The Sex Difference in Tumor Incidence Is Related to the Female Condition: Models for Europe and Italy.A remarkable aspect of cancer distribution in Europe is the large spatial variability Spatial variability is characterized by different values for an observed attribute or property that are measured at different geographic locations in an area. The geographic locations are recorded using GPS (global positioning systems) while the attribute's spatial variability is of the male-female incidence ratio, from no difference up to 50%. Given the evidence of the predominantly environmental origin of cancer, we studied the ability of a set of socioeconomic indicators of the female condition to model the spatial variability of the sex difference in tumor tumor: see neoplasm. incidence at two different scales: between countries (Europe) and between provinces (Italy). The sex difference in tumor incidence correlated with female socioeconomic condition indicators at the same extent (r = 0.73) in both situations, but in opposite directions. In the European study the higher the sexual social equality "Equal Rights" redirects here. for the motto, see Equal Rights (motto) Social equality is a social state of affairs in which certain different people have the same status in a certain respect, at the very least in voting rights, freedom of speech and assembly, the extent of the lower the differential tumor incidence, whereas the opposite result was shown by the between-provinces Italian study. We also investigated the relation of the female condition indicator with other social and cultural descriptors of the same populations, and we suggest explanatory models linking female condition and pathology at the continental and local scales. Overall, our analysis supports the predominantly environmental origin of cancer and stresses the importance of relating cancer patterns to societal determinants. Our analysis also suggests that the sex difference in tumor incidence is a very useful probe for exploring the social-economic cultural correlates of cancer in human populations. We emphasize the need for a thorough analysis of the empirical correlations highlighted in ecologic studies. Key words: ecological studies, environmental carcinogenesis car·ci·no·gen·e·sis n. The production of cancer. carcinogenesis production of cancer. biological carcinogenesis viruses and some parasites are capable of initiating neoplasia. , epidemiology, principal component analysis, public health, socioeconomics. Environ Health Perspect 109:705-709 (2001). [Online 2 July 2001] http://ehpnetl.niehs.nih.gov/docs/2001/109p705-709benigni/abstract.html The evidence for the predominantly environmental origin of cancer is extremely large and varied. Classical studies have shown that cancer incidences vary to a large extent from one geographic area to another, and that the people who migrate to a country with different habits acquire--in one or two generations--the pattern of tumors that characterizes their new place (1). Similar conclusions were obtained by studies that focused on cancer incidence in twins. For example, a recent paper has demonstrated that for most cancers, identical twins identical twins pl.n. Twins derived from the same fertilized ovum that at an early stage of development becomes separated into independently growing cell aggregations, giving rise to two individuals of the same sex, identical genetic makeup, and (i.e., those with identical genes) have similar cancers no more than do fraternal twins fraternal twins pl.n. Twins that derive from separately fertilized ova and that have different genetic makeup. They may be of the same or opposite sex. (i.e., those with only 50% genetic similarity) (2); similar evidence was provided by a previous study (3). Other investigators have studied whether the genetic differences among European populations reflected similar differences in the incidence of the various types of tumors: Very little correlation existed between genetic settings and cancer incidence (4,5). Overall, all these results agree in pointing out that the environment is the major determinant of cancer; a commonly shared opinion is that the environment is responsible for at least 50-80% of cancers (1). On the other hand, this is only one facet of the more general influence that societal changes exert on the health of a population, as demonstrated, for example, by a series of studies on mortality and differential sex mortality ratios (6-9). The complexity of and the reciprocal influences among social, economic, and demographic factors and living conditions living conditions npl → condiciones fpl de vida living conditions npl → conditions fpl de vie living conditions living is demonstrated eloquently by historical research as well: The temporal sequence of economic recession, food shortage, epidemics, and an increase in mortality has been described in classic historical works (10). An unfortunate current example of this is the situation in some countries of the former Soviet Union (7,11). Within the above perspective, we have studied the geographic variability of the sex difference in tumor incidence in Europe, 1988-1992. Together with a large variation of global tumor incidence from area to area, Europe shows a concomitant large variation of sex ratios in tumor incidence, ranging from the absence of any difference (e.g., Denmark) to 50% difference in Calais, France (see Table 1). This variability looks too high to be caused by any plausible genetic difference among the European populations in terms of male-female biology; in our opinion it calls for a global environmental explanation, possibly involving a large range of the socioeconomic and cultural factors that have shaped European differences in lifestyle during its history. In particular, we focused on the available socioeconomic descriptors that best point to the changing role of the female population in European societies. Table 1. Cancer registries and differential male-female incidence. Area [Delta]N Denmark 0.06712 Oxford (UK) 0.07307 Sweden 0.07500 Mersey (UK) 0.09326 South West Region (UK) 0.09369 Iceland 0.11337 South Thames (UK) 0.11414 Cork (Eire) 0.14848 Total UK 0.14911 Yorkshire (UK) 0.16050 Birmingham (UK) 0.16132 Norway 0.17681 Manchester (UK) 0.17776 Total Scotland (UK) 0.18379 West Scotland (UK) 0.21187 Ragusa (Italy) 0.22608 Finland 0.23085 East Germany 0.25615 Warsaw (Poland) 0.26150 Modena (Italy) 0.30839 Forli (Italy) 0.31113 Ferrara (Italy) 0.31968 Parma (Italy) 0.32363 Isere (France) 0.32798 Florence (Italy) 0.33129 Saarland (Germany) 0.33857 Macerata (Italy) 0.34100 Czech Republic 0.34680 Latina (Italy) 0.35499 Turin (Italy) 0.36381 Slovenia 0.36704 Slesia (Poland) 0.36999 Navarra (Spain) 0.37382 Genoa (Italy) 0.37720 Tarragona (Spain) 0.38274 Venice (Italy) 0.39837 Trieste (Italy) 0.40496 Tarn (France) 0.40677 Varese (Italy) 0.41748 Murcia (Spain) 0.42107 Stovakia 0.42381 Doubs (France) 0.42769 Granada (Spain) 0.44292 Zaragoza (Spain) 0.44420 Bas Rhin (France) 0.44593 Belarus 0.45744 Somme (France) 0.45887 Calais (France) 0.46918 We presented a preliminary analysis of the geographic distribution of the sex difference in tumor incidence in Europe in a previous paper (12). Here we show that the relation between sex difference in tumor incidence and the socioeconomic description of female condition follows different models in Europe and in Italy. Moreover, we show that this conflict between contradictory results can be solved only by assuming a broad perspective on the history of the studied populations. Thus we need an interdisciplinary effort that combines humanistic hu·man·ist n. 1. A believer in the principles of humanism. 2. One who is concerned with the interests and welfare of humans. 3. a. A classical scholar. b. A student of the liberal arts. and naturalistic nat·u·ral·is·tic adj. 1. Imitating or producing the effect or appearance of nature. 2. Of or in accordance with the doctrines of naturalism. competences to successfully approach ecologic epidemiology studies (13). Data and Methods Cancer incidence data. The areas analyzed are those relative to the cancer registries A cancer registry is a systematic collection of data about cancer and tumor diseases. The data is collected by Cancer Registrars. Cancer Registrars capture a complete summary of patient history, diagnosis, treatment, and status for every cancer patient in the United States, and present in the 1988-1992 International Agency for Research on Cancer The International Agency for Research on Cancer (IARC, or CIRC in its French acronym) is an intergovernmental agency forming part of the World Health Organisation of the United Nations. Its main offices are in Lyon, France. (Lyon, France) compilation (14). The statistical index we used to formalize the cancer incidence data was the normalized difference between male and female global tumor incidence: [Delta]N = (PM-PF)/PM, with PM representing the whole incidence of tumors in males and PF the whole incidence of tumors in females (normalized per 1,0000 inhabitants
The game is based loosely on the concepts from SameGame. ). Table 1 lists the cancer registries with their [Delta]N values. In the study on Europe, the average value of[Delta]N for each country was computed from the available local cancer registries. Socioeconomic data. We collected socioeconomic data relative to the female condition (11 variables) (Table 2) for the 95 Italian provinces, including the 13 areas relative to the Italian cancer registries (see Table 1). We summarized all data relative to the 95 Italian provinces by applying principal component analysis, and we used the scores relative to the 13 cancer registries for the subsequent analyses. Table 2. Female condition in Italy, including correlation coefficients (factor loadings) of the original variables with the principal components. Condition ITFEM1 ITFEM2 Females with secondary school certificate(a) 0.665 -0.250 Illiterate females(a) -0.821(b) -0.222 Divorced females(c) 0.549 -0.077 Employed females out of the active population 0.907(b) 0.314 Employed females out of employed males 0.752(b) 0.232 Females seeking employment(d) -0.912(b) -0.283 Hospital beds per inhabitant 0.550 0.077 Infant deaths in the first year of life(e) -0.123 0.808(b) Kindergartens per live infant births 0.166 -0.718(b) Average number of members per household -0.037 -0.230 Added value at factor cost per inhabitant 0.888(b) 0.158 % Explained variance 43.5 15.8 Condition ITFEM3 Females with secondary school certificate(a) 0.394 Illiterate females(a) -0.168 Divorced females(c) -0.728(b) Employed females out of the active population 0.135 Employed females out of employed males 0.278 Females seeking employment(d) -0.079 Hospital beds per inhabitant -0.028 Infant deaths in the first year of life(e) 0.037 Kindergartens per live infant births 0.197 Average number of members per household 0.897(b) Added value at factor cost per inhabitant 0.283 % Explained variance 14.1 (a) Proportion of the female population [is greater than or equal to] 16 years of age. [b] These values point to the variables most important for the interpretation of the PCs. [c] proportion of married females, [d] proportion of the female population 14-29 years of age. [e] Proportion of live infant births. For the European analysis on the female condition, the data collected refer to 37 European countries. These include 16 countries for which incidence data were available (see Table 1), plus Albania, Austria, Belgium, Bosnia, Bulgaria, Croatia, Estonia, Greece, Hungary, Latvia, Lithuania, Luxembourg, Macedonia, Malta, The Netherlands, Portugal, Romania, Russia, Switzerland, Ukraine, and Yugoslavia. The European variables are listed in Table 3. We summarized the socioeconomic variables relative to these countries by principal component analysis, and used the resulting scores for the 16 countries with cancer incidence data in the subsequent correlation analyses. Table 3. Female condition in Europe, including correlation coefficients (factor loadings) of the original variables with the principal components. Condition EUFEM1 EUFEM2 Population density 0.046 0.046 Migration rate 0.211 -0.456 Birth rate 0.229 0.907(a) Fertility rate -0.006 0.815(a) Mother mean age at birth 0.858(a) 0.304 Male mean age 0.796(a) 0.391 Female mean age 0.888(a) 0.182 Infant mortality rate -0.829(a) 0.353 Urbanization rate 0.491 -0.444 Female occupation rate -0.305 -0.105 Male ocupation rate -0.073 0.200 GNP per capita 0.941(a) 0.037 Percentage of service 0.873(a) -0.252 industry workers (male) Percentage of service 0.864(a) -0.074 industry workers (female) Percentage of female ministers 0.821(a) 0.050 % Explained variance 46 20 Condition EUFEM3 EUFEM4 Population density 0.912(a) -0.051 Migration rate 0.060 0.546(a) Birth rate 0.141 -0.109 Fertility rate -0.021 -0.333 Mother mean age at birth 0.188 0.127 Male mean age 0.257 0.217 Female mean age 0.194 0.140 Infant mortality rate -0.093 -0.115 Urbanization rate 0.447 -0.168 Female occupation rate -0.675(a) -0.460(a) Male ocupation rate -0.005 -0.838(a) GNP per capita 0.038 0.037 Percentage of service 0.260 -0.056 industry workers (male) Percentage of service 0.215 0.209 industry workers (female) Percentage of female ministers -0.280 0.032 % Explained variance 9.9 4.9 GNP, gross national product. (a) These values point to the variables most important for the interpretation of the PCs. To put the constructed indicators of the female condition within a correct perspective, we contrasted them with several general socioeconomic indicators that describe the European countries and the Italian provinces, respectively. We derived and discussed in detail these general socieconomic indices in a previous work (15). Principal component analysis (PCA (tool, programming) PCA - A dynamic analyser from DEC giving information on run-time performance and code use. ). We used PCA to reduce the variables listed in Tables 2 and 3 into summary scores; in the subsequent step, we used the summary scores for the female condition in the various correlation analyses. The theory of principal components states that every symmetric covariance Covariance A measure of the degree to which returns on two risky assets move in tandem. A positive covariance means that asset returns move together. A negative covariance means returns vary inversely. or correlation matrix Noun 1. correlation matrix - a matrix giving the correlations between all pairs of data sets statistics - a branch of applied mathematics concerned with the collection and interpretation of quantitative data and the use of probability theory to estimate population relating p variables x1, x2, ... xn can be transformed into particular linear combinations by rotating the matrix into a new coordinate system coordinate system Arrangement of reference lines or curves used to identify the location of points in space. In two dimensions, the most common system is the Cartesian (after René Descartes) system. . This rotation is produced by multiplying each of the original data by appropriate coefficients. The original matrix is rotated such that the axis defined by the first principal component (PC1) is aligned in the direction of greatest variance. This procedure is repeated until a set of [Delta]N orthogonal At right angles. The term is used to describe electronic signals that appear at 90 degree angles to each other. It is also widely used to describe conditions that are contradictory, or opposite, rather than in parallel or in sync with each other. (uncorrelated) components is obtained, arranged in descending order of variance. In this transformation, none of the information contained within the original variables is lost, and the derived components can be manipulated statistically in the same way as the original variables. Moreover, the transformation is useful because most of the significant total variance is concentrated within the first few uncorrelated PCs, whereas the remaining PCs mainly contain noise (16,17). Results The strategy adopted in this study was the following: The female condition in Europe (country-based analysis) and in Italy (province-based analysis) was separately parameterized by summary indicators obtained by PCA of several variables selected for their relevance to the female condition. As a further check, the specificity of the summary indicators (PCs) of the female condition was controlled against general socioeconomic descriptors relative to the same areas. After this check, the female condition indicators were contrasted with the sex differences in tumor incidence ([Delta]N values). We had demonstrated previously the time invariance in·var·i·ant adj. 1. Not varying; constant. 2. Mathematics Unaffected by a designated operation, as a transformation of coordinates. n. An invariant quantity, function, configuration, or system. of the incidence data between the 1985-1988 and 1988-1992 periods (4). Moreover, we had demonstrated the existence of a marked country effect in the tumor distribution in Europe (12). The country effect for [Delta]N (analysis of variance) scored an F value of 59.5 correspondent to p [is less than] 0.0001. This reassured us about the correctness of using incidence data averaged at the country level for the European analysis. We used the incidence data relative to the 13 Italian locations as such in the province scale model, which we elaborated separately from the analysis on whole Europe. The Italian study. The female condition in the Italian provinces was described by 11 indicators selected from the 1991 Census data (18); these included a range of different aspects (cultural conditions, welfare, occupation, health). We condensed con·dense v. con·densed, con·dens·ing, con·dens·es v.tr. 1. To reduce the volume or compass of. 2. To make more concise; abridge or shorten. 3. Physics a. this information into summary indicators by applying PCA, which produced three components (ITFEM1-ITFEM3), collectively explaining 73% of total variance. ITFEM1 alone explained 43.5% of total variability. The correlation matrix between original variables and factors (factor loadings matrix) is reported in Table 2. The inspection of the factor loadings indicated that the first factor (ITFEM1) pointed to the general level of economic development and, more important, to the female occupation and the percentage of the female graduates, but it was inversely correlated with the percentage of illiterate ILLITERATE. This term is applied to one unacquainted with letters. 2. When an ignorant man, unable to read, signs a deed or agreement, or makes his mark instead of a signature, and he alleges, and can provide that it was falsely read to him, he is not bound by females and the rate of unemployment of young females. The first factor was by far the most relevant component (more than 40% of explained variability) of the local differences in terms of female condition. The second factor (ITFEM2) was positively correlated with the rate of infant deaths Noun 1. infant death - sudden and unexpected death of an apparently healthy infant during sleep cot death, crib death, SIDS, sudden infant death syndrome and negatively correlated with the number of kindergartens for each live birth. Thus, this factor described the level of medical and social assistance in a given area. The third factor was negatively correlated with the divorce rate and positively correlated with the average number of members per household. This factor can be considered a descirptor of the female relational condition (i.e., the changing approach of women to family and extrafamily matters). Thus the first PC (ITFEM1) can be considered the best summary score of the advancement of the female condition in the different Italian areas (provinces). We checked the specificity of ITFEM1 by contrasting it with other general socioeconomic indicators of Italy. Using PCA in a previous work (15), we analyzed 36 general descriptors of the Italian society and we derived two summary indicators: ITDEM1 and ITDEM2. ITDEM1 is a general indicator of economic development and follows the well known north-south gradient that characterizes many aspects of Italian society; the second component, ITDEM2, was related to the urban-nonurban character of the provinces studied. The correlation coefficient Correlation Coefficient A measure that determines the degree to which two variable's movements are associated. The correlation coefficient is calculated as: between ITFEM1 and the first component of general (not sex-related) socioeconomic indicators (ITDEM1) was relatively weak (r = 0.47), though statistically significant (p [is less than] 0.001). ITFEM1 therefore conveys some important sex-related specific information not simply assimilable as·sim·i·la·ble adj. That can be assimilated: assimilable nutrients; assimilable information. as·sim to the economic development. The next step of the analysis was to compare the sex difference in cancer incidence ([Delta]N) with the three factors (ITFEM1-ITFEM3) describing the female socioeconomic condition. Notwithstanding the small sample size (13 provinces), the [Delta]N values of the Italian provinces were sufficiently widespread, ranging from around 20% sex difference (Ragusa: [Delta]N = 0.226) to 40% (Varese: [Delta]N = 0.417), The Pearson correlation coefficient of [Delta]N with the socioeconomic components was, respectively: r (ITFEM1, [Delta]N) = 0.729, r (ITFEM2, [Delta]N) = -0.261, r (ITFEM3, [Delta]N) = -0.306. Only the correlation between ITFEM1 (already selected as summary descriptor (1) A word or phrase that identifies a document in an indexed information retrieval system. (2) A category name used to identify data. (operating system) descriptor of the female condition) and [Delta]N was statistically significant (p [is less than] 0.005). Figure 1 reports the observed correlation. [GRAPH OMITTED] As a further check for the specificity of the observed relations, we performed a similar analysis having as pathologic end point the observed incidence of AIDS cases (both sexes) in the 95 Italian provinces. In the case of AIDS, a disease with a natural history completely different from cancer, the female condition components were not correlated with the relative incidence of the pathology, whereas ITDEM1 (as well as, marginally, ITDEM2) was significantly related to AIDS incidence. This result further demonstrates the specificity of the female condition indicator as predictor of the sex difference in cancer incidence. The positive sign of the relationship between ITFEM1 and [Delta]N should be noted: its meaning will discussed below in the Discussion section. Table 4 summarizes the main results presented above.
Table 4. Italian descriptors: Pearson correlation matrix.
ITFEM1 ITDEM1 ITDEM2 AIDS
ITFEM1 1.00 0.47 -0.14 0.25
ITDEM1 1.00 0.00 0.58
ITDEM2 1.00 0.30
AIDS 1.00
[Delta]N
PM
PF
ITFEM1 [Delta]N PM PF
ITDEM1 0.73(a) 0.40 0.16
ITDEM2 0.45 0.78(a) 0.86(a)
AIDS 0.59 0.43 0.24
[Delta]N 0.39 0.34 0.29
PM 1.00 0.82(a) 0.58
PF 1.00 0.94(a)
1.00
(a) These values point to the variables most important for
the interpretation of the PCs.
European study. The female condition in the various European countries is described by 15 variables. A PCA of this socioeconomic data set produced four PCs (EUFEM1-EUFEM4), which collectively explain 86% of variance (Table 3). Component 1 (EUFEM1) alone explains 46% of total variability. Inspection of the variables maximally max·i·mal adj. 1. Of, relating to, or consisting of a maximum. 2. Being the greatest or highest possible. n. Mathematics An element in an ordered set that is followed by no other. loaded on EUFEM1 shows that this component summarizes the advancement of the female socioeconomic condition occurring in the last decades in the most developed countries: In fact, the reaching of apical apical /ap·i·cal/ (ap´i-k'l) pertaining to an apex. a·pi·cal adj. 1. Relating to the apex of a pyramidal or pointed structure. 2. positions for women (percentage of female ministers, loading = 0.821) goes hand in hand with the per capita [Latin, By the heads or polls.] A term used in the Descent and Distribution of the estate of one who dies without a will. It means to share and share alike according to the number of individuals. GNP GNP See: Gross National Product (loading= 0.941) and the increase of mother's mean age at birth (loading = 0.858). EUFEM2 is related to the birth and fertility rates Noun 1. fertility rate - the ratio of live births in an area to the population of that area; expressed per 1000 population per year birth rate, birthrate, fertility, natality , and represents the relative extent and timing of the demographic transition Demographic transition occurs in societies that transition from high birth rates and high death rates to low birth rates and low death rates as part of the economic development of a country from a pre-industrial to an industrialized economy. (contraction of the population increase) experienced by developed societies in recent decades. EUFEM3 points to a (probably spurious spu·ri·ous adj. Similar in appearance or symptoms but unrelated in morphology or pathology; false. spurious simulated; not genuine; false. ) relation between population density and female occupation rate, whereas EUFEM4 mainly describes problems linked to unemployment and migration, with no sex connotation con·no·ta·tion n. 1. The act or process of connoting. 2. a. An idea or meaning suggested by or associated with a word or thing: . We compared EUFEM1 (the best summary score for the female condition in Europe) with three general socioeconomic indicators relative to the European countries (EUDEM1-EUDEM3); We had obtained EUDEM1-EUDEM3 in a previous study (15). EUFEM1 scored Pearson correlation coefficients of 0.66 and 0.65 with EUDEM1 and EUDEM2, respectively. This result indicates that, unlike in Italy, the female condition indicator for the rest of Europe was largely coincident co·in·ci·dent adj. 1. Occupying the same area in space or happening at the same time: a series of coincident events. See Synonyms at contemporary. 2. with the information carried by socioeconomic indicators that are not directly sex-related. Both EUFEM1 and EUDEM1 scored a significant correlation with [Delta]N: r = -0.74, p [is less than] 0.001 and r = -0.80, p [is less than] 0.001, respectively, whereas EUDEM2 was not significantly correlated with [Delta]N. None of the above composite indices was able to predict the global incidence of infective infective /in·fec·tive/ (in-fek´tiv) 1. capable of producing infection. 2. infectious (1). in·fec·tive adj. Capable of producing infection; infectious. diseases, thus confirming the specificity of the measured demographic and socioeconomic indices for tumor pathology. Table 5 summarizes the above correlations.
Table 5. European descriptors: Pearson
correlation matrix.
EUFEM1 EUDEM1 EUDEM2 EUDEM3
EUFEM1 1.00 0.66(a) 0.65(a) -0.17
EUDEM1 1.00 0.00 0.00
EUDEM2 1.00 0.00
EUDEM3 1.00
INF
[Delta]N
PM
PF
INF [Delta]N PM PF
EUFEM1 0.28 -0.74(a) -0.49 0.59
EUDEM1 -0.03 -0.80(a) -0.37 0.74
EUDEM2 0.28 -0.24 -0.06 0.25
EUDEM3 0.28 0.01 0.33 0.23
INF 1.00 0.01 -0.10 0.00
[Delta]N 1.00 0.61(a) -0.84(a)
PM 1.00 -0.10
PF 1.00
(a) These values point to the variables most important
for the interpretation of the PCs.
Discussion Figure 2 displays the relation between EUFEM1 and [Delta]N. It shows that the earlier and more pronounced the advancement of female condition (higher values of EUFEM1, Northern Europe), the lower the sex differences of tumor incidence (lower values of [Delta]N, inverse relationship A inverse or negative relationship is a mathematical relationship in which one variable decreases as another increases. For example, there is an inverse relationship between education and unemployment — that is, as education increases, the rate of unemployment between EUFEM1 and [Delta]N). This is exactly what should be expected by the simplest line of reasoning Noun 1. line of reasoning - a course of reasoning aimed at demonstrating a truth or falsehood; the methodical process of logical reasoning; "I can't follow your line of reasoning" logical argument, argumentation, argument, line : Progress toward socioeconomic equality between the sexes equalizes lifestyles and thus lowers differences in pathology profiles. This simple picture, though it remains at the European macro scale (between-countries variability) was contradicted at the micro scale studied (Italian within-country variability), where we found a positive relationship between[Delta]N and ITFEM1 (Table 4, Figure 1). The incidence of tumors in males is uniformly greater than that in females, both in Italy and in all of Europe (see Table 1): High [Delta]N values point to a relatively better differential condition of females than of males. This trend implies that in Europe the socioeconomic advancement of women meant that they lost the relative benefits of a cancer incidence lower than that of men. In Italy, the positive relation observed between [Delta]N and ITFEM1 implies that the highest differential between sexes in terms of pathology parallels the improvement of female socioeconomic conditions, which is the exact contrary of what we observed in Europe on a general ground. [ILLUSTRATION OMITTED] The Italian result is, at a first sight, paradoxical: Given that progress in the female condition usually is interpreted as reduced socioeconomic differences between sexes, we should observe a parallel reduced heterogeneity het·er·o·ge·ne·i·ty n. The quality or state of being heterogeneous. heterogeneity the state of being heterogeneous. in terms of pathology and thus a negative correlation Noun 1. negative correlation - a correlation in which large values of one variable are associated with small values of the other; the correlation coefficient is between 0 and -1 indirect correlation between ITFEM1 and [Delta]N, as in the rest of Europe. But if we consider the peculiarities of Italian history (19), the observed results are less paradoxical. In Italy, the progressive emancipation of women has followed industrialization industrialization Process of converting to a socioeconomic order in which industry is dominant. The changes that took place in Britain during the Industrial Revolution of the late 18th and 19th century led the way for the early industrializing nations of western Europe and , which in turn, until the last 34 years, has practically involved only the northern-central part of the country. This implies that ITFEM1 is an indirect index of the relative precocity precocity /pre·coc·i·ty/ (-kos´it-e) unusually early development of mental or physical traits.preco´cious sexual precocity precocious puberty. and intensity of industrialization. In fact, Table 2 shows that where industrialization was more intense and prolonged, the percentages of occupation are more homogeneous between sexes and, in general, allowed more women to enter the labor force. However, the emancipation of Italian women followed industrialization two or three generations after industrialization. Only 30 years ago, it was common for the husband to be employed outside the home and the wife to be busy with domestic duties. Thus, men in the more industrialized in·dus·tri·al·ize v. in·dus·tri·al·ized, in·dus·tri·al·iz·ing, in·dus·tri·al·iz·es v.tr. 1. To develop industry in (a country or society, for example). 2. Italian areas experienced environmental and life-style conditions quite different from those of the female population, given the relatively serious health hazards health hazard Occupational safety Any agent or activity posing a potential hazard to health. Cf Physical hazard. linked to industrial work (now drastically reduced within approximately one generation) and the concomitant diffusion of such unhealthy habits as cigarette smoking. Conversely, agricultural work provided a much more homogeneous environment Hardware and system software from one vendor; for example, an all-IBM or all-Windows shop. Contrast with heterogeneous environment. for both sexes. Given the latency time of tumor induction (20 years on average) and the fact that our data refer to the 1988-1992 period, our results should be interpreted as a consequence of the different timings of industrialization and female emancipation within the same areas. For Italy, therefore, we are observing a transient phenomenon linked to the over-60s population, the last generation that experienced the industrial environment before female emancipation. This interpretation is strengthened by the extent of the correlation between global cancer incidence in males (PM) and females (PF) at the two scales: Although they are highly correlated at the scale of the Italian provinces (r = 0.94 for Italy), they are independent at the scale of the European countries (r = -0.10 for the whole Europe) (Tables 4 and 5). This points to the coexistence co·ex·ist intr.v. co·ex·ist·ed, co·ex·ist·ing, co·ex·ists 1. To exist together, at the same time, or in the same place. 2. of specific national models, which can be different from the Europe model. Obviously, the value of the above conclusions depends on the reliability of the measures we used to define the elusive concept female condition. In both cases we analyzed with PCA a set of variables related both to the type of society and to specific characteristics of the female population. We were unable to retrieve the same variables in the existing public databases, so the sets of variables used in the two cases were different. However, the female condition indicators derived for Italy (ITFEM1) and for Europe (EUFEM1) have similar meaning: They point to affluent societies affluent society, term coined by John Kenneth Galbraith in The Affluent Society (1958) to describe the United States after World War II. An affluent society, as the term was used ironically by Galbraith, is rich in private resources but poor in public ones (northern Italy Northern Italy comprises of two areas belonging to NUTS level 1:
Both the European and Italian models proposed are informative, but their differences must be considered in attempting to explain observed empiric correlations. The observed scale effect restricts the generalizability of ecologic studies and points to the need for interdisciplinarity in interpreting them. It is impossible to derive a comprehensive (biologic?) theory that includes society, individuals, and cells because different phenomena and mechanisms act at different levels. Thus, when performing ecologic studies we are dealing with empiric evidence calling for an operational and not a biologically mechanistic mech·a·nis·tic adj. 1. Mechanically determined. 2. Of or relating to the philosophy of mechanism, especially one that tends to explain phenomena only by reference to physical or biological causes. interpretation. What is needed is the possibility of expressing the empiric models in terms of operationally modifiable variates, to make a consequent public health intervention health intervention Health care An activity undertaken to prevent, improve, or stabilize a medical condition possible. On a more theoretical ground, we share the vision of S. Levine (20): [There] is no single correct scale of investigation.... [The] pattern exists at all levels and on all scales, and recognition of this multiplicity of scales is fundamental to describing and understanding ecosystems.... [T]here can be no "correct" scale level of aggregation.... [A] central challenge in ... theory must be an elaboration of ... how scales relate, and how the measurement and dynamics of scale phenomena vary across scales.... We must recognize explicitly the multiplicity of scales ... and develop a perspective that looks across scales and that builds upon a multiplicity of models rather than seeking a single "correct" one. As a matter of fact, the investigations aimed at linking different size and temporal scales In snakes, the temporal scales are those scales on the side of the head between the parietals and the supralabials, and behind the postoculars.[1] There are two types of temporal scales:[1]
In this respect, the loss indicators of female condition lose specificity, going from the more detailed Italian picture to the coarser grain European picture. In the Italian provinces data, ITFEM1 is only loosely correlated with the more general socioeconomic variables, whereas EUFEM1 is largely reconstructible from the socioeconomic descriptors of the European countries. In fact, this is a general characteristic of every type of empiric correlations: The coarser the grain of the representation, the less specific the picture. This is the obvious consequence of collapsing all the local models into an average general model, which reflects only what is common to the various local models. This also makes the various scales of analysis largely independent from one another. This very general feature of systems analysis (23) is driven in this case by the different historical determinants that shaped the female condition variability at the macro scale (economic development) and at the micro scale (time delay between economic development and female emancipation). The need to consider simultaneously different scales of phenomena makes the debate surrounding the "real biomedical bi·o·med·i·cal adj. 1. Of or relating to biomedicine. 2. Of, relating to, or involving biological, medical, and physical sciences. explanation" of pathologic events largely devoid of immediate applicative ap·pli·ca·tive adj. 1. Characterized by actual application; applied. 2. Practical; applicatory. ap interest, whereas practical health problems require an efficient interdisciplinary collaboration between scientists coming from different fields. Our results confirmed the hypothesis that the sex difference in cancer incidence in Europe is largely attributable to differences in lifestyle and environmental factors. This suggests that the sex difference in cancer incidence can be a useful probe for environmental factors in the epidemiologic studies epidemiologic study A study that compares 2 groups of people who are alike except for one factor, such as exposure to a chemical or the presence of a health effect; the investigators try to determine if any factor is associated with the health effect . Address correspondence to R. Benigni, Laboratory of Comparative Toxicology toxicology, study of poisons, or toxins, from the standpoint of detection, isolation, identification, and determination of their effects on the human body. 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(12.) Benigni R, Giaimo R, Matranga D, Giuliani A. The cultural heritage shapes the pattern of tumor profiles in Europe: a correlation study. J Epidemiol Community Health 54:262-268 (2000). (13.) Garruto RM, Little MA, James GD, Brown DE. Natural experimental models: the global search for biomedical paradigms among traditional, modernizing and modern populations. Proc Natl Acad Sci 96:10536-10543 (1999). (14.) Cancer Incidence in Five Continents, Vol 7. Lyon: International Agency for Research on Cancer, (1997). (15.) Benigni R, Giuliani A. Tumor geography of Italy This article describes the geography of Italy.
(16.) Lebart L, Morineau A, Warwick KM. Multivariate The use of multiple variables in a forecasting model. Descriptive Statistical Analysis. New York New York, state, United States New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of :Wiley, 1984. (17.) Benigni R, Giuliani A. Quantitative modeling and biology: the multivariate approach. Am J Physiol 266:R1697-R1704 (1994). (18.) ISTAT ISTAT Istituto Centrale di Statistica (Italian: Italian National Statistics Institute) ISTAT International Society of Transport Aircraft Trading . Census 1991. Rome: Istituto Nazionale di Statistica Istituto Nazionale di Statistica (Istat) is the Italian national statistical institute. It was created in 1926 to collect and organize essential data about the nation. Administering the census is one of its activities. . Available: http://www.istat.it [cited 17 May 2001]. (19.) Mack Smith D. Modern Italy: A Political History. Ann Arbor Ann Arbor, city (1990 pop. 109,592), seat of Washtenaw co., S Mich., on the Huron River; inc. 1851. It is a research and educational center, with a large number of government and industrial research and development firms, many in high-technology fields such as , MI:University of Michigan (body, education) University of Michigan - A large cosmopolitan university in the Midwest USA. Over 50000 students are enrolled at the University of Michigan's three campuses. The students come from 50 states and over 100 foreign countries. Press, 1997. (20.) Levine S. Ecology in theory and application. In: Applied Mathematical Ecology (Hallam T, Gross L, Levine S, eds). New York:Springer springer a North American term commonly used to describe heifers close to term with their first calf. , 1989;3-10. (21.) Laughlin RB, Pines D, Schmalian J, Stojkovic BP, Wolynes P. The middle way. Proc Natl Acad Sci 97:32-37 (2000). (22.) Feynman R. The Character of Physical Law. London:BBC BBC in full British Broadcasting Corp. Publicly financed broadcasting system in Britain. A private company at its founding in 1922, it was replaced by a public corporation under royal charter in 1927. , 1965. (23.) Rand DA, Wilson HB. Using spatio-temporal chaos and intermediate-scale determinism to quantify spatially extended ecosystems Proc R Soc Lond B Biol Sci 259:111-117 (1995). Romualdo Benigni, (1) Rosa Giaimo,(2) Domenica Matranga,(3) and Alessandro Giuliani(1) (1)Laboratory of Comparative Toxicology and Ecotoxicology, Istituto Superiore di Sanita, Rome, Italy; (2)Department of Statistics, Faculty of Economics, University of Palermo The University of Palermo (Italian: Università degli Studi di Palermo) is a university located in Palermo, Italy, and founded in 1806. It is organized in 12 Faculties. , Italy; (3)Italian Institute of Statistics (ISTAT), Palermo Branch, Palermo, Italy |
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