The association between antihypertensive treatment and serious adverse events by age and frailty: A cohort study.

BACKGROUND: Antihypertensives are effective at reducing the risk of cardiovascular disease, but limited data exist quantifying their association with serious adverse events, particularly in older people with frailty. This study aimed to examine this association using nationally representative electronic health record data. METHODS AND FINDINGS: This was a retrospective cohort study utilising linked data from 1,256 general practices across England held within the Clinical Practice Research Datalink between 1998 and 2018. Included patients were aged 40+ years, with a systolic blood pressure reading between 130 and 179 mm Hg, and not previously prescribed antihypertensive treatment. The main exposure was defined as a first prescription of antihypertensive treatment. The primary outcome was hospitalisation or death within 10 years from falls. Secondary outcomes were hypotension, syncope, fractures, acute kidney injury, electrolyte abnormalities, and primary care attendance with gout. The association between treatment and these serious adverse events was examined by Cox regression adjusted for propensity score. This propensity score was generated from a multivariable logistic regression model with patient characteristics, medical history and medication prescriptions as covariates, and new antihypertensive treatment as the outcome. Subgroup analyses were undertaken by age and frailty. Of 3,834,056 patients followed for a median of 7.1 years, 484,187 (12.6%) were prescribed new antihypertensive treatment in the 12 months before the index date (baseline). Antihypertensives were associated with an increased risk of hospitalisation or death from falls (adjusted hazard ratio [aHR] 1.23, 95% confidence interval (CI) 1.21 to 1.26), hypotension (aHR 1.32, 95% CI 1.29 to 1.35), syncope (aHR 1.20, 95% CI 1.17 to 1.22), acute kidney injury (aHR 1.44, 95% CI 1.41 to 1.47), electrolyte abnormalities (aHR 1.45, 95% CI 1.43 to 1.48), and primary care attendance with gout (aHR 1.35, 95% CI 1.32 to 1.37). The absolute risk of serious adverse events with treatment was very low, with 6 fall events per 10,000 patients treated per year. In older patients (80 to 89 years) and those with severe frailty, this absolute risk was increased, with 61 and 84 fall events per 10,000 patients treated per year (respectively). Findings were consistent in sensitivity analyses using different approaches to address confounding and taking into account the competing risk of death. A strength of this analysis is that it provides evidence regarding the association between antihypertensive treatment and serious adverse events, in a population of patients more representative than those enrolled in previous randomised controlled trials. Although treatment effect estimates fell within the 95% CIs of those from such trials, these analyses were observational in nature and so bias from unmeasured confounding cannot be ruled out. CONCLUSIONS: Antihypertensive treatment was associated with serious adverse events. Overall, the absolute risk of this harm was low, with the exception of older patients and those with moderate to severe frailty, where the risks were similar to the likelihood of benefit from treatment. In these populations, physicians may want to consider alternative approaches to management of blood pressure and refrain from prescribing new treatment.

Classification of patients with osteoarthritis through clusters of comorbidities using 633 330 individuals from Spain.

OBJECTIVES: To explore clustering of comorbidities among patients with a new diagnosis of OA and estimate the 10-year mortality risk for each identified cluster. METHODS: This is a population-based cohort study of individuals with first incident diagnosis of OA of the hip, knee, ankle/foot, wrist/hand or 'unspecified' site between 2006 and 2020, using SIDIAP (a primary care database representative of Catalonia, Spain). At the time of OA diagnosis, conditions associated with OA in the literature that were found in ≥1% of the individuals (n = 35) were fitted into two cluster algorithms, k-means and latent class analysis. Models were assessed using a range of internal and external evaluation procedures. Mortality risk of the obtained clusters was assessed by survival analysis using Cox proportional hazards. RESULTS: We identified 633 330 patients with a diagnosis of OA. Our proposed best solution used latent class analysis to identify four clusters: 'low-morbidity' (relatively low number of comorbidities), 'back/neck pain plus mental health', 'metabolic syndrome' and 'multimorbidity' (higher prevalence of all studied comorbidities). Compared with the 'low-morbidity' cluster, the 'multimorbidity' cluster had the highest risk of 10-year mortality (adjusted hazard ratio [HR]: 2.19 [95% CI: 2.15, 2.23]), followed by the 'metabolic syndrome' cluster (adjusted HR: 1.24 [95% CI: 1.22, 1.27]) and the 'back/neck pain plus mental health' cluster (adjusted HR: 1.12 [95% CI: 1.09, 1.15]). CONCLUSION: Patients with a new diagnosis of OA can be clustered into groups based on their comorbidity profile, with significant differences in 10-year mortality risk. Further research is required to understand the interplay between OA and particular comorbidity groups, and the clinical significance of such results.

Reliability of detection of ultrasound and MRI features of hand osteoarthritis: a systematic review and meta-analysis.

OBJECTIVES: To systematically review the literature on inter- and intra-rater reliability of scoring US and MRI changes in hand OA. METHODS: MEDLINE, EMBASE, CINHAL, Web of Science and AMED were searched from inception to January 2020. Kappa (κ), weighted kappa (κw) and intra-class correlation coefficients for dichotomous, semi-quantitative and summated scores, respectively, and their 95% CI were pooled using a random-effects model. Heterogeneity between studies was assessed and reliability estimates were interpreted using the Landis-Koch classification. RESULTS: Fifty studies met the inclusion criteria (29 US, 17 MRI, 4 involving both modalities). The pooled κ (95% CI) for inter-rater reliability was substantial for US-detected osteophytes [0.66 (0.54, 0.79)], grey-scale synovitis [0.64 (0.32, 0.97)] and power Doppler [0.76, (0.47, 1.05)], whereas intra-rater reliability was almost perfect for osteophytes [0.82 (0.80, 0.84)], central bone erosions (CBEs) [0.83 (0.78, 0.89)] and effusion [0.83 (0.74, 0.91)], and substantial for grey-scale synovitis [0.64 (0.49, 0.79)] and power Doppler [0.70 (0.59, 0.80)]. Inter-rater reliability for dichotomous assessment was substantial for MRI-detected CBEs [0.75 (0.67, 0.83)] and synovitis [0.69 (0.51, 0.87)], slight for osteophytes [0.14 (0.04, 0.25)], and almost perfect for sum score of osteophytes, CBEs, joint space narrowing (JSN), and bone marrow lesions (BMLs) (0.81-0.89). Intra-rater reliability was almost perfect for sum score of MRI synovitis [0.92 (0.87, 0.96)], BMLs [0.88 (0.78, 0.98)], osteophytes [0.86 (0.74, 0.98)], CBEs [0.83 (0.66, 1.00)] and JSN [0.91 (0.87, 0.91)]. CONCLUSION: US and MRI are reliable in detecting hand OA features. US may be preferred due to low cost and increasing availability.

Constitutional morphological features and risk of hip osteoarthritis: a case-control study using standard radiographs.

OBJECTIVES: To evaluate the risk of association with hip osteoarthritis (OA) of 14 morphological features measured on standard antero-posterior pelvis radiographs. METHODS: A case-control study of 566 symptomatic unilateral hip OA cases and 1108 controls without hip OA, using the Genetics of OA and Lifestyle database. Unaffected hips of cases were assumed to reflect pre-OA morphology of the contralateral affected hip. ORs with 95% CI adjusted for confounding factors were calculated using logistic regression. Hierarchical clustering on principal component method was used to identify clusters of morphological features. Proportional risk contribution (PRC) of these morphological features in the context of other risk factors of hip OA was estimated using receiver operating characteristic analysis. RESULTS: All morphological features showed right-left symmetry in controls. Each feature was associated with hip OA after adjusting for age, gender and body mass index. Increased sourcil angle had the strongest association (OR: 6.93, 95% CI 5.16 to 9.32). Three clusters were identified. The PRC varied between individual features, as well as between clusters. It was 35% (95% CI 31% to 40%) for all 14 morphological features, compared to 21% (95% CI 19% to 24%) for all other well-established risk factors. CONCLUSIONS: Constitutional morphological variation strongly associates with hip OA development and may explain much of its heritability. Relevant morphological measures can be assessed readily on standard radiographs to help predict risk of hip OA. Prospective studies are required to provide further support for causality.

Initial analgesic prescriptions for osteoarthritis in the United Kingdom, 2000-2016.

OBJECTIVES: To examine trends in the initial prescription of commonly-prescribed analgesics and patient- as well as practice-level factors related to their selection in incident OA. METHODS: Patients consulting with incident clinical OA between 2000-2016 were identified within The Health Improvement Network in the United Kingdom (UK) general practice. Excluded were patients who had history of cancer or were prescribed the analgesics of interest within 6 months before diagnosis of OA. Initial analgesic prescription included oral non-selective NSAID, oral selective cyclooxygenase-2 inhibitor, topical NSAID, paracetamol, topical salicylate or oral/transdermal opioid within 1 month after OA diagnosis. RESULTS: ∼44% of patients with incident OA (n = 125 696) were prescribed one of these analgesics. Incidence of oral NSAID prescriptions decreased whereas other analgesic prescriptions, including oral opioid prescriptions, increased (all P-for-trend < 0.001). Patients with a history of gastrointestinal disease were more likely to receive topical NSAIDs, paracetamol or oral/transdermal opioids. Only 38% of patients with history of gastrointestinal disease and 21% of patients without it had co-prescription of gastroprotective agent with oral NSAIDs. Oral/transdermal opioid prescription was higher among the elderly (≥65 years), women, obesity, current smoker, and patients with gastrointestinal, cardiovascular or chronic kidney disease. Prescription of oral opioids increased with social deprivation (P-for-trend < 0.05) and was highest in Scotland, whereas transdermal opioid prescription was highest in Northern Ireland (all P-for-homogeneity-test < 0.05). CONCLUSION: The initial prescription pattern of analgesics for OA has changed over time in the UK. Co-prescription of gastroprotective agents with oral NSAIDs remains suboptimal, even among those with prior gastrointestinal disease.

Temporal relationship between osteoarthritis and comorbidities: a combined case control and cohort study in the UK primary care setting.

OBJECTIVE: To determine the burden of comorbidities in OA and their temporal relationships in the UK. METHODS: The Clinical Practice Research Datalink (CPRD) GOLD was used to identify people with incident OA and age, gender and practice matched non-OA controls from UK primary care. Controls were assigned the same index date as matched cases (date of OA diagnosis). Associations between OA and 49 individual comorbidities and multimorbidities (two or more comorbidities excluding OA) both before and after OA diagnosis were estimated, adjusting for covariates, using odds ratios (aORs) and hazard ratios (aHRs), respectively. RESULTS: During 1997-2017, we identified 221 807 incident OA cases and 221 807 matched controls. Of 49 comorbidities examined, 38 were associated with OA both prior to and following the diagnosis of OA and 2 (dementia and systemic lupus erythematosus) were associated with OA only following the diagnosis of OA. People with OA had a higher risk of developing heart failure [aHR 1.63 (95% CI 1.56, 1.71)], dementia [aHR 1.62 (95% CI 1.56, 1.68)], liver diseases [aHR 1.51 (95% CI 1.37, 1.67)], irritable bowel syndrome [aHR 1.51 (95% CI 1.45, 1.58)], gastrointestinal bleeding [aHR 1.49 (95% CI 1.39, 1.59)], 10 musculoskeletal conditions and 25 other conditions following OA diagnosis. The aOR for multimorbidity prior to the index date was 1.71 (95% CI 1.69, 1.74), whereas the aHR for multimorbidity after the index date was 1.29 (95% CI 1.28, 1.30). CONCLUSIONS: People with OA are more likely to have other chronic conditions both before and after the OA diagnosis. Further study on shared aetiology and causality of these associations is needed.

Magnitude and determinants of multimorbidity and health care utilization among patients attending public versus private primary care: a cross-sectional study from Odisha, India.

BACKGROUND: Multimorbidity in primary care is a challenge not only for developing countries but also for low and medium income countries (LMIC). Health services in LMIC countries are being provided by both public and private health care providers. However, a critical knowledge gap exists on understanding the true extent of multimorbidity in both types of primary care settings. METHODS: We undertook a study to identify multimorbidity prevalence and healthcare utilization among both public and private primary care attendees in Odisha state of India. A total of 1649 patients attending 40 primary care facilities were interviewed using a structured multimorbidity assessment questionnaire collecting information on 22 chronic diseases, medication use, number of hospitalization and number of outpatient visits. RESULT: The overall prevalence of multimorbidity was 28.3% and nearly one third of patients of public facilities and one fourth from private facilities had multimorbidity. Leading diseases among patients visiting public facilities included acid peptic diseases, arthritis and chronic back pain. No significant difference in reporting of hypertension and diabetes across the facilities was seen. Besides age, predictors of multimorbidity among patients attending public facilities were, females [AOR: 1.6; 95% CI 1.1-1.3] and non-aboriginal groups [AOR: 1.6; 95%CI 1.1-2.3] whereas, in private females [AOR: 1.6; 95%CI 1.1-2.4], better socioeconomic conditions [AOR 1.4; 95% CI 1.0-2.1] and higher educational status [primary school completed [AOR 2.6; 95%CI 1.6-4.2] and secondary schooling and above [AOR 2.0; 95%CI 1.1-3.6] with reference to no education were seen to be the determinants of multimorbidity. Increased number of hospital visits to public facilities were higher among lower educational status patients [IRR: 1.57; 95% CI 1.13-2.18] whereas, among private patients, the mean number of hospital visits was 1.70 times more in higher educational status [IRR: 1.70; 95%CI 1.01-3.69]. The mean number of medicines taken per day was higher among patients attending private hospitals. CONCLUSION: Our findings suggest that, multimorbidity is being more reported in public primary care facilities. The pattern and health care utilization in both types of settings are different. A comprehensive care approach must be designed for private care providers.

High- and low-inpatients' serum magnesium levels are associated with in-hospital mortality in elderly patients: a neglected marker?

BACKGROUND: Altered serum magnesium (Mg) level in the human body has been hypothesized to have a role in the prediction of hospitalization and mortality; however, the reported outcomes are not conclusive. AIMS: The present study aimed to analyze the relationship between serum Mg and in-hospital mortality (IHM) in patients admitted to the medical ward of two hospitals in the Veneto region (Italy). METHODS: Patients > 18 years hospitalized in the medical wards of the hospitals of Vittorio Veneto and Conegliano, Italy (from January 12, 2011, through December 27, 2016) with at least one measurement of serum Mg were included in the study. A logistic regression model was used to assess the unadjusted and adjusted (by age, gender, Charlson Comorbidity index, discharge diagnosis' class) association of serum Mg and IHM. RESULTS: In total 5024 patients were analyzed, corresponding to 6980 total admissions. The unadjusted analysis showed that IHM risk was significantly higher with 0.2 mg/dl incremental serum Mg level change from 2.4 mg/dl to 2.6, (OR 1.71 95% CI 1.55-1.89) and with 0.2 mg/dl change from serum Mg level of 1.4 mg/dl to 1.2 mg/dl, (OR 1.28 95% CI 1.17-1.40). Such results were confirmed at adjusted analysis. DISCUSSION: Present findings have relevant implications for the clinical management of patients suffering from medical conditions, highlighting the need for analyzing Mg concentration carefully. CONCLUSIONS: Serum Mg levels seem to be a good predictor of IHM.

At which temperature do the deleterious effects of ambient heat "kick-in" to affect all-cause mortality? An exploration of this threshold from an eastern Indian city.

Despite experiencing hot weathers, limited studies from India explored relationships between ambient heat and health. We studied associations between heat and all-cause mortality to estimate heat threshold(s) affecting health, and examine other affecting dimensions. We conducted time-series analysis with daily maximum temperature and all-cause mortality data of Bhubaneswar city (March-July, 2007-2017), and explored their interactions. Mortality risks rose when daily maximum temperatures were >36.2°C (lower threshold), and even more when >40.5°C (upper threshold). Every degree above36.2°C increased the mortality risk by 2% (mortality rate ratio: 1.02; 95% CI 1.01, 1.03). The effects of maximum temperature increased on days when minimum temperatures were >25.6°C (median). The effect of heat was immediate and lasted for 0-1 day with no lagged effect. Two temperature thresholds with varying mortality risks provided an opportunity for a graded heat warning system. The accentuation of the deleterious effects of heat by the higher minimum temperature calls for its inclusion in the heat warning system in future.

Health related quality of life in multimorbidity: a primary-care based study from Odisha, India.

BACKGROUND: Multimorbidity, the coexistence of two or more chronic conditions is increasingly prevalent in primary care populations. Despite reports on its adverse impact on health outcomes, functioning and well-being, it's association with quality of life is not well known in low and middle income countries. We assessed the health-related quality of life (HRQoL) of primary care patients with multimorbidity and identified the influencing factors. METHODS: This cross-sectional study was done across 20 public and 20 private primary care facilities in Odisha, India. Data were collected from 1649 adult out-patients using a structured multimorbidity assessment questionnaire for primary care (MAQ-PC). HRQoL was assessed by the 12-item short-form health survey (SF-12). Both physical (PCS) and mental components scores (MCS) were calculated. Multiple regression analysis was performed to determine the association of HRQoL with socio-demographics, number, severity and typology of chronic conditions. RESULTS: Around 28.3% [95% CI: 25.9-30.7] of patients had multimorbidity. Mean physical component scope (PCS) and mental component score (MCS) of QoL in the study population was 43.56 [95% CI: 43.26-43.86] and 43.69 [95% CI: 43.22-44.16], respectively. Patients with multimorbidity reported poorer mean PCS [43.23, 95% CI: 42.62-43.84] and MCS [41.58, 95% CI: 40.74-42.43] compared to those without. After adjusting for other variables, morbidity severity burden score was found to be negatively associated with MCS [adjusted coefficient: -0.24, 95% CI - 0.41 to - 0.08], whereas no significant association was seen with PCS. Hypertension and diabetes with arthritis and acid peptic diseases were found to be negatively related with MCS. Within multimorbidity, lower education was inversely associated with mental QoL and positively associated with physical QoL score after adjusting for other variables. CONCLUSION: Our findings demonstrate the diverse negative effects of multimorbidity on HRQoL and reveal that apart from count of chronic conditions, severity and pattern also influence HRQoL negatively. Health care providers should consider severity as an outcome measure to improve QoL especially in individuals with physical multimorbidity. Given the differences observed between age groups, it is important to identify specific care needs for each group. Musculoskeletal clusters need prioritised attention while designing clinical guidelines for multimorbidity.

'Health Promoting School' Model in Prevention of Vector-Borne Diseases in Odisha: A Pilot Intervention.

We tested 'Health-Promoting School model' for vector-borne diseases (VBDs) through behavioural changes among students in India for better control of the diseases in the community. A total of 1098 students from eight co-ed schools (four controls and four interventions) of Odisha participated in the study. Intervention was 12 h of class room teaching and activities on mosquito dynamics, source identification, prevention and management of VBDs. Control schools were provided with the leaflets and posters covering the objectives. Impact was assessed through change in knowledge and practice through pre- and post-test. Total mean transformed score in pre-intervention survey in both the groups was nearly 33, which increased to 38.6 and 53.9 in control and intervention group, respectively. In intervention group the mean days of absenteeism due to VBDs was reduced nearly by four times. The model can be used as effective health-promoting measures for vector control.

Prevalence, correlates, and outcomes of multimorbidity among patients attending primary care in Odisha, India.

PURPOSE: Little information is available on multimorbidity in primary care in India. Because primary care is the first contact of health care for most of the population and important for coordinating chronic care, we wanted to examine the prevalence and correlates of multimorbidity in India and its association with health care utilization. METHODS: Using a structured multimorbidity assessment protocol, we conducted a cross-sectional study, collecting information on 22 self-reported chronic conditions in a representative sample of 1,649 adult primary care patients in Odisha, India. RESULTS: The overall age- and sex-adjusted prevalence of multimorbidity was 28.3% (95% CI, 24.3-28.6) ranging from 5.8% in patients aged 18 to 29 years to 45% in those aged older than 70 years. Older age, female sex, higher education, and high income were associated with significantly higher odds of multimorbidity. After adjusting for age, sex, socioeconomic status (SES), education, and ethnicity, the addition of each chronic condition, as well as consultation at private hospitals, was associated with significant increase in the number of medicines intake per person per day. Increasing age and higher education status significantly raised the number of hospital visits per person per year for patients with multiple chronic conditions. CONCLUSION: Our findings of higher prevalence of multimorbidity and hospitalizations in higher SES individuals contrast with findings in Western countries, where lower SES is associated with a greater morbidity burden.

Non communicable disease multimorbidity and associated health care utilization and expenditures in India: cross-sectional study.

BACKGROUND: Non communicable disease (NCD) multimorbidity is increasingly becoming common in high income settings but little is known about its epidemiology and associated impacts on citizens and health systems in low and middle-income countries (LMICs). We aim to examine the socio-demographic distribution of NCD multimorbidity (≥2 diseases) and its implications for health care utilization and out-of-pocket expenditure (OOPE) in India. METHODS: We analyzed cross-sectional nationally representative data from the World Health Organisaion Study on Global Ageing and Adult Health (WHO-SAGE), conducted in India during 2007. Multiple logistic regression was used to determine socio-demographic predictors of self-reported multimorbidity. A two part model was used to assess the relationship between number of NCDs and health care utilization including OOPE. RESULTS: 28.5% of the sample population had at least one NCD and 8.9% had NCD multimorbidity. The prevalence of multimorbidity increased from 1.3% in 18-29 year olds to 30.6% in those aged 70 years and above. Mean outpatient visits in the preceding 12 months increased from 2.2 to 6.2 and the percentage reporting an overnight hospital stay in the past 3 years increased from 9% to 29% in those with no NCD and ≥2 NCDs respectively (p <0.001).OOPE incurred during the last outpatient visit increased from INR 272.1 (95% CI = 249.0-295.2) in respondents with no NCDs to INR 454.1 (95% CI = 407.8-500.4) in respondents with ≥2 NCDs. However, we did not find an increase in OOPE during the last inpatient visit with number of NCDs (7865.9 INR for those with zero NCDs compared with 7301.3 for those with ≥2 NCDs). For both outpatient and inpatient OOPE, medicine constitutes the largest proportion of spending (70.7% for outpatient, 53.6% for inpatient visit), followed by spending for health care provider (14.0% for outpatient, 12.2% for inpatient visit). CONCLUSION: NCD multimorbidity is common in the Indian adult population and is associated with substantially higher healthcare utilization and OOPE. Strategies to address the growing burden of NCDs in LMICs should include efforts to improve the management of patients with multimorbidity and reduce associated financial burden to individuals and households.

Risk behaviors related to violence and injury among school-going adolescents in Karnataka, Southern India.

BACKGROUND: Adolescence is a crucial stage of life. The development and practice of various risk behaviors predisposes the risk of getting injured and consequences in later life. STUDY METHODS: This cross-sectional study was conducted among 381 adolescents (15-19 years) studying in different schools and colleges of Udupi. The Youth Risk Behavior Survey (YRBS), Center for Disease Control (CDC) Questionnaire and the Atlanta Questionnaire and Guidelines was adopted for data collection. Behaviors such as poor obeying traffic rules while driving, violence at school premises, and suicidal thoughts of the participants were explored. Univariate analysis followed by multivariate logistic regression was done to estimate the predictors of violence-related behavior using the Statistical Packages for Social Sciences (SPSS) v. 20. RESULTS: In our study, 27.03% of students (total=381) had knowledge of traffic rules in detail, where 65% drove a motorized vehicle. Nearly 75% of students did not use a helmet or seatbelt while driving and 17% used a mobile phone for either talking or texting while driving. Considering all violence risk behaviors, 33.07% of students had at least and 18% had at least two violence-related risk behaviors. Nearly 21.78% thought of hitting somebody, 16.34% of boys and 9.5% of girls carried sharp objects to school, 18.81% of boys and 10.39% of girls damaged or stole other students' property, 18.37% bullied others in the past month at the school campus, and 11.32% were involved in serious fights. Out of 381 students, 114 (30.32%) were bullied, 10% had been slapped intentionally, and 18% of girls felt unsafe to go out of their home because of threat compared with 15% of boys. In total, 71 (18.93%) students thought of suicide and 22 of them attempted it. Logistic regression showed that boys [odds ratio (OR): 1.72, 95% confidence interval (CI): 1.02-2.93) and students of 16 years of age (OR: 3.02, 95% CI: 1.06-9.02) affected or victimized by violent activities at school (OR: 3.23, 95% CI: 1.76-5.93) and bullied by others (OR: 2.6, 95% CI: 1.55-4.36) were determining factors for violence-related behaviors after adjusting for other variables. CONCLUSION: There is a need to identify students at risk and for intervention addressing the risk factors. Further qualitative studies could provide more insight.

Causal associations of central and peripheral risk factors with knee osteoarthritis: a longitudinal and Mendelian Randomisation study using UK Biobank data.

ABSTRACT: Our aim was to investigate relative contributions of central and peripheral mechanisms to knee osteoarthritis (OA) diagnosis and their independent causal association with knee OA. We performed longitudinal analysis using data from UK-Biobank participants. Knee OA was defined using International Classification of Diseases manual 10 codes from participants' hospital records. Central mechanisms were proxied using multisite chronic pain (MCP) and peripheral mechanisms using body mass index (BMI). Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated, and proportional risk contribution (PRC) was estimated from receiver-operator-characteristic (ROC) analysis. To estimate the causal effects, we performed 2-sample multivariable Mendelian Randomisation (MR) analysis. We selected genetic instruments from the largest Genome Wide Association Study of BMI (N = 806,834) and MCP (N = 387,649) and estimated the instruments genetic associations with knee OA in the largest available dataset (62,497 cases and 333,557 control subjects). The multivariable MR was performed using modified inverse-variance weighting methods. Of the 203,410 participants, 6% developed knee OA. Both MCP (OR 1.23, 95% CI; 1.21-1.24) and BMI (1.10, 95% CI; 1.10-1.11) were associated with knee OA diagnosis. The PRC was 6.9% (95% CI; 6.7%-7.1%) for MCP and 21.9% (95% CI; 21.4%-22.5%) for BMI; the combined PRC was 38.8% (95% CI; 37.9%-39.8%). Body mass index and MCP had independent causal effects on knee OA (OR 1.76 [95% CI, 1.64-1.88] and 1.83 [95% CI, 1.54-2.16] per unit change, respectively). In conclusion, peripheral risk factors (eg, BMI) contribute more to the development of knee OA than central risk factors (eg, MCP). Peripheral and central factors are independently causal on knee OA.

Healthcare utilisation and mortality in people with osteoarthritis in the UK: findings from a national primary care database.

BACKGROUND: The burden of osteoarthritis (OA) in UK primary care has not been investigated thoroughly. AIM: To estimate healthcare use and mortality in people with OA (overall and joint specific). DESIGN AND SETTING: A matched cohort study of adults with an incident diagnosis of OA in primary care were selected for the study using UK national Clinical Practice Research Datalink (CPRD) electronic records. METHOD: Healthcare utilisation was measured as the annual average number of primary care consultations and admissions to hospital after the index date for any cause and all-cause mortality data in 221 807 people with OA and an equal number of controls (with no OA diagnosis) who were matched to the case patients by age (standard deviation 2 years), sex, practice, and year of registration. The associations between OA and healthcare utilisation and all-cause mortality were estimated using multinomial logistic regression and Cox regression, respectively, adjusting for covariates. RESULTS: The mean age of the study population was 61 years and 58% were female. In the OA group, the median number of primary care consultations per year after the index date was 10.91 compared with 9.43 in the non-OA control group (P = 0.001) OA was associated with an increased risk of GP consultation and admission to hospital. The adjusted hazard ratio for all-cause mortality was 1.89 (95% confidence interval [CI] = 1.85 to 1.93) for any OA, 2.09 (95% CI = 2.01 to 2.19) for knee OA, 2.08 (95% CI = 1.95 to 2.21) for hip OA, and 1.80 (95% CI = 1.58 to 2.06) for wrist/hand OA, compared with the respective non-OA control group. CONCLUSION: People with OA had increased rates of GP consultations, admissions to hospital, and all-cause mortality that varied across joint sites.

Causal association between subtypes of osteoarthritis and common comorbidities: A Mendelian randomisation study.

Objective: To investigate the causal association between Osteoarthritis (OA) and five comorbidities: depression, tiredness, multisite chronic pain, irritable bowel syndrome (IBS) and gout. Design: This study used two-sample Mendelian Randomisation (MR). To select the OA genetic instruments, we used data from the largest recent genome-wide association study (GWAS) of OA (GO Consortium), with a focus on OA of the knee (62,497 cases, 333,557 controls), hip (35,445 cases, 316,943 controls) and hand (20,901 cases, 282,881 controls). Genetic associations for comorbidities were selected from GWAS for depression (246,363 cases, 561,190 controls), tiredness (449,019 participants), multisite chronic pain (387,649 participants), IBS (53,400 cases, 433,201 controls) and gout (6543 cases, 456,390 controls). We performed a bidirectional MR analysis using the inverse variance weighted method, for both joint specific and overall OA. Results: Hip OA had a causal effect on multisite chronic pain (per unit change 0.02, 95% CI 0.01 to 0.04). Multisite chronic pain had a causal effect on knee (odd ratio (OR) 2.74, 95% CI 2.20 to 3.41), hip (OR 2.12, 95% CI 1.54 to 2.92), hand (OR 2.24, 95% CI 1.59 to 3.16) and overall OA (OR 2.44, 95% CI, 2.06 to 2.86). In addition, depression and tiredness had causal effects on knee and hand, but not hip, OA. Conclusions: Apart from Hip OA to multisite chronic pain, other joint OA did not have causal effects on these comorbidities. In contrast, multisite chronic pain had a causal effect on any painful OA.

BLOod Test Trend for cancEr Detection (BLOTTED): protocol for an observational and prediction model development study using English primary care electronic health record data.

BACKGROUND: Simple blood tests can play an important role in identifying patients for cancer investigation. The current evidence base is limited almost entirely to tests used in isolation. However, recent evidence suggests combining multiple types of blood tests and investigating trends in blood test results over time could be more useful to select patients for further cancer investigation. Such trends could increase cancer yield and reduce unnecessary referrals. We aim to explore whether trends in blood test results are more useful than symptoms or single blood test results in selecting primary care patients for cancer investigation. We aim to develop clinical prediction models that incorporate trends in blood tests to identify the risk of cancer. METHODS: Primary care electronic health record data from the English Clinical Practice Research Datalink Aurum primary care database will be accessed and linked to cancer registrations and secondary care datasets. Using a cohort study design, we will describe patterns in blood testing (aim 1) and explore associations between covariates and trends in blood tests with cancer using mixed-effects, Cox, and dynamic models (aim 2). To build the predictive models for the risk of cancer, we will use dynamic risk modelling (such as multivariate joint modelling) and machine learning, incorporating simultaneous trends in multiple blood tests, together with other covariates (aim 3). Model performance will be assessed using various performance measures, including c-statistic and calibration plots. DISCUSSION: These models will form decision rules to help general practitioners find patients who need a referral for further investigation of cancer. This could increase cancer yield, reduce unnecessary referrals, and give more patients the opportunity for treatment and improved outcomes.

Health care utilization and out-of-pocket expenditure of type 2 diabetic patients: A study in primary care in Bhubaneswar, India.

Background: Globally, noncommunicable diseases (NCD) demand a higher healthcare expenditure. Among NCDs, diabetes mellitus is often associated with multiple, co-existing chronic conditions. In low- and middle-income countries where most of the healthcare expenditure is borne out of pocket, diabetes management may pose a significant financial stress. Methods: A cross-sectional study was conducted in 17 urban primary healthcare facilities of Bhubaneswar to assess the healthcare utilization and out-of-pocket expenditure among type 2 diabetes patients attending these facilities. Healthcare utilization was determined by the number of visits to healthcare facilities in the last 6 months, and out-of-pocket expenditure was assessed by outpatient consultation fees, medicines, travels to health care facilities, and diagnostic tests. Total out-of-pocket expenditure was defined as the sum of these costs. Results: The median number of visits in 6 months for diabetes patients with any comorbidity was 4 and 5 for diabetes patients with more than 4 comorbidities. Among the comorbid conditions, depression, stroke, auditory impairment, and acid peptic disease were associated with higher healthcare utilization. The total out-of-pocket expense was 2.3 times higher among diabetes patients with any comorbid condition compared to patients with diabetes only. The total median expenditure was higher for diabetes patients having stroke, heart diseases, kidney diseases, and cancer compared with other comorbid conditions. The association of comorbidity in diabetes patients with health care utilization and out-of-pocket expenditure is statistically significant after adjustment for sociodemographic characteristics and diabetes duration. Conclusion: Considerable expenditure is incurred by diabetes patients attending primary healthcare facilities for the management of diabetes and other chronic conditions. This is a significant burden for diabetes patients below the poverty line and with limited or no insurance cover. There is a need to increase the coverage of insurance schemes to address the chronic conditions management expenditure of outpatients.

Comorbidities and use of analgesics in people with knee pain: a study in the Nottingham Knee Pain and Health in the Community (KPIC) cohort.

Objectives: The aims were to examine the prevalence of comorbidities and role of oral analgesic use in people with knee pain (KP) compared with those without. Methods: The Knee Pain and related health In the Community (KPIC) cohort comprises community-derived adults aged ≥40 years, irrespective of knee pain. Thirty-six comorbidities across 10 systems were compared between people with KP and controls without KP or knee OA. Multivariable logistic regression analysis was used to determine the adjusted odds ratio (aOR) and 95% CI for multimorbidity (at least two chronic conditions) and each specific comorbidity. Both prescribed and over-the-counter analgesics were included in the model, and their interactions with KP for comorbidity outcomes were examined. Results: Two thousand eight hundred and thirty-two cases with KP and 2518 controls were selected from 9506 baseline participants. The mean age of KP cases was 62.2 years, and 57% were women. Overall, 29% of the total study population had multimorbidity (KP cases 34.4%; controls 23.8%). After adjustment for age, sex, BMI and analgesic use, KP was significantly associated with multimorbidity (aOR 1.35; 95% CI 1.17, 1.56) and with cardiovascular (aOR 1.25; 95% CI 1.08, 1.44), gastrointestinal (aOR 1.34; 95% CI 1.04, 1.92), chronic widespread pain (aOR 1.54; 95% CI 1.29, 1.86) and neurological (aOR 1.32; 95% CI 1.01, 1.76) comorbidities. For multimorbidity, the use of paracetamol and opioids interacted positively with KP, whereas the use of NSAIDs interacted negatively for seven comorbidities. Conclusion: People with KP are more likely to have other chronic conditions. The long-term benefits and harms of this change remain to be investigated. Trial registration: ClinicalTrials.gov, http://clinicaltrials.gov, NCT02098070.