Spat Spatiotemporal Epidemiol - Spatial prevalence and associations among respiratory diseases in Maine.

Tópicos

{ spatial(1525) area(1432) region(1030) }
{ data(2317) use(1299) case(1017) }
{ risk(3053) factor(974) diseas(938) }
{ blood(1257) pressur(1144) flow(957) }
{ age(1611) year(1155) adult(843) }
{ studi(1119) effect(1106) posit(819) }
{ patient(2837) hospit(1953) medic(668) }
{ first(2504) two(1366) second(1323) }
{ can(774) often(719) complex(702) }
{ howev(809) still(633) remain(590) }
{ studi(1410) differ(1259) use(1210) }
{ group(2977) signific(1463) compar(1072) }
{ patient(1821) servic(1111) care(1106) }
{ estim(2440) model(1874) function(577) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ data(1737) use(1416) pattern(1282) }
{ bind(1733) structur(1185) ligand(1036) }
{ patient(2315) diseas(1263) diabet(1191) }
{ chang(1828) time(1643) increas(1301) }
{ method(984) reconstruct(947) comput(926) }
{ perform(999) metric(946) measur(919) }
{ model(2341) predict(2261) use(1141) }
{ monitor(1329) mobil(1314) devic(1160) }
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{ activ(1138) subject(705) human(624) }
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{ structur(1116) can(940) graph(676) }
{ use(976) code(926) identifi(902) }
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{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
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{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
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{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ record(1888) medic(1808) patient(1693) }
{ health(3367) inform(1360) care(1135) }
{ model(3480) simul(1196) paramet(876) }
{ ehr(2073) health(1662) electron(1139) }
{ state(1844) use(1261) util(961) }
{ research(1218) medic(880) student(794) }
{ model(2656) set(1616) predict(1553) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ can(981) present(881) function(850) }
{ health(1844) social(1437) communiti(874) }
{ high(1669) rate(1365) level(1280) }
{ cancer(2502) breast(956) screen(824) }
{ use(1733) differ(960) four(931) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(2212) result(1239) propos(1039) }

Resumo

Chronic respiratory diseases rank among the leading global disease burdens. Maine's respiratory disease prevalence exceeds the US average, despite limited urbanization/industrialization. To provide insight into potential etiologic factors among this unique, rural population, we analyzed the spatial distributions of, and potential associations among asthma, COPD, pneumonia, and URI adult outpatient data (n=47,099) from all outpatient transactions (n=5,052,900) in 2009 for Maine hospitals and affiliate clinics, using spatial scan statistic, geographic weighted regression (GWR), and a Delaunay graph algorithm. Non-random high prevalence regions were identified, the majority of which (84% of the population underlying all regions) exhibited clusters for all four respiratory diseases. GWR provided further evidence of spatial correlation (R(2)=0.991) between the communicable and noncommunicable diseases under investigation, suggesting spatial interdependence in risk. Sensitivity analyses of known respiratory disease risks did not fully explain our results. Prospective epidemiology studies are needed to clarify all contributors to risk.

Resumo Limpo

chronic respiratori diseas rank among lead global diseas burden main respiratori diseas preval exceed us averag despit limit urbanizationindustri provid insight potenti etiolog factor among uniqu rural popul analyz spatial distribut potenti associ among asthma copd pneumonia uri adult outpati data n outpati transact n main hospit affili clinic use spatial scan statist geograph weight regress gwr delaunay graph algorithm nonrandom high preval region identifi major popul under region exhibit cluster four respiratori diseas gwr provid evid spatial correl r communic noncommunic diseas investig suggest spatial interdepend risk sensit analys known respiratori diseas risk fulli explain result prospect epidemiolog studi need clarifi contributor risk

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