Foundation Day Lecture 2008

Prof. Anthony J McMichaelHuman population health is a very important component of climate change debate..our economy, physical infrastructure, production capabilities and material growth risk are also things that bear on human health-Prof A J McMichael

A.J. McMichael

National Centre forEpidemiology and Population Health The Australian National University Canberra

Environment, Climate and Health: An Expanded Public Health Research and Policy Agenda for the 21st Century

Public Health Foundation of IndiaMarch 28, 2008

Human Wellbeing and Health: Key Criterion of SustainabilityFor humans, the central criterion/measure of sustainability is the capacity of a societys way of life to sustain the wellbeing and good health of the population . across generations, and without detriment to health in populations elsewhere (now and in future).

OutlineEnvironment: Cinderella of disease causationPopulation Perspective: thinking ecologicallyLarge-scale, human-induced, environmental changeSystemic character; new conceptsClimate change recent science Health impactsCategories and examples of researchFood, nutrition, healthInfectious diseasesMitigation and Adaptation: research needsConclusion

5%75%Main focus of Health-Care SystemImpact of Social Policies: Environment Urban Planning Technology choices Public health programs Health Promotion Etc.High-Risk Gp:accounts for 20% of casesMedium-Risk Gp,accounts for 80% of casesDisease Risk 'Scoree.g. blood pressure, relative weightNo. of personsPopulation Strategy for Improving HealthLow riskHigh riskPopulation distribution for some specified risk factor20%Medium risk

GlobalisationIndustrialisationModernisationFuture190018002000Infectious diseasesObesityUrban air pollutionRoad traumaGreenhouse gas emissions climate changeRise-and-Fall of Urban Health PenaltiesThe Developed Country experienceNot SustainableHealth risk/impact (indicative)TimeMcMichael, 2007

Estimated Atmospheric PM10 (respirable particulates) Concentration in World Cities (popn >100,000)PM10 (g/m3). 5-14. 15-29. 30-59. 60-99. 100-254Cohen AJ et al. 2004

PM10 Annual Average Ambient Concentrations in Asian Cities, 2005City020406080100120140160BangkokBeijingChiang MaiColomboDhakaHo Chi MinhHong KongKathmanduShanghaiSingaporeSurabayaTaipei,ChinaXi'anconcentrations in g/m3KolkataMumbaiNew DelhiWHO 2005 Guideline Value for Annual Average of PM10 = 20 g/m3

Life expectancy at birth, by GDP per head, 2000GDP per head, 2000 (purchasing power parity, in $US)Life expectancy (years)Source: Deaton, 2004

Obesity, diabetes, rising fast

Number of overweight and obese (~I billion) likely to reach 1.5.billion by 2015Obese children: already 155 million worldwidePrevalence of (type 2) diabetes, ~120 million, may increase three-fold by 2030 Accounts for 7% of all deathsHighest prevalence rates in India, China, USA, Indonesia, Russia, Japan, Pakistan, Brazil, Italy

International Obesity Task Force 2005 New York Herald Tribune 12 September 2005WHO, World Heart Day, September 2005

Prevalence of overweight varies with wealth, by State, in IndiaEcon Survey India, 1999, NFHS-2 1998-99From M Vaz, St. Johns, IPHCRPercent over-weight (i.e., BMI > 25)

PopulationsSub-groupsIndividuals18001850 19001950 2000Esp. occupational groupingsEpidemiology since ~1850: Changes in emphasis on different levels/units of analysis Germ TheoryMicronutrientsNon-Infectious Dis: Individual-level risk factorsMolecules and genesSocial epiSustaining population health: Understanding effects of systemic changes

Year 2003 Moderate business as usual (to 2050) Slow shift in economic and social practices Ecological Footprint, 1961-2003Source: World Wildlife Fund, 2006See also Wackernagel et al, PNAS, 2002 1961 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 1.8

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2

We Are Now Living Beyond Earths Limits[WWF Living Planet Report 2006]No. of Planet Earths needed to meet human demandsRapid transition to environmental sustainability No. of Planet Earths available Three Ecological Footprint Scenarios, to 2100

Eyes (cataracts, etc.)Skin damage/cancerLand cover (forest, etc)Direct impactsThermal stress: death, disease events, injuryStorms, cyclones, floods, firesSea-level rise: physical hazards, displacementBiodiversity changes, & ecosystem disruptionChanges in host species, vectors (mosquitoes, etc.) Infectious disease risksFood yields: nutrition and healthHuman predationStratospheric ozone depletionPoverty, slum, hygiene; physical hazards; infectious disease risks (mobility, density)e.g. pollinationAvian flu, Nipah virus, etc.Land useWatersheds, systems Food-production systemsUrbanisation; human settlementsClimate change Immune suppressionGlobal Environmental Changes: paths, health risks

UNDP Human Development Report, 2007/2008Fighting Climate Change: Human Solidarity in a Divided World

Excerpt from UNDP Press Release, Nov 27

6 different GHG emissions scenarios3 of the 6 emissions scenariosUncertainty range: 1 standard deviationNo. of modelsused

A2

B1

A1F1

1.8 - 4.0 oCProjected warming, to 2100: for six future global greenhouse gas emissions scenarios

Intergovernmental Panel on Climate Change, 2007: Wkg Gp I

A1BWarming already in pipeline from recent/current GHG levels (~0.6oC)Warming (oC)Year+ 2oC+ 4oC

23 models (tested against recent record)

1980-99 baselinetemperature

16-21 models used for each scenarioA1TB2A1F11900 2000 2100

More Variable Weather in FutureIntergovernmental Panel on Climate Change (2007)very likely that hot extremes, heatwaves and heavy precipitation events will continue to become more frequent.UN World Meteorological Organization (2007)increasing trend in extreme events observed over the last 50 years. . Record extremes in many regions across the world since Jan 2007.

Flooding, after drought, produced cholera outbreak, and high abundance of mosquitoes Extreme rainfall event, 2006, in Somalia/Kenya border region

Climate Change: Now Faster than ExpectedIPCC Report 4 (2007) is already conservative/out-dated

Recent research shows increasing rates of:Global GHG emissionsSurface temperature rise - esp. polar regionsIce melt (Arctic, Antarctic, mountain glaciers)Sea-level riseSaturation of carbon sinks (oceans, terrestrium)

Arctic Sea Ice Meltdown: A hundred years ahead of schedule? projectionIPCC Projections: Serious under-estimates?Bjeknes Centre for Climate Research, Svalbard, Norway (2008)Satellite observationsIPCC central projection

Environmental/Climate Change: Health Impacts, Responses and Research NeedsGlobal Environmental Changes, affecting:

Climate Water Food yields Other materials Physical envtl. safety Microbial patterns Cultural assetsNatural processes and forcingsaHuman society: culture, institutions economic activity demographyFeedback changePressure on environmentMitigation: Reduce pressure on environmentFeedback changecbBased on: McMichael et al., Brit med J, 2008Adaptation: Reduce impactsImpacts on human society: livelihoods economic productivity social stability health

Climate Change and Health Five Core Research Categories1. Determine baseline climate/health relations2. Detect impacts3. Estimate current CC-related burden4. Prediction of future risks (modelling)Empirical data-based studiesScenario-based health risk projectionsPastFuturePresent5. Adaptive strategies

Heatwave: August 200335-50,000 extra deaths over a 2-week periodLand surface temperatures, summer of 2003, vs. summers of 2000-04. NASA satellite spectrometry

Seasonal variation in daily mortality, Delhi, 1991-94Daily deaths 19910204060From: ISOTHURM Study, McMichael et al., in press199219931994summerwinter

Relative mortalityDaily mean temperature, oCHeat-related daily mortality, Delhi, 1991-94 Generalised additive model, cubic-spline smoothing 01020304080100120140Uncertainty range: 95% CI

Delhi: Decadal changes, 1960s-present, in key climate variables and heat index WBGT-at-workFrom: Kjellstrom T, Lemke B, Hales S, unpublished0.05.010.015.020.025.030.035.01960-791980-891990-992000-07DecadeDegrees C, hPa, m/sxWBGTAbs HumidityWind speedTemp, maxTemp, averageTemp, min

From: Kjellstrom T, Lemke B, Hales S, unpublishedDelhi, reduced workable days with climate change050100150200250300+1oC+2oC+3oC+4oC+5oC+6oC+7oCIncrease in WBGT-at-workFully workable days in Delhi per year, with future temperature increases of up to 7o C

200 Watts (light)500 Watts (heavy)Av temp 2000-07

Deaths (thousands)DALYs (millions)20002030Disease Burden attributable to Climate Change: 2000, 2030Selected conditions in developing countries

McMichael et al/WHO, 2004

Now (2000)Future (2030)DeathsTotal Burden

0-22-44-7070-120No dataDeaths per million population ,Based on: McMichael et al., 2004 (WHO Comparative Risk Assessment 2000)** Deaths from malaria & dengue fever, diarrhoea, malnutrition, flooding and (in OECD countries) heatwavesEstimated Regional Mortality* Attributable to Climate Change in Year 2000 (relative to 1961-90 average climate)

Cartogram: Emissions of greenhouse gasesCountries scaled according to cumulative emissions in billion tonnes carbon equivalent in 2002. (Patz, Gibbs, et al, 2007)

Cartogram: Health impacts of climate change

WHO regions scaled according to estimated mortality (per million people) in the year 2000, attributable to the climate change that occurred from 1970s to 2000 (Patz, Gibbs, et al, 2007)Deaths from malaria & dengue fever, diarrhoea, malnutrition, flooding and (OECD countries) heatwaves

Two Major Risks to HealthFood and NutritionDecreased food yields, affordability malnutrition

Infectious Disease PatternsGeographic range, seasonality and outbreak rates

March 2008: UN World Food Program anticipates global hunger crisisThreat of widespread malnutrition due to dramatic rise in world food prices

Price increases of up to 40% in 2007 (highest on record)

WFP head, J Sheeran, describes price rise as due to perfect storm - demand for animal feed (China, India, etc.)- biofuels production- climate change- rising costs of fertiliser and fuel-energy

Sources: FAO 2005; WHO 2006; UNICEF 2005MostLeastGlobal Hunger IndexGlobal Hunger Map, 2005-06No dataNot included

Drought: Recent and likely future expansion under climate changePercentage of worlds land area in droughtExtreme drought (1% circa 2000)Severe drought (5% circa 2000) Burke EJ, Brown SJ, Christidis N. 2006. Journal of Hydrometeorology

1960 1980 2000 2020 2040 2060 2080 2100

50

40

30

20

10

0

% land in drought

Photo-synthetic yield20o C30o C40o CGeneral Relationship of Temperature and Photosynthesis0%100%2oC 2oC C Field, D Lobell. Env Res Letters, 2007: A 1oC increase reduces global cereal grain crop yields by 6-10%. So, a rise of 2oC could cause 12-20% fall in global production. [Note: this estimate is higher than most others.]

Expected climate change impacts on global cereal grain production,1990-2080 (% change)

World -0.6 to -0.9

Developed countries +2.7 to +9.0

Developing countries -3.3 to -7.2

Southeast Asia -2.5 to -7.8 South Asia -18.2 to -22.1 Sub-Saharan Africa -3.9 to -7.5 Latin America +5.2 to +12.5

Based on: Tubiello and Fischer 2007

Poor Countries: More vulnerable to Climate Change impacts on food productionGeography (hotter, less rain, more variable weather)High dependence on agriculture and natural resources (forests, wetlands, fisheries)Limited infrastructure (e.g. 95% of agriculture in SSAfrica is rain-fed)Poverty, poor access (entitlement)Existing malnutrition, infectious diseaseOverall, lower adaptive capacity (professional, technical, health-care system, etc.)

Pattern of Influence of Seasonal Rainfall, Surface Water, and Crowding on Cholera Occurrence, Madras region, 1901-40 Water DepthShallowFloodRo primary (water-borne) transmissionRo secondary (human-to-human) transmission1.01.0Based on: Ruiz-Moreno et al, EcoHealth 2007; 4: 52-62. Study of 26 districts, Madras Presidency, south-east India, 1901-1940. waterdilution effecthuman crowding effectRo = reproductive numberCholera Risk

Rain Forest, with seasonal fruiting bat diet Forest clearingNipah Virus Disease: Outbreak in Malaysian Pig Farmers, 1997-1999 El Nio dryingFruit bats (with their virus: ~40% positive)

Tong et al, date?Rainfall, mosquito density and RRV in BrisbaneYearRRV DiseaseRainfallMosquito Density

RRV Incidence (per 105) Mosquito Density; Rainfall (mm)Positive relationship between rainfall and mosquito density, with increased mosquito density after lag of 1-2 months.Tong S, McMichael A, et alRoss River Virus, Rainfall and Mosquito Density: Queensland, 1998-2001

Sheet1

dateMOSDRRVRainfallMinTRHTideRRVlagRRVlag1mosdlagmonthseasontwoseasonRainfalllag2RHlag1tidelag2mintlag1mosdlag2SOISOIlag1SOIlag2tidelag1tidelag2rhlag2mintlag2rainfalllag1SOIlag3Mosdlag3

Nov-98134.200.46205.816.6059.33205.8..1141.....12.5......9.8

Dec-9885.551.4820919.4861.062090.46134.201211.59.33.16.60.13.312.5.205.8...205.811.1

Jan-99196.274214.7721.0569.13214.771.480.4685.55111205.861.06205.819.48134.2015.613.312.5209205.859.3316.6020910.9

Feb-99819.277.19214.1420.6071.54214.1441.48196.2721120969.1320921.0585.558.615.613.3214.7720961.0619.48214.7712.5134.20

Mar-99490.8416.33204.2820.0973.03204.287.194819.27321214.7771.54214.7720.60196.278.98.615.6214.14214.7769.1321.05214.1413.385.55

Apr-99177.4015.53214.8515.5266.33214.8516.337.19490.84422214.1473.03214.1420.09819.2718.58.98.6204.28214.1471.5420.60204.2815.6196.27

May-9924.367.65210.7814.5175.97210.7815.5316.33177.40522204.2866.33204.2815.52490.841.318.58.9214.85204.2873.0320.09214.858.6819.27

Jun-992.06200.9910.7169.57200.997.6515.5324.36632214.8575.97214.8514.51177.4011.318.5210.78214.8566.3315.52210.788.9490.84

Jul-990.57205.1811.1375.74205.182.067.65732210.7869.57210.7810.7124.364.811.3200.99210.7875.9714.51200.9918.5177.40

Aug-991.14200.7110.7470.97200.710.572.06832200.9975.74200.9911.132.14.81205.18200.9969.5710.71205.181.324.36

Sep-990.57196.213.1167.07196.21.140.57942205.1870.97205.1810.74-0.42.14.8200.71205.1875.7411.13200.711

Oct-99165.180.91200.0416.4368.52200.040.571.141042200.7167.07200.7113.119.1-0.42.1196.2200.7170.9710.74196.24.8

Nov-9985.270.69202.9516.3159.9202.950.910.57165.181141196.268.52196.216.4313.19.1-0.4200.04196.267.0713.11200.042.1

Dec-99148.440.69204.6217.7163.9204.620.690.9185.271211200.0459.9200.0416.31165.1812.813.19.1202.95200.0468.5216.43202.95-0.4

Jan-00112.401.37205.6519.6362.74205.650.690.69148.44111202.9563.9202.9517.7185.275.112.813.1204.62202.9559.916.31204.629.1165.18

Feb-00241.130.69192.9820.1062.34192.981.370.69112.40211204.6262.74204.6219.63148.4412.95.112.8205.65204.6263.917.71205.6513.185.27

Mar-00161.151.14200.9419.7970.81200.940.691.37241.13321205.6562.34205.6520.10112.409.412.95.1192.98205.6562.7419.63192.9812.8148.44

Apr-0082.650.5720816.9571.072081.140.69161.15422192.9870.81192.9819.79241.1316.89.412.9200.94192.9862.3420.10200.945.1112.40

May-0035.141.26209.2213.6967.65209.220.571.1482.65522200.9471.07200.9416.95161.153.616.89.4208200.9470.8119.7920812.9241.13

Jun-001.250.46199.199.1968.77199.191.260.5735.1463220867.6520813.6982.65-5.53.616.8209.2220871.0716.95209.229.4161.15

Jul-000.770.57209.537.3864.97209.530.461.261.25732209.2268.77209.229.1935.14-3.7-5.53.6199.19209.2267.6513.69199.1916.882.65

Aug-001.440.11206.98.5356.9206.90.570.460.77832199.1964.97199.197.381.255.3-3.7-5.5209.53199.1968.779.19209.533.635.14

Sep-0010.290.46202.3612.3052.5202.360.110.571.44942209.5356.9209.538.530.779.95.3-3.7206.9209.5364.977.38206.9-5.51.25

Oct-007.281.03201.1216.0060.68201.120.460.1110.291042206.952.5206.912.301.449.79.95.3202.36206.956.98.53202.36-3.70.77

Nov-0069.650.91195.1817.3065.3195.181.030.467.281141202.3660.68202.3616.0010.2922.49.79.9201.12202.3652.512.30201.125.31.44

Dec-0026.850.69202.2119.9260.39202.210.911.0369.651211201.1265.3201.1217.307.287.722.49.7195.18201.1260.6816.00195.189.910.29

Jan-01204.053.31205.8420.9362.97205.840.690.9126.85111195.1860.39195.1819.9269.658.97.722.4202.21195.1865.317.30202.219.77.28

Feb-01226.124.45203.9619.7568.89203.963.310.69204.05211202.2162.97202.2120.9326.8511.98.97.7205.84202.2160.3919.92205.8422.469.65

Mar-01466.036.17214.520.4465.71214.54.453.31226.12321205.8468.89205.8419.75204.056.711.98.9203.96205.8462.9720.93203.967.726.85

Apr-01248.1111.31210.1516.4668.97210.156.174.45466.03422203.9665.71203.9620.44226.120.36.711.9214.5203.9668.8919.75214.58.9204.05

May-0112.606.85210.1911.7563.39210.1911.316.17248.11522214.568.97214.516.46466.03-90.36.7210.15214.565.7120.44210.1511.9226.12

Jun-013.871.83201.9510.8371.07201.956.8511.3112.60632210.1563.39210.1511.75248.111.8-90.3210.19210.1568.9716.46210.196.7466.03

Jul-019.781.37201.188.8062.35201.181.836.853.87732210.1971.07210.1910.8312.60-31.8-9201.95210.1963.3911.75201.950.3248.11

Aug-011.530.11205.98.6852.77205.91.371.839.78832201.9562.35201.958.803.87-8.9-31.8201.18201.9571.0710.83201.18-912.60

Sep-015.880.23197.2811.7555.03197.280.111.371.53942201.1852.77201.188.689.781.4-8.9-3205.9201.1862.358.80205.91.83.87

Oct-019.500.57208.8414.9654.58208.840.230.115.881042205.955.03205.911.751.53-1.91.4-8.9197.28205.952.778.68197.28-39.78

Nov-01102.950.34206.2817.4861.87206.280.570.239.501141197.2854.58197.2814.965.887.2-1.91.4208.84197.2855.0311.75-8.91.53

Dec-01250.350.46201.6820.6864.84201.680.340.57102.951211208.8461.87208.8417.489.50-9.17.2-1.9206.28208.8454.5814.961.45.88

Chart1

0.46134.2110.2

1.4885.5588.2

4196.2727272727244.4

7.19819.2727272727331.6

16.33490.8421052632158.8

15.53177.477.4

7.6524.363636363676

2.0636312198.2

0.5736342140.8

1.143637364.8

0.573640470

0.91165.1818181818215.8

0.6985.272727272762.4

0.69148.4444444444176

1.37112.445

0.69241.12592.4

1.14161.153846153836.4

0.5782.65217391338.8

1.2635.137931034552.8

0.461.2541

0.570.771428571424.6

0.111.444444444425.8

0.4610.28571428570.8

1.037.27586206955.2

0.9169.652173913106.4

0.6926.852941176553.4

3.31204.0547.2

4.45226.1166666667173.6

6.17466.0285714286154.4

11.31248.105263157933.6

6.8512.602564102637

1.833.869565217417.2

1.379.777777777828.8

0.111.526315789512

0.235.87512.6

0.579.592.8

0.34102.9473684211212.6

0.46250.354838709777.6

Incidence rate of RRV

Mosquito density

Rainfall

Year

Incidence rate of RRV (1/100,000)

Mosquito density/Rainfall (mm)

Sheet2

dateRRVMosquitoSOIRainfallVig

Nov-980.46134.2012.5110.20118.80

Dec-981.4885.5513.388.2048.70

Jan-994196.2715.6244.4098.27

Feb-997.19819.278.6331.60177.27

Mar-9916.33490.848.9158.80100.74

Apr-9915.53177.4018.577.408.73

May-997.6524.361.376.002.45

Jun-992.061198.20

Jul-990.574.8140.80

Aug-991.142.164.80

Sep-990.57-0.470.00

Oct-990.91165.189.1215.8030.64

Nov-990.6985.2713.162.405.36

Dec-990.69148.4412.8176.0018.22

Jan-001.37112.405.145.0020.00

Feb-000.69241.1312.992.4051.75

Mar-001.14161.159.436.4035.50

Apr-000.5782.6516.838.8037.57

May-001.2635.143.652.809.69

Jun-000.461.25-5.541.000.33

Jul-000.570.77-3.724.600.37

Aug-000.111.445.325.800.33

Sep-000.4610.299.90.801.54

Oct-001.037.289.755.201.76

Nov-000.9169.6522.4106.4011.43

Dec-000.6926.857.753.4010.56

Jan-013.31204.058.947.2083.60

Feb-014.45226.1211.9173.6089.42

Mar-016.17466.036.7154.40144.03

Apr-0111.31248.110.333.6048.11

May-016.8512.60-937.005.45

Jun-011.833.871.817.201.00

Jul-011.379.78-328.800.17

Aug-010.111.53-8.912.000.74

Sep-010.235.881.412.602.80

Oct-010.579.50-1.992.806.35

Nov-010.34102.957.2212.6071.08

Dec-010.46250.35-9.177.6086.94

Sheet3

Dengues principal vector: Aedes aegypti

Effects of Temperature Rise on Dengue Transmission Faster viral incubation in mosquitoShorter mosquito breeding cycleIncreased mosquito feeding frequencyMore efficient transmission of dengue virus from mosquito to human

(WHO)Countries/areas at risk of dengue transmission, 2007 (DengueNet, WHO)

Estimated regions at risk of Dengue Fever under climate change: 2085 vs 1990Hales et al. Lancet, 2002

Zhou X-N, Yang G-J, et al. Potential Impact of Climate Change on Schistosomiasis Transmission in ChinaRecent data suggest that schisto-somiasis is re-emerging in some settings that had previously reached the [successful disease control] criteria of either transmission control or transmission interruption. . Along with other reasons, climate change and ecologic transformations have been suggested as the underlying causes.

Mitigation and AdaptationMitigation (avoiding the unmanageable)First-order taskEspecially since climate change is accelerating. Also: assess health impacts (hopefully mostly benefits) of mitigation strategies

Adaptation (managing the unavoidable)Necessary transitional taskSpontaneous adaptation (need to study/understand)Planned adaptation near-term and long-term health protectionImplement and evaluate adaptive strategies

Health Promotion

linking the individual with the planet!

Think Global, Act Local

CC and Health: Main Types of Adaptive StrategiesPublic education and awarenessEarly-alert systems: heatwaves, other impending weather extremes, infectious disease outbreaksCommunity-based neighbourhood support/watch schemesClimate-proofed housing design, and cooler urban layoutDisaster preparedness, incl. health-system surge capacityEnhanced infectious disease control programs vaccines, vector control, case detection and treatmentImproved surveillance: Risk indicators (e.g. mosquito numbers, aeroallergen concentration) Health outcomes (e.g. inf dis outbreaks, rural suicide rates, seasonal asthma peaks) Appropriate workforce training and mid-career development

Use of climate-health time-series data to develop a Malaria Early Warning System (Botswana)Thomson M, et al. Summer rain and subsequent malaria annual incidence in Botswana. Nature 2006; 439: 576-9 Highest malaria incidence years

(versus)Lowest malaria incidence yearsPrecipitation anomaly (mm / day)Observed summer (Dec-Feb) rainForecast (advance- modelled) summer rainRelationship between summer rainfall and subsequent annual malaria incidence Log malaria incidenceSummer Precipitation (mm / day)

1. Recognition of health risks will potentiate true primary prevention i.e. reduction of GHG emissions (mitigation). 2. Mitigation acitivities can/should provide health benefits and may, thus, help revitalise Health Promotion. 3. Health risks already exist, and more are unavoidable. So, we must define them in order to develop and evaluate adaptive (secondary prevention) strategies. Climate Change and Health Triple Purpose of Research

My best wishes to the Public Health Foundation of India an important, bold and very timely initiative

*Our solution was to estimate annual average PM concentrations for the 3,200 cities in the world with populations >100K using a model developed by Kiran Dev Pandey at the World Bank. These estimates, developed using the available measurements and econometric, socio-demographic and other data, indicated that the highest levels of PM, the tan and brown points, were concentrated in the developing countries of Asia, Africa, and Latin America. The model, however, was incapable of estimating concentrations in smaller cities, suburbs or rural areas, leaving an estimated 4 billion people unaccounted for in the burden estimates. Finer scale exposures, such as those related to proximity to traffic, were also not estimated. ***Handling domesticated animals

*Linking these projections to a series of climate-health models, take conservative simplistic account of csocioeconomic factors, come up with the following projections.

Insights:

Small changes in big burdens may be more important than large relative changes in small burden s (floods vs. diarrheoa)

Of course, climate is not the only influence on these disease, we need to take account of changes in socioeconomic conditions etc.

We address this in several ways. Firstly, we apply our proportional changes to disease burdens, and future projections, that already take account of socioeconomic influences over time, and between different WHO regions.. Small proportional changes in areas with a large disease burden therefore weigh much heavier than changes in areas where socioeconomic conditions and health services keep diseases in check.

Secondly, we make a series of reasonable conservative assumptions on SE effects. For example, based on previous analyses carried out at LSHTM we think it is unlikely that climate change will lead to re-establishment of malaria in Western Europe.

Finally, we make conservative assumptions about the climate sensitivity of these diseases. In the diarrhoea example, we have no evidence on the effect of temp on all cause DD in developed countries

****