Economics and Human Biology 15 (2014) 56–66

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Are chronic diseases related to height? Results from the Portuguese National Health Interview Survey Julian Perelman * Escola Nacional de Sau´de Pu´blica, Universidade Nova de Lisboa, Avenida Padre Cruz, 1600-560 Lisbon, Portugal

A R T I C L E I N F O

A B S T R A C T

Article history: Received 24 December 2013 Received in revised form 18 June 2014 Accepted 21 June 2014 Available online 8 July 2014

This paper analyze the association between height and chronic diseases in Portugal and the extent to which this relationship is mediated by education. The sample upon which the analysis is based comprised those participants in the 2005/2006 Portuguese National Health Interview Survey (n = 28,433) aged 25–79. Logistic regressions measured the association of height with ten chronic diseases, adjusting for age, lifestyle, education, and other socioeconomic factors. Among women, an additional centimeter in stature significantly decreased the prevalence of asthma, chronic pain, and acute cardiac disease, by 0.057, 0.221, and 0.033 percentage points, respectively. Also, mental disorders were significantly less prevalent in the last quartile of height. Among men, an additional centimeter in height was associated with a 0.074 lower prevalence of asthma, and men in the last quartile of height were significantly less at risk of acute cardiovascular disease. There was no significant association between height and the risk of diabetes, high blood pressure, cancer, and pulmonary diseases. As for the impact of education, women with a tertiary level were on average 5.3 cm taller than those with no schooling; among men, the difference was almost 9 cm. Adjusting for education reduced the height-related excess risk of ill health by 36% on average among men, and by 7% among women. The analysis indicates that there is a significant association of height with several chronic conditions, and that education plays a mediating role in the height–health connection. By emphasizing the role of height and education as determinants of chronic conditions, this paper also highlights the role of conditions related to childhood health and socioeconomic background. ß 2014 Elsevier B.V. All rights reserved.

Keywords: Height Education Chronic disease Portugal

1. Introduction Since the second part of the 20th century, Portugal has undergone a dramatic increase in personal wealth and, not coincidentally, in average stature, generally considered a reliable indicator of standard of living and well-being (McKeown, 1976; Steckel, 1995, 2008); between 1904 and 2000, men’s average height increased by almost 9 cm

* Tel.: +351 217512196; fax: +351 217582754. E-mail address: [email protected] http://dx.doi.org/10.1016/j.ehb.2014.06.001 1570-677X/ß 2014 Elsevier B.V. All rights reserved.

(Padez, 2003). Children’s health and education have likewise undergone impressive improvements. In 1960,the infant mortality rate in Portugal was 77.5 per 1000 live births, a value that is comparable to that observed today in the poorest countries of the world; the current rate is 3 per 1000, among the lowest worldwide (World Health Organization, 2013). The illiteracy rate was 25.7% in 1970, compared with 5.2% today. Scrutiny of such large changes over a relatively short period of time is necessary if we are to understand the country’s current state of health, since a significant segment of its adult population went through those changes as they were

J. Perelman / Economics and Human Biology 15 (2014) 56–66

growing and/or as adults. The purpose of this paper is dual: to examine how height, as a marker of early-life circumstances, is associated with chronic diseases in Portugal, and to determine the extent to which this relationship is mediated by education. The association between height and mortality has been observed in several studies that have tracked cohorts over long periods of time (Engeland et al., 2003; Jousilahti et al., 2000; Koch, 2011; Peck and Va˚gero¨, 1989; Waaler, 1984). There is a moderate negative correlation between height and mortality from cardiovascular disease and a close negative correlation between height and mortality from respiratory disease (Leon et al., 1995). The one significant exception to this rule is the positive correlation among women between height and the risk of several cancers unrelated to smoking (Green et al., 2011). The most cited explanation for the height–health connection is that height is a marker of health hazards and socioeconomic conditions in childhood and adolescence (Batty et al., 2006, 2009; Case and Paxson, 2010; Jousilahti et al., 2000). Through the systematic analysis of the height patterning of several chronic diseases, this paper contributes to the growing body of evidence featuring conditions that have received relatively little attention, such as asthma, depression, diabetes, high blood pressure, and chronic pain. As noncommunicable conditions represent the highest disease burden in high-income countries (Lopez et al., 2006), this focus offers insights into population-wide health trends and therefore into health-policy implications. Showing that there is a connection between height and health does not, however, elucidate the causal pathways between these factors. Indeed, the same early-life factors that influence height also shape an individual’s socioeconomic position in adulthood, in particular educational achievement. In addition, there is evidence that height is a determinant of education and income (Steckel, 1995).

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Consequently, we may erroneously contribute to height the well-known social inequalities in health. This paper therefore features a theoretical model of the height patterning of health, and tests the mediating role of education in the height–health connection. 2. Background The following simple model of the height–health connection charts two possible pathways from height to health (Fig. 1): height is directly associated to health (Hypothesis 1); and the height–health association is mediated by education (Hypothesis 2). Hypothesis 1. Height is directly associated to health. The first and most obvious explanation for the height– health connection is that height proxies childhood circumstances. That is, relatively tall people are, on the whole, healthier than others because their health during childhood, which determined their height and health once they have attained adulthood, was better than that of other children. Height variation is largely explained by heritability – above 80% according to recent estimates (Perola et al., 2007; Silventoinen et al., 2003) – but the large worldwide increase in height over the last century has shifted the focus to childhood environment (Beard and Blaser, 2002; Steckel, 1995). Childhood nutrition has long been emphasized as a key long-term factor (Fogel, 2004). Recently, Crimmins and Finch (2006) argued that infectious diseases and microbial infections by diverting energy from growth, and by provoking organ insufficiency or vital-organ damage, largely account for stunting. Several studies reveal a negative correlation between the infant mortality rate during the birth year, on the one hand, and height and adult mortality, on the other, for older cohorts (Crimmins and Finch, 2006) and more recent alike (Bozzoli et al., 2009).

Fig. 1. The association of height and health: possible causal pathways.

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Both nutrition and health during childhood are closely related with living conditions, and the literature consistently shows that socioeconomic background is a reliable predictor of height (Cavelaars et al., 2000; Finch and Beck, 2011; Padez and Johnston, 1999; Power et al., 2002; Silventoinen, 2003). In Fig. 1, these determinants of stature are depicted by pathways P1 and P2. Simultaneously, there is a general consensus that poor health in childhood elevates the risk of poor adult health (for a review, see Elo and Preston (1992)). Wadsworth and Kuh (1997) summarize the results from the National Health Survey and Development carried out in the UK, which followed newborns in March 1946; among other findings is the significant impact of serious illnesses in childhood on later physical disability, of low birth weight on raised systolic blood pressure, and of respiratory illness in the first two years of life on adult chronic obstructive pulmonary disease. Later results from the National Child Development Study, which followed babies born in the UK in 1958, show that uterine development influences health at age forty-two, as measured by, for instance, the number of chronic diseases during childhood and adolescence (Case et al., 2005). In Fig. 1, these early-life effects are depicted by pathways P5 and P6. The second explanation is based on Batty et al. (2009), who suggest several causal mechanisms for the impact of on health outcomes of height alone, independent of its early-life determinants. To better understand how this hypothesis differs from the previous one, let us consider a child afflicted with a severe infectious pulmonary disease, which in turn provokes organ damage and leads to chronic pulmonary disease in adulthood. The childhood disease causes stunting; according to the first, ‘‘height as proxy’’, explanation, the adult disease is unrelated the stunting, which only proxies early-life conditions. On the other hand, if the childhood pulmonary infection affects growth, leading to lower lung capacity, which in turns provokes pulmonary disease during adulthood, then this disease is the direct consequence of stunting. Other direct influences include the higher lung capacity and greater vessel diameter of the taller members of the population, which protect against cardiovascular disease, and insulin-like growth factor (IGF), which determines both stature and atherosclerosis. Conversely, height may be positively correlated with cancer because a greater number of cells means a greater risk of malignancy; diet, too may stimulate growth and thus the risk of cancer. Green et al. (2011) also mention that IGF in adulthood affects cancer risk. These hypothesized pathways linking height to health are depicted by H1 in Fig. 1. The height–health connection (H1) may be directly related to the effect of stature; on the other hand, it may stem from the fact that height reflects early-life circumstances that affect adults’ health. Findings by Koch (2011) support to this hypothesis, since the effect of height on mortality is not affected when education is accounted for.

agreed that a positive correlation between education and health in adulthood prevails across countries (Huisman et al., 2005) and time periods (Goldman and Smith, 2011; Mackenbach et al., 2003). There is a close negative correlation between educational level and both the rate of mortality (Kunst and Mackenbach, 1994; Lleras-Muney, 2005) and the prevalence of certain diseases (Huisman et al., 2005) for different age groups (Dupre, 2007; Lynch, 2003). This positive correlation may involve factors associated with a relatively high educational level, including a healthful lifestyle, access to information, the ability to process information, and access to economic resources and to an advantageous social position (Cutler and Lleras-Muney, 2006). The second hypothesis is that the height–health connection is mainly due to the association between education and height (hypothesized pathway H2a in Fig. 1), while education is the main health determinant (hypothesized pathway H2b). The rationale for this hypothesis is that both stature and education are largely determined by biological health in early life and by socioeconomic background. The determinants of educational level are very similar to those of physical stature, presented in the previous section. Using data from the United States, Belzil and Hansen (2003) show that household background accounts for 68% of explained variation in school attainment. Evidence about inter-generational transmission of education has been confirmed for the UK (Galindo-Rueda and Vignoles, 2005; Machin and Vignoles, 2004), Switzerland (Bauer and Riphahn, 2007), Germany (Riphahn and Tru¨bswetter, 2013), and Portugal (Carneiro, 2008), among other countries. As for the impact of childhood health on educational achievement, the evidence is consistent, too (Case et al., 2005; Currie, 2009). In Fig. 1 these determinants of education are depicted by pathways P3 and P4. Likewise, the literature emphasizes that height predicts educational achievement (Magnusson et al., 2006) and earnings later in life (Persico et al., 2004; Steckel, 1995). Again, the explanation is that both height and education are influenced by common factors; these factors are more precisely measured in recent studies. Case and Paxson (2008), for example, find a positive association between height and cognitive ability, using data on test scores of children five to eleven years old. Cinnirella et al. (2011) find that the educational premium is related to noncognitive skills; their data for children two to three years of age show among boys height is positively correlated with social and motor skills, and that among girls it is positively correlated with verbal and motor skills. According to these authors, both cognitive and non-cognitive skills are thus the determinants of both educational attainment and height. However, they do not rule out the possibility of discrimination among shorter children, a potentiality that is also mentioned by Magnusson et al. (2006). 3. Materials and methods

Hypothesis 2. The height–health association is mediated by education.

3.1.1. Data

Studies for different countries reveal that the association between height and education is worldwide (Cavelaars et al., 2000; Singh-Manoux et al., 2010); likewise, it is generally

The data used derived from the last wave of the Portuguese National Health Interview Survey (NHIS) conducted in 2005–2006 in all regions of Continental

J. Perelman / Economics and Human Biology 15 (2014) 56–66

Portugal on 41,193 subjects. The information was collected by trained interviewers during face-to-face interviews with a household representative, and included several questions about health, socioeconomic, and demographic conditions. The population sample was designed to be representative of the seven statistical regions of the country. The sample design was complex, including systematic stratification and selection of conglomerates (for more information see Instituto Nacional de Sau´de (2008)). The database included sampling weights to be used in data analysis, computed based on the inverse of the probability of selection of each sampling unit, and further corrected for non-responses and for the effective number of subjects evaluated (Dourado et al., 2013). The analysis comprised data on 28,433 persons aged 25–79. Data on younger participants were excluded because they had yet to attain their adult height and complete their education, and because including in the computations the near absence of serious diseases among this age set would have skewed the results. 3.2. Variables Chronic conditions were summarized by thirteen selfreported chronic diseases surveyed in the NHIS, all of which represent a reduction in quality of life (diabetes, asthma, high blood pressure, chronic pain, rheumatic disease, osteoporosis, malignancy/cancer, renal failure, chronic anxiety, depression, stroke, myocardial infarction, and chronic obstructive pulmonary disease (COPD)/chronic bronchitis). For each disease, respondents were asked ‘‘Do you have or have you ever had [name of the disease]?’’. Answers were either ‘‘Yes’’ or ‘‘No’’; thus all variables were coded as dichotomous. In order to obtain a larger number of cases and aggregate conditions sharing similar characteristics, the following diseases were paired: rheumatic disease and osteoporosis; chronic anxiety and depression; stroke and myocardial infarction. To limit the potential self-reporting bias, those cases that were undiagnosed by a health professional as determined by the respondents’ answers to the question ‘‘Did a physician or a nurse tell you that you have this disease?’’ were excluded. The percentage of excluded cases was lower than 5% for all conditions except rheumatic disease (7%) and mental disorders (9%). To complete the analysis, a global subjective health indicator was also studied. Self-assessed or subjective health was measured by asking respondents, ‘‘From a general viewpoint, how do you consider your health state?’’. Responses were on a five-point scale, ranging from ‘‘very bad’’ to ‘‘very good’’. This variable was recoded as a dichotomous variable with value ‘‘1’’ for those reporting ‘‘bad’’ or ‘‘very bad’’ health, and ‘‘0’’ for those who did not. The explanatory variables of interest were height and education. Height was measured by means of the question ‘‘How tall are you in your stocking feet?’’. Height was first modeled as continuous and then as a categorical variable using quartiles. Education was measured as credentials rather than years of education because credentials provide a better measure of educational achievement. Education groups were constructed on the basis of the International Standard Classification of Education (ISCED 97).

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Demographic, lifestyle, and other socioeconomic characteristics, being potential confounding factors, were included among the variables. Demographic variables included age and its squared value, to account for nonlinearities in the association between age and prevalence of disease. Regarding lifestyle factors, studies show a significant positive association between height and obesity among adults (Bosy-Westphal et al., 2009; Komlos, 2010). The issue of smoking prompts a similar question since it is negatively correlated with height (Batty et al., 2009). Gunnell et al. (2001) thus refer to the BMI (body mass index), socioeconomic position, and smoking as the most relevant and commonly included confounding factors in studies of the height–health connection. Obesity was defined as a dichotomous variable with a value ‘‘1’’ in case of a BMI equal or above30. Obesity was preferred to physical activity and diet that are likely to be biased by reverse causation (i.e., health condition determines the amount of exercise and the type of diet, and not the reverse). Ever-smoked status was preferred to current smoking for the same reason (those who are in bad health are more likely than others to stop smoking); this variable was also defined as dichotomous, with a value ‘‘1’’ if the person reported either smoking at least one cigarette per day, or being a former smoker. Income and employment status were used as additional controls for socioeconomic status (SES), also following the recommendation of Gunnell et al. (2001). Regarding income, respondents were presented with a list of ten income categories and asked to tick the one that was the best proxy of his/her disposable household income for the previous month. In order to calculate individual income, the ‘‘OECD-modified’’ equivalence scale was used. According to this scale, a weight of 1 was attributed to the first adult, 0.5 to the second adult and each subsequent person aged 14 and over, and 0.3 to anyone under 14. Employment status was one of five categories: employee, unemployed, housewife, retired, and those out of the labor force for other reasons (mainly disabled persons and students). 3.3. Analysis To measure the association between height and health while controlling for age, age squared, smoking status, obesity, education, and socioeconomic status (SES), the following model was estimated: hi ¼ a0 þ a1 height i þ a2 educi þ a3 demoi þ a4 li fei þ a4 SESi þ ui

(Model 1)

where hi is the disease of individual i; heighti his/her height; educi his/her education level; denoi and lifei his/her vector of demographic and lifestyle characteristics, respectively; SESi his/her socioeconomic status (income and employment), and ui is the error term. In order to estimate how education affected the height– health connection, two additional models were estimated: hi ¼ b0 þ b1 height i þ b2 demoi þ b3 li fei þ ui

(Model 2)

hi ¼ d0 þ d1 height i þ d2 educi þ d3 demoi þ d4 li fei þ ui

(Model 3)

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Next, the contribution of education to the mediation of the height influence was measured by calculating the following indicator:

Deduc ¼ ðd1  b1 Þ=b1 This value measured the percentage of decrease in the excess risk related to height that is obtained by the introduction of the education variable. This strategy was inspired by Jusot and Khlat (2013), and helped provide some insights about possible height–health causalities, through testing Hypothesis 2. The two other socioeconomic variables, income and employment, were not included at that stage, given their collinearity with education, which would hinder the identification of the specific mediating role of education. Models were estimated using multivariate logistic regressions using sampling weights. Regressions were performed first considering height as a continuous variable, and then as a categorical variable using quartiles to identify non-linearities. All analyses were performed

separately for men and women. Thus a total of 132 regressions – 3 (models) 11 (conditions) 2 (sex) 2 (linear versus non-linear) – were performed. Marginal effects from regressions are reported in order to directly interpret results as changes in prevalence related to variations in height. 4. Results The sample included 52.8% of women and 47.2% of men (Table 1). The women were on average older than men, self-reported worse health, and featured a higher prevalence of all chronic conditions except cardiovascular diseases. Ever-smokers were more prevalent among men (62.3% vs. 16.9%), but a higher proportion of women were obese (25.1% vs. 22.1%). A higher percentage of women had no education (18.3%vs. 11.0%), and a lower percentage were employed (21.9%vs. 65.7%). As for the average height of the youngest and oldest age categories, it increased by 5.0 cm among women and by

Table 1 Demographic health and socioeconomic characteristics of the sample. Variable

Total

No. of cases (%) Total

Women

28,433

15,007

(52.8)

Men 13,426

(47.2)

Age categories 25–34 35–44 45–54 55–64 65–74 75–79

4754 5728 5837 5189 5053 1872

(16.7) (20.2) (20.5) (18.3) (17.8) (6.6)

2343 2982 3011 2792 2834 1045

(15.6) (19.9) (20.1) (18.6) (18.9) (7.0)

4754 5728 5837 5189 5053 1872

(16.7) (20.1) (20.5) (18.2) (17.8) (6.6)

Self-assessed health Bad/very bad Fair Good/very good

4127 8605 7814

(20.1) (41.9) (38.0)

2828 5346 4044

(23.1) (43.8) (33.1)

1299 3259 3770

(15.6) (39.1) (45.3)

Chronic diseases Diabetes Asthma High blood pressure Chronic pain Rheumatism/osteoporosis Malignancy/cancer Renal failure Anxiety/depression Stroke/myocardial infarction COPD/chronic bronchitis

2659 1415 7908 5233 6532 702 442 3142 1049 1017

(9.4) (5.0) (27.8) (18.4) (23.0) (2.5) (1.6) (11.1) (3.7) (3.6)

1522 844 4794 3274 4680 448 243 2457 411 550

(10.1) (5.6) (32.0) (21.8) (31.2) (3.0) (1.6) (16.4) (2.7) (3.7)

1137 571 3114 1959 1852 254 199 685 638 467

(8.5) (4.2) (23.2) (14.6) (13.8) (1.9) (1.5) (5.1) (4.7) (3.5)

Lifestyle Obesity Ever-smoker

5343 10,892

(18.8) (38.2)

2981 2530

(19.9) (16.9)

2362 8362

(17.6) (62.3)

Education No education Low primary Primary Secondary Tertiary

4234 11,434 7102 2882 2781

(14.9) (40.2) (25.0) (10.1) (9.8)

2754 5814 3384 1442 1613

(18.3) (38.7) (22.6) (9.6) (10.8)

1480 5620 3718 1440 1168

(11.0) (41.9) (27.7) (10.7) (8.7)

Employment Housewife Unemployed Retired Employed Income [mean (std. dev)]

3297 1295 6627 16,046 631

(11.6) (4.6) (23.3) (56.4) (566)

551 3279 672 3287 617

(3.7) (21.9) (4.5) (21.9) (559)

18 623 3340 8828 646

(0.1) (4.6) (24.9) (65.7) (573)

J. Perelman / Economics and Human Biology 15 (2014) 56–66 Table 2 Mean height, in cm, by categories (n = 28,433). Variable

Mean height (std. error) Men

Women Total

158.94

(0.11)

169.92

(0.12)

Age categories 25–34 35–44 45–54 55–64 65–74 75–79

161.75 160.21 158.54 158.21 157.29 156.70

(0.27) (0.25) (0.21) (0.22) (0.24) (0.42)

173.51 171.42 169.76 168.39 167.00 167.00

(0.29) (0.26) (0.23) (0.28) (0.26) (0.48)

Self-assessed health Bad/very bad Fair Good/very good

157.22 158.19 159.76

(0.24) (0.17) (0.20)

167.52 169.04 171.56

(0.35) (0.23) (0.22)

157.07 158.23 157.67 157.85 157.81

(0.34) (0.45) (0.17) (0.21) (0.18)

167.88 168.71 168.50 168.47 168.15

(0.40) (0.58) (0.24) (0.29) (0.30)

158.14 158.83 158.58 157.30

(0.64) (0.75) (0.22) (0.57)

168.46 166.87 169.94 166.98

(0.69) (0.92) (0.50) (0.49)

157.43

(0.48)

168.20

(0.62)

Lifestyle Obesity Ever-smoker

156.83 160.81

(0.25) (0.25)

168.01 170.02

(0.31) (0.15)

Education No education Low primary Primary Secondary Tertiary

156.21 158.23 159.95 161.65 161.50

(0.25) (0.16) (0.21) (0.34) (0.29)

165.74 167.93 171.52 172.69 174.71

(0.35) (0.17) (0.24) (0.37) (0.34)

Employment Housewife Unemployed Retired Employed

158.17 158.92 157.20 159.96

(0.22) (0.49) (0.22) (0.15)

166.86 169.51 167.49 170.81

(3.72) (0.56) (0.23) (0.15)

Chronic diseases Diabetes Asthma High blood pressure Chronic pain Rheumatism/ osteoporosis Malignancy/Cancer Renal failure Anxiety/depression Stroke/myocardial infarction COPD/chronic bronchitis

6.5 cm among men (Table 2). Women who reported good or very good health were on average 2.5 cm taller than those who reported bad or very bad health. For men, the difference was 4.0 cm. Women who suffered from any chronic disease were of below-average height; this was also the case for men except for those suffering from anxiety and depression. Women with tertiary education were on average 5.3 cm taller than those with no schooling; the figure was even more striking among men: almost 9 cm. Finally, unemployed men and women were shorter than employees. Fig. 2 completes this descriptive picture by showing the age-adjusted height differences between educational categories, across birth cohorts. Differences were positive, across the board, confirming a stable association between education and stature. Among women, those with tertiary education were slightly shorter on average for some

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cohorts than those whose education did not go beyond secondary school; amore distinct gradient was found among men. Inequalities between the no-education and tertiary-education categories increased among women, from 3.5 to 7.7 cm, between the 1926–1940 and the 1971– 1981 generations. Results from the complete regression (Model 1) are presented in Table 3. Taller women were significantly less likely than others to suffer from asthma, at a 10% significance level, and from chronic pain and acute cardiovascular disease, at a 5% significance level. The marginal effect for asthma was of 0.057; in other words, a 0.057 percentage-point decrease in prevalence per additional centimeter in stature. For chronic pain and acute cardiovascular disease, the marginal effects were of 0.221 and 0.033, respectively. The prevalence of asthma was 0.074 percentage points lower per additional centimeter in height among men. The non-linear models with height as categorical variable allowed for refining of the analysis. Among women, asthma and anxiety/depression were significantly less prevalent among the tallest at a 10% significance level. The shortest women were significantly more affected by acute cardiovascular disease, while the reverse held for the tallest men. No significant linear or non-linear height effect was found for the risk of cancer, diabetes, high blood pressure, rheumatic disease, renal failure, and COPD. Fig. 3 shows marginal estimates for linear models, first adjusting for neither education nor other SES factors (Model 2), and then adjusting for education (Model 3). Among women, the negative association between height and self-assessed health and between height and diabetes disappeared when education was factored in. Among men, when this was done, the significance of the link between height and chronic pain and the link for renal failure disappeared. For these chronic diseases, the excess risk related to shorter stature was reduced by 47% and 34%, respectively. The models’ comparison with height as a categorical variable was quite similar and is therefore not reported. The average reduction in the height-related excess risk when education was factored in was 7% and 36% for women and men, respectively. The height gradient was fully eliminated for self-assessed health, rheumatic disease and pulmonary disease among men. 5. Discussion Height has been considered a reliable indicator of a given population’s standard of living, reflecting as it does childhood circumstances (Steckel, 1995, 2008). Its association with overall mortality has been demonstrated in several studies (Engeland et al., 2003; Jousilahti et al., 2000; Koch, 2011; Waaler, 1984). However, evidence is scarcer for the height–morbidity connection, and may be biased by educational achievement. A simple model indicates that two mechanisms may explain the height– health connection: (i) a direct association with stature; (ii) an indirect association, where the link between height and health is mediated by education. The association between height and several chronic diseases that have not been systematically considered in the literature was therefore

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Fig. 2. Age-adjusted height differences between education categories compared to ‘‘no schooling’’, by birth cohort.

tested, and the mediating role of education in the height patterning of these conditions was examined. Results can be organized into three categories. First, they indicated that there is a close association between height and several chronic diseases after controlling for age, lifestyle, education, and other socioeconomic factors – in line with the first hypothesis, postulating an independent effect of stature on health. Stature was negatively associated with the risk of asthma, chronic pain, acute cardiovascular disease, and mental disorders among women. Among men, the prevalence of asthma and acute cardiovascular disease was negatively associated with height. These findings confirm most of the earlier evidence for high-income countries regarding chronic heart disease and stroke (for a systematic review, see Paajanen et al., 2010). They also confirm earlier findings for the inverse relationship between height and asthma (Huovinen et al., 2003), and between height and depression and anxiety (Bjerkeset et al., 2008; Case and Paxson, 2010). To our best knowledge, the association between stature and chronic pain has not been demonstrated so far.

Conversely, no association was found between height and other life-threatening conditions such as diabetes, high blood pressure, cancer, and COPD. Recent evidence consistently shows a positive correlation between height and cancers unrelated to smoking (Green et al., 2011). That the Portuguese data used in this study did not permit a breakdown of cancers according to type helps to explain these inconclusive findings. The small size of the sample (702 people reported malignancy) may also contribute to the non-significant result. The literature shows that the risk of developing COPD is strongly associated with adult height (Ward and Hubbard, 2011). This relationship however varies with age groups, being stronger among younger people; again, the non-stratification in this study helps explain the absence of a significant association. By contrast, the literature about the link between height and diabetes confirms a weak association (Lawlor et al., 2002); for the link between height and hypertension, findings are likewise inconclusive (Olatunbosun and Bella, 2000). Second, results indicate that while in Portugal average height has dramatically increased in recent decades, the association between stature and education remained remarkably stable and significant across birth cohorts, confirming earlier results for Portugal (Cardoso and Caninas, 2010; Padez and Johnston, 1999) and other countries as well (Cavelaars et al., 2000). In other words, if height is a marker of living conditions, these conditions have improved equally across groups without any convergence in the trends. Several mechanisms help to explain the positive height– education connection. Growth is affected by nutrition and infectious diseases in childhood (Crimmins and Finch, 2006), which are affected by living conditions, by access to care, and by parents’ information, support, and knowledge; these conditions also affect educational achievement (Belzil and Hansen, 2003); for example, poor health and poor nutrition limit school attendance and performance, while parents’ support determines children success. That there is a close connection between height and education is thus no surprise. That the positive height–education connection was higher among men than among women confirms previous evidence (Cavelaars et al., 2000). This finding is somewhat puzzling, since there is no apparent reason for this gender discrepancy. A study conducted by Cinnirella et al. (2011), however, shows that the height–education correlation among male teenagers is due to a positive correlation between height and social skills, which does not exist among their female counterparts. Discrimination against shorter-than-average boys (and not against shorter-thanaverage girls) is presented by these authors as a second plausible mechanism. The fact that, on account of this discrimination, teenage boys of below-average height are at a disadvantage when it comes to educational attainment leads to their suffering disadvantages in regard to acquisition of skills, access to resources and employment, and – last but not least – to health. Third, results indicate that the association between height and morbidity was largely mediated by education. This finding supports the second hypothesis of the theoretical model. If indeed both height and education reflect childhood circumstances, education appears to

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Table 3 Adjusted association between height and health, usinglinear and non-linear specifications of height. Men

Women Marginal effect

(S.E.)

Marginal effect

(S.E.)

Self-assessed health Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

0.113 0.181 0.008 0.019

0.074

0.027 0.222 0.001 0.000

0.044

Diabetes Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

0.077 0.100 0.007 0.003

Asthma Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

0.057 0.022 0.010 0.010

High blood pressure Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

0.100 0.171 0.010 0.038

Chronic pain Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

0.221 0.057 0.012 0.014

Stroke/MI Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2 Rheumatism/osteop. Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

0.181

0.057 0.008 0.010

0.008 0.009

0.119 0.016 0.019

0.101

0.039 0.034 0.012 0.027

0.015 0.017

0.085 0.014 0.017 0.042

0.017 0.003 0.004

0.015

0.023 0.189 0.001 0.006

0.002 0.003 0.191

0.112 0.016 0.020

0.085 0.141 0.001 0.018

0.065 0.010 0.014 0.140

0.041 0.005 0.006

0.009 0.127 0.001 0.002

0.035

0.015 0.002 0.004 0.128

0.027 0.004 0.004

0.014

0.015 0.108 0.003 0.000

0.075

0.002 0.004 0.108

0.084 0.013 0.014

0.026 0.042 0.005 0.015

0.035

0.028

0.014 0.019

0.102 0.042 0.021 0.002

0.166

Anxiety/depression Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

0.093

0.153

0.138

0.029 0.075 0.004 0.001

0.008 0.007

0.033 0.153 0.003 0.003

0.057

Renal failure Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

0.043

0.028

0.172

0.013 0.035 0.001 0.001

0.006 0.009

0.074 0.026 0.013 0.006

0.023

0.063 0.166 0.006 0.019

0.041

0.026 0.129 0.005 0.000 0.129

0.055

0.033 0.136 0.006 0.002

0.006 0.009 0.223

0.099

Malignancy/cancer Height – continuous Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

COPD/Chron. bronch. Height – continuous

0.011 0.013

0.054 0.009 0.011 0.044

0.041

0.021

0.035

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J. Perelman / Economics and Human Biology 15 (2014) 56–66

Table 3 (Continued ) Men

Women Marginal effect Pseudo-R2 Height – quartile 1 Height – quartile 4 Pseudo-R2

(S.E.)

0.052 0.007 0.003

0.006 0.008 0.052

Marginal effect

(S.E.)

0.103 0.001 0.002

. 0.008 0.101

Note: values in bold indicate p-value < 0.05; values in bold-italics indicate p-value < 0.10. Adjustment is performed with respect to age, age squared, obesity, ever-smoker, education, income, and employment status.

strongly mediate these circumstances as we seek to evaluate their impact on health. It should be noted that the mediating role of education was much higher among men (a 36% reduction in height-related excess risk) than among women (7%). This finding is certainly related to the fact that the positive height–education connection was greater among men than among women, with the greater height-related educational premium among boys translating into a positive height–health connection later in their lives.

The important mediating role of education contradicts findings by Koch (2011), based on an in-depth analysis of mortality rates in Norway. Several factors may explain the discrepancy between Portugal and Norway in regard to height–education effect. According to Carneiro (2008), Portugal exhibits ‘‘one of the highest levels of income inequality in Europe, and low wages and unemployment are concentrated among low skills individuals’’ (p.17). It is also among the EU countries with the highest

Fig. 3. Association between height and health obtained from logistic regressions. Notes. *The first bar (1) for each condition provides the 90% confidence interval of the marginal effect for height, adjusted or age and lifestyle, but unadjusted for education (Model 2). **The second bar (2) for each condition provides the 90% confidence interval of the marginal effect for height adjusted for age, lifestyle, and education (Model 3). The bars represent confidence intervals, i.e., when the bar intersects the zero value, this means that the height association is not significant. The percentages indicate the variation in height-related excess risk when adjusting for education.

J. Perelman / Economics and Human Biology 15 (2014) 56–66

socioeconomic inequality in primary-school performance, an inequality related to significant inequalities in socioeconomic background and access to educational resources (Martins and Veiga, 2010). These figures, coupled with a significant socioeconomic inequity in healthcare use (Van Doorslaer et al., 2006), help to explain the impact of education on health, and why earlylife circumstances and height both exert influence through education. Findings partly reflect those obtained by Deaton and Arora (2009), who show the negative correlation between height and negative experiences, like physical pain, disappears when income and education are factored in. The cross-sectional nature of the data used in this analysis was certainly a limitation, as it did not allow for an accurate observation of childhood health and the timing of growth, of educational attainment, or of health hazards, all of which would have helped to establish a causal pathway. Indeed, unobservable effects related to early life probably cause an over-estimation of the effect of height, and of the mediating role of education. A longitudinal database would have been particularly helpful in two respects. First, it would help to determine the extent to which height acts as a marker of early circumstances or as a direct cause of illness. Second, it would help to determine the role of education in the height–health connection; it may be that both height and educational attainment are driven by cognitive and non-cognitive skills, as some postulate (Case and Paxson, 2008; Cinnirella et al., 2011), but these in turn may be explained by socioeconomic conditions during childhood, such as parents’ support, knowledge and information, access to school programs and to medical services, and environmental conditions. Tracking the respondents over a considerable period of time would permit the inclusion of individual fixed effects that would reduce unobserved heterogeneity (see e.g. Coneus and Spiess (2012)). Despite these limitations, the present study shows, by means of cross-sectional data alone, that height and educational attainment, which are determined early in life, are key to an understanding of the causes of chronic diseases. Another limitation of this analysis is due to the fact that national health surveys have limitations. On the one hand, height and diseases are self-reported. Regarding height, Coneus and Spiess (2012) observe the fact that among adults height is stable over time, which minimizes the magnitude of measurement errors. Height data from Spanish national health surveys have been used recently and validated (Spijker et al., 2012). The fact that the respondents with little education may have more trouble than others reporting their height can bias inequality measurements; however, there is no reason to believe that respondents systematically misreport values and there is no evident pattern to the bias. The stability argument also applies to education, which is not likely to be misreported. By contrast, the measurement bias may be greater for selfreported diseases; however, evidence suggests that selfreported morbidity is a reliable predictor of disability, in that it does not differ largely from physician-reported values (Ferraro and Su, 2000). In the case of the present study, the exclusion of cases undiagnosed by a health

65

professional no doubt reduced this bias as well. On the positive side, the national health survey provides a large number of observations including information on a wide range of diseases, behavioral risk factors, and socioeconomic factors, which can be controlled for. From a long-term perspective, height is a reliable predictor of mortality; data for Portugal confirm its link with chronic conditions, although not for the most lifethreatening diseases. Height is closely connected with education because they share certain early-life determinants, although education proves to be more influential than height. From a policy viewpoint, this study emphasizes the role of height and education as determinants of chronic conditions, which nowadays represent the major burden of disease in high-income countries (Lopez et al., 2006). By extension, it also emphasizes the role of conditions related to childhood health and socioeconomic conditions, and thereby identifies the direction that further investigations concerning the causalities of chronic conditions should take, using longitudinal data with information on early-life living conditions. Findings about the mediating role of education highlight the critical role of the educational system as a barrier against the detrimental effects of ill health and poor socioeconomic position in childhood. In addition, early public-health interventions, which have demonstrated their high economic return (Doyle et al., 2009), serve to increase access to secondary and even tertiary education and this in turn leads to improvements in health and well-being. References Batty, G.D., Shipley, M.J., Langenberg, C., Marmot, M.G., Davey Smith, G., 2006. Adult height in relation to mortality from 14 cancer sites in men in London (UK): evidence from the original Whitehall study. Ann. Oncol. 17 (1), 157–166. Batty, G.D., Shipley, M.J., Gunnell, D., Huxley, R., Kivimaki, M., Woodward, M., Smith, G.D., 2009. Height, wealth, and health: an overview with new data from three longitudinal studies. Econ. Hum. Biol. 7 (2), 137– 152. Bauer, P., Riphahn, R.T., 2007. Heterogeneity in the intergenerational transmission of educational attainment: evidence from Switzerland on natives and second-generation immigrants. J. Popul. Econ. 20 (1), 121–148. Beard, A.S., Blaser, M.J., 2002. The ecology of height: the effect of microbial transmission on human height. Perspect. Biol. Med. 45 (4), 475–498. Belzil, C., Hansen, J., 2003. Structural estimates of the intergenerational education correlation. J. Appl. Econmetr. 18 (6), 679–696. Bjerkeset, O., Romundstad, P., Evans, J., Gunnell, D., 2008. Association of adult body mass index and height with anxiety, depression, and suicide in the general population The HUNT Study. Am. J. Epidemiol. 167 (2), 193–202. Bosy-Westphal, A., Plachta-Danielzik, S., Do¨rho¨fer, R., Mu¨ller, M.J., 2009. Short stature and obesity: positive association in adults but inverse association in children and adolescents. Br. J. Nutr. 102 (03), 453–461. Bozzoli, C., Deaton, A., Quintana-Domeque, C., 2009. Adult height and childhood disease. Demography 46 (4), 647–669. Cardoso, H., Caninas, M., 2010. Secular trends in social class differences of height, weight and BMI of boys from two schools in Lisbon, Portugal (1910–2000). Econ. Hum. Biol. 8 (1), 111–120. Carneiro, P., 2008. Equality of opportunity and educational achievement in Portugal. Portuguese Econ. J. 7 (1), 17–41. Case, A., Paxson, C., 2008. Stature and status: Height, ability, and labor market outcomes. J. Pol. Econ. 116 (3), 499–532. Case, A., Paxson, C., 2010. Causes and consequences of early-life health. Demography 47 (1), S65–S85. Case, A., Fertig, A., Paxson, C., 2005. The lasting impact of childhood health and circumstance. J. Health Econ. 24 (2), 365–389. Cavelaars, A., Kunst, A., Geurts, J., Crialesi, R., Gro¨tvedt, L., Helmert, U., Rasmussen, N., 2000. Persistent variations in average height between

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