This article has Open Peer Review reports available.
Application of disability-adjusted life years to predict the burden of injuries and fatalities due to public exposure to engineering technologies
© Veeramany and Mangalam; licensee BioMed Central Ltd. 2014
Received: 26 July 2012
Accepted: 19 March 2014
Published: 28 March 2014
As a public safety regulator, the Technical Standards and Safety Authority (TSSA) of Ontario, Canada predicts and measures the burden of injuries and fatalities as its primary means of characterizing the state of public safety and for decision-making purposes through the use of a simulation model. The paper proposes a simulation-based predictive model and the use of disability-adjusted life years (DALYs) as a population health metric for the purposes of reporting, benchmarking, public safety decision-making, and organizational goal setting. The proposed approach could be viewed as advancement in the application of traditional population health metrics, used primarily for public health policy decisions, for the measurement and prediction of safety risks across a wide variety of engineering technologies to which the general public is exposed.
The proposed model is generic and applicable to a wide range of devices and technologies that are typically used by the general public. As an example, a measure of predicted risk that could result from the use of and exposure to elevating devices in the province of Ontario is presented in terms of the DALY metric. The predictions are further categorized in terms of the causal attribution of the risks for the purposes of identifying and focusing decision-making efforts. The results are also presented by taking into consideration factors such as near-misses or precursor events as termed in certain industries.
The ability to predict potential health impacts has three significant advantages for a public safety regulator – external reporting, decision-making to ensure public safety, and organizational benchmarking. The application of the well-known Monte Carlo simulation has been proposed to predict the health impacts expressed in terms of DALYs. The practicality of the proposed ideas has been demonstrated through the application of the prediction model to characterizing and managing risks associated with elevating devices in the province of Ontario, Canada.
A judicious choice of a population health metric for the purposes of quantifying risk that is predictive in nature and a risk-acceptability criterion as a benchmarking standard for organizational goal setting are essential. Hence, the objective of this paper is to propose a technique for measuring and quantifying risk in the context of regulated devices and explore the application of disability-adjusted life years (DALYs) as an appropriate metric to predict future health burden.
The TSSA is a not-for-profit self-funded organization that administers and enforces public safety laws in various sectors under Ontario’s Technical Standards and Safety Act. The regulated sectors include elevating and amusement devices, fuels, boilers and pressure vessels, operating engineers, ski lifts, and upholstered and stuffed articles. There are a large number of devices, technologies, and products (herewith known as technical systems and products) with which the public interacts on a daily basis across the province of Ontario. The state of compliance of these technical systems and products to the specific codes and standards is ensured through a collective application of regulatory instruments that span the life cycle of these systems. Active information on the state of compliance is monitored and collected primarily through periodic inspections of the technical systems and sometimes, through random ad hoc inspections. If noncompliances are identified, they are enforced and logged in to an information system. Despite the efforts, there are sometimes incidents and near misses, some of which lead to health impacts in the general public. For the purposes of distinction, an incident may result in a consequence such as an injury or property damage, while a near miss results in an elevated exposure that could potentially lead to such consequences. Incidents and near misses are collectively known as occurrences at the TSSA.
The DALY has been explored by various public safety authorities in Canada and around the world including the Australian Institute of Health and Welfare , National Center for Infectious Diseases, USA , National Institute for Public Health and the Environment, Netherlands , Public Health Agency of Canada , Statistics Canada , and Health Canada . The TSSA in particular has been using DALYs as a reporting metric since 2010 . A DALY is the loss of a year in an individual’s life who would have otherwise led a healthy life . The intent is to utilize DALYs in a decision-making setting as a single dimensional metric  resulting from aggregating morbidity and mortality outcomes. TSSA has already used this approach to risk measurement in prioritizing and managing several known safety issues associated with elevating devices .
The concept of using the DALY metric in the future domain has been explored by studies related to the Global Burden of Disease (GBD) [11, 12]. Disease-specific projection studies elsewhere have taken inspiration from the GBD literature . These studies have extensively applied multiple linear regression on large amounts of global individual mortality data available to the World Health Organization (WHO). The objective of these studies is to estimate the mortality rate with dependency on various covariates such as smoking, level of education, age. etc. The future burden of disease is then reported in terms of DALYs. The TSSA has unique challenges in the sense that it does not collect in-depth device-specific intrinsic factors, which could potentially be causes of unfavorable human health impacts in the event of occurrences. Some of the factors are age-related susceptibility to degradation mechanisms that accumulate over time, making a device high risk in nature. It is quite common in the engineering failure analysis literature to use this information to predict the state of a device in its future course of usage. As a result of the missing covariate information, the well-explored concept of regression analysis is not applicable for prediction purposes at the TSSA.
However, there is a wealth of credible historic occurrence data along with actual health impacts collected over a period of several years. Hence, the present paper effectively utilizes the available occurrence data in order to predict potential health impacts by implementing a second-order Monte Carlo simulation. The predicted health impacts in terms of DALYs are reported as a range of percentiles so that readers can appreciate the extremities along with the average estimates.
Keeping in view the importance of separating variability and uncertainty through a second-order simulation , uncertainty in λ could be exclusively dealt with in a second layer of simulation through the reporting bias. This separation increases the model complexity and becomes computationally prohibitive .
Establishing an occurrence rate
The TSSA database has an account of each reported occurrence along with factors such as the date of occurrence, whether the occurrence is an incident or a near miss, the number of injured victims, and number and type of injuries sustained by each victim. Actual or observed DALYs do not account for near misses and incidents without injury. Hence, an occurrence rate based solely on past occurrences with health impacts does not truly reflect what would potentially happen if some of the near misses manifest themselves as occurrences with health impacts in the future. But then the uncertainty lies in how many of the nonhealth impact occurrences turn in to incidents with injuries. We assume two approaches to account for the benefit uncertainty: (1) assume that every such occurrence carries an injury potential in the future (r = 1) or (2) assume a near miss to incident ratio based on the safety pyramid rule  from the chemical processing industry (r > 1). The second assumption has the motivation in the belief that some of the near misses may never have negative health impacts purely by design. For example, activation of relief valves or rupture discs on pressure vessels is designed to manage overpressure scenarios. These activations may be reported as near-miss pressure boundary failures.
This expression determines the number of occurrences for an iteration of the simulation as a sample from Poisson distribution with λ as the mean occurrence rate. It is assumed that the number of occurrences has been constant over the years. However, it is of future interest to move toward a time-dependent occurrence rate λ(t) so that the number of occurrences in a future year is predictive rather than assuming a past average. A nonhomogeneous Poisson process with an underlying power law model is an alternative.
Number of injured victims
The number of injured victims in the event of an occurrence is a random variable. This number is dependent on the technical system or product under consideration. For example, in the context of elevating devices, the number could be anywhere between 0 and the maximum observed in the history or the mean number M of passengers a typical device could affect. It is assumed that the number of injured victims follows a discrete uniform distribution with parameters a = 0 and b = M. Historically, there have not been more than three injured victims in a single occurrence associated with elevating devices.
Injury type distribution
Determining the DALY
Disability weights related to TSSA specific injury types
Burden of disease injury type
TSSA injury type
Intracranial injuries 
Injured spinal cord 
Sprained or twisted
Internal injuries 
Other internal injury
Upper respiratory infections – pharyngitis 
Rheumatic heart disease and heart failure 
Aches or pains
Electric shock minor
Electric shock severe
A DALY estimate from Equation (2) for fatality of an individual is determined by setting the short-term parameters (disability weight and duration) to zero, the long-term parameters (disability weight and fraction) to one, and long-term duration as the remaining life expectancy of the deceased. The result is a DALY value which is equal to the victim’s expected life expectancy at the age of death. In a general setting, if the remaining life is set to median life expectancy in Ontario (44.4 years; subject to change), the resulting DALY associated with mortality could be used as a benchmark and guiding principle for decision-makers.
Adaptation of AIHW disability weights to specific TSSA injuries
The injuries observed from occurrences resulting from TSSA-regulated technologies (Table 1) do not correspond exactly with the injury types in the AIHW tabulation . Where the TSSA-defined category did not evidently correspond to one from the Victorian study, the most appropriate proxy was considered, e.g., intracranial injuries for concussion. If a certain TSSA injury type was associated with subcategories in other studies, the most conservative one was chosen. As an example, for respiratory infections, the disability weight associated with pharyngitis was chosen from the GBD 2004 update . Some injury types such as heart attacks have been reported to the field inspectors in the past. These are possible health outcomes from several scenarios such as entrapments, electrical shocks, falls, etc. during elevator rides and hence have been included in the list of injury types. Rheumatic heart disease was chosen for short-term disability and heart failure was chosen as the long-term one . In case of fatality, disability weight and fraction of long-term duration are both assigned a one so that the DALYs are set to the life expectancy of the victim. All parameters are set to zero for “no injury” in order to keep track of the number of occurrences where there was no injury. TSSA regulates various sectors, the injury types across which range from minor injuries to permanent ones, including mortality. The current choice of injury types is subject to revision based on shortcomings identified  in relation to differentiating high-incidence low-severity injuries from low-incidence high-severity injuries and also based on the improved methodology to measure disability weights . As a proof of concept, high-incidence injury types (e.g., aches and pains) that do not correspond to the GBD study have been assigned a very low disability weight (< 0.02) and an approximate duration of 1 week (< 0.02). These assumptions are needed until a more robust theory is articulated on determining DALYs for injuries rather than for diseases. The authors are of the opinion that public safety decision-making in sectors dominated by injuries is better informed with certain assumptions for disability weights, although some of them cannot be directly mapped to the burden of disease studies. This scheme is more informative than making decisions solely based on actual number of observed infrequent fatalities.
Occurrence cause classification
It is believed that incident and near miss occurrences are the ultimate realization of safety risks and can result from any one of three causal safety impact measurement (SIM) categories:
SIM 1 – causal factors related to potential inadequacies in available regulatory controls
SIM2 – causal factors related to noncompliance with current regulatory controls
SIM 3 – external causal factors not within the purview of current regulatory controls (e.g., behavioral factors, weather conditions, etc.)
Occurrences that do not have an established root cause after inspection are contained in a fourth category, root cause not established (RCNE).
There are sectors that TSSA regulates in which high-incidence low-disability conditions are quite common. In such circumstances, TSSA continues to focus on frequency of system-induced failures and causal reasoning alongside the risk knowledge driven by the DALY metric in order to undertake strategic public safety decisions.
Application to elevating devices
In the context of elevating devices, there have been four fatalities observed in the past, and the predicted expected health impact is about 22 DALYs per million people per year in the absence of reporting bias. DALYs for the cases without near misses and with r = 10 in Figure 7 are the same (0.5), signifying the possibility that though there are apparently many near misses happening in the province of Ontario, only a few are being actively reported to TSSA. Assuming a one-to-one ratio between incidents and near misses, the DALY estimate is 26.6. The predicted SIM3 risk (1.9 DALYs) indicates that occurrences related to elevators in Ontario are dominated by user behavior and factors external to the regulatory system. A number of occurrences under RCNE were recategorized at the end of the year as user behavior-related. In order to reduce risk related to aging systems that do not conform to the current elevator codes, the elevator risk reduction group analyzed elevators with single- and two-speed motion control and the associated risks to passengers when the elevators do not stop at the prescribed level. This analysis could lead to a replacement of older control systems in the near future. In order to tackle public safety risk due to noncompliance, TSSA issued a communication to elevating device owners reminding them of their legal responsibilities with regard to elevator maintenance and oversight of their maintenance contractors. The owners were advised of new requirements and the timeline in which they will become effective so they can better plan for improved compliance. Figure 8 shows the predicted health impacts as a distribution to illustrate the range of health impacts predicted in the elevating devices sector.
The proposed model supports risk-informed decision-making and hence has a potential to influence policy implications at the organizational level. In particular, the model has significant importance for technical systems, such as those involving high pressure boilers and vessels, where there is possibility of a high-consequence, low-probability incident with health impacts to the population, even though most of such occurrences are rare events making it difficult for policymakers to take risk-informed safety initiatives. Secondly, the model could be explored for new technologies being brought in to the purview of the regulatory system for which there is no health impact data or where there is an effort to strengthen the data management system of an existing technology. While the proposed model strives to bring predictive power to risk assessments, traditional systems analysis of engineering devices based on failure frequencies continues to additionally inform recommendations for public safety decision-making.
The present paper explored the application of DALYs, traditionally used as a population health metric, for purposes of measuring and quantifying public safety risk in the context of engineering devices and technologies. In the absence of extensive covariate information, it was shown that using a Monte Carlo simulation, technology-induced health impacts could be predicted for the future. The simulations combine mortality and morbidity to yield future health impacts (or public safety risks) in terms of DALYs expected in a given year. The categorization of DALYs into causal factors allows for policy-setting and resource allocation. The proposed model was demonstrated in the context of characterizing and managing public safety risk for elevating devices in the province of Ontario, Canada.
The authors are thankful to the senior management at Technical Standards and Safety Authority (TSSA) for supporting the initiative and for providing the encouragement to have the concept validated by the scientific community. A special thanks to Lency Mulamootil, Christine Ho, Supraja Sridharan, Kavitha Ravindran, Dwight Reid, and Jorge Larez at the TSSA for their ideas and analytical and quality assurance support. The authors would also like to express their gratitude to Cole Lepine at the TSSA and Mudassir Nazir at the Memorial University, St. Johns, Canada for leaving behind a legacy of information as a platform to build the model. The authors would like to acknowledge Greg Paoli at Risk Sciences International, Ottawa, Canada for providing critical comments on the conceptual thinking and mathematical rigor.
- Begg S, Vos T, Barker B, Stevenson C, Stanley L, Lopez A: The Burden Of Disease And Injury In Australia 2003. Cat. No. Phe 82. Canberra: AIHW; 2007.Google Scholar
- Meltzer MI, Rigau-Pérez JG, Clark GG, Reiter P, Gubler DJ: Using disability-adjusted life years to assess the economic impact of dengue in Puerto Rico: 1984–1994. Am J Trop Med Hyg 1998, 59: 265-271.PubMedGoogle Scholar
- Knol AB, Staatsen BAM: Trends In The Environmental Burden Of Disease In The Netherlands, 1980–2020. 2005. Rijksinstituut voor Volksgezondheid en Milieu (RIVM) official report; RIVM, Netherlands.Google Scholar
- William F, Jane BP, Le Petit Christel BJM: Estimating summary measures of health: a structured workbook approach. Popul Health Metr 2005, 3: 5. 10.1186/1478-7954-3-5View ArticleGoogle Scholar
- Paradis G, Tremblay MS, Janssen I, Chiolero A, Bushnik T: Blood Pressure In Canadian Children And Adolescents. 2010. Health Report, Catalogue no. 82-003-X Statistics Canada, CanadaGoogle Scholar
- Beveridge C, Bodo K, Brodsky M, Huntley G, Joy W, Khan H, Lawrence R, Lefrançois L, McFadyen S, Nowakowski C, Poupart L, Robertson W, Rowe JBR, Rowse J, Ruf F, Soroczan C, Van ME, Vassos T, Vouk I, Woods S, Brooks T: Canadian Guidelines for Household Reclaimed Water for Use in Toilet and Urinal Flushing. 2007. Draft for consultation prepared for Health Canada, Ottawa, CanadaGoogle Scholar
- Technical Standards and Safety Authority (TSSA)Public Safety Performance Reports, Retrieved July 13, 2012 from http://tssa.org/regulated/about/publicSafetyReports.aspx
- Lopez AD, Mathers CD, Ezzati M, Jamison DT, Murray JLC: The Global Burden Of Disease And Risk Factors. World Bank: Oxford University Press; 2006.View ArticleGoogle Scholar
- Hofstetter P, Hammitt JK: Selecting human health metrics for environmental decision-support tools. Risk Anal 2002, 22: 965-983. 10.1111/1539-6924.00264View ArticlePubMedGoogle Scholar
- Mangalam S, Nazir M, Mulamootil L: Risk Informed Scheduling Of Regulatory Inspections - A Deterministic Approach To Regulating Operational Risks Of Elevating Devices. In Proceedings of the Annual European Safety and Reliability (ESREL) Conference. Helsinki, Finland; 2012.Google Scholar
- Mathers CD, Loncar D: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Med 2006, 3: 2011-2030.View ArticleGoogle Scholar
- Murray CJL, Lopez AD: Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet 1997, 349: 1498-1504. 10.1016/S0140-6736(96)07492-2View ArticlePubMedGoogle Scholar
- Murthy NS, Nandakumar BS, Pruthvish S, George PS, Mathew A: Disability adjusted life years for cancer patients in India. Asian Pac J Cancer Prev 2010, 11: 633-40.PubMedGoogle Scholar
- Burgman MA: Risks And Decisions For Conservation And Environmental Management. Cambridge, UK: Cambridge University Press; 2005.View ArticleGoogle Scholar
- World Health Organization, Food and Agriculture Organization of the United Nations: Exposure Assessment Of Microbiological Hazards In Food: Guidelines. Microbial Risk Assessment Series; 2008. ISBN-13?9789241546898Google Scholar
- Phimister JR, Oktem U, Kleindorfer PR, Kunreuther H: Near-miss incident management in the chemical process industry. Risk Anal 2003, 23: 445-459. 10.1111/1539-6924.00326View ArticlePubMedGoogle Scholar
- Australian Institute of Health and Welfare (AIHW)Injury Notes & Assumptions.xls retrieved July 7, 2012 from http://www.aihw.gov.au/publication-detail/?id=6442467088 Injury Notes & Assumptions.xls retrieved July 7, 2012 from
- Beggs S, Tomijima N Proceedings of the 4th International Measuring the Burden of Injuries Conference. In Global burden of injury in the year 2000: an overview of methods. Montreal, Canada; 2002.Google Scholar
- World Health Organization (WHO)Global Burden Disease 2004 Update: Disability Weights for Diseases and Conditions, Retrieved June 3, 2013 from http://www.who.int/healthinfo/global_burden_disease/GBD2004_DisabilityWeights.pdf
- Heart FoundationThe shifting burden of cardiovascular disease in Australia, report by Access Economics Pty Ltd. 2005. Retrieved June 3, 2013 from http://www.heartfoundation.org.au/sitecollectiondocuments/hf-shifting_burden-cvd-accecons-2005-may.Pdf
- Haagsma JA, van Beeck EF, Polinder S, Hoeymans N, Mulder S, Bonsel GJ: Novel empirical disability weights to assess the burden of non-fatal injury. Inj Prev 2008, 14: 5-10. 10.1136/ip.2007.017178View ArticlePubMedGoogle Scholar
- Joshua AS, Vos T, Hogan DR, Gagnon M, Naghavi M, Mokdad A, Begum N, Shah R, Karyana M, Kosen S, Farje MR, Moncada G, Dutta A, Sazawal S, Dyer A, Seiler J, Aboyans V, Baker L, Baxter A, Benjamin EJ, Bhalla K, Bin Abdulhak A, Blyth F, Bourne R, Braithwaite T, Brooks P, Brugha TS, Bryan-Hancock C, Buchbinder R, Burney P, et al.: Common values in assessing health outcomes from disease and injury: disability weights measurement study for the Global Burden of Disease Study 2010. Lancet 2013, 380: 2129-2143.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.