Abstract

This research aimed to investigate the occurrence of clusters of cardiovascular risk behaviors and their association with social demographic and occupational characteristics in a population of industrial workers in the metropolitan area of Recife, Brazil. It was a transversal study with 727 workers of both genders. We identified cluster distribution from the variables: smoking, alcohol consumption, physical activity and waist circumference, by a combination of hierarchical and non-hierarchical analysis. We later tested the association with the social demographic and occupational variables with a multi-varied analysis. We have identified a protection cluster (sufficient physical activity, moderate alcohol consumption) and a risk cluster (high waist circumference, sedentarism, smoking, excessive alcohol consumption). The protection cluster was positively associated with night shift or variable shift work (RP: 1.66, IC95%: 1.26-2.17), and the risk cluster was associated with women (RP: 1.15, IC95%: 1.01-1.31). Cluster analysis allowed to identify that, for day shift workers and women, the shortest daytime outside the work environment can influence the adoption of cardiovascular risk behaviors.

Key words:
Occupational health; Cluster analysis; Shift work; Health behaviors

Resumo

Esta pesquisa teve como objetivo investigar a ocorrência de clusters de comportamentos de risco cardiovascular e sua associação com características sociodemográficas e ocupacionais em uma população de trabalhadores da indústria da região metropolitana de Recife, Brasil. Trata-se de um estudo transversal com 727 trabalhadores de ambos os gêneros. Identificou-se a distribuição do cluster a partir das variáveis: tabagismo, etilismo, atividade física e circunferência da cintura, por meio de uma combinação de análise hierárquica e não hierárquica. Posteriormente, testamos a associação às variáveis sociodemográficas e ocupacionais com uma análise multivariada. Identificamos a formação consistente de um cluster de proteção (atividade física suficiente, consumo moderado de álcool) e de um cluster de risco (circunferência da cintura elevada, sedentarismo, tabagismo, consumo excessivo de álcool). O cluster de proteção associou-se positivamente ao turno noturno ou turnos variáveis (RP: 1,66, IC95%: 1,26-2,17) e o cluster de risco esteve associado às mulheres (RP: 1,15, IC95%: 1,01-1,31). A análise de cluster permitiu identificar que, para trabalhadores diurnos e mulheres, a menor jornada diurna fora do trabalho pode influenciar na adoção de comportamentos de risco cardiovascular.

Palavras-chave:
Saúde ocupacional; Análise por conglomerados; Trabalho em turnos; Comportamento de saúde

Introduction

The main factors for cardiovascular risk that are considered to be modifiable are related to life behaviors adopted by individuals, and the main five are smoking, excess weight, high blood pressure, diabetes, and high cholesterol¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.. Although they are responsive to behavioral changes, the prevalence of these factors has increased in Brazil and worldwide²2 Sacco RL, Smith SC, Holmes D, Shurin S, Brawley O, Cazap E, Glass R, Komajda M, Koroshetz W, Mayer-Davis E, Mbanya JC, Sledge G, Varmus H. Accelerating progress on non-communicable diseases. Lancet 2013; 382:e4-e5.^,33 Malta DC, Santos MAS, Andrade SSCA, Oliveira TP, Stopa SR, Oliveira MM, Jaime P. Time trend in adult obesity indicators in Brazilian state capitals, 2006-2013. Cien Saude Colet 2016; 21(4):1061-1069..

The occurrence of these risk factors is influenced by social demographic characteristics such as gender, age, education, social class and cultural aspects of the individual and population groups¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.^,44 Pereira JC, Barreto SM, Passos VMA. Cardiovascular risk profile and health self-evaluation in Brazil: a population-based study. Rev Panam Salud Publica 2009; 25:491-498.

5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^-66 Loch MR, Bortoletto MSS, Tanno De Souza RK, Mesas AE. Simultaneity of health risk behaviors and associated factors in a population-based study. Cad Saude Colet 2015; 23:180-187.. In addition to that, studies in occupational health have pointed to the place of work⁷7 Kim W, Kim TH, Lee TH, Choi JW, Park E. The impact of shift and night work on health related quality of life of working women: findings from the Korea Health Panel. Health Qual Life Outcomes 2016; 14:162-168.^,88 Bae M, Song Y, Shin J, Choi B, Keum J, Lee E. The Association Between Shift Work and Health Behavior: Findings from the Korean National Health and Nutrition Examination Survey. Korean J Fam Med 2017; 38:86-92., position⁹9 Porto DB, Arruda GA, Altimari LR, Cardoso Júnior CG. Self-perceived health among workers at a University Hospital and associations with indicators of adiposity, arterial blood pressure and physical activity habits. Cien Saude Colet 2016; 21(4):1113-1122., social capital¹⁰10 Pattussi MP, Olinto MT, Canuto R, Garcez AS, Paniz VMV, Kawachi I. Workplace social capital, mental health and health behaviors among Brazilian female workers. Soc Psychiatry Psychiatr Epidemiol 2016; 51:1321-1330., and the shift of work¹¹11 Freitas ES, Canuto R, Henn RL, Olinto BA, Macagnan JBA, Pattussi MP, Busnello FM, Olinto MTA. Alteration in eating habits among shift workers of a poultry processing plant in southern Brazil. Cien Saude Colet 2015; 20(8):2401-2410.

12 Meader N, King K, Moe-Byrne T, Wright K, Graham H, Petticrew M, Power C, White M, Sowden AJ. A systematic review on the clustering and co-occurrence of multiple risk behaviours. Bmc Public Health 2016; 16:657-666.

13 Sun M, Feng W, Wang F, Li P, Li Z, Li M, Tse G, Vlaanderen J, Vermeulen R, Tse LA. Meta-analysis on shift work and risks of specific obesity types. Obes Rev 2017; 19:28-40.^-1414 Souza RV, Sarmento RA, Almeida JC, Canuto R. The effect of shift work on eating habits: a systematic review. Scand J Work Environ Health 2019; 45:7-21. as contributing factors to the increase in the occurrence of non-communicable chronic diseases, cardiovascular disease, and metabolic disturbances, due to their influence in the adoption of risk behaviors related to health. This highlights the importance of studies that can investigate the association between occupational variables and cardiovascular risk behaviors.

Also, the occurrence of these behaviors and risk factors has been studied in isolation, both in the general population¹⁵15 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009. and among workers¹⁶16 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68.. The risk factors studied including smoking¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.^,55 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,1515 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009.^,1616 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68.

17 Lacombe J, Armstrong MEG, Wright FL, Foster C. The impact of physical activity and an additional behavioural risk factor on cardiovascular disease, cancer and all-cause mortality: a systematic review. BMC Public Health 2019; 19(1):900.^-1818 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107., alcohol⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,1515 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009.^,1616 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68.^,1818 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107., physical activity⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,1515 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009.^,1616 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68.^,1818 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107., diet⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,1515 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009.^,1616 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68.^,1818 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107., overweight¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.^,1515 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009.^,1616 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68., diabetes¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.^,1515 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009.^,1616 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68., dyslipidemia¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.^,1515 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009.^,1616 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68., hypertension¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.^,1515 World Health Organization (WHO). Global health risks: Mortality and burden of disease attributable to selected major risks. Geneva: WHO; 2009. and stress¹⁶16 Ulguim FO, Renner JDP, Pohl HH, Oliveira CF, Bragança GCM. Health workers: cardiovascular risk and occupational stress. Rev Bras Med Trab 2020; 17(1):61-68.. However, this approach seems to be insufficient, since most of the time, a positive or negative health behavior happens in association to other behaviors⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49. revealing a synergic effect in the simultaneous presentation of behaviors and consequently in a heightened risk of cardiovascular diseases and general morbidity¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.^,1818 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107.. An in-depth analysis of how behaviors are distributed and associated with the construction of life habits for individuals might better show cardiovascular risk and its associated factors¹⁷17 Lacombe J, Armstrong MEG, Wright FL, Foster C. The impact of physical activity and an additional behavioural risk factor on cardiovascular disease, cancer and all-cause mortality: a systematic review. BMC Public Health 2019; 19(1):900..

The use of cluster analysis has intensified in the past decade as an advanced statistical technique that may be able to identify the underlying associations among health behaviors demonstrating the increased risk associated with combined behavioral exposure⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,77 Kim W, Kim TH, Lee TH, Choi JW, Park E. The impact of shift and night work on health related quality of life of working women: findings from the Korea Health Panel. Health Qual Life Outcomes 2016; 14:162-168. and even to find the existence of a common causal chain¹⁹19 Hofstetter H, Dusseldorp E, Van Empelen P, Pauçussen TWGM. A primer on the use of cluster analysis or factor analysis to assess co-occurrence of risk behaviors. Prev Med 2014; 67:141-146.. Some studies have described cluster cardiovascular risk of combination between alcohol, smoking and sedentarism⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,2020 Siqueira K, Griep RH, Rotenberg L, Costa A, Melo E, Fonseca MJ. Interrelationships between nursing workers' state of nutrition, socio demographic factors, work and health habits. Cien Saude Colet 2015; 20:1925-1935.

21 Falkstedt D, Möller J, Zeebari Z, Engström K. Prevalence, co-occurrence, and clustering of health-risk behaviors among people with different socio-economic trajectories: A population-based study. Prev Med 2016; 93:64-69.^-2222 Wang TT, Lin B, Cui WX, Zhang MZ, Zhang YH, Zhang SY. Clustering of Cardiovascular Risk Factors and Diabetes: A Prospective Cohort Study on the Inner Mongolian Population in China. Biomed Environ Sci 2018; 31:749-756., other clusters compound of smoking, abdominal fat and sedentarism²²22 Wang TT, Lin B, Cui WX, Zhang MZ, Zhang YH, Zhang SY. Clustering of Cardiovascular Risk Factors and Diabetes: A Prospective Cohort Study on the Inner Mongolian Population in China. Biomed Environ Sci 2018; 31:749-756.

23 Pimenta AM, Felisbino-Mendes MS, Velasquez-Melendez G. Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population. Sao Paulo Med J 2013; 131:213-219.^-2424 Tzeng C-R, Chang YI, Chang Y, Wang C-W, Chen C-H, Hsu M-I. Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertil Steril 2014; 101:1404-1410..

However, studies that have used cluster analysis in the investigation of risk behaviors are concentrated on the general population¹⁸18 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107.^,2121 Falkstedt D, Möller J, Zeebari Z, Engström K. Prevalence, co-occurrence, and clustering of health-risk behaviors among people with different socio-economic trajectories: A population-based study. Prev Med 2016; 93:64-69.^,2222 Wang TT, Lin B, Cui WX, Zhang MZ, Zhang YH, Zhang SY. Clustering of Cardiovascular Risk Factors and Diabetes: A Prospective Cohort Study on the Inner Mongolian Population in China. Biomed Environ Sci 2018; 31:749-756.^,2424 Tzeng C-R, Chang YI, Chang Y, Wang C-W, Chen C-H, Hsu M-I. Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertil Steril 2014; 101:1404-1410. mainly in Asian countries⁷7 Kim W, Kim TH, Lee TH, Choi JW, Park E. The impact of shift and night work on health related quality of life of working women: findings from the Korea Health Panel. Health Qual Life Outcomes 2016; 14:162-168.^,2222 Wang TT, Lin B, Cui WX, Zhang MZ, Zhang YH, Zhang SY. Clustering of Cardiovascular Risk Factors and Diabetes: A Prospective Cohort Study on the Inner Mongolian Population in China. Biomed Environ Sci 2018; 31:749-756.^,2424 Tzeng C-R, Chang YI, Chang Y, Wang C-W, Chen C-H, Hsu M-I. Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertil Steril 2014; 101:1404-1410. and in Europe¹²12 Meader N, King K, Moe-Byrne T, Wright K, Graham H, Petticrew M, Power C, White M, Sowden AJ. A systematic review on the clustering and co-occurrence of multiple risk behaviours. Bmc Public Health 2016; 16:657-666.^,1818 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107.^,1919 Hofstetter H, Dusseldorp E, Van Empelen P, Pauçussen TWGM. A primer on the use of cluster analysis or factor analysis to assess co-occurrence of risk behaviors. Prev Med 2014; 67:141-146.^,2121 Falkstedt D, Möller J, Zeebari Z, Engström K. Prevalence, co-occurrence, and clustering of health-risk behaviors among people with different socio-economic trajectories: A population-based study. Prev Med 2016; 93:64-69.. A study among Chinese adults showed smoking, overweight and dyslipidemia as the main cardiovascular risk factors, and when evaluating them in clusters, they observed that combinations occurred frequently among dyslipidemia, overweight, hypertension and smoking. Revealing a synergistic effect in the simultaneous presentation of factors and an increased risk of cardiovascular events, cardiovascular diseases and general mortality¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13..

In this context, despite several studies investigating occupational variables associated with the occurrence of cardiovascular risk behaviors in Brazilian industry workers⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,1010 Pattussi MP, Olinto MT, Canuto R, Garcez AS, Paniz VMV, Kawachi I. Workplace social capital, mental health and health behaviors among Brazilian female workers. Soc Psychiatry Psychiatr Epidemiol 2016; 51:1321-1330.^,1111 Freitas ES, Canuto R, Henn RL, Olinto BA, Macagnan JBA, Pattussi MP, Busnello FM, Olinto MTA. Alteration in eating habits among shift workers of a poultry processing plant in southern Brazil. Cien Saude Colet 2015; 20(8):2401-2410.^,2020 Siqueira K, Griep RH, Rotenberg L, Costa A, Melo E, Fonseca MJ. Interrelationships between nursing workers' state of nutrition, socio demographic factors, work and health habits. Cien Saude Colet 2015; 20:1925-1935.^,2525 Carvalho FC, Godinho MR, Ferreira AP. Cardiovascular risk factors among oil refinery workers: ecological study. Rev Bras Med Trab 2020; 18(1):11-19.

26 Diniz AP, Alves ME, Fajardo VC, Freitas SN, Batista GAS, Athadeu BFM, Machado-Coelho GLL, Oliveira FLP, Pimenta FAP, Neto RMN. Body fat indicators for cardiometabolic risk screening among shift workers. Rev Bras Med Trab 2020; 18(2):125-132.^-2727 Vinholes DB, Bassanesi SL, Chaves Junior HDC. Associação das características do local de trabalho e da população com a prevalência de hipertensão entre trabalhadores da indústria brasileira: uma análise multinível. BMJ Open 2017; 7:e015755., as far as we can tell, this is the first study to identify a behavioral cluster in Brazilian workers and its association with occupational characteristics. Therefore, this study investigates the occurrence of clusters of cardiovascular risk behavior and their association with sociodemographic and occupational characteristics in the population of factory workers in the metropolitan area of Northeast - Brazil.

Methods

This was a transversal study with adult subjects (≥18 years old) of both genders from factories in the metropolitan area of Recife - Pernambuco, Brazil - a region composed of 15 cities all closely linked together.

For the formation of the sample, we have considered variables such as being overweight, obesity, glycemia, cholesterol, triglycerides, and high blood pressure as indicators of non-communicable chronic diseases in the State of Pernambuco²⁸28 Pinho CPS, Diniz AS, Arruda IKG, Batista Filho M, Coelho PC, Sequeira LAS, Lira PIC. Prevalence of abdominal obesity and associated factors among individuals 25 to 59 years of age in Pernambuco State, Brazil. Cad Saude Publica 2013; 29:313-324.. We considered a confidence level of 95%, a maximum sample error of 4% and a design effect of 1.5; with a 20% increase for possible losses, resulting in a sample size of around 630 subjects. Following the rule proposed by Hair et al.²⁹29 Hair JF, Black WC, Babin BJ, Anderson RE. Multivariate Data Analysis. 7th ed. New Jersey: Prentice Hall; 2009. for cluster analysis, it is necessary a sample larger than 100 subjects.

Sample building happened in two stages; first, an invitation was sent to factories in the metropolitan area with more than a hundred employees and who are part of the National Program for Workers Nutrition [Programa de Alimentação do Trabalhador - PAT]. From the 66 companies invited, 16 factories from 7 cities agreed to participate in the study. A proportional sample of workers was established, varying from 4.5 to 8% of the total number of employees in each factory. In the end, the total sample size was 727.

Data collection happened between January and June 2015. Standardized questionnaires pre-coded and previously tested in a pilot study. The current project followed all applicable laws of ethics in research with human subjects. It was approved by the Committee of Ethics in Research in the Center for Health Sciences at the Federal University of Pernambuco (UFPE) (CAAE n^o 37098814.0.0000.5208). Each participant signed a form of free consent.

Social demographic variables were: gender (male/female), age (≤27 years-old; 28 to 34 years-old; 35 to 44 years-old; >44 years-old), and schooling (<12 years of education; 12 years of education; >12 years of education); occupational: shift (day shift - from 6 am to 2 pm; night shift or rotating shift - from 2 pm to 6 am or rotating 8 hours/day); position (director/management; engineering administrative assistants and auxiliaries; production coordination and technicians; production; others (receptionists and nursing staff), and time of employment at the same factory (<2 years; 2 to 4 years; 5 to 9 years; ≥10 years).

The following behavioral variables were considered: smoking (yes or no); alcohol consumption (excessive consumption in men was 5 or more doses in a single occasion at least once in the past 30 days; for women, it was 4 or more doses - any amount smaller than that was considered as a moderate consumption. For determining the level of physical activity, participants stated the kind, the frequency and duration of any physical activity practiced in their free time in the week prior to the interview, being classified as sedentary (<10 minutes per week of moderate physical activity), insufficiently active (10 to 149 minutes per week of moderate physical activity), and sufficiently active (≥150 minutes per week of moderate physical activity)³⁰30 Haskell WL, Lee IM, Pate RR, Powell KE, Blair SN, Franklin BA, Macera CA, Heath GW, Thompson PD, Bauman A. Physical Activity and Public Health. Med Sci Sports Exerc 2007; 39:1423-1434..

For waist circumference measurements, we have used the mean figure from two measurement takings, using a non-elastic measuring tape (brand: Seca®) with 200 cm in length and 1 mm precision, positioned in the middle point between the last rib arch and the iliac. When the difference between the two measurement takings was over 0.5 mm a third measurement was taken, and the two closest measurements were considered, and the most dissonant one was discarded. A regular waist circumference was <80 cm for women and <94 cm for men; a high waist circumference was from ≥80 to <88 cm for women and from ≥94 to <102 cm for men, and an extremely high waist circumference was ≥88 cm for women and ≥102 for men³¹31 Han TS, Van Leer EM, Seidell JC, Lean ME. Waist circumference action levels in the identification of cardiovascular risk factors: prevalence study in a random sample. BMJ 1995; 311(7017): 1401-1405..

Data were entered in a double-entry method and checked with VALIDATE, a module of the software Epi-info version 3.5.2, to assure consistency and validity. Statistical analysis was done with the Statistical Package for Social Sciences (SPSS version 17) and Stata (version 15).

To identify the behavioral clusters, we used a combination of hierarchical and non-hierarchical analysis. In the hierarchical cluster analysis, we used the method of Euclidian square distances for binary variables. The possible clusters found at this stage, considering the analysis of Dendrogram and Screen Plot, were used in the non-hierarchical analysis, when the k-mean procedure was used, based on the Euclidian square distances. As the seeds for the initial randomization, we used the clusters’ centroids in the hierarchical analysis and 10,000 iterations to further refine the preliminary solution and to optimize the classification. The final cluster solution was selected based on its ability to interpret the data. Finally, each cluster generated was saved as dichotomy variable (0/1).

The associations between clusters of behavioral characteristics and independent variables were tested by Pearson’s chi-square test and linear tendency. The prevalence ratios, crude and adjusted, with their respective intervals of 95% of confidence, were obtained by Poisson’s regression method with a robust variation. The analysis followed a conceptual model (1^st level: social-demographic variables; 2^nd level: occupational variables). In the multi-varied analysis, only the exposition variables obtained p<0.2 in the bi-varied analysis were included. The same cut was applied to the other variables in the subsequent levels. Associations with p≤0.05 were considered as statistically significant.

Results

There was a loss of 8 participants due to the absence of an answer to the variables physical activity (1) and waist circumference (7), so the final sample contained 719 workers. Of these workers, with a mean age of 36.4 (standard deviation 10.7), most were male (75,8%). Most of them declare to have 12 years of education (56.9%), followed by those who said they had more than 12 years (31.2%). The great majority of the subjects worked in a day shift (95.0%), predominantly in production positions (57.2%). The mean time of employment at the same factory was 4.08 years (interquartile range 1.92:10).

Cluster analysis identified 2 behavioral clusters. Initially, they were named cluster 1 and cluster 2, and their distribution is shown in Table 1. Cluster 1 had an evident predominance of sufficient physical activity and a moderate alcohol consumption pattern, which are factors of cardiovascular protection. On the other hand, risky behaviors concentrated on Cluster 2, with the predominance of high waist circumference, sedentarism, smoking, and excessive alcohol consumption. Cluster 2 included the majority of the participants. According to each cluster’s predominant behaviors, they were renamed as Protection Cluster and Risk Cluster, respectively.

In Table 2 we can observe both clusters according to social demographic and occupational variables. Only the shifts seemed to be associated with the behavior clusters, and for all the other social demographic and occupational variables, there was not a significant association to the composition of the clusters.

The multivariable analysis of the Protection cluster (Table 3) shows that women and older workers had a lower probability of being a part of the protection cluster; on the other hand, night shift workers had a higher probability of being a part of the same cluster compared to day workers. In Table 4, the multivariable analysis of the Risk cluster indicated a positive association of females, had more than 44 years old, and risk, while night shift or variable shift work was inversely associated with the Risk cluster.

Discussion

This study investigated the aggregate occurrence of cardiovascular risk behavior in a population of factory workers in the metropolitan area of Recife and identified the formation of two clusters associated with gender and the shift in which industrial employees work.

In the cluster analysis, there were a consistent determination of a group composed more frequently or protection behaviors (sufficient physical activity, moderate alcohol consumption) and another with behaviors that represent a risk for cardiovascular health (augmented waist circumference, sedentarism, smoking, excessive alcohol consumption) and the largest cluster was the second. These findings suggest that individuals may simultaneously adopt risk behaviors as well as protection behaviors, which reinforces the hypothesis that these behaviors happen together⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49. and that there is a synergy in the basic mechanisms that lead to cardiovascular diseases²³23 Pimenta AM, Felisbino-Mendes MS, Velasquez-Melendez G. Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population. Sao Paulo Med J 2013; 131:213-219..

Mawditt et al.¹⁸18 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107. found similar cluster compositions in 21,019 adult subjects in Great Britain - a Conventional cluster (more frequent physical activity and lower alcohol consumption, as well as not smoking) and a Risk cluster (smoking and excessive alcohol consumption). Other studies have also found a combination of alcohol abuse, smoking and physical activity level in cardiovascular risk clusters⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,2020 Siqueira K, Griep RH, Rotenberg L, Costa A, Melo E, Fonseca MJ. Interrelationships between nursing workers' state of nutrition, socio demographic factors, work and health habits. Cien Saude Colet 2015; 20:1925-1935.

21 Falkstedt D, Möller J, Zeebari Z, Engström K. Prevalence, co-occurrence, and clustering of health-risk behaviors among people with different socio-economic trajectories: A population-based study. Prev Med 2016; 93:64-69.^-2222 Wang TT, Lin B, Cui WX, Zhang MZ, Zhang YH, Zhang SY. Clustering of Cardiovascular Risk Factors and Diabetes: A Prospective Cohort Study on the Inner Mongolian Population in China. Biomed Environ Sci 2018; 31:749-756.. In the same sense, other studies have found a significant association between smoking, abdominal fat and the higher probability of any combination of cardiovascular risk in sedentary individuals²²22 Wang TT, Lin B, Cui WX, Zhang MZ, Zhang YH, Zhang SY. Clustering of Cardiovascular Risk Factors and Diabetes: A Prospective Cohort Study on the Inner Mongolian Population in China. Biomed Environ Sci 2018; 31:749-756.

23 Pimenta AM, Felisbino-Mendes MS, Velasquez-Melendez G. Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population. Sao Paulo Med J 2013; 131:213-219.^-2424 Tzeng C-R, Chang YI, Chang Y, Wang C-W, Chen C-H, Hsu M-I. Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertil Steril 2014; 101:1404-1410., suggesting that sedentarism is a risk behavior with a propensity to combine with others¹1 Hong X, Ye Q, He J, Wang Z, Yang H, Qi S, Chen X, Wang C, Zhou H, Li C, Qin Z, Xu F. Prevalence and clustering of cardiovascular risk factors: a cross-sectional survey among Nanjing adults in China. BMJ Open 2018; 8:1-13.. In this study, all sedentary participants presented behaviors that were distributed in the risk cluster.

Risk cluster was associated with women. Several studies done with cluster analysis have shown a higher number of risk behaviors in women⁷7 Kim W, Kim TH, Lee TH, Choi JW, Park E. The impact of shift and night work on health related quality of life of working women: findings from the Korea Health Panel. Health Qual Life Outcomes 2016; 14:162-168.^,1212 Meader N, King K, Moe-Byrne T, Wright K, Graham H, Petticrew M, Power C, White M, Sowden AJ. A systematic review on the clustering and co-occurrence of multiple risk behaviours. Bmc Public Health 2016; 16:657-666.^,1818 Mawditt C, Sacker A, Britton A, Kelly Y, Cable N. The clustering of health-related behaviours in a British population sample: Testing for cohort differences. Prev Med 2016; 88:95-107.^,1919 Hofstetter H, Dusseldorp E, Van Empelen P, Pauçussen TWGM. A primer on the use of cluster analysis or factor analysis to assess co-occurrence of risk behaviors. Prev Med 2014; 67:141-146.^,2121 Falkstedt D, Möller J, Zeebari Z, Engström K. Prevalence, co-occurrence, and clustering of health-risk behaviors among people with different socio-economic trajectories: A population-based study. Prev Med 2016; 93:64-69.^,2222 Wang TT, Lin B, Cui WX, Zhang MZ, Zhang YH, Zhang SY. Clustering of Cardiovascular Risk Factors and Diabetes: A Prospective Cohort Study on the Inner Mongolian Population in China. Biomed Environ Sci 2018; 31:749-756.^,2424 Tzeng C-R, Chang YI, Chang Y, Wang C-W, Chen C-H, Hsu M-I. Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertil Steril 2014; 101:1404-1410.. The association of gender and these risk factors can be related in a certain measure by the stress and lack of leisure time generated by the accumulation of formal work and house work³²32 Pitanga FJG, Matos SMA, Almeida MC, Molina MCB, Aquino EML. Factors associated with leisure time physical activity among ELSA-Brasil participants: Ecological model. Prev Med 2016; 90:17-25.^,3333 Pinto KA, Griep RH, Rotenberg L, Almeida MCC, Barreto RS, Aquino EML. Gender, time use and overweight and obesity in adults: Results of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). PLoS One 2018; 13(3):e0194190., as shown in the previous studies⁷7 Kim W, Kim TH, Lee TH, Choi JW, Park E. The impact of shift and night work on health related quality of life of working women: findings from the Korea Health Panel. Health Qual Life Outcomes 2016; 14:162-168.^,3333 Pinto KA, Griep RH, Rotenberg L, Almeida MCC, Barreto RS, Aquino EML. Gender, time use and overweight and obesity in adults: Results of the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). PLoS One 2018; 13(3):e0194190.

34 Olinto MTA, Canuto R, Garcez AS. Work and Abdominal Obesity Risk. In: Watson RR, organizador. Nutrition in the prevention and treatment of abdominal obesity. San Diego: Elsevier; 2014. p. 17-25.^-3535 Rissardi VF, Schaffrath E. Labor market: gender inequalities and coping with conflict. Unoesc Cienc ACBS 2014; 5:187-194.. In addition to that, individuals who were a part of this cluster have the highest prevalence of sedentarism and abdominal obesity, in accordance with the literature that repeatedly suggests the association of sedentarism and the other risk behaviors, especially an increase in abdominal circumference in women⁵5 Tassitano RM, Feitosa WMN, Júnior GLS, Tenório MCM. Simultaneity of health risk behaviors and associated factors in industrial workers. Rev Bras Ativ Fis Saude 2010; 15:42-49.^,2323 Pimenta AM, Felisbino-Mendes MS, Velasquez-Melendez G. Clustering and combining pattern of metabolic syndrome components in a rural Brazilian adult population. Sao Paulo Med J 2013; 131:213-219.^,2424 Tzeng C-R, Chang YI, Chang Y, Wang C-W, Chen C-H, Hsu M-I. Cluster analysis of cardiovascular and metabolic risk factors in women of reproductive age. Fertil Steril 2014; 101:1404-1410.^,3636 Veloso HJF, Silva AAM. Prevalence and factors associated with abdominal obesity and excess weight among adults from Maranhão, Brazil. Rev Bras Epidemiol 2010; 13:400-412.

37 Paulitsch RG, Dumith SC, Susin LRO. Simultaneity of behavioral risk factors for cardiovascular disease in university students. Rev Bras Epidemiol 2017; 20:624-635.^-3838 Jin Y, Kim D, Cho J, Lee I, Choi K, Hang H. Association between Obesity and Carotid Intima-Media Thickness in Korean Office Workers: The Mediating Effect of Physical Activity. Biomed Res Int 2018; 2018:1-10..

Population aging combined with metabolic risks is associated with an increased incidence of cardiovascular disease and mortality³⁹39 GBD 2017 Risk Factor Collaborators. Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018; 392(10159):1923-1994.. Aging was associated with the risk cluster in this study, probably due to abdominal obesity being part of that cluster. Central adiposity concentration over the years was observed among rural and urban workers, and among women aged >40 or in the menopausal period⁴⁰40 Luz TC, Cattafesta M, Petarli GB, Meneghetti JP, Zandonade E, Bezerra OMPA. Fatores de risco cardiovascular em uma população rural brasileira. Cien Saude Colet 2020; 25(10):3921-3932.

41 Arbués ER, Martínez-Abadía B, Gracía-Tabuenca T, Yuste-Gran C, Pellicer-García B, Juárez-Vela R. Prevalencia de sobrepeso/obesidad y su asociación con diabetes, hipertensión, dislipemia y síndrome metabólico: estudio transversal de una muestra de trabajadores en Aragón, España. Nutr Hosp 2019; 36(1):51-59.^-4242 Barroso TA, Marins LB, Alves R, Gonçalves ACS, Barroso SG, Rocha GS. Association of Central Obesity with The Incidence of Cardiovascular Diseases and Risk Factors. Int J Cardiovasc Sci 2017; 30(5):416-424., corroborating our results.

In this study, the day shift was associated with the risk cluster. However, the literature has demonstrated consistent associations with night shift or variable shift work and metabolic diseases¹³13 Sun M, Feng W, Wang F, Li P, Li Z, Li M, Tse G, Vlaanderen J, Vermeulen R, Tse LA. Meta-analysis on shift work and risks of specific obesity types. Obes Rev 2017; 19:28-40.^,1414 Souza RV, Sarmento RA, Almeida JC, Canuto R. The effect of shift work on eating habits: a systematic review. Scand J Work Environ Health 2019; 45:7-21. and cardiovascular diseases⁸8 Bae M, Song Y, Shin J, Choi B, Keum J, Lee E. The Association Between Shift Work and Health Behavior: Findings from the Korean National Health and Nutrition Examination Survey. Korean J Fam Med 2017; 38:86-92., the causal mechanism of these associations still is controversial, and it would involve metabolic and behavioral changes. Metabolic changes could be explained by the fact that these workers are exposed to the physiological effects that the rupture in the circadian rhythm can trigger¹⁴14 Souza RV, Sarmento RA, Almeida JC, Canuto R. The effect of shift work on eating habits: a systematic review. Scand J Work Environ Health 2019; 45:7-21., either by hormonal changes brought about by the inversion of the sleep cycle and associated with the increase of abdominal fat²⁰20 Siqueira K, Griep RH, Rotenberg L, Costa A, Melo E, Fonseca MJ. Interrelationships between nursing workers' state of nutrition, socio demographic factors, work and health habits. Cien Saude Colet 2015; 20:1925-1935.^,4343 Padilha HG, Crispim CA, Zimberg IZ, Folkard S, Tufik S, Mello MT. Metabolic responses on the early shift. Chronobiol Int 2010; 27:1080-1092.

44 Crispim CA, Waterhouse J, Dâmaso AR, Zimberg IZ, Padilha HG, Oyama LM, Tufik S, Mello MT. Hormonal appetite control is altered by shift work: a preliminary study. Metabolism 2011; 60:1726-1735.^-4545 Macagnan J, Pattussi MP, Canuto R, Henn RL, Fassa AG, Olinto MTA. Impact of Nightshift Work on Overweight and Abdominal Obesity Among Workers of a Poultry Processing Plant in Southern Brazil. Chronobiol Int 2012; 29:336-343., or by the lower production of melatonin triggered by the constant exposition to light, even if it is at night¹³13 Sun M, Feng W, Wang F, Li P, Li Z, Li M, Tse G, Vlaanderen J, Vermeulen R, Tse LA. Meta-analysis on shift work and risks of specific obesity types. Obes Rev 2017; 19:28-40.. However, behavioral changes such as eating habits, smoking and physical activity may occur due to reorganize daily activities, since during the day, instead of being able to practice these common activities, the worker has to rest¹¹11 Freitas ES, Canuto R, Henn RL, Olinto BA, Macagnan JBA, Pattussi MP, Busnello FM, Olinto MTA. Alteration in eating habits among shift workers of a poultry processing plant in southern Brazil. Cien Saude Colet 2015; 20(8):2401-2410.^,4646 Crispim CA, Zimberg IZ, Dattilo M, Padilha HG, Tufik S, Mello MT. Shift work and nutritional aspects: a review. Nutrire Rev Soc Bras Aliment Nutr 2009; 34:213-227.^,4747 Balieiro LCT, Rossato LT, Waterhouse J, Paim SL, Mota MC, Crispim CA. Nutritional status and eating habits of bus drivers during the day and night. Chronobiol Int 2014; 31:1123-1129.. In fact, some studies have suggested that night shift or variable shift work may not negatively influence healthy behaviors⁸8 Bae M, Song Y, Shin J, Choi B, Keum J, Lee E. The Association Between Shift Work and Health Behavior: Findings from the Korean National Health and Nutrition Examination Survey. Korean J Fam Med 2017; 38:86-92.^,1111 Freitas ES, Canuto R, Henn RL, Olinto BA, Macagnan JBA, Pattussi MP, Busnello FM, Olinto MTA. Alteration in eating habits among shift workers of a poultry processing plant in southern Brazil. Cien Saude Colet 2015; 20(8):2401-2410.^,1414 Souza RV, Sarmento RA, Almeida JC, Canuto R. The effect of shift work on eating habits: a systematic review. Scand J Work Environ Health 2019; 45:7-21.^,4848 Canuto R, Garcez AS, Olinto MTA. Metabolic syndrome and shift work: A systematic review. Sleep Med Rev 2013; 17:425-431. and may even favor them, as shown in some studies that associated night shift or variable shift work to an increase of time available for regular physical activity⁴⁹49 Jacobsen HB, Reme SE, Sembajwe G, Hopcia K, Stiles TC, Sorensen G, Porter JH, Marino M, Buxton OM. Work stress, sleep deficiency, and predicted 10-year cardiometabolic risk in a female patient care worker population. Am J Ind Med 2014; 57:940-949.^,5050 Garcez AS, Canuto R, Paniz VMV, Olinto BA, Macagnan J, Henn RL, Pattussi P, Olinto MTA. Association between work shift and the practice of physical activity among workers of a poultry processing plant in Southern Brazil. Nutr Hosp 2015; 31:2174-2181.. However, it is important to consider some study limitations in interpreting these results: the work shift was not detail investigated, and the sample had a small number of shift workers compared to daytime workers.

From these findings, we hypothesized that for day shift workers, the shortest socially valuable time (daytime) out of a formal work environment can limit the adoption of a profile of cardiovascular protective behaviors, such as physical activity practice. Besides, for women, who also showed higher risk behavior, this can be intensified for the double shift imposed by domestic work. These hypotheses should be verified in further studies.

The present study has possibly been limited by its transversal design, which does not determine temporal or causal relationships in the association between social-demographic and behavioral factors, even if most of the interviewed workers have worked at the same factory for over 2 years. On the other hand, this study has cluster analysis as a strong point in identifying patterns of cardiovascular risk behavior in the workers, which allowed us to demonstrate how risk or protection behaviors can occur simultaneously. Besides, considering the lack of evidence on this topic with data from workers who live in Latin America, the results of this study contributed to a better understanding of this issue and to assist public policy formulation aiming to change cardiovascular risk behaviors in this population.

Considering the great diversity of work environments, our results indicated the importance of future studies on the simultaneous occurrence of health behaviors in other worker places, and how these behaviors are associated with social demographic and occupational variables. Studies on this theme are essential for investigating possible iniquities involved in adopting health behaviors by workers and a deeper understanding of the factors that influence time management outside work, as demonstrated in this study. Also, the knowledge of the impact of simultaneous risk behaviors on the population’s quality of life and their iniquities is essential for the implementation of worker’s health public policies informed by evidence and socially referenced.

Acknowledgements

This study was funded by a grant from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) Foundation in Graduate Program in Nutrition, Federal University of Pernambuco. Thank you for supporting Brazilian scientific development.

References

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