Comput Math Methods Med - Medical cost trajectories and onsets of cancer and noncancer diseases in US elderly population.

Tópicos

{ patient(2315) diseas(1263) diabet(1191) }
{ state(1844) use(1261) util(961) }
{ cancer(2502) breast(956) screen(824) }
{ cost(1906) reduc(1198) effect(832) }
{ perform(1367) use(1326) method(1137) }
{ medic(1828) order(1363) alert(1069) }
{ treatment(1704) effect(941) patient(846) }
{ spatial(1525) area(1432) region(1030) }
{ use(976) code(926) identifi(902) }
{ model(3404) distribut(989) bayesian(671) }
{ inform(2794) health(2639) internet(1427) }
{ surgeri(1148) surgic(1085) robot(1054) }
{ algorithm(1844) comput(1787) effici(935) }
{ method(1557) propos(1049) approach(1037) }
{ data(1714) softwar(1251) tool(1186) }
{ studi(1410) differ(1259) use(1210) }
{ model(3480) simul(1196) paramet(876) }
{ data(2317) use(1299) case(1017) }
{ age(1611) year(1155) adult(843) }
{ patient(1821) servic(1111) care(1106) }
{ can(774) often(719) complex(702) }
{ imag(2675) segment(2577) method(1081) }
{ framework(1458) process(801) describ(734) }
{ design(1359) user(1324) use(1319) }
{ care(1570) inform(1187) nurs(1089) }
{ health(3367) inform(1360) care(1135) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ data(3008) multipl(1320) sourc(1022) }
{ first(2504) two(1366) second(1323) }
{ can(981) present(881) function(850) }
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{ survey(1388) particip(1329) question(1065) }
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{ method(2212) result(1239) propos(1039) }
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{ risk(3053) factor(974) diseas(938) }
{ perform(999) metric(946) measur(919) }
{ research(1085) discuss(1038) issu(1018) }
{ system(1050) medic(1026) inform(1018) }
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{ model(2341) predict(2261) use(1141) }
{ visual(1396) interact(850) tool(830) }
{ compound(1573) activ(1297) structur(1058) }
{ studi(1119) effect(1106) posit(819) }
{ blood(1257) pressur(1144) flow(957) }
{ record(1888) medic(1808) patient(1693) }
{ monitor(1329) mobil(1314) devic(1160) }
{ ehr(2073) health(1662) electron(1139) }
{ research(1218) medic(880) student(794) }
{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
{ signal(2180) analysi(812) frequenc(800) }
{ group(2977) signific(1463) compar(1072) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ time(1939) patient(1703) rate(768) }
{ use(2086) technolog(871) perceiv(783) }
{ analysi(2126) use(1163) compon(1037) }
{ structur(1116) can(940) graph(676) }
{ high(1669) rate(1365) level(1280) }
{ use(1733) differ(960) four(931) }
{ drug(1928) target(777) effect(648) }
{ result(1111) use(1088) new(759) }
{ implement(1333) system(1263) develop(1122) }
{ decis(3086) make(1611) patient(1517) }
{ process(1125) use(805) approach(778) }
{ method(1969) cluster(1462) data(1082) }
{ detect(2391) sensit(1101) algorithm(908) }

Resumo

Time trajectories of medical costs-associated with onset of twelve aging-related cancer and chronic noncancer diseases were analyzed using the National Long-Term Care Survey data linked to Medicare Service Use files. A special procedure for selecting individuals with onset of each disease was developed and used for identification of the date at disease onset. Medical cost trajectories were found to be represented by a parametric model with four easily interpretable parameters reflecting: (i) prediagnosis cost (associated with initial comorbidity), (ii) cost of the disease onset, (iii) population recovery representing reduction of the medical expenses associated with a disease since diagnosis was made, and (iv) acquired comorbidity representing the difference between post- and pre diagnosis medical cost levels. These parameters were evaluated for the entire US population as well as for the subpopulation conditional on age, disability and comorbidity states, and survival (2.5 years after the date of onset). The developed approach results in a family of new forecasting models with covariates.

Resumo Limpo

time trajectori medic costsassoci onset twelv agingrel cancer chronic noncanc diseas analyz use nation longterm care survey data link medicar servic use file special procedur select individu onset diseas develop use identif date diseas onset medic cost trajectori found repres parametr model four easili interpret paramet reflect prediagnosi cost associ initi comorbid ii cost diseas onset iii popul recoveri repres reduct medic expens associ diseas sinc diagnosi made iv acquir comorbid repres differ post pre diagnosi medic cost level paramet evalu entir us popul well subpopul condit age disabl comorbid state surviv year date onset develop approach result famili new forecast model covari

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