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Comparative risk assessment

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GHME 2013 Conference …

GHME 2013 Conference
Session: Global Burden of Diseases, Injuries, and Risk Factors Study 2010: workshop on methods and key findings
Date: June 18 2013
Presenter: Steve Lim
Institute:
Institute for Health Metrics and Evaluation (IHME),
University of Washington

Published in: Health & Medicine

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  • Chris (and others) presenting the overall flow chart for estimating the global burden of diseases, injuries and risk factors. I will be covering four steps shown in the bottom left hand corner of the flow chart outlined in green. The first step in computing risk factor burden, however, is not shown on this diagram and that is the selection of risk-outcome pairs to be included in the quantification of risk burden. An example of a risk outcome pair is systolic blood pressure and its effects on ischemic heart disease. Once risk outcome pairs are selected, the next steps are to estimate the current exposure distribution to each risk factor. For blood pressure this would be the mean and standard deviation of systolic blood pressure in the population. The theoretical minimum risk exposure distribution to which the current exposure will be compared to, and the relative risk per exposure unit for each of the risk-outcome pairs. These three steps allow us to calculate the fraction of the disease burden for each of the outcomes that is currently attributable to the risk factor, namely the population attributable fraction or PAF for each risk-outcome pair.PAFs are then multiplied by the corresponding YLL and YLDs for the specific outcome to determine the YLLs, YLDs and DALYs attributable to the risk factor. Uncertainty in the estimation of risk attributable burden is computed by generating a 1,000 draws of each of the corresponding inputs.
  • For the selection of risk outcome pairs, we used a set of four criteria to guide these choices.
  • The four risk inclusion criteria for GBD 2010 were :2. For example, while household surveys and censuses routinely collect information on water and sanitation, information on hygiene exposure is extremely limited and it was not subsequently included in the GBD20103. Importantly, we also require sufficient epidemiological evidence to estimate outcome-specific effect sizes. For example, there is a large body of evidence documenting the effect of maternal education on child mortality but the literature is predominantly focused on all-cause child mortality outcomes. 4. For example, we did not include the effects of intimate partner violence on HIV burden as longitudinal evidence is only available from South Africa and there is uncertainty about how the effect sizes might be applied to other populations which may have very different transmission dynamics.
  • Based on these criteria we included:
  • -
  • This flow chart provides a summary of the exposure estimation process, including the types of data sources used which ranged from household surveys, administrative data and censuses as well as trade sales and consumption data and as I will show in a moment satellite imagery. Similar to the previous presentations, we make a number of corrections for representativeness and selection and importantly for risk factors, cross walk between different definitions of risk exposure so that the measure of exposure matches best with the effect size estimates. For example, for computing the burden due to high sodium consumption, we cross walk between dietary based measures of sodium consumption and urinary sodium as a gold standard. We utilize a range of statistical procedures that generate predictions based on time, space and covariates to produce exposures by risk, age, sex, year and country.
  • A good example of this estimation process is for ambient particulate matter pollution. This map shows the availability of data on particulate matter measures as PM2.5 from ground-based monitoring stations. As you can see data are largely restricted to cities and are unavailable for many populations globally, for example for most parts of Africa.
  • To estimate exposure distributions for all populations globally, we used sattelite-based measures of aerosol
  • The third steps is to choose a counterfactual exposure distribution. The choice of the TMRED was guided by the epidemiological literature in terms of how
  • WASH and seafood omega-3s are posters
  • For ambient air pollution, as we saw in the previous map there are large populations such as those in East Asia that are exposed to high levels – greater than 80 ug per cubic meter - of PM2.5. The epidemiological studies of the health effects of ambient PM2.5 are largely restricted to North American and European populations with lower levels of exposure. To quantify the health effects of high ambient PM2.5 exposure we integrated evidence across different sources of PM2.5 as shown in this figure. On the y-axis we have the relative risk of lung cancer and on the x-axis we have log-transformed PM2.5. The red circles indicate the various ambient PM2.5 epidemiological studies, green household or indoor air pollution and the blue circles various categories of cigarette consumption. By fitting non-linear functions to this data, we are then able to estimate the health effects of PM2.5 exposure for populations with higher levels of ambient PM exposure, that is, largely between the ambient PM studies shown in red and the green household air pollution studies.
  • If we focus on the top 25 risk factors and risk factor clusters, in 2010, the cluster of dietary risks were the leading risk factors in terms of global disability adjusted life years, accounting for almost a tenth of global disability adjusted life years, followed by high blood pressure, tobacco smoking, including second hand smoke and HAP. The colors on this figure indicate the underlying cause attributable to the risk factor. For example, the effects of high blood pressure are primarily via cardiovascular disease while the effects of alcohol use are across a more diverse range of outcomes including cancer, cardiovascular disease, injuries, and communicable diseases. Many of the leading risk factors such as high BMI, high fasting plasma glucose, physical inactivity, and high total cholesterol have effects on primarily non-communicable disease. Household air pollution and ambient particulate matter pollution which have effects on both communicable disease and non-communicable disease were ranked fourth and 9th, respectivelyThe leading communicable disease risk factors in 2010 were childhood underweight ranked 8th and accounting for more than 3% of total health burden with iron deficiency and suboptimal breastfeeding accounting for more than 2% of health burden.
  • The picture in 2010 reflects a dramatic shift away from communicable disease risk factors towards non-communicable disease risk factors as shown in this arrow diagram. This diagram depicts the leading risk factors in 1990 on the left and the leading risk factors in 2010 on the right. Risks are color coded according to the cluster of risk factors. For example, red are the maternal and child undernutrition risks and blue are the physiological risks for chronic disease. The lines connect the same risk. Numbers in the right hand most column represent the % change in the risk factor between 1990 and 2010. In 1990 childhood underweight was the leading risk factor accounting for almost 8% of total health burden in 1990; by 2010 it had more than halved and was the 8th ranked risk factor. Similar declines are present for other communicable disease risks as denoted by the dotted lines. These include suboptimal breastfeeding, unimproved sanitation and water as well as micronutrient deficiencies such as Vitamin A and Zinc. Solid lines denote whether the risk increased in rank. For example, high blood pressure was previously the 5th leading risk in 1990 and the 2nd leading risk in 2010. Overall, the burden of non-communicable disease risk factors has increased with the two of the other more notable being high body mass index for which the burden increased by more than 80% and high fasting plasma glucose that increased by more than 50%.
  • The global results mask considerable variation by region. This heatmap shows the top 25 global risk factors as rows ordered by the global rank and I have included just the Asian regions as columns ordered by the mean age at death, a marker of the epidemiological transition. Cells are shaded according to the rank of the risk factor in the corresponding region with dark red indicating the 5 leading risk factors and green indicating ranks 21-25 or greater. A number of the important patterns to note are:The cluster of dietary risk factors for chronic disease, SBP and Tobacco are generally among the top 5 ranked risk factors for all regions outside of SSA. Alcohol is a leading risk factor in Southern sub-Saharan Africa, Eastern Europe and Latin AmericaHousehold air pollution is an important cause of disease burden in many parts of Asia and sub-Saharan Africa. Ambient particularly matter pollution is the 4th leading risk factor in East Asia. Despite declines, the cluster child and maternal undernutrition risk factors remain the leading risk factors in Western, Central and Eastern sub-Saharan Africa
  • Transcript

    • 1. Comparative risk assessment June 18, 2013 Stephen Lim Associate Professor of Global Health Global Burden of Diseases, Injuries, and Risk Factors Study 2010: workshop on methods and key findings
    • 2. Outline 1. Methods for estimating Burden of Disease attributable to risk factors 2. Summary of key findings 2
    • 3. Outline 1. Methods for estimating Burden of Disease attributable to risk factors 2. Summary of key findings 3
    • 4. 4
    • 5. Calculating risk factor burden 1. Select risk-outcome pairs 2. Estimate exposure distributions to each risk factor in the population 3. Choose a counterfactual exposure distribution: theoretical minimum risk exposure distribution (TMRED) 4. Estimate cause effect sizes: relative risk per unit of exposure for each risk-outcome pair 5. Compute attributable burden, including uncertainty 5
    • 6. Risk-outcome pair inclusion criteria 1. Likely importance of a risk factor to disease burden or policy 2. Availability of sufficient data and methods to enable estimation of exposure distributions by country for at least one of the study periods 3. Sufficient evidence for causal effect (convincing or probable evidence) and to estimate outcome-specific effect sizes 4. Evidence to support generalizability of effect sizes to populations other than those included in epidemiological studies 6
    • 7. GBD 2010: risks quantified Unimproved water and sanitation Unimproved water Unimproved sanitation Air pollution Ambient particulate matter pollution Household air pollution from solid fuels Ambient ozone pollution Other environmental risks Residential radon Lead exposure Child and maternal undernutrition Suboptimal breastfeeding Nonexclusive breastfeeding Discontinued breastfeeding Childhood underweight Iron deficiency Vitamin A deficiency Zinc deficiency Tobacco smoking and secondhand smoke Tobacco smoking Secondhand smoke Alcohol and other drugs Alcohol use Drug use (opioids, cannabis, amphetamines) Physical inactivity and low physical activity Physiological chronic disease risks High fasting plasma glucose High total cholesterol High systolic blood pressure High body mass index Low bone mineral density Sexual abuse and violence Childhood sexual abuse Intimate partner violence 7
    • 8. GBD 2010: risks quantified (cont’d) Dietary risk factors Diet low in fruits Diet low in vegetables Diet low in whole grains Diet low in nuts/seeds Diet low in milk Diet high in unprocessed red meat Diet high in processed meat Sugar-sweetened beverages Diet low in fiber Diet low in calcium Diet low in seafood omega-3 Diet low in polyunsaturated fatty acid (PUFA) Diet high in trans fatty acids Diet high in sodium Occupational exposures Asbestos Arsenic Benzene Beryllium Cadmium Chromium Diesel Formaldehyde Nickel PAHs Secondhand smoke Silica Sulfuric acid Asthmagens Particulates and gases Noise Occupational injury Low back pain 8
    • 9. Calculating risk factor burden 1. Select risk-outcome pairs 2. Estimate exposure distributions to each risk factor in the population 3. Choose a counterfactual exposure distribution: theoretical minimum risk exposure distribution (TMRED) 4. Estimate cause effect sizes: relative risk per unit of exposure for each risk-outcome pair 5. Compute attributable burden, including uncertainty 9
    • 10. Measuring risk factor exposure 10
    • 11. Example: ambient PM pollution 11
    • 12. 12 PM2.5 (µg per m3) Example: ambient PM pollution (cont’d) • Satellite-based measures of aerosol optical depth (AOD) • TM5 chemical transport models • Cross-walk to ground-based PM2.5 sensor data
    • 13. Calculating risk factor burden 1. Select risk-outcome pairs 2. Estimate exposure distributions to each risk factor in the population 3. Choose a counterfactual exposure distribution: theoretical minimum risk exposure distribution (TMRED) 4. Estimate cause effect sizes: relative risk per unit of exposure for each risk-outcome pair 5. Compute attributable burden, including uncertainty 13
    • 14. Calculating risk factor burden 1. Select risk-outcome pairs 2. Estimate exposure distributions to each risk factor in the population 3. Choose a counterfactual exposure distribution: theoretical minimum risk exposure distribution (TMRED) 4. Estimate cause effect sizes: relative risk per unit of exposure for each risk-outcome pair 5. Compute attributable burden, including uncertainty 14
    • 15. Risk-outcome effect sizes 1. Recent or new systematic reviews and meta-analyses 2. New/updated effect size estimates conducted for: • Water and sanitation • Dietary risk factors • Air pollution: integrated exposure response (IERs) 3. Examined validity of single dietary risk factor effect sizes: • Dietary pattern studies, e.g., Mediterranean diet • Randomized controlled feeding studies, e.g., DASH, OMNI Heart 15
    • 16. 16 Example: integrated exposure-response Lung cancer as a function of ambient PM2.5 exposure
    • 17. Calculating risk factor burden 1. Select risk-outcome pairs 2. Estimate exposure distributions to each risk factor in the population 3. Choose a counterfactual exposure distribution: theoretical minimum risk exposure distribution (TMRED) 4. Estimate cause effect sizes: relative risk per unit of exposure for each risk-outcome pair 5. Compute attributable burden, including uncertainty 17
    • 18. Population attributable fractions • Continuous risk factors: • Categorical risk factors: • Joint effects of risk factor cluster: 18 R r rPAFPAF 1 )1(1
    • 19. Limitations • Few risks for major communicable diseases • Exclusion of risk-outcomes based on insufficient data • Limited exposure distribution data • Potential for residual confounding, especially in the absence of intervention studies • Uncertainty about generalizability of effect sizes across populations • Approximation of joint effects of risk factor clusters 19
    • 20. Outline 1. Methods for estimating Burden of Disease attributable to risk factors 2. Summary of key findings 20
    • 21. Burden of Disease attributable to 25 leading risk factors as a percentage of global DALYs, both sexes, 2010 Residential radon Ambient ozone pollution Low bone mineral density Unimproved water source Childhood sexual abuse Zinc deficiency Vitamin A deficiency Lead exposure Unimproved sanitation Intimate partner violence Drug use High total cholesterol Suboptimal breastfeeding Iron deficiency Occupational risks Physical inactivity and low physical activity Ambient particulate matter pollution Childhood underweight High fasting plasma glucose High body-mass index Alcohol use Household air pollution from solid fuels Tobacco smoking High blood pressure Dietary risks 0 2 4 6 8 10 DALYs (%) Cancer Cardiovascular and circulatory diseases Chronic respiratory diseases Cirrhosis Digestive diseases Neurological disorders Mental and behavioural disorders Diabetes, urogenital, blood, and endocrine Musculoskeletal disorders Other non-communicable diseases HIV/AIDS and tuberculosis Diarrhoea, lower respiratory infections, & other common infectious diseases Neglected tropical diseases and malaria Maternal disorders Neonatal disorders Nutritional deficiencies Other communicable diseases Transport injuries Unintentional injuries Intentional injuries War and disaster Global, 2010 Leading risk factors, percent of total DALYs 21
    • 22. Global risk factor ranks and percentage change with 95% UI for all ages and sexes combined, 1990 and 2010 22
    • 23. Regional variation in leading risk factors 23
    • 24. National variation in leading risk factors 24
    • 25. Key risk factor messages • Dramatic shift away from communicable disease risks in children toward noncommunicable disease risks in adults • Global rise in high BMI and glucose emphasizes research priorities given the absence of effective interventions • More nuanced understanding of the role of diet in preventing chronic disease • Considerable variation in risk factor burden by region and country • In much of sub-Saharan Africa, the leading risks continue to be those associated with poverty 25

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