ライフサイエンスコーパス: surveillance data

1 al biases and underreporting inherent in the surveillance data.

2 31, 2007, by linking national laboratory and surveillance data.

3 using GeoSentinel surveillance data or other surveillance data.

4 demiology of cholera in Togo, using national surveillance data.

5 from the continuous capture of institutional surveillance data.

6 9-2010 were analyzed using enhanced national surveillance data.

7 ssion intensity of future epidemics by using surveillance data.

8 ysis using only Arizona-specific outcome and surveillance data.

9 CRC, inflammatory bowel disease, or without surveillance data.

10 nt class and latent transition models to HIV surveillance data.

11 s are still hindered by the lack of reliable surveillance data.

12 bacterial nosocomial pathogens using routine surveillance data.

13 much lower than expected from epidemiologic surveillance data.

14 y risks, yet there are a paucity of national surveillance data.

15 open-source, laboratory-confirmed influenza surveillance data.

16 ase Control and Prevention respiratory virus surveillance data.

17 This change is mirrored in passive national surveillance data.

18 ease was estimated using hospital laboratory surveillance data.

19 for real-time estimation of AVR fitness from surveillance data.

20 g influenza-related mortality and laboratory surveillance data.

21 g this drug pending additional postmarketing surveillance data.

22 ng with AIDS, estimated on the basis of AIDS surveillance data.

23 f these control measures by use of available surveillance data.

24 In addition, race is often misclassified in surveillance data.

25 mitted infections (STI) are largely based on surveillance data.

26 from 2 yrs of primary bloodstream infection surveillance data.

27 months of pre- and post-vaccine introduction surveillance data.

28 erization of W family clones supplemented by surveillance data.

29 odels with vital death records and influenza surveillance data.

30 imple method for estimating AVR fitness from surveillance data.

31 to estimate sparse Markov networks from AMR surveillance data.

32 ue cohort-specific prevalence, using disease surveillance data.

33 S. influenza season with the help of digital surveillance data.

34 rameters and true herd-level prevalence from surveillance data.

35 Surveillance data (2000-2010) and mortality data (2000-2

36 Our results from national surveillance data accentuate the need for public health

37 on of adults with CHD and the development of surveillance data across the life span to provide empiri

38 Information from national surveillance data allows us to evaluate the adequacy of

39 National influenza surveillance data also have been used in suitable models

40 mmunications & community engagement, disease surveillance & data analysis, technical quality & capaci

41 ood levels, and use them in conjunction with surveillance data and a data assimilation method to fore

42 s conducted using routinely collected health surveillance data and chloroplatinate exposure data.

43 Only the systems collecting syndromic surveillance data and detection system data were designe

44 Here, we use detailed geographic surveillance data and epidemic models to estimate the cr

45 partmental model to synthesise evidence from surveillance data and epidemiological and behavioural st

46 We analyzed surveillance data and estimated case fatality rates (CFR

47 quired immunodeficiency syndrome (AIDS) case surveillance data and estimates of the time from HIV inf

48 Here, from the joint analysis of surveillance data and holiday timing in France, we quant

49 Surveillance data and modelling will help country stakeh

50 ance Program ensure the generation of useful surveillance data and result in the continued improvemen

51 ance Program ensure the generation of useful surveillance data and result in the continued improvemen

52 djunct treatment for severe malaria using US surveillance data and reviewed the literature to update

53 nfection is necessary to complement clinical surveillance data and statistical models.

54 ort study based on the linkage of laboratory surveillance data and the Danish Civil Registration Syst

55 understood owing to the absence of reliable surveillance data and the simplistic approaches underlyi

56 We quantified the links between mosquito surveillance data and the spatiotemporal patterns of 3,8

57 t fully capture uncertainty due to imperfect surveillance data and uncertainty about the transmission

58 ose derived from traditional epidemiological surveillance data and with those reported for prior outb

59 mined the number of cases using 2004 U.S. TB surveillance data, and calculated case rates using popul

60 h there are important limitations in passive surveillance data, and caution in their interpretation i

61 ational parasite movement, utilize real time surveillance data, and relax the steady state assumption

62 ogy can aid in the interpretation of disease surveillance data, and the results can potentially refin

63 amics, discernible from noisy and incomplete surveillance data, and the underlying, imperfectly obser

64 Based on this surveillance data, approximately 0.1% of children who we

65 High-quality, comprehensive, and real-time surveillance data are essential to reduce the burden of

66 Existing epilepsy surveillance data are inadequate to address factors such

67 tification, health promotion, and capture of surveillance data are integral aspects of the eSHC.

68 Genotype surveillance data are needed from many countries to impr

69 ative about HIV donors in ways that standard surveillance data are not.

70 anisms to ensure timely and complete cholera surveillance data are reported to the national level sho

71 We analysed national surveillance data around PsA-TT introduction to investig

72 f three STDs but were not listed in Oklahoma surveillance data as AI/AN.

73 using national influenza and RSV laboratory surveillance data as covariates.

74 s that incorporated weekly influenza and RSV surveillance data as covariates.

75 Trends were compared to provincial Chlamydia surveillance data by time-series analysis, using the cro

76 , tailoring these models to certain types of surveillance data can be challenging, and overly complex

77 s of influenza A(H3N2), so that sequence and surveillance data can be used synergistically.

78 We considered how participatory syndromic surveillance data can be used to estimate influenza atta

79 ngs demonstrate that participatory syndromic surveillance data can be used to gauge influenza attack

80 These surveillance data can be used to improve viral food-born

81 We show that the temporal extent of surveillance data can have a dramatic impact on inferenc

82 Surveillance data can identify neighborhoods with a pers

83 Racial misclassification in state surveillance data causes inaccuracies in characterizing

84 lyses identified unobserved risk patterns in surveillance data, characterized high-risk MSM, and quan

85 lies in Hawaii by using actively ascertained surveillance data collected between 1987 and 1996 by the

86 Surveillance data collected by specialist clinics may no

87 Limitation: Surveillance data collected by specialized clinics may n

88 eal-time--two weeks earlier than traditional surveillance data collected by the U.S. Centers for Dise

89 ed a latent transition analysis technique to surveillance data collected during clinic visits.

90 Retrospective analysis of longitudinal surveillance data collected from 2005-2006 through 2013-

91 tors for poor outcomes, we analyzed national surveillance data collected from December 1980 through N

92 lyze syndromic, virological, and serological surveillance data collected in England in 2009-2011 and

93 Surveillance data collected in Philadelphia during Septe

94 rtality rates were calculated using national surveillance data collected through the Surveillance, Ep

95 Prospective patient surveillance, data collection, and implementation of an

96 ed in this study provides important sentinel surveillance data concerning groups at risk for tubercul

97 Inaccurate or incomplete surveillance data delay a translational approach to the

98 Indeed, our current local surveillance data demonstrate that approximately half of

99 Combined population behavior and surveillance data demonstrate the high PPB for STIs attr

100 trends in its utilization, but national HIV surveillance data do not include PrEP uptake.

101 3 Member States and analyzed rubella and CRS surveillance data during 2005-2009.

102 Surveillance data enabled the computation of severity sc

103 ple molecular techniques in conjunction with surveillance data enabled us to identify a previously un

104 Weekly clinical and laboratory influenza surveillance data, environmental temperature and humidit

105 We reviewed published studies and available surveillance data evaluating links between HIV infection

106 inents, contributed clinician-based sentinel surveillance data for 17,353 ill returned travelers.

107 Oklahoma State STD surveillance data for 1995 were matched with the Oklahom

108 National STD surveillance data for 1999 were used to determine diseas

109 001, we analyzed regional measles case-based surveillance data for 2002-2009.

110 We analyzed surveillance data for all persons aged >/=13 years with

111 We collected regional syndromic surveillance data for epidemiological weeks 23 to 44, 20

112 ate recommended standards for the use of HAI surveillance data for external facility assessment to en

113 sed the challenges associated with using HAI surveillance data for external quality reporting, includ

114 where available and validated using national surveillance data for incidence of NAFLD-related HCC.

115 and May for isolate activity level based on surveillance data for influenza, respiratory syncytial v

116 and Prevention's Emerging Infections Program surveillance data for invasive community-associated MRSA

117 We summarize US population-based surveillance data for invasive listeriosis from 2004 thr

118 edictability based on high-quality influenza surveillance data for Israel; the model fit is corrobora

119 observational cohort study, we used national surveillance data for meningococcal serogroup W and sero

120 We compiled and analysed surveillance data for nine countries in the meningitis b

121 ce to the Ministry of Health (MOH), national surveillance data for poliomyelitis and charts of cases

122 e Cost and Utilization Project and influenza surveillance data for regions encompassing these states.

123 idemiologic studies should carefully dissect surveillance data for sex-specific effects.

124 Nationwide weekly surveillance data for suspect malaria cases reported bet

125 By use of human resources and incident surveillance data for the hourly population at 6 US alum

126 Center for Disease Control and Prevention's surveillance data for these infections.

127 We aimed to assess European surveillance data for travel-related illness to profile

128 rs and >/=65 years) and scaled by laboratory surveillance data for viral types and subtypes, in the p

129 Prospectively collected MRSA surveillance data from 10 general wards at Guy's and St.

130 IPD surveillance data from 1986 to 2009 and carriage survey

131 t bloodstream infection (BSI) and meningitis surveillance data from 1998 to 2014.

132 extracted prospectively acquired Australian surveillance data from 2 studies nested within the Paedi

133 We analyzed surveillance data from 2001-2006 at 4 hospitals located

134 Using the national surveillance data from 2010 to 2013, we conducted this r

135 We used nationally representative surveillance data from 63 emergency departments obtained

136 We analyse 18 years (1990-2007) of surveillance data from a paediatric ward in a malaria-en

137 e examined 10 years (1995 to 2004) of active surveillance data from a sentinel population of 350,000

138 ic analysis of HIV genetic sequence data and surveillance data from a US population of men who have s

139 Retrospective review of surveillance data from all cases of cholera reported to

140 cent trends in cholera in the United States, surveillance data from all cases of laboratory-confirmed

141 s this question with use of measles outbreak surveillance data from Bangladesh from the period 2004-2

142 Surveillance data from both COVIS and FoodNet indicate t

143 plements a Bayesian model using strain-typed surveillance data from both human cases and source sampl

144 xamined national population-based meningitis surveillance data from Burkina Faso using two sources, o

145 We also analysed surveillance data from camps included in UN High Commiss

146 Descriptive analysis of US TB surveillance data from case reports submitted from 1993

147 through 2005 using active, population-based surveillance data from eight sites in the United States.

148 We looked at surveillance data from England and Wales to ascertain th

149 We calibrated the model to match the HIV surveillance data from LAC across a 10-year period, star

150 ation in West Africa, we collected influenza surveillance data from ministries of health and influenz

151 Although surveillance data from most Western European countries s

152 Several sources of population-based surveillance data from New York City, covering 1982 thro

153 We used acute flaccid paralysis (AFP) surveillance data from Nigeria collected between January

154 d through application to bovine tuberculosis surveillance data from Northern and the Republic of Irel

155 Laboratory surveillance data from recent years provided independent

156 Using routine national surveillance data from Swaziland (a sub-Saharan country

157 o human rabies autopsy data and human rabies surveillance data from Tamil Nadu.

158 We assessed surveillance data from the 4 years since introduction of

159 Surveillance data from the California tuberculosis regis

160 d active population and laboratory-based IPD surveillance data from the Centers for Disease Control a

161 We also included surveillance data from the Centers for Disease Control a

162 Combining features of long-term surveillance data from The Netherlands with features of

163 since 1966 and analyzed national aggregated surveillance data from the period 2003-2008 and case-bas

164 Using a stochastic model fit to seasonal flu surveillance data from the United States, we find that s

165 ity for 125 countries using laboratory-based surveillance data from the WHO's FLUNET database and com

166 Current surveillance data from these programs, together with nat

167 ence of infection are derived and applied to surveillance data from three regions.

168 Influenza surveillance data from tropical, sub-Saharan African cou

169 Longitudinal surveillance data from two randomized controlled trials

170 extracted prospectively acquired Australian surveillance data from two studies nested within the Pae

171 Analysis of AMR surveillance data has focused on resistance to individua

172 old greater burden of infection than routine surveillance data have suggested.

173 These surveillance data help characterize the clinical manifes

174 Conclusion: These surveillance data help characterize the clinical manifes

175 We reviewed national surveillance data housed in the National Ministry of Hea

176 fections are deduced primarily from outbreak surveillance data; however, in the United States, only a

177 Surveillance data imperfectly indicate current prevalenc

178 Conclusions Preliminary surveillance data in Colombia suggest that maternal infe

179 We analyzed varicella surveillance data in Connecticut for 2001-2005, to descr

180 nd analysed nationwide case-based meningitis surveillance data in Niger.

181 misclassification of American Indians in STD surveillance data in Oklahoma.

182 Comparison of active surveillance data in the same health zone from the 1980s

183 = 0.70) and regional (R(2) = 0.74) norovirus surveillance data in the United States.

184 e of chronic shedding was only apparent when surveillance data included at least two outbreaks and th

185 Surveillance data included numbers of residents diagnose

186 Current surveillance data indicate a recent decline in reported

187 FINDINGS: Overall trends of the ANC surveillance data indicated a substantial HIV prevalence

188 proposes a framework to integrate influenza surveillance data into transmission models.

189 Continued close attention to surveillance data is needed to monitor the impact of rec

190 ct and analyze mortality and hospitalization surveillance data is needed to rapidly establish the sev

191 -risk schools, as identified by school-level surveillance data, may experience substantial caries-pre

192 socioeconomic information in most US health surveillance data necessitates using area-based socioeco

193 tment factors from the national HIV sentinel surveillance data obtained annually from antenatal clini

194 trospective analysis of population-based IPD surveillance data of the general population residing in

195 An annual update of antimicrobial resistance surveillance data of uropathogens may permit targeted tr

196 ral Brong Ahafo region in Ghana, we combined surveillance data on 11,274 deliveries with quality of c

197 We reviewed US surveillance data on confirmed measles cases reported to

198 and characteristics of infections, national surveillance data on diagnoses in England and Wales from

199 tilization surveys, verbal autopsy data, and surveillance data on diarrheal disease were used to dete

200 93 were examined along with laboratory-based surveillance data on HPIV-1 activity in the United State

201 Surveillance data on IMD for patients aged 15 to 64 year

202 A retrospective study using Dutch surveillance data on IMD from June 1999 to June 2011.

203 Surveillance data on influenza virus activity permitted

204 lign with those derived from epidemiological surveillance data on MERS and Ebola, underscoring the im

205 We analyzed National HIV Behavioral Surveillance data on MSM from 20 cities.

206 In the absence of quality surveillance data on privately treated patients, commerc

207 We fit Poisson harmonic regression models to surveillance data on RRV, BFV, and dengue (from 1993, 19

208 other settings using analogous, multiseason surveillance data on self-reported ILI together with sep

209 ailability of weekly Web-based participatory surveillance data on self-reported influenza-like illnes

210 We analysed surveillance data on suspected and confirmed cases of me

211 Using recent surveillance data on virologically confirmed infections

212 Surveillance data on water quality and diarrhea were col

213 hese pandemic waves, we examined prospective surveillance data on Wisconsin residents who were hospit

214 yndrome analytical studies using GeoSentinel surveillance data or other surveillance data.

215 e available literature and the postmarketing surveillance data, proposed a clinically based grading o

216 D PATIENTS: Retrospective cohort study using surveillance data prospectively submitted by emergency m

217 We analyzed surveillance data prospectively submitted from 29 U.S. s

218 Malaria surveillance data provide opportunity to develop forecas

219 V Synthesis Model, to multiple data sources (surveillance data provided by Public Health England and

220 According to surveillance data provided by the Centers for Disease Co

221 CV was developed using data synthesized from surveillance data, published literature, expert opinion,

222 ffects logistic regression models to routine surveillance data recording the presence of poliomyeliti

223 Recent national surveillance data report stable rates of invasive GAS di

224 HCV surveillance data reported to the Philadelphia Departmen

225 Cholera surveillance data reported to the Uganda Ministry of Hea

226 We reviewed the cholera surveillance data reported to the World Health Organizat

227 , children were matched with NC kindergarten-surveillance data representing school-level mean untreat

228 sed inference schemes to analyze the largest surveillance data set of Shigella sonnei in the United S

229 uartile from administrative data, use of the surveillance data set resulted in performance grades of

230 cted a case-cohort study using public health surveillance data sets to examine perinatal risk factors

231 National surveillance data show recent, marked reductions in morb

232 Starting in 1999, active and passive surveillance data showed sharp decreases in varicella di

233 Analysis of national STI surveillance data showed that the PPB for new episodes o

234 Surveillance data suggest that HIV infection and MDR-TB

235 ccine development has been unsuccessful, but surveillance data suggest that outer membrane vesicle me

236 US surveillance data suggest that the case fatality ratio i

237 Early surveillance data suggest that the prevalence of oseltam

238 nd uses more medical resources than official surveillance data suggest.

239 An initial analysis of surveillance data suggested that such a polymorphism in

240 red (April-May and September-November), with surveillance data suggesting locally acquired infections

241 ata on antiretroviral therapy or viral load, surveillance data suggests that a small proportion of me

242 h organizations collect additional influenza surveillance data that complement the CDC's surveillance

243 the potential to generate rich epidemiologic surveillance data that will be widely accessible to mala

244 After 3 years of follow-up and review of surveillance data, the ICCPE declared that wild poliovir

245 Using published surveillance data, they estimated proportions of US mult

246 rom the medical literature and from national surveillance data through May 2002.

247 We analyzed California surveillance data to characterize the outcomes of patien

248 tion of chikungunya in 2015, by using active surveillance data to correct reported dengue case data f

249 eriod from 1992 to 1997, we used local viral surveillance data to define periods in Washington State

250 spital service with national epidemiological surveillance data to describe the use of surgical proced

251 hasize the importance of using comprehensive surveillance data to detect changing characteristics and

252 care utilization survey and population-based surveillance data to estimate disease incidence.

253 We used serological and surveillance data to estimate the probability of infecti

254 ddition to providing high-quality laboratory surveillance data to help guide disease control, elimina

255 Genotyping was combined with surveillance data to identify the S75 group and to eluci

256 developed methods to aggregate county-level surveillance data to inform provincial-level analysis, a

257 on and academic scientists, these models use surveillance data to make quantitative predictions regar

258 In this analysis, we use surveillance data to provide an estimate of influenza-as

259 cation months of ventilator-associated event surveillance data to the National Healthcare Safety Netw

260 In this study, we use all available U.S. surveillance data to: 1) fit a set of mathematical model

261 ission, informed by detailed behavioural and surveillance data, to assess the effect of seven differe

262 from vital registration, verbal autopsy, and surveillance data using the Cause of Death Ensemble Mode

263 A descriptive analysis of surveillance data was performed.

264 artin on pages 368-9.)Using population-based surveillance data, we analyzed antiviral treatment among

265 Using active surveillance data, we evaluated geographic and temporal

266 From surveillance data, we found that two of these substituti

267 strict to state influenza-like illness (ILI) surveillance data, we measured its effect on community l

268 Coupled with Ebola virus surveillance data, we modelled the expected number of in

269 e data used by ALERT are routinely collected surveillance data: weekly case counts of laboratory-conf

270 bacteria for which robust weekly laboratory surveillance data were available.

271 Individual-level malaria surveillance data were collected from 1 outpatient depar

272 Surveillance data were collected through physician and p

273 Ten years of Austrian surveillance data were compared, including 10 960 labora

274 United States national surveillance data were gathered from the Centers for Dis

275 Annual medical evaluations and injury surveillance data were linked to compare levels of aerob

276 Health surveillance data were obtained from 1437 households wit

277 Pertussis surveillance data were reviewed and a retrospective coho

278 Rubella surveillance data were reviewed for Kyrgyzstan (1981-200

279 California surveillance data were reviewed to identify all children

280 California Department of Public Health surveillance data were reviewed to identify cases; demog

281 Case surveillance data were used primarily to derive stages 1

282 Surveillance data were used to describe injury trends, i

283 National unlinked sentinel surveillance data were used to describe trends in preval

284 hia Department of Public Health and enhanced surveillance data were used to determine where individua

285 boratory-confirmed influenza hospitalization surveillance data were used to examine the association b

286 METHODS AND Routinely-collected hospital surveillance data were used to undertake a pragmatic com

287 rably according to whether administrative or surveillance data were used, suggesting that administrat

288 ate was 0.15% (95% CI, 0.13% to 0.17%); when surveillance data were used, the rate was 2.0% (CI, 1.8%

289 ta was compared with the grade assigned when surveillance data were used.

290 We reviewed national surveillance data where available.

291 Regional or state-level surveillance data will be needed to describe the changin

292 Combining active surveillance data with routine dengue reports improved n

293 dministrative data) or examined adults (from surveillance data) with at least 1 stage II or greater H