a Gaussian distribution than did the untransformed values. Therefore, natural logarithms of TS (ln TS) were used in this
analysis.
For the analyses presented here, the hemoglobin and ln TS values were pooled across ages 18 through 44 years AJPH September 1983, Vol. 73, No. 9
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because neither analysis of variance nor regression analysis showed any association of these values with age in this age group of women. Analysis 1: Prevalence of Anemia in Women Examined Two methods were used to estimate the prevalence of anemia. The cut-off method assumes a well-defined normal range below which an indicator is pathological. Thus hemoglobin values less than 12 g/dl in women are conventionally considered indicative of anemia.2' Nevertheless, supplementation trials in which anemia is defined by measuring hemoglobin response to iron therapy show that hemoglobin values of anemic (responding) and non-anemic (nonresponding) populations overlap."'2 Thus the assumption is incorrect and will result in incorrect prevalence estimates unless one is able to correct for the overlap of the anemic and non-anemic populations.22,23 The second technique, mixed-distribution analysis is based on two assumptions: 1) the distributions of the hemoglobin values for non-anemic and anemic women are different; and 2) hemoglobin values for the populations of nonanemic women have a Gaussian distribution. According to these assumptions, anemic women are those whose hemoglobin values are not part of the Gaussian distribution of hemoglobin values, i.e., their values fall outside the Gaussian distribution of hemoglobin values. The first assumption, that there are separate distributions of anemic and non-anemic women, has been validated by supplementation trials."'2 The second, that hemoglobin values in a non-anemic population have a Gaussian distribution, is generally accepted as biologically logical and has been supported by at least four types of evidence: 1) early investigations of the hemoglobin distributions of "presumably healthy persons" report Gaussian distributions;24 25 2) iron supplementation trials indicate that the hemoglobin distributions of populations whose hemoglobin values do not increase with therapy (nonresponders or non-anemics) approximate a Gaussian distribution;8.9"'1.12 3) the hemoglobin distributions of children26 and women (Figure 2) defined as non-anemic using Mean Corpuscular Volume and TS, respectively, are approximately Gaussian; and 4) we examined the distribution of hemoglobin values of white men 18-44 years old from NHANES I who were expected to have a low prevalence of anemia and found that their hemoglobin values approximated a Gaussian distribution. Cook, et al,'3 used a mixed-distribution analysis that required the hemoglobin values of the anemic population to have a Gaussian distribution. However, we know of no empirical evidence to support this assumption. In fact, two supplementation trials8' 2 suggest that the distributions are not Gaussian. Therefore, our method does not "force'" a Gaussian distribution for anemic women. When a cumulative Gaussian frequency distribution of hemoglobin values is plotted on probability paper, it is linear. If anemia is found in the population, the plot will also contain a non-linear component. When the prevalence of anemia is low, the linear portion, when extrapolated, represents the Gaussian distribution of hemoglobin values for the population of non-anemic women. The non-linear portion in that case is a composite of the hemoglobin distribution of the anemic women plus that of the non-anemic women, i.e., the non-linear portion includes both anemic and non-anemic women with low hemoglobin levels. The estimated proportion of anemics is found by determining the difference between the total (curved) and non-anemic (linear) populaAJPH September 1983, Vol. 73, No. 9
tions at ascending hemoglobin levels. The Appendix describes and Appendix Table 1 shows how the estimated proportion of anemics rises with increasing levels of hemoglobin and then stabilizes once all anemics have been identified. Because of sampling variability, the calculated proportion of anemics fluctuates about the true prevalence in this area of stability. The first fall in estimated proportions (referred to as the first reversal) indicates that one is within the area of stability. Thus the prevalence of anemia can be taken as the largest estimate prior to the first reversal. (See Appendix for formulae.) The variance of the chosen prevalence estimate was estimated through simulation techniques using these data.27 The cumulative frequency distributions of hemoglobin values for Blacks and Whites were plotted and analyzed separately because a previous analysis of these data28 indicated that a highly significant (p < .001) one-gram difference in hemoglobin between Blacks and Whites represents racial differences which appear unrelated to anemia and iron nutrition. Analysis 2: Contribution of Iron Deficiency to Anemia A mixed-distribution analysis for iron deficiency anemia would identify those persons who are anemic, iron deficient, or both* from among all those whose hemoglobin and/or TS levels lie outside the bivariate frequency volume encompassing all individuals normal in both hemoglobin and TS. Unfortunately, this methodology is not yet available. Therefore we have estimated the contribution of iron deficiency to the anemia prevalence by examining by race the disappearance of anemia from the cumulative frequency distribution of hemoglobin values when iron deficient women are excluded. To do this, a TS cut-off point for identifying iron deficient women was chosen to identify women with low TS caused only by iron deficiency, i.e., the cut-off point must have a high positive predictive value;29 a TS cut-off of 10 per cent was chosen for these analyses. We chose this cut-off rather than the more commonly used one of 16 per cent to ensure selection of women with anemia caused by iron deficiency and to exclude women with low TS caused by infection for whom TS levels between 10 and 20 per cent are common.20 30 If iron deficiency is the major cause of anemia, one can also estimate the prevalence of iron deficiency anemia by using a cut-off for hemoglobin which includes essentially all of the anemic population (high sensitivity for anemia) and the cutoff selected above for TS. Analysis 3: Extrapolation of Prevalence to US Population
The women included in this sample were drawn from a multi-stage, stratified probability sample of the US civilian noninstitutionalized population 1 through 74 years of age.'4 Five thousand three hundred eighty-three White and 1,340 Black women 18 through 44 years old were chosen for examination by the US Bureau of the Census, with a deliberate over-sampling of the poor. Ninety-nine per cent of the women were subsequently interviewed and 74 per cent were examined. Of the women examined who were neither pregnant nor lactating, information on the variables of interest for this paper was available for 3,816 (80 per cent). *Iron deficiency anemia is the final stage in the depletion of body iron. Iron deficiency anemia follows a stage in which iron stores are depleted and transport iron decreased, but hemoglobin has not yet begun to decrease.'9 Thus a person may be iron deficient but not yet anemic. Further, a person with low hemoglobin from causes other than iron lack could be termed anemic but not iron deficient.
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Since not all women who were interviewed were also examined, possible bias leading to over- or under-estimation of prevalence of iron deficiency anemia may have been introduced by self-selection of women who came to be examined. This bias must be investigated before any inferences can be made about the total US population of Black and White nonpregnant, nonlactating women 18 through 44 years old. If self-selection for examination biases estimates of the prevalence of iron deficiency anemia, one would expect the data obtained in the survey to be associated with both iron deficiency anemia and attendance at the examination. Therefore we identified the medical history and demographic variables that were related to both iron deficiency anemia and examination attendance and examined the direction and strength of the relationship. In these analyses, only one variable was included if the correlation among two or more variables was greater than .85. The accuracy of presenting national estimates based on raw data rather than on statistically weighted data was also examined. Before national estimates of disease prevalence are computed, the NHANES I raw (unweighted) data are adjusted with statistical weights. This is done because sample persons selected by the Bureau of the Census represent different numbers of persons in the US population depending upon specific age, sex, and income characteristics.'4 Because of measurement and laboratory error, hemoglobin data collected at three examination centers and transferrin saturation data from three other centers were discarded. New weighting schemes were therefore developed separately for persons remaining with hemoglobin values and those with transferrin saturation values. At the time of this analysis no appropriate weighting scheme was available for analyses using hemoglobin and transferrin saturation together and we feared incorrect use of weights might mask true physiological relationships. Thus we decided to use unweighted values if we could show that the weighted and unweighted distributions of hemoglobin and of transferrin saturation were similar. This similarity was determined by inspecting weighted and unweighted frequency distributions for hemoglobin and for transferrmn saturation, since statistical testing is not possible between these two nonindependent populations. Some differences in statistics introduced by the NHANES I cluster sampling procedures should also be considered before inferences drawn from this sample can be extrapolated to the US population. Cluster sampling may result in larger estimates for variances than does a simple random sample of the same size,3' but it does not affect the estimated proportions outside the Gaussian curve at given hemoglobin levels. Results Prevalence of Anemia in Sample
Figure 1 presents statistically weighted and unweighted cumulative frequency distributions of hemoglobin values for the samples of Black and White women. Only the unweighted data are relevant to estimates of the prevalence of anemia in Analysis 1. According to the assumptions of the method, probability plots presented in Figure 1 indicate that within each sample of women a population of hemoglobin values of anemic women was superimposed on the normal Gaussian distribution. Appendix Table 1 presents by race the propor1 044
,0
99
98
95 w
-* 90
z 80 F
BLACK WOMEN
X 60 w
>- 40
g-J o)
20 F
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O O
5 3
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* unweighted
weighted
2
8
9
10
14 13 12 HEMOGLOBIN g/dI
11
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16
FIGURE 1-Hemoglobin Distribution for Black and White Women using Weighted and Unweighted Data. Probability plot of the cumulative frequency distributions of hemoglobin values for unweighted and statistically weighted data for Black and White women ages 18 through 44 who are not pregnant nor lactating. Extrapolation of the upper linear portion of each distribution is also shown. Data from NHANES I, 1971-1974. Sample size = 3,074 White and 742 Black women. Unweighted mean ± standard deviation for this sample is 13.8 ± 1.1 g/dl for White and 13.0 ± 1.3 g/dl for Black women.
tion of women in the total population (column 2) and the proportions falling under (column 3) and outside (column 4) the Gaussian hemoglobin distribution at selected hemoglobin levels (column 1) over the range where the proportion of women outside the Gaussian curve and the corresponding variances are stable. The largest estimate of prevalence in this stable range is 1.1 per cent for White women and 2.6 per cent for Black women. The prevalence estimate of anemia calculated by using the accepted hemoglobin cut-off point of 12 g/dl is 4.5 ± 0.4 per cent (mean + standard error) for White women and 20.4 + 1.5 per cent for Black women. Computer simulations of these data suggest that the standard errors of the prevalences estimated by mixed distribution analysis are on the order of 0.3 per cent for White and 1.7 per cent for Black women27 and that the 95 per cent confidence limits of the prevalence estimates from mixed-distribution analysis27 do not overlap those calculated by using the conventional hemoglobin cut-off. Contribution of Iron Deficiency to Anemia
Figure 2 presents the plots of the cumulative frequency distributions of hemoglobin for the populations of women with TS greater than 10 per cent (i.e., excluding women certain to be iron deficient). The hemoglobin distribution
curve of Black women has a much smaller excess of values outside the Gaussian curve than in Figure 1. The hemoglobin distribution of White women no longer has an excess of values outside the Gaussian curve, and is, in fact, slightly over-depleted relative to a Gaussian distribution. The number of women with values below both the lowest hemoglobin cut-offs with 100 per cent sensitivity for anemia (all anemics identified) from Appendix Table 1 (11.3 g/dl for White and 10.7 gIdl for Black women) and the TS cut-off of 10 per cent is 36 ± 6 White women and 17 ± 4 Black women. AJPH September 1983, Vol. 73, No. 9
ANEMIA AND IRON DEFICIENCY ANEMIA IN WOMEN 99
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98
cr
w
TRANFERRIN SATURATION MORE TOTAL THAN 10 SUPERIMPOSED ON DATA FROM POPULATION I-) AND ON EXTRAPOLATION OF LINEAR SEGMENT(---)
60 90--
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OBSERVED DATA (0,0) OF WOMEN WITH PERCENT 0
95
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8 601
ITE WOMEN
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HEMOGLOBI N g/dI FIGURE Hemoglobin Distributions for Black and White Women with TS Greater than 10 per cent. Cumulative frequency distributions of hemoglobin values for 698 Black women and 2,950 White women with TS values greater than 10.0 per cent who are not pregnant nor lactating and who are 18 through 44 old. The extrapolated linear curve for the total sample of 742 Black and of years -J 20~~~~~~~~1 3,074 White women is also shown. This extrapolated curve represents a Gaussian distribution of hemoglobin values. Data from NHANES I, 1971-1974.
Comparability of Sample to US Population Examination of possible biases in response indicated that 6 out of 146 variables for White women and 5 out of 124 variables for Black women showed a significant difference (p < .05) between iron deficient anemic and non-iron deficient anemic groups of women and also showed a significant difference (p < .05) between those women examined and included in these analyses and women from all sampling sites who were only interviewed.3 The variables related to both examination attendance and anemia for the White women were: 1) the number of people in the house; 2) whether the woman had ever been diagnosed with anemia caused by childbirth; 3) whether the woman was under treatment for anemia at the time of the interview; 4) whether the woman had ever been diagnosed with blood loss from stomach or bowels; 5) frequency of alcohol consumption; and 6) type of alcohol consumed. The direction of values for the first three variables suggests that the sample examined has more women with characteristics of anemia than the interviewed women, but the opposite is true for the remaining three variables. Thus overall the direction of the differences suggests that there is little response bias among the White women sampled. Furthermore while the means or proportions between the examined and interviewed women are statistically different (p < .05), the actual differences are quite small and less than the differences found between anemics and non-anemics. The five variables related to anemia and examination attendance for Black women were: 1) presence of piped water; 2) presence of hot and cold piped water; 3) whether a language other than English was spoken at home; 4) employment status the previous two weeks; and 5) whether the woman worked outside the home the past three months. For all but the last variable, the direction of the difference AJPH September 1983, Vol. 73, No. 9
suggests that the sample examined has more characteristics associated with anemia than the women only interviewed, although again the magnitude of the differences is quite small. Weighted and unweighted cumulative frequency distributions of hemoglobin values are presented in Figure 1. Similar results were found for transferrin saturation distributions. Since the weighted and unweighted distributions are similar, the prevalence estimates by race are based on unweighted data. Exact estimates of the effect of the clustered design have not been determined. However, a recent analysis of NHANES I data33 estimated design effects of eight variables expected to show clustering characteristics associated with anemia and iron deficiency. That analysis showed a maximum increase in the standard errors of 1.5 times the simple random sample standard errors.33 In fact, for the variable analyzed that was most likely to tap the clustering effect associated with anemia ("Have you ever had anemia?"), the design effect was only 1.09.33 Discussion A principal purpose of NHANES is to measure "the whole population's nutritional status. " 14 This requires two steps: estimating the prevalence of the condition in the sample collected in NHANES, and extrapolating that prevalence to the noninstitutionalized US population. The two steps are usually interrelated by the sampling design, which determines for how many separate subsamples prevalence must be estimated. In this case, biological reasons dictate separate prevalence estimates for Blacks and Whites. According to the comparison of the weighted and unweighted distributions within the Black and White populations, race is sufficiently related to those sampling variables which affect the prevalence of iron deficiency anemia (above all poverty32) that further estimates of prevalence in additional subsamples do not appear to be necessary. The first step, estimating the prevalence of iron deficiency anemia in the sample examined, presented difficulties so that we first estimated prevalence of all anemia in the sample and then showed that the vast majority of the anemia is due to iron deficiency-thus the extrapolation step is based primarily on the estimated prevalence of anemia in the
sample. Prevalence of Anemia in Sample
Approximately 1 per cent of the White and 3 per cent of the Black women in this sample fall outside a Gaussian distribution for hemoglobin and are considered anemic. These proportions are considerably lower than either the 4 per cent of White or 20 per cent of Black women in the sample whose hemoglobin concentrations are less than 12 g/ dl. The most likely explanation for the difference between the two methods is that the cut-off point of 12 g/dl overestimates the true population prevalence of anemia because it is too high for this population, even though it may be correct for populations with high prevalence of anemia where the positive and negative diagnostic misclassifications cancel out.2229 Errors in classification can be corrected for even when the misclassifications do not cancel out if the sensitivity and specificity of the cut-off are known in similar populations.23 However, these data are not available for anemia. An additional explanation for an overestimate, at least 1 045
MEYERS, ET AL.
in the sample of Black women, is that the conventional use for Black women of a hemoglobin cut-off derived from White populations'5 iS incorrect. Analysis of the hemoglobin distributions in these Black and White women shows lower hemoglobin levels for Black women throughout the range of the hemoglobin distributions which cannot be primarily related to different prevalences of anemia or of iron deficiency between races.28 This finding suggests that use of a 12 gIdl cut-off point for Blacks overestimated the prevalence of anemia. Measurement imprecision could also contribute to an overestimate. If errors of measurement are large relative to the hemoglobin variance in the population, the estimated distributions will be broader than the true distributions. Thus a 10.5 gId1 cut-off in these data might correspond to a 12.0 g/ dl cut-off for hemoglobin values measured more precisely. Such gross imprecision (imprecision34 standard error ± .86 gIdl) seems most unlikely, however, given NHANES quality control procedures.'16 The fact that a cut-off point of 12 gId1 overestimates the prevalence of anemia when the total population prevalence is low was borne out with regard to iron deficiency anemia by Garby, et al,8 who studied the response of hematological indicators to iron therapy in Swedish women. Such an overestimate may also explain the disappointing response of women (considered to be moderately anemic (9-12.9 gIdl)) to iron supplementation trials.35 It is also conceivable, although unlikely, that mixeddistribution analysis underestimates the prevalence. Underestimation would occur if: 1) some women lying under the Gaussian curve are anemic; 2) factors that depress hemoglobin (e.g., infection, poor iron nutrition) shift the entire hemoglobin distribution downward; or 3) the technique inherently overestimates the proportion falling under the Gaussian curve (non-anemics). Results of classic supplementation trials (e.g., Garby, et al,8) suggest that some women with hemoglobin levels higher than 12 gId1 do respond to iron therapy, i.e., some women under the Gaussian curve in our data are indeed iron deficient anemic. However, their number is negligible even when the prevalence of iron deficiency anemia is much higher than it appears here. Thus the first explanation for an underestimate is unlikely. We discarded the second explanation (shifting the total distribution), at least with regard to the anemic effect of iron deficiency, by showing that exclusion of iron deficient women does not shift the whole hemoglobin distribution upward, but only decreases its skewness (Figure
2). The third explanation (inherent overestimate of nonanemics at a given hemoglobin level) is also unlikely at the low prevalences reported here, although it is a problem at high prevalences unless an iterative procedure is used. The results presented here, however, agree with those found by using both an iterative procedure27 and a simulation of the technique used by Cook, et al,'II that are not distorted at high
prevalences.
Contribution of Iron Deficiency to Anemia in Sample The contribution of iron deficiency to anemia estimated
by comparing the cumulative distributions of hemoglobin in the total population (Figure 1) with that of the population excluding women certain to be iron deficient (Figure 2) indicates that most of the anemia in this sample is caused by inadequate iron nutriture. If the anemia were not caused by iron deficiency, removal of women with low TS from the 1046
1046
total sample would not have affected the lower tail of the hemoglobin curve so markedly. Comparison of Figure 1 with Figure 2 indicates that essentially all anemia disappears in Whites when a TS cut-off of 10 per cent is chosen. If one assumes that women with TS values at or below 10 per cent are concentrated among those women with low hemoglobin values outside the Gaussian distribution, as these data suggest, then iron deficient anemic women may be enumerated. The enumeration supports the above finding that essentially all anemia in Whites and most, but not all, in Blacks is associated with. iron deficiency. As shown in Figure 2, when all White women with TS below 10 per cent are excluded from the sample, more women with low hemoglobin values are excluded than are necessary to eliminate all those with anemia. This probably occurs because hemoglobin and In TS are associated even at healthy levels of transferrin saturation."* Therefore, some of the TS values less than or equal to 10 per cent are probably not related to iron deficiency but are still associated with low hemoglobin levels. Thus we have overcorrected the iron deficient anemics relative to what would be expected by a bivariate mixed-distribution analysis. Extrapolation of Findings to US Population Effects of the sampling design must be determined before the prevalences of the above unweighted samples are extrapolated to the US population. A bias caused by selfselection for hematological examination could vitiate any prevalence estimates for the US population. This was shown to be unlikely in this analysis because the 3,074 White and the 742 Black women examined were representative, for variables associated with iron deficiency anemia, of noninstitutionalized nonpregnant and nonlactating Black and White women 18 through 44 years old in the US. A bias resulting from differential sampling of specific subgroups appears to be absent within each race because weighted and unweighted hemoglobin distributions are similar. Prevalence estimates for each race separately can thus be made from the unweighted data. Prevalence estimates for anemia or iron deficiency anemia for the races combined would have to take the oversampling of Blacks into account, however. The above considerations suggest that the prevalences of anemia estimated by either method and the contribution of iron deficiency to anemia presented here for Black and White women in the US are not underestimated because of biases in the selection of women examined or because of the oversampling of specific groups. Yet the estimates we drew from these data, even using the conventional hemoglobin cut-off of 12 g/dl, are lower than most previous estimates. '-4 This may reflect a true change in prevalence. More likely it reflects differences in survey design' 2 and generalizability3 in the previous surveys that resulted in estimates which could not be extrapolated to the US population at large, and are thus not comparable to NHANES I. NHANES I was the first US national probability survey to collect hemoglobin data. These data should provide a baseline for future national surveys designed to monitor the health and nutrition of the US population. One such survey, NHANES 11,36 was completed in February 1980 and data are **In the NHANES I data, both Black and White races and males and females show the same change in grams of hemoglobin concentration (A Hb) with a similar change in per cent transferrin saturation (A TS) over the range of 25 to 50 per cent transferrin saturation. A Hb = (.0097 ± .0030) A TS, P < .01), unpublished WH Pan.)
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just becoming available. Because of their sampling design, the two NHANES surveys are potentially comparable. This comparability is an important prerequisite for monitoring in nutrition surveillance that has not yet been met by previous surveys. However, biases such as those investigated in Analysis 3 must still be excluded or taken into account before even these surveys can be compared. The major proportion of the discrepancy between our estimates of iron deficiency anemia and those of others'-3 are, however, not due to sampling design but to the more basic fact that as prevalence falls the proportion of false positive diagnoses of iron deficiency anemia based on cut-off points of a single or of multiple variables will rise, i.e., the positive predictive value falls.29 Mixed-distribution analysis does not suffer from this vagary. If our estimates are true, they will have major public health implications when substantiated by appropriate iron supplementation population trials.32 This is because proposed public health measures to reduce iron deficiency anemia3738 in the United States have presumed higher prevalences, and therefore higher positive predictive values for those receiving iron fortified foods. Lower positive predictive values (smaller proportion of true benefiters to recipients) will reduce cost-effectiveness of presently proposed interventions and lower the ratio of benefiters to those who will suffer detrimental side effects of increased iron intake, e.g., hemochromatosis. Even though the prevalences estimated here are lower than previously reported, the more than 400,000 women suffering from putatively preventable anemia should not be dismissed as presenting an unimportant problem. The 1.1 per cent or 2.6 per cent prevalence of a disease is a statement of fact and not a probability statement such as is made on finding in a single patient a hemoglobin value which lies outside the 95th percentile and thus indicates a low probability of anemia in that patient. In summary, even our lower estimates represent an important problem for those women affected. However, the appropriate public health intervention will probably differ depending upon whether our estimates of prevalence are correct. Therefore, the prevalence estimates presented here must be verified by appropriate intervention trials. Based on that outcome the appropriate public health interventions can be designed. REFERENCES 1. Cook JD, Finch CA, Smith NJ: Evaluation of the iron status of a population. Blood 1976; 48:449-455. 2. O'Neal RM, Abrahams OG, Kohrs MB, et al: The incidence of anemia in residents of Missouri. Am J Clin Nutr 1976; 29:1158-1166. 3. US Department of Health, Education, and Welfare, Centers for Disease Control: Ten-State Nutrition Survey, 1968-70. Chapter IV-Biochemistry and Highlights. Washington DC, 1972. DHEW Pub. No. (HSM) 723132 and 8134. 4. White H, Johnson E: Hemoglobin concentrations of 2263 university women. J Am College Health Assoc 1969; 17:255-256. 5. World Health Organization: Methodology of Nutritional Surveillance. WHO Tech Rept Ser No 593. Geneva, Switzerland: WHO, 1976; 16-17. 6. Beaton GH: Epidemiology of iron deficiency. In: Jacobs A, Worwood M, (eds): Iron in Biochemistry and Medicine. New York: Academic Press, 1974, 477-528. 7. World Health Organization: Nutritional Anaemias. WHO Tech Rept Ser No 503. Geneva. Switzerland:WHO, 1972. 8. Garby L, Irnell L, Werner I: Iron deficiency in women of fertile age in a Swedish community: III. Estimation of prevalence based on response to iron supplementation. Acta Med Scand 1969; 185:113-117. 9. Nativig H, Vellar OD. Studies on hemoglobin values in Norway: VIII. Hemoglobin, hematocrit and MCHC values in adult men and women. Acta Med Scand 1967: 182: 193-205.
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10. Driggers DA, Reeves JD, Lo EYT, Dallman PR: Iron deficiency in oneyear-old infants: comparison of results of a therapeutic trial in infants with anemia or low-normal hemoglobin values. J Pediatr 1981; 98:753-758. 11. Margolis HS, Hardison HH, Bender TR, Dallman PR: Iron deficiency in children: the relationship between pretreatment laboratory tests and subsequent hemoglobin response to iron therapy. Am J Clin Nutr 1981; 34:2158-2168. 12. Freire WB: Use of Hemoglobin Levels to Determine Iron Deficiency in High Prevalence Areas of Iron Deficiency Anemias. PhD Dissertation. Ithaca, NY: Cornell University, 1982. 13. Cook JD, Alvarado J, Gutnisky A, et al: Nutritional deficiency and anemias in Latin America: A collaborative study. Blood 1971; 38:591603. 14. National Center for Health Statistics: Plan and Operation of the Health and Nutrition Examination Survey, United States-1971-1973. Part A and Part B. Washington DC:US GPO, 1973. DHEW Pub. No. (HRA) 761310 and (HSM) 73-1310. (Part A reprinted 1976.) 15. National Institutes of Health, Interdepartmental Committee on Nutrition for National Defense: Manual for Nutrition Surveys, 2nd ed. Washington DC:US GPO, 1963. 16. National Center for Health Statistics: HANES I: Hematology and Clinical Chemistry Procedures Developed or Utilized by the Center for Disease Control, Bureau of Laboratories, 1971-1975. Instruction Manual: Data collection, part 16. Washington DC:US GPO, 1979. 17. Giovanniello TJ, DiBenedetto G, Palmer DW, et al: Fully and semiautomated methods for the determination of serum iron and total iron-binding capacity. J Lab Clin Med 1968; 71:874-883. 18. Ramsey WNM: The determination of the total iron-binding capacity of serum. Clin Chem Acta 1957; 2:221-226. 19. Bainton DF, Finch CA: The diagnosis of iron deficiency anemia. Am J Med 1964; 37:62-70. 20. Bothwell TH, Charlton RW, Cook JD, Finch CA: Iron Metabolism in Man. London: Blackwell Scientific, 1979, Chapter 2, pp 44-81. 21. World Health Organization: Nutritional Anaemias. WHO Tech Rept Ser No 405. Geneva, Switzerland:WHO, 1968. 22. Habicht J-P. Meyers LD, Brownie CB: Indicators for identifying and counting the improperly nourished. Am J Clin Nutr 1982; 35:1241-1254. 23. Rogan WJ, Gladen B: Estimating prevalence from the results of a screening test. Am J Epidemiol 1978; 107:71-76. 24. Osgood EE: Hemoglobin, color index, saturation index and volume index standards. Arch Int Med 1926; 37:685-706. 25. McGeorge M: Haematological variations in fifty normal adult males. J Path Bacteriol 1936; 42:67-73. 26. Dallman PR, Siimes MA: Percentile curves for hemoglobin and red cell volume in infancy and childhood. J Pediatr 1979; 94:26-31. 27. Brownie C, Habicht J-P, Robson DS: A Class of Mixtures with Application to Clinical Chemistry and Nutritional Survey Data. Ithaca NY: Cornell University, 1981. Biometrics Unit Mimeograph Series, no. BU744-M. 28. Meyers LD, Habicht J-P, Johnson CL: Components of the difference in hemoglobin concentrations in blood between black and white women in the United States. Am J Epidemiol 1979; 109:539-549. 29. Vecchio TJ: Predictive value of a single diagnostic test in unselected populations. N Engl J Med 1966; 274:1171-1173. 30. Bothwell TH, Finch CA: Iron Metabolism. Boston: Little, Brown and Co., 1962, p 328. 31. Simmons WR, Bean JA: Impact of design and estimation components on inference. In: Johnson NL, Smith H (eds): New Developments in Survey Sampling. New York:Wiley-Interscience, 1969, 601-605. 32. Meyers LD: Definition, Prevalence, and Correlates of Iron Deficiency Anemia in Black and White American Women-An Epidemiologic Analysis. PhD Dissertation, Ithaca NY:Cornell University, 1978. 33. Singer JD, Granahan P, Goodrich NN, Meyers LD, Johnson CL: Diet and Iron Status, A Study of Relationships: United States 1971-1974. Hyattsville MD: National Center for Health Statistics, 1982. Vital and Health Statistics Series 11 No. 229. 34. Habicht J-P, Yarbrough C, Martorell R: Anthropometric field methods: criteria for selection. In: Jelliffe DB, Jelliffe EFP (eds): Human Nutrition, Vol 2:Nutrition and Growth. New York:Plenum Press, 1979, 365-387. 35. Gershoff SN, Brusis OA, Nino HV, Huber AM: Studies of the elderly in Boston. I. The effects of iron fortification on moderately anemic people. Am J Clin Nutr 1977; 30:226-234. 36. National Center for Health Statistics: Plan and Operation of the Second National Health and Nutrition Examination Survey, 1976-80. Washington DC:US GPO, 1981. Vital and Health Statistics Series 1, no. 15. PHS Pub. No. (PHS)81-1347. 37. Iron fortification of flour and bread: proposed statement of reasons, proposed findings of fact, proposed conclusions of law, and tentative order. Federal Register 1977; 42(223):59513-59518. 38. Iron fortification of flour and bread; findings of fact, conclusions, and final order. Federal Register 1978; 43(168):38575-38578.
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ACKNOWLEDGMENTS The authors thank Robert S. Murphy, Director, Division of Health Examination Statistics, National Center for Health Statistics, for his advice and encouragement. This paper is a report of research through an Intergovernmental Personnel Agreement. The research is drawn, in part, from a thesis submitted by the first author (Meyers) in partial fulfillment of the requirements for the doctoral degree. The research summarized in the Appendix was supported by US Department of Agriculture under agreement number 59010410-9-03140.
APPENDIX The method of analysis described here is only appropriate when prevalence of the non-Gaussian population is low, i.e., less than 5 per cent. An iterative version of the procedure has been developed which is appropriate at higher prevalences.2' The method also requires that overlap of the two component populations occur in the "tail" region of the Gaussian component and that the samples be large. The following notation is used. Let G(-) denote the cumulative distribution function of the Gaussian population, let A(-) denote the cumulative distribution of the non-Gaussian population, and let F(-) denote the cumulative distribution function of the mixture. Then if p is the prevalence of the non-Gaussian population in the mixture, we have F(x) = p A(x) + (I - p) G(x) for all x. (1)
These estimates are inefficient but are easily obtained and almost unbiased for low (L, such that A(x) = I for all x-x0 and hence
lx)I- -G(x) G(x
(x) =
= p for all x 2 Xo .
Although x0 (the hemoglobin value above which there are no more anemics) is unknown, we can make use of this relationship to obtain an estimate of p. The estimates F(x) from column 2, and 6(x) from column 3 of Appendix Table I are used to obtain estimates
)
F(x) - 6(x) 1 - G(x)
Inspection of column 4 shows that, as expected, the values of X(x) increase gradually and then fluctuate about some unknown maximum. If the assumptions on which the analysis is based are valid, this maximum should reflect the required prevalence p.,It should be possible therefore to obtain an estimate p from the values of X(x) and corresponding standard errors in column 4. This is not a trivial matter, however, because of the following: (i) A(x) is not unbiased as an estimator of X(x), and the bias depends on x, (ii) the X(x) are highly correlated for adjacent x-values, (iii) Var X(x)) increases as x and G(x) increase. Various methods for obtaining p can be suggested. One is to plot A(x) against x, for all x, and determine p by inspection from a region of relative stability of the X curve. A second, and somewhat more objective method involves looking for the first reversal in the X(x;) as xi increases, and is called the first reversal rule. It is prompted by the notion that the probability of a reversal, i.e., P [K(x,) > X(x+1)] increases as xi increases beyond x.. The prevalence (p) is determined as follows:
For predetermined x values (xi, i =1, . n) ordered so that xi < xj if i < j, evaluate X(xi), i = 1, . . n. Determine xm = min {x; X(x,) > X(xi, i)}. I i n Then p = 1/2[(xm) + X(xm+,)I. These calculations are explained in more detail in Brownie, et al.27
Nutrition in Cancer Causation and Prevention Supplement to Cancer Research Journal
1
The proceedings of a workshop conference on "Nutrition in Cancer Causation and Prevention" have been published in a supplement to the May 1983 issue of the journal Cancer Research, which is the official publication of the American Association for Cancer Research. This workshop was attended by 40 leading experts in nutrition and cancer research. The supplement, entitled Workshop Conference on Nutrition in Cancer Causation and Prevention, summarizes the participants' work on the relationship between diet and cancer and presents their recommendations for future research strategies. Copies of the supplement, publication of which was paid for by the American Cancer Society, are available by contacting: Cancer Research Editorial Office Temple Univ. School of Medicine Fels Research Institute West Building, Room 301 Philadelphia, PA 19140 AJPH September 1983, Vol. 73, No. 9
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