Interrelationships between nursing workers’ state of nutrition, socio demographic factors, work and health habits

Kali Siqueira Rosane Harter Griep Lúcia Rotenberg Aline Costa Enirtes Melo Maria de Jesus Fonseca About the authors

Abstract

The interrelationships between professional nursing workers’ state of nutrition, variables relating to their socio demographic relationships, their professional work, and health behavior, were examined based on a correspondence analysis technique. This is a sectional study carried out involving 917 nursing professionals in a public hospital in Rio de Janeiro. The results show the formation of four groups, three of them grouped under BMI (body mass index) categories. The obese individuals group included poor health, current socio economic conditions, unfavorable past conditions, and former night shift workers. The low/adequate group showed the most favorable conditions, while the group of overweight individuals also included smoking, alcohol consumption, and current night shift work (up to five nights per two-week period). Specifically, among the interrelationships between the states of nutrition levels, we highlight those relating to current and previously evaluated socio economic conditions, and underscore the life-long importance of social indicators.

Body Mass Index; Obesity; Night shift work; Nursing; Cluster Analysis


Introduction

Obesity is a chronic disease, of multiple factor origin, characterized by excess body fat. It is closely associated with chronic degenerative disease11. Klaus S. Adipose tissue as a regulator of energy balance. Curr Drug Targets 2004; 5(3):241-250.44. Park SK, Park JH, Kwon YC, Kim HS, Yoon MS, Park HT. The effect of combined aerobic and resistance exercise training on abdominal fat in bese middle-aged women. J Physiol Anthropol Appl Human Sci 2003; 22(3):129-135., whose risks increase substantially when the BMI exceeds 25kg/m2 55. Lyznicki JM, Young DC, Riggs JA, Davis RM. Obesity: assessment and management in primary care. Am Fam Physician 2001; 63(11):2185-2196..

In Brazil, the results of the 2008-2009 Family Budget Survey disclosed an accelerated increase in excess weight. Over a period of 34 years (1974/1975 through 2008/2009), the trend to excess weight among men increased from 18.5% to 50.1%. The respective statistics for women were 28.7% to 48.0%. Over this same period, the trend to obesity increased by more than four times for men and more than doubled for women66. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de orçamento familiar (POF) 2008-2009: Despesas, Rendimentos e Condições de Vida. Rio de Janeiro: IBGE; 2010..

Contemporary life styles differences, as compared with prior decades, have been considered a major reason for the prevalence of excess weight. These changes comprise the so-called “obesogenic environment”77. Ulijaszek SJ. Frameworks of population obesity and the use of cultural consensus modeling in the study of environments contributing to obesity. Econ Hum Biol 2007; 5(3):443-457., a term coined by Swinburn et al.88. Swinburn B, Egger G, Raza F. Dissecting obesogenic environments: the development and application of a framework for identifying and prioritizing environmental interventions for obesity. Prev Med 1999; 29(6 Pt 1):563-570. referring to the physical, economic, social, and cultural aspects of an environment that could encourage a balance of positive energy, thereby promoting obesity.

Some authors suggest an inverse association between the socio economic status (SES), both current and previous, and weight gain99. Sobal J, Stunkard AJ. Socioeconomic status and obesity: a review of the literature. Psychol Bull 1989; 105(2):260-275.1212. Fonseca MJM, França RF, Faerstein E, Werneck GL, Chor D. Escolaridade e padrões de ganho de peso na vida adulta no Brasil: Estudo Pró-Saúde. Rev Panam Salud Publica 2012; 32(5):376-380.. This association suggests that the differential weight gain rhythm, according to the SES could, possibly, begin early in life, influenced by parental SES10,13.

In addition to these factors, work environment aspects have also been associated with an increased risk of excess weight. Highlighted among these is shift work, particularly night shifts1414. Pan A, Schernhammer ES, Sun Q, Hu FB. Rotating night shift work and risk of type 2 diabetes: two prospective cohort studies in women. Plos Medicine 2011; 8(12):1-8.1717. Di Lorenzo L, De Pergola G, Zocchetti C, L’Abbate N, Basso A, Pannacciulli N, Cignarelli M, Giorgino R, Soleo L. Effect of shift work on body mass index: results of a study performed in 319 glucose-tolerant men working in a Southern Italian industry. Int J Obes Relat Metab Disord 2003; 27(11):1353-1358., excessive work load1818. Han K, Trinkoff AM, Storr CL, Geiger-Brown J. Job stress and work schedules in relation to nurse obesity. J Nurs Adm 2011; 41(11):488-495., and psychosocial stress in the workplace1919. Kouvonen A, Kivimäki M, Cox SJ, Cox T, Vahtera J. Relationship between work stress and body mass index among 45,810 female and male employees. Psychosom Med 2005; 67(4):577-583..

These factors are typical of nursing work in Brazil, where night shifts2020. Teixeira RC, Mantovani MF. Enfermeiros com doença crônica: as relações com o adoecimento, a prevenção e o processo de trabalho. Rev Esc Enferm USP 2009; 43(2):415-421. are worked, in addition to long day shifts (12 hours). Moreover, here, multiple work bonds are common in the nursing profession2121. Portela LF, Rotenberg L, Waissmann W. Health, sleep and lack of time: relations to domestic and paid work in nurses. Rev Saude Publica 2005; 39(5):802-808.. The interaction of these factors can negatively impact such workers, both physiologically as psychologically2222. Admi H, Tzischinsky O, Epstein R, Herer P, Lavie P. Shift work in nursing: is it really a risk factor for nurses’ health and patients’ safety? Nurs Econ 2008; 26(4):250-257.,2323. Schluter PJ, Turner C, Benefer C. Long working hours and alcohol risk among Australian and New Zealand nurses and midwives: a cross-sectional study. Int J Nurs Stud 2012; 49(6):701-709., added to a potential for increase in weight.

Although a number of different studies have analyzed the determining factors of obesity and excess weight, our bibliographical search revealed no articles based on the correspondence analysis technique, aimed at visualizing BMI distribution and its relationship with several variables that influence weight gain. This approach becomes important in studies of the multi-factor results, as described by Carvalho and Struchiner2424. Carvalho MS, Struchiner CJ. Análise de correspondência: uma aplicação do método à avaliação de serviços de vacinação. Cad Saude Publica 1992; 8(3):287-301., since it enables a simultaneous study of the relationships between a sizeable group of variables.

The aim of this study is to examine the interrelationship between the state of nutrition, socio demographic related variables, work, and the health behavior of nursing professionals working in a Rio de Janeiro public hospital, based on the correspondence analysis technique.

Methods

Population and information source

This is a sectional study carried out involving 1,182 nursing workers (nurses, nursing technicians, and nursing assistants) of a Rio de Janeiro public hospital, all of whom answered a questionnaire in 2006 when they took part in a survey entitled “Type, work, and health of nursing professionals: morbidity and its association with night shift work, long day shifts, and housework”.

Definition of the variables

The variables applied in this study reflect aspects of state of nutrition, socio demographic conditions, work-related variables, and health behavior.

State of nutrition was classified based on weight and height data supplied. The BMI was classified under low/adequate (BMI1 ≤ 24,9), excess weight (BMI2 = 25,0 −29,9), and obesity (BMI 3 ≥ 30,0)33. World Health Organization (WHO). Obesity and Overweight. Geneva: WHO; 2012..

The socio demographic variables used were: sex (female/male); age divided into averages (ages 16 to 44, 45 to 70); stated skin color (Caucasian, mixed race, Afro-Brazilian); education (elementary, high school, college level); per capita family income in US Dollars (USD), based on the foreign exchange rate of the period in which the data was collated (12/29/2006) – classified in accordance with the base salary of the period in which the data was collated (> $421.00 up to $421.00); marital status (married, single, divorced, separated, widowed); children (yes, no); prior economic circumstances based on the interviewee's perception of his/her family situation at the age of twelve, in relation to his/her current circumstances (moderately wealthy, poor, very poor); and maternal educational level (did not attend school, up to elementary, high school, or higher).

In the work context, the following variables were examined: professional category (nurse, auxiliary nurse, nursing technician); the working bond (permanent or impermanent); night shift (never worked a night shift, former night shift worker, working up to five night shifts per two-week period, six or more night shifts per two-week period)); professional work load (up to 40 hours a week, > 40 hours a week); and hours of housework, classified into averages (up 11 hours a week, > 11 hours a week).

The health-related variables were: auto perception of health (good, bad); self diagnosed hypertension (yes, no); insomnia (yes, no); smoking (smoker, ex-smoker, never smoked); alcohol consumption (yes, no); physical exercise (yes, no); fried food consumption (at least once a week, less than three times a month); fruit consumption (at least once a week, less than three times a month); vegetable consumption (at least once a week, less than three times a month).

Statistical analysis

The descriptive age analysis was based on the average, the standard deviation, and categorical variable percentages. The Chi Square test was applied based on a significance level of 20%, to test categorical variable associations.

Correspondence analysis and cluster analysis were applied to examine relationships between state of nutrition and socio demographic conditions, and work-related variables and health behavior.

Correspondence analysis is a multi-varied statistical technique, one that is both exploratory and descriptive, utilized for categorical data analysis2525. Greenacre MJ. Practical correspondence analysis. In: Looking at Multivariate Data. New York: J. Wiley&Sons; 1981. Cap. III. This method enables visualization of the major relationships of a significant number of variables.

Data was treated in the stages described below. At first, the graphic representation obtained in the correspondence analysis enabled visualization of the distribution of relationship between all the categories. Each category was represented by one point, and the differences between the points represented the inter-category relationships.

Each auto valuation was considered to correspond to one of the axes that define multidimensional space, representing a total variance percentage. This percentage enabled evaluation of the capacity of each axis to represent a points cloud. The greater the percentage, the better the representation of the distribution of the points in space and, consequently, of the existing relationships between the categories2424. Carvalho MS, Struchiner CJ. Análise de correspondência: uma aplicação do método à avaliação de serviços de vacinação. Cad Saude Publica 1992; 8(3):287-301..

The importance of each category of variable in the construction of the axes was evaluated via an absolute contribution. This absolute contribution analysis of the categories, in conjunction with observation of the position of the points on the graph in relation to the axes, aided in the interpretation of the factors and contributed to the conceptual categorization of the axes.

The cluster analysis complemented the correspondence analysis in identifying and dividing the groups.

The R program, version 2.15.0 () was utilized for these analyses.

Ethical aspects

This study was submitted to, and approved by, the Research Ethics Committees of Fiocruz and of the hospital involved in the study.

Results

Of the 1,182 nursing professionals investigated, the data of 265 (22.4%) were excluded, due to incomplete data on variables in the questionnaire. The group analyzed (N = 917) did not differ from the sample excluded from the study in respect of age, sex, education, BMI, and professional category (chi square test, p > 0,05).

Of the group studied, 85,9% were of the female sex. The average age was 40.9 (DP = 13,3). There was a majority of Caucasians; college level education; per capita family income of up to US$421.00; married individuals; and childless individuals. The BMI distribution of the population studied was 30.1% of overweight individuals and 16% of obese individuals.

Table 1 describes the BMI-based data. The predominance of excess weight and obesity was noted among the men (35.7% and 18.6, respectively), within an age range of 45 to 70 (37.6% and 21.8%, respectively), with education levels up to elementary level (40.5% and 27.8%, respectively), with children (35.4% and 20.4%, respectively), of Afro-Brazilian descent (37.4% and 17.8%, respectively), per capita income up to US$421.00 (31.4% and 17.2%, respectively), and with self diagnosed hypertension (37.2% and 30.2%, respectively).

Table 1
Distribution of the survey population according to state of nutrition and Absolute Contribution in the two axes of greater auto value.

The absolute contribution for each variables category can be interpreted as being part of the variation of the axis explained by the respective category (Table 1). Accordingly, the greater the absolute contribution, the greater the importance of the category in the axis. In this case, the categories that most contributed to the first axis were the age range of 45 to 70 and ‘impermanent bond’; the variable that contributed the least was ‘alcohol consumption’. In the second axis, the categories that most contributed were ‘education levels up to high school levels’ and ‘nurses’, and the variable that contributed the least was ‘auto perception of health’.

Table 2 shows self valuations and percentages of variances explained by each axis, which defines the multidimensional space. The first two dimensions explained 18.88% of the global data variation, the first contributing 12.95%, and the second 5.93%. Both these dimensions were utilized for subsequent analyses, since, from the second axis onwards, the explanation percentage diminished, became homogeneous, and relatively insignificant.

Table 2
Dimensions, auto values, variation percentage, and accumulated percentage.

The graphic analysis of the first two axes (Figure 1) enabled visualization of the distribution of each category of variable, by configuring a points cloud in a multidimensional space, displaying the proximity between the variables categories. Groupings were defined based on the distribution of variables and their interrelationships. As a result, four groups were found to be formed, all of them heterogeneous with one another, selected based on a visual inspection of the graph generated by the correspondence analysis and confirmed by the dendrogram (Figure 2). This figure shows the formation of four groups when we applied a cut-off point at the stage of 2.5, and confirms the same grouping identified in the graphic representation arrived at via correspondence analysis.

Figure 1
Graphic representation of the first two correspondence analysis axes.
Figure 2
Dendrogram of the coordinates of the first two axes.

Predominant in the group within the obesity category (BMI3) are the following features: auto perception of bad health, insomnia, self-diagnosed hypertension, marital separation, divorce, and widowhood, of being Afro-Brazilians, permanent working bond, older (aged between 45 and 70); former night shift workers, low fried food consumption, mother who did not attend school, prior low economic position, and lower level of education.

The group including the ‘overweight’ category (BMI 2) showed a link with ‘smokers’, ‘alcohol consumption, ‘professionals who work up to five nights a week per two-week period’, and ‘men’.

The ‘low/adequate’ category (BMI 1) comprised nurses, professionals with a ‘college education’, ‘higher per capita family income’ (greater than US$421.00), ‘moderately wealthy prior economic circumstances’, ‘skin color auto perceived as Caucasian’, maternal education to high school or higher level, impermanent work bond, younger ‘(aged from 16 to 44)’, ‘lower housework load (up to 11 hours a week)’, ‘longer professional working hours (more than 40 hours per week)’, ‘greater exposure to night shift work’ (more than six nights per two-week period), ‘unmarried, ‘childless, ‘low fruit and vegetable consumption’.

A fourth group consists mainly of nursing technicians and assistants, with a ‘per capita family income of up to US$421,00’, ‘prior economic situation of poverty’, ‘skin color referred to as being of mixed race’, ‘high school education, ‘self perception of good health’, ‘no insomnia, ‘no auto-diagnosis of hypertension’, ‘greater fried food consumption’, ‘maternal education ‘up to elementary level, ‘greater fruit and vegetable consumption’, ‘non-smokers’, ‘no alcohol consumption, ‘women, ‘physical exercise (yes and no)’, ‘never any night shift work’, ‘housework more than eleven hours a week 11 hours a week’, ‘fewer working hours (up to 40 hours a week)’, ‘ex-smokers’, married and some with children.

Discussion

The most important interrelationships of categories associated with the levels of states of nutrition identified were those relating to the individuals’ current and prior socio economic circumstances, thus underscoring the importance of social determinants throughout life. Although, the less favorable current and prior socio economic conditions were maintained in the obese group, these were more positive for the ‘low/adequate’ group. In fact, over the last few decades, an accumulation of evidence has been observed indicating that the incidence and progression of illnesses and incapacitation involve a complex relation of socio economic, behavioral, demographic, and psychosocial determinants. As noted by Kaplan et al.2626. Kaplan GA, Baltrus PT, Raghunathan TE. The shape of health to come: prospective study of the determinants of 30-year health trajectories in the Alameda County Study. Int J Epidemiol 2007; 36(3):542-548., these factors tend to be associated with occurrences of illness and also with the direction of an individual's health during a significant period of his/her life. This is how epidemiological models that examine the direction of life and health view the effects of exposure throughout life, particularly during more vulnerable periods in biological and social terms, and which could influence health when a more advanced age is reached2727. Lynch J, Smith GD. A life course approach to chronic disease epidemiology. Annu Rev Public Health 2005; 26:1-35.. In addition to genetic factors impacting chronic diseases, such as obesity, several social determinant aspects throughout life could favor or prevent weight gain, and this includes the influence of life in utero2828. Rhee KE. Phelan S, McCaffery J. Early determinants of obesity: genetic, epigenetic, and in utero influences. Int J Pediatrics 2012; 2012: 463850.. Models examining lifetime exposures show that the impact of a given exposure could be dependent on the age of, or on the stage of development to which, the individual was exposed2929. Osypuk TL. Integrating a life-course perspective and social theory to advance research on residential segregation and health. Am J Epidemiol 2013; 177(4):310-315.. Accordingly, obesity prevention strategies should begin very early in life.

The aspects of this work show that the obese group consisted of former night shift workers, i.e., individuals who work during the day but have past experience of working night shifts. The remit of this work is the impact of shift work, particularly night shifts, on weight gain3030. Suwazono Y, Dochi M, Sakata K, Okubo Y, Oishi M, Tanaka K, Kobayashi E, Kido T, Nogawa K. A longitudinal study on the effect of shift work on weight gain in male Japanese workers. Obesity 2008; 6(8):1887-1893.. Studies evaluating former night shift workers noted a greater weight gain in this group, as compared with individuals who had never worked a night shift3131. Wang X-S, Travis RC, Reeves G, Green J, Allen NE, Key TJ, Roddam AW, Beral V. Characteristics of the Milion Women Study participants who have and have not worked at night. Scand J Work Environ Health 2012; 38(6):590-599.,3232. Griep RH, Fonseca MJM, Melo ECP, Portela LF, Rotenberg L. Enfermeiros dos grandes hospitais públicos no Rio de Janeiro: características sociodemográficas e relacionadas ao trabalho. Rev Bras Enferm 2013; 66:151-157.. Moreover, on leaving night shift work, they were more likely to continue experiencing sleeping difficulties3131. Wang X-S, Travis RC, Reeves G, Green J, Allen NE, Key TJ, Roddam AW, Beral V. Characteristics of the Milion Women Study participants who have and have not worked at night. Scand J Work Environ Health 2012; 38(6):590-599.,3333. Rotenberg L, Costa AS, Diniz TB, Griep RH. Long-term deleterious effects of night work on sleep. Sleep Science 2011; 4:13-20.. It is not uncommon for sleep disruption to become associated with obesity and other health issues1616. Antunes LC, Levandovski R, Dantas G, Caumo W, Hidalgo MP. Obesity and shift work: chronobiological aspects. Nutr Res Rev 2010; 23(1):155-168..

In fact, the category of individuals with no night shift experience leveled with the categories reflecting the best state of health, including no self diagnosed hypertension, a good perception of own health, and the absence of insomnia. Conversely, the unfavorable health aspects analyzed (auto perception of bad health, insomnia, and self diagnosed hypertension) were all linked under obesity.

In turn, excess weight was associated with individuals who work up to five night shifts per two-week period. A number of factors explain this greater weight gain among night shift workers. Chief among these is circadian maladjustment and changes in health behavior pat-terns1515. Morikawa Y, Nakagawa H, Miura K, Soyama Y, Ishizaki M, Kido T, Naruse Y, Suwazono Y, Nogawa K. Effect of shift work on body mass index and metabolic parameters. Scand J Work Environ Health 2007; 33(1):45-50.,3434. Parkes KR. Shift work and age as interactive predictors of body mass index among offshore workers. Scand J Work Environ Health 2002; 28(1):64-71.3636. Atkinson G, Fullick S, Grindey C, Maclaren D, Waterhouse J. Exercise, Energy Balance and the Shift Worker. Sports Med 2008; 38(8):671-685.. These workers suffer from chronic nocturnal sleep deprivation. This changes successive biochemical events that, in turn, alter the release of hormones during sleep, such as the growth hormone, leptin, ghrelin, and cortisol3737. Scheer FAJL, Hiltona MF, Mantzorosb CS, Shea SA. Adverse metabolic and cardiovascular consequences of circadian misalignment. PNAS 2009; 106(11):4453-4458.. Sleep deprivation and associated fatigue stimulate the urge to eat and reduce energy expended to increase energy reserves, thus resulting in weight gain3838. Geliebter A, Tanowitz M, Aronoff NJ, Zammit GK. Work-shift period and weight change. Nutrition 2000; 16(1):27-29.. Allied to these factors are difficulties in absorbing food during the night, based on the circadian organization function which is adapted to daytime activities3939. Lowden A, Moreno C, Holmback U, Lennernas M, Tucker P. Eating and shift work-effects on habits, metabolism and performance. Scand J Work Environ Health 2010; 36(2):150-162..

Interacting with the biological aspects, night shift workers’ behavior also affects weight gain, since they develop a tendency to more irregular feeding patterns than those of day shift workers. This includes a greater number of meals which can be easily prepared snacks during the night shift4040. Waterhouse J, Buckley P, Edwards B, Reilly T. Measurement of, and some reasons for, differences in eating habits between night and day workers. Chronobiol Int 2003; 20(6):1075-1092.. Lastly, this excessive weight gain could also arise from the lack of, or decrease in, physical exercise, particularly, on the day after the night shift4141. Silva AA, Rotenberg L, Fischer FM. Nursing work hours: individual needs versus working conditions. Rev Saude Publica 2011; 45(6):1117-1126., as a result of exhaustion and physical and mental tension.

A relationship between working hours and age could explain the grouping of the low/adequate individuals with frequent night shift work (six or more nights per two-week period) and with younger workers. Other studies of this population show that night work is more frequent and common among younger people4242. Diniz TB, Costa AS, Griep RH, Rotenberg L. Minor psychiatric disorders among nursing workers – is there an association with current or former night work? Work 2012; 41:2887-2892. who, in turn may have been exposed to night shift work for a shorter time. Here, the evidence of increase in BMI, with the increase of time of exposure to night shift work3232. Griep RH, Fonseca MJM, Melo ECP, Portela LF, Rotenberg L. Enfermeiros dos grandes hospitais públicos no Rio de Janeiro: características sociodemográficas e relacionadas ao trabalho. Rev Bras Enferm 2013; 66:151-157.,4343. Marqueze EC, Lemos LC, Soares N, Lorenzi-Filho G, Moreno CRC. Weight gain relation to night work among nurses. Work 2012; 41:2043-2048. showing a lower BMI among younger people, explains this variables grouping. This is because the younger age and the lower period of exposure to night shift work contribute to the chances of eutrophy. However, the length of exposure to night shift work was not analyzed, which is one of the limitations of this study.

Another feature noted in the grouping of individuals in a low/adequate state of nutrition was the minimal consumption of fruit and vegetables. However, this category also consists of younger individuals. Studies point a positive association between the consumption of fruit and vegetables and age, since older people tend to recognize and value the relationship between eating habits and health4444. Jaime PC, Monteiro CA. Fruit and vegetable intake by Brazilian adults, 2003. Cad Saude Publica 2005; 21(Supl. 1):S19-24.. Furthermore, the regular consumption of fruit and vegetables is relatively low among the Brazilian population4444. Jaime PC, Monteiro CA. Fruit and vegetable intake by Brazilian adults, 2003. Cad Saude Publica 2005; 21(Supl. 1):S19-24.4646. Castanho GKF, Marsola FC, Mclellan KCP, Nicola M, Moreto F, Burini RC. Consumo de frutas, verduras e legumes associado à Síndrome Metabólica e seus componentes em amostra populacional adulta. Cien Saude Colet 2013; 18(2):3985-3992..

The overweight category showed a grouping with smoking, alcohol consumption, and men. Smoking and excess alcohol consumption are life style patterns relating to social, cultural, environmental, and behavioral changes, all of which have contributed to weight gain4747. World Health Organization (WHO). Food and Agriculture Organization. Expert consultation. Diet, nutrition and the prevention of chronic diseases. Geneva: WHO; 2003.4949. Berto SJP, Carvalhaes MABL, Moura ECD. Tabagismo associado a outros fatores comportamentais de risco de doenças e agravos crônicos não transmissíveis. Cad Saude Publica 2010; 26(8):1573-1582.. Our findings bear out conclusions reached over the last few years of the trend to excess weight, mostly among men66. Instituto Brasileiro de Geografia e Estatística (IBGE). Pesquisa de orçamento familiar (POF) 2008-2009: Despesas, Rendimentos e Condições de Vida. Rio de Janeiro: IBGE; 2010.,5050. São Paulo. Secretaria Municipal da Saúde (SMS). Coordenação de Epidemiologia e Informação. Estado nutricional, insatisfação em relação ao peso atual e comportamento relacionado ao desejo de emagrecer na cidade de São Paulo. São Paulo: SMS; 2010.,5151. Block KV, Klein CH, Silva NAS, Nogueira AR, Campos LHS. Hipertensão arterial e obesidade na Ilha do Governador – Rio de Janeiro. Arq Bras Cardiol 1994; 62(1):17-22.. A comparison between the surveys carried out by the IBGE (Brazilian Institute of Geography and Statistics) from 1974 through 2009, such as the National Family Expenditure Study (1974-1975), the National Health and Nutrition Survey (1989), and Family Budget Surveys (2002-2003 and 2008-2009), stress that, over the last few years, men tend to gain the most weight.

There is one group representing a varied pattern, which showed a connection between intermediary socio demographic conditions, improved states of health (auto perception of good health, no insomnia, and no self diagnosed hypertension), healthier life styles (greater consumption of fruit and vegetables, non-smokers, no alcohol consumption) and some unfavorable categories (lower income levels, more housework, and greater consumption of fried foods). However, this group was not linked with any state of nutrition categories. A partial explanation for this profile is the fact that, in this modeling type, no a priori selection is made of the variables that will compose the final model, and all variables are analyzed simultaneously. The groupings formed by measured features (categories of variables) are homogeneous among themselves and heterogeneous in relation to other groups5252. Mingoti SA. Análise de dados através de métodos de estatística multivariada: uma abordagem aplicada. Belo Horizonte: Editora UFMG; 2005..

The complex nature of obesity etiology is inherent to an understanding at various levels. There are countless factors of differing levels of influence (biological, behavioral, group, and macro social levels)5353. Wanderley EN, Ferreira VA. Obesidade: uma perspectiva plural. Cien Saude Colet 2010; 15(1):185-194., all of them with implications for the development of the disease; these factors influence one another and, furthermore, are occasionally influenced by the disease itself5454. Galea S, Riddle M, Kaplan GA. Causal thinking and complex system approaches in epidemiology. Int J Epidemiol 2010; 39(1):97-106..

In addition to the points raised in this study, certain limitations must be stressed. An important one is the application of BMI to analyze the state of nutrition of the individuals concerned, given that this does not enable us to analyze the body composition. However, the ease of obtaining body and stature composition in population basis studies, and its good correlation with mortality and morbidity justify its use in epidemiological studies as an adiposity marker5555. World Health Organization (WHO). Physical status: the use and interpretation of anthropometry. Report of a WHO expert committee. World Health Organ Tech Rep Ser 1995. 1-452.5757. Seidell JC, Kahn HS, Williamson DF, Lissner L, Valdez R. Report from a Centers for Disease Control and Prevention Workshop on use of adult anthropometry for public health and primary health care. Am J Clin Nutr 2001; 73(1):123-126..

Correspondence analysis enabled us to examine the interrelationship between state of nutrition and work, health, and socio demographic features, resulting in the extraction of the maximum possible data. It was a useful tool and enabled us to identify well defined groups in terms of state of nutrition. If, on the one hand this correspondence analysis was extremely useful in identifying states of nutrition profiles, given the convoluted nature of the problem and its connection with articulated complex systems, on the other hand, the conformity of the groups did not permit inferences to be made regarding the findings. Accordingly, the results described herein are restricted to the population studied.

We hope that the results of this study will contribute to promoting preventive public health strategies. These would aid in the planning of preventive actions and to the management of excess weight and of obesity, and would also encourage periodic monitoring of health in a work context.

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Publication Dates

  • Publication in this collection
    June 2015

History

  • Received
    06 Mar 2014
  • Reviewed
    31 Oct 2014
  • Accepted
    02 Nov 2014
ABRASCO - Associação Brasileira de Saúde Coletiva Rio de Janeiro - RJ - Brazil
E-mail: revscol@fiocruz.br