Title: What is the effect of reduced street lighting on crime and road traffic accidents at night? A mixed methods study
Funder: Department of Health (England)

Abstract: Objectives:(1) To conduct a nationwide analysis of street-level data for all local authorities (LAs) where street lighting reduction schemes have been introduced by 2012; to quantify (with adequate power and precision) the impact of these schemes on night-time traffic injuries and on all crimes. (2) To use qualitative research methods to identify articulated public views about street lighting reduction schemes and their consequences and the role these views have played in local decision making.(3) To develop an inventory of all quantified costs and benefits of street lighting reduction schemes and to examine variation in the costs and benefits of schemes.Objective 1Design: Controlled interrupted time series analysis.Setting: All local authorities of England & Wales.Target population: People who were victims of road traffic collisions or street crime.Intervention being evaluated: Any street lighting reduction schemes implemented by 2012 (to include switching selected street lights off permanently; switching selected street lights off for part of the night; ‘trimming’ the period where lights are switched on; dimming the brightness in the evening and during the early hours of morning). We shall classify each road according to the type of lighting change implemented.Measurement of outcomes and duration of follow up: STATS19 road traffic injury data will be obtained for the period 2000 to 2012. These data include date time location of collision by type of casualty (pedestrian car occupant etc.). Crime data will be obtained for the period up to 2012 from police.uk including the month location and type of crime.Geographical Information System (GIS) methods: We will link data sets to a road segment database including the characteristics of all roads in England & Wales. We will classify each road segment according to road type type of street lighting reduction scheme (e.g. part-night switch-off; ‘dimming’) the period at night when lighting is reduced and the Super Output Area in which it is located.Sample size: We propose to assemble and analyse data for all LAs in England & Wales that have implemented sizeable street lighting reduction schemes by 2012. This will enable reasonably precise quantification of the impact of such schemes on road injuries and crimes even on the assumption that the schemes only affect roads on which 1% of road injuries and crimes occur. For road injuries we will maximise power by using data for at least 10 years before schemes were implemented; for the more numerous crime events data may be available for only one year before implementation. We estimate that across all LAs the expected annual number of road injuries at night on roads with reduced lighting will be around 150 yielding study power of 90% to detect an increase of 32% above pre-implementation road casualty levels. For crime we estimate that across all study areas the expected number of all (day and night-time) crimes on intervention roads will be around 20000 per year. This number will provide 90% power to detect a 5% increase in all (day and night-time) crimes above pre-intervention levels (equivalent to a 20% increase in night-time crimes alone) and for major sub-categories such as burglary we will have 90% power to detect increases of about 10% in all (day and night-time) crimes. By obtaining data from a sample of four police forces (one tenth of all forces) we can expect 400 detailed night-time crime events per year. Using data for one year before lighting reduction and one year after we will have 90% power to detect a 26% increase in crimes above pre-intervention levels. There will be similar power to detect increases of 40% in major sub-categories of crime (e.g. violence against the person).Planned analyses: Analyses will adapt methods previously applied by the applicants to quantify the effect of 20mph zones on road injuries (Grundy et al. 2009; Steinbach et al. 2010). These are based on conditional Poisson models applied to events (road injuries and crimes) for every individual road segment (over 100000 road segments in total) across study areas. Comparisons will be made of the change in events on roads before and after the street lighting changes controlling for: (i) changes over time in day-time road injuries and all crimes on the same road segments (i.e. ‘within-roads’ comparisons); and (ii) year-to-year trends in night-time road injuries and all crimes on all other roads of a similar kind (i.e. ‘between-roads’ comparisons). Objective 2Design: this will utilise elements of ethnographic rapid appraisal traditionally developed for research with rural populations in low income settings but increasingly used as a complement to quantitative epidemiological methods in high income urban settings when more focused qualitative data are required on topics for which good quality epidemiological data do not exist (Trotter 2001).- Rapid appraisal: Using the review of proposed lighting reduction schemes (DeFRA 2011) and our contacts with all LAs of England & Wales (for Objective 1) we will identify a purposive sample of eight LAs to provide a range of rural & part-urban settings across England with lighting reduction schemes in place or proposed. The rapid appraisal will generate data from web searches phone/email interviews and a 2-3 day site visits as follows: - Survey of web and public sources (e.g. LA websites minutes of committee meetings) to identify LAs where lighting has been or is to be reduced; public consultations on street lighting to identify methods used and the content of concerns expressed; and contact details for key informants in each locality. - Key informant interviews to identify local consultation processes to date; local stakeholders’ views of public concerns; how concerns are incorporated into policy decisions; other factors that influence policy decisions around street lighting. These key informants will help to identify sites for survey interviews with residents and for ethnographic interviews and to provide data for Objective 3 (below). In each area we aim to include: councillors local MPs LA Engineers Departments Police Community Liaison Officers and other local actors identified in interviews.- One-to-one interviews with a sample of 20 people in two contrasting sets of affected streets in each area. Interviews will cover views of current street lighting levels preferences for more/less lighting impacts of any changes noticed. Using data on area deprivation (in the GIS) the streets will be selected to include contrasting areas of socio-economic status.- Ethnographic interviews and observations using natural groups where possible (e.g. groups encountered in cafés community meetings suggested by local key informants). These interviews will be used to generate more narrative talk about street lighting and possible reductions and the impact of this on health and wellbeing. Analysis: Data from key informant interviews will be thematically analysed to identify the range of ways public consultations are incorporated into decision making and how public health evidence (of the type generated in Objective 1) could or would be utilised. Resident survey interviews will be analysed using descriptive statistics comparing views in areas where lighting has been reduced to those changes are proposed and comparing higher and lower socio-economic settings. Key informant and ethnographic interviews will be analysed thematically using both deductive methods (i.e. identifying how far issues such as sleep anxiety fear of going out after dark are expressed) and a more inductive analysis to identify the meaning and salience of street lighting for residents and other road users in terms of impact on health and wellbeing. Key issues for data analysis will include salience of concerns; comparisons of views before and after changes; comparison of concerns expressed in public consultations survey interviews and ethnographic interviews.Objective 3We will assemble evidence from objectives 1 and 2 (above) of all costs and benefits. We will assemble data from local authorities on the costs of providing types of street lighting (i.e. infrastructure cost maintenance costs energy consumption). We will also assemble data on the monetary values (i.e. economic and societal costs) of road traffic casualties by level of severity and the economic and societal costs of crimes by type of crime using Home Office definitions (Home Office 2005). We will then compare societal costs of street lighting schemes against the societal benefits in a cost-benefit analysis framework. We will assess the impacts of street lighting schemes on inequalities by conducting stratified analyses (e.g. by: area deprivation resident population density crime and traffic injury rates pre-implementation of schemes). Using sensitivity analyses we also propose to examine the impact on our results of the uncertainty in our effect estimates (to derive credible limits of the estimated societal costs and benefits of street lighting reduction schemes).


Research activity codes: 3.2 INTERVENTIONS TO ALTER PHYSICAL AND BIOLOGICAL ENVIRONMENTAL RISKS
Health categories: INJURIES AND ACCIDENTS;GENERIC HEALTH RELEVANCE