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Handbook of Spatial Epidemiology

Andrew B. Lawson, Sudipto Banerjee, Robert P. Haining, María Dolores Ugarte

Interpreting Clusters of Health Events

Publication details https://www.routledgehandbooks.com/doi/10.1201/b19470-5 Geoffrey Jacquez, Jared Aldstadt Published online on: 04 Apr 2016

How to cite :- Geoffrey Jacquez, Jared Aldstadt. 04 Apr 2016, Interpreting Clusters of Health Events from: Handbook of Spatial Epidemiology CRC Press Accessed on: 29 Sep 2021 https://www.routledgehandbooks.com/doi/10.1201/b19470-5

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Downloaded By: 10.3.98.104 At: 17:52 29 Sep 2021; For: 9781482253023, chapter3, 10.1201/b19470-5 ovriuiemnn MDnl ta.20) h lseigo ekma ypoa and lymphoma, leukemia, of clustering the 2004); related a al. exposures of asbestos et finding from (McDonald cancer a mining lung making of vermiculite small, cluster specific to Montana often to Libby linked is as the successfully cases actual include such clusters long, disease exposures of diseases often Chronic number is chronic difficult. the latency For excess disease significant and 2011). since statistically measured, unclear, al. often not et is are cause (Commins putative exposures bites a to tick link association and the the cancer, allergy and 1981) food and al. 1995). et a discov- 1938), (Friedman-Kien (Harada include between syndrome events (Dean disease deficiency health immune Minamata’s caries adverse acquired of of dental of clustering cause ery and by the triggered water as connections dis- drinking etiological poisoning the Other in mercury 1855), fluoride of (Snow identification between cholera frequently the of link Applications investigation the clear. Snow’s less of contribu- include is covery the epidemiology exemplars citizens, spatial successful concerned to in by as response analysis cluster health cited cluster possible public a of analysis. a of role cluster of reports and part disease to tion be of or may disease, utility analysis of the cluster outbreak regarding that an recognized years widely the is over it debate While some been has There Introduction 3.1 Introduction 3.1 CONTENTS Buffalo York at New York Buffalo, New of University State Geography of Department Aldstadt Jared Buffalo York at New York Buffalo, New of University State Geography of Department Jacquez Geoffrey Events Health of Clusters Interpreting 3 . o r lse tde sdadWa sTerFunction? Their Is What and Used Studies Cluster Are How 3.2 . Conclusions References Study Case 3.5 Issues Relevant 3.4 3.3 .. ofimtr tde (association) studies Confirmatory 3.2.2 .. ulchat response health Public 3.2.1 .. r lse tde cec rl fcutrsuisi scientific in studies cluster of (role science studies cluster Are knowledge) (advancing testing Hypothesis 3.2.4 3.2.3 ...... inquiry)? ...... & a K39 K23899 — #K23899 Cat T&F ...... 03—pg 7—31/06—7:45 — 3/10/2016 — 57 page — C003 ...... 57 57 58 58 59 63 63 62 61 59 59 Downloaded By: 10.3.98.104 At: 17:52 29 Sep 2021; For: 9781482253023, chapter3, 10.1201/b19470-5 eue nvciain cenn,adohratvte htrdc ies udn The burden. disease reduce called that be may framework activities response health other public a the and are within methods screening, studies clustering reliably cluster vaccination, of might suggested application resources in (1990) those result when Neutra used to especially why failure agencies, be reasons This health significant the public 1988). statistically for of Aldrich enterprise a and one wasteful Warner find is 1987; not findings al. do positive et they in (Schulte by cases is, disease that results—that majority disease of negative is the excess local have practice, rationale In apparent studies probability. The higher cluster “preselect” with positives). of clusters to within chance (false sharpshooter act report issues thereby error “Texas citizens and Attendant I excesses environment, the 2007). type their as al. the surveying colloquially et constantly increase known may Kingsley bias, which 1990; concerned preselection a problem,” by CDC of reported include 2004; context and framework identified the al. events this within health et used for of (Buehler be Centers clustering the may a citizens at they to studies how response cluster and health of (CDC) review public Prevention useful a and provide Control others Disease and (2007) al. response et Kingsley health Public scientific advance health 3.2.1 systematically a to turn. and framework in exposure testing these environmental of hypothesis studies each putative confirmatory a consider a We in in knowledge. between (2) (3) citizens, association and concerned an response outcome, Function? by health for public forward Their a search brought of Is part that allegation What as cluster (1) and capacities: a three Used in to used Studies commonly are Cluster studies Cluster Are How 3.2 to (refer methods with not ourselves concern processes? disease we of Here, disease understanding of ters. our causes advanced underlying analyses their plausible cluster as alone the have let analysis are how clusters, cluster then of And view of What existence clusters? to 1990). majority the others into (Neutra the and insights causes little Neutra studies, underlying yields leading possi- cluster that excess, trimer endeavor Jersey, positive significant expensive (SAN) New a some an styrene-acrylonitrile find River, been not and Toms have do acrylonitrile, in them there possible styrene, cancers Although a to 1997). and systems exposures (ATSDR 2010); nervous by (ATSDR central water caused and drinking bly brain from trichloroethy- of carcinogens to other exposures cluster to and due Carolina, benzene, North lene, Lejeune, Camp in outcomes birth adverse 58 h ml ubr rbe,adapcso netit.W ekt dniywa a be demonstrate can to avoided. what included be is identify can study ascertainment, to pitfalls case case the seek A way. as of We the uncertainty. several such along that of encountered issues pitfalls relevant aspects the and and and inquiry, known, problem, have scientific numbers studies in small cluster role when the discuss their we used, rather, surveillance); been and clustering for tests statistical hscatrpoie noeve fise eta oteitrrtto fdsaeclus- disease of interpretation the to central issues of overview an provides chapter This hscatri raie rudtreqetoso topics: or questions three around organized is chapter This .Wa r h sust osdrwe nepeigcutrsuyresults? study cluster interpreting scientific when in consider analysis to cluster issues of the are role What the is 3. what and function? science, their studies is cluster what Are and used, studies 2. cluster are How 1. inquiry? & a K39 K23899 — #K23899 Cat T&F 03—pg 8—31/06—7:45 — 3/10/2016 — 58 page — C003 adoko pta Epidemiology Spatial of Handbook hpe 8 Chapter nti ouefor volume this in Downloaded By: 10.3.98.104 At: 17:52 29 Sep 2021; For: 9781482253023, chapter3, 10.1201/b19470-5 l) eea usin n susms eadesdwe nepeigcutr nspatial mod- in neutral clusters interpreting (e.g., when support clusters addressed that observed hypotheses be explain null must might (2) issues and epidemiology: and that incident registry questions factors of disease Several known census a els). complete by of an provided a inclusion from from be to the usually may or studies as design, cohort) cluster such or sampling for cases case–control sound order (e.g., a in study (1) necessary epidemiological knowledge: components scientific epidemiological two spatial in identified advance studies discussion cluster above of The (role science studies cluster Are epidemiology. patterns spatial 3.2.4 spatial in the methods regarding clustering hypotheses of makes test utility This to the 2005). increasing analysis may 2004, thereby cluster factors Jacquez disease, disease explanatory and of use known (Goovaerts to in models” possible patterns “neutral nuisance) sta- it using Spatial identify (e.g., for account. to explanatory accounted studies into known cluster be taken provided use been disease, to have of possible cancers is factors excesses that breast it available local and cases, are significant incident 2011), registries all, tistically disease al. not good-quality et if When (Schmiedel most, others. document among diabetes par- 2013), and (Sloan al. a cancers leukemia et successfully testicular in (Jacquez childhood of been identified 2013), clustering has causal potential factors al. approach the identify et risk This to beyond study. studies and the case–control epidemiological are above by in parent are employed methods explained the then analysis in identified not identified so cluster is 2009a, factors clusters Here, al. that Any post-epidemiology. study. et risk epidemiological as (Meliker residual ent to structure the referred inference to clus- robust be applied with more may designs study and a and epidemiological Popper supports 2009b) of by techniques coupling employ forward analysis The not put 1968). ter do method Popper associations scientific thus 1964; identify definition the and (Platt to by variables, with public others seek environmental consistent are the studies framework of they inferential in Confirmatory patterns as an clusters. studies spatial design, alleged and cluster sampling to clusters above, sound between response noted a hoc As spatial lack post of disease. often a think under- of framework better can correlates to response We patterns and epidemiology? health disease spatial causes spatial in of the analysis studies stand and cluster documentation of the as role epidemiology the is knowledge) then, (advancing What, testing Hypothesis epidemiology. spatial perspective, 3.2.3 for that appeal From have little 2004). not have (Jacquez does association predictions it for hypotheses, test search causal that or studies suggest hypotheses may confirmatory reject activity to an exposures power is environmental such with it the While associations found, factors. for been searching other have by and events causes possible health seek of to clusters tempting significant statistically geographically (association) Once studies Confirmatory demonstrated. been has 3.2.2 sound cluster epidemiologically significant an statistically a and after lacking only be imposed may is sample design study designed a since pre-epidemiology Events Health of Clusters Interpreting 1. on,uulywt hi pta xet ouainsz ffce,ada estimate an and are affected, clusters size those population where extent, report spatial some- methods their risk clustering with excess local usually of area; existence found, the study to the refers in clustering where global Here, tests. focused or excess? an there Is inquiry)? & a K39 K23899 — #K23899 Cat T&F otcutrn ehd aludrterbi fgoa,local, global, of rubric the under fall methods clustering Most 03—pg 9—31/06—7:45 — 3/10/2016 — 59 page — C003 59 Downloaded By: 10.3.98.104 At: 17:52 29 Sep 2021; For: 9781482253023, chapter3, 10.1201/b19470-5 60 4. 3. 2. rnpr nantoki upce,oemgtcos oln oain sn the using locations link if to But choose might locations. to nearby one locations for suspected, adjacent 1 is link of network weight to a spatial sense on a make transport specify exam- may example, For for it process. another, suspected, cluster one is underlying diffusion the fully when reflects that ple, is manner covariates a hypothesis. the in null accomplished in reasonable a structure be spatial expected not minus unless may (observed CSR But residuals for, pattern. the risk accounted known spatial covariate inspect incorporating then for the while and risk) risk for model, expected the account the into that model factors also age, procedures may Monte as One randomization or structure. such randomization with use covariates employ to methods sel- to with is this is solution Carlo associated solution one practice, data, be Another In area-based rates. may area. with covariate-adjusted working study risk When the ethnicity. disease absence and across the since income, uniform in true, be risk, is will disease ran- disease dom spatial process, spatial underlying of cluster the complete tests a that of statistical of implies use many usually in the hypothesis CSR is the null clustering. example disease the Often, One underlying as 1995). the hypothesis thought. (CSR) of domness (Griffiths little alternative terms given weights in the hypotheses is spatial while alternative process and the process, null of the cluster of specification meaning a pattern in of spatial embodied the absence is regarding the statement a in is expected hypothesis null The hypotheses. tive workers employed, hypothesis? be Dobrzy´nski the 2010). to and What’s (Fornalski order health in example sound where An comparatively effect, cluster. worker in to healthy be noncases the must disease or mod- is elevated cases and latter of either facilities, the treatment cause of absence near that the behaviors be in to in moving ifications hypotheses together patients Case-attractor include cases Examples processes. bring risk. disease that the and processes and covariates, exposures describe in causative may pattern of explanations spatial of action set hypotheses, The systematic case-attractor information. a chance, or in data include excluded additional enu- of be been analysis have may by of these explanations fashion set Once alternative the excess. specific observed of these the identification merated, underlie the may involve that may causes This possible earnest. in begins mean? interpretation it does be real. must What is significance, cluster statistical a a as whether of assessing well expla- comprised when biological as plausible considered plausibility, are common, with biological clusters a cases Hence, with True of nation. cases causes. comprised of unrelated excess be significant may with statistically The clus- and illnesses False findings. explanation 1990). or cluster biological (CDC false symptoms clusters plausible to true a rise and chance. lack giving false by well, ters between expected as distinction are role a events a makes dis- health play CDC in of can excess excess factors underlying an true, other a of But reflecting findings of Spurious sense risk. the in ease real, is cluster disease the may real? it excess Is that where and iden- excess, to significant 1991; used statistically found. Newell are a be and methods is clustering (Besag there Statistical focus whether 1995). or tify in al. risk location et elevated Waller suspected is 1989; a there Lawson whether of assesses vicinity clustering immediate focused the and risk; excess the of & a K39 K23899 — #K23899 Cat T&F o h lentv yohss h pcfiaino pta egt hudbe should weights spatial of specification the hypothesis, alternative the For h dnicto fasaitclysgicn xesde o enthat mean not does excess significant statistically a of identification The hnara xeshsbe dnie,tecalneof challenge the identified, been has excess real a When lotaltssfrcutrn mlynl n alterna- and null employ clustering for tests all Almost 03—pg 0—31/06—7:45 — 3/10/2016 — 60 page — C003 adoko pta Epidemiology Spatial of Handbook Downloaded By: 10.3.98.104 At: 17:52 29 Sep 2021; For: 9781482253023, chapter3, 10.1201/b19470-5 ficmlt eotn ssailysrcue,i a iers otefidn fcutr when clusters of finding the to rise give can it structured, spatially is reporting incomplete of bandtruhantfibedsaerprigsse ramnsrtv elhrecords health administrative 2009). are or Svenson data system and whether Yiannakoulias reporting 2012; on disease al. WNV depending that et notifiable human considerably, shown (Jones have vary of a studies results clusters through comparative virulence, Additionally, clustering case, obtained strain risk. and this or infection characteristics, analysis of In host human indicator spatial diagnoses, 2006). an Most in al. as variability 2005). that well of et al. features result as (Davis et its clinical a Hayes misdiagnosed be common since may very 2010; be America illnesses in al. to result North et many likely in Ruiz and are 2001; transmission asymptomatic, understanding al. are (WNV) and infections et virus describing WNV (Nash Nile into 1999 West in gone of introduction has patterns findings. effort spatial cluster Considerable the misdiagnoses? of there interpretation and are problem, the numbers impact small the can error. reporting, that location incomplete geocoding ascertainment, issues case other include These several are There Issues Relevant 3.3 Events Health of Clusters Interpreting nopeereporting Incomplete Accurate 6. 5. eetdsae ndffrn iutoscngv iet iia pc–iepatterns space–time similar to is dif- rise since this give occurrence. information, But can case additional 1). situations of of table different absence 2013, in regarding the diseases al. in inferences ferent et especially construct Jacquez prospect, to e.g., difficult possible (see, space–time a process and be disease may temporal, are underlying spatial, it data an for used, the tests are when and clustering several But fashion, disease be cluster. systematic infer- will a disease make in usually observed to sampled there an difficult since for is it explanations processes, citizens, alternative disease concerned underlying by regarding reported ences processes? cases epidemiological disease of underlying parent consist regarding data drawn the be in can inferences sample What the apply which will from drawn. inference was of population study registry. scope study the the study, by the used, the covered When is to in population framework sample. entire included postepidemiological the the be a to to could When apply only conceivably then and would cases apply absent, inferences all would is and analyzed, thus population are drawn designed study data be a underlying for registry may Here, an that encountered, citizens. of inferences be concerned representative any by may is the reported data that within been sample the Especially have design. that above, cases sampling mentioned example, and framework frame health sampling the public of design? on results and premised the frame be sampling on the scale is spatial What of effect be anecdotal, may the scales analysis. or of spatial cluster absent analysis different the be sensitivity to may a corresponding in clustering sets weight employed, the spatial of case certain scale which coupled In in the hypothesis. be, alternative of might each requires knowledge for hypotheses weights This instances, alternative spatial set. of the weight of set alterna- construction spatial of the with suite own what a of its explore enumeration to to careful wish corresponding may each thus hypotheses, one tive practice, In network. road actual & a K39 K23899 — #K23899 Cat T&F aeascertainment case a euetepwrt eetatu lse.Adwe h extent the when And cluster. true a detect to power the reduce can sciia.Aecsscretydansd n owa extent what to and diagnosed, correctly cases Are critical. is 03—pg 1—31/06—7:45 — 3/10/2016 — 61 page — C003 nepeaino lse nigmust finding cluster a of Interpretation hnthe When 61 Downloaded By: 10.3.98.104 At: 17:52 29 Sep 2021; For: 9781482253023, chapter3, 10.1201/b19470-5 oesta s ecddlctos(audre l 08.Hwvr ttetm fthis results of cluster time of exposure the 2006). interpretation into at al. the However, bias in et ignored 2008). Whitsel introducing routinely Whitsel 2012; al. and is (Jacquez 2005; et error 2012) (Mazumdar al. location al. locations et geocoding et statistical writing, Oliver geocoded decreasing Zandbergen use 2003; by 2006; that al. analysis al. models et cluster disease et Bonner uncer- impacts (Rushton 2001; location uncertainty power al. associated Location an et 2006). have al. (Krieger geographic address) et (the known an well geocodes geocoding of That is result 2008). tainty the Goldberg are 2008; unreliable. that Stinchcomb are coordinates and results (Abe analysis events cluster health null the the CSR), in is for studies accounted the not most into is in to autocorrelation autocorrelation (which prior spurious spatial hypothesis this smoother spurious Since popula- Bayesian introduces rates. empirical for practitioners smoothed smoothing an account resulting Some Such using positives). not analysis. smoothed (false does Moran findings be rates, local cluster rates Moran disease spurious disease local analysis raw yield that The can cluster to do. recommend population) hence them Many applied at-risk and of decreases. often the size, all denominator is not tion of but the example, size size, in for the population population statistic, in by differences at-risk divided for the e.g., account of methods cases, size incident the of number as the as lated neaiaino motn gso xoueadacut o h aec eidbetween for allows period latency cases the of for alignment accounts temporal and The diagnosis. exposure diagnosis. of and to ages exposure important prior of time cases examination of or alignment an diagnosis temporal the at for allows residential 2013). age also of al. by and the Inclusion et mobility system. address (Nordsborg population registration for group to the accounts control from histories obtained the of obtained diagnosed was choice also from controls to cases were obtained histories due of 3297 Residential are were set results include controls second clustering and birth-date-matched that The possibility of Registry System. sets Registration Cancer Two Civil Danish Danish 2003. the and from 1991 taken between of are hypothesis data null 2005). case the al. et test (Jacquez rigorously Q-statistics to among nearest-neighbor-based employed exposure and with are data, shared risk factors history uniform of risk residential locations personal detailed design, detecting for study adjustment by Case–control hypotheses and patients. unexplained, generate cancer or largely testicular spatial to is are cancer seeks there testicular study for whether risk determine this Excess to risk. cases of cancer clusters testicular spatial–temporal examine (2015) al. et StudySloan Case 3.4 cause 2010). a al. et as associations Meter apparent suggested (Van the incidence been and autism has California, and levels in reporting education incidence the incomplete parental autism at between example, of lie clustering For may obtaining geographical behavior to problem. of treatment-seeking barriers this in social of and differences inten- Economic root associated heterogeneous analysis. Passive and cluster spatially units. healthcare confound to administrative proper that susceptible across disease reporting particularly differences in of are difference sity reporting dramat- varies true by systems rate a surveillance explained disease reflect often disease the may where is this maps it is While burden, this boundaries. of administrative example across One absent. ically fact, in are, they 62 ding Geoco h td sbsdo eibecs aaadargru aecnrlsuydsg.The design. study case–control rigorous a and data case reliable on based is study The The ml ubr problem numbers small & a K39 K23899 — #K23899 Cat T&F sfeunl sdi lse nlsst dniytesailcodntsof coordinates spatial the identify to analysis cluster in used frequently is eest h nraei ainei ies ae (calcu- rates disease in variance in increase the to refers 03—pg 2—31/06—7:45 — 3/10/2016 — 62 page — C003 adoko pta Epidemiology Spatial of Handbook Downloaded By: 10.3.98.104 At: 17:52 29 Sep 2021; For: 9781482253023, chapter3, 10.1201/b19470-5 TD Aec o oi usacsadDsaeRgsr) 19) hlho cancer Childhood (1997). Registry). Disease and Substances Toxic for (Agency ATSDR b,T n .Siccm.(08.Gooigpatcsi acrrgsre.In registries. cancer in practices Geocoding (2008). Stinchcomb. D. and T. Abe, References how is analysis streams. data cluster such in on research without designs future known, and of partially frames of only area sampling kinds is representative important These method impose An engines. sampling to design. search the sampling Google and formal and heterogeneous, a Twitter be from to unstruc- those tend using as data into are such case systems studies streams, included surveillance cluster data health moved The tured Increasingly, have post-epidemiology. analysis. designs, methodologies of sampling cluster and realm rigorous of data the available of structure that use inferential demonstrates the the study especially strengthened years, greatly recent have in advances events. analysis, health pattern cluster of regression, clusters inferential inferential including of on from approaches, interpretation primarily the concentrated modeling techniques, and has to chapter analysis of This pattern clustering) spectrum models. disease Bayesian a and for geostatistical, of tests (e.g., consists analysis analysis cluster Disease Conclusions 3.5 Events Health of Clusters Interpreting nfr hogotDnakaea xml.I ihrcs,teerbs eut ilbe studies cancer. future will testicular of for results design risks robust the environmental by in these practically and or case, detected behavioral is regions either the different be that examining from In can factor studies example. environmental that aggregating indicate an when an scale also important are or a may Denmark result households at throughout environ- The vary within uniform risk. that not Exposures cancer be do method. testicular may exposures in this It role environmental a generating. important play hypothesis that not and do instructive exposures itself testing. mental multiple is of multiple result face of “negative” the face in the system spatial robust in for is registration reexamined clusters which were same significant Q-statistics SaTScan, for the the using with threshold clustering from detected a clusters data provide space–time Local history to procedure. testing. study residential SaTScan per- simulation by employed the a addressed with (2012) in was correspondence al. testing for et multiple checking of Sloan incorporated and problem was studies and The simulation risk analysis. increased forming detection of cluster predictor significant history the only Family into the was status. cancer socioeconomic testicular of community-level and characteristics individual-level aadu ieHat vlainPorm iiino pdmooy niomna and Environmental Services, Epidemiology, Health. Senior of Occupational and Division Health Health Program, of of Evaluation Department Health Department Site Jersey U.S. Hazardous New and GA: ATSDR, Atlanta, Services, for Jersey. Human 1979–1995 and New data, registry County), cancer (Ocean of Township analysis Dover and review A consultation: health Press. incidence CRC FL: Raton, Boca 111–125. Zimmerman, D. and West, M. M. elhData Health hl h 90 n 90 ih ecretytpfida neao r-pdmooyin pre-epidemiology of era an as typified correctly be might 1990s and 1980s the While hssuyddntfidcnicn vdneo lseso ikfrtsiua acr This cancer. testicular for risk of clusters of evidence convincing find not did study This included that model regression logistic conditional a in first examined were factors Risk & a K39 K23899 — #K23899 Cat T&F d .Rstn .P rsrn,J ite,B .Gen,C .Pavlik, E. C. Greene, R. B. Gittler, J. Armstrong, P. M. 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