ライフサイエンスコーパス: marital status

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

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

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

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

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

6 nformation on educational qualifications and marital status.

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

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

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

10 conditions were found between spouses or by marital status.

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

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

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

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

15 on leads to substantial misclassification of marital status.

16 = 0.29), but did not differ by age, sex, or marital status.

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

18 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,

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

58 Age, marital status, and comorbidities influenced the probabi

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

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

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

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

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

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

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

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

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

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

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

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

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

72 other subthreshold symptoms, age, sex, race, marital status, and perceived physical health status, on

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

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

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

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

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

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

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

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

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

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

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

84 [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

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

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

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

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

89 Marital status became the second most important factor a

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

105 istic regression, with control for age, sex, marital status, date, education, cigarette smoking, alco

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

137 Marital status, employment, education, geographic locati

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

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

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

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

142 ge, father's social class, own social class, marital status, fibrinogen and cholesterol concentration

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

186 endance, part-time work status, self-esteem, marital status, living arrangements, and number of biwee

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

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

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

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

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

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

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

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

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

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

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

198 y associated with low income; unemployment a marital status of single, divorced, or separated; and ur

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

217 Marital status, premorbid functional status, clinical se

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

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

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

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

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

223 Participants were matched on age, sex, marital status, race, and the nature and severity of the

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

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

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

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

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

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

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

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

232 After controlling for age, marital status, residence, total energy intake, and tran

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

259 Marital status was found to be an independent prognostic

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

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

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

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

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

265 ,000 dollars (OR = 5.6, 95% CI, 4.5 to 7.1), marital status (widowed/divorced/separated; OR = 1.3; 95

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

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

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

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