Explore the vast range of titles available.

In Table 1, the codes under “Observed variables [range], adopted response” were incorrectly placed under “Latent variables”. Latent variables Observed variables [range], adopted response Data available, n (%) Applicable sample, n (%) Mean (SD) Median (25%, 75% IQR) Personal factors p1 Age, y 477 (100.0) 65.4 (12.4) 67 (57, 74) p2 Sex, male 475 (99.6) 261 (54.9) p3 Body mass index, kg/m2 465 (97.5) 21.4 (3.3) 21.2 (19.1, 23.7) p4 Educational background, post-secondary 474 (99.4) 291 (61.0) Medical factors m1 Disease type, complex 466 (97.7) 330 (70.8) m2 Disease duration, years 448 (93.9) 13.8 (11.0) 10.8 (6.0, 18.2) m3 Number of common complications 402 (84.3) 0.6 (0.8) 0 (0, 1) m4 Number of disease-related complications 402 (84.3) 0.5 (0.8) 0 (0, 1) m5 Number of medications 467 (97.9) 4.4 (3.5) 4 (2, 6) m6 Drugs for cerebellar symptoms, yes 435 (91.2) 284 (65.3) m7 Drugs for Parkinsonism, yes 434 (91.0) 76 (17.5) m8 History of falls within the past year, ≥ 1 458 (96.0) 365 (79.7) Environmental factors s1 Type of residence, home 475 (99.6) 444 (93.5) s2 Environmental improvement, yes 467 (97.9) 367 (78.6) s3 Cohabitation, yes 457 (95.8) 399 (87.3) s4 Caregiver support, yes 436 (91.4) 384 (88.1) s5 Work, yes 447 (93.7) 104 (23.3) s6 Need for social support [0–10] 459 (96.2) 7.6 (2.5) 8 (6, 10) s7 Satisfaction with social support [0–10] 456 (95.6) 5.6 (2.3) 5 (5, 7) Impairments i1 Ataxia [0–3], ≥ 1 471 (98.7) 468 (99.4) 2.4 (0.8) 3 (2, 3) i2 Weakness [0–3], ≥ 1 473 (99.2) 419 (88.6) 1.8 (1.0) 2 (1, 3) i3 Rigidity [0–3], ≥ 1 467 (97.9) 335 (71.7) 1.3 (1.1) 1 (0, 2) i4 Spasticity [0–3], ≥ 1 462 (96.9) 294 (63.6) 1.2 (1.1) 1 (0, 2) i5 Imbalance [0–3], ≥ 1 470 (98.5) 462 (98.3) 2.5 (0.8) 3 (2, 3) i6 Malalignment [0–3], ≥ 1 466 (97.7) 290 (62.2) 1.1 (1.1) 1 (0, 2) i7 Fatigue [0–3], ≥ 1 464 (97.3) 388 (83.6) 1.6 (1.0) 2 (1, 2) i8 Pain [0–3], ≥ 1 452 (94.8) 193 (42.7) 0.8 (1.0) 0 (0, 2) i9 Numbness [0–3], ≥ 1 446 (93.5) 150 (33.6) 0.6 (0.9) 0 (0, 1) i10 Sensory disturbances [0–3], ≥ 1 467 (97.9) 268 (57.4) 1.0 (1.0) 1 (0, 2) i11 Tremor [0–3], ≥ 1 468 (98.1) 289 (61.8) 1.1 (1.1) 1 (0, 2) i12 Involuntary movements [0–3], ≥ 1 454 (95.2) 235 (51.8) 0.9 (1.0) 1 (0, 1) i13 Orthostatic hypotension [0–3], ≥ 1 469 (98.3) 186 (39.7) 0.7 (1.0) 0 (0, 1) i14 Poor sleep [0–3], ≥ 1 468 (98.1) 228 (48.7) 0.8 (1.0) 0 (0, 1) i15 Respiratory disorder [0–3], ≥ 1 466 (97.7) 175 (37.6) 0.6 (0.9) 0 (0, 1) i16 Speech dysarthria [0–3], ≥ 1 473 (99.2) 437 (92.4) 2.0 (1.0) 2 (1, 3) i17 Dysphagia [0–3], ≥ 1 474 (99.4) 389 (82.1) 1.5 (1.0) 1 (1, 2) i18 Visual impairment [0–3], ≥ 1 472 (99.0) 314 (66.5) 1.2 (1.1) 1 (0, 2) i19 Urinary impairment [0–3], ≥ 1 472 (99.0) 294 (62.3) 1.3 (1.2) 1 (0, 2) i20 Bowel impairment [0–3], ≥ 1 476 (99.8) 282 (59.2) 1.2 (1.2) 1 (0, 2) i21 Cognitive impairment [0–3], ≥ 1 475 (99.6) 210 (44.2) 0.6 (0.8) 0 (0, 1) i22 PHQ-2 score [0–6], ≥ 1 458 (96.0) 286 (62.4) 1.8 (1.8) 1 (0, 3) Activity limitations a1 mRS score [0–5], ≤ 2 (= independence) 461 (96.6) 110 (23.9) 3.4 (1.1) 4 (3, 4) a2 Indoor mobility1,2,3,4,5, ≥ 4 (= ambulatory) 461 (96.6) 165 (35.8) 3.6 1.4 4 (2, 5) a3 Outdoor mobility1,2,3,4,5, ≥ 4 (= ambulatory) 469 (98.3) 149 (31.8) 2.8 1.3 3 (2, 4) a4 BI [0–100] 474 (99.4) 70.1 (36.6) 90 (45, 100) a5 LSA score [0–120] 475 (99.6) 36.7 (28.4) 30 (17, 48.5) Quantity of rehabilitation r1 Types of rehabilitation implemented, ≥ 1 477 (100.0) 368 (77.1) r2 Types of therapy, maximum of 3 477 (100.0) 1.3 (1.0) 1 (0, 2) r3 Total rehabilitation time, min/month 477 (100.0) 543.4 (609.5) 320 (160, 720) r4 Duration of rehabilitation, months 476 (99.8) 49.4 (64.0) 30 (2, 71.5) r5 Types of rehabilitation program 477 (100.0) 5.9 (4.4) 5 (2.5, 9) r6 Self-rehabilitation, yes 450 (94.3) 324 (72.0) Quality of rehabilitation 368 (77.1) r7 Effects of rehabilitation [0–10] 356 (96.7) 6.4 (2.4) 6 (5, 8) r8 Self-efficacy of rehabilitation [0–10] 355 (96.5) 6.1 (2.5) 6 (5, 8) r9 Satisfaction with rehabilitation [0–10] 354 (96.2) 6.5 (2.4) 7 (5, 8) r10 Motivation for rehabilitation [0–10] 354 (96.2) 7.2 (2.4) 8 (6, 9) Quality of life q1 EQ index [0.00–1.00] 456 (95.6) 0.49 (0.23) 0.51 (0.31, 0.66) q2 EQ-VAS [0–100] 440 (92.2) 57.9 (23.9) 60 (45, 78.5) q3 Life satisfaction score [0–10] 456 (95.6) 5.1 (2.3) 5 (4, 7) Table 2. Unstandardized and standardized regression coefficients of specified paths within the final model (rehabilitation effects model). Latent variables Observed indicators B Unstandardized estimate Lower 95% CI Upper 95% CI Β Standardized estimate Lower 95% CI Upper 95% CI P value Personal factors  →  p1 Age − 3.523 − 4.908 − 2.138 − 0.285 − 0.394 − 0.176  < 0.001  →  p2 Sex 0.112 − 0.032 0.255 0.112 − 0.032 0.255 0.200  →  p3 Body mass index 1.592 1.206 1.978 0.487 0.376 0.599  < 0.001  →  p4 Educational background 0.184 0.064 0.303 0.184 0.064 0.303 0.011 Medical factors  →  m1 Disease type − 0.683 − 0.785 − 0.581 − 0.683 − 0.785 − 0.581  < 0.001  →  m4 Number of disease-related complications − 0.599 − 0.701 − 0.496 − 0.599 − 0.701 − 0.496  < 0.001  →  m5 Number of medications − 0.153 − 0.179 − 0.126 − 0.552 − 0.636 − 0.468  < 0.001  →  m6 Drugs for cerebellar symptoms 0.307 0.180 0.434 0.307 0.180 0.434  < 0.001  →  m7 Drugs for Parkinsonism − 0.747 − 0.872 − 0.623 − 0.747 − 0.872 − 0.623  < 0.001  →  m8 History of falls within the past year 0.149 0.042 0.257 0.146 0.043 0.250 0.020 Environmental factors  →  s1 Type of residence 0.565 0.390 0.739 0.565 0.390 0.739  < 0.001  →  s3 Cohabitation − 0.363 − 0.513 − 0.212 − 0.363 − 0.513 − 0.212  < 0.001  →  s4 Caregiver support − 0.773 − 0.905 − 0.641 − 0.773 − 0.905 − 0.641  < 0.001  →  s6 Need for social support − 1.075 − 1.334 − 0.816 − 0.438 − 0.534 − 0.343  < 0.001  →  s7 Satisfaction with social support − 0.377 − 0.599 − 0.155 − 0.164 − 0.259 − 0.069 0.005 Primary impairments  →  i1 Ataxia 0.308 0.169 0.446 0.751 0.699 0.804  < 0.001  →  i5 Imbalance 0.256 0.141 0.371 0.624 0.564 0.684  < 0.001  →  i11 Tremor 0.262 0.146 0.377 0.64 0.584 0.695  < 0.001  →  i16 Speech dysarthria 0.285 0.158 0.413 0.697 0.648 0.746  < 0.001  →  i17 Dysphagia 0.311 0.172 0.45 0.76 0.719 0.802  < 0.001  →  i18 Visual impairment 0.181 0.099 0.264 0.443 0.372 0.514  < 0.001  →  i19 Urinary impairment 0.294 0.163 0.425 0.717 0.667 0.767  < 0.001  →  i20 Bowel impairment 0.325 0.18 0.47 0.794 0.751 0.836  < 0.001 Secondary impairments  →  i2 Weakness 0.363 0.309 0.417 0.824 0.785 0.863  < 0.001  →  i3 Rigidity 0.335 0.287 0.382 0.759 0.717 0.802  < 0.001  →  i6 Malalignment 0.337 0.288 0.385 0.764 0.721 0.807  < 0.001  →  i7 Fatigue 0.336 0.291 0.381 0.762 0.721 0.804  < 0.001  →  i8 Pain 0.279 0.234 0.323 0.632 0.567 0.697  < 0.001  →  i10 Sensory disturbances 0.339 0.292 0.387 0.770 0.726 0.814  < 0.001  →  i22 PHQ-2 score 0.258 0.217 0.300 0.586 0.525 0.648  < 0.001 Activity limitations  →  a2 Indoor mobility − 0.382 − 0.476 − 0.287 − 0.862 − 0.903 − 0.820  < 0.001  →  a3 Outdoor mobility − 0.250 − 0.317 − 0.184 − 0.565 − 0.627 − 0.504  < 0.001  →  a4 BI − 0.400 − 0.516 − 0.284 − 0.903 − 0.959 − 0.846  < 0.001  →  a5 LSA − 0.320 − 0.403 − 0.236 − 0.722 − 0.771 − 0.673  < 0.001 QoL  →  q1 EQ index 0.124 0.102 0.146 1.029 0.981 1.076  < 0.001  →  q2 EQ-VAS score 0.304 0.253 0.354 0.591 0.533 0.649  < 0.001  →  q3 Life satisfaction score 0.3 0.203 0.396 0.249 0.168 0.331  < 0.001 Quantity of rehabilitation  →  r1 Types of rehabilitation implemented 0.802 0.750 0.854 0.865 0.819 0.912  < 0.001  →  r2 Types of therapy 0.807 0.757 0.858 0.871 0.827 0.915  < 0.001  →  r3 Total rehabilitation time 0.693 0.544 0.843 0.665 0.574 0.757  < 0.001  →  r4 Duration of rehabilitation 0.470 0.383 0.557 0.644 0.572 0.716  < 0.001  →  r5 Types of rehabilitation program 2.150 1.811 2.489 0.529 0.460 0.598  < 0.001 Quality of rehabilitation  →  r7 Effects of rehabilitation 2.057 1.858 2.256 0.911 0.877 0.945  < 0.001  →  r9 Satisfaction with rehabilitation 2.175 1.943 2.408 0.942 0.907 0.977  < 0.001  →  r10 Motivation for rehabilitation 1.906 1.671 2.142 0.791 0.743 0.839  < 0.001 Personal factors  →  Primary impairments − 1.565 − 0.648 − 2.482 − 0.641 − 0.749 − 0.533  < 0.001 Medical factors  →  Primary impairments − 1.585 − 0.792 − 2.378 − 0.649 − 0.721 − 0.577  < 0.001 Medical factors  →  Secondary impairments − 0.391 − 0.173 − 0.610 − 0.172 − 0.265 − 0.079 0.002 Primary impairments  →  Secondary impairments 0.744 0.393 1.095 0.800 0.723 0.877  < 0.001 Quantity of rehabilitation  →  Secondary impairments − 0.254 − 0.116 − 0.392 − 0.121 − 0.181 − 0.060 0.001 Primary impairments  →  Activity limitations 0.418 0.186 0.650 0.452 0.270 0.634  < 0.001 Environmental factors  →  Activity limitations − 1.181 − 0.490 − 1.871 − 0.523 − 0.721 − 0.324  < 0.001 Environmental factors  →  Quantity of rehabilitation − 0.406 − 0.511 − 0.301 − 0.376 − 0.460 − 0.293  < 0.001 Quantity of rehabilitation  →  Quality of rehabilitation 0.844 0.712 0.976 0.680 0.620 0.741  < 0.001 Personal factors  →  Quality of rehabilitation 0.262 0.106 0.417 0.195 0.086 0.305

Degenerative cerebellar ataxias (DCAs) are progressive diseases that reduce quality of life (QoL). This study aimed to assess the impact of rehabilitation on QoL in patients with DCAs using structural equation modeling (SEM). This cross-sectional survey included members of a national Japanese DCAs patient association. Assessed latent variables included personal, medical, and environmental factors, impairments, activity limitations, rehabilitation (participation quantity and patient-reported quality), and QoL. SEM was used to explore causal relationships between these latent variables. Overall, 477 participants (mean age 65.4 years; 45.1% female) were included. Impairments were categorized as primary and secondary, based on preliminary analyses. The final model demonstrated acceptable-to-good fit indices, explaining 74% of the variance in QoL. The model paths showed that activity limitations, secondary impairments, and rehabilitation quality had a direct effect on QoL, in that order. The quantity of rehabilitation had an indirect effect on QoL through its direct effect on secondary impairments and quality of rehabilitation. These findings suggest that rehabilitation interventions improve QoL in the DCA population, but its effects vary depending on the quantity and quality of rehabilitation. However, the cross-sectional study design limits the ability to draw causal conclusions and longitudinal studies are needed for confirmation.

The COVID-19 pandemic forced many educational institutions to turn to electronic learning to allow education to continue under the stay-at-home orders/requests that were commonly instituted in early 2020. In this cross-sectional study, we evaluated the effects of the COVID-19 pandemic on medical education in terms of students’ attitudes toward online classes and their online accessibility; additionally, we examined the impacts of any disruption caused by the pandemic on achievement test performance based on the test results. The participants were 674 students (412 in pre-clinical, 262 in clinical) at Juntendo University Faculty of Medicine; descriptive analysis was used to examine the respondents’ characteristics and responses. The majority of respondents (54.2%) preferred asynchronous classes. Mann–Whitney U tests revealed that while pre-clinical students preferred asynchronous classes significantly more than clinical students (39.6%, p < .001), students who preferred face-to-face classes had significantly higher total achievement test scores ( U = 1082, p = .021, r = .22). To examine the impacts of pandemic-induced changes in learning, we conducted Kruskal–Wallis tests and found that the 2020 and 2021 scores were significantly higher than those over the last three years. These results suggest that while medical students may have experienced challenges adapting to electronic learning, the impact of this means of study on their performance on achievement tests was relatively low. Our study found that if possible, face-to-face classes are preferable in an electronic learning environment. However, the benefit of asynchronous classes, such as those that allow multiple viewings, should continue to be recognized even after the pandemic.

Background Fetal head malrotation is associated with prolonged labor, instrumental delivery, and perinatal complications. Previous studies have suggested an association between malrotation and labor analgesia, but this remains controversial. This study aimed to clarify whether malrotation increases with the use of labor analgesia. Methods This retrospective cohort study using propensity score matching. Medical records from January 2020 to January 2023 were reviewed. The study subjects were full-term pregnant women with singleton cephalic fetuses whose cervixes were fully dilated. The group without labor analgesia (Group C) was matched with the labor analgesia group (Group A) by propensity score matching. The primary outcome was the occurrence of malrotation. Secondary outcomes included rates of normal vaginal delivery, instrumental delivery, cesarean section, and success rate of attempted manual rotation. Pearson’s chi-square test was used to assess the association between the use of labor analgesia and outcomes. Results During the study period, 3868 women were included, 1164 cases were excluded due to the exclusion criteria, and 971 cases were further excluded due to missing data. Of 1735 eligible women, 88.4% received labor analgesia. After propensity score matching, 804 women were included, of whom 75% received labor analgesia (Group A) and 25% did not (Group C). The rate of malrotation was significantly higher in Group A compared to that in Group C (11.6% vs. 6.5%, p  = 0.03). The rate of instrumental delivery was significantly higher in Group A (25.7% vs. 14.9%, p  = 0.001). The distribution of the instrumental deliveries in Group A was as follows: Naegele forceps delivery was used in 87.1% of the cases, Kielland forceps in 5.2%, and vacuum extraction in 7.7%. There was no significant difference in the rate of vaginal delivery. Manual rotation was attempted in 84.3% of malrotation cases in Group A with a 64.4% success rate, with no significant difference of the success rate between the two groups. Conclusions Labor analgesia is associated with an increased rate of malrotation and instrumental delivery. However, it does not seem to decrease the rate of vaginal delivery, given the high attempt and success rates of manual rotation and the availability of Kielland forceps delivery.

Hypertension and diabetes are prevalent among older people and may be associated with cancer. Although several network analyses have been conducted to visualize the associations between diseases and relevant factors, to the best of our knowledge, none have focused on visualizing the associations between cancer and other diseases. We conducted a network analysis to explore the associations between cancer, hypertension, and diabetes. This study used a large-scale clinical dataset of 1,026,305 hospitalized patients aged ≥ 65 years, collected between April 2008 and December 2020. Diseases were categorized using the International Classification of Diseases-10 (2019 version) codes. The analysis focused on diseases with a prevalence of ≥ 1%. A multimorbidity network was constructed for the entire patient cohort, and the same analysis was applied specifically to cancer patients. Hypertension (degree centrality: 58/61) and diabetes (degree centrality: 56/61) were connected to several diseases, indicating significant multimorbidity in the cohort. The associations (observed-to-expected ratio) between digestive system cancers and hypertension and diabetes were relatively stronger than those between the diseases and other cancers. Type 2 diabetes and essential hypertension may be risk factors of cancers at multiple digestive system sites. Early treatment of these conditions could prevent or delay the progression of digestive system cancers.

Studies have reported that health care professionals experienced a lack of sleep during the coronavirus disease 2019 (COVID-19) pandemic and that such lack of sleep and working environment affect their performance. However, to the authors’ knowledge, no study has yet investigated the relationship between sleep duration and working environment among Japanese physiotherapists during the COVID-19 pandemic. This study retrospectively investigated the sleep duration of physiotherapists directly providing physiotherapy to patients with COVID-19 within the red zone and analyzed the association between sleep duration and working environment using logistic regression analysis. Among the 565 physiotherapists studied, the average sleep duration was 6 (6–7) h, and 381 (67.4%) had an average sleep duration of ≤6 h. Less experienced physiotherapists were 1.03 times more likely to sleep ≤6 h, and those in charge of patients with COVID-19 as the supervisor ordered were 0.64 times more likely to sleep ≤6 h. Moreover, physiotherapists with a significant increase in the frequency of internal online meetings and those who had been providing physiotherapy to patients with COVID-19 for >6 months were 2.34 and 2.05 times more likely to sleep ≤6 h, respectively. During the COVID-19 pandemic in Japan, two-thirds of the physiotherapists directly providing physiotherapy to patients with COVID-19 slept less than the recommended duration. This study highlights the need for appropriate workload and work hour management for physiotherapists according to their experience and workload, as well as establishing a medical care system that includes work rotation to ensure that the recommended sleep duration is satisfied.

Few reports exist regarding the therapeutic effects of probiotics on chronic constipation in elderly individuals. This study evaluated the effects of Bifidobacterium longum BB536 in elderly individuals with chronic constipation. This was a randomized, double-blind placebo-controlled, parallel-group superiority trial in Japan (UMIN 000033031). Eighty older adults diagnosed with chronic constipation were randomly assigned (1:1) to receive either probiotics ( B. longum BB536, 5 × 10 10 colony-forming unit, n = 39) or placebo (n = 41) once daily for up to 4 weeks. The severity of constipation was evaluated using the Constipation Scoring System. The primary end point was the difference in the changes from baseline in the constipation scoring system total score between the 2 groups at week 4. A total of 79 patients (mean age of 77.9 years), including 38 patients in the BB536 group and 41 in the placebo group, completed the study. The primary end point was not significant ( P = 0.074), although there was significant improvement ( P < 0.01) in the BB536 group from baseline to week 4, but there were no significant changes in the placebo group. There was a significant difference and a tendency toward a difference in the changes from baseline on the stool frequency ( P = 0.008) and failure of evacuation ( P = 0.051) subscales, respectively, at week 4 between the 2 groups. Few adverse events related to the probiotics were observed. The primary end points were not significant. However, probiotic supplementation significantly improved bowel movements. These results suggest that B. longum BB536 supplementation is safe and partially effective for improving chronic constipation in elderly individuals.

During the coronavirus disease 2019 (COVID-19) pandemic in Japan, the state of emergency, as a public health measure to control the spread of COVID-19, and the Go To campaign, which included the Go To Travel and Go To Eat campaigns and was purposed to stimulate economic activities, were implemented. This study investigated the impact of these government policies on COVID-19 spread. This ecological study included all 47 prefectures in Japan as samples between February 3 and December 27, 2020. We used COVID-19 cases and mobility as variables. Additionally, places where social contacts could accrue, defined as restaurants, companies, transportation, and tourist spots; mean temperature and humidity; the number of inhabitants in their twenties to fifties; and the number of COVID-19 cases in the previous period, which were factors or covariates in the graphical modeling analysis, were divided into five periods according to the timing of the implementation of the state of emergency and Go To campaign. Graphical changes occurred throughout all five periods of COVID-19. During the state of emergency (period 2), a correlation between COVID-19 cases and those before the state of emergency (period 1) was observed, although this correlation was not significant in the period after the state of emergency was lifted (period 3). During the implementation of Go To Travel and the Go To Eat campaigns (period 5), the number of places where social contacts could accrue was correlated with COVID-19 cases, with complex associations and mobility. This study confirms that the state of emergency affected the control of COVID-19 spread and that the Go To campaign led to increased COVID-19 cases due to increased mobility by changing behavior in the social environment where social contacts potentially accrue.

The intestinal microbiome changes dynamically in early infancy. Colonisation by Bifidobacterium and Bacteroides and development of intestinal immunity is interconnected. We performed a prospective observational cohort study to determine the influence of antibiotics taken by the mother immediately before delivery on the intestinal microbiome of 130 healthy Japanese infants. Faecal samples (383) were collected at 1, 3, and 6 months and analysed using next-generation sequencing. Cefazolin was administered before caesarean sections, whereas ampicillin was administered in cases with premature rupture of the membranes and in Group B Streptococcus -positive cases. Bifidobacterium and Bacteroides were dominant (60–70% mean combined occupancy) at all ages. A low abundance of Bifidobacterium was observed in infants exposed to antibiotics at delivery and at 1 and 3 months, with no difference between delivery methods. A lower abundance of Bacteroides was observed after caesarean section than vaginal delivery, irrespective of antibiotic exposure. Additionally, occupancy by Bifidobacterium at 1 and 3 months and by Bacteroides at 3 months differed between infants with and without siblings. All these differences disappeared at 6 months. Infants exposed to intrapartum antibiotics displayed altered Bifidobacterium abundance, whereas abundance of Bacteroides was largely associated with the delivery method. Existence of siblings also significantly influenced the microbiota composition of infants.

ObjectiveTo investigate whether osteoarthritis (OA) is a risk factor for cardiovascular disease (CVD); whether there are differences concerning ischaemic heart disease (IHD), congestive heart failure (CHF) and stroke; and whether there are differences between OA sites (hips, knees and hand) in predicting CVD onset.DesignPopulation-based matched case–control study.SettingHealth insurance claims data among Japanese patients.ParticipantsJapanese patients aged ≥65 years with newly diagnosed CVD and hospitalised between January 2015 and December 2020 (cases) and age-matched and sex-matched 1:1 individuals (controls).Main outcome measuresA conditional logistic regression model was used to estimate the adjusted ORs and their 95% CIs for CVD, IHD, CHF and stroke risk, adjusting for covariates.ResultsA total of 79 296 patients were included, with respect to CVD (39 648 patients with newly diagnosed CVD and 39 648 controls). After adjustment for covariates, the exposure odds of knee OA (KOA), hip OA (HipOA) and hand OA (HandOA) for CVD were 1.192 (95% CI 1.115 to 1.274), 1.057 (95% CI 0.919 to 1.215) and 1.035 (95% CI 0.684 to 1.566), respectively, showing an association only for KOA. The exposure odds of KOA, HipOA and HandOA for IHD were 1.187 (95% CI 1.086 to 1.297), 1.078 (95% CI 0.891 to 1.306) and 1.099 (95% CI 0.677 to 1.784), respectively. The exposure odds of KOA, HipOA and HandOA for stroke were 1.221 (95% CI 1.099 to 1.356), 0.918 (95% CI 0.723 to 1.165) and 1.169 (95% CI 0.635 to 2.151), respectively. Similar to CVD, only KOA was associated with both. For CHF, neither KOA nor HipOA and HandOA were associated with CHF development.ConclusionThis study confirms the association of KOA with CVD, particularly IHD and stroke, in the Japanese population. The finding that patients with KOA have a higher CVD risk can potentially assist in guiding future treatment strategies.