Background Merkel cell (neuroendocrine) carcinoma is a small round blue cell malignant neoplasm that primarily presents in the skin. The diagnosis of Merkel cell carcinoma in a pleural fluid is challenging because of the morphological similarity to many other malignant neoplasms. Immunohistochemical stains can be essential to establish the diagnosis of Merkel cell carcinoma.
Background
Merkel cell (neuroendocrine) carcinoma is a small round blue cell malignant neoplasm that primarily presents in the skin. The diagnosis of Merkel cell carcinoma in a pleural fluid is challenging because of the morphological similarity to many other malignant neoplasms. Immunohistochemical stains can be essential to establish the diagnosis of Merkel cell carcinoma.Case presentation A 77 year-old woman presented with a mass in her right buttock thought clinically to be a boil or sebaceous cyst. Upon histopathologic review including immunohistochemical analysis, a diagnosis of Merkel cell carcinoma was rendered. Wide-excision and sentinel lymph node biopsy revealed negative margins and no evidence of metastasis. Ten months later she complained of bone pain and a bone scan revealed multiple lesions. An abdominal CT scan revealed a T4 vertebral mass and local radiotherapy was administered. Two months later the patient presented with shortness of breath. A chest radiograph showed an effusion and thoracentesis was performed. The fluid was confirmed to contain metastatic Merkel cell carcinoma by cytology and immunohistochemical analysis.
Case presentation
A 77 year-old woman presented with a mass in her right buttock thought clinically to be a boil or sebaceous cyst. Upon histopathologic review including immunohistochemical analysis, a diagnosis of Merkel cell carcinoma was rendered. Wide-excision and sentinel lymph node biopsy revealed negative margins and no evidence of metastasis. Ten months later she complained of bone pain and a bone scan revealed multiple lesions. An abdominal CT scan revealed a T4 vertebral mass and local radiotherapy was administered. Two months later the patient presented with shortness of breath. A chest radiograph showed an effusion and thoracentesis was performed. The fluid was confirmed to contain metastatic Merkel cell carcinoma by cytology and immunohistochemical analysis.Conclusions Merkel cell carcinoma is an aggressive neoplasm that can, despite careful surgical management, occasionally present as a malignant pleural effusion in a relatively short time period. Immunohistochemical analysis can aid in confirming this rare outcome.
Conclusions
Merkel cell carcinoma is an aggressive neoplasm that can, despite careful surgical management, occasionally present as a malignant pleural effusion in a relatively short time period. Immunohistochemical analysis can aid in confirming this rare outcome.

Background The Merkel cell is the namesake of an F. Merkel who in 1875 described distinct round cells in the basal layer of the epidermis [ 1 ]. The distinctive Merkel cell with its round shape and salt and pepper chromatin is known to be present in the epidermis and dermis [ 2 ]. Toker first described Merkel cell carcinoma in 1972 and along with Tang concluded the cells of origin to be derived from the neural crest [ 3 , 4 ]. More recently, Sibley discussed the idea that the cells may be derived from the pleuripotential basal cells of the epidermis that surround hair follicles and function as touch receptors in the skin [ 5 ]. Merkel cell carcinoma is a tumor of the dermis and can easily be mistaken for other malignant small round blue cell tumors such as lymphoma, amelanotic melanoma, and metastatic small cell carcinoma [ 6 ]. Historically, electron microscopy demonstrating cytoplasmic microfilaments and neurosecretory granules was the key to diagnosing Merkel cell carcinoma [ 7 ]. Immunohistochemical approaches are now used to confirm a diagnosis of Merkel cell carcinoma.
Background
The Merkel cell is the namesake of an F. Merkel who in 1875 described distinct round cells in the basal layer of the epidermis [ 1 ]. The distinctive Merkel cell with its round shape and salt and pepper chromatin is known to be present in the epidermis and dermis [ 2 ]. Toker first described Merkel cell carcinoma in 1972 and along with Tang concluded the cells of origin to be derived from the neural crest [ 3 , 4 ]. More recently, Sibley discussed the idea that the cells may be derived from the pleuripotential basal cells of the epidermis that surround hair follicles and function as touch receptors in the skin [ 5 ]. Merkel cell carcinoma is a tumor of the dermis and can easily be mistaken for other malignant small round blue cell tumors such as lymphoma, amelanotic melanoma, and metastatic small cell carcinoma [ 6 ]. Historically, electron microscopy demonstrating cytoplasmic microfilaments and neurosecretory granules was the key to diagnosing Merkel cell carcinoma [ 7 ]. Immunohistochemical approaches are now used to confirm a diagnosis of Merkel cell carcinoma.Case Report A 77 year old female presented to her primary care physician with a complaint of a right buttock mass. Her past medical history was significant for colonic adenocarcinoma treated surgically, twelve years prior. The skin overlying the 2 × 3 cm firm, non-fluctuant, non-tender mass was erythematous. Initially the mass was thought to be a boil or carbuncle. Six weeks later during a visit with a surgical oncologist for routine follow-up of her colonic adenocarcinoma resected 13 years prior, the patient noted that the mass was becoming quite painful and enlarging rapidly. The patient was then taken to the OR for local excision. Pathology revealed a small round blue cell tumor forming large nests and infiltrating as single cells between thick strands of fibrous tissue in the dermis and extending to the subcutaneous fat. There were abundant mitoses, a lack of necrosis or obvious vascular invasion. The epidermis was uninvolved. The histologic and immunochemical findings supported the diagnosis of Merkel cell carcinoma. A wide local excision and sentinel lymph node biopsy were performed. One sentinel lymph node was negative for malignancy and there was no residual tumor identified in the excision. No adjuvant therapy was given. Ten months after the initial presentation the patient had increasing shoulder pain that radiated into the chest and flank. A CT of the thoracic spine revealed a 3.0 cm mass in the area of the fourth thoracic (T4) vertebra. A CT guided needle biopsy of the area revealed metastatic Merkel cell carcinoma. A bone scan revealed additional areas of concern in the right humerus and bilateral distal femurs. The patient received radiation therapy to the thoracic spine and right distal femur for palliation of bone pain. A year after first presenting for a right buttock mass, the patient was admitted to the hospital for increasing dyspnea. A chest radiograph revealed a large right pleural effusion, which was drained for both therapeutic and diagnostic purposes. The fluid was grossly bloody and cytology revealed abundant, round, basophilic single cells, many in mitosis, with characteristic granular, "salt and pepper" nuclear chromatin (Figure  1 ). A cellblock was made and immunohistochemical staining with CK20 revealed characteristic positivity in a dot like rim pattern (Figures  2 ). Figure 1 Pleural fluid at 630x demonstrating small round cells with salt and pepper chromatin. Note multiple mitotic figures Figure 2 Cell block stained with CK20 demonstrating sharp perinuclear dot like pattern.
Case Report
A 77 year old female presented to her primary care physician with a complaint of a right buttock mass. Her past medical history was significant for colonic adenocarcinoma treated surgically, twelve years prior. The skin overlying the 2 × 3 cm firm, non-fluctuant, non-tender mass was erythematous. Initially the mass was thought to be a boil or carbuncle. Six weeks later during a visit with a surgical oncologist for routine follow-up of her colonic adenocarcinoma resected 13 years prior, the patient noted that the mass was becoming quite painful and enlarging rapidly. The patient was then taken to the OR for local excision. Pathology revealed a small round blue cell tumor forming large nests and infiltrating as single cells between thick strands of fibrous tissue in the dermis and extending to the subcutaneous fat. There were abundant mitoses, a lack of necrosis or obvious vascular invasion. The epidermis was uninvolved. The histologic and immunochemical findings supported the diagnosis of Merkel cell carcinoma. A wide local excision and sentinel lymph node biopsy were performed. One sentinel lymph node was negative for malignancy and there was no residual tumor identified in the excision. No adjuvant therapy was given.Ten months after the initial presentation the patient had increasing shoulder pain that radiated into the chest and flank. A CT of the thoracic spine revealed a 3.0 cm mass in the area of the fourth thoracic (T4) vertebra. A CT guided needle biopsy of the area revealed metastatic Merkel cell carcinoma. A bone scan revealed additional areas of concern in the right humerus and bilateral distal femurs. The patient received radiation therapy to the thoracic spine and right distal femur for palliation of bone pain.A year after first presenting for a right buttock mass, the patient was admitted to the hospital for increasing dyspnea. A chest radiograph revealed a large right pleural effusion, which was drained for both therapeutic and diagnostic purposes. The fluid was grossly bloody and cytology revealed abundant, round, basophilic single cells, many in mitosis, with characteristic granular, "salt and pepper" nuclear chromatin (Figure  1 ). A cellblock was made and immunohistochemical staining with CK20 revealed characteristic positivity in a dot like rim pattern (Figures  2 ).Conclusions The diagnosis of Merkel cell carcinoma in pleural fluid is challenging partially because of its rarity. To our knowledge this is the first case report of Merkel cell carcinoma in a pleural fluid characterized with immunohistochemical stains. Wason and Friedman described the only other reported case of pleural effusion due to metastatic Merkel cell carcinoma in 1985 [ 7 ]. In that report, the diagnosis was confirmed by electron microscopy that demonstrated cytoplasmic microfilaments and numerous dense-core, peripheral, neurosecretory granules. The light microscopic morphologic characteristics of Merkel cells in pleural fluid yields a large list of possible diagnosis including small cell lung carcinoma, carcinoid tumor, lymphoma, plasmacytoma, pancreatic carcinoma small cell type, and peripheral neuroectodermal tumor (PNET). Since Merkel cell carcinoma rarely appears in pleural fluid and small cell lung carcinoma frequently does the pathologist must have the salient cytomorphologic features of Merkel cell carcinoma in mind to make the diagnosis in the absence of clinical history. The most characteristic features of Merkel cell carcinoma in pleural fluid include convolution of nuclei and lack of prominent nuclear molding. Once Merkel cell carcinoma has entered the differential diagnosis the case can be pursued. With immunohistochemistry, it is now possible to definitively diagnose Merkel cell carcinoma in pleural fluid without electron microscopy. The immunohistochemical profile of Merkel cell carcinoma is distinct and has been well established on paraffin embedded tissue [ 8 ]. Tumor cells consistently express low molecular weight keratins in the form of a perinuclear dot like pattern typical of neuroendocrine carcinomas. Merkel cell carcinomas also label for neuron specific enolase (virtually all), chromogranin B (100%), chromogranin A (72%), secretoneurin (22%), and synaptophysin (39%) [ 5 , 9 ]. Among neuroendocrine carcinomas, CK20 positivity in a dot like rim pattern is seen only in Merkel cell and salivary gland small cell carcinomas [ 10 ] Other small cell tumors that are CK20 positive include bronchogenic small cell carcinoma (0.03%), small cell cervical carcinomas (9%), and small cell carcinomas of salivary glands (60%) but not in the characteristic dot-like pattern [ 11 ]. Neuron specific enolase is positive in almost all Merkel cell carcinomas [ 5 ]. Carcinoid tumors stain for synaptophysin and chromogranin, but not CK20. Reactivity with CD45 is diagnostic of a hematopoietic lineage; in addition, apart from vimentin, hematologic malignancies do not generally react with antibodies to other lineage specific markers. Plasmacytomas express CD138 and CK8 [ 10 ]. CD 99 positivity suggests PNET; however, one series found CD99 to be positive in 40% of Merkel cell tumors [ 12 ]. Metastatic Merkel cell carcinoma as the cause of a malignant pleural effusion is a rare occurrence. However, such an aggressive neoplasm as Merkel cell, which is often widely metastatic, may be a more common cause of malignant pleural effusion than recognized. Morphologically, the tumor cells in a body fluid could easily be mistaken for a malignancy of another origin. In the work-up of the pleural effusion caused by a small round blue cell process, inclusion of immunohistochemical markers specific to Merkel cell carcinoma may be prudent.
Conclusions
The diagnosis of Merkel cell carcinoma in pleural fluid is challenging partially because of its rarity. To our knowledge this is the first case report of Merkel cell carcinoma in a pleural fluid characterized with immunohistochemical stains. Wason and Friedman described the only other reported case of pleural effusion due to metastatic Merkel cell carcinoma in 1985 [ 7 ]. In that report, the diagnosis was confirmed by electron microscopy that demonstrated cytoplasmic microfilaments and numerous dense-core, peripheral, neurosecretory granules. The light microscopic morphologic characteristics of Merkel cells in pleural fluid yields a large list of possible diagnosis including small cell lung carcinoma, carcinoid tumor, lymphoma, plasmacytoma, pancreatic carcinoma small cell type, and peripheral neuroectodermal tumor (PNET).Since Merkel cell carcinoma rarely appears in pleural fluid and small cell lung carcinoma frequently does the pathologist must have the salient cytomorphologic features of Merkel cell carcinoma in mind to make the diagnosis in the absence of clinical history. The most characteristic features of Merkel cell carcinoma in pleural fluid include convolution of nuclei and lack of prominent nuclear molding. Once Merkel cell carcinoma has entered the differential diagnosis the case can be pursued.With immunohistochemistry, it is now possible to definitively diagnose Merkel cell carcinoma in pleural fluid without electron microscopy. The immunohistochemical profile of Merkel cell carcinoma is distinct and has been well established on paraffin embedded tissue [ 8 ]. Tumor cells consistently express low molecular weight keratins in the form of a perinuclear dot like pattern typical of neuroendocrine carcinomas. Merkel cell carcinomas also label for neuron specific enolase (virtually all), chromogranin B (100%), chromogranin A (72%), secretoneurin (22%), and synaptophysin (39%) [ 5 , 9 ]. Among neuroendocrine carcinomas, CK20 positivity in a dot like rim pattern is seen only in Merkel cell and salivary gland small cell carcinomas [ 10 ] Other small cell tumors that are CK20 positive include bronchogenic small cell carcinoma (0.03%), small cell cervical carcinomas (9%), and small cell carcinomas of salivary glands (60%) but not in the characteristic dot-like pattern [ 11 ]. Neuron specific enolase is positive in almost all Merkel cell carcinomas [ 5 ]. Carcinoid tumors stain for synaptophysin and chromogranin, but not CK20. Reactivity with CD45 is diagnostic of a hematopoietic lineage; in addition, apart from vimentin, hematologic malignancies do not generally react with antibodies to other lineage specific markers. Plasmacytomas express CD138 and CK8 [ 10 ]. CD 99 positivity suggests PNET; however, one series found CD99 to be positive in 40% of Merkel cell tumors [ 12 ].Metastatic Merkel cell carcinoma as the cause of a malignant pleural effusion is a rare occurrence. However, such an aggressive neoplasm as Merkel cell, which is often widely metastatic, may be a more common cause of malignant pleural effusion than recognized. Morphologically, the tumor cells in a body fluid could easily be mistaken for a malignancy of another origin. In the work-up of the pleural effusion caused by a small round blue cell process, inclusion of immunohistochemical markers specific to Merkel cell carcinoma may be prudent.Competing interests The authors declare that they have no competing interests.
Competing interests
The authors declare that they have no competing interests.Authors' contributions
Authors' contributions


Background Understanding the influences of educational and economic variables on food consumption may be useful to explain food behaviour and nutrition policymaking. The aim of this study was to evaluate the importance of educational and economic factors in determining food pattern in Portuguese adults.
Background
Understanding the influences of educational and economic variables on food consumption may be useful to explain food behaviour and nutrition policymaking. The aim of this study was to evaluate the importance of educational and economic factors in determining food pattern in Portuguese adults.Methods A cross-sectional study in a representative sample of Portuguese adults (20977 women and 18663 men). Participants were distributed in four categories according to years of education (≤4, 5–9, 10–12, and >12) and income (≤314 euros, 315–547 euros, 548–815 euros, and >815 euros). Logistic regression models were fitted to estimate the magnitude of the association between food groups and education/income, adjusting for confounders.
Methods
A cross-sectional study in a representative sample of Portuguese adults (20977 women and 18663 men). Participants were distributed in four categories according to years of education (≤4, 5–9, 10–12, and >12) and income (≤314 euros, 315–547 euros, 548–815 euros, and >815 euros). Logistic regression models were fitted to estimate the magnitude of the association between food groups and education/income, adjusting for confounders.Results In both genders, the odds favouring milk, vegetable soup, vegetables, fruit, and fish consumption, increased significantly with education, for those having >12 years of education compared to those with ≤4 years; the odds favouring wine, and spirits consumption decreased significantly with education, for those having >12 years of education compared to those with ≤4 years. In males, the odds favouring starchy foods and meat consumption decreased significantly with income, while for milk, the odds increased with higher income (those having >815 euros compared to those with ≤314 euros).
Results
In both genders, the odds favouring milk, vegetable soup, vegetables, fruit, and fish consumption, increased significantly with education, for those having >12 years of education compared to those with ≤4 years; the odds favouring wine, and spirits consumption decreased significantly with education, for those having >12 years of education compared to those with ≤4 years. In males, the odds favouring starchy foods and meat consumption decreased significantly with income, while for milk, the odds increased with higher income (those having >815 euros compared to those with ≤314 euros).Conclusions The low and high income groups are or tend to be similar in regard to several food groups consumption, and access to education/information appears to be the key element to a better food pattern as indicated by higher frequency of milk, vegetable soup, vegetables, fruit, and fish consumption.
Conclusions
The low and high income groups are or tend to be similar in regard to several food groups consumption, and access to education/information appears to be the key element to a better food pattern as indicated by higher frequency of milk, vegetable soup, vegetables, fruit, and fish consumption.

Background There is a large published literature on associations between socio-economic position and chronic disease, with socioeconomically disadvantaged groups experiencing higher mortality and morbidity rates for coronary heart disease, noninsulin dependent diabetes mellitus and some cancers [ 1 - 4 ]. Chronic diseases are largely preventable diseases, and diet has been known fore many years to play a key role as a risk factor for chronic diseases. While age, sex and genetic susceptibility are non-modifiable, many of the risks associated with age and sex are modifiable. Such risks include a complex mixture of interacting socio-economic, cultural and other environmental factors [ 5 - 8 ]. The relationship between socio-economic factors and diet has been examined on the basis of food and nutrient intake and the results are mixed, sometimes contradictory, and often the observed differences are small [ 9 , 10 ]. When differences are found, it is usually the case that persons from socioeconomically disadvantage backgrounds have food intakes consistent with their higher rates of chronic diseases [ 11 - 17 ]. In the context of European countries, economic development and increased purchasing power have recently changed the food availability situation [ 18 ]. On the other hand, socio-cultural influences may contribute, along with economic constraints, to particular food choices, which may explain the still substantial differences in food consumption across European countries [ 19 ]. Understanding the influences of socio-economic variables on food consumption may be useful to predict the outcome of interventions, to change food behaviour, and generate hypotheses concerning food consumption in diverse circumstances, as well as to explain observations in epidemiological studies. The aim of this study was to evaluate the importance of educational and economic factors in determining food choice in a representative sample of the Portuguese general adult population.
Background
There is a large published literature on associations between socio-economic position and chronic disease, with socioeconomically disadvantaged groups experiencing higher mortality and morbidity rates for coronary heart disease, noninsulin dependent diabetes mellitus and some cancers [ 1 - 4 ]. Chronic diseases are largely preventable diseases, and diet has been known fore many years to play a key role as a risk factor for chronic diseases. While age, sex and genetic susceptibility are non-modifiable, many of the risks associated with age and sex are modifiable. Such risks include a complex mixture of interacting socio-economic, cultural and other environmental factors [ 5 - 8 ].The relationship between socio-economic factors and diet has been examined on the basis of food and nutrient intake and the results are mixed, sometimes contradictory, and often the observed differences are small [ 9 , 10 ]. When differences are found, it is usually the case that persons from socioeconomically disadvantage backgrounds have food intakes consistent with their higher rates of chronic diseases [ 11 - 17 ].In the context of European countries, economic development and increased purchasing power have recently changed the food availability situation [ 18 ]. On the other hand, socio-cultural influences may contribute, along with economic constraints, to particular food choices, which may explain the still substantial differences in food consumption across European countries [ 19 ]. Understanding the influences of socio-economic variables on food consumption may be useful to predict the outcome of interventions, to change food behaviour, and generate hypotheses concerning food consumption in diverse circumstances, as well as to explain observations in epidemiological studies.The aim of this study was to evaluate the importance of educational and economic factors in determining food choice in a representative sample of the Portuguese general adult population.Methods Subjects and general characteristics Data for this study were drawn from the Portuguese third National Health Survey (National Health Systems Observatory, National Institute of Health – Dr. Ricardo Jorge, Ministry of Health) carried out in 1998–1999. The study sample included all subjects (20977 women and 18663 men) older than 18 years, who reported their education level, income, physical activity, smoking habits, weight, height, and food intake when participating in the National Health Survey. Subjects were selected from 21808 households distributed according to the five regions of Portugal (there are five regions in mainland Portugal, namely Norte, Centro, Lisboa/Vale do Tejo, Alentejo, and Algarve; these regions are the portuguese NUTS II subdivisions), using a multi-stage random probability design. This probabilistic sample is representative of the Portuguese population from the Continental area (Azores and Madeira islands were not included). The survey response rate was 82%. Trained interviewers conducted face-to-face interviews with the person in each household and inquired participants on social and demographic characteristics, smoking status (non-smokers, ex-smokers, smoking less than one cigarette per day, and smoking one or more cigarettes per day), weight, height (those anthropometric measures were self-reported and body mass index – BMI – was then calculated), food and beverages intake, and daily physical activity (occupational and leisure-time physical activity). Physical activity Occupational physical activity was measured using the respondent's own occupation at the time of the survey. Respondents were asked about what best characterized their daily occupational activity, namely: usually seated and walking during short periods of time; standing activities or walking during long periods of time without carrying loads to often; carrying light objects or walking upstairs/downstairs several times; heavy physical work or carrying heavy objects; or don't know. Respondents were asked to describe their leisure-activity using the following classification: heavy training and competitive sports more than once a week; running or practicing recreational sports or gardening activities ≥4 hours per week; walking for pleasure, bicycling (light effort) or doing other light activities ≥4 hours per week; reading, watching television or other sedentary activities; and don't know. Respondents were also asked to provide information about whether they had regular activities (once or more per week) such as running or bicycling (enough to make them feel tired). Food and beverages intake Respondents were asked twelve questions related to their intake of central food groups and beverages, namely milk, vegetable soup, meat, fish, vegetables, fruit, bread, starchy foods (pasta/rice/potatoes), beer, spirits, Port Wine, and wine, and the consumption was recorded as a yes (when the respondent indicated the consumption of the food) or no answer. Because the data were collected by interviewers within the framework of an epidemiological study that was not specifically designed to assess quantitative aspects of nutritional and food intake, the dietary assessment method employed generic classifications of food groups, rather than specific varieties or species (fish rather than fatty fish or salmon, etc.), or quantitative measures. Consumption of these food items was determined by asking "For each of the listed food items please indicate those consumed": "during the day before the interview" (vegetable soup, meat, fish, vegetables, fruit, bread, and starchy foods – pasta, rice and potatoes); "during the week before interview" (beer, spirits, and Port Wine); and "daily consumed" (milk and wine). Education Respondents were asked to provide information about whether they had attained further education since leaving school and if so, the highest qualification completed. Respondent's education was subsequently classified in four levels of education: less than 4 years, 5–9 years, 10–12 years, and more than 12 years. Income Respondent's were asked to estimate the total income (including pensions, allowances and investments) received by all household members in the last month and to indicate this using a single measure comprising ten narrow-ranged income categories. This measure was subsequently re-coded into four categories according the number of salaries: less than 315 euros, 315–547 euros, 548–815 euros, and >815 euros. Statistical analysis Separate logistic regression models were fitted for male and female to estimate the magnitude of the association between food groups consumption and education or income categories, adjusting for age, BMI, smoking habits, physical activity and income/education. An exploratory approach was chosen in the selection of explanatory variables in order to control for as many potentially significant variables as possible in the regression model. The choice of variables (age, BMI, smoking habits and physical activity) was based on findings reported in the literature, our own experience with specifically Portuguese factors associated with food consumption and their associations with the variables of interest; education was also adjusted for income and vice versa. Student's t-tests, ANOVA, Spearman rank correlation analyses and chi-squared tests were used to compare BMI, age, frequency of smoking, physical activity categories between genders to determine the degree to which those variables correlated with education and income. A  p -value of less than 0.05 was considered statistically significant. Statistics were performed using SPSS 12.0.
Methods
Subjects and general characteristics Data for this study were drawn from the Portuguese third National Health Survey (National Health Systems Observatory, National Institute of Health – Dr. Ricardo Jorge, Ministry of Health) carried out in 1998–1999. The study sample included all subjects (20977 women and 18663 men) older than 18 years, who reported their education level, income, physical activity, smoking habits, weight, height, and food intake when participating in the National Health Survey. Subjects were selected from 21808 households distributed according to the five regions of Portugal (there are five regions in mainland Portugal, namely Norte, Centro, Lisboa/Vale do Tejo, Alentejo, and Algarve; these regions are the portuguese NUTS II subdivisions), using a multi-stage random probability design. This probabilistic sample is representative of the Portuguese population from the Continental area (Azores and Madeira islands were not included). The survey response rate was 82%. Trained interviewers conducted face-to-face interviews with the person in each household and inquired participants on social and demographic characteristics, smoking status (non-smokers, ex-smokers, smoking less than one cigarette per day, and smoking one or more cigarettes per day), weight, height (those anthropometric measures were self-reported and body mass index – BMI – was then calculated), food and beverages intake, and daily physical activity (occupational and leisure-time physical activity).
Subjects and general characteristics
Data for this study were drawn from the Portuguese third National Health Survey (National Health Systems Observatory, National Institute of Health – Dr. Ricardo Jorge, Ministry of Health) carried out in 1998–1999. The study sample included all subjects (20977 women and 18663 men) older than 18 years, who reported their education level, income, physical activity, smoking habits, weight, height, and food intake when participating in the National Health Survey. Subjects were selected from 21808 households distributed according to the five regions of Portugal (there are five regions in mainland Portugal, namely Norte, Centro, Lisboa/Vale do Tejo, Alentejo, and Algarve; these regions are the portuguese NUTS II subdivisions), using a multi-stage random probability design. This probabilistic sample is representative of the Portuguese population from the Continental area (Azores and Madeira islands were not included). The survey response rate was 82%.Trained interviewers conducted face-to-face interviews with the person in each household and inquired participants on social and demographic characteristics, smoking status (non-smokers, ex-smokers, smoking less than one cigarette per day, and smoking one or more cigarettes per day), weight, height (those anthropometric measures were self-reported and body mass index – BMI – was then calculated), food and beverages intake, and daily physical activity (occupational and leisure-time physical activity).Physical activity Occupational physical activity was measured using the respondent's own occupation at the time of the survey. Respondents were asked about what best characterized their daily occupational activity, namely: usually seated and walking during short periods of time; standing activities or walking during long periods of time without carrying loads to often; carrying light objects or walking upstairs/downstairs several times; heavy physical work or carrying heavy objects; or don't know. Respondents were asked to describe their leisure-activity using the following classification: heavy training and competitive sports more than once a week; running or practicing recreational sports or gardening activities ≥4 hours per week; walking for pleasure, bicycling (light effort) or doing other light activities ≥4 hours per week; reading, watching television or other sedentary activities; and don't know. Respondents were also asked to provide information about whether they had regular activities (once or more per week) such as running or bicycling (enough to make them feel tired).
Physical activity
Occupational physical activity was measured using the respondent's own occupation at the time of the survey. Respondents were asked about what best characterized their daily occupational activity, namely: usually seated and walking during short periods of time; standing activities or walking during long periods of time without carrying loads to often; carrying light objects or walking upstairs/downstairs several times; heavy physical work or carrying heavy objects; or don't know.Respondents were asked to describe their leisure-activity using the following classification: heavy training and competitive sports more than once a week; running or practicing recreational sports or gardening activities ≥4 hours per week; walking for pleasure, bicycling (light effort) or doing other light activities ≥4 hours per week; reading, watching television or other sedentary activities; and don't know. Respondents were also asked to provide information about whether they had regular activities (once or more per week) such as running or bicycling (enough to make them feel tired).Food and beverages intake Respondents were asked twelve questions related to their intake of central food groups and beverages, namely milk, vegetable soup, meat, fish, vegetables, fruit, bread, starchy foods (pasta/rice/potatoes), beer, spirits, Port Wine, and wine, and the consumption was recorded as a yes (when the respondent indicated the consumption of the food) or no answer. Because the data were collected by interviewers within the framework of an epidemiological study that was not specifically designed to assess quantitative aspects of nutritional and food intake, the dietary assessment method employed generic classifications of food groups, rather than specific varieties or species (fish rather than fatty fish or salmon, etc.), or quantitative measures. Consumption of these food items was determined by asking "For each of the listed food items please indicate those consumed": "during the day before the interview" (vegetable soup, meat, fish, vegetables, fruit, bread, and starchy foods – pasta, rice and potatoes); "during the week before interview" (beer, spirits, and Port Wine); and "daily consumed" (milk and wine).
Food and beverages intake
Respondents were asked twelve questions related to their intake of central food groups and beverages, namely milk, vegetable soup, meat, fish, vegetables, fruit, bread, starchy foods (pasta/rice/potatoes), beer, spirits, Port Wine, and wine, and the consumption was recorded as a yes (when the respondent indicated the consumption of the food) or no answer. Because the data were collected by interviewers within the framework of an epidemiological study that was not specifically designed to assess quantitative aspects of nutritional and food intake, the dietary assessment method employed generic classifications of food groups, rather than specific varieties or species (fish rather than fatty fish or salmon, etc.), or quantitative measures. Consumption of these food items was determined by asking "For each of the listed food items please indicate those consumed": "during the day before the interview" (vegetable soup, meat, fish, vegetables, fruit, bread, and starchy foods – pasta, rice and potatoes); "during the week before interview" (beer, spirits, and Port Wine); and "daily consumed" (milk and wine).Education Respondents were asked to provide information about whether they had attained further education since leaving school and if so, the highest qualification completed. Respondent's education was subsequently classified in four levels of education: less than 4 years, 5–9 years, 10–12 years, and more than 12 years.
Education
Respondents were asked to provide information about whether they had attained further education since leaving school and if so, the highest qualification completed. Respondent's education was subsequently classified in four levels of education: less than 4 years, 5–9 years, 10–12 years, and more than 12 years.Income Respondent's were asked to estimate the total income (including pensions, allowances and investments) received by all household members in the last month and to indicate this using a single measure comprising ten narrow-ranged income categories. This measure was subsequently re-coded into four categories according the number of salaries: less than 315 euros, 315–547 euros, 548–815 euros, and >815 euros.
Income
Respondent's were asked to estimate the total income (including pensions, allowances and investments) received by all household members in the last month and to indicate this using a single measure comprising ten narrow-ranged income categories. This measure was subsequently re-coded into four categories according the number of salaries: less than 315 euros, 315–547 euros, 548–815 euros, and >815 euros.Statistical analysis Separate logistic regression models were fitted for male and female to estimate the magnitude of the association between food groups consumption and education or income categories, adjusting for age, BMI, smoking habits, physical activity and income/education. An exploratory approach was chosen in the selection of explanatory variables in order to control for as many potentially significant variables as possible in the regression model. The choice of variables (age, BMI, smoking habits and physical activity) was based on findings reported in the literature, our own experience with specifically Portuguese factors associated with food consumption and their associations with the variables of interest; education was also adjusted for income and vice versa. Student's t-tests, ANOVA, Spearman rank correlation analyses and chi-squared tests were used to compare BMI, age, frequency of smoking, physical activity categories between genders to determine the degree to which those variables correlated with education and income. A  p -value of less than 0.05 was considered statistically significant. Statistics were performed using SPSS 12.0.
Statistical analysis
Separate logistic regression models were fitted for male and female to estimate the magnitude of the association between food groups consumption and education or income categories, adjusting for age, BMI, smoking habits, physical activity and income/education. An exploratory approach was chosen in the selection of explanatory variables in order to control for as many potentially significant variables as possible in the regression model. The choice of variables (age, BMI, smoking habits and physical activity) was based on findings reported in the literature, our own experience with specifically Portuguese factors associated with food consumption and their associations with the variables of interest; education was also adjusted for income and vice versa.Student's t-tests, ANOVA, Spearman rank correlation analyses and chi-squared tests were used to compare BMI, age, frequency of smoking, physical activity categories between genders to determine the degree to which those variables correlated with education and income. A  p -value of less than 0.05 was considered statistically significant. Statistics were performed using SPSS 12.0.pResults The study sample comprised 20977 women (52,9%) and 18663 men, with mean ages of 50.3 (±18.88) and 47.7 (±18.51) years, respectively; BMI was significantly lower in women than in men (25.1 ± 4.53 Kg/m 2  versus  25.6 ± 3.83, p < 0.001). There was a lower proportion of smokers among women compared to men (8.2%  versus  30.5%, p < 0.001). General characteristics (gender, age, BMI, smoking status, and physical activity) by education and income categories are presented in Tables  1  and  2 . Table 1 Characteristics of Portuguese adults by education categories Education ≤4 years 5–9 years 10–12 years >12 years Gender  Female 54.0% 22.8% 12.6% 10.6%  Male 49.7% 28.7% 13.2% 8.4% Age (years)  Female 55.6 ± 14.47 37.8 ± 14.44 31.7 ± 13.63 34.7 ± 13.50  Male 55.3 ± 14.72 37.0 ± 14.99 33.0 ± 14.44 38.7 ± 16.19 BMI (Kg/m 2 )  Female 26.4 ± 4.45 24.3 ± 4.03 22.5 ± 3.51 22.3 ± 3.45  Male 26.3 ± 3.91 25.1 ± 3.63 24.5 ± 3.52 24.6 ± 3.19 Smokers  Female 3.1% 16.1% 19.1% 18.7%  Male 26.0% 42.9% 32.6% 29.2% Physical activity in females  Daily occupational activity best characterized by   Usually seated, walking short periods of time 29.2% 28.8% 30.2% 30.4%   Standing, walking long periods of time 44.5% 46.1% 41.7% 42.0%   Carrying light objects, walking up/downstairs 11.3% 11.0% 12.2% 11.1%   Heavy physical work or carrying heavy objects 14.9% 14.1% 15.8% 16.5%   Don't know 0.1% 0% 0.1% 0.1%  Leisure-activity best characterized by   Heavy training/competitive sports (>1x/wk) 3.4% 3.3% 3.6% 3.1%   Running/recreational sports/gardening (≥4 h/wk) 9.3% 7.8% 9.0% 9.7%   Walking for pleasure, bicycling light (≥4 h/wk) 20.4% 21.2% 19.3% 21.2%   Reading, watching TV, sedentary activities 66.8% 67.7% 68.0% 66.0%   Don't know 0.1% 0% 0.1% 0%  Regular activity such as running or bicycling, enough to feel tired   Yes 13.1% 11.9% 13.2% 14.2%   No 86.8% 88.1% 86.7% 85.8%   Don't know 0.1% 0% 0.1% 0% Physical activity in males  Daily occupational activity best characterized by   Usually seated, walking short periods of time 36.4% 37.3% 37.5% 36.2%   Standing, walking long periods of time 51.6% 51.5% 50.9% 50.0%   Carrying light objects, walking up/downstairs 8.3% 8.2% 7.5% 10.5%   Heavy physical work or carrying heavy objects 3.6% 3.0% 3.9% 3.3%   Don't know 0.1% 0% 0.2% 0%  Leisure-activity best characterized by   Heavy training/competitive sports (>1x/wk) 0.9% 0.5% 1.1% 1.2%   Running/recreational sports/gardening (≥4 h/wk) 5.1% 4.6% 4.1% 4.9%   Walking for pleasure, bicycling light (≥4 h/wk) 15.9% 15.7% 16.8% 16.7%   Reading, watching TV, sedentary activities 78.1% 79.1% 77.8% 77.2%   Don't know 0.1% 0.1% 0.2% 0%  Regular activity such as running or bicycling, enough to feel tired   Yes 7.0% 6.0% 6.7% 7.2%   No 92.9% 94.0% 93.2% 92.8%   Don't know 0.1% 0.0% 0.2% 0% Table 2 Characteristics of Portuguese adults by income categories Income ≤314 euros 315–547 euros 548–815 euros >815 euros Gender  Female 16.3% 25.4% 24.5% 33.8%  Male 20.8% 24.3% 22.8% 32.2% Age (years)  Female 50.7 ± 18.97 50.3 ± 18.90 50.1 ± 18.68 50.2 ± 18.94  Male 47.9 ± 18.54 47.2 ± 18.45 47.6 ± 18.44 48.1 ± 18.55 BMI (Kg/m 2 )  Female 25.3 ± 4,55 25.0 ± 4.51 25.1 ± 4.52 25.1 ± 4.55  Male 25.6 ± 3.70 25.4 ± 3.95 25.6 ± 3.74 25.7 ± 3.88 Smokers  Female 7.4% 8.0% 8.6% 8.2%  Male 28.5% 31.9% 30.3% 30.7% Physical activity in females  Daily occupational activity best characterized by   Usually seated, walking short periods of time 30.3% 26.0% 24.6% 35.7%   Standing, walking long periods of time 46.4% 43.8% 42.8% 43.6%   Carrying light objects, walking up/downstairs 10.3% 11.4% 13.5% 10.0%   Heavy physical work or carrying heavy objects 12.7% 18.7% 19.0% 10.6%   Don't know 0.2% 0% 0.1% 0.1%%  Leisure-activity best characterized by   Heavy training/competitive sports (>1x/wk) 1.2% 2.3% 3.1% 5.4%   Running/recreational sports/gardening (≥4 h/wk) 4.0% 7.3% 11.1% 11.1%   Walking for pleasure, bicycling light (≥4 h/wk) 16.7% 19.2% 19.0% 24.0%   Reading, watching TV, sedentary activities 77.9% 71.2% 66.7% 59.5%   Don't know 0.1% 0% 0.1% 0.1%  Regular activity such as running or bicycling, enough to feel tired   Yes 5.8% 9.1% 12.0% 19.1%   No 94.1% 90.9% 87.9% 80.8%   Don't know 0.1% 0% 0.1% 0.1% Physical activity in males  Daily occupational activity best characterized by   Usually seated, walking short periods of time 41.8% 33.5% 32.2% 39.3%   Standing, walking long periods of time 48.1% 53.2% 53.1% 50.8%   Carrying light objects, walking up/downstairs 7.6% 9.6% 10.0% 7.0%   Heavy physical work or carrying heavy objects 2.4% 3.6% 4.7% 2.8%   Don't know 0.1% 0.1% 0% 0%  Leisure-activity best characterized by   Heavy training/competitive sports (>1x/wk) 0.3% 0.5% 0.8% 1.4%   Running/recreational sports/gardening (≥4 h/wk) 2.9% 4.4% 5.5% 5.4%   Walking for pleasure, bicycling light (≥4 h/wk) 10.3% 13.1% 16.3% 21.5%   Reading, watching TV, sedentary activities 86.4% 81.9% 77.4% 71.7%   Don't know 0.1% 0.2% 0% 0%  Regular activity such as running or bicycling, enough to feel tired   Yes 2.2% 4.7% 5.4% 11.7%   No 97.6% 95.2% 94.6% 88.3%   Don't know 0.1% 0.1% 0% 0% In women, the odds favouring milk, vegetable soup, vegetables, fruit, and fish consumption, increased with increasing education (p-values for trends were always ≤0.046), being the odds ratios, respectively, 2.60 (2.24–3.01), 1.20 (1.05–1.38), 1.75 (1.44–2.13), 1.92 (1.49–2.49), and 1.40 (1.23–1.60) for those having >12 years of education compared to those with ≤4 years, after adjusting for age, BMI, smoking habits, physical activity and income (Tables  3  and  4 ). The odds favouring bread, starchy foods (other than bread), wine, and spirits consumption in women decreased with increasing education (p trend ≤ 0.002), being the odds ratios, respectively, 0.44 (0.34–0.56), 0.68 (0.53–0.87), 0.51 (0.41–0.62), and 0.13 (0.03–0.53) for those having >12 years of education compared to those with ≤4 years (Tables  3  and  4 ). Table 3 Odds ratios for food consumption according level of education, adjusted for age, BMI, smoking habits, physical activity and income Women Men OR IC(95%) P trend OR IC(95%) P trend Vegetable soup Vegetable soup Education Education ≤4 years (reference) ≤4 years (reference) 5–9 years 0.90 0.82–1.00 5–9 years 0.99 0.90–1.09 10–12 years 0.94 0.83–1.07 10–12 years 1.07 0.94–1.21 >12 years 1.20 1.05–1.38 0.046 >12 years 1.15 1.00–1.32 0.045 Vegetables Vegetables Education Education ≤4 years (reference) ≤4 years (reference) 5–9 years 1.05 0.92–1.20 5–9 years 1.09 0.97–1.23 10–12 years 1.17 0.99–1.39 10–12 years 1.38 1.17–1.62 >12 years 1.75 1.44–2.13 <0.001 >12 years 1.44 1.19–1.74 <0.001 Fruit Fruit Education Education ≤4 years (reference) ≤4 years (reference) 5–9 years 1.30 1.09–1.55 5–9 years 1.30 1.13–1.49 10–12 years 1.63 1.29–2.06 10–12 years 1.75 1.45–2.13 >12 years 1.92 1.49–2.49 <0.001 >12 years 1.68 1.35–2.10 <0.001 Bread Bread Education Education ≤4 years (reference) ≤4 years (reference) 5–9 years 0.78 0.63–0.96 5–9 years 0.97 0.75–1.25 10–12 years 0.50 0.39–0.64 10–12 years 0.73 0.54–1.00 >12 years 0.44 0.34–0.56 <0.001 >12 years 0.44 0.33–0.59 <0.001 Other starchy Other starchy Education Education ≤4 years (reference) ≤4 years (reference) 5–9 years 0.99 0.81–1.20 5–9 years 1.09 0.88–1.35 10–12 years 0.72 0.57-0.92 10-12 years 1.07 0.80-1.43 >12 years 0.68 0.53-0.87 <0.001 >12 years 1.15 0.83-1.60 0.355 Fish Fish Education Education ≤4 years (reference) ≤4 years (reference) 5-9 years 1.06 0.96-1.17 5-9 years 1.14 1.04-1.25 10-12 years 1.24 1.09-1.40 10-12 years 1.36 1.20-1.54 >12 years 1.40 1.23-1.60 <0.001 >12 years 1.50 1.31-1.72 <0.001 Meat Meat Education Education ≤4 years (reference) ≤4 years (reference) 5-9 years 1.01 0.89-1.14 5-9 years 1.09 0.96-1.23 10-12 years 1.02 0.86-1.21 10-12 years 1.27 1.06-1.52 >12 years 0.95 0.80-1.13 0.693 >12 years 1.16 0.96-1.41 0.014 Table 4 Odds ratios for beverage consumption according level of education, adjusted for age, BMI, smoking habits, physical activity and income Women Men OR IC(95%) P trend OR IC(95%) P trend Milk Milk Education Education ≤4 years (reference) ≤4 years (reference) 5-9 years 1.54 1.38-1.70 5-9 years 1.53 1.39-1.68 10-12 years 2.24 1.95-2.57 10-12 years 3.00 2.62-3.44 >12 years 2.60 2.24-3.01 <0.001 >12 years 3.07 2.62-3.59 <0.001 Wine Wine Education Education ≤4 years (reference) ≤4 years (reference) 5-9 years 0.79 0.68-0.93 5-9 years 0.73 0.63-0.84 10-12 years 0.45 0.36-0.55 10-12 years 0.41 0.35-0.49 >12 years 0.51 0.41-0.62 <0.001 >12 years 0.46 0.38-0.56 <0.001 Beer Beer Education Education ≤4 years (reference) ≤4 years (reference) 5-9 years 0.92 0.72-1.18 5-9 years 0.86 0.77-0.97 10-12 years 0.76 0.56-1.03 10-12 years 0.61 0.52-0.71 >12 years 0.82 0.61-1.10 0.097 >12 years 0.57 0.48-0.68 <0.001 Spirits Spirits Education Education ≤4 years (reference) ≤4 years (reference) 5-9 years 0.66 0.27-1.58 5-9 years 0.72 0.60-0.87 10-12 years 0.15 0.03-0.74 10-12 years 0.46 0.34-0.62 >12 years 0.13 0.03-0.53 0.002 >12 years 0.27 0.19-0.40 <0.001 Port Wine Port Wine Education Education ≤4 years (reference) ≤4 years (reference) 5-9 years 1.44 1.03-2.01 5-9 years 1.00 0.81-1.24 10-12 years 1.22 0.80-1.88 10-12 years 0.97 0.73-1.30 >12 years 1.34 0.90-2.00 0.265 >12 years 1.42 1.07-1.89 0.093 In men, similar odds ratios were observed for milk, vegetable soup, vegetables, fruit, fish, bread, wine, and spirits (Tables  3  and  4 ). However, in men but not in women, odds favouring meat consumption increased with increasing education (OR = 1.16 (0.96–1.41) for those having >12 years of education compared to those with ≤4 years), while for beer consumption, odds decreased with increasing education (OR = 0.57 (0.48–0.68) for those having >12 years of education compared to those with ≤4 years). No such significant trends were observed for these food groups and income with the exceptions of meat and starchy foods (other than bread) consumption, in men, which decreased with increasing income (p trend ≤ 0.022), and milk consumption which increased with increasing income (Tables  3  and  4 ).
Results
The study sample comprised 20977 women (52,9%) and 18663 men, with mean ages of 50.3 (±18.88) and 47.7 (±18.51) years, respectively; BMI was significantly lower in women than in men (25.1 ± 4.53 Kg/m 2  versus  25.6 ± 3.83, p < 0.001). There was a lower proportion of smokers among women compared to men (8.2%  versus  30.5%, p < 0.001). General characteristics (gender, age, BMI, smoking status, and physical activity) by education and income categories are presented in Tables  1  and  2 .versusversusCharacteristics of Portuguese adults by education categoriesCharacteristics of Portuguese adults by income categoriesIn women, the odds favouring milk, vegetable soup, vegetables, fruit, and fish consumption, increased with increasing education (p-values for trends were always ≤0.046), being the odds ratios, respectively, 2.60 (2.24–3.01), 1.20 (1.05–1.38), 1.75 (1.44–2.13), 1.92 (1.49–2.49), and 1.40 (1.23–1.60) for those having >12 years of education compared to those with ≤4 years, after adjusting for age, BMI, smoking habits, physical activity and income (Tables  3  and  4 ). The odds favouring bread, starchy foods (other than bread), wine, and spirits consumption in women decreased with increasing education (p trend ≤ 0.002), being the odds ratios, respectively, 0.44 (0.34–0.56), 0.68 (0.53–0.87), 0.51 (0.41–0.62), and 0.13 (0.03–0.53) for those having >12 years of education compared to those with ≤4 years (Tables  3  and  4 ).Odds ratios for food consumption according level of education, adjusted for age, BMI, smoking habits, physical activity and incomeWomenMenORIC(95%)P trendORIC(95%)P trendVegetable soupVegetable soupVegetablesVegetablesFruitFruitBreadBreadOther starchyOther starchyFishFishMeatMeatOdds ratios for beverage consumption according level of education, adjusted for age, BMI, smoking habits, physical activity and incomeWomenMenORIC(95%)P trendORIC(95%)P trendMilkMilkWineWineBeerBeerSpiritsSpiritsPort WinePort WineIn men, similar odds ratios were observed for milk, vegetable soup, vegetables, fruit, fish, bread, wine, and spirits (Tables  3  and  4 ). However, in men but not in women, odds favouring meat consumption increased with increasing education (OR = 1.16 (0.96–1.41) for those having >12 years of education compared to those with ≤4 years), while for beer consumption, odds decreased with increasing education (OR = 0.57 (0.48–0.68) for those having >12 years of education compared to those with ≤4 years).No such significant trends were observed for these food groups and income with the exceptions of meat and starchy foods (other than bread) consumption, in men, which decreased with increasing income (p trend ≤ 0.022), and milk consumption which increased with increasing income (Tables  3  and  4 ).Discussion The main finding of the present study is that educational attainment was more frequently associated with food choices than income. There is general agreement among researchers [ 20 - 23 ] that education and income are conceptually distinct, and that they are likely to make separate and unique contributions to health-related outcomes [ 24 ]. In our study, the most educated consumed more frequently fruit, vegetables, milk and fish, and less wine and spirits, than their counterparts from less educated groups. Over the last years, several studies have attempted to identify the influence of socioeconomic factors on individual's dietary intake [ 25 - 28 ]. Our interest in educational and economic determinants of food choice in Portuguese adults relate to these particular characteristics in the population. Portugal, according European standards, is a small and relatively poor country, exhibiting the highest level of social inequalities in the European Union [ 29 ]. Nevertheless, Portugal had significantly and positively changed in the last four decades, in several domains such as the economy and culture, although the census of 1991 revealed that 15.3% of the Portuguese were illiterate. That of 2000 showed that, despite the improvements and changes in the education of adults, 7% can still not read or write [ 29 ]. This is a reality that classifies Portugal as the country with the higher percentage of individuals with low level of education in all the European Union [ 29 ]. From the employment perspective, Portugal's unemployment rates in the last 25 years never surpassed 10% of the active population, which is a better indicator than the observed levels in the majority of the European countries. However, the percentage of individuals with low-remuneration in Portugal is much higher than the EU average [ 29 ]. Several studies have concluded that a strong relationship exists between countries' per capita national incomes and nutrition [ 30 - 32 ]. The economic issue is of considerable significance, and it is sometimes suggested that this is probably the key variable of all in influencing food choice [ 30 ]. Household income is expected to influence food choices, especially for relatively high-priced food items such as fish, fresh fruit and vegetables [ 33 ]. Nevertheless, this not seems to be the case when we compared income and education levels as determinants of intake of significant food groups in Portuguese adults. Our data shows, in both genders, a significant positive trend in the consumption of vegetables, vegetable soup, fruits, milk and fish, with higher levels of education, which did not occurred in relation to income with the exception of milk. In our study, education was adjusted for income and vice versa. While the majority of investigators use two or more indicators of socio-economic position, several [ 34 - 37 ] do not simultaneously adjust for the unmeasured effects of each indicator on the other. Two types of bias may result from this practice: (1) using a single indicator such as education may bias the point estimate (food choice) because the education variable is allowed to account for some of the variation that is actually the product of unmeasured socio-economic influences; as a result, if we did not simultaneously adjusted education for income and vice versa, our claims about the influence of education level on food choice probably would have been overestimated; (2) the use of a single indicator may result in the overall or total socio-economic effect being underestimated. Data from the Portuguese Household Budget Surveys (using the DAta Food Networking – DAFNE – classification system), shows similar results to ours in relation to the positive association between education attainment and the availability of fruits, fish, milk and alcoholic beverages but some different data in regard to other foods (availability of vegetables and cereal products is fairly stable or tends to decline with education) [ 18 ]. Curiously, we found that meat consumption in men was positively associated with level of education, as in the DAFNE study [ 18 ], although our study showed a significant reduction of meat consumption with higher categories of income, in men. In Portugal [ 18 ] fish is more available among the trend-leading educated individuals which may be more advantageous to their cardiovascular health [ 38 ]. In our study, there was also a significant trend in the consumption of milk in men, being more frequently ingested with increasing income. As suggested by Axelson, [ 39 ] positive health relationships between dietary patterns and income may reflect a growing concern about health in the higher socio-economic groups. The association between milk consumption and socio-economic position is sometimes contradictory. Cristofar and Basiotis [ 40 ], for example, reported lower intake of milk among low-income women, while Roos et al. [ 41 ] found that higher educational and income groups from both genders consumed less milk. Consumption of alcoholic beverages, such as wine and spirits, in both genders, and beer in men, exhibited significant decreases in their frequency of intake, with increasing education levels. In Portugal, alcoholic beverages consumption is a major public health problem [ 42 ]. In DAFNE study, [ 18 ] using Portuguese Household Budget Surveys, alcoholic beverages availability was also higher in the lower educated households. Interestingly, in our study, the consumption of bread (in both genders) and other starchy foods (in women), decreased with increasing number of years of education; men seem to abandon starchy foods (other than bread) consumption under condition of higher incomes. It is possible that higher educated individuals tend to avoid foods that are considered as being more fattening or rich in energy, such as bread and other starchy foods [ 43 , 44 ]. Research has demonstrated that for a given body size, higher educated women are more dissatisfied with or concerned about their bodies and are more likely to have dieted in the past than lower educated women [ 45 , 46 ]. One of the most interesting findings in terms of economic constraints and food consumption relationship in our study, is the few significant associations between income and food choices, even though the well established links between economic and material resources, food availability and dietary quality [ 47 ]. By contrast to our results, Turrell et al. [ 23 ] showed household income to be the strongest and most robust independent predictor of food purchasing behaviour, and the effects of education to be substantially attenuated (to non-significance or marginal significance). In our study, the specificity of the relationship between education and food choice probably reflect each respondent's individual contribution to food choice, whereas household income was possibly capturing the combined contextual effects of numerous individuals, as well as many other within-household processes [ 23 ], and thus showed a weaker relationship with food choice. Our results may also reflect lesser difficulties faced by low-income groups when selecting the food groups that we studied. In several urban and rural areas of Portugal, there are many people who own plots of land that are too small to make a living, but allow them to work the land and produce foods (e.g., fruit, vegetables and poultry) for their own consumption. Although they produced a limited range of foods that is not accounted in official agricultural statistics, probably, if they stopped working the land they would experience greater difficulties in obtaining access to those particular foods. A potential limitation of our study and most nationwide population surveys is that the poor are usually not well presented. We know from previous research into survey participation that population-based samples typically under-represent the most socio-economically disadvantaged and over-represent the advantaged [ 48 , 49 ], because homeless and unemployed may be difficult to reach, and this may debilitates the interpretation of our results. In our study, it remains to be explained the different pattern of associations between income and important food groups (milk versus fruit and vegetables, for example) and the different pattern of associations between food choices and income in each gender (e.g., milk and starchy foods). Several reasons may explain specific differences in the findings of our study compared with those of previous mentioned studies, including differences in populations sampled (e.g., both genders versus women only, different cultural backgrounds ranges), differences in assessment of education or income, differences in dietary assessment (e.g., qualitative food data versus 24-h dietary recalls or food frequency questionnaires) and differences in analytic methods (e.g., covariates included in statistical models). Nonetheless, results from our study indicate that the associations between food choice were stronger in relation to educational attainment than income categories. Differences in food choices according the level of education reflect that more knowledge may influence the perceived relationship between diet and health as well as the perceived outcomes of following a healthy diet [ 33 ]. Despite differences in food consumption according education and income, in our study we could not assess if these differences were also evident on the energy and nutrient level, which was a limitation. British data point to micronutrient and antioxidant intakes as the most likely nutritional influences on health inequalities [ 50 ]. Nevertheless, according Galobardes et al., [ 10 ] it is also possible that despite differences in food consumption, nutrient intake is similar among socio-economic groups, as these may not be substantial enough to translate into differences in nutrient intake. If a country like Portugal wants to change the adult food choice behaviour, or in other words, wants to reach certain dietary goals, the support of applied research like ours is needed in order to plan the right strategies for promoting healthy diets. Confidence in a significant positive causal link between per capita national income and individual nutrition reinforces the importance of economic growth [ 51 ] but also implies that public policy should stress education as a mean for improving healthy food choices. Education might influence food choice by facilitating or constraining one's ability to understand the information communicated in nutrition education or in food labels [ 52 , 53 ]. Whereas income-related dietary differences suggest ameliorative responses through the potential of the economic system, differences based on education point to initiatives such as nutrition education programmes [ 22 , 54 ]. According to Geraldes,[ 55 ]in Portugal it may sometimes be more appropriate to correct inequalities in the domains of education or nutrition than that of health. Given the poor education level of the majority of Portuguese adults, a move towards an increased acquisition of general knowledge and personal development through compulsory and higher education, lifelong learning and improved qualifications of the population, is desirable to promote the development of a knowledge society and improve the level and quality of national education which, in turn, may relate to healthier food choices. It is well recognized that changes in dietary behaviour may be brought about, not by direct modification of food habits, but by alteration or manipulation of the education and culture [ 8 ].
Discussion
The main finding of the present study is that educational attainment was more frequently associated with food choices than income. There is general agreement among researchers [ 20 - 23 ] that education and income are conceptually distinct, and that they are likely to make separate and unique contributions to health-related outcomes [ 24 ]. In our study, the most educated consumed more frequently fruit, vegetables, milk and fish, and less wine and spirits, than their counterparts from less educated groups.Over the last years, several studies have attempted to identify the influence of socioeconomic factors on individual's dietary intake [ 25 - 28 ]. Our interest in educational and economic determinants of food choice in Portuguese adults relate to these particular characteristics in the population. Portugal, according European standards, is a small and relatively poor country, exhibiting the highest level of social inequalities in the European Union [ 29 ]. Nevertheless, Portugal had significantly and positively changed in the last four decades, in several domains such as the economy and culture, although the census of 1991 revealed that 15.3% of the Portuguese were illiterate. That of 2000 showed that, despite the improvements and changes in the education of adults, 7% can still not read or write [ 29 ]. This is a reality that classifies Portugal as the country with the higher percentage of individuals with low level of education in all the European Union [ 29 ]. From the employment perspective, Portugal's unemployment rates in the last 25 years never surpassed 10% of the active population, which is a better indicator than the observed levels in the majority of the European countries. However, the percentage of individuals with low-remuneration in Portugal is much higher than the EU average [ 29 ].Several studies have concluded that a strong relationship exists between countries' per capita national incomes and nutrition [ 30 - 32 ]. The economic issue is of considerable significance, and it is sometimes suggested that this is probably the key variable of all in influencing food choice [ 30 ]. Household income is expected to influence food choices, especially for relatively high-priced food items such as fish, fresh fruit and vegetables [ 33 ]. Nevertheless, this not seems to be the case when we compared income and education levels as determinants of intake of significant food groups in Portuguese adults. Our data shows, in both genders, a significant positive trend in the consumption of vegetables, vegetable soup, fruits, milk and fish, with higher levels of education, which did not occurred in relation to income with the exception of milk. In our study, education was adjusted for income and vice versa. While the majority of investigators use two or more indicators of socio-economic position, several [ 34 - 37 ] do not simultaneously adjust for the unmeasured effects of each indicator on the other. Two types of bias may result from this practice: (1) using a single indicator such as education may bias the point estimate (food choice) because the education variable is allowed to account for some of the variation that is actually the product of unmeasured socio-economic influences; as a result, if we did not simultaneously adjusted education for income and vice versa, our claims about the influence of education level on food choice probably would have been overestimated; (2) the use of a single indicator may result in the overall or total socio-economic effect being underestimated.Data from the Portuguese Household Budget Surveys (using the DAta Food Networking – DAFNE – classification system), shows similar results to ours in relation to the positive association between education attainment and the availability of fruits, fish, milk and alcoholic beverages but some different data in regard to other foods (availability of vegetables and cereal products is fairly stable or tends to decline with education) [ 18 ]. Curiously, we found that meat consumption in men was positively associated with level of education, as in the DAFNE study [ 18 ], although our study showed a significant reduction of meat consumption with higher categories of income, in men. In Portugal [ 18 ] fish is more available among the trend-leading educated individuals which may be more advantageous to their cardiovascular health [ 38 ].In our study, there was also a significant trend in the consumption of milk in men, being more frequently ingested with increasing income. As suggested by Axelson, [ 39 ] positive health relationships between dietary patterns and income may reflect a growing concern about health in the higher socio-economic groups.The association between milk consumption and socio-economic position is sometimes contradictory. Cristofar and Basiotis [ 40 ], for example, reported lower intake of milk among low-income women, while Roos et al. [ 41 ] found that higher educational and income groups from both genders consumed less milk.Consumption of alcoholic beverages, such as wine and spirits, in both genders, and beer in men, exhibited significant decreases in their frequency of intake, with increasing education levels. In Portugal, alcoholic beverages consumption is a major public health problem [ 42 ]. In DAFNE study, [ 18 ] using Portuguese Household Budget Surveys, alcoholic beverages availability was also higher in the lower educated households.Interestingly, in our study, the consumption of bread (in both genders) and other starchy foods (in women), decreased with increasing number of years of education; men seem to abandon starchy foods (other than bread) consumption under condition of higher incomes. It is possible that higher educated individuals tend to avoid foods that are considered as being more fattening or rich in energy, such as bread and other starchy foods [ 43 , 44 ]. Research has demonstrated that for a given body size, higher educated women are more dissatisfied with or concerned about their bodies and are more likely to have dieted in the past than lower educated women [ 45 , 46 ].One of the most interesting findings in terms of economic constraints and food consumption relationship in our study, is the few significant associations between income and food choices, even though the well established links between economic and material resources, food availability and dietary quality [ 47 ]. By contrast to our results, Turrell et al. [ 23 ] showed household income to be the strongest and most robust independent predictor of food purchasing behaviour, and the effects of education to be substantially attenuated (to non-significance or marginal significance). In our study, the specificity of the relationship between education and food choice probably reflect each respondent's individual contribution to food choice, whereas household income was possibly capturing the combined contextual effects of numerous individuals, as well as many other within-household processes [ 23 ], and thus showed a weaker relationship with food choice. Our results may also reflect lesser difficulties faced by low-income groups when selecting the food groups that we studied. In several urban and rural areas of Portugal, there are many people who own plots of land that are too small to make a living, but allow them to work the land and produce foods (e.g., fruit, vegetables and poultry) for their own consumption. Although they produced a limited range of foods that is not accounted in official agricultural statistics, probably, if they stopped working the land they would experience greater difficulties in obtaining access to those particular foods. A potential limitation of our study and most nationwide population surveys is that the poor are usually not well presented. We know from previous research into survey participation that population-based samples typically under-represent the most socio-economically disadvantaged and over-represent the advantaged [ 48 , 49 ], because homeless and unemployed may be difficult to reach, and this may debilitates the interpretation of our results.In our study, it remains to be explained the different pattern of associations between income and important food groups (milk versus fruit and vegetables, for example) and the different pattern of associations between food choices and income in each gender (e.g., milk and starchy foods). Several reasons may explain specific differences in the findings of our study compared with those of previous mentioned studies, including differences in populations sampled (e.g., both genders versus women only, different cultural backgrounds ranges), differences in assessment of education or income, differences in dietary assessment (e.g., qualitative food data versus 24-h dietary recalls or food frequency questionnaires) and differences in analytic methods (e.g., covariates included in statistical models). Nonetheless, results from our study indicate that the associations between food choice were stronger in relation to educational attainment than income categories. Differences in food choices according the level of education reflect that more knowledge may influence the perceived relationship between diet and health as well as the perceived outcomes of following a healthy diet [ 33 ].Despite differences in food consumption according education and income, in our study we could not assess if these differences were also evident on the energy and nutrient level, which was a limitation. British data point to micronutrient and antioxidant intakes as the most likely nutritional influences on health inequalities [ 50 ]. Nevertheless, according Galobardes et al., [ 10 ] it is also possible that despite differences in food consumption, nutrient intake is similar among socio-economic groups, as these may not be substantial enough to translate into differences in nutrient intake.If a country like Portugal wants to change the adult food choice behaviour, or in other words, wants to reach certain dietary goals, the support of applied research like ours is needed in order to plan the right strategies for promoting healthy diets. Confidence in a significant positive causal link between per capita national income and individual nutrition reinforces the importance of economic growth [ 51 ] but also implies that public policy should stress education as a mean for improving healthy food choices. Education might influence food choice by facilitating or constraining one's ability to understand the information communicated in nutrition education or in food labels [ 52 , 53 ].Whereas income-related dietary differences suggest ameliorative responses through the potential of the economic system, differences based on education point to initiatives such as nutrition education programmes [ 22 , 54 ]. According to Geraldes,[ 55 ]in Portugal it may sometimes be more appropriate to correct inequalities in the domains of education or nutrition than that of health. Given the poor education level of the majority of Portuguese adults, a move towards an increased acquisition of general knowledge and personal development through compulsory and higher education, lifelong learning and improved qualifications of the population, is desirable to promote the development of a knowledge society and improve the level and quality of national education which, in turn, may relate to healthier food choices. It is well recognized that changes in dietary behaviour may be brought about, not by direct modification of food habits, but by alteration or manipulation of the education and culture [ 8 ].Conclusions Regardless of the reasons explaining the complex and diversified patterns of economic and educational associations of food consumption found in Portugal, the findings of this study suggest that education and income have distinct associations with food choice. The low and high income groups are or tend to be similar in regard to the majority of food choices, and access to education appears to be the key element to a better food pattern as indicated by higher frequency of milk, vegetable soup, vegetables, fruit, and fish consumption.
Conclusions
Regardless of the reasons explaining the complex and diversified patterns of economic and educational associations of food consumption found in Portugal, the findings of this study suggest that education and income have distinct associations with food choice. The low and high income groups are or tend to be similar in regard to the majority of food choices, and access to education appears to be the key element to a better food pattern as indicated by higher frequency of milk, vegetable soup, vegetables, fruit, and fish consumption.Competing interests The author(s) declare that they have no competing interests.
Competing interests
The author(s) declare that they have no competing interests.Authors' contributions PM and PP designed the study. PM and PP did the statistical analysis, and PM wrote the paper. PM and PP reviewed the final version of the paper. Table 5 Odds ratios for food consumption according level of income, adjusted for age, BMI, smoking habits, physical activity and education Women Men OR IC(95%) P trend OR IC(95%) P trend Vegetable soup Vegetable soup Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 1.02 0.91-1.14 315-547 euros 0.95 0.85-1.05 548-815 euros 1.01 0.90-1.14 548-815 euros 0.95 0.85-1.06 >815 euros 0.96 0.86-1.07 0.281 >815 euros 0.92 0.83-1.02 0.195 Vegetables Vegetables Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 1.11 0.96-1.29 315-547 euros 1.22 1.06-1.39 548-815 euros 1.07 0.92-1.24 548-815 euros 1.16 1.01-1.33 >815 euros 1.05 0.92-1.21 0.768 >815 euros 1.06 0.93-1.20 0.796 Fruit Fruit Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 0.94 0.81-1.21 315-547 euros 0.99 0.84-1.15 548-815 euros 0.98 0.80-1.19 548-815 euros 0.95 0.81-1.12 >815 euros 1.01 0.84-1.23 0.983 >815 euros 1.03 0.89-1.20 0.769 Bread Bread Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 1.16 0.92-1.47 315-547 euros 1.05 0.80-1.39 548-815 euros 1.06 0.84-1.34 548-815 euros 1.05 0.79-1.40 >815 euros 1.00 0.81-1.25 0.502 >815 euros 0.85 0.66-1.10 0.139 Other starchy Other starchy Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 0.86 0.69-1.06 315-547 euros 0.91 0.71-1.17 548-815 euros 0.95 0.77-1.19 548-815 euros 0.84 0.65-1.07 >815 euros 0.88 0.72-1.08 0.466 >815 euros 0.72 0.57-0.90 0.002 Fish Fish Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 0.86 0.77-0.96 315-547 euros 0.99 0.89-1.10 548-815 euros 0.84 0.75-0.93 548-815 euros 0.95 0.85-1.06 >815 euros 0.92 0.83-1.03 0.409 >815 euros 0.98 0.88-1.08 0.457 Meat Meat Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 0.98 0.86-1.12 315-547 euros 0.95 0.82-1.09 548-815 euros 1.09 0.95-1.25 548-815 euros 0.92 0.80-1.06 >815 euros 1.04 0.92-1.19 0.299 >815 euros 0.87 0.76-0.99 0.026 Table 6 Odds ratios for beverage consumption according level of income, adjusted for age, BMI, smoking habits, physical activity and education Women Men OR IC(95%) P trend OR IC(95%) P trend Milk Milk Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 0.94 0.83-1.05 315-547 euros 1.01 0.90-1.12 548-815 euros 1.02 0.91-1.15 548-815 euros 1.05 0.94-1.18 >815 euros 0.99 0.89-1.11 0.575 >815 euros 1.12 1.01-1.24 0.017 Wine Wine Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 0.97 0.81-1.17 315-547 euros 1.07 0.93-1.22 548-815 euros 0.92 0.76-1.11 548-815 euros 1.00 0.87-1.14 >815 euros 0.86 0.72-1.03 0.065 >815 euros 0.89 0.79-1.01 0.757 Beer Beer Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 0.89 0.67-1.19 315-547 euros 0.95 0.83-1.09 548-815 euros 0.89 0.66-1.20 548-815 euros 1.05 0.92-1.21 >815 euros 1.00 0.76-1.31 0.671 >815 euros 1.06 0.93-1.21 0.139 Spirits Spirits Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 2.18 0.74-6.43 315-547 euros 1.14 0.92-1.41 548-815 euros 2.52 0.86-7.43 548-815 euros 1.12 0.89-1.40 >815 euros 1.28 0.44-3.78 0.968 >815 euros 1.09 0.88-1.34 0.229 Port Wine Port Wine Income Income ≤314 euros (reference) ≤314 euros (reference) 315-547 euros 1.15 0.78-1.70 315-547 euros 0.98 0.77-1.24 548-815 euros 1.28 0.87-1.89 548-815 euros 1.20 0.94-1.53 >815 euros 1.09 0.75-1.59 0.704 >815 euros 1.05 0.84-1.33 0.483
Authors' contributions
PM and PP designed the study. PM and PP did the statistical analysis, and PM wrote the paper. PM and PP reviewed the final version of the paper.Odds ratios for food consumption according level of income, adjusted for age, BMI, smoking habits, physical activity and educationWomenMenORIC(95%)P trendORIC(95%)P trendVegetable soupVegetable soupVegetablesVegetablesFruitFruitBreadBreadOther starchyOther starchyFishFishMeatMeatOdds ratios for beverage consumption according level of income, adjusted for age, BMI, smoking habits, physical activity and educationWomenMenORIC(95%)P trendORIC(95%)P trendMilkMilkWineWineBeerBeerSpiritsSpiritsPort WinePort WinePre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


Highlights of Bela Julesz's scientific career in visual neuroscience

Throughout his career, Bela Julesz created new scientific disciplines by remarkable combinations of seemingly disparate approaches. The selection of his major discipline, which would eventually be called visual neuroscience, may have been serendipity or choice.When the unexpected Soviet invasion of Hungary in 1956 spurred his emigration to the United States, Bela Julesz, with his Hungarian doctorate in engineering, joined the numerous mathematical luminaries working at AT&T Bell Laboratories, such as John Tukey, Harry Nyquist, Claude Shannon, and John Kelly. One of the projects underway at the time was the creation of long random-number binary sequences that did not repeat. Bela told the story that he was assigned the problem of testing these number generators; he decided to use the best pattern recognizer that he knew of—the human visual system. The random bits of zeros and ones drawn from the random number sequences were plotted as sequential rows in an image. Any repeats, any correlations across space, would be instantly seen by the human visual system as patterns in the random dots. What caused Bela to choose this unusual approach to looking for patterns, combining computers and vision? His doctoral thesis research in network theory and television signals clearly influenced him, but it was quintessential Bela to give himself a hand up into a new field by building on his base of knowledge, moving in a new and unexpected direction using mathematical and psychological insight. He termed this talent “scientific bilingualism” ( Julesz 1994 ).This success in exploiting the visual system, and the intellectual freedom intrinsic to the design of Bell Labs, provided Bela with the opportunity to use these new random dot patterns to explore the visual system. Most of us know well that we can use the small differences in the images in each eye to see depth. Sir Charles Wheatstone showed in 1838 that if two different perspective images were observed through a stereoscope so that each eye observed only one view, a startlingly realistic three-dimensional image occurred. Oliver Wendell Holmes, stereoscope enthusiast, wrote of the experience that “the shutting out of surrounding objects, and the concentration of the whole attention, which is a consequence of this, produce a dreamlike exaltation…in which we seem to leave the body behind us and sail away into one strange scene after another, like disembodied spirits” ( Holmes 1861 ).The basis of this three-dimensional perception was hotly debated between Wheatstone and fellow physicist Sir David Brewster. (Though it may seem odd for physicists to concern themselves with the physiology of optics, this was felt to be a natural extension of the study of the physics of optics.) Brewster opined that perspective was the source of the apprehension of an object's shape. Wheatstone insisted that the images in the each eye had identifiable landmarks that were combined to assign depth to the landmarks. Bela read much of the literature of that time, and he must have seen two greats as wrestling without either finding the overwhelming hold to pin down the other. More than one hundred twenty years after Brewster and Wheatstone, Bela realized that his random dot patterns could be used to probe this question. What Bela did was create a pair of identical random dot patterns. When viewed binocularly through a stereoscope (i.e., fused), they would be seen as a single surface. Then Bela took a central region from the right random dot pattern and displaced it minutely to the right. Now when the two patterns were fused, the central square was not seen double, but after a moment or two, eerily moved into depth, behind the surrounding region. In 1960, Bela's experiment with what eventually became known as Julesz random dot stereograms unambiguously demonstrated that stereoscopic depth could be computed in the absence of any identifiable objects, in the absence of any perspective, in the absence of any cues available to either eye alone. It was a perfect combination of psychological and mathematical insight and technology that solved this puzzle. (It is an interesting aside that Bela sent his first report to the  Journal of the Optical Society of America , where it was rejected; the  Bell Labs Technical Journal  holds the now classic paper [ Julesz 1960 ]. The  Journal of the Optical Society of America  published Bela's second paper [ Julesz 1963 ].) The stereoscope had existed 125 years.Journal of the Optical Society of AmericaBell Labs Technical JournalJournal of the Optical Society of AmericaBela proposed in his book  Foundations of Cyclopean Perception  (1971) that early in the vision process the two images from the two eyes were combined to form a single view, imbued with inherent depth information. The perceptual “cyclops within us” was proposed to analyze the visual world first, before the motion, color, and contrast systems began their perceptual operations. Bela's book is full of powerful visual experiments that make this point irrefragably; from his psychophysical analysis, binocular vision forces unexpected constraints on the rest of vision,  Q.E.D. Foundations of Cyclopean Perception  is still considered one of the classics of modern psychophysics and continues to have profound relevance to both those entering the field and established investigators—over thirty years after its publication. At the time of his death, Bela had begun working on a second edition.Foundations of Cyclopean PerceptionQ.E.D. Foundations of Cyclopean PerceptionHis success in determining the sequence of visual processing using random dot stereograms led Bela to propose that the anatomical hierarchy of the visual system could be understood in part through visual psychophysics—he termed this approach “psychoanatomy.” His ingenious use of the stereogram established a new approach in the field of vision research and presaged the now common use of carefully controlled computational techniques in brain science. By this time Bela's reputation was established, and in 1983, he received a prestigious MacArthur Fellowship—the “genius award.” He used the funds for travel, including an annual peregrination to the California Institute of Technology, where I first met Bela in 1985.His seminars and lecture courses were enthusiastically received and endorsed by countless students, post-doctoral trainees, and faculty, as evidenced both by his formidable reputation and through the numerous citations of his work. His approach to presenting his research was modest and gently self-deprecating. He always encouraged young scientists; his joy and passion in their science were transmitted both through his warm persona and his suggestions of directions for future study. His insights guided my development of random dot kinematograms (i.e., movies) to examine how motion could be used to construct three-dimensional form ( Siegel and Andersen 1988 ). He collaborated with Derek Fender, David Van Essen, and John Allman at the California Institute of Technology on the combination of the computer, the psychophysical approach, and the physiological experiment.Bela was a fount of ideas, each building on the prior's advance. His later passions were explorations of texture and attention, notably with Jonathan Victor and Dov Sagi. Bela's appealing hypothesis that textons (putative elements of textures) are represented at a cellular level is now questionable ( Julesz et al. 1978 ). Bela was groping for an overarching computational theory for the representation of random geometry, but none was to be had. Nonetheless, the texton elements served useful duty in the demonstration that there were two stages to early vision—an effortless phase preceding attention and a guided identification phase ( Sagi and Julesz 1985 ). Many contemporary laboratories examining vision, studying either perception or the activity of neurons, now incorporate designed, complicated, yet highly controlled stimuli that have evolved (knowingly or not) from Bela's original forays in the 1960s and 1970s. His continuing impact was recognized by his election to the National Academy of Science in 1987.In 1989, Bela retired from Bell Labs (by then he was a department head) and joined the Department of Psychology at Rutgers University to establish the Laboratory of Vision Research. Bela continued investigating mechanisms of form, texture, and stereopsis; his presence led to numerous studies into the implications of his original findings as well as new investigations into computational vision. His collaborations greatly aided the establishment of neuroscience at Rutgers. Bela wrote  Dialogues on Perception  (1995), a wide-ranging intellectual effort, in which he uses classic dialectics to question both his own successes and those of his chosen field. In the book one reads of two competing intellects, a Bela who believes in his contributions to science and another Bela who is constantly belittling and judging his contributions.Dialogues on PerceptionThroughout his career Bela Julesz was able to add language after language to his research imperative, becoming a true scientific polyglot. Although his arrival in the United States was propelled by political events beyond his control, his intellectual directions followed a chosen path “less traveled by, and that has made all the difference.” In 1956, an engineer set out from Hungary. By 2003, his unique combination of mathematical precision combined with deep biological insight had carried him to elegant solutions for seemingly intractable problems in visual neuroscience. Bela was always in dialogue, often with others, and often with himself. In the process, he would gently drive each of us, and himself, forward to our final destination of understanding the brain. Bela Julesz died on December 31, 2003, forty-seven years to the day after starting at Bell Laboratories.