Occupational Ventilatory Defects Among Workers Employed in Tea Gardens, A Cross-Sectional Study in Siliguri Subdivision of Darjeeling District, West Bengal.

Introduction: During the refining and packing of tea, a very fine dust is formed which is called the tea fluff. Exposure to this fluff has been reported to cause ventilatory defects on chronic exposure. Objective: To determine the association between air quality of the different work sections of tea gardens and the ventilatory functions of the workers. Methodology: An observational analytical study with cross sectional design was conducted on 400 apparently healthy individuals working in different sections of 4 tea gardens. Data on sociodemographic characters of the population, particulate matter (PM) and air quality (AQI) was recorded. All participants underwent spirometric evaluation.
Results: The mean (SD) age of the study population was 37.8 (8) years. Most (67%) of the study participants were females. The participants were engaged in the job for a mean (SD) of 4.7 (2.7) years. The median distribution of PM2.5, PM 10 and AQI are progressively higher from the garden section to the dry section. A significant correlation between FVC and PM2.5 and PM10 is seen. FEV1 and FEV1/FVC shows a significant correlation with all air quality parameters while FEF25-75 is correlated to none of them. Comparison of the three models to predict the spirometric variables show that even after adjustments, the FEV1 is significantly associated with air quality parameters, FEV1/FVC is significantly associated with the air quality parameters and age while the FEF25-75% is dependent on the age of the participant.
Conclusion: There is a strong correlation between the ventilatory parameters and the cumulative exposure of PM2.5, PM10 and AQI, which persists even after adjustment for confounders. Copyright:

INTRODUCTION

India is one of the largest tea industries in the world, accounting for 31% of global production and engaging more than 1.1 million workers.[1] Tea plantations are located throughout the country, with the northeastern, southern, and a few northern states producing the bulk. There are about 450 tea gardens spread in the Darjeeling District and Dooars region alone.[2]

Three types of workers are employed in the tea industry. Garden workers, workers who are responsible for plantation and plucking of tea leaves, and factory workers, who are employed at two different sections of the factory, the wet section and the dry section. During refining and packing of tea, a fine dust is formed which is called “tea fluff.” Exposure to this fluff has been reported to cause a group of respiratory disorders among workers historically described as tea maker's asthma, tea factory cough, and tea taster's disease since as early as in 1970.[3] Chronic symptoms of tea dust exposure include byssinosis, chronic cough, and dyspnoea.[4]

Particulate matters (PM) constitute a measure of the air pollution level of a particular location. One group of PMs, the PM2.5, has small diameters but large surface areas and pass through the filtration of nose hair, reaching the end of the respiratory tract with airflow and accumulates there by diffusion.[5] These PM2.5s, along with PM10, which have diameters between 2.5 and 10 μm, are associated with several adverse health impacts. Long-term exposures have been associated with increased hospital admissions for heart or lung causes, acute and chronic bronchitis, asthma attacks, emergency room visits, and other respiratory symptoms.[6] Worldwide, PM2.5 is associated with the greatest proportion of adverse health effects related to air pollution, as reported in the World Health Organization's Global Burden of Disease.[7]

Objective

To determine the association between air quality of different work sections of tea gardens and the ventilatory functions of study participants.

METHODOLOGY

Study design and population

We conducted an observational study with cross-sectional design from May 2018 to August 2019 on apparently healthy individuals working in tea gardens of four blocks of Siliguri Sub-division, Darjeeling district, West Bengal. Unwilling workers, workers with self-reported history of any lung diseases prior to placement in the industry, or with diagnosed lung problems other than obstructive lung disease were excluded from the study. Workers with congenital anomalies that may influence spirometry, like cleft lip, cleft palate, and musculoskeletal deformities causing difficulty in performance and interpretation of results of spirometry were also excluded.

Sample size and sampling techniques

A multistage sampling technique was used. In the first stage, one tea garden from each block in the plains of Darjeeling was selected by purposive sampling. Workers were stratified into three groups namely (a) Garden workers, (b) wet section, and (c) dry section factory workers. From each selected garden, participants were recruited based on a proportionate sampling method.

In the absence of any published literature on the prevalence of ventilatory defects among tea garden workers, sample size was calculated using an anticipated prevalence of 50% to yield the maximum sample size. The sample size was calculated using the following formula: N = [Z2 (1 − α/2) P (1 – P)]/d2, where: Z (1 − α/2) = 1.96 (at 95% confidence interval), P = anticipated prevalence of ventilatory defect among industry workers (50%) d = absolute precision (7.5%). A design effect of 2 and a nonresponse of 15% were considered to derive a final sample size of 391.

Research tools

A predesigned pretested questionnaire was used to collect data on sociodemographic characters of the population, educational attainment, and underlying medical conditions.

Assessment of air pollution

Each subject's exposure to occupational air pollution was estimated based on their place of work. Air quality monitors use a laser-scattering principle. The scattered light is transformed into electrical signals and these signals are amplified and processed. The number and diameter of particles are obtained by analysis because the signal waveform has certain relations with the particles’ diameter. An air quality monitor from Airveda (model 1207170014) was placed at the nose level of the average worker at the point of maximum generation of dust. The monitor measures PM 2.5 (range 0–250) and PM 10 (0–430 in) μg/m3 with a minimum resolution of <0.3 μm and a maximum relative error of ± 15% and ± 10 μg/m3. The monitor is tested and calibrated against BAM, Grimm 11-E, Dylos DC100, and provides the Indian AQI categories and ranges corresponding to different values of PM2.5 and PM10.[8]

Spirometry

Spirometry measurements were performed by a trained spirometry technician using Spirolab II (Spirolab II, SDI, USA) auto-calibrating device. The apparatus was calibrated daily. The precise technique of executing various lung function tests for the present study was based on the operation manual of the instrument with special reference to the official statement of the British Thoracic Society of Standardization of Spirometry.[9] The forced vital capacity (FVC), forced expiratory volume in 1 second (FEV1), FEV1/FVC ratio, and forced expiratory flow rates at different lung volumes (including FEF25%, FEF50%, FEF75%, and FEF25–75%) were measured. We collected at least three acceptable spirograms per participant.

Average cumulative index (ACI)

ACI for each individual was derived by multiplying the measured air quality parameters (PM2.5, PM10, and AQI) with the hours of work per day and the total number of years engaged in the job.

Statistical methods

Collected data were checked for consistency and completeness and were entered into Microsoft Excel spreadsheet. Inconsistencies were resolved by comparing with the raw data. Data were analyzed using IBM Statistical Package of Social Sciences (SPSS) Version 22.0 and presented using the principles of descriptive and inferential statistics.

Ethical issues

The study was approved by the Institution Ethics Committee of the North Bengal Medical College (IEC/2017-18/28 dated 06/01/2018).

RESULTS

The mean (SD) age of the study population was 37.8 (8) years. Most (67%) of the study participants were females. The mean (SD) height of the cohort was 155.0 (8.0) cm and mean weight was 50.1 (6.2) kg. More than half of the study participants were smokers (56.3%). Participants were engaged in the job for a mean (SD) of 4.7 (2.7) years. Table 1 shows the sociodemographic and spirometric variables of the study cohort based on the nature of job. There was a significant difference in all sociodemographic and spirometric parameters between the groups. The cumulative exposure to PM2.5, PM 10, and AQI has been shown in the box and whisker plot [Figure 2], which shows that the median distribution of all air quality parameters was higher in the dry section of the factory compared to wet section, which in turn is higher than the garden section [Table 2]. Table 3 shows a significant correlation between FVC and PM2.5 and PM10. FEV1 and FEV1/FVC showed a significant correlation with all air quality parameters, while FEF25-75 was correlated to none of them. We constructed three models using the three air quality indicators and important confounding factors for the spirometric measurements. Comparison of the three models shows that even after adjustments the FEV1 was significantly associated with PM2.5, PM10, and AQI. FEV1/FVC was significantly associated with the air quality parameters and age while FEF25–75% was dependent solely on the age of the participant after adjustment for confounding [Table 4].

Table 1

Indian AQI categories and ranges corresponding to different values of PM 2.5 and PM 10

AQI category	PM 2.5 (µg/m3)	PM 10 (µg/m3)
Good (0-50)	0-30	0-50
Satisfactory (51-100)	31-60	51-100
Moderately polluted (101-200)	61-90	101-250
Poor (201-300)	91-120	251-350
Very poor (301-400)	121-250	351-430
Severe (401-500)	250+	430+

Figure 2

Box and whisker plot of cumulative PM 2.5, PM10 exposure, and AQI based on work sections

Table 2

Demographic characteristics and spirometric variables in the study population (n=391)

Variables	Garden section	Wet section	Dry section	Statistic	P
Age (year) (mean±SD)	37.3±8.8	36.35±7.2	40.37±7.1	F=5.644	0.004*
Sex
Male	46 (35.7%)	36 (27.9%)	47 (36.4%)	χ2=46.730	0.000*
Female	186 (71.0%)	41 (15.6%)	35 (13.4%)
Height (cm) (mean±SD)	152.76±6.73	157.62±8.39	159.02±8.61	F=26.831	0.000*
Weight (kg) (mean±SD)	48.92±5.13	50.79±7.43	52.62±6.83	F=12.176	0.000*
Education level
Less than primary	87 (64.9%)	27 (20.1%)	20 (14.9%)	χ2=11.759	0.019*
Primary	100 (59.9%)	35 (21.0%)	32 (19.2%)
Middle school and above	45 (50.0%)	15 (16.7%)	30 (33.3%)
Smoking
Current smokers	121 (55.0%)	36 (16.4%)	63 (28.6%)	χ2=18.516	0.000*
Non smokers	111 (64.9%)	41 (24.0%)	19 (11.1%)
Duration in job (year) (mean±SD)	4.69±2.57	3.77±2.91	5.7±2.43	F=10.878	0.000*
Pulmonary function†
FVC (L) (mean±SD)	2.89±0.70	2.8±0.80	2.65±0.73	F=3.246	0.040*
FEV1 (L) (mean±SD)	2.3±0.65	2.27±0.69	2.01±0.63	F=6.138	0.002*
FEV1/FVC (mean±SD)	79.34±8.29	81.85±9.07	74.7±13.42	F=11.424	0.000*
FEF25-75 (%) (mean±SD)	2.35±1.04	2.58±1.20	2.06±1.17	F=4.477	0.012*

FVC forced vital capacity, FEV1: 1-second forced expiratory volume, MMF: Maximal mid-expiratory flow rate ‡ pred=predicted value, *P<0.05(statistically significant) F statistics: One-way ANOVA, χ2=Chi-square test/Fisher’s exact test

Table 3

Correlation coefficients between cumulative exposures and pulmonary function in the study population

Spirometric parameters	PM 2.5 ACI	PM10 ACI	AQI ACI
FVC	−.123**	−.095	−.109*
FEV1	−.166**	−.162**	−.168**
FEV1/FVC	−.158**	−.219**	−.198**
FEF25-75	−.068	−.093	−.086
ACI Average cumulative index

**Correlation is significant at the 0.01 level (two-tailed); *Correlation is significant at the 0.05 level (two-tailed)

Table 4

Multiple regression analyses of pulmonary function tests

Variables	FVC (L)		FEV1 (L)		FEV1/FVC (%)		FEF 25-75% (L)
	β [95% CI]	P	β [95% CI]	P	β [95% CI]	P	β [95% CI]	P
Model 1
Age (year)	−0.001 (−0.01, 0.008)	0.857	−0.006 (−0.014, 0.002)	0.135	−0.222 (−0.344, −0.099)	0.000*	−0.014 (−0.028, 0.000)	0.045*
Height (cm)	−0.007 (−0.027, 0.013)	0.479	−0.008 (−0.026, 0.009)	0.363	−0.103 (−0.365, 0.159)	0.441	−0.012 (−0.041, 0.018)	0.444
Weight (kg)	0.000 (−0.017, 0.016)	0.955	0.006 (−0.009, 0.02)	0.457	0.17 (−0.051, 0.391)	0.13	0.012 (−0.013, 0.037)	0.354
Smoking	0.019 (−0.142, 0.179)	0.818	0.03 (−0.113, 0.173)	0.681	1.613 (−0.524, 3.75)	0.139	0.12 (−0.123, 0.362)	0.332
PM 2.5 ACI	−0.004 (−0.009, 0.000)	0.065	−0.006 (−0.01, −0.002)	0.005*	−0.082 (−0.142, −0.022)	0.008*	−0.003 (−0.01, 0.003)	0.336
Model 2
Age (year) (mean±SD)	−0.001 (−0.01, 0.008)	0.856	−0.006 (−0.014, 0.002)	0.157	−0.208 (−0.33, −0.087)	0.001*	−0.014 (−0.028, 0.000)	0.054
Height (cm) (mean±SD)	−0.007 (−0.027, 0.013)	0.497	−0.008 (−0.025, 0.01)	0.38	−0.102 (−0.362, 0.158)	0.441	−0.012 (−0.041, 0.018)	0.444
Weight (kg) (mean±SD)	−0.002 (−0.019, 0.014)	0.793	0.004 (−0.011, 0.019)	0.588	0.168 (−0.048, 0.384)	0.127	0.012 (−0.013, 0.036)	0.35
Smoking	0.012 (−0.15, 0.173)	0.888	0.015 (−0.13, 0.159)	0.84	1.247 (−0.882, 3.376)	0.25	0.105 (−0.139, 0.348)	0.399
PM 10 ACI	−0.001 (−0.003, 0.001)	0.188	−0.002 (−0.003, −0.001)	0.008*	−0.042 (−0.064, −0.021)	0.000*	−0.002 (−0.004, 0.001)	0.168
Model 3
Age (year)	−0.001 (−0.01, 0.009)	0.878	−0.006 (−0.014, 0.002)	0.16	−0.212 (−0.334, −0.089)	0.001*	−0.014 (−0.028, 0.000)	0.052
Height (cm)	−0.007 (−0.027, 0.013)	0.478	−0.008 (−0.026, 0.009)	0.355	−0.108 (−0.369, 0.153)	0.416	−0.012 (−0.042, 0.018)	0.434
Weight (kg)	−0.001 (−0.018, 0.015)	0.872	0.005 (−0.01, 0.02)	0.507	0.176 (−0.043, 0.394)	0.114	0.012 (−0.013, 0.037)	0.338
Smoking	0.013 (−0.148, 0.174)	0.873	0.02 (−0.124, 0.164)	0.784	1.408 (−0.722, 3.539)	0.195	0.111 (−0.132, 0.354)	0.369
AQI ACI	−0.002 (−0.004, 0.000)	0.113	−0.002 (−0.004, −0.001)	0.005*	−0.045 (−0.071, −0.019)	0.001*	−0.002 (−0.005, 0.001)	0.207

*Statistically significant FVC forced vital capacity, FEV1 1-second forced expiratory volume, FEF 25-75% forced expiratory flow 25-75% ACI Average cumulative index, Beta unstandardized coefficient

Flow chart showing recruitment of cases

DISCUSSION

A huge section of the labor force of West Bengal is engaged in the tea industry. Most of these laborers are casual workers and engaged in the industry only during certain months of the year. Many are engaged in other professions in the interim, exposing them to other organic or inorganic dusts. Since the present study includes only workers that are permanent/semipermanent in nature and living within garden premises throughout the year, the assessment of pulmonary functions remains uncomplicated by other exposures.

The highest dust generation, both PM 2.5 and PM 10, and the worst air quality (AQI) were seen in the dry section of the industry followed by wet section. The garden section generated the least amount of dust and had the best air quality. In addition to the particulate matter levels, duration of exposure is an important predictor of respiratory morbidities. We developed a composite index based on the average exposure rates to PM 2.5 and PM10, the hours of work per day, and the years engaged in the present occupation. This cumulative index gives a rough idea of the total amount of exposure of a person till date. In addition, the cumulative exposure to the air quality over the working hour-years was also used in the present study. Although the cross-sectional study design makes it difficult to draw conclusions on causality, a strong dose–response relation was noted in the present study.

Data from the present study revealed a significant decrease in mean FEV1 and FEV1/FVC values among all tea workers. Changes were most marked in workers of dry section, followed by the wet section and the garden sections and the difference between them were statistically significant. Most observed changes were obstructive in nature, similar to those reported in previous studies.[510111213]

Only three of our study participants had purely restrictive impairment on spirometry. However, confirmation with radiography or diffusion capacity was beyond the scope of the study. These patients were not included in the final calculations. Restrictive patterns in a small segment of patients exposed to tea dust were also reported from a study in Taiwan. Further studies, with adequate sample size, should be conducted to evaluate if long-term high-level tea dust exposure can lead to restrictive changes.

Significantly lower FVCs and FEF25–75% in relation to the cumulative exposures were seen in the workers from the dry section of the factories. FEF25–75% value denotes small airways function and is generally low in low measures of the FEV1. Similar degree of small airways obstruction in workers exposed to tea dust has also been reported from most studies.[1415]

Women, having a lower mean height and weight than men, are preferentially employed for garden work while men are proportionately more in number in factory work. Thus, the mean height and weight of the female workers can confound the results of the study. Other factors likely to affect the relation between occupational exposure and ventilatory defects include age, smoking status, and the type of particulate matter.

In order to minimize this, three models were developed to predict the changes in the ventilatory parameters. The cumulative index, developed for the study, was used in the models. Even after adjustment for confounders, the relation between the FEV1 and FEV1/FVC and PM 2.5, PM 10 and AQI remained consistent. However, no relation was seen between FVC and FEF25–75.

Limitations

The method of area sampling as employed in the present study assesses the distribution of dusts in the environment, which does not necessarily reflect a worker's true exposure. Personal sampling with portable gravimeters would have given much more accurate estimate of the exposures. Furthermore, tea dust is also known to contain in addition to particulate matter, pollen, fungal spores and hyphae, mycotoxins, bacteria, and endotoxins.[16] These allergens could affect the respiratory system of the workers, resulting in changes in the ventilatory function.

CONCLUSION

The study showed high exposures to PM 2.5 and PM 10 in factory workers compared to the garden workers. Among factory workers, workers working in the dry section had more cumulative exposures and worse pulmonary functions than the other two groups.

Financial support and sponsorship

Funded by ICMR for MD thesis grant for the first author vide letter number 3/2/June – 2017/PG- Thesis HRD (29) dated 13.03.2018.

Conflicts of interest

There are no conflicts of interest.