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Multimorbidity is increasingly the primary concern of healthcare systems globally with substantial implications for patient outcomes and resource cost. A critical knowledge gap exists as to the magnitude of multimorbidity in primary care practice in low and middle income countries with available information limited to prevalence. In India, primary care forms the bulk of the health care delivery being provided through both public (community health center) and private general practice setting. We undertook a study to identify multimorbidity patterns and relate these patterns to severity among 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. Multimorbidity patterns (dyad and triad) were identified for 21 chronic conditions, functional limitation was assessed as a proxy measure of severity and the mean severity score for each pattern, was determined after adjusting for age. The leading dyads in younger age group i.e. 18-29 years were acid peptic disease with arthritis/ chronic back ache/tuberculosis /chronic lung disease, while older age groups had more frequent combinations of hypertension + arthritis/ chronic lung disease/vision difficulty, and arthritis + chronic back ache. The triad of acid peptic disease + arthritis + chronic backache was common in men in all age groups. Tuberculosis and lung diseases were associated with significantly higher age-adjusted mean severity score (poorer functional ability). Among men, arthritis, chronic backache, chronic lung disease and vision impairment were observed to have highest severity) whereas women reported higher severity for combinations of hypertension, chronic back ache and arthritis. Given the paucity of studies on multimorbidity patterns in low and middle income countries, future studies should seek to assess the reproducibility of our findings in other populations and settings. Another task is the potential implications of different multimorbidity clusters for designing care protocols, as currently the protocols are disease specific, hardly taking comorbidity into account.

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.

[This corrects the article DOI: 10.1371/journal.pmed.1004223.].

The balance of benefits and risks associated with lowering blood pressure levels in individuals with dementia remains controversial with a lack of evidence for possible harms associated with antihypertensive treatment. We examined the association between antihypertensive medication and serious adverse events in individuals with dementia compared to those without dementia. This was a retrospective analysis using nationally representative UK general practice population between 1998 and 2018, from electronic health records (Clinical Practice Research Datalink, CPRD, GOLD). Eligible individuals were aged ≥40 years, with a systolic blood pressure 130-179 mmHg, and not previously prescribed antihypertensive treatment. The diagnosis of dementia was based on clinical codes in the electronic health record. Individuals were allocated to the exposure group if they were prescribed at least one antihypertensive medication during a 12-month exposure period. Those who were not prescribed any antihypertensive medication during the exposure period were allocated to the control group. The primary outcome was the first hospitalisation or death from a fall within 10 years of the follow-up period. Secondary outcomes were first hospitalisation or death from hypotension, syncope, and fracture. In a population of 1,219,732 individuals, 23,510 had dementia. Antihypertensive medications were newly prescribed in 4,062/23,510 (17.3%) individuals with dementia and 142,385/1,196,222 (11.9%) individuals without dementia in the 12-month exposure period. In the primary analyses, which adjusted for the propensity score and a previous history of the outcome of interest, antihypertensive treatments were associated with a small increased risk of hospitalisation or death from falls (adjusted hazard ratio [aHR] 1.15, 95% confidence interval [CI] 1.08, 1.22), hypotension (aHR 1.51, 95%CI 1.29, 1.78), syncope (aHR 1.34, 95%CI 1.11, 1.61), but not fracture (aHR 1.05, 95%CI 0.96, 1.15), in individuals with dementia. These findings were consistent across different analytic approaches, including multivariable adjustment, propensity score matching, and inverse probability treatment weighting. In individuals without dementia, the association between antihypertensive treatment and serious adverse events was similar, with a small increased risk of hospitalisation or death from falls (aHR 1.07, 95%CI 1.05, 1.10). However, the absolute fall risk associated with antihypertensive treatment was significantly higher in individuals with dementia (47 per 10,000 individuals per year, 95%CI 26, 70) compared to those without (14 per 10,000 individuals per year, 95%CI 10, 18). The absolute risks of hypotension and syncope with antihypertensive treatment were also higher in the individuals with dementia compared to those without. The main limitation is the possibility of unmeasured confounding, and heterogeneity in dementia diagnoses based on coded entries in the electronic health record. Antihypertensive treatment was associated with increased risk of serious adverse events in individuals with and without dementia, however, the absolute risk of harm was more than double in individuals with dementia. These data suggest that clinicians, patients, and their carers should consider these risks before starting new antihypertensive medications, particularly in the context of dementia.

Antihypertensives are associated with increased risk of syncope, hypotension, and fractures, but the highest-risk individuals are unclear. This study aimed to develop and validate three models to predict these outcomes in patients with an indication for antihypertensive treatment. A cohort study was conducted using data from Clinical Practice Research Datalink (CPRD). Patients aged 40+ with systolic blood pressure 130-179 mmHg were included. Outcomes were first hypotension, syncope, or fracture leading to hospitalization or death within 10 years. Models were derived from CPRD GOLD data ( n  = 1,773,224) and validated with CPRD Aurum data ( n  = 3,805,366). Each model had 31-37 predictors. Validation demonstrated strong discriminative ability (10-year C-statistic: hypotension 0.824; syncope 0.819; fracture 0.790), with close agreement between predicted and observed risks for the hypotension and syncope models. Some underprediction was observed for the fracture model. These models could be used to help reassure patients about the relatively low risk of harm from antihypertensive treatment, or identify the small number of individuals with a higher risk where additional monitoring may be indicated. This study developed three clinical prediction models using UK primary care  data which accurately identified patients eligible for antihypertensive treatment who were at higher risk of hypotension, syncope, or fracture.

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.

BackgroundAltered 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.AimsThe 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).MethodsPatients > 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.ResultsIn 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.DiscussionPresent findings have relevant implications for the clinical management of patients suffering from medical conditions, highlighting the need for analyzing Mg concentration carefully.ConclusionsSerum Mg levels seem to be a good predictor of IHM.

IntroductionOsteoarthritis (OA) is one of the leading chronic conditions in the older population. People with OA are more likely to have one or more other chronic conditions than those without. However, the temporal associations, clusters of the comorbidities, role of analgesics and the causality and variation between populations are yet to be investigated. This paper describes the protocol of a multinational study in four European countries (UK, Netherlands, Sweden and Spain) exploring comorbidities in people with OA.Methods and analysisThis multinational study will investigate (1) the temporal associations of 61 identified comorbidities with OA, (2) the clusters and trajectories of comorbidities in people with OA, (3) the role of analgesics on incidence of comorbidities in people with OA, (4) the potential biomarkers and causality between OA and the comorbidities, and (5) variations between countries.A combined case–control and cohort study will be conducted to find the temporal association of OA with the comorbidities using the national or regional health databases. Latent class analysis will be performed to identify the clusters at baseline and joint latent class analysis will be used to examine trajectories during the follow-up. A cohort study will be undertaken to evaluate the role of non-steroidal anti-inflammatory drugs (NSAIDs), opioids and paracetamol on the incidence of comorbidities. Mendelian randomisation will be performed to investigate the potential biomarkers for causality between OA and the comorbidities using the UK Biobank and the Rotterdam Study databases. Finally, a meta-analyses will be used to examine the variations and pool the results from different countries.Ethics and disseminationResearch ethics was obtained according to each database requirement. Results will be disseminated through the FOREUM website, scientific meetings, publications and in partnership with patient organisations.

This study is aimed to estimate the epidemiological burden of dengue in Odisha, India using the disability adjusted life year (DALY) methods and to explore the associated factors in the year 2010–2016. During the period of 2010–2016, 27 772 cases (68.4% male) were reported in the state. Mean age (years) of male and female was 31.63 and 33.82, respectively. Mean district wise disability adjusted life years (DALY) per 100 000 people was higher in the year 2016 (0.45) and mean DALY lost per person was highest in the year 2015 (34.90 years). Adjusted regression model indicates, every unit increase in humidity and population density increases DALY by 1.05 and 1.02 units respectively. Whereas, unit change in sex ratio (females per 1000 males) and forest coverage increases the DALY by 0.98 units. Our results indicate geographical variation of DALY in Odisha, which is associated with population density, humidity and forest cover. Discrepancies identified between standard incidence and DALY maps suggests, latter can be used to present disease burden more effectively. More prevalence among young males suggests the need of strengthening the targeted prevention and control measures.

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.