How to measure health and safety performance

All businesses understand the importance of measurement. Financial measurement is a must of businesses of all size to determine if the business model is viable, if pricing is right and margins are healthy. 

Production

 measurement is usually in place to ensure the workforce is performing efficiently with deadlines to meet, targets to achieve and bonuses on offer.

Health and safety is another important aspect of your business, and most organisations recognise this, but fail to measure health and safety performance in the same context as other important business functions. Why is this? Because health and safety performance cannot be measured through a one-size fits all approach.

Financial measurement is universal. You can easily determine the money coming into, and out of your business (or you should be able to!), and a few simple calculations allows you to measure, profit, loss, overheads, and other important financial information. The result you’re looking for is a figure, the bigger the better.

There is no single measure for good health and safety performance. The measure holding the most weight is accident statistics. The result you’re looking for is a figure, the smaller the better!

But should you just measure accident statistics? No. Accident statistics alone cannot be used as adiagnostic tool to determine where things are going wrong. Often, by the time poor health and safety performance is reflected in accident statistics it can be too late, for those injured, and for your organisation. The damage has been done.

So how should you measure health and safety performance? You need to implement not one, but several measures across your health and safety activities. Each health and safety initiative and management activity should be measured in some form, otherwise, you can't know how successful that initiative is, or if there is a problem.

You should not only measure failure, through accident statistics and other reactive monitoring, but also measure success - through activities such as health and safety inspections, near miss reporting, health and safety culture, training achievements, good housekeeping and following the correct procedures.

You can measure through direct observations of conditions and behaviour, through gathering information through questionnaires, meetings and reviews, and through facts and figures examining written documents, records and reports.

What, exactly are you measuring for? There must be a purpose to you measurement. If you get 25 near miss reports in a month is that good or bad? Before you start measuring, you need to consider:

What outcome do you want?

When do you want to achieve the outcome?

How will you know when the outcome has been achieved?

What result should the outcome produce?

How would you know that people are doing what they should be doing?

Then, through establishing baseline data (how you are currently performing) and setting goals or targets (how you want to be performing) you can start to take action to improve your health and safety performance.

Burden of disease and injury in Australia

Burden of disease and injury in Australia in the new millennium: measuring health loss from diseases, injuries and risk factors

Abstract

Objective: To describe the magnitude and distribution of health problems in Australia, in order to identify key opportunities for health gain.

Design: Descriptive epidemiological models for a comprehensive set of diseases and injuries of public health importance in Australia were developed using a range of data sources, methods and assumptions. Health loss associated with each condition was derived using normative techniques and quantified for various subpopulations, risks to health, and points in time. The baseline year for comparisons was 2003.

Main outcome measures: Health loss expressed as disability-adjusted life years (DALYs) and presented as proportions of total DALYs and DALY rates (crude and age-standardised) per 1000 population.

Results: A third of total health loss in 2003 was explained by 14 selected health risks. DALY rates were 31.7% higher in the lowest socioeconomic quintile than in the highest, and 26.5% higher in remote areas than in major cities. Total DALY rates were estimated to decline for most conditions over the 20 years from 2003 to 2023, but for some causes, most notably diabetes, they were projected to increase.

Conclusion: Despite steady improvements in Australia’s health over the past decade, there are still opportunities for further progress. Significant gains can be made through achievable changes in exposure to a limited number of well established health risks.

Information on the magnitude and distribution of health problems in a population is important for health policy decision making. Popular epidemiological measures such as mortality, incidence and prevalence are available for many health problems, but can not be compared across causes as indicators of population-level health. Summary measures of population health, on the other hand, extend the utility of descriptive epidemiology by combining information on mortality and non-fatal health problems into a common measure that can be used to provide a comprehensive picture of the health status of a population.1

We present here a reanalysis of a large body of work that used summary measures to describe the health of Australians in the new millennium.2 The research on which it is based follows a comparable study for the year 1996 reported in the Journal in 2000.3 Both studies use a particular summary measure — the “disability-adjusted life year” (DALY) — to quantify health loss from a comprehensive set of diseases, injuries and health risks of public health importance in Australia. The DALY, in turn, has its origins in an assessment of global health for the World Bank.4,5 One DALY is equivalent to one lost year of healthy life and represents the gap between current health status and an ideal situation of the whole population living into old age in full health. This gap is referred to here as “health loss”, rather than the less accurate but more commonly used term “burden of disease”.

The DALY combines the descriptive epidemiology of each health condition of interest with a multidimensional numerical weighting for the severity of that condition. As the weighting given to each dimension implies a judgement about its relative importance to the total measure, the DALY has obvious normative characteristics that make it not necessarily compatible with other classifications of health (eg, the World Health Organization’s International classification of functioning, disability and health6). For this reason, others have highlighted the importance of limiting interpretation of the DALY to the specific purposes for which it is being used7 — which, in this case, is as a comparative measure of health loss.

Our article provides an assessment of the magnitude and distribution of health problems in Australia in order to identify key opportunities for health gain. Our specific objectives were to calculate:

• DALYs by cause, age and sex for the year 2003;
• DALYs attributable to past and current exposure to major modifiable health risks;
• Differentials in DALY rates between subpopulations (eg, between state and territory jurisdictions, socioeconomic groups, and remoteness categories); and
• DALYs projected 10 and 20 years beyond 2003.

Methods

Health loss was estimated for a comprehensive set of diseases and injuries of public health importance in Australia, using DALYs as the outcome measure. Diseases and injuries were the smallest reported unit of disaggregation, and are referred to here as “specific causes” or “conditions”. Each is mutually exclusive and belongs to one of 22 “broad cause groups”, most of which correspond to chapter-level headings of the International classification of diseases.8 Each broad cause group, in turn, belongs to one of three broad clusters: (a) communicable, maternal, neonatal and nutritional conditions; (b) non-communicable diseases; and (c) injuries. Further details on methods and assumptions are provided elsewhere.2

Baseline models

Baseline models describing the epidemiology of each specific cause for Australia in 2003 were developed using a range of data sources, methods and assumptions. Typical inputs included prevalence (from surveys), incidence (from disease registers), case fatality (from cohort studies), remission (from cohort and intervention studies), clinical judgement, and information about changes over time in any of these variables. Complete and internally consistent cross-sectional epidemiological models were derived from three of these inputs using modelling software.9

Epidemiological trends

Trends in observed cause-specific mortality over the period 1979–2003 were analysed and projected to 2023 using a combination of regression techniques. Transition hazards for conditions that cause mortality were extrapolated from baseline using assumptions about the relative contribution of incidence and case fatality to changes in cause-specific mortality. For non-fatal conditions, incidence was the only transition hazard for which extrapolations were made. Estimates for each specific cause through time were calculated in a model that accounted for changes in all-cause mortality as well as changes in incidence and case fatality (where appropriate) at all points throughout the study period. Absolute numbers of incident and prevalent cases were derived by applying the population rates from these analyses to Australian Bureau of Statistics population projections.10

DALY estimates

DALYs were calculated by applying severity weights (range, 0–1) to the estimated number of incident cases and average duration for each condition. Weights were derived from two sources,4,11 with extrapolations based on alternative methods in some cases. Adjustments were made to account for the possibility of two or more conditions occurring simultaneously in the same person, either by chance or because the conditions are related. These corrections were achieved by determining numbers of people for every combination of causes of ill health as measured by various surveys and hospital admission data.

Health risk assessment

Past and current exposure to 14 selected risk factors (listed in Box 3) were analysed for their contribution to health loss in 2003. Analyses were based on the theoretical framework developed for the WHO-initiated Comparative Risk Assessment project.12 This approach incorporates a “hypothetical minimum” as the alternative exposure distribution against which health loss is calculated, and uses continuous rather than categorical measures of exposure where appropriate. Results were also calculated for the combined effect of health risks.

Health differentials

Health differentials were assessed by comparing subpopulation-specific age-standardised DALY rates derived from disaggregated national DALY estimates. Disaggregation was achieved in two stages, whereby condition-specific estimates of incidence and mortality were first apportioned to states and territories and then to a 15-cell matrix of subpopulations. The matrix was composed of three remoteness categories (major cities, regional areas and remote areas) by five socioeconomic quintiles within each jurisdiction.

To disaggregate conditions with a predominantly fatal impact, preference was given to mortality data. For the remaining conditions, preference was given to the data source on which the baseline model was based (eg, hospital data, health survey data). Condition-specific estimates of prevalence and duration for each subpopulation were derived from subpopulation-specific incidence and all-cause mortality rates, as well as national assumptions regarding remission and case fatality. Subpopulation-specific DALYs were calculated using comorbidity-corrected national severity weights.

Results

Key findings are presented here at two levels of aggregation: “broad cause groups” and “specific conditions”. Both levels are referred to as “causes” and are ranked in terms of “leading” causes compared with others at the same level of aggregation.

Leading broad cause groups

Total health loss in Australia in 2003 was 2.63 million DALYs or 132 DALYs lost per 1000 people. Fifty-one per cent of the loss was from non-fatal causes. Over 75% was accounted for by the six leading broad cause groups: cancer, cardiovascular disease, mental disorders, neurological and sense organ disorders, chronic respiratory diseases, and injuries (Box 1).

Broad cause groups, by age

DALY rates increased steeply with age, apart from small but significant peaks in infancy and early adulthood. Injuries (particularly in males) and mental disorders accounted for the majority of DALYs in early adulthood, after which cancer, cardiovascular disease, and neurological and sense organ disorders were more prominent. The contribution from cancer peaked at age 70 years then declined, leaving cardiovascular disease as the major cause of DALYs in the very old.

Leading specific conditions, by sex

Ischaemic heart disease was the leading specific cause of health loss in males, followed by type 2 diabetes, anxiety/depression, lung cancer and stroke. For females, anxiety/depression was the leading specific cause of health loss, followed by ischaemic heart disease, stroke, type 2 diabetes and dementia (Box 2).

Risks to health, by broad cause group

The 14 selected risk factors together explained almost a third of health loss (expressed as total DALYs). Ten risk factors explained 32.9% of cancer-related health loss, tobacco use being the most important. Twelve risk factors explained 69.3% of health loss from cardiovascular disease, with high blood pressure and high blood cholesterol levels being the largest contributors. Four risk factors explained 26.9% of health loss from mental disorders, with alcohol and illicit drug use contributing in roughly equal proportions. Seven risk factors explained 31.7% of injury-related health loss, alcohol consumption being the dominant risk. Two risk factors explained 60.1% of health loss from type 2 diabetes, high body mass being the largest contributor (Box 3).

Health differentials

Age-standardised DALY rates were 31.7% higher in the lowest socioeconomic quintile than in the highest, and 26.5% higher in remote areas than in major cities. Age-standardised DALY rates in the Northern Territory were 88.7% higher than in the Australian Capital Territory, these jurisdictions having the highest and lowest rates, respectively (Box 4).

Past, present and future health loss

Age-standardised DALY rates declined from 151.0/1000 people to 132.4/1000 people over the period 1993–2003, and are projected to decline by 0.8% per year to 111.4/1000 people by the year 2023. Over the period 2003–2023, age-standardised DALY rates associated with cardiovascular disease are expected to experience the greatest annual rate of decline (− 2.5%), followed by cancer (− 1.4%), injuries (− 1.1%) and chronic respiratory conditions (− 0.9%). On the other hand, age-standardised DALY rates associated with diabetes are projected to grow by 1.8% a year over the period 2003–2023. Age-standardised DALY rates for other broad cause groups are likely to experience much smaller changes over this period (Box 5).

Discussion

Our findings emphasise that, despite steady improvements in Australia’s health over the past decade, significant opportunities for further progress remain at the beginning of the 21st century.

The strength of our analysis is that it is based on an internally consistent assessment of the incidence, prevalence, duration and mortality for a mutually exclusive and comprehensive set of diseases and injuries of importance in Australia. Health loss from these causes was quantified for different periods, subpopulations and risks to health using methods that incorporate fatal and non-fatal health outcomes and include adjustments to account for individuals who simultaneously experience multiple conditions. Health loss is likely to be over-estimated without such corrections, as the severity weights used to derive DALYs were originally determined for health states in isolation, without reference to coexisting conditions.13

A potential limitation is that the severity weights used in our analysis were derived from international sources4,11 and applied without evidence of their validity in Australia. However, studies conducted elsewhere suggest that there are only minor variations across populations in the values people ascribe to different health states.4

We have not quantified uncertainty in our analysis, although a qualitative assessment suggests it is unlikely to be excessive. Overall, about half of the total estimated health loss is due to mortality, for which estimates are fairly robust. Of the remainder, half is due to non-fatal outcomes from conditions for which reasonably good data are available (including cardiovascular disease, cancers, diabetes, common mental disorders and injuries), leaving a quarter with varying and probably higher levels of uncertainty. Precision varies between causes, with estimates for hearing loss, neurological conditions, osteoarthritis and cirrhosis being the most inaccurate.

Our results are not directly comparable with previous DALY estimates for Australia,3 owing to the different methods used. First, a number of the epidemiological models in our analysis benefit from more accurate inputs, particularly the cardiovascular disease models, which incorporated linked data from Western Australia. Second, unlike in the previous analysis, the comorbidity adjustments here capture the dependent nature of certain health states (eg, diabetes increases the risk of heart disease). Third, the current risk attribution methods incorporate a number of methodological advances absent from previous health risk analyses.3,14,15 Because of this lack of comparability, we back-calculated estimates for 1993 based on methods that were consistent with estimates for 2003.

Several implications for policy are worth emphasising. All of the health risks examined here are amenable to modification through intervention, and together explain a large proportion of health loss in Australia. In addition, the large health differentials between subpopulations are due, in part, to differential exposure to these risks. Significant health gains are likely to be achieved through realistic changes to future levels of exposure to health risks, given that even small changes in distribution of exposures can lead to substantial reductions in population-level risk.16

The predicted strong growth in DALY rates associated with diabetes is notable in that it is mostly due to increasing body mass. Given that current strategies have failed to mitigate this risk, new approaches are critical. The impact of increasing diabetes incidence will be magnified by reductions in case fatality from cardiovascular disease through successful strategies to reduce smoking and lower cholesterol levels and blood pressure.2,17Increased survival will result in a greater number of people with diabetes developing other health conditions such as renal failure, retinopathy, neuropathy and peripheral vascular disease. Notwithstanding the apparent intractability of diabetes, further reductions in cardiovascular disease could be achieved, given that most of the health loss from this condition continues to be explained by exposure to known health risks.

The much higher DALY rates in the NT compared with other jurisdictions are largely explained by a higher concentration of Indigenous people in the NT. Health loss in this particular population is considered elsewhere.18,19

Several areas for further research flow from this work. First, health loss and expenditure under a “business as usual” approach to health risk management have been projected into the future,2,20 and such analyses could usefully be extended to include various “what if?” risk-reduction scenarios. Second, simulation methods have been used elsewhere to quantify uncertainty in DALY estimates,21 and would enhance interpretability if applied to these findings. Third, developments in health state valuation methods could, if applied in Australia, increase confidence in the use of the DALY as a valid comparative measure of health loss.

Finally, our analysis is undermined, to some degree, by significant gaps in Australia’s health information infrastructure. In particular, there is limited information on mental disorders, neurological conditions, hearing loss, chronic respiratory diseases and musculoskeletal disorders. Even more importantly, Australia, unlike other countries, has no mechanism for regularly collecting measurement data on biomedical indicators such as body mass, blood pressure, and blood glucose and cholesterol levels. Better and more frequent monitoring in each of these areas would strengthen future comparative assessments of health in Australia, thus enhancing their value for policy and program development.

Preventing Chronic Disease: Public Health Research, Practice and Policy

Abstract

Effective health policies and allocation of public health resources can substantially improve public health. An objective of public health practitioners and researchers is to identify key metrics that would help improve effective policies and terminate poor ones. We review articles published in 2008 surrounding measurement issues for public health policy and present a set of recommendations for future emphasis. We found that a set of consensus metrics for population health performance should be developed. However, considerable work is needed to develop appropriate metrics covering policy approaches that can affect large populations, intervention approaches within organizations, and individual-level behavioral approaches for prevention or disease management.

Introduction

Effective health policies and allocation of public health resources can substantially improve public health (1). For example, each of the 10 great public health achievements of the 20th century (2) was influenced by policy change, such as seat belt laws or regulations governing permissible workplace exposures. To improve public health outcomes, evidence-based policy is developed through a continuous process that uses the best available quantitative and qualitative evidence (3). To broaden the evidence base, a “pay-for-performance” concept that has been widely applied to medical care (4) should be considered for population- and policy-related outcomes (5). In the pay-for-performance approach, providers are rewarded for meeting targets for health care services. For public health, the analogous example might be if public health laws were based in part on policies that are the most cost-effective.

A difference between individual-level health care and population-level approaches for improving health is that public health interventions often occur at multiple levels (6). Upstream interventions involve policy approaches that can affect large populations through regulation, increased access, or economic incentives. For example, increasing tobacco taxes is an effective method for controlling tobacco-related diseases (7). Midstream interventions occur within organizations. For example, worksite-based programs that increase employee access to facilities for physical activity show promise in improving health. Most research has been conducted on downstream interventions, which often involve individual-level behavioral approaches for prevention or disease management. A set of metrics (ie, a group of related measures to quantify some characteristic) can be developed corresponding to these 3 levels. For example, for tobacco control, 3 metrics might be the number of state laws that ban smoking (upstream), the number of private worksites that ban smoking in states with weak laws (midstream), and the rate of self-reported exposure to secondhand smoke (downstream).

In addition to these levels of change, the policy process also must be considered. The framework of Kingdon (8) is useful in illustrating the policy-making process. Kingdon suggests that policies move forward when elements of 3 “streams” come together. (These “streams” are different than the upstream, midstream, and downstream metrics noted above.) The first of these streams is the definition of the problem (eg, a high cancer rate). The second is the development of potential policies to solve that problem (eg, identification of policy measures to achieve an effective cancer control strategy). The third is the role of politics and public opinion (eg, interest groups supporting or opposing the policy). Policy change occurs when a “window of opportunity” opens and the 3 streams push through policy change. A tenet of Kingdon’s model is that policy makers are on the receiving end of sometimes disconnected, random, and chaotic data (8,9). Therefore, a key objective of public health practitioners and researchers is to identify metrics for assessing burden, setting priorities, and measuring progress. Such a set of metrics would help public health decision makers as they seek to improve, expand, or terminate policies.

To illustrate the measurement-related issues for public health policy, we review the literature that sets up recommendations. To reach public health goals, we need metrics for the policy environment, just as we do for other environments relevant to public health progress (eg, air, water, the built environment, health care settings).

Analysis of Metrics in the Literature

Methods

To better understand the use of policy metrics, we reviewed articles published in 14 public health and preventive medicine journals. The journals chosen were broad, general public health journals and not specific to a single topic such as nutrition or disease. Journals that focused solely on policy and journal supplements were not included. We examined the following journals:

1. American Journal of Health Behavior
2. American Journal of Health Promotion
3. American Journal of Preventive Medicine
4. American Journal of Public Health
5. Australian and New Zealand Journal of Public Health
6. Health Education and Behavior
7. Health Education Research
8. Health Promotion International
9. Health Psychology
10. Journal of Behavioral Medicine
11. Journal of Public Health Management and Practice
12. Journal of School Health
13. Public Health Reports
14. Social Science and Medicine

We defined a policy article as one that explicitly describes a policy, law, or regulation (including development, implementation, and evaluation). Using online archives, we conducted a systematic audit of articles published in 2008. Tables of contents were collected from each journal issue for that year. Two researchers reviewed the table of contents in each issue and compiled a list of policy-related articles. If the policy content was unclear from the title of the article, the abstract or full text was used. Any articles in question were reconciled by the research team until consensus was reached.

Once the list of policy articles was compiled, the titles were sorted by policy category. To examine policy metrics in detail, 78 articles from 2008 were analyzed. Editorials, commentaries, and reviews were excluded, resulting in 47 articles from which metrics were summarized. For articles that presented data analysis, we assessed policy metrics across several categories:

• the evaluation design
• whether the evaluation was quantitative, qualitative, or both
• the outcome (dependent) variables
• whether metrics were at an upstream, midstream, or downstream level
• whether measurement properties of the metrics were reported
• whether there was specific attention to health disparities
• presence or absence of economic data

Results

The articles examined were a mixture of both “big P” policy studies (eg, formal laws, rules, regulations enacted by elected officials) and “small p” policy research (eg, organizational guidelines, internal agency decisions or memoranda, social norms guiding behavior) (3). Articles were categorized as child health; maternal health; HIV/AIDS; drug use prevention; tobacco control; violence control; environmental and disaster preparedness and biosecurity; school health; special populations; worksite health; international health; advocacy; general policy; or health care.

The topics that were most represented were tobacco control, international health, and school health. Among international articles, health care was the most common topic. The Journal of School Health and the American Journal of Public Health published the most policy-related articles.

Most articles (74.5%) relied on a cross-sectional design (Table 1). Only 3 studies reported any economic or cost data. Fourteen studies reported on psychometric properties of the metrics. Most presented new data on psychometric testing (n = 10), while some referred to previous articles (n = 4). The testing most often reported was for reliability (eg, interrater reliability), internal consistency, or key informant validation of methods. When categorizing according to 3 levels of outcomes, most were downstream (n = 31), followed by midstream (n = 13) and upstream (n = 3). Detailed data on health disparities (eg, subgroup analysis for vulnerable populations) were available for only 2 studies. Both of these studies (10,11) explicitly investigated differences among disparate groups; 1 studied how national laws that increased tobacco prices affected smoking prevalence among different socioeconomic groups (by sex, occupation, and birth cohort), and the other investigated differences in the use of skilled birth attendants by women of varying wealth in several countries.

Most of these studies dealt with the effectiveness or evaluation of a given policy that is in effect. Three studies focused on characteristics of or influences on policies that are successfully “passed.”

Recommendations for Policy-Related Metrics

Expand sources of evidence

Policy outcomes can be monitored by accumulating evidence from many sources to gain insight into a particular topic, often combining quantitative and qualitative data to understand content and track progress. Consensus on valid and useful measures is needed (12). Successfully monitoring outcomes will also require sources beyond the usual public health data sets (eg, tax revenue, polling, and marketing data). We used the 3 domains of evidence-based policy (process, content, outcome) to present sample metrics across the 3 domains (Table 2). Metrics are quantitative (eg, the percentage of the population with a particular health behavior) and qualitative (eg, the content of a certain policy). Most studies in this review were cross-sectional; stronger study designs are needed to improve the evidence base.

Consider the paradox of local policy evidence

Although much of the effect of public health policy occurs locally, in many jurisdictions high-quality data are lacking at the city, county, or metropolitan levels. Some attempts have been made to identify local-level indicators (13), but a set of consensus policy metrics needs to be developed for local areas, as has been done at the national and state levels.

Develop systems for policy surveillance

A public health adage is “what gets measured gets done” (14). This has typically been applied to downstream endpoints; however, for policy approaches, midstream and upstream metrics are needed. A few efforts are under way to develop public health policy surveillance systems. For example, a group of federal and voluntary agencies has developed policy surveillance systems for tobacco, alcohol, and more recently, school-based nutrition and physical education (3).

Increase understanding of practice-based evidence

Policy-relevant evidence should come from settings and organizations that reflect public health practice and policy. For example, efforts such as the Steps to a HealthierUS initiative, YMCA’s Activate America, and faith-based interventions demonstrate that existing approaches for leadership development can enhance the use of evidence for promoting physical activity (15). As these efforts are documented, specific attention should be given to the key metrics for measuring progress.

Make research more accessible for policy audiences

Researchers and policy makers sometimes exist in parallel universes because of decision-making differences, poor timing, ambiguous findings, and lack of relevant data (16). Metrics may become relevant to policy makers when the effects of a health outcome are framed in terms of the direct impact on one’s community, family, or constituents (17). An excellent example comes from the Rudd Center Revenue Calculator (www.yaleruddcenter.org/sodatax.aspx), which shows the revenue that could be generated from a 1-cent excise tax per ounce of sugar-sweetened beverages by state or municipality.

Improve and clarify metrics relevant to health disparities

Eliminating health disparities is a policy imperative. To achieve this goal, we need to better articulate the key domains of inequality. For example, variables have included race/ethnicity, socioeconomic status or social class, geography, age, and sex (18). Our review of the existing literature showed sparse attention to metrics for health disparities and policy.

Improve incorporation of economic metrics

In deciding whether to take action and how to prioritize resources, policy makers often ask 3 questions: 1) Is there a problem? 2) Do we know how to fix the problem? and 3) How much will it cost? We probably have the most data for answering the first question (19), an intermediate amount for the second (20), and the least data for the economic issues (21). Studies of disease burden that use comparative units of analysis (eg, quality-adjusted life years) provide a basis for economic evaluations (22). Since much of the literature on pay-for-performance has focused on financial incentives, more work is needed to understand how the concepts apply to population-level public health policy.

Learn by analogy

Although public health research and practice are often segregated into “silos” because of categorical funding streams and interest groups (23), much can be learned across content areas. For example, several authors have examined the lessons from tobacco control that can be applied to the obesity epidemic (24,25). Similar areas in public health where policy measurement is advanced may provide beneficial insights to developing topics.

Conclusion

Much of what has been learned from surveillance of diseases and risk factors can probably be applied in the policy arena. A full spectrum of outcomes is needed spanning upstream, midstream, and downstream domains. Arriving at these metrics will require creative thinking and application of alternative study designs. For example, adherence to a strict hierarchy of study designs may reinforce an “inverse evidence law” by which interventions most likely to influence whole populations (eg, policy change) are least valued in an evidence matrix emphasizing randomized designs (26). To establish a system that rewards policies for improved population health (5), considerable work is needed on the appropriate metrics.

Measuring Health and Disease

Measuring Health and Disease I: Introduction to Epidemiology Module Guide

This module was designed to meet the growing need for an applied course in the measurement of a variety of health indicators and outcomes. Whether you manage a healthprogramme, a health facility, or simply have to interpret health data in the course of your work, this module sets out to increase your capacity to deal with health and disease information. It aims to assist you in applying epidemiological knowledge and skills to a variety of Public Health problems such as:

• Is your DOTS programme succeeding?
• What does it mean if a TB prevalence is 850/100 000?
• Is this a Public Health problem or not?
• What is the “burden of disease” in different communities?

Disease burden

Disease burden is the impact of a health problem as measured by financial cost, mortality, morbidity, or other indicators. It is often quantified in terms of quality-adjusted life years (QALYs) or disability-adjusted life years (DALYs), both of which quantify the number of years lost due to disease. One DALY can be thought of as one year of healthy life lost, and the overall disease burden can be thought of as a measure of the gap between current health status and the ideal health status (where the individual lives to old age free from disease and disability).^[1][2][3] The environmental burden of disease is defined as the number of DALYs that can be attributed to environmental factors.^[3][4][5] These measures allow for comparison of disease burdens, and have also been used to forecast the possible impacts of health interventions.

Implementation and interpretation

The public health impacts of air pollution (annual means of PM10 and ozone), noise pollution, and radiation (radon and UV), can be quantified using DALYs. For each disease, a DALY is calculated as:

DALYs = number of people with the disease × duration of the disease (or loss of life expectancy in the case of mortality) × severity (varying from 0 for perfect health to 1 for death)

Necessary data include prevalence data, exposure-response relationships, and weighting factors that give an indication of the severity of a certain disorder. When information is missing or vague, experts will be consulted in order to decide which alternative data sources to use. An uncertainty analysis is carried out so as to analyze the effects of different assumptions.^[15]

Uncertainty[edit]

When estimating the environmental burden of disease, a number of potential sources of error may arise in the measure of exposure and exposure-risk relationship, assumptions made in applying the exposure or exposure-risk relationship to the relevant country, health statistics, and, if used, expert opinions.

Generally, it is not possible to estimate a formal confidence interval, but it is possible to estimate a range of possible values the environmental disease burden may take based on different input parameters and assumptions. When more than one definition has to be made about a certain element in the assessment, multiple analyses can be run, using different sets of definitions. Sensitivity and decision analyses can help determine which sources of uncertainty affect the final results the most.

Representative examples

The Netherlands

In the Netherlands, air pollution is associated with respiratory and cardiovascular diseases, and exposure to certain forms of radiation can lead to the development of cancer. Quantification of the health impact of the environment was done by calculating DALYs for air pollution, noise, radon, UV, and indoor dampness for the period 1980 to 2020. In the Netherlands, 2–5% of the total disease burden in 2000 could be attributed to the effects of (short-term) exposure to air pollution, noise, radon, natural UV radiation, and dampnessin houses. The percentage can increase to up to 13% due to uncertainty, assuming no threshold.

Among the investigated factors, long-term PM10 exposure have the greatest impact on public health. As levels of PM10 decrease, related disease burden is also expected to decrease. Noise exposure and its associated disease burden is likely to increase to a level where the disease burden is similar to that of traffic accidents. The rough estimates do not provide a complete picture of the environmental health burden, because data are uncertain, not all environmental-health relationships are known, not all environmental factors have been included, and it was not possible to assess all potential health effects. The effects of a number of these assumptions were evaluated in an uncertainty analysis.^[15]

Canada

Exposure to environmental hazards may cause chronic diseases, so the magnitude of their contribution to the Canada's total disease burden is not well-understood. In order to give an initial estimate of the environmental burden of disease for four major categories of disease, the EAF developed by the WHO, EAFs developed by other researchers, and data from Canadian public health institutions were used.^[17] Results showed a total of 10,000–25,000 deaths, with 78,000–194,000 hospitalizations; 600,000–1.5 million days spent in hospital; 1.1–1.8 million restricted activity days for sufferers of asthma; 8000–24,000 new cases of cancer; 500–2,500 babies with low birth weights; and C$3.6–9.1 billion in costs each year due to respiratory disease, cardiovascular illness, cancer, and congenital affliction associated with adverse environmental exposures.^[17]

Criticism[edit]

DALYs are a simplification of a complex reality, and therefore only give a crude indication of environmental health impact. Relying on DALYs may make donors take a narrow approach to health care programs. Foreign aid is most often directed at diseases with the highest DALYs, ignoring the fact that other diseases, despite having lower DALYs, are still major contributors to disease burden. Less-publicized diseases thus have little or no funding for health efforts. For example, maternal death (one of the top three killers in most poor countries) and pediatric respiratory and intestinal infections maintain a high disease burden, and safe pregnancy and the prevention of coughs in infants do not receive adequate funding.

Disease burden methodologies such as DALYs also do not capture other aspects of disease and illness, such as pain and suffering, deterioration in quality of life, and emotional and physical impacts on families