|ORIGINAL ARTICLE: DR. S. D. GAUR BEST PAPER AWARD ON ENVIRONMENTAL HEALTH
|Year : 2020 | Volume
| Issue : 3 | Page : 229-235
Effect of light pollution on self-reported sleep quality and its components: Comparative assessment among healthy adult populations in a rural and an Urban area of West Bengal, India
Arista Lahiri1, Arup Chakraborty2, Amal Kumar Sinha Roy3, Urmila Dasgupta4, Krishnadas Bhattacharyya5
1 Senior Resident, Department of Community Medicine, College of Medicine and Sagore Dutta Hospital, Kolkata, West Bengal, India
2 Associate Professor, Department of Community Medicine, Medical College and Hospital, Kolkata, West Bengal, India
3 Associate Professor, Department of Community Medicine, Malda Medical College, Malda, West Bengal, India
4 Professor, Department of Community Medicine, Medical College and Hospital, Kolkata, West Bengal, India
5 Professor and Head, Department of Community Medicine, Medical College and Hospital, Kolkata, West Bengal, India
|Date of Submission||05-Apr-2020|
|Date of Decision||24-May-2020|
|Date of Acceptance||10-Jun-2020|
|Date of Web Publication||22-Sep-2020|
240, Golpukur Road, P.O-Baruipur, South 24-Parganas, Kolkata - 700 144, West Bengal
Source of Support: None, Conflict of Interest: None
| Abstract|| |
Background: Light pollution is inappropriate or excessive use of artificial light. Nighttime sky radiance is an effective measure to study its effects on individual sleep quality. Objectives: The study is aimed to measure the effect of light pollution on the sleep quality and compare among people residing in selected rural and urban areas of West Bengal, India. Methods: A comparative cross-sectional study was conducted during September–October 2018 in 10 villages of Barasat II block and 10 wards of Kolkata Municipal Corporation. Two hundred and sixty-three participants from urban and 249 participants from rural areas were selected through multi-stage sampling. Data regarding sleep quality and other selected factors were geotagged along with the radiance data. Multi-level linear regression models were built. Results: The mean age of the participants from rural and urban areas were respectively 37.65 (±10.77) years and 38.10 (±11.02) years. Gender-wise the participants were distributed almost evenly in urban and rural areas. Among the urban and rural population, the observed mean global Pittsburgh Sleep Quality Index scores were 12.63 (±3.04) and 9.23 (±2.27), respectively. Poorer sleep quality was observed to be statistically significant with increasing level of exposure. Multi-level models show that, at an exposure of >40.0 nW/cm2/sr the adjusted coefficient was 11.52 (95% confidence interval [CI]: 9.65, 13.40) in the overall model and 12.84 (95% CI: 12.31, 13.37) for urban participants. Conclusion: The disturbance in sleep is associated with higher levels of night-time radiance of the sky strongly observed among the urban population.
Keywords: Light pollution, multi-level, Pittsburgh sleep quality index, radiance, sky glow, sleep quality
|How to cite this article:|
Lahiri A, Chakraborty A, Roy AK, Dasgupta U, Bhattacharyya K. Effect of light pollution on self-reported sleep quality and its components: Comparative assessment among healthy adult populations in a rural and an Urban area of West Bengal, India. Indian J Public Health 2020;64:229-35
|How to cite this URL:|
Lahiri A, Chakraborty A, Roy AK, Dasgupta U, Bhattacharyya K. Effect of light pollution on self-reported sleep quality and its components: Comparative assessment among healthy adult populations in a rural and an Urban area of West Bengal, India. Indian J Public Health [serial online] 2020 [cited 2023 Mar 23];64:229-35. Available from: https://www.ijph.in/text.asp?2020/64/3/229/295788
| Introduction|| |
In an era of the growing movement to save the environment, awareness about pollution and intent to act is on the rise globally, yet polluting the dark sky remains a topic not much discussed. The international dark-sky association has defined light pollution as the inappropriate or excessive use of artificial light. Light pollution can have serious environmental consequences for humans, wildlife, and climate. While sky-glow, a phenomenon most commonly noticed, provides some idea about the growing menace of light pollution, the health effects such as stress, insomnia, poor sleep quality often go unnoticed as a direct impact of light pollution.,, Light trespass, glare, and over-illumination also leads to light pollution, with the effect being a simple distraction to resetting the circadian rhythm and even beyond sleep disturbance leading to altered hormonal homeostasis. The World Health Organization recommends cutting down wastage of light energy that is leading to pollution of the dark sky to attain a sustainable development with focusing on the adequacy of natural and artificial lighting.
Sleep quality is an important determinant of quality of life., There are several studies reporting age, gender difference, and morbidities such as diabetes mellitus, chronic pain as determinants of poor sleep quality.,, Obesity, alcohol, and tobacco addiction are also independent risk factors resulting in poor sleep quality, while physiological conditions such as pregnancy can also alter sleep patterns., Along with the biological, behavioral and individual determinants of sleep quality light pollution appears to be perhaps the most important environmental determinant.,, However, light pollution is also linked with urbanization and economic growth. Therefore, it is important in a delicate way to identify the true effect of light pollution on sleep quality, adjusting for the geo-spatial variation, individual and social variation. In fact, there is a scarcity of studies regarding quantification of the effect of light pollution on sleep quality globally and even more in the Indian context.
In this context, the present study aimed to find out the quality of sleep and measure the effect of light pollution independently on sleep quality among people residing in selected rural and urban areas of West Bengal, India. The study also compared the observed effects in urban and rural settings.
| Materials and Methods|| |
Study design and study population
A comparative cross-sectional study was conducted among Bengali and Hindi speaking adults (18–60 years) in a rural (Barasat-II block, North 24 Parganas district) and urban (Kolkata Municipal Corporation) area of West Bengal. The study subjects were permanent residents of selected villages and selected wards of the rural and urban study areas. Among female participants, pregnant and lactating mothers were excluded. Any male or female participant having diagnosed physical or mental morbidity, and/or suffering from serious illness and/or bed-ridden at the time of data collection were also excluded, as the variation in their sleep quality is most likely to be contributed by their underlying condition(s).
Sampling and selection of study participants
Multi-stage sampling was used for the selection of the participants. The sample size calculation was done based on the Fleiss method to detect differences in mean scores of sleep quality in urban and rural groups with 80% power and 5% precision. From the observations of a pilot study by the researchers, the mean scores of the Pittsburgh Sleep Quality Index (PSQI) were 12.98 (±4.06) and 11.59 (±3.52), respectively, in an urban and a rural area. The sample size after inflating with the design effect of 2 and a nonresponse of 10% was 260 in each rural and urban setting.
A total of 10 villages and 10 wards were selected from the rural and urban areas, respectively. Sample size for each unit (village/ward) was calculated using population proportionate to size technique. Considering observations from a pilot study about number of available and eligible participants from each household, it was decided by the researchers that in each village or ward, minimum 13 and maximum 25 households were to be selected. The selection of household was made randomly. From each selected household one male and one female member were chosen randomly. Where a selected household was inhabited by a single eligible male or female member, then only the available member was included. Finally, maintaining the sampling methodology, 249 individuals in the rural area and 263 in urban areas were surveyed from 142 and 157 households, respectively.
Tools and techniques
Data collection for the study was carried out during September 2018–December 2018. Data collection was done through house-to-house visits. A geo-locator (Garmin GPS etrex 10, Garmin) was used to locate the coordinates (latitudes and longitudes) of the households surveyed, a process called geotagging. A predesigned pretested questionnaire comprising of selected socio-demographic variables (age, gender, area of residence [rural/urban], addiction, physical activity) and translated versions of PSQI and 10-item Perceived Stress Scale (PSS) (both translated to Bengali and Hindi and validated beforehand) was used.,, The height and weight of the respondents were measured in order to calculate body mass index (BMI). Waist and hip circumferences were measured to calculate the Waist-Hip ratio (WHR) to detect abdominal fatness or central obesity. Measurement of light pollution was obtained from the Visible Infrared Imaging Radiometer Suite (VIIRS) 2019 dataset.
The nighttime radiance (1 radiance unit = 10 722;9 W/cm2/sr) measurement was considered as a measure of light pollution. As per the VIIRS 2019 data, and the latitude and longitude of the participants' locations the nighttime radiance data was retrieved and tagged using QGIS open-source geographic information application., Light pollution clusters were identified from the VIIRS 2019 data for observed nighttime radiance of the sky., These clusters were considered as exposure categories for light pollution. Nighttime radiance noted were categorized into six graded exposure categories such as ≤1.0 unit, 1.0–3.0 units, 3.0–6.0 units, 6.0–20.0 units, 20.0–40.0 units, and >40.0 units.
The analysis of self-reported sleep quality from PSQI was performed following the guidelines. The PSQI is composed of seven components, namely subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, self-reported sleep disturbance, daytime dysfunction, and use of sleeping medication, measured through a set of nineteen questions. Each component is scored from 0 to 3 in a best to worst manner. The PSQI global score is the summation of the scores of the seven components, with a higher score indicating a greater sleep disturbance resulting in a poorer sleep quality.
Stress and nutritional status
Stress, being an important determinant in the sleep quality of the apparently healthy individuals, was measured by PSS. As per the total score obtained, stress levels for individuals were categorized as low (0–13), moderate (14–26), and high (27–40). Nutritional status measured by BMI was categorized into underweight, normal weight, preobese, obese I, and obese II. Obese I and obese II were merged into obese for multivariate analysis. Abdominal fatness or central obesity was identified using specified WHR cutoffs for males and females.
To measure the effect of light pollution on sleep quality, three multi-level mixed-effect linear models predicting PSQI global score were created to account for the clustering., The analyses were performed using STATA 14.2 (StataCorp. LLC, College Station, Texas, USA) software. In the overall model, a three-stage nesting was utilized with the exposure category of radiance nested within the area of residence (urban/rural), village or ward level nested in exposure category, and finally household level nested within village or ward level. On post-hoc analysis, the intra-cluster correlation at levels of exposure category and village/ward level were 0.117 and 0.127, respectively. Two separate multilevel models were created for urban and rural areas taking exposure category, village/ward, and household as the level variables contributing to clustering. All the models were built using maximum likelihood estimation and robust standard errors were used to account for outliers in the dependent variable. Models were found to be statistically appropriate through an indicative conservative Likelihood Ratio Test (Pχ2< 0.001). The models adjusted for age, gender and addiction habits, included physical activity (sedentary/non-sedentary), nutritional status, abdominal fatness, perception of stress as predictor variables along with exposure categories of radiance of nighttime sky, which also served as a level variable to account for clustering thereby yielding an unbiased estimate of effects of light pollution on self-reported sleep quality.
The current study was conducted as a part of a larger study. Ethical approval was obtained from The Institutional Ethics Committee of Medical College and Hospital, Kolkata (Ref. No. MC/KOL/IEC/ACADEMIC/313/04-2014). Written informed consent was obtained from the study participants before data collection.
| Results|| |
Background information of the participants
Overall, 511 participants were studied: 248 from rural areas and 263 from urban areas. The mean ages of the participants from rural and urban areas were respectively 37.65 (±10.77) years and 38.10 (±11.02) years, which were not statistically different. [Table 1] summarizes the major findings regarding sociodemographic profile and addiction behavior of the participants. Gender-wise, the participants were distributed almost evenly in urban and rural areas, with 51.32% of the study participants overall being females. Overall, 9.8% were chronic consumers of alcohol, with the proportion being higher in urban areas (12.53%). Considering the use of any form of tobacco currently, 38.21% were found to be current tobacco users.
|Table 1: Distribution of the study participants from rural and urban areas according to Socio-demographic profile and addiction habits|
Click here to view
Anthropometric profile and perceived stress level
The anthropometric profile and the perceived level of stress are depicted in [Table 2]. Nutritional status assessed by BMI and abdominal fatness assessed by WHR did not differ statistically between urban and rural respondents. However, mean BMI among rural and urban study participants was 26.51 (±4.08) and 26.14 (±3.96) Kg/m2, respectively, which were not statistically significantly different. Abdominal fatness was observed among 28.01% of the respondents overall. The level of perceived stress was found to be statistically significantly different among urban and rural participants. A high level of perceived stress was observed in a significantly higher proportion among the urban respondents (25.11%) compared to rural residents (15.73%).
|Table 2: Distribution of the study participants from rural and urban areas according to anthropometric parameters and perceived level of stress|
Click here to view
Exposure to light pollution
Considering night-time brightness of the sky, the minimum radiance value was observed to be 0.63 units while maximum 63.07 units in the study areas. Mean radiance in urban and rural areas were, respectively, 3.49 (±2.03) and 30.33 (±19.40) units. This difference was statistically significant. While classifying participants in the predecided radiance categories, it was observed that maximum numbers of participants were exposed to the radiance of the range 3.0–6.0 units (30.11%). However, the radiance of >20.0 units was observed only in urban areas.
Self-reported sleep quality, its components, and exposure categories
Overall, subjective sleep quality, sleep latency, sleep duration, and habitual sleep efficiency had a higher score (score of 3), indicating poorer quality of sleep among 18.61%, 18.41%, 25.59%, and 6.72% of participants, respectively. There were significantly higher proportions of participants in the urban areas reporting poorer quality of sleep with respect to the mentioned components. Self-reported sleep disturbance did not differ significantly among urban and rural populations. Higher use of sleeping medication was found in urban areas (24.31% taking medications ≥3 times/week) in stark contrast to that in rural areas (2.01% taking medications ≥3 times/week). The observed trend of poorer sleep quality in terms of sleeping medication use and daytime dysfunction among the urban population was significant statistically. When compared against different exposure categories, daytime dysfunction and poor subjective sleep quality were found to differ significantly and as associated with a higher level of exposure to light pollution.
The mean global PSQI score was 10.98 (±3.18), with an overall median of 11. Among the urban and rural population, the observed mean scores were respectively12.63 (±3.04) and 9.23 (±2.27). A better sleep quality (PSQI global score ≤ median) was observed among 82.30% of the rural respondents, while 69.20% of the urban participants had a poor sleep quality (PSQI global score > median) based on the cumulative component scores. The differences were statistically significant. [Figure 1] depicts the distribution of PSQI global scores in urban and rural areas as per exposure category for night time radiance. In unadjusted analysis the trend of higher PSQI global score, i.e., poorer sleep quality was observed to be statistically significant with increasing level of exposure.
|Figure 1: Box and whisker plot showing comparison of Pittsburgh Sleep Quality Index global scores' distribution as per different levels of exposure among the rural and urban respondents.|
Click here to view
Effect of light pollution on self-reported sleep quality
[Table 3] depicts the clustering adjusted effects of light pollution on sleep quality of individuals in three different multi-level linear models, one overall model, one model for urban area, and another for the rural area. In all the three models, the global score of PSQI increases with advancing exposure category. At exposure level of >6.0 the PSQI global score increases more as compared to the levels ≤6.0. At an exposure of >40.0, the adjusted coefficient predicting an increase in PSQI global score was 11.52 (95% confidence interval [CI]: 9.65, 13.40) in the overall model and 12.84 (95% CI: 12.31, 13.37) for urban participants. In the rural population, the highest exposure category was 6.0–20.0, for which the adjusted coefficient was found to be 8.20 (95% CI: 6.06, 10.34). While for the said level of exposure, PSQI global score increases by 11.02 (95% CI: 7.20, 14.83) overall and 13.13 (95% CI: 12.29, 13.98) among urban population as compared to exposure category of ≤1.0. The models show a significant independent effect of perceived stress on a higher PSQI global score, which were found to be statistically significant.
|Table 3: Effect of light pollution and other selected predictors on self-reported sleep quality in three multi-level linear models|
Click here to view
| Discussion|| |
Upon analysis, it was observed that light pollution was clearly higher in urban areas, being consistent with the common notion of urbanization., For the observed statistical significance in the trend of exposure categories, area of residence (urban/rural) has been considered as a potential confounder while assessing the effect of light pollution on sleep quality.
The glow of the night sky exposes the population to a baseline chronic exposure to light during the night. The effect of the same category of exposure in bringing about a poorer sleep quality was higher among the urban population as compared to rural participants. The higher values of PSQI global scores in high exposure categories indicated a higher degree of sleep disturbance. Comparing the effect sizes, the effects of different levels of exposure to light pollution was found to be the major determinant for poorer self-reported sleep quality. The current study identified the effect of this exposure to be independently hazardous in terms of sleep quality. The findings are in consonance with Obayashi et al., who in a cohort study among the elderly population established the effect of light exposure at night and insomnia. A similar study examining the effects of artificial light exposure at night concluded with the findings of disruption of circadian rhythm and thereby altered sleep pattern.
The night-time radiance being a direct effect of artificial lighting, the current study also infers that artificial lighting at night leads to increased sleep disturbances. Cho et al. (2016) concluded that exposure to dim artificial light leads to disruption of REM sleep and causes awakening. In accordance with this finding, it can be conceptualized that in Indian context it is very common practice to keep the windows open, resulting in constant exposure to the sky-glow leading to a higher proportion of poorer sleep quality as observed in the current article.
A study from the USA concluded that light pollution was negatively associated with sleep outcomes, while the practical effect sizes were found to be small, suggesting negligible effects at the population level. The current study, however, did not measure sleeping time objectively, but analyzing self-reported sleep quality found out sizeable effect of nighttime brightness of the sky on sleep hygiene of individuals in contradiction to the theory of the idiosyncratic effect of nighttime light pollution.
In the current research, self-reported scales to measure sleep quality and stress were used which might have incurred response biases. A more robust yet highly resource-intensive strategy may be the use of polysomnography and electroencephalography set-up. The current study was a cross-sectional study. Therefore, comments are valid only in terms of the association of light pollution with poor sleep quality. The establishment of causality in this regard may warrant a longitudinal study with yearly follow-up to address the cumulative seasonal effects of sky-glow. Still, analysis of sleep quality by graded levels of exposure gives an idea about dose-response relationship in this context.
Despite these limitations, the current study boasts its strength in the application of multi-stage spatial sampling method using geographic information system and analysis by accounting spatial clustering at each level. The study tried to ensure comparability at each level of sampling. Most of the studies trying to find out the effect of light pollution do not take into account the common factors modifying sleep, for example, stress, obesity, physical activity, addiction. The current article adjusted the effects of these factors in multi-level regression models to yield unbiased independent effect of night time radiance on sleep. Assuming the sleep pattern of patients with diagnosed morbidity to be different, the current study focused on healthier individuals. Most of all, the current study is one among the only few studies conducted in India trying to find out the effect of light pollution on sleep pattern.
| Conclusion|| |
Perceived stress, obesity, physical activity, all are important determinants of sleep quality in healthy adults. Adjusting for all these along with age, gender, and addiction habits, the association of light pollution with poorer sleep quality appears to be strongest. The disturbance in sleep increases with higher levels of exposure to light pollution, with the majority observed among the urban respondents. A practical way of tackling the problem of light pollution could be through the legislative approach, along with intense mass awareness campaigns. Reduction in light pollution has two direct effects on human life. First saving energy increases domestic yield and second, a better sleep at night would lead to a better quality of life. With the advances in geographic information systems, compelling evidence of light pollution affecting health warrant further studies to measure the effects in different aspects of individual and community health and also assess impact in policy decisions, especially in the Indian context.
The authors would like to acknowledge the study participants and their family members. The authors also acknowledge the contribution of the health-care staff working at the field level for their assistance during the phase of data collection.
Financial support and sponsorship
Self-funded by the authors.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Chepesiuk R. Missing the dark: Health effects of light pollution. Environ Health Perspect 2009;117:A20-7.
Lo CM, Lee PH. Prevalence and impacts of poor sleep on quality of life and associated factors of good sleepers in a sample of older Chinese adults. Health Qual Life Outcomes 2012;10:72.
Berhanu H, Mossie A, Tadesse S, Geleta D. Prevalence and associated factors of sleep quality among adults in Jimma Town, Southwest Ethiopia: A community-based cross-sectional study. Sleep Disord 2018;4:1-11.
Dong X, Wang Y, Chen Y, Wang X, Zhu J, Wang N, et al
. Poor sleep quality and influencing factors among rural adults in Deqing, China. Sleep Breath 2018;22:1213-20.
Chiang GS, Sim BL, Lee JJ, Quah JH. Determinants of poor sleep quality in elderly patients with diabetes mellitus, hyperlipidemia and hypertension in Singapore. Prim Health Care Res Dev 2018;19:610-5.
Khadka R, Hong SA, Chang YS. Prevalence and determinants of poor sleep quality and depression among postpartum women: A community-based study in Ramechhap district, Nepal. Int Health 2020;12:125-31.
Smolensky MH, Sackett-Lundeen LL, Portaluppi F. Nocturnal light pollution and underexposure to daytime sunlight: Complementary mechanisms of circadian disruption and related diseases. Chronobiol Int 2015;32:1029-48.
Obayashi K, Saeki K, Kurumatani N. Association between light exposure at night and insomnia in the general elderly population: The HEIJO-KYO cohort. Chronobiol Int 2014;31:976-82.
Cho MJ, Bae JN, Suh GH, Hahm BJ, Kim JK, Lee DW, et al
. Validation of geriatric depression scale, Korean Version (GDS) in the assessment of DSM-III-R major depression. J Korean Neuropsychiatry Assoc 1999;38:48-63.
Ghosh T, Anderson S, Elvidge C, Sutton P. Using nighttime satellite imagery as a proxy measure of human well-being. Sustainability 2013;5:4988-5019.
Fleiss JL. Statistical Methods for Rates and Proportions. Second Edition. Wiley, John and Sons, Incorporated, New York, N.Y.; 1981.
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983;24:385-96.
World Health Organization. Waist Circumference and Waist-Hip Ratio: Report of a WHO Expert Consultation, Geneva, 8-11 December 2008. Geneva: World Health Organization; 2011.
Cheah BC. Clustering standard errors or modeling multilevel data. University of Columbia. 2009:2-4.
Cho CH, Lee HJ, Yoon HK, Kang SG, Bok KN, Jung KY, et al
. Exposure to dim artificial light at night increases REM sleep and awakenings in humans. Chronobiol Int 2016;33:117-23.
Patel PC. Light pollution and insufficient sleep: Evidence from the United States. Am J Hum Biol 2019;31:e23300.
[Table 1], [Table 2], [Table 3]
|This article has been cited by|
||How to measure light pollution - a systematic review of methods and applications
| ||Susan Mander, Fakhrul Alam, Ruggiero Lovreglio, Melanie Ooi |
| ||Sustainable Cities and Society. 2023; : 104465 |
|[Pubmed] | [DOI]|
||The Efficacy of a Community-based Intervention in Bangladesh for Enhancing Successful Ageing Indicators
| ||Prosannajid Sarkar, P. S. Aithal |
| ||International Journal of Management, Technology, and Social Sciences. 2022; : 233 |
|[Pubmed] | [DOI]|
||Sleep impairment: Is it an overlooked burden in painful diabetic peripheral neuropathy? A single-centre, cross-sectional study from south India
| ||Adlin Lawrence, Himsikhar Khataniar, Sinimol Joseph, Thenmozhi Nagarajan, Soumya Umesh, John Michael Raj A |
| ||Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2022; : 102568 |
|[Pubmed] | [DOI]|