Multimorbidity is a huge public health issue that affects people's health significantly on a global scale. Many chronic diseases have been linked to exposure to workplace dangers. The goal of this study was to comprehend the existing degree of multimorbidity among steel workers, the connection between cumulative occupational hazard exposure and multimorbidity, and to build the groundwork for the creation of preventative and control strategies for multimorbidity among steelworkers. This cross-sectional study included 3577 steel workers who had occupational health examinations in 2017. The relationship between occupational risks and the prevalence of multimorbidity in steel workers was examined using multiple logistic regression. We discovered that the rate of multimorbidity was 61.3%. The risk of multimorbidity among employees exposed to high levels of shift work, heat, noise, and dust was 1.520 (95% CI: 1.147 to 2.014), 3.376 (95% CI: 2.583 to 4.413), 2.069 (95% CI: 1.653 to 2.590), and 1.509 (95% CI: 1.1262.023) respectively in a multifactorial logistic regression analysis. Steelworkers need stronger protection against each occupational hazard since multimorbidity is highly common within the steel sector as a whole and is made more likely by occupational hazard exposure.
Research Article
Association between exposure to Occupational hazard factors and multimorbidity in steelworkers: A Cross-Sectional Study
https://doi.org/10.21203/rs.3.rs-2964176/v1
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multimorbidity
steelworkers
Cumulative exposure
Family history of multiple diseases
Occupational hazard factors
Multimorbidity is the term used to describe a person having two or more chronic conditions at the same time [1]. Multimorbidity has increased in frequency throughout time, and current studies have noted an upward trend in the prevalence of multimorbidity [2–6]. When compared to the beginning of a single disease, multimorbidity presents more complex and difficult healthcare challenges. Compared to people without multimorbidity, those with multimorbidity have a higher chance of impairment and a lower quality of life. There is proof that multimorbidity reduces productivity at work. Furthermore, the impact on production is more severe as the number of disorders rises [7, 8]. The majority of research today, which concentrate on the senior population over 60 years old, view multimorbidity as an indicator of aging [9]. But, from an economic perspective, workers are those who work and provide value at the workplace, making them a crucial group. And therefore, any changes to employee productivity will have a direct impact on the factory's performance. Multimorbidity is substantially correlated with extended sick leave, absence from work or lack thereof, and early termination from paid employment, according to several research [8, 10–12]. Studies on multimorbidity and occupational populations, however, are still scarce overall. The most recent research on multimorbidity in occupational populations demonstrates that different professional populations with different employment have different multimorbidity results [13–18]. This can be because various occupations are subject to distinct occupational dangers. Initiatives to enhance or preserve the work capacity of steel employees should be given high importance because iron and steel firms are a significant pillar industry in the national economy of China and there are millions of steelworkers in China. High temperatures, noise, dust, and other risk factors are present at steel plants throughout various production processes for making iron, steel, and steel rolling, and steel workers must deal with a number of occupational risks like shift work and occupational stress [19–21]. The multimorbidity of steelworkers, however, has received less academic attention, hence this area need urgent investigation. In this study, we used physical ex-amination data and questionnaires to investigate the multimorbidity status of steel-workers in a steel plant. We also classified the onset of multimorbidity among steel workers to explore the distribution of different multimorbidity patterns among steel workers and to investigate the effects of various demographic information, behavioral patterns, and occupational exposure hazards among steel workers. The objective is to identify the factors influencing polyvalence and the high-risk worker groups in this steel plant, to enrich the study of polyvalence in the occupational population, and to provide a reference basis for the formulation of policies related to enhancing or maintaining steelworkers' working abilities and mitigating polyvalence.
2.1 Research Object
The data for this study were obtained from the China Key Research and Development Program "Cohort Study on the Health Impact of Occupational Populations in Beijing-Tianjin-Hebei Region".
2.2 Questionnaire
Using questionnaires, data on general demographic factors, such as age, gender, marital status, education level, total monthly family income, and the number of household members, as well as lifestyle factors, such as smoking, alcohol usage, diet, and physical activity, were gathered.
The Job Content Questionnaire has three dimensions: Job Requirements, Job Autonomy, and Social Support. Each dimension has five, nine, and eight items, respectively. Each item is scored from one to four points, and the sum of all item scores represents the score for each dimension. The higher the score for each dimension, the stronger the Job Requirements, Job Autonomy, and Social Support, respectively. To assess the level of occupational strain, the ratio of job autonomy to job requirements (D/C ratio) is utilized. The D/C ratio can be calculated using the following formula.
D/C ratio = Job requirement factor score / (Job autonomy factor score × C)
In the equation above, C stands for the 5/9 ratio of job autonomy entries to job requirements; D/C ratio>1 denotes the presence of occupational tension, while D/C ratio1 denotes the absence of occupational strain.
The Effort-Reward Imbalance Scale consists of three components: effort, reward and over-commitment. These dimensions each have 6, 11, and 6 items. Each item is given a score between 1 and 5, and the total of all the item scores determines the score for each dimension; the higher the score for any given dimension, the stronger the sense of pay, gain, or overload, correspondingly; The ERI index is used to assess the level of occupational stress, and its formula is as follows.
ERI Index = Give Factor Score / (Get Factor Score × C)
ERI index > 1 shows the presence of occupational tension, whereas ERI index 1 indicates the absence of occupational tension. C in the equation above denotes the ratio of the number of paid entries to the number of acquired entries, or 6/11.
Employment history, including the number of years worked, the sort of work performed, the shift worked, and any previous exposure to heat, noise, dust, etc.
Ask if they have taken medication in the last two weeks for any personal diseases they may have, such as hypertension, diabetes, coronary heart disease, chronic bronchitis, duodenal or gastric ulcers, fatty liver, and hyperlipidemia. You should also in-quire if they have any other diseases they may have and when they were discovered.
Family history includes diabetes, coronary artery disease, hypertension, and other illnesses.
The 8-item Asens Insomnia Scale, which is widely used, was used to assess insomnia in the study patients.
Self-assessment of anxiety: This scale consists of 20 items, each of which is scored on a scale of 1 to 4, with 15 items receiving positive scores and 5 receiving negative ones. The sum of the scores for each item determines the total score, which is then multiplied by 1.25 and rounded to the nearest whole number.
Depression self-assessment scale: The scale has 20 items with a total score that is the sum of each item's scores, rounded to the nearest whole number, and graded on a range of 1 to 4, with 10 items receiving positive scores and 10 receiving negative scores.
2.3 On-site physical examination
The study participants' height and weight were measured using an ultrasonic measuring device. A nonelastic leather ruler was used by qualified researchers to measure subjects' waist and hip circumferences. The person has to stand up straight, take off or pull up their shirt, have a relaxed abdomen, and let both of their arms drop down freely. The subject's waist was measured along the mid-axillary line, beyond the iliac crest and the middle of the lower rib arch. The subject's greater trochanter on the femur was used to determine hip circumference. The average of the two measurements was calculated after the measurement was conducted twice.
A clinician takes blood pressure readings with the UN-EX-TWIN completely automated electronic blood pressure monitor. Before to the measurement, the individual is instructed to sit quietly for around 5 minutes and rest. The subject was requested to remove their outer clothing, expose their right upper arm, and make sure that their right upper arm was not being crushed by their clothing before being instructed to sit or lie down while the sphygmomanometer's wristband was opened and the measurement was taken. The average of the three measures was calculated for each individual after three measurements were collected at intervals of at least one and a half minutes.
A clinical ultrasonographer who has the necessary training performs the B ultra-sound examination.
2.4 Laboratory tests
Before blood samples were taken, all subjects were instructed to go 12 hours without eating or drinking. Every day between 7:30 and 9:30 a.m., a clinical nurse took blood samples. then brought to the hospital for routine blood testing and blood bio-chemistry.
2.5 Survey of occupational hazard factors
Details on workplace dangers such high temperatures, noise, and dust are derived from the steel company's yearly inspection report.
2.6 Definition of relevant factors
2.6.1 The 13 chronic illnesses or health issues defined in this study are: hypertension, coronary heart disease, carotid atherosclerosis, diabetes, fatty liver, asthma, pep-tic ulcer, chronic bronchitis, cancer or tumor, psychosomatic issues, hyperuricemia, dyslipidemia and biliary disorders.
2.6.2 Multimorbidity: a patient who has two or more of the 13 chronic illnesses or health issues outlined in this study.
2.6.3 Hypertension: blood pressure 140/90 mmHg (1 mmHg = 0.133 kPa) as determined by the on-site physical examination [22, 23] or by self-reporting hypertension or the use of anti-hypertensive medication in the questionnaire.
2.6.4 Diabetes: Self-reported having diabetes or self-reported using diabetes-related medication in the questionnaire, and physical examination measurement of FPG ≥7.0 mmol/L [24].
2.6.5 Carotid atherosclerosis: Carotid intima-media thickness of 1.0 mm was deemed normal, whereas carotid atherosclerosis was defined as thickness greater than 1.0 mm[25]. Or self-reported in the survey that they had carotid atherosclerosis or that they were receiving treatment for the condition.
2.6.6 Psychiatric problems: having both anxiety and depression as well as one or both. The Anxiety Self-Rating Scale was used to quantify anxiety, and a score of 50 or more was regarded as anxiety [26]. Depression is diagnosed based on the Depression Self-Rating Scale, with a score of 50 or higher indicating depression [27].
2.6.7 Hyperuricemia: A history of gout as well as blood uric acid levels of ≥420 mol/L (7.0 mg/dL) in men and ≥360 mol/L (6.0 mg/dL) in women [28]
2.6.8 Insomnia: A score of 4 or less indicates no sleep disturbance, 4 to 6 indicates possible insomnia, and 6 or more indicates actual insomnia [29].
2.6.9 Dyslipidemia: When at least one of these criteria was met, as well as self-reporting in the questionnaire that they had dyslipidemia or self-reporting that they were taking dyslipidemia medications, serum total cholesterol≥6.2 mmol/L (240 mg/dl), triglycerides≥2.3 mmol/L (200 mg/dl), LDL cholesterol≥4.1 mmol/L (160 mg/dl), and non-HDL cholesterol≥4.9 mmol/L (190 mg/dl) were all defined as dyslipidemia.
2.6.10 Self-reported data from questionnaires included information on coronary heart disease, asthma, peptic ulcer, chronic bronchitis, fatty liver, cancer or tumor, and biliary disease.
2.6.11 There were three categories for smoking status: never smoked, ever smoked and quit, and currently smoking. Never smoked was defined as having never smoked; ever smoked was defined as having never smoked up to and including the time of the questionnaire and having never smoked; present smoking was defined as having smoked at least one cigarette a day for at least six months or longer.
2.6.12 Three categories were used to categorize a person's drinking history: never, occasionally, and currently. Never drinking indicates that person never drank; ever drinking means that person had quit drinking by the time of the survey; and current drinking means that person currently drank at least twice per week, ingested at least 50 grams of ethanol each time, and has done so for at least six months.
2.6.13 Body Mass Index: Underweight range is BMI < 18.5 kg/m2, normal weight range is 18.5 kg/m2 ≤ BMI < 24 kg/m2, overweight range is 24.0 kg/m2 ≤ BMI < 28.0 kg/m2, and obese range is BMI ≥ 28.0 kg/m2.
2.6.14 Exposure to occupational hazards:
Ln is the average exposure over Tn time [20].
2.6.15 Family history of multiple diseases: family history of at least two illnesses.
2.6.16 Occupational Stress: Those who are evaluated to be occupationally stressed by the Job Content Questionnaire or the Effort-Reward Imbalance.
2.7 Statistical Analysis
Means between groups were compared using the two independent samples t-test, and normally distributed and non-normally distributed variables, respectively, were measured using `x±s and M (P25, P75). The c2 test was used to compare the descriptive use rate or composition ratio of the count data between groups. Binary logistic regression was used to perform univariate and multifactorial analyses, and the multiple-fill technique was used to fill in the exposures to missing occupational hazards.
3.1 General demographic characteristics of steelworkers
3,662 individuals, representing 5 plant departments, took part in the occupational health exams. With a response rate of 92.9% on the 3,403 questionnaires that were gathered for this study, 3,357 study participants in total were included after the questionnaires with missing data were removed. They had an average age of 43.328.1 years, with 3050 males (90.9%) and 307 females (9.1%) among them. In the age groups under 30, 30~, 40~, and 50~, respectively, the prevalence of multimorbidity was 31.1%, 51.5%, 68.2%, and 68.4%; Men were more likely than women to have multimorbidity (63.5% for males and 40.4% for women); In subjects with lower secondary education, the prevalence of multimorbidity was 72.5%, in sub-jects with high school or secondary education it was 61.6%, and in subjects with university education and beyond it was 50.1%.T In those with a per capita monthly income of less than RMB1,500 and those with an average monthly income of RMB1,500 or more, the prevalence of multimorbidity was 68.2% and 59.0%, respectively. In people with a family history of many illnesses, the prevalence of multimorbidity was 71.2%, compared to 60.5% in people without such a history. Multimorbidity was present in 46.9% of respondents who were of normal weight, 65.9% of subjects who were overweight, and 78.9% of subjects who were obese. In participants without occupational stress, the prevalence of multimorbidity was 56.2%, while in subjects with occupational stress, it was 62.5%. Age, gender, education level, per capita monthly income, married status, sleep hygiene, smoking, alcohol use, family history of multiple diseases, BMI, and occupational stress all exhibited data that indicated a P value less than 0.05 and statistically significant differences. The groupings' specifics are displayed in Table 1.
Table 1. Relationship analysis between multimorbidity in steelworkers and general demographic traits
|
Variables |
Total population n=3357 |
Multimorbidity n=2059 |
Non-multimorbidity n=1298 |
Z/χ2 值 |
P |
|
age |
43.32±8.1 |
44.55±7.576 |
41.37±8.514 |
-11.008 |
＜0.001 |
|
Age groups |
|
|
|
153.127 |
＜0.001 |
|
＜30 |
182（5.4） |
57（2.8） |
125（9.6） |
|
|
|
30~ |
980（29.2） |
506（24.6） |
474（36.5） |
|
|
|
40~ |
1319（39.3） |
898（43.6） |
421（32.4） |
|
|
|
50~ |
876（26.1） |
598（29.0） |
278（21.4） |
|
|
|
Gender |
|
|
|
62.694 |
＜0.001 |
|
Male |
3050（90.9） |
1936（94.0） |
1114（85.9） |
|
|
|
Female |
307（9.1） |
124（6.0） |
183（14.1） |
|
|
|
Educational level |
|
|
|
88.760 |
＜0.001 |
|
junior high school and below |
825（24.6） |
598（29.0） |
227（17.5） |
|
|
|
high school or secondary school |
1676（49.9） |
1033（50.1） |
643（49.9） |
|
|
|
college and above |
856（25.5） |
429（20.8） |
427（32.9） |
|
|
|
Income status |
|
|
|
23.448 |
＜0.001 |
|
＜1500 |
872（26.0） |
595（28.9） |
277（21.3） |
|
|
|
≥1500 |
2485（74.0） |
1465（71.1） |
1020(78.7) |
|
|
|
Marital Status |
|
|
|
51.006 |
＜0.001 |
|
Unwed |
136（4.1） |
44（2.1） |
92（1.0） |
|
|
|
Married. |
3162（94.2） |
1976(95.9) |
1186（96.7） |
|
|
|
Divorce |
59（1.8） |
40（1.9） |
19（1.5） |
|
|
|
Quality of sleep |
|
|
|
7.565 |
0.023 |
|
No sleep disorder |
1590（47.3） |
941（45.7） |
649（50.0） |
|
|
|
Suspected insomnia |
738（22.0） |
455（22.1） |
283（21.8） |
|
|
|
Insomnia |
1029（30.7） |
664（32.2） |
365（28.1） |
|
|
|
Smoking |
|
|
|
36.089 |
＜0.001 |
|
Never |
1439（42.9） |
803（39.0） |
636（49.0） |
|
|
|
Once |
335（10.0） |
205（10.0） |
130（10.0） |
|
|
|
Now |
1583（47.1） |
1052（51.1） |
531（40.9） |
|
|
|
Drinking |
|
|
|
9.497 |
0.009 |
|
Never |
1962（58.4） |
1166（56.6） |
796（61.4） |
|
|
|
Once |
53（1.6） |
29（1.4） |
24（1.9） |
|
|
|
Now |
1342（40.0） |
865（42.0） |
477（36.8） |
|
|
|
Family history of multiple diseases |
|
|
|
12.448 |
＜0.001 |
|
No |
3076（91.6） |
1860（90.3） |
1216（93.8） |
|
|
|
Yes |
281（8.4） |
200（9.7） |
81（6.2） |
|
|
|
BMI |
|
|
|
212.950 |
＜0.001 |
|
Normal |
1258（37.5） |
589（28.6） |
669（51.5） |
|
|
|
Overweight |
1436（42.8） |
945（45.9） |
491（37.8） |
|
|
|
Obesity |
663（19.7） |
525（255.5） |
138（10.6） |
|
|
|
Occupational Stress |
|
|
|
8.028 |
0.005 |
|
No |
594（17.7） |
334（16.3） |
260（20.0） |
|
|
|
Yes |
2763（82.3） |
1726（83.7） |
1037（80.0） |
|
|
3.2 Prevalence and classification of chronic diseases or chronic health problems among steelworkers
Steelworkers had a crude prevalence rate of multimorbidity of 61.3%, with 63.5% of male workers and 40.1% of female workers being multimorbid. The five most common chronic diseases or health issues out of the 13 are carotid atherosclerosis, hyper-lipidemia, hypertension, hyperuricemia, and fatty liver.
The prevalence pattern of multimorbidity among steelworkers was separated into two groups based on the clustering results: a non-cardiovascular metabolic pattern and a cardiovascular metabolic pattern with fatty liver, hyperuricemia, hyperlipidemia, hypertension, carotid atherosclerosis, and diabetes aggregation. About cardiovascular metabolic trends, there were 1,682 patients. 377 patients were involved in the non-cardiovascular metabolic pattern.
3.3 Cumulative exposure to occupational hazards
The cumulative days of shift work were separated into three dose groups of low, medium, and high using their tertile as the node, with zero exposure serving as the control group. Similar to how shift work days were categorized, high temperature exposure and noise exposure cumulatively were as well. In that order, the trichotomies for cumulative shift work days, cumulative heat exposure, and cumulative noise exposure were (1326.1, 2234.7), (464.8, 675.7), and (1111.9, 1574.0). With zero exposure serving as the control group and those who had cumulative exposure, the cumulative dust expo-sure was split into two dose groups of low and high by median (30.1). In the control, low-dose, medium-dose, and high-dose groups, respectively, the prevalence of multi-morbidity in the cumulative days of shift work was 53.4%, 59.2%, 60.2%, and 67.9%, with c2 findings less than 0.05 indicating that differences between groups were statistically significant. In the control, low-dose, medium-dose, and high-dose groups, respectively, the prevalence of multimorbidity in the cumulative amount of high temperature was 57.4%, 60.6%, 59.7%, and 77.7%, and the c2 result was less than 0.05, making the difference between groups statistically significant. In the control, low-dose, medium-dose, and high-dose groups, respectively, the prevalence of multimorbidity in the noise accumulation was 57.0%, 62.1%, 59.2%, and 69.0%, with c2 findings less than 0.05 indicating that differences between groups were statistically significant. In the control, low-dose, and high-dose groups, the prevalence of multimorbidity in dust buildup was, respectively, 59.6%, 68.8%, and 69.8%, with c2 findings less than 0.05 indicating that differences between groups were statistically significant. Table 2 displays information on each category in detail.
Table 2. Accumulation of occupational hazard factors for steelworkers
|
Variables |
Total population n=3357 |
Multimorbidity n=2059 |
Non-multimorbidity n=1298 |
Z/χ2 值 |
P |
|
|
Shift |
|
|
|
30.584 |
＜0.001 |
|
|
0 |
388（11.6） |
207（10.0） |
181（14.0） |
|
|
|
|
low |
988（29.4） |
585（28.4） |
403（31.1） |
|
|
|
|
medium |
1003（29.9） |
604（29.3） |
399（30.8） |
|
|
|
|
high |
978（29.1） |
664（32.2） |
314（24.2） |
|
|
|
|
High Temperature |
|
|
|
70.339 |
＜0.001 |
|
|
0 |
1811（53.9） |
1040（50.5） |
771（59.4） |
|
|
|
|
low |
515（15.3） |
312（15.1） |
203（15.7） |
|
|
|
|
medium |
516（15.4） |
308（15.0） |
208（160） |
|
|
|
|
high |
515（15.3） |
400（19.4） |
115（8.9） |
|
|
|
|
Noise |
|
|
|
28.976 |
＜0.001 |
|
|
0 |
1082（32.2） |
617（30.0） |
465（35.9） |
|
|
|
|
low |
758（22.6） |
471（22.9） |
287（22.1） |
|
|
|
|
medium |
759（22.6） |
449（21.8） |
310（23.9） |
|
|
|
|
high |
758（22.6） |
523（25.4） |
235（18.1） |
|
|
|
|
Dust |
|
|
|
19.396 |
＜0.001 |
|
|
0 |
2755（82.1） |
1643（79.8） |
1112（85.7） |
|
|
|
|
low |
301（9.0） |
207（10.0） |
94（7.2） |
|
|
|
|
high |
301（9.0） |
210（10.2） |
91（7.0） |
|
|
|
3.4 Multivariate logistic regression of multimorbidity in steelworkers
The multifactor logistic regression used factors that were statistically significant in the univariate analysis as adjustment variables. The multifactorial logistic regression took into account cumulative shift, cumulative heat exposure, cumulative noise exposure, and cumulative dust exposure. For the sake of study, these statistics were combined with those who smoked or drank regularly because there weren't many regular smokers or drinkers. Table 7 demonstrates the assignment method. Multivariate covariance was used to diagnose the findings, with no component covariance and all VIF values below 3.
Table 3. The variable assignment method for the influencing factor.
|
Variable Name |
Variable Meaning |
Assignment Method |
|
Y |
multimorbidity |
0=No，1=Yes |
|
X1 |
Gender |
1= Female，2= Male |
|
X2 |
Age |
1=~30，2=30~40，3=40~5，4=50~ |
|
X3 |
Education status |
1= junior high school and below，2= high school or secondary school，3= college and above |
|
X4 |
Marital Status |
1= Unwed，2= Married，3= Divorce |
|
X5 |
Smoking status |
0=No，1=Yes |
|
X6 |
Drinking status |
0=No，1=Yes |
|
X7 |
Income status |
1=＜1500，2=≥1500 |
|
X8 |
Quality of sleep |
1= No sleep disorder，2= Suspected insomnia，3= Insomnia |
|
X9 |
BMI |
1= Normal，2= Overweight，3= Obesity |
|
X10 |
Shift |
1=0，2= low，3= medium，4= high |
|
X11 |
High temperature |
1=0，2= low，3= medium，4= high |
|
X12 |
Noise |
1=0，2= low，3= medium，4= high |
|
X13 |
Dust |
1=0，2= low，3= high |
|
X14 |
Occupational Stress |
0=No，1=Yes |
|
X15 |
Family history of multiple diseases |
0=No，1=Yes |
After controlling for confounding variables, the results of the multifactorial logistic regression analysis revealed that the cumulative days of shift work with 0 as the reference group, the medium-dose group, and the high-dose group, respectively, had 1.361 times and 1.520 times the risk of having multiple diseases than the reference group. The risk of sickness was 1.921, 1.369, and 3.376 times higher in the low-dose, medium-dose, and high-dose groups than in the reference group in the cumulative exposure to heat with 0 as the control group. The risk of disease in the low-dose, medium-dose, and high-dose groups was 1.756, 1.504, and 2.069 times higher than that in the reference group in the cumulative exposure to noise with 0 as the control group. In the low-dose and high-dose groups, respectively, with 0 serving as the reference group, the cumulative exposure to dust was 1.720 times and 1.509 times higher than in the reference group.
Table 4. Multifactorial Logistic Regression of Steelworkers Multimorbidity
|
Variables |
B |
SE |
Wald c2 |
OR（95%CI） |
P |
|
Age |
|
|
|
|
|
|
＜30 |
|
|
|
|
|
|
30~ |
0.625 |
0.207 |
9.910 |
1.92（1.279，2.883） |
0.002 |
|
40~ |
1.561 |
0.214 |
53.221 |
4.762（3.131，7.243） |
＜0.001 |
|
50~ |
1.468 |
0.221 |
44.096 |
4.339（2.814，6.692） |
＜0.001 |
|
Gender |
|
|
|
|
|
|
Female |
|
|
|
|
|
|
Male |
1.074 |
0.149 |
51.527 |
2.917（2.178，3.908） |
＜0.001 |
|
Education status |
|
|
|
|
|
|
junior high school and below |
|
|
|
1 |
|
|
high school or secondary school |
-0.315 |
0.105 |
8.928 |
0.73（0.593，0.897） |
0.003 |
|
college and above |
-0.576 |
0.131 |
19.439 |
0.562（0.435，0.726） |
＜0.001 |
|
Marital Status |
|
|
|
|
|
|
Unwed |
|
|
|
1 |
|
|
Married. |
0.513 |
0.235 |
4.747 |
1.67（1.053，2.648） |
0.029 |
|
Divorce |
0.626 |
0.381 |
2.703 |
1.871（0.887，3.948） |
0.100 |
|
Income status |
|
|
|
|
|
|
＜1500 |
|
|
|
1 |
|
|
≥1500 |
-0.298 |
0.094 |
9.945 |
0.742（0.617，0.893） |
0.002 |
|
Family history of multiple diseases |
|
|
|
|
|
|
No |
|
|
|
1 |
|
|
Yes |
0.502 |
0.152 |
10.923 |
1.652（1.227，2.225） |
0.001 |
|
Smoking status |
|
|
|
|
|
|
No |
|
|
|
1 |
|
|
Yes |
0.172 |
0.086 |
4.012 |
1.188（1.004，1.406） |
0.045 |
|
Drinking |
|
|
|
|
|
|
No |
|
|
|
1 |
|
|
Yes |
0.288 |
0.082 |
12.343 |
1.333（1.136，1.566） |
＜0.001 |
|
Occupational Stress |
|
|
|
|
|
|
No |
|
|
|
|
|
|
Yes |
0.263 |
0.108 |
5.949 |
1.301（1.053，1.608） |
0.015 |
|
Quality of sleep |
|
|
|
|
|
|
No sleep disorder |
|
|
|
1 |
|
|
Suspected insomnia |
0.227 |
0.105 |
4.437 |
1.255（1.021，1.543） |
0.031 |
|
Insomnia |
0.260 |
0.096 |
7.282 |
1.297（1.074，1.567） |
0.007 |
|
BMI |
|
|
|
|
|
|
Normal |
|
|
|
1 |
|
|
Overweight |
0.831 |
0.088 |
89.208 |
2.296（1.933，2.729） |
＜0.001 |
|
Obesity |
1.667 |
0.123 |
184.289 |
5.298（4.165，6.74） |
＜0.001 |
|
Shift |
|
|
|
|
|
|
0 |
|
|
|
1 |
|
|
low |
0.099 |
0.137 |
0.521 |
1.104（0.844，1.445） |
0.470 |
|
medium |
0.308 |
0.139 |
4.914 |
1.361（1.036，1.788） |
0.027 |
|
high |
0.419 |
0.144 |
8.502 |
1.52（1.147，2.014） |
0.004 |
|
High temperature |
|
|
|
|
|
|
0 |
|
|
|
1 |
|
|
low |
0.653 |
0.125 |
27.132 |
1.921（1.502，2.455） |
＜0.001 |
|
medium |
0.314 |
0.121 |
6.782 |
1.369（1.081，1.734） |
0.009 |
|
high |
1.217 |
0.137 |
79.324 |
3.376（2.583，4.413） |
＜0.001 |
|
Noise |
|
|
|
|
|
|
0 |
|
|
|
1 |
|
|
low |
0.563 |
0.113 |
24.635 |
1.756（1.406，2.193） |
＜0.001 |
|
medium |
0.408 |
0.111 |
13.498 |
1.504（1.21，1.871） |
＜0.001 |
|
high |
0.727 |
0.115 |
40.304 |
2.069（1.653，2.59） |
＜0.001 |
|
Dust |
|
|
|
|
|
|
0 |
|
|
|
1 |
|
|
low |
0.548 |
0.145 |
14.323 |
1.73（1.303，2.299） |
＜0.001 |
|
high |
0.412 |
0.149 |
7.594 |
1.509（1.126，2.023） |
0.006 |
3.5 Subgroup Analysis
Age, gender, BMI, smoking and alcohol use status, as well as monthly per capita income, were used to divide subgroups. Each subgroup's association between cumulative occupational risk exposure and multimorbidity was examined. The results for each occupational hazard factor and multimorbidity are presented below after controlling for other confounding factors. Due to the small number of participants in the younger than 30 group, age was separated into two subgroups: less than 40 years and older than 40 years.
4.1 Main result
1. 61.3% of steelworkers had multimorbidity, which was a significant and dangerous prevalence. 2. Male steelworkers had a substantially higher prevalence of multimorbidity than female steel employees. 3. A significant risk factor for multi-morbidity was a history of many diseases in the family. 4. Compared to the group that had no exposure to occupational risks, the high-dose accumulation group had a greater incidence of multimorbidity. 5. The cardiovascular metabolic pattern of hypertension, hyperlipidemia, fatty liver, diabetes, and hyperuricemia aggregation dominates the prevalence pattern of multimorbidity in steelworkers.
4.2 Association of general demographic characteristics with multimorbidity in steelworkers
From the findings of this study, we deduced that multimorbidity in steelworkers was substantially correlated with age, gender, smoking, alcohol use, sleep quality, BMI, educational attainment, and per capita monthly income. In particular, it is clear that the frequency of multimorbidity among steelworkers declines as economic income levels rise and that multimorbidity has a negative correlation with education level [30-33]. Multimorbidity is more likely in steelworkers as they get older, use more alcohol and tobacco, get less sleep, have higher BMI, and have a history of multiple diseases in their families. The findings of the present study are in agreement with those of earlier investigations conducted in the general population. [4, 13, 34-38].
However, in contrast to the findings of Troelstra, Kalpana Singh, et al [8, 30, 34, 39, 40], it was discovered in this study of steelworkers that the prevalence of multimorbidity was substantially higher in males than in women. First off, this can be as a result of the fact that the population of this study differs from that of earlier studies. The bulk of individuals employed in front-line manufacturing in the steel sector are male employees because it is a unique job that necessitates hard physical effort. During the manufacture of steel, front-line employees are subject to environmental dangers like heat, noise, and dust. Women who work for steel businesses are less likely to be involved in production's front lines and are exposed to fewer workplace dangers. Second, compared to female employees, a higher percentage of male employees smoke and consume alcohol. Last but not least, there may be disparities in the results between research due to variations in the definition and measurement of multimorbidity and disease severity across studies as well as variations in the methodology used for data collecting[41, 42].
The study's findings indicate that multimorbidity is strongly influenced by a family history of multiple diseases. Multimorbidity is 1.65 times more likely to occur in people with a family history of multiple diseases than in people without such a history. Prior research has mostly focused on the family history of a single disease, with very few cases discussing the study of numerous diseases in the same family. Findings from earlier research suggest that having a family history of the disease increases the likelihood of getting it in cases of hypertension [43, 44], diabetes, cancer, dyslipidemia, and other diseases. Most research on the mechanism points to heritable susceptibility genes as the primary cause[45-48]. Studies on hypertension have shown that diabetes mellitus and family history of hypertensive disease are independent risk fac-tors for the development of hypertension, and that the risk of hypertension is increased when both conditions are present. This means that having a family history of multiple diseases has a greater impact on the disease than having a family history of a single disease. A family history of a single disease, on the other hand, can influence the incidence of its corresponding disease. People with a family history of multiple diseases are more likely to develop multiple corresponding diseases, and the incidence of multi-morbidity is increased by the increased incidence of a single disease, thus this may result in an increase in the incidence of their multimorbidity. Family history of disease can be further investigated since it can suggest the link between particular family habits and sickness in addition to reflecting genetic inheritance and disease information.
4.3 The association between occupational hazards and multimorbidity in steelworkers
According to the current study's findings, there is a dose-response relationship between the prevalence of multimorbidity in steelworkers and cumulative exposure to shift work, heat, noise, and dust. The risk of multimorbidity among steelworkers is influenced by an increase in cumulative shift days, cumulative heat exposure, and cumulative noise exposure. With increased cumulative exposure to occupational hazards, steelworkers' risk of multimorbidity was found to increase, according to the findings of both univariate and multifactorial analyses, and the majority of subgroups showed consistency in these findings. This leads to the conclusion that among steelworkers, high levels of cumulative exposure to occupational hazards are a significant risk factor for multimorbidity. According to the findings, the risk ratio for cumulative dust expo-sure for the high dosage group was lower than for the low dose group. This may be connected to the movement of workers in the steel sector, where individuals with dust allergies will experience symptoms more quickly after exposure and will subsequently be moved out of the exposed position. The remaining steelworkers were less likely to get the same ailments and were more tolerant of the dust. This might be the cause of the phenomenon.
The prevalence of multimorbidity among steelworkers increased with the cumulative number of days of shift work, with cumulative exposure to heat, and with cumulative noise exposure. The increased risk of polyvalence with increasing cumulative exposure was observed in both univariate and multifactorial analyses and was consistent across most subgroups, suggesting that higher cumulative occupational hazard exposure is a high-risk factor for polyvalence. The decrease in risk in the high-dose group compared to the low-dose group in the cumulative dust exposure may be related to the transfer of workers in steel companies, where those who are intolerant to dust or have occupational contraindications are transferred out of the dust-catching position, while those who remain are relatively tolerant to dust and therefore may contribute to this phenomenon. In the subgroup analysis, an increase in risk with increasing dose was observed in the group aged over 40 years, which may be due to the increased risk of polyvalence with increasing dust accumulation observed in these more tolerant groups after the change of jobs. Those over the age of 40, however, showed an increase in risk with increasing dosage in the subgroup analysis, which may account for the higher risk of multimorbidity shown with increasing dust buildup in these more tolerant individuals.
Long-term shift work was linked to an increased incidence of AF as well as disorders like coronary heart disease, diabetes, cancer, asthma, cerebrovascular disease, and poor sleep quality, among others, according to a study on shift work by Ningjian Wang, Vetter, Barul, Maidstone et al. [49-55]. Steelworkers are more likely to develop kidney stones, dyslipidemia, hypertension, and cardiovascular system disorders as a result of continuous operations in a heated atmosphere [56]. Noise damage that has accumulated over time increases the risk of hypertension, renal dysfunction, and the ability to cause ischemic heart disease [57-59]. A Danish cohort study of nurses found that air pollution and noise had a negative impact on atrial fibrillation and chronic obstructive pulmonary disease [60, 61]. Dust can cause allergic reactions, asthma, emphysema, chronic bronchitis, and other respiratory conditions in humans [62, 63]. It is commonly known that shift work, extreme heat, noise, and dust are among the workplace risks that increase the chance of illness start. The prevalence of multimorbidity rises when one particular disease in the population becomes more common.
Also, it has been discovered in earlier studies that shift work over an extended period of time disrupts circadian rhythms in the human body [64], which affect the synthesis of liver enzymes as well as the typical pattern of employees' activities. As a result, the liver sustains extracellular damage, impairing liver function. There is also evidence that many of the genes in the mammalian liver have circadian rhythms, and that long-term shift employment can harm the liver by disrupting these cycles. Acute effects of disrupted circadian rhythms on body metabolism include dysregulated levels of glucose, insulin, and leptin in the blood as well as a decreased resting metabolic rate. Disrupted circadian rhythms also cause disturbances in the secretion of important biomarkers, such as melatonin and cortisol. Acute circadian disruptions brought on by working nights may function as a biological stressor, altering gut flora and perhaps causing metabolic changes in the body [65].
Workplace noise primarily harms the auditory system, but it also negatively im-pacts the cardiovascular, neurological, digestive, and endocrine systems. Stressors like noise have the potential to alter stress-related hormone levels, which can cause a variety of physiological alterations in the endocrine and cardiovascular systems [66].
Worker exposure to high temperatures will result in heat stress, which will cause the body to go through a series of physiological changes in thermoregulation, water and salt metabolism, neuroendocrine system, circulatory system, urinary system, etc. These changes include an increase in systolic pressure and pulse pressure, a decrease in vascular elasticity, damage to the heart's pumping and contraction functions, increased cardiac load, and eventually the induction of various cardiovascular.
This explains the cumulative effect of occupational hazards on the occurrence of multiple diseases and a portion of the phenomenon of disease clustering. These occupational hazards not only affect one aspect of the human body but also cause issues in multiple tissues, organs, and systems, leading to a cascade of related diseases.
Previous studies on steel workers have primarily concentrated on the effects of occupational hazards on a single disease, but steel workers do not only suffer from one disease, and disease prevention and control are typically focused on a single disease, which cannot effectively handle the complex situation of multiple diseases occurring at the same time. The impact of occupational risks on a variety of diseases was examined in this study, and the dose-response connection was examined using cumulative exposure. This study's drawback is that it is cross-sectional, which makes it difficult to explain the causal link between occupational hazards and a variety of diseases. Second, this study's total exposure to workplace risks is external exposure, which does not accurately rep-resent the magnitude of the impact on human body production. Moreover, because the study group was somewhat small and it focused on steel workers who were already at work, it is important to use caution when extrapolating the findings.
Steelworkers have a high multimorbidity rate and the most cardiovascular metabolic patterns. Because exposure to industrial hazards raises the chance of multimorbidity among workers, steel companies must do more to protect their employees from each of these workplace dangers.
Author Contributions: Conceptualization, Z.Z. and L.J.; Data curation, X.L.; Funding acquisition, X.L.; Investigation, H.W.（Huan Wang）, Y.Z. and J.C.; Methodology, J.H. and L.J.; Software, Z.S. and H.W.（Hui Wang）; Supervision, J.W., L.X., X.X., X.L. and L.J.; Validation, X.W. and R.M.; Writing – original draft, H.L.
Funding: This research was funded by Science and Technology Program of Tangshan, grant number 22130234H.
Institutional Review Board Statement: The study was reviewed and approved by the Ethics Committee of North China University of Technology (approval number: 2022147).
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.
Data Availability Statement: Because the data are being researched and are private, the data have not been made public during the study, but can be obtained from the corresponding author upon reasonable request.
Acknowledgments: We would like to thank North China University of Science and Technology for providing the platform. I would also like to thank teachers for their guidance in the research, and thank my classmates and friends for their help.
Conflicts of Interest: The authors declare that they have no competing interest.
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Competing interest reported. The authors declare that they have no competing interest.