Taking globally consistent health impact projections to the next level

Despite intensive research activity within the area of climate change, substantial knowledge gaps still remain regarding potential future impacts change on human health. A key shortcoming in scientific understanding these is lack studies that are conducted a coordinated and consistent fashion, producing directly comparable outputs. This Viewpoint discusses exemplifies bottom-up initiative generating new evidence more way compared with previous efforts. It describes one largest model comparisons projected health due to so far. Yet, included constitute only selection variety geographical locations, therefore not comprehensive assessment all possible impact pathways consequences. The findings shed light complex multidirectional health, where can be both adverse or beneficial. However, dominate overall, especially scenarios greenhouse gas forcing. Overall, population at risk disease incidence rates predicted increase substantially, but highly location-specific disease-specific fashion. Greenhouse emission mitigation substantially reduce resultant morbidity mortality. positive adaptation has been models applied, thus remains major source uncertainty. lays out strategy brings meaningful outputs calls for greater coordination initiatives across community. Climate increasingly recognised as potent globe its associated changes hazards, exposure, risks populations can, some extent, already observed today.1Watts N Amann M Arnell et al.The 2020 report Lancet Countdown change: responding converging crises.Lancet. 2020; 397: 129-170Summary Full Text PDF PubMed Scopus (403) Google Scholar, 2Smith K Woodward Campell-Lendrum D al.Human health: impacts, adaptation, co-benefits.in: Change 2014: vulnerability. Part A: global sectoral aspects. Contribution Working Group II fifth Intergovernmental Panel Change. Cambridge University Press, Cambridge2014: 709-754Google Scholar intrinsically related many ways, from direct stress heat exposure indirect through ecological systems vector-borne water-borne diseases, food production nutrition, extreme climatic events. Currently, rarely systematically investigated, community does exhibit strong mechanisms shown by other communities (eg, agriculture, water). reason this likely scattered different medical disciplines, common denominator restricted specific types exposures—ie, nutrition epidemiology, infectious occupational environmental epidemiology. These groups generally assemble meetings, there cross-talk among various issues. made public rare. To generate richer insights field exists an important need break researchers' silos promote impacts. overall continuous systematic domain stark contrast sciences, long-standing tradition assess such influences uncertainties, most prominently Coupled Model Intercomparison Project (CMIP).3Eyring V Bony S Meehl GA al.Overview Phase 6 (CMIP6) experimental design organization.Geosci Dev. 2016; 9: 1937-1958Crossref (2247) engages large researchers contributes independent synchronised manner Beyond science, non-health sectors also advanced joining efforts reveal additional benefits intercomparisons have offer.4Rosenzweig C Jones JW Hatfield JL Agricultural Improvement (AgMIP): Protocols pilot studies.Agr Forest Meteorol. 2013; 170: 166-182Crossref (586) 5Hagemann Chen Clark DB al.Climate available water resources obtained using multiple hydrology models.Earth Syst Dyn. 4: 129-144Crossref (220) Inter-Sectoral Impact (ISIMIP) projects aiming conduct sectors.6Huber Schellnhuber HJ NW research: beyond patchwork.Earth 2014; 5: 399-408Crossref (22) 7Warszawski L Frieler Huber Piontek F Serdeczny O Schewe J inter-sectoral intercomparison project (ISI–MIP): framework.Proc Natl Acad Sci USA. 111: 3228-3232Crossref (673) 8Frieler Levermann Elliott al.A framework cross-sectoral integration multi-model projections: land use decisions under uncertainties.Earth 2015; 6: 447-460Crossref (23) ISIMIP strives develop innovative approaches estimates harmonising input data (climatic non-climatic) defining simulation protocols. One benefit following approach become models. together working 12 sectors, encompassing water, marine ecosystems, biomes, forests, lakes, energy. hosts repository which makes socioeconomic data, well results achieved, entire interested entities. sector represented since foundation, initially focused diseases.9Caminade Kovats Rocklov al.Impact malaria distribution.Proc 3286-3291Crossref (299) process-based used closely resemble dynamic modelling were good first candidates inclusion into framework. More recently, expanded climate-sensitive outcomes, including empirical commonly ISIMIP—besides comparability output generated—has it required undertaking projections based accessible epidemiologists little no training sciences. special issue Planetary Health, includes six Articles10Martínez-Solanas E Quijal-Zamorano Achebak H al.Projections temperature-attributable mortality Europe: time series analysis 147 contiguous regions 16 countries.Lancet Planet Health. 2021; e446-e454Summary (8) 11Chua PLC Ng CFS al.Global enteric infections: study.Lancet e436-e445Summary (4) 12Colón-González FJ Sewe MO Tompkins AM al.Projecting mosquito-borne diseases warmer populated world: multi-model, multi-scenario e404-e414Summary (17) 13Trinanes Martinez-Urtaza Future Vibrio infections warming planet: mapping e426-e435Summary 14Dasgupta van Maanen Gosling SN Otto Schleussner C-F Effects combined labour productivity supply: empirical, e455-e465Summary (7) 15Zhao Q Guo Y Ye T al.Global, regional, national burden non-optimal ambient temperatures 2000 2019: three-stage e415-e425Summary (36) result activity, kicked off workshop Barcelona 2018 brought modellers disciplines. Most adhere guidelines developed second round ISIMIP, focus (ISIMIP2b). Bias-corrected derived CMIP5 up four models, concentration (Representative Concentration Pathways: RCP2.6, RCP4.5, RCP6.0, RCP8.5, numbers refer actual radiative forcing anthroponegic gases atmosphere, ie, 2·6, 4·5, 6·0, 8·5 W/m2), several (Shared Socioeconomic Pathways, SSPs; table). With exception paper, focuses Europe,10Martínez-Solanas assessments united scale. In addition, although Zhao colleagues15Zhao do present projections, they estimate grid, forming perfect starting point produce reliable scale.TableOverview criteria hotspot locationsHealth indicatorsTime periodsScenariosHotspot figuresMartinez-Solanas al, 202110Martínez-Solanas ScholarExcess attributable heat, cold, temperatures; European scaleMid-century (2035–64) End-century (2070–99) vs present-day (1976–2005)RCP2.6, RCP8.5; SSP narrativesAnomaly fraction >2% mid-century >4% end-century figures 3, 4 Europe onlyChua 202111Chua ScholarTemperature-attributable infections, considering ten aetiologiesReporting period: 2080–95 Temperature baseline: 1976–2005RCP2.6 SSP1 RCP4.5 SSP2 RCP6.0 SSP3 study-specific indicatorsAnnual mean number deaths combining aetiologies >0 0·5° × grid cell (with low investment); figure 3Colón-González 202112Colón-González ScholarSpatiotemporal suitability dengue exposure2006–99 1951–2005 baselineRCP2.6 &2 RCP8.5 SSP2&51 ?3 month increases length transmission season pathogens RCP 8.5 scenario; 2Trinanes Martinez-Urtaza, 202113Trinanes ScholarVibrio (sea surface temperature >18°C salinity <28 psu) risk2015–2100 1850–2014 baselineSSP245 SSP585 (CMIP6)1 every 30 years temporal SSP245; 4Dasgupta 202114Dasgupta ScholarLabour supply, productivity, (effective labour)GCM level-specific 20 year periods baseline (1986–2005)1·5°C, 2°C, 3°C above pre-industrial data>20% reduction effective outdoor work 4Zhao 202115Zhao ScholarPercentage excess temperatures2000–19Retrospective studyThis study was map because did include projectionsRCP=Representative Pathway. SSP=Shared CMIP=Coupled Project. GCM=General Circulation Model. Open table tab RCP=Representative Only few date. frequent being monitors development indicators how their trends over past decades current time, attempt impacts.1Watts Before indicator report, 2014, WHO published last assessment—a assessment.16WHOQuantitative effects selected causes death, 2030s 2050s. World Health Organization, Geneva2014Google sensitive covering diarrhoeal disease, heat-related mortality, malaria, dengue, undernutrition. All topics assessed likewise same data. each scenario, so-called ensembles, investigate uncertainties. statistical less terms non-linearity, lags, spatial covariance, neither captured details dynamics processes ecology, nor allowed interplay between aetiological agents hosts. Studies contrasting independently effort structural uncertainty hitherto presented here, primarily (ie, differing scenarios) incorporating range Colon-Gonzalez al12Colón-González structurally transmission. An earlier study9Caminade took similar approach, found larger than RCPs. Thus, uncertainties should strive diseases. standardised, example, prediction validity datasets. paragraphs, we briefly highlight value summarising five papers climate-associated burdens. We condense hotspots (figure; Non-optimal temperature-related impose high worldwide.17Gasparrini Hashizume al.Mortality temperature: multicountry observational study.Lancet. 386: 369-375Summary (16) Martínez-Solanas colleagues analysed 1998 2012 countries 420 million people.10Martínez-Solanas estimated 7·2% all-cause registered temperature, majority cold (6·5%). They then end century scenarios, represent trajectories, reference period trend. Cold-related decrease quickly trend RCPs 2.6 6.0 (low medium respectively), whereas expected far faster increasing trend, (high scenario). By century, total exceed levels would occur scenario. particular, Europe, Mediterranean region affected three (figure). Combined analyses useful could exacerbate alleviate hence what strategies might appropriate where. Enteric bacterial viral encompass suite collectively significant burden, low-income middle-income (LMICs). Global anticipated lead temperature-dependent growth indirectly quality.18Naumova EN Jagai JS Matyas B al.Seasonality enterically transmitted temperature.Epidemiol Infect. 2007; 135: 281-292Crossref (186) sensitivities Chua colleagues11Chua address will differentially pathogens. Focussing (sustainability “taking green road”), (middle road), (regional rivalry “a rocky rate ignoring warming. Then mortalities per agent anomalies aetiology-specific 6·0. Without consideration trends, (ten) significantly scenarios. worsen RCP6.0. worsening projection mainly shigellosis, cryptosporidiosis, typhoid, particularly sub-Saharan Africa South Asia (figure), bear brunt infections. On note, notably rotaviruses, importance. underlines importance outcomes. Mosquito-borne pathogens, parasites arboviruses (dengue, Zika, chikungunya) huge world's population, tropics subtropics, extend ranges.19Kraemer MU Reiner Jr, RC Brady OJ al.Past spread arbovirus vectors Aedes aegypti albopictus.Nat Microbiol. 2019; 854-863Crossref (290) 20Liu-Helmersson Stenlund Wilder-Smith Rocklöv Vectorial capacity aegypti: implications epidemic potential.PLoS One. 9e89783Crossref (222) potentially effect rainfall mosquito abundance vectorial capacity.21Liu-Helmersson Brännström Å Semenza JC Estimating past, present, distribution vector scenarios.Front Public 7: 148Crossref (29) Models previously relatively set SSPs.9Caminade issue, implemented ensemble narratives.12Colón-González authors combine regions: Africa, eastern Mediterranean, Americas, southeast Asia, western Pacific. Using predictions, find extends small degree Pacific regions, altitude. Under Americas intermediate altitudes. extension scenario Stratifying urban rural areas density, heterogeneities revealed. means (see appendix Colón-González Scholar). Significant areas. For notable very (>1 month) density Africa. vibriosis rising spreading conditions had considered pathogenic spp. Rising sea decreasing identified drivers emergence.22Semenza Trinanes Lohr W al.Environmental climate: early warning system.Environ Perspect. 2017; 125107004Crossref (55) web-based tool (the ECDC Map Viewer) monitor environmentally suitable quasi-real lacks any SSPs. Martinez-Urtaza13Trinanes applied subset generation CMIP SSP–RCP combinations provide realistic risk. Projections clear pattern expansion 183