Spat Spatiotemporal Epidemiol - Prostate cancer incidence in light of the spatial distribution of another screening-detectable cancer.

Tópicos

{ spatial(1525) area(1432) region(1030) }
{ age(1611) year(1155) adult(843) }
{ blood(1257) pressur(1144) flow(957) }
{ can(981) present(881) function(850) }
{ group(2977) signific(1463) compar(1072) }
{ patient(2315) diseas(1263) diabet(1191) }
{ analysi(2126) use(1163) compon(1037) }
{ cancer(2502) breast(956) screen(824) }
{ sequenc(1873) structur(1644) protein(1328) }
{ health(3367) inform(1360) care(1135) }
{ cost(1906) reduc(1198) effect(832) }
{ result(1111) use(1088) new(759) }
{ detect(2391) sensit(1101) algorithm(908) }
{ model(3404) distribut(989) bayesian(671) }
{ studi(2440) review(1878) systemat(933) }
{ assess(1506) score(1403) qualiti(1306) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ search(2224) databas(1162) retriev(909) }
{ case(1353) use(1143) diagnosi(1136) }
{ howev(809) still(633) remain(590) }
{ state(1844) use(1261) util(961) }
{ model(2656) set(1616) predict(1553) }
{ data(2317) use(1299) case(1017) }
{ signal(2180) analysi(812) frequenc(800) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ time(1939) patient(1703) rate(768) }
{ method(1969) cluster(1462) data(1082) }
{ can(774) often(719) complex(702) }
{ imag(1947) propos(1133) code(1026) }
{ data(1737) use(1416) pattern(1282) }
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{ measur(2081) correl(1212) valu(896) }
{ imag(1057) registr(996) error(939) }
{ bind(1733) structur(1185) ligand(1036) }
{ method(1219) similar(1157) match(930) }
{ featur(3375) classif(2383) classifi(1994) }
{ imag(2830) propos(1344) filter(1198) }
{ network(2748) neural(1063) input(814) }
{ imag(2675) segment(2577) method(1081) }
{ take(945) account(800) differ(722) }
{ motion(1329) object(1292) video(1091) }
{ treatment(1704) effect(941) patient(846) }
{ framework(1458) process(801) describ(734) }
{ problem(2511) optim(1539) algorithm(950) }
{ error(1145) method(1030) estim(1020) }
{ chang(1828) time(1643) increas(1301) }
{ learn(2355) train(1041) set(1003) }
{ concept(1167) ontolog(924) domain(897) }
{ clinic(1479) use(1117) guidelin(835) }
{ algorithm(1844) comput(1787) effici(935) }
{ extract(1171) text(1153) clinic(932) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ design(1359) user(1324) use(1319) }
{ control(1307) perform(991) simul(935) }
{ model(2220) cell(1177) simul(1124) }
{ care(1570) inform(1187) nurs(1089) }
{ general(901) number(790) one(736) }
{ method(984) reconstruct(947) comput(926) }
{ featur(1941) imag(1645) propos(1176) }
{ data(3963) clinic(1234) research(1004) }
{ studi(1410) differ(1259) use(1210) }
{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
{ import(1318) role(1303) understand(862) }
{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ perform(1367) use(1326) method(1137) }
{ studi(1119) effect(1106) posit(819) }
{ record(1888) medic(1808) patient(1693) }
{ model(3480) simul(1196) paramet(876) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ medic(1828) order(1363) alert(1069) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
{ use(2086) technolog(871) perceiv(783) }
{ health(1844) social(1437) communiti(874) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(976) code(926) identifi(902) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ implement(1333) system(1263) develop(1122) }
{ survey(1388) particip(1329) question(1065) }
{ estim(2440) model(1874) function(577) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ activ(1452) weight(1219) physic(1104) }
{ method(2212) result(1239) propos(1039) }

Resumo

Bias in spatial analyses that overlook compositional and contextual factors of communities can be substantial. We first examined spatial patterns among 11,728 prostate cancer cases across Connecticut, 1994-98. A spatial scan statistic (SatScan?) identified two locations where average annual incidence rates significantly exceeded the statewide level and two locations with significantly lower disease rates. Extending the analysis to adjust rates for age and race/ethnicity greatly minimized, but did not eliminate, geographic variation. Adjustment for age and poverty level of communities eliminated significant variability across locales. Similarly, analysis adjusted for age and covariation of colorectal cancer incidence rates across the state accounted for all significant variation previously observed. These results suggest that accounting for a "detection effect" due to clinical patterns of another screenable condition may be as useful as adjusting spatial data for variability of socio-economic conditions.

Resumo Limpo

bias spatial analys overlook composit contextu factor communiti can substanti first examin spatial pattern among prostat cancer case across connecticut spatial scan statist satscan identifi two locat averag annual incid rate signific exceed statewid level two locat signific lower diseas rate extend analysi adjust rate age raceethn great minim elimin geograph variat adjust age poverti level communiti elimin signific variabl across local similar analysi adjust age covari colorect cancer incid rate across state account signific variat previous observ result suggest account detect effect due clinic pattern anoth screenabl condit may use adjust spatial data variabl socioeconom condit

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