{"id":"https://openalex.org/W7155405083","doi":"https://doi.org/10.1097/ee9.0000000000000483","title":"When does algorithmic complexity add causal value in environmental epidemiology?","display_name":"When does algorithmic complexity add causal value in environmental epidemiology?","publication_year":2026,"publication_date":"2026-04-23","ids":{"openalex":"https://openalex.org/W7155405083","doi":"https://doi.org/10.1097/ee9.0000000000000483","pmid":"https://pubmed.ncbi.nlm.nih.gov/42038062","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13108649"},"language":"en","primary_location":{"id":"doi:10.1097/ee9.0000000000000483","doi":"https://doi.org/10.1097/ee9.0000000000000483","is_oa":true,"landing_page_url":"https://doi.org/10.1097/ee9.0000000000000483","pdf_url":null,"source":{"id":"https://openalex.org/S4210236793","display_name":"Environmental Epidemiology","issn_l":"2474-7882","issn":["2474-7882"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-20","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310318547","host_organization_name":"Wolters Kluwer","host_organization_lineage":["https://openalex.org/P4310318547"],"host_organization_lineage_names":["Wolters Kluwer"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Environmental Epidemiology","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1097/ee9.0000000000000483","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068380736","display_name":"Perla Simons Morales","orcid":"https://orcid.org/0000-0003-1754-9828","observed_orcids":["https://orcid.org/0000-0003-1754-9828"]},"institutions":[{"id":"https://openalex.org/I169009895","display_name":"National Autonomous University of Honduras","ror":"https://ror.org/03xyve152","country_code":"HN","type":"education","lineage":["https://openalex.org/I169009895"]}],"countries":["HN"],"is_corresponding":false,"raw_author_name":"Perla Simons","raw_affiliation_strings":["Faculty of Medical Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras"],"raw_orcid":"https://orcid.org/0000-0003-1754-9828","affiliations":[{"raw_affiliation_string":"Faculty of Medical Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras","institution_ids":["https://openalex.org/I169009895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062883554","display_name":"Salvador Diaz","orcid":"https://orcid.org/0000-0002-6656-0233","observed_orcids":["https://orcid.org/0000-0002-6656-0233"]},"institutions":[{"id":"https://openalex.org/I169009895","display_name":"National Autonomous University of Honduras","ror":"https://ror.org/03xyve152","country_code":"HN","type":"education","lineage":["https://openalex.org/I169009895"]}],"countries":["HN"],"is_corresponding":false,"raw_author_name":"Salvador Diaz","raw_affiliation_strings":["Faculty of Medical Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras"],"raw_orcid":"https://orcid.org/0000-0002-6656-0233","affiliations":[{"raw_affiliation_string":"Faculty of Medical Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras","institution_ids":["https://openalex.org/I169009895"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123336798","display_name":"Antonio Garcia Loureiro","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I200284239","display_name":"Universidade de Santiago de Compostela","ror":"https://ror.org/030eybx10","country_code":"ES","type":"education","lineage":["https://openalex.org/I200284239"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio Garcia Loureiro","raw_affiliation_strings":["Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain"],"raw_orcid":"https://orcid.org/0000-0003-0574-1513","affiliations":[{"raw_affiliation_string":"Department of Electronics and Computer Science, University of Santiago de Compostela, Santiago de Compostela, Spain","institution_ids":["https://openalex.org/I200284239"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006097761","display_name":"Isaac Zablah","orcid":"https://orcid.org/0000-0002-1410-7189","observed_orcids":["https://orcid.org/0000-0002-1410-7189"]},"institutions":[{"id":"https://openalex.org/I169009895","display_name":"National Autonomous University of Honduras","ror":"https://ror.org/03xyve152","country_code":"HN","type":"education","lineage":["https://openalex.org/I169009895"]}],"countries":["HN"],"is_corresponding":true,"raw_author_name":"Isaac Zablah","raw_affiliation_strings":["Faculty of Medical Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras"],"raw_orcid":"https://orcid.org/0000-0002-1410-7189","affiliations":[{"raw_affiliation_string":"Faculty of Medical Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras","institution_ids":["https://openalex.org/I169009895"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5006097761"],"corresponding_institution_ids":["https://openalex.org/I169009895"],"apc_list":{"value":1200,"currency":"USD","value_usd":1200},"apc_paid":{"value":1200,"currency":"USD","value_usd":1200},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"10","issue":"3","first_page":"e483","last_page":"e483"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.7128999829292297,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.7128999829292297,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10845","display_name":"Advanced Causal Inference Techniques","score":0.12880000472068787,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T14374","display_name":"Statistical Methods in Epidemiology","score":0.12770000100135803,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/targeted-minimum-loss-based-estimation","display_name":"targeted minimum loss-based estimation","score":0.8679999709129333},{"id":"https://openalex.org/keywords/super-learner","display_name":"super learner","score":0.9789999723434448},{"id":"https://openalex.org/keywords/algorithmic-complexity","display_name":"algorithmic complexity","score":0.7639999985694885},{"id":"https://openalex.org/keywords/bias-variance-trade-off","display_name":"bias-variance trade-off","score":0.9380000233650208},{"id":"https://openalex.org/keywords/pm2-5","display_name":"PM2.5","score":0.8830000162124634},{"id":"https://openalex.org/keywords/mortality-benefit","display_name":"mortality benefit","score":0.675000011920929},{"id":"https://openalex.org/keywords/causal-inference","display_name":"causal inference","score":0.5239999890327454},{"id":"https://openalex.org/keywords/g-computation","display_name":"g-computation","score":0.7250000238418579},{"id":"https://openalex.org/keywords/doubly-robust-estimation","display_name":"doubly robust estimation","score":0.7689999938011169},{"id":"https://openalex.org/keywords/exposure-heterogeneity","display_name":"exposure heterogeneity","score":0.7670000195503235},{"id":"https://openalex.org/keywords/hyperparameter-robustness","display_name":"hyperparameter robustness","score":0.8769999742507935},{"id":"https://openalex.org/keywords/exposure-response-relationship","display_name":"exposure-response relationship","score":0.49399998784065247},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"mean squared error","score":0.5149999856948853},{"id":"https://openalex.org/keywords/low-dose-exposure","display_name":"low-dose exposure","score":0.49000000953674316}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.47850000858306885},{"id":"https://openalex.org/C2776291640","wikidata":"https://www.wikidata.org/wiki/Q2912517","display_name":"Value (mathematics)","level":2,"score":0.4742000102996826},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33239999413490295},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.30379998683929443},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29330000281333923},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2856000065803528},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2572999894618988}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1097/ee9.0000000000000483","doi":"https://doi.org/10.1097/ee9.0000000000000483","is_oa":true,"landing_page_url":"https://doi.org/10.1097/ee9.0000000000000483","pdf_url":null,"source":{"id":"https://openalex.org/S4210236793","display_name":"Environmental Epidemiology","issn_l":"2474-7882","issn":["2474-7882"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-20","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310318547","host_organization_name":"Wolters Kluwer","host_organization_lineage":["https://openalex.org/P4310318547"],"host_organization_lineage_names":["Wolters Kluwer"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Environmental Epidemiology","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13108649","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13108649/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13108649/pdf/ee9-10-e483.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Environ Epidemiol","raw_type":"Text"},{"id":"pmid:42038062","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42038062","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Environmental epidemiology (Philadelphia, Pa.)","raw_type":"Letter"}],"best_oa_location":{"id":"doi:10.1097/ee9.0000000000000483","doi":"https://doi.org/10.1097/ee9.0000000000000483","is_oa":true,"landing_page_url":"https://doi.org/10.1097/ee9.0000000000000483","pdf_url":null,"source":{"id":"https://openalex.org/S4210236793","display_name":"Environmental Epidemiology","issn_l":"2474-7882","issn":["2474-7882"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","dongbi-c","jufo-1","ki-jl-1","norway-1","oarl","poland-20","russia-white-list-2","vabb-shw"],"host_organization":"https://openalex.org/P4310318547","host_organization_name":"Wolters Kluwer","host_organization_lineage":["https://openalex.org/P4310318547"],"host_organization_lineage_names":["Wolters Kluwer"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Environmental Epidemiology","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.8048999905586243,"display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W2946169684","https://openalex.org/W3018283450","https://openalex.org/W4323921070","https://openalex.org/W4404319887","https://openalex.org/W4408149471"],"related_works":["https://openalex.org/W4412398530","https://openalex.org/W4408149471","https://openalex.org/W2301601600","https://openalex.org/W4406444135","https://openalex.org/W3198426176","https://openalex.org/W4413140357","https://openalex.org/W3202205457","https://openalex.org/W7133585989","https://openalex.org/W7162465161","https://openalex.org/W2796166690"],"abstract_inverted_index":{"To":[0,249,435],"the":[1,46,96,122,131,137,184,246,307,346,450,468,475,574,667,685,689,754,816,819,848,881],"Editor:":[2],"The":[3,118,658,725,740,868],"short":[4],"communication":[5],"by":[6,650,757],"Chen":[7,269,584,758,777],"et":[8,270,585,759,778],"al.1":[9,586,760],"delivers":[10],"a":[11,37,60,100,168,401,424,428,441,456,628,640,729,735,787,844],"rigorous":[12],"comparative":[13],"analysis":[14,154],"of":[15,103,121,173,183,186,349,358,444,478,590,661,847,865,876,883],"three":[16],"causal":[17,303,340,472,595],"estimators":[18],"used":[19],"to":[20,74,253,369,391,573,880],"quantify":[21],"mortality":[22,483],"benefits":[23],"associated":[24],"with":[25,31,52,76,91,161,209,214,275,539,587,600,753,772,878],"reductions":[26],"in":[27,95,295,471,619,680,715,720,791,801,861],"fine":[28,211,597,769],"particulate":[29,212,598,770],"matter":[30,213,300,599,771],"aerodynamic":[32,215,601,773],"diameter":[33,216,602,774],"\u22642.5":[34,217,603,775],"\u03bcm":[35,218],"within":[36,167],"Canadian":[38,251,287,668,741],"national":[39,513,556],"cohort.":[40],"Their":[41,151,797],"central":[42],"finding":[43],"is":[44,86,125,134,205,625,743,794,840],"that":[45,159,244,333,691,800,824,836,871],"targeted":[47],"minimum":[48],"loss-based":[49],"estimation":[50],"(TMLE)":[51],"SuperLearner,":[53],"despite":[54],"incorporating":[55],"six":[56],"candidate":[57],"algorithms,":[58],"produced":[59],"risk":[61],"difference":[62],"(\u22120.67":[63],"per":[64],"1,000":[65],"participants;":[66],"95%":[67,80,321],"CI":[68,81,322],"=":[69,82,323],"\u22121.27,":[70],"\u22120.06)":[71],"statistically":[72],"comparable":[73],"TMLE":[75,160,165,315,328],"parametric":[77,164,486,710],"models":[78],"(\u22120.83;":[79],"\u22121.24,":[83],"\u22120.43),":[84],"which":[85,851],"striking.":[87],"This":[88,177,330,578,623],"concordance,":[89],"coupled":[90],"substantially":[92],"greater":[93,337],"variance":[94,145,247,810],"flexible":[97,394,692,806],"estimator,":[98],"highlights":[99],"structural":[101],"condition":[102],"low-concentration,":[104],"relatively":[105],"homogeneous":[106,804],"exposure":[107,203,408,481,492,651,700],"settings":[108,564,717],"where":[109,130,232,718],"algorithmic":[110,367,418,611,833,853],"complexity":[111,419,612],"may":[112,239,448,613,695,712,808,855,857],"not":[113,313,664,673,781,858],"deliver":[114],"meaningful":[115,241],"inferential":[116,398],"gains.":[117],"author\u2019s":[119],"treatment":[120],"bias-variance":[123,452],"tradeoff":[124],"methodologically":[126,261],"convincing.":[127],"In":[128,207,835],"contexts":[129,255],"exposure-response":[132],"relationship":[133],"smooth":[135],"and":[136,170,220,285,326,382,412,569,594,653,671,704,828],"signal-to-noise":[138],"ratio":[139],"modest,":[140],"ensemble":[141],"learners":[142],"often":[143],"accumulate":[144],"without":[146,256,811],"achieving":[147],"sufficient":[148],"bias":[149,242,721],"reduction.2":[150],"mean-squared":[152],"error":[153],"reinforces":[155],"this":[156,278,292,356,884],"interpretation,":[157,437],"showing":[158],"SuperLearner":[162],"surpasses":[163],"only":[166,745,782,842],"narrow":[169],"implausible":[171],"range":[172,493],"hypothetical":[174,641],"true":[175],"values.":[176],"finding,":[178],"while":[179],"persuasive,":[180],"reminds":[181],"us":[182],"importance":[185,359],"contextualizing":[187],"methodological":[188,347,581,788],"conclusions":[189],"rather":[190,426,454,632,733],"than":[191,342,427,455,633,734],"generalizing":[192],"across":[193,505],"diverse":[194],"environmental":[195,620,862],"settings.":[196],"Three":[197],"dimensions":[198],"deserve":[199],"closer":[200],"attention.":[201],"First,":[202],"heterogeneity":[204,508,652],"decisive.":[206],"regions":[208],"broader":[210],"distributions":[219],"steeper":[221],"concentration":[222],"gradients,":[223],"such":[224,254],"as":[225,293,362,423,627,676,699,746,843],"South":[226],"Asian":[227],"or":[228,512,518,544,549,557,562,616,856],"urban":[229,511],"African":[230],"environments":[231],"concentrations":[233],"frequently":[234],"exceed":[235],"40":[236],"\u03bcg/m3\u2014adaptive":[237],"algorithms":[238,395,807],"provide":[240],"reduction":[243,679,722],"justifies":[245],"cost.3":[248],"extrapolate":[250],"findings":[252,755,798],"empirical":[257,635,783],"validation":[258],"would":[259],"be":[260,571,614,674,859],"unsound.":[262],"Second,":[263],"hyperparameter":[264],"robustness":[265],"demands":[266],"caution.":[267],"Although":[268],"al1":[271,779],"tested":[272],"28":[273],"specifications":[274,316],"minimal":[276],"variation,":[277],"stability":[279],"likely":[280],"reflects":[281],"limited":[282],"confounder":[283,410],"dimensionality":[284,531],"uniform":[286],"exposure.4":[288],"Personally,":[289,353],"I":[290,354],"see":[291,432],"context-specific;":[294],"heterogeneous":[296],"datasets,":[297],"sensitivity":[298],"could":[299],"greatly,":[301],"altering":[302],"inference":[304],"reliability.":[305,371],"Third,":[306],"most":[308],"analytically":[309],"instructive":[310],"contrast":[311],"lies":[312],"between":[314,318,379],"but":[317,785],"g-computation":[319],"(\u22120.23;":[320],"\u22120.46,":[324],"0.00)":[325],"both":[327],"approaches.":[329,384],"divergence":[331],"suggests":[332],"estimator":[334,370,656,826],"choice":[335,827],"exerts":[336],"influence":[338],"on":[339,482,645],"estimates":[341,784],"algorithm":[343],"selection,":[344],"reinforcing":[345],"priority":[348],"doubly":[350,591],"robust":[351,592],"frameworks.2":[352],"find":[355],"hierarchy":[357],"particularly":[360,795],"compelling,":[361],"it":[363],"redirects":[364],"attention":[365],"from":[366,583,666],"novelty":[368],"Future":[372],"research":[373],"should":[374,403,570,672],"move":[375],"beyond":[376],"broad":[377,503],"dichotomies":[378],"\u201cmachine":[380],"learning\u201d":[381],"\u201cparametric\u201d":[383],"Instead,":[385],"explicit":[386],"decision":[387,458,464,738],"criteria":[388,831],"are":[389,567,723],"needed":[390],"determine":[392],"when":[393,610],"genuinely":[396],"add":[397],"value.":[399],"Such":[400],"framework":[402,465,579,608],"consider":[404],"effective":[405],"sample":[406,522,702],"support,":[407,703],"heterogeneity,":[409,701],"dimensionality,":[411],"plausible":[413,705],"nonlinearity.5":[414],"Without":[415],"these":[416,446],"criteria,":[417],"risks":[420],"being":[421],"adopted":[422],"default":[425],"justified":[429],"analytical":[430,469],"decision,":[431],"Table":[433,460],"1.":[434,461],"aid":[436],"Figure":[438,605,838],"1":[439,839],"provides":[440],"conceptual":[442,630,845],"schematic":[443,631],"how":[445],"features":[447],"shape":[449],"expected":[451,654],"tradeoff,":[453],"quantitative":[457],"rule.":[459],"-":[462],"Proposed":[463],"for":[466,609,821,832],"selecting":[467],"method":[470],"studies":[473],"estimating":[474],"health":[476],"effects":[477],"long-term":[479],"PM2.5":[480,491],"Criterion":[484],"Favors":[485,488],"approach":[487,490],"machine-learning":[489,693],"Narrow":[494],"(\u22648":[495],"\u03bcg/m3;":[496,502],"near":[497],"regulatory":[498],"threshold)":[499],"Wide":[500],"(>20":[501],"gradient":[504],"population)":[506],"Exposure":[507],"Low":[509,532],"(uniform":[510],"settings)":[514],"High":[515,536],"(mixed":[516],"urban-rural":[517],"transboundary":[519],"pollution)":[520],"Effective":[521],"size":[523],"Moderate":[524],"(<50,000":[525],"person-years)":[526,529],"Large":[527],"(>100,000":[528],"Confounder":[530],"(<15":[533],"baseline":[534],"variables)":[535],"(>30":[537],"variables":[538],"interactions)":[540],"Hypothesized":[541],"nonlinearity":[542,706],"Mild":[543],"approximately":[545],"linear":[546],"Pronounced":[547],"(supra-linear":[548],"threshold":[550],"effects)":[551],"Policy":[552],"application":[553],"context":[554],"Established":[555],"regional":[558],"standards":[559],"Local":[560],"interventions":[561],"high-heterogeneity":[563],"All":[565],"thresholds":[566],"approximate":[568],"adapted":[572],"specific":[575],"epidemiological":[576],"context.":[577],"synthesizes":[580],"considerations":[582],"general":[588],"principles":[589],"estimation2,4":[593],"transportability.5PM2.5,":[596],"\u03bcm.":[604],"1.:":[606],"Conceptual":[607],"more":[615,697],"less":[617],"advantageous":[618],"epidemiology.":[621,863],"Note.":[622],"figure":[624,686],"intended":[626,841],"qualitative":[629],"an":[634,646,677,747],"estimate.":[636],"Each":[637],"point":[638],"represents":[639,728],"study":[642,817],"setting":[643],"positioned":[644],"illustrative":[647,748],"scale":[648],"defined":[649],"relative":[655],"performance.":[657],"vertical":[659],"placement":[660],"points":[662],"was":[663],"estimated":[665,737],"cohort":[669],"data":[670],"interpreted":[675],"observed":[678],"mean":[681,762],"squared":[682,763],"error.":[683],"Rather,":[684],"heuristically":[687],"summarizes":[688],"idea":[690],"approaches":[694,711],"become":[696],"attractive":[698],"increase,":[707],"whereas":[708],"simpler":[709],"remain":[713],"preferable":[714],"lower-heterogeneity":[716],"gains":[719],"limited.":[724],"dashed":[726],"curve":[727],"heuristic":[730],"transition":[731],"zone":[732],"formally":[736],"boundary.":[739],"example":[742],"included":[744],"low-exposure,":[749],"low-heterogeneity":[750],"setting,":[751],"consistent":[752],"reported":[756],"SE,":[761],"error;":[764],"ML,":[765],"machine":[766],"learning;":[767],"PM2.5,":[768],"\u03bcm.Briefly,":[776],"contribute":[780],"also":[786],"reflection":[789],"that,":[790],"our":[792],"view,":[793],"valuable.":[796],"show":[799],"low":[802],"exposure,":[803],"contexts,":[805],"inflate":[809],"reducing":[812],"bias.":[813],"More":[814],"broadly,":[815],"underscores":[818],"need":[820],"context-sensitive":[822],"frameworks":[823],"prioritize":[825],"define":[829],"clear":[830],"complexity.":[834],"spirit,":[837],"summary":[846],"conditions":[849],"under":[850],"added":[852],"flexibility":[854],"worthwhile":[860],"Conflicts":[864],"interest":[866,877],"statement":[867],"authors":[869],"declare":[870],"they":[872],"have":[873],"no":[874],"conflicts":[875],"regard":[879],"content":[882],"report.":[885]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-04-24T00:00:00"}
