ライフサイエンスコーパス: marital status

1 tal health status, maternal IQ, and maternal marital status).

2 y quintiles, highest level of education, and marital status).

3 , diabetes mellitus, years of education, and marital status).

4 untry of birth, language spoken at home, and marital status.

5 = 0.29), but did not differ by age, sex, or marital status.

6 tion, knowledge about malaria/ITNs, age, and marital status.

7 breastfeeding, age, education, smoking, and marital status.

8 economic status, rural residence, and single marital status.

9 n, household wealth quintile, education, and marital status.

10 nformation on educational qualifications and marital status.

11 .9) after adjustment for age, education, and marital status.

12 , poor sleep, health status, employment, and marital status.

13 y, parity, height, body mass index, race, or marital status.

14 conditions were found between spouses or by marital status.

15 ptor (PR) status, differentiation, race, and marital status.

16 ies according to age, race or ethnicity, and marital status.

17 lly by age, residence in Greater London, and marital status.

18 n, health behaviours, employment status, and marital status.

19 sidered included maternal IQ, education, and marital status.

20 on leads to substantial misclassification of marital status.

21 io 1.23, 95% CI 1.14-1.32; p<0.0001), single marital status (1.47, 1.18-1.83; p<0.0001), unemployment

22 k ratio [RR] 1.52, 95% CI 1.48-1.56), single marital status (1.70, 1.66-1.75), age 30-39 years (1.58,

23 cts could be explained by differences in: 1) marital status; 2) gender; and 3) ethnicity.

24 unknown 35%, HIV-negative 8%; p < 0.001) and marital status (28% unmarried versus married 21%; p = 0.

25 ustment for age, sex, education, income, and marital status, acculturation was negatively associated

26 were each associated with prevalent RP (for marital status adjusted odds ratio [OR] 2.3, 95% confide

27 e, race, ethnicity, geographic location, and marital status (adjusted hazard ratio, 2.19; 95% CI, 1.8

28 HPV among women in the control arm included marital status (adjusted odds ratio [AOR], 3.2; 95% conf

29 d with race/ethnicity, education, income, or marital status after adjusting for age.

30 ustment for sex, age, race, education level, marital status, age at first alcohol intoxication, and h

31 Results were similar after adjustment for marital status, age, and education.

32 fects meta-analysis, controlling for region, marital status, age, number of sex partners, and condom

33 to maternal variables (age, race, education, marital status, alcohol during pregnancy) or child varia

34 rthritis, pulmonary disease, stroke, asthma, marital status also contribute to MCI risk, which is les

35 Medication use, education, and marital status also influenced who underwent biopsy.

36 poverty, low level of education, and single marital status among teenage mothers.

37 An effect size data analysis identified marital status and a diagnosis of schizophrenia as the v

38 ntuitive that circumstantial factors such as marital status and age influence loneliness, there is al

39 vaccination (before and after adjustment for marital status and age) was 0.9 (95% CI, 0.5-1.4; P =.55

40 In women, marital status and alcohol use were each associated with

41 th RP in men only, while the associations of marital status and alcohol use with RP were observed in

42 f women of reproductive age defined by their marital status and contraceptive need and use, and the s

43 ationship, adjusted for age and sex, between marital status and dementia.

44 nt for family socioeconomic status, maternal marital status and education, children's nutrition, and

45 age at outcome assessment, prepregnancy BMI, marital status and insurance at delivery, race, smoking

46 ation ratio) and individual-level mediators (marital status and last dental visit).

47 Maternal IQ, education, and marital status and low birth weight predicted IQ at age

48 l measures oversimplify the relation between marital status and mortality and that sex differences ar

49 ibutions to research on the relation between marital status and mortality.

50 iety and depression, IQ, educational status, marital status and pending litigation.

51 investigated one of the SNPs for measures of marital status and quality.

52 analyses, many nonclinical factors, such as marital status and region of the country, had a greater

53 ith infection were: maternal age, education, marital status and religion; household drinking water so

54 The association between marital status and risk for first registration for alcoh

55 dered personal and life circumstances (e.g., marital status and seniority) when discussing intention

56 Medical conditions as well as ethnic and marital status and smoking habits were considered.

57 We ascertained history of marital status and spouse's death by record linkage to t

58 alysis of studies of the association between marital status and the risk of developing dementia.

59 ing to personal characteristics such as age, marital status and urban or rural residence.

60 ecific antigen (PSA) levels, Gleason scores, marital statuses and bone metastasis statuses was compar

61 anisms that lead from specific dimensions of marital status (and family structure more broadly) to mo

62 e, sex, race/ethnicity, education level, and marital status) and health factors (OA severity, knee sy

63 ), sociodemographic characteristics (age and marital status), and treatment (surgery and radiation th

64 After adjustment for age, marital status, and antidepressant use, the relative ris

65 ation functional class, depression symptoms, marital status, and baseline beta-blockers, angiotensin-

66 After controlling for age, marital status, and characteristics of dependency at dis

67 Age, marital status, and comorbidities influenced the probabi

68 re adjusted for age, sex, educational level, marital status, and CVD risk factors.

69 ter further adjustment for health behaviors, marital status, and education.

70 sted for height, weight, smoking, education, marital status, and energy intake.

71 erval: 0.09 to 0.72), adjusting for smoking, marital status, and gender.

72 tation correlated with sex, race, ethnicity, marital status, and geographic region (ethnicity, P = .0

73 lower mammography rates based on age, race, marital status, and geographic region.

74 husband's level of education, nested within marital status, and having pets in the home were related

75 r the correlated variables of family income, marital status, and health insurance type.

76 , adjusting for age, sex, educational level, marital status, and healthcare worker status.

77 ng for child's age and sex, mother's age and marital status, and household size.

78 r adjusting for insurance, hospitalizations, marital status, and illness severity.

79 ent risk factors for HPV detection were age, marital status, and increasing numbers of lifetime and r

80 ic variables including sex, race, ethnicity, marital status, and insurance status are associated with

81 al surgery, higher disease stage, older age, marital status, and lower comorbidity.

82 ting for maternal education, race/ethnicity, marital status, and maternal age; separately examining h

83 evel data on diagnosis, age, sex, ethnicity, marital status, and occupational status, and our outcome

84 tment for matching characteristics, smoking, marital status, and race/ethnicity using logistic regres

85 Asian, or Indigenous, and educational level, marital status, and region of residence) across the stud

86 eg, sex, age, education, income, urbanicity, marital status, and regional differences) and mental, ne

87 ifferences in employment, social assistance, marital status, and reproduction were no longer signific

88 Baseline variables of age, sex, marital status, and risk did not modify vaccine efficacy

89 Age group, education, marital status, and sexual behavior were associated with

90 bling group, own socioeconomic position, own marital status, and socioeconomic rank within the siblin

91 regression with covariates for site, gender, marital status, and socioeconomic status was used to est

92 information in our analyses, such as gender, marital status, and the presence of current or previous

93 ohol use, smoking history, age at menopause, marital status, and the use of hormone replacement thera

94 In men, marital status, and to a lesser extent network score (bu

95 The predictive value of age, race, marital status, and tumor characteristics were compared.

96 emale sex in Alaska only, whereas education, marital status, and urban residency were associated with

97 tentially confounding variables such as sex, marital status, antidepressant medication, arthritis med

98 [aOR] 0.5 [95% CI, .3-.9] >/=28 vs 22-23]); marital status (aOR 2.3 [95% CI, 1.5-3.5] single vs marr

99 atus, tumor size, differentiation, race, and marital status are valuable for prognostication in breas

100 ite race, Hispanic ethnicity, education, and marital status, as well as zip code population character

101 an differences in antisocial behavior across marital status at age 29 years were present even at 17 a

102 After adjustment for confounders (marital status at first pregnancy and age at first pregn

103 Marital status became the second most important factor a

104 lus, crown coverage, age, income, education, marital status, body mass index, diabetes, and vitamin C

105 associated with age, being African American, marital status, body mass index, lack of exercise, alcoh

106 king and variables to control for education, marital status, body mass, alcohol consumption, occupati

107 Moreover, no association of marital status, body weight, body mass index, blood pres

108 s, by gender, with adjustment for age, race, marital status, branch of service, and type of unit.

109 roups were similar in age, gender ratio, and marital status, but those legally committed for involunt

110 trate how the resulting misclassification of marital status can produce substantial bias in estimates

111 functional independence measure score), age, marital status, chronic conditions, and prestroke ambula

112 relationship between depressive symptoms and marital status, cigarette smoking, nulliparity, and prem

113 ce with adjustment for individual (age, sex, marital status), clinical (histologic grade, surgery, ir

114 Network score and marital status combined explained 27% (95% confidence in

115 demographics (age, sex, racial/ethnic group, marital status), comorbid conditions, and economic resou

116 ompletion of treatment, including age, race, marital status, comorbidities, and sociodemographic fact

117 evated child problem behaviors, and divorced marital status conveyed elevated risk for psychiatric di

118 d the association of gender, age, education, marital status, current physical activity, body weight,

119 moking, social class, long-standing illness, marital status, diabetes, hypertension)-adjusted hazard

120 Maternal age and marital status did not account for the birth weight tren

121 requently cited "red flag." Age, gender, and marital status did not affect how applicants rated facto

122 weight, height, smoking history, education, marital status, diet, alcohol consumption, and occupatio

123 Prospective studies that examine marital status differences in health and mortality frequ

124 d with the use of radiation therapy included marital status, disease stage, and type of lymph node su

125 igh-income versus low-income, and racial and marital status disparities.

126 characteristics (age, sex, race, education, marital status, distance to nearest acute care hospital)

127 ion, 197 control), similar across groups for marital status, duration of HIV diagnosis, and distance

128 sociation between self-assessment scores and marital status, education level, performance status, or

129 on and adjusted for maternal race/ethnicity, marital status, education, age, and ozone.

130 ssion, adjusted for maternal race/ethnicity, marital status, education, age, smoking, maximum tempera

131 goal were use of lipid-lowering medication, marital status, education, body mass index, exercise, hy

132 s are presented by age, sex, race/ethnicity, marital status, education, employment status, and income

133 ing variables of maternal age, parity, race, marital status, education, family income, smoking, alcoh

134 The authors examined age, gender, marital status, education, financial strain, chronic med

135 emographic features (age, gender, ethnicity, marital status, education, occupation, poverty, and heal

136 model: study site, maternal age, gravidity, marital status, education, race/ethnicity, smoking, and

137 nam but were not POWs, matched on race, age, marital status, education, rank, year of entry into the

138 ty, baseline weight (and their interaction), marital status, education, smoking, calorie intake, and

139 r controlling for age, time since diagnosis, marital status, education, tumor site and stage, comorbi

140 fter adjusting for age, sex, race/ethnicity, marital status, educational attainment, employment, heal

141 ing individual characteristics (gender, age, marital status, educational background and self-rated he

142 were categorized by patient characteristics (marital status, educational level) and tumor characteris

143 er of people in household, country of birth, marital status, educational level, and highest employmen

144 nces in kappa statistics based on age, race, marital status, educational level, and income.

145 fter adjustment for comorbidity score, race, marital status, educational status, clinical stage, and

146 m survival models indicate that estimates of marital status effects are sensitive to whether and how

147 istics (i.e., age, sex, race, education, and marital status), employment status at year of diagnosis,

148 cioeconomic markers (education, age, income, marital status, employment status) on these conditions.

149 f age, sex, social class, educational level, marital status, employment status, body mass index, phys

150 patient age, race, education, health status, marital status, employment status, distance from the cen

151 h state and trait anxiety were: age, gender, marital status, employment status, level of education, s

152 Marital status, employment, education, geographic locati

153 ogistic regression, adjusting for sex, race, marital status, employment, education, health literacy,

154 demographic variables (age, race, education, marital status, employment, total family income, and num

155 ctive women aged 15-49 years irrespective of marital status, except in Argentina and Brazil, where an

156 -ethnicity, breastfeeding, mode of delivery, marital status, exposure to environmental tobacco smoke,

157 idence after controlling for age, education, marital status, fasting glucose, body mass index, high-d

158 elf-explanatory outcomes such as employment, marital status, financial independence and housing.

159 re consistent with finer-grained analyses by marital status, gender, and age.

160 ds models, adjusted for age, sex, ethnicity, marital status, geographical region, childhood and adult

161 3) Marital status has a limited impact on periodontal healt

162 aditional measures such as socioeconomic and marital status, health habits, and education may require

163 djusting for patient age, gender, education, marital status, health status, and length of the patient

164 Age, marital status, health status, stressful life events, an

165 Assessment of marital status, heart failure symptomatology, and percei

166 was associated with excellent health, single marital status, higher education, lower body mass index,

167 rs of better QOL included college education, marital status, higher household income, private health

168 y performance status, stage, sex, age, race, marital status, histology, tumor location, hemoglobin, t

169 rs, after adjustment for smoking, education, marital status, history of heart disease, parity, race/e

170 SV-2 seropositivity were HIV seropositivity, marital status, history of sexually transmitted disease

171 mic status; residence area; occupation type; marital status; history of hypertension, diabetes mellit

172 al cancer varies substantially by age, race, marital status, hospital volume, and individual hospital

173 ust, controlling for age, gender, education, marital status, household income and religiosity.

174 Age, marital status, household income, education, parity, tim

175 Age, gender, marital status, household structure, and occupation did

176 Hazard ratios (HRs) were adjusted for marital status, immigration status, income quartile (sin

177 For example, updating marital status in hazard models from interview informati

178 tion between periodontitis risk, gender, and marital status in older adults.

179 ctivity, alcohol consumption, education, and marital status in pooled data from 19 prospective studie

180 ity frequently fail to update information on marital status in statistical models.

181 lyze the survival difference among different marital status in the United States.

182 lic and diastolic blood pressure, education, marital status, income, and occupation.

183 ratios (aOR), by adjusting for nationality, marital status, income, and site of testing.

184 factors including age, ethnicity, education, marital status, income, employment, and drug and alcohol

185 Correlates of depressive symptoms include marital status, income, kidney function, history of affe

186 er adjusting for age, background, education, marital status, income, nativity, smoking, physical acti

187 respect to age, ethnicity, race, education, marital status, income, obstetric history, and language.

188 groups (sex, age, race/ethnicity, education, marital status, income, urban/rural, and region).

189 ancy BMI, previous history of preterm birth, marital status, infant sex, and initiation of prenatal c

190 regnancy, previous history of preterm birth, marital status, infant sex, and timing of initiation of

191 conomic variables including race, ethnicity, marital status, insurance status, and geographic locatio

192 6) after adjusting for age, smoking, parity, marital status, insurance status, and weight.

193 ex), adjusting for survey year, region, age, marital status, insurance, educational attainment, and i

194 r controlling for age, sex, race, education, marital status, interval between final interview and dea

195 ss race-ethnicity, geography, education, and marital status is compatible with a physiologic response

196 tus effects are sensitive to whether and how marital status is updated after baseline interviews.

197 ing status and intensity, educational level, marital status, job status, energy intake, and physical

198 unger age, nonwhite race, male sex, divorced marital status, lack of advance directives, a recent dec

199 djusted for age, baseline educational level, marital status, leisure time physical exercise, walking/

200 physical health status, depressive symptoms, marital status, level of education, and severity of illn

201 s of education, occupational status, income, marital status, life satisfaction, disability, and heigh

202 er controlling for age, sex, race/ethnicity, marital status, living alone or not, education, income,

203 ciprofloxacin-resistant gonococci included: marital status, living alone, duration of sex work, and

204 logistic regression models adjusted for age, marital status, living arrangement, family size, and sev

205 health, education level, employment status, marital status, living arrangements, and birth rate were

206 ssociated with lower age, male sex, divorced marital status, living with children, lack of satisfacti

207 onomic status, lived in urban areas, married marital status, lower PSA levels and lower Gleason score

208 ents, age at presentation, older age, single marital status, lower socioeconomic status, higher PSA l

209 adults to investigate the impact of current marital status, marriage timing, divorce and widow trans

210 orrelates included in our analyses were age, marital status, marriage type, whether pregnant or post

211 After adjusting for age, marital status, material deprivation history, smoking, d

212 ages over childhood irrespective of parental marital status, may anticipate or otherwise accommodate

213 on, physical activity, body weight, smoking, marital status, medical conditions, and medications.

214 Information regarding income, education, marital status, medical history, and cardiovascular risk

215 alyses revealed that age more than 65 years, marital status, minority populations, and primary tumor

216 y, child's ethnicity, mother's age, mother's marital status, mother's depression score at 18 and 32 w

217 usting for country, maternal age, education, marital status, neonate weight at birth, and neonate sex

218 ties differed from whites in sex proportion, marital status, number of children, geographic location,

219 e no significant differences with respect to marital status, number of children, or number of hours w

220 and MCS after adjustment for age, education, marital status, number of comorbidities, smoking, cancer

221 Fisher exact test showed that gender, age, marital status, occupation and level of education had no

222 higher risk for periodontitis, regardless of marital status (odds ratio: 1.5, 95% CI: 1.05 to 2.04, P

223 er tumor size (OR, 0.37; 95% CI, 0.27-0.51), marital status of being separated at the time of diagnos

224 ers, 50% were single and 20.6% were married (marital status of the remainder was unknown).

225 ent, how many children parents have, and the marital statuses of parents and children affect the exte

226 The influence of marital status on racial/ethnic disparities was stronger

227 factors were not associated with RP in men (marital status OR 1.4, 95% CI 0.6-3.5; alcohol use OR 1.

228 rder (OR = 1.35), widowed/separated/divorced marital status (OR = 1.28), and arthritis (OR = 1.27) we

229 of receiving chemotherapy included unmarried marital status (OR, 0.59; p = 0.0002) and more comorbidi

230 ikelihood of receiving RT included unmarried marital status (OR, 0.64; p < 0.0001) and more comorbidi

231 fered in gender distribution but not in age, marital status, or educational achievement.

232 ucation, employment, hours of work per week, marital status, or satisfaction with life.

233 acteristics (age [P = .007], race [P = .05], marital status [P = .04]), personality (extroversion [P

234 ollowing characteristics: gender, age, race, marital status, parental status, additional graduate deg

235 or smoking, body mass index, alcohol intake, marital status, parity, menopausal status, and history o

236 After adjustment for race/ethnicity, marital status, parity, prepregnancy physical activity,

237 f melanoma, institution, histologic subtype, marital status, performance of skin self-examination, nu

238 ing status, alcohol intake, body mass index, marital status, physical activity, and education level.

239 results were controlled for age, education, marital status, physician's health rating, dieting for m

240 ation level, sex, US region, race/ethnicity, marital status, political affiliation, likelihood to vot

241 ation problems, but when maternal education, marital status, poverty level, and race are controlled,

242 Marital status, premorbid functional status, clinical se

243 founding by maternal age, education, parity, marital status, prenatal care, smoking, and previous pre

244 logistic regression with adjustment for age, marital status, prescription drug monitoring programs, a

245 ance coverage, years of education, literacy, marital status, pretransplantation compliance, and histo

246 For CD and ND in the EA population, marital status proved to be a significant moderator in t

247 Data for age, sex, ethnicity, marital status, psychotic disorder diagnosis, subsequent

248 rate ratio (before and after adjustment for marital status, race, and age) comparing vaccinated with

249 sible modifiers of concordance: patient age, marital status, race, educational level, Eastern Coopera

250 After adjustment for maternal age, marital status, race/ethnicity, and education, the risk

251 After controlling for age, gender, marital status, race/ethnicity, city, and time, the auth

252 ed from matched spouse records with reported marital status recorded in LSOA interviews demonstrate t

253 ction, race/ethnicity, socioeconomic status, marital status, referral source, and referral year.

254 Age, ethnicity, marital status, religion, and advance directives were no

255 ent for a participant's age, sex, education, marital status, religion, and study site.

256 regression that adjusted for age, race, sex, marital status, residence, percent of county below feder

257 When adjusted for age, marital status, residential area, clinical characteristi

258 After adjusting for age, education, race, marital status, self-efficacy, and dental knowledge, mul

259 conomic variables, including age, race, sex, marital status, service connection, prescription copay,

260 ge, surgery weight, surgery body mass index, marital status, sex, educational level, site, Internatio

261 Social determinants of health, including marital status (SHR [95% CI]: 0.6 [0.4-0.9]), religious

262 fter adjusting for changes in disability and marital status since baseline (OR, 1.72; 95% CI, 0.99-2.

263 ham Offspring Study, the association of age, marital status, smoking, alcohol use, diabetes, hyperten

264 for country, age, race, education duration, marital status, smoking, alcohol, and number of recent s

265 ed to age, state, race/ethnicity, education, marital status, smoking, and alcohol consumption.

266 after adjusting for age, gender, education, marital status, smoking, hypertension, diabetes mellitus

267 Sex, age, ethnicity, marital status, social deprivation, severity of psychopa

268 Covariates were age, sex, education, marital status, social isolation and social support, chr

269 mined the effects of age, gender, education, marital status, social isolation, functional impairment,

270 Three components (marital status, social network size, and religious servi

271 s measured with a 7-item index that included marital status, social network size, frequency of contac

272 s measured with a 7-item index that included marital status, social network size, frequency of contac

273 ciations were not accounted for by sex, age, marital status, socioeconomic position, place of work, s

274 ciocultural factors, such as age, gender and marital status, strongly influence the probability of un

275 e includes 5 domains (employment, education, marital status, substance abuse and income), each with a

276 ces of neighborhood socioeconomic status and marital status suggest that social determinants, support

277 al radiation, age, sex, race, education, and marital status, survivor hair loss increased risk of anx

278 fter adjustment for age, race/ethnicity, and marital status, the odds of high-risk HPV infection were

279 phic factors at study inclusion such as age, marital status, the presence of children in the home, ar

280 adults to examine the association of current marital status, timing of first marriage, number and kin

281 atements, white race, older age, and married marital status to be associated with higher adherence (a

282 e than a century of empirical evidence links marital status to mortality.

283 ables included individual factors (age, sex, marital status, underlying cause of death) and measures

284 Higher LS was significantly related to marital status, unilateral affliction, and higher educat

285 xyvitamin D levels and age, body mass index, marital status, use of hormone therapy, physical activit

286 Marital status was found to be an independent prognostic

287 ariable regression analysis showed that only marital status was independently associated with higher

288 delay by patients, and strong evidence that marital status was unrelated to delays by patients.

289 interaction; and the PFS model also included marital status, weight loss, and p16 x Zubrod interactio

290 blings, and monozygotic twins discordant for marital status were as strong as that seen in the genera

291 Parent education and marital status were significantly associated with BA and

292 causes, but education, occupation, sex, and marital status were typically included as well.

293 ctors (age, education, household income, and marital status) were included in the analysis.

294 ditionally, patients with single or divorced marital status, who were living in rural places had high

295 ,000 dollars (OR = 5.6, 95% CI, 4.5 to 7.1), marital status (widowed/divorced/separated; OR = 1.3; 95

296 Symptom profiles, comorbidity, and marital status with major depression.

297 of marriage, and durations spent in various marital statuses with the risk of all-cause mortality.

298 adjusted for hormonal contraceptive use and marital status, women reporting multiple male partners o

299 rlson score, residency training program, and marital status, women with early-stage disease were sign

300 ber and specific causes), age at death, sex, marital status, year of death, index of multiple depriva