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      The effect of reduced street lighting on road casualties and crime in England and Wales: controlled interrupted time series analysis

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          Abstract

          Background

          Many local authorities in England and Wales have reduced street lighting at night to save money and reduce carbon emissions. There is no evidence to date on whether these reductions impact on public health. We quantified the effect of 4 street lighting adaptation strategies (switch off, part-night lighting, dimming and white light) on casualties and crime in England and Wales.

          Methods

          Observational study based on analysis of geographically coded police data on road traffic collisions and crime in 62 local authorities. Conditional Poisson models were used to analyse longitudinal changes in the counts of night-time collisions occurring on affected roads during 2000–2013, and crime within census Middle Super Output Areas during 2010–2013. Effect estimates were adjusted for regional temporal trends in casualties and crime.

          Results

          There was no evidence that any street lighting adaptation strategy was associated with a change in collisions at night. There was significant statistical heterogeneity in the effects on crime estimated at police force level. Overall, there was no evidence for an association between the aggregate count of crime and switch off (RR 0.11; 95% CI 0.01 to 2.75) or part-night lighting (RR 0.96; 95% CI 0.86 to 1.06). There was weak evidence for a reduction in the aggregate count of crime and dimming (RR 0.84; 95% CI 0.70 to 1.02) and white light (RR 0.89; 95% CI 0.77 to 1.03).

          Conclusions

          This study found little evidence of harmful effects of switch off, part-night lighting, dimming, or changes to white light/LEDs on road collisions or crime in England and Wales.

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          The dark side of light at night: physiological, epidemiological, and ecological consequences.

          Kristen Navara, Randy J Nelson (2007)
          Organisms must adapt to the temporal characteristics of their surroundings to successfully survive and reproduce. Variation in the daily light cycle, for example, acts through endocrine and neurobiological mechanisms to control several downstream physiological and behavioral processes. Interruptions in normal circadian light cycles and the resulting disruption of normal melatonin rhythms cause widespread disruptive effects involving multiple body systems, the results of which can have serious medical consequences for individuals, as well as large-scale ecological implications for populations. With the invention of electrical lights about a century ago, the temporal organization of the environment has been drastically altered for many species, including humans. In addition to the incidental exposure to light at night through light pollution, humans also engage in increasing amounts of shift-work, resulting in repeated and often long-term circadian disruption. The increasing prevalence of exposure to light at night has significant social, ecological, behavioral, and health consequences that are only now becoming apparent. This review addresses the complicated web of potential behavioral and physiological consequences resulting from exposure to light at night, as well as the large-scale medical and ecological implications that may result.
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            Limiting the impact of light pollution on human health, environment and stellar visibility.

            Christopher D. Elvidge, Fabio Falchi, Abraham Haim … (2011)
            Light pollution is one of the most rapidly increasing types of environmental degradation. Its levels have been growing exponentially over the natural nocturnal lighting levels provided by starlight and moonlight. To limit this pollution several effective practices have been defined: the use of shielding on lighting fixture to prevent direct upward light, particularly at low angles above the horizon; no over lighting, i.e. avoid using higher lighting levels than strictly needed for the task, constraining illumination to the area where it is needed and the time it will be used. Nevertheless, even after the best control of the light distribution is reached and when the proper quantity of light is used, some upward light emission remains, due to reflections from the lit surfaces and atmospheric scatter. The environmental impact of this "residual light pollution", cannot be neglected and should be limited too. Here we propose a new way to limit the effects of this residual light pollution on wildlife, human health and stellar visibility. We performed analysis of the spectra of common types of lamps for external use, including the new LEDs. We evaluated their emissions relative to the spectral response functions of human eye photoreceptors, in the photopic, scotopic and the 'meltopic' melatonin suppressing bands. We found that the amount of pollution is strongly dependent on the spectral characteristics of the lamps, with the more environmentally friendly lamps being low pressure sodium, followed by high pressure sodium. Most polluting are the lamps with a strong blue emission, like Metal Halide and white LEDs. Migration from the now widely used sodium lamps to white lamps (MH and LEDs) would produce an increase of pollution in the scotopic and melatonin suppression bands of more than five times the present levels, supposing the same photopic installed flux. This increase will exacerbate known and possible unknown effects of light pollution on human health, environment and on visual perception of the Universe by humans. We present quantitative criteria to evaluate the lamps based on their spectral emissions and we suggest regulatory limits for future lighting. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              Conditional Poisson models: a flexible alternative to conditional logistic case cross-over analysis

              Ben G. Armstrong, Antonio Gasparrini, Aurelio Tobías (2014)
              Background The time stratified case cross-over approach is a popular alternative to conventional time series regression for analysing associations between time series of environmental exposures (air pollution, weather) and counts of health outcomes. These are almost always analyzed using conditional logistic regression on data expanded to case–control (case crossover) format, but this has some limitations. In particular adjusting for overdispersion and auto-correlation in the counts is not possible. It has been established that a Poisson model for counts with stratum indicators gives identical estimates to those from conditional logistic regression and does not have these limitations, but it is little used, probably because of the overheads in estimating many stratum parameters. Methods The conditional Poisson model avoids estimating stratum parameters by conditioning on the total event count in each stratum, thus simplifying the computing and increasing the number of strata for which fitting is feasible compared with the standard unconditional Poisson model. Unlike the conditional logistic model, the conditional Poisson model does not require expanding the data, and can adjust for overdispersion and auto-correlation. It is available in Stata, R, and other packages. Results By applying to some real data and using simulations, we demonstrate that conditional Poisson models were simpler to code and shorter to run than are conditional logistic analyses and can be fitted to larger data sets than possible with standard Poisson models. Allowing for overdispersion or autocorrelation was possible with the conditional Poisson model but when not required this model gave identical estimates to those from conditional logistic regression. Conclusions Conditional Poisson regression models provide an alternative to case crossover analysis of stratified time series data with some advantages. The conditional Poisson model can also be used in other contexts in which primary control for confounding is by fine stratification. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-122) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Epidemiol Community Health
                Journal ID (iso-abbrev): J Epidemiol Community Health
                Journal ID (hwp): jech
                Journal ID (publisher-id): jech
                Title: Journal of Epidemiology and Community Health
                Publisher: BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                ISSN (Print): 0143-005X
                ISSN (Electronic): 1470-2738
                Publication date (Print): November 2015
                Publication date (Electronic): 29 July 2015
                Volume: 69
                Issue: 11
                Pages: 1118-1124
                Affiliations
                [1 ]Department of Social and Environmental Health Research, London School of Hygiene & Tropical Medicine , London, UK
                [2 ]Department of Population Health, London School of Hygiene & Tropical Medicine , London, UK
                [3 ]Department of Security and Crime Science, University College London , London, UK
                [4 ]Department of Health Services Research, London School of Hygiene & Tropical Medicine , London, UK
                Author notes
                [Correspondence to ] Dr Phil Edwards, Department of Population Health, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK; Phil.Edwards@ 123456lshtm.ac.uk
                Author information
                http://orcid.org/0000-0003-4431-8822
                Article
                Publisher ID: jech-2015-206012
                DOI: 10.1136/jech-2015-206012
                PMC ID: 4680141
                PubMed ID: 26219885
                SO-VID: 7b85832c-aeae-4f68-ba45-ca3643ac3672
                Copyright © Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions
                License:

                This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

                History
                Date received : 6 May 2015
                Date revision received : 2 June 2015
                Date accepted : 3 June 2015
                Categories
                Subject: 1506
                Subject: Other Topics
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                ScienceOpen disciplines: Public health
                Keywords: accidents,spatial analysis,environmental epidemiology
                Data availability:
                ScienceOpen disciplines: Public health
                Keywords: accidents, spatial analysis, environmental epidemiology

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