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Objective: Preventable hospitalizations for heart failure result in a large proportion of hospitalizations. The primary objective of this study was to describe longitudinal trends in the association of race/ethnicity with preventable hospitalizations for heart failure in Connecticut and differences in disparities by age. Methods: We analyzed data on hospitalizations in all civilian acute-care hospitals in Connecticut during a 7-year period, 2009 through 2015. We used raking methodology to weight the nonhospitalized population to create a reference population representative of the state’s general population. Multivariate regression models examined racial/ethnic disparities among adults aged 35-64, controlling for age, sex, and type of health insurance. For adults aged ≥65, regression models controlled for age and sex. Results: After controlling for age and sex, the non-Hispanic black to non-Hispanic white odds ratio for preventable hospitalizations for heart failure ranged from 5.2-6.4 during the study period among adults aged 35-64. Among adults aged ≥65, non-Hispanic black adults had significantly higher odds (range, 1.2-1.8) of preventable hospitalizations than non-Hispanic white adults. Rates among Hispanic adults were significantly higher than rates among non-Hispanic adults after controlling for age and sex among adults aged ≥65 in 2014 and 2015. Conclusions: This research provides information for clinical and population-based interventions targeting racial/ethnic gaps in heart failure hospitalizations. Demonstrating the persistent black–white disparity and age differences in racial/ethnic disparities, this study emphasizes the need for focused prevention among vulnerable populations. Raking methodology is an innovative approach to eliminating selection bias in hospital discharge data.

Statistical models, including those based on electronic health records, can accurately identify patients at high risk for a suicide attempt or death, leading to implementation of risk prediction models for population-based suicide prevention in health systems. However, some have questioned whether statistical predictions can really inform clinical decisions. Appropriately reconciling statistical algorithms with traditional clinician assessment depends on whether predictions from these two methods are competing, complementary, or merely duplicative. In June 2019, the National Institute of Mental Health convened a meeting, "Identifying Research Priorities for Risk Algorithms Applications in Healthcare Settings to Improve Suicide Prevention." Here, participants of this meeting summarize key issues regarding the potential clinical application of suicide prediction models. The authors attempt to clarify the key conceptual and technical differences between traditional risk prediction by clinicians and predictions from statistical models, review the limited evidence regarding both the accuracy of and the concordance between these alternative methods of prediction, present a conceptual framework for understanding agreement and disagreement between statistical and clinician predictions, identify priorities for improving data regarding suicide risk, and propose priority questions for future research. Future suicide risk assessment will likely combine statistical prediction with traditional clinician assessment, but research is needed to determine the optimal combination of these two methods.

Age-adjusted suicide rates in the US have increased over the past two decades across all age groups. The ability to identify risk factors for suicidal behavior is critical to selected and indicated prevention efforts among those at elevated risk of suicide. We used widely available statewide hospitalization data to identify and test the joint predictive power of clinical risk factors associated with death by suicide for patients previously hospitalized for a suicide attempt (N = 19,057). Twenty-eight clinical factors from the prior suicide attempt were found to be significantly associated with the hazard of subsequent suicide mortality. These risk factors and their two-way interactions were used to build a joint predictive model via stepwise regression, in which the predicted individual survival probability was found to be a valid measure of risk for later suicide death. A high-risk group with a four-fold increase in suicide mortality risk was identified based on the out-of-sample predicted survival probabilities. This study demonstrates that the combination of state-level hospital discharge and mortality data can be used to identify suicide attempters who are at high risk of subsequent suicide death.

Accurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children’s Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10–18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53–62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents.

Cloud computing is the emerging way of computing that is being used by wide range of users. At present 80 % of workload is on cloud. There are many safety risks in cloud computing, “Cloud Security Alliance” (CSA) listed top 7 security threats as Maltreat and Obnoxious utilization of cloud, Uncertain API, Vicious Infiltrators, Distributed mechanism challenges, Information drop, Unidentified Risk Report and Account, Service and Traffic Hijacking. An IT company spends 42 percent of its funds for the security of cloud and 80 percent of the funds will be bounded to attain the solution to security issues. Researchers have proposed no. of solutions to cloud security. Proposed solutions fall under three primary categories, Cryptographic, data storage and data semantics. In this paper we have reviewed the concerns in cloud computing with some of the subsequent proposed models to tackle the security issues in cloud computing.

Digital and mhealth interventions can be effective in improving health outcomes among minority patients with diabetes, congestive heart failure, and chronic respiratory diseases. A number of electronic and digital approaches to individual and population-level interventions involving telephones, internet and web-based resources, and mobile platforms have been deployed to improve chronic disease outcomes. This paper summarizes the evidence supporting the efficacy of various behavioral and digital interventions targeting intermediate outcomes and hospitalizations with particular emphasis on studies examining the effects of these interventions on racial and ethnic minority population.

Hospitalizations due to ambulatory care sensitive conditions (ACSCs) result in high morbidity and economic burden on the American healthcare system. Admissions due to chronic ACSCs, in particular, cost the American healthcare system over 30 billion dollars annually.This paper presents the current research on racial and ethnic disparities in the burden of hospitalizations due to chronic ACSCs. For this narrative review, we evaluated over 800 abstracts from MEDLINE and Google Scholar and cited 62 articles.Since 1998, racial and ethnic disparities in hospitalizations from chronic ACSCs have increased resulting in over 430,000 excess hospitalizations among non-Hispanic Blacks compared to non-Hispanic Whites.Racial disparities in chronic ACSCs hospitalizations are pervasive in the USA. There is need for more research on the pathways through which an individual's race modifies the risk for hospitalizations due to chronic ACSCs.

Directly observed therapy (DOT) for monitoring tuberculosis (TB) treatment is intended to reduce disease transmission, mortality and acquired drug resistance by facilitating treatment adherence and support. Synchronous (S-VOT) and asynchronous (A-VOT) video observed therapy are mHealth solutions for remotely monitoring medication ingestion. This paper synthesizes literature through December 2018 to describe existing VOT approaches, summarize evidence, identify knowledge gaps, evaluate VOT strengths and weaknesses, and examine patient and provider factors influencing VOT feasibility and acceptability. High rates of adherence and patient acceptance were obtained using both VOT methods. VOT reduced travel time for TB program staff and/or patients, improving program efficiency compared to in-person DOT while maintaining high patient satisfaction. The impact of VOT on TB treatment outcomes, such as cure and relapse, require further study with longer follow-up. Individual patient, provider and program factors should be considered in selecting either or both VOT approaches for provision of patient-centered care. Keywords: Directly observed therapy, Latent tuberculosis infection, Medication adherence monitoring, Mobile health, Patient-centered care, Antimicrobial resistance

Background The United States has experienced a significant rise in suicide. As decision makers identify how to address this national concern, healthcare systems have been identified as an optimal location for prevention. Objective To compare variation in patterns of healthcare use, by health setting, between individuals who died by suicide and the general population. Design Case-Control Study. Setting Eight healthcare systems across the United States. Participants 2674 individuals who died by suicide between 2000 and 2013 along with 267,400 individuals matched on time-period of health plan membership and health system affiliation. Measurements Healthcare use in the emergency room, inpatient hospital, primary care, and outpatient specialty setting measured using electronic health record data during the 7-, 30-, 60-, 90-, 180-, and 365-day time periods before suicide and matched index date for controls. Results Healthcare use was more common across all healthcare settings for individuals who died by suicide. Nearly 30% of individuals had a healthcare visit in the 7-days before suicide (6.5% emergency, 16.3% outpatient specialty, and 9.5% primary care), over half within 30 days, and >90% within 365 days. Those who died by suicide averaged 16.7 healthcare visits during the year. The relative risk of suicide was greatest for individuals who received care in the inpatient setting (aOR = 6.23). There was both a large relative risk (aOR = 3.08) and absolute utilization rate (43.8%) in the emergency room before suicide. Limitations Participant race/ethnicity was not available. The sample did not include uninsured individuals. Conclusions This study provides important data about how care utilization differs for those who die by suicide compared to the general population and can inform decision makers on targeting of suicide prevention activities within health systems.

Our objective is to assess epidemiological transition in urban Maharashtra in India in past two decades. We used the medically certified causes of death (MCCD) data from urban areas of Maharashtra, 1990–2006. Cause-specific death rate was estimated, standardized for age groups, and projected by using an exponential linear regression model. The results indicate that the burden of mortality due to noncommunicable conditions increased by 25% between 1990 and 2006 and will add 20% more by 2020. Among specific causes, the “diseases of the circulatory system” were consistently the leading CoD between 1990 and 2006. The “infectious and parasitic disease” and “diseases related to respiratory system” were the second and third leading causes of death, respectively. For children and young population, the leading cause of death was the “certain conditions originating in the prenatal period” and “injury and poisoning,” respectively, among both sexes. Among adults, the leading cause of death was “infectious and parasitic diseases.” In case of the adult female and elderly population, “diseases of circulatory system” caused the most deaths. Overall the findings foster that socioeconomically developed and demographically advanced urban Maharashtra bears the double burden of disease-specific mortality.

Countries aiming to reduce their tuberculosis (TB) burden by 2035 to the levels envisaged by the World Health Organization End TB Strategy need to innovate, with approaches such as digital health (electronic and mobile health) in support of patient care, surveillance, programme management, training and communication. Alongside the large-scale roll-out required for such interventions to make a significant impact, products must stay abreast of advancing technology over time. The integration of artificial intelligence into new software promises to make processes more effective and efficient, endowing them with a potential hitherto unimaginable. Users can benefit from artificial intelligence-enabled pattern recognition software for tasks ranging from reading radiographs to adverse event monitoring, sifting through vast datasets to personalise a patient's care plan or to customise training materials. Many experts forecast the imminent transformation of the delivery of healthcare services. We discuss how artificial intelligence and machine learning could revolutionise the management of TB., Tuberculosis control, and the where and why of artificial intelligence http://ow.ly/GwuY30bCXkJ

PurposeThe purpose of this study is to investigate the association between the marriage among blood relatives and resulting adverse pregnancy outcomes.Design/methodology/approachThis study uses data from India Human Development Survey in 2005. The methods of analyses include bivariate, trivariate estimates and Cox proportional hazard regression model.FindingsThe results reveal that the occurrence of consanguineous marriages is more predominant in southern India and among socioeconomically disadvantageous groups. Moreover, women in consanguineous unions are more likely to have adverse pregnancy outcomes including stillbirths (RR=1.59, p‐valuep‐valuep‐valuep‐valuePractical implicationsIn order to avoid loss of pregnancy and related reproductive health problems in India, it is imperative to create awareness regarding the adverse effects of consanguineous marriages, focusing on the regions with high prevalence.Originality/valueThis unique study comprehensively examines the occurrence of consanguineous marriages and their association with adverse pregnancy outcomes by using advanced statistical analyses and nationally representative data.

Objectives To determine the impact of tethered personal health record (PHR) use on patient engagement and intermediate health outcomes among patients with coronary artery disease (CAD). Methods Adult CAD patients ( N = 200) were enrolled in this prospective, quasi-experimental observational study. Each patient received a PHR account and training on its use. PHRs were populated with information from patient electronic medical records, hosted by a Health Information Exchange. Intermediate health outcomes including blood pressure, body mass index, and hemoglobin A1c (HbA1c) were evaluated through electronic medical record review or laboratory tests. Trends in patient activation measure® (PAM) were determined through three surveys conducted at baseline, 6 and 12 months. Frequency of PHR use data was collected and used to classify participants into groups for analysis: Low , Active , and Super users. Results There was no statistically significant improvement in patient engagement as measured by PAM scores during the study period. HbA1c levels improved significantly in the Active and Super user groups at 6 months; however, no other health outcome measures improved significantly. Higher PAM scores were associated with lower body mass index and lower HbA1c, but there was no association between changes in PAM scores and changes in health outcomes. Use of the PHR health diary increased significantly following PHR education offered at the 6-month study visit and an elective group refresher course. Conclusions The study findings show that PHR use had minimal impact on intermediate health outcomes and no significant impact on patient engagement among CAD patients.

This article addresses an important routing problem that arises in surveillance applications involving two heterogeneous vehicles. As the addressed routing problem is NP-Hard, we develop an approximation algorithm and heuristics to solve the problem. Our approach involves solving the routing problem in two main steps: Partitioning and Sequencing. Partitioning involves finding a distinct set of targets to be visited by each vehicle. Sequencing provides the order in which each vehicle must visit the subset of targets assigned to it. The problem of partitioning is tackled by solving a linear program (LP) obtained by relaxing some of the constraints of an integer programming model for the problem. We consider two LP models for partitioning. The first LP model is obtained by mainly relaxing both the integrality and degree constraints, whereas the second model relaxes mainly the integrality constraints. Once the targets are partitioned, the sequencing problem can be solved either by Hoogeveen's algorithm or by the Lin–Kernighan heuristic to yield an approximately optimal solution. Computational results show that the algorithms based on the second LP model, on an average, provided better (closer to the optimum) solutions as compared with those based on the first LP model. We also observed that for both the LP models, the average quality of solutions given by the heuristics were found to be within 4% of the optimum, whereas the average quality of solutions obtained from the approximation algorithms were within 8–20% of the optimum depending on the problem size. Copyright © 2011 John Wiley & Sons, Ltd.

Introduction: Every year about 70% of coronary heart disease deaths in the United States occur out of hospital, usually presenting as ‘sudden death’ due to cardiac arrest. Despite the improvement of survival with advanced cardiac interventions, mortality remains high. Therapeutic hypothermia (TH) has been shown to be neuroprotective after cardiac arrest. We explored factors associated with good recovery of neurological function following out-of-hospital cardiac arrest (OHCA). Methods: This retrospective study included review of electronic medical records from a major healthcare system in Northeast Indiana. Individuals who suffered OHCA from January 2011 to June 2014 were included. Neurological function was evaluated by Modified Rankin Scale (mRS) at discharge. The neurological outcome was defined as good (mRS 0-3), poor (mRS 4-5), and deceased (mRS 6) to assess the association of examined variables. Results: Among the 111 patients meeting inclusion criteria, the mortality at discharge was 68.6% in TH-treated patients and 84% in normothermia patients. For almost half (47.5%) of the patients undergoing TH who died prior to discharge, brain-related causes were the primary cause of death. 21 patients who had imaging or pathological examinations all showed evidences of ischemic brain injury. Among TH-treated survivors, patients with return of spontaneous cardiac rhythm (ROSC) within 20 minutes of onset were 1.4 times the odds more likely to have a good neurological outcome at discharge (p=0.02). Patients with ventricular fibrillation had 2 times the odds more likely retaining good neurological function at discharge after receiving TH treatment (p=0.012). The time to initiate TH (mean 2.3 ± 1.5 hours) and time to reach target temperature (mean 7.2 ± 2.3 hours) were not associated with neurological outcome at discharge. Conclusions: Initial rhythm and time to ROSC were identified as reliable predictors of good neurological function following OHCA. TH has been found to be insufficient in preventing brain injury. This study emphasizes the need for future studies to develop new neuroprotective strategies to improve survival among OHCA patients.

Gavgani et al., through their systematic review, explore the existing evidence in the area of information prescription and information therapy. The application of Information prescription and information therapy has been explored in a number of developed countries through randomized trials and observational studies. The low awareness about these concepts among medical practitioners has been highlighted. This commentary highlights the routes for IP/Ix dissemination and its application in current medical practice.

Background/Objective Children and women comprise vulnerable populations in terms of health and are gravely affected by the impact of economic inequalities through multi-dimensional channels. Urban areas are believed to have better socioeconomic and maternal and child health indicators than rural areas. This perception leads to the implementation of health policies ignorant of intra-urban health inequalities. Therefore, the objective of this study is to explain the pathways of economic inequalities in maternal and child health indicators among the urban population of India. Methods Using data from the third wave of the National Family Health Survey (NFHS, 2005–06), this study calculated relative contribution of socioeconomic factors to inequalities in key maternal and child health indicators such as antenatal check-ups (ANCs), institutional deliveries, proportion of children with complete immunization, proportion of underweight children, and Infant Mortality Rate (IMR). Along with regular CI estimates, this study applied widely used regression-based Inequality Decomposition model proposed by Wagstaff and colleagues. Results The CI estimates show considerable economic inequalities in women with less than 3 ANCs (CI = −0.3501), institutional delivery (CI = −0.3214), children without fully immunization (CI = −0.18340), underweight children (CI = −0.19420), and infant deaths (CI = −0.15596). Results of the decomposition model reveal that illiteracy among women and her partner, poor economic status, and mass media exposure are the critical factors contributing to economic inequalities in maternal and child health indicators. The residuals in all the decomposition models are very less; this implies that the above mentioned factors explained maximum inequalities in maternal and child health of urban population in India. Conclusion Findings suggest that illiteracy among women and her partner, poor economic status, and mass media exposure are the critical pathways through which economic factors operate on inequalities in maternal and child health outcomes in urban India.

Introduction Leprosy has been a major public health problem in India for centuries. In India, between 2001 and 2005, the prevalence of leprosy was reduced by 80%. This sharp decline in the prevalence of leprosy alongside the cessation of active surveillance for detection of leprosy cases has raised a sense of alarm in the scientific community. Materials and methods This is a total population survey aiming to estimate the prevalence of undetected active cases of leprosy in the community in defined rural (Panvel Taluka, Raigad District) and urban (M-East Ward, Mumbai) areas by health workers from Kushth Nivaran Samiti (Panvel) and Lok Seva Sangam (Mumbai). Those provisionally diagnosed with leprosy were subjected to an independent verification using clinical, bacteriological and histopathological investigations at the Foundation for Medical Research. Findings A population of 196,694 and 600,247 was covered in defined rural and urban areas respectively. In the rural area on examining 178,646 individuals, 120 provisionally diagnosed leprosy cases were detected, of which 65 were paucibacillary (PB) and 55 were multibacillary (MB) based on the WHO operational classification used by health workers at field level. In the urban area, of the 512,434 individuals who were examined, 134 provisionally diagnosed leprosy cases were detected with 92 PB and 42 MB cases. Among the clinically confirmed cases, 35.6% (32/90) and 34.9% (36/109) in rural and urban areas respectively were children. Conclusions There are large numbers of undetected leprosy cases in the community with a high proportion of MB patients and children among them. This indicates active transmission pointing to the need for a paradigm shift in leprosy care services and control programme.

Tuberculosis has deep cultural roots in India, and patients face discrimination and social isolation, write Rajasree Pai Rand colleagues

Two years later, Allen Ugargol and colleagues describe their visit to a village hit by the 2004 tsunami