Cardiovascular disease risk and comparison of different strategies for blood pressure management in rural India.

BACKGROUND: Non-optimal blood pressure (BP) levels are a major cause of disease burden globally. We describe current BP and treatment patterns in rural India and compare different approaches to BP lowering in this setting. METHODS: All individuals aged ≥40 years from 54 villages in a South Indian district were invited and 62,194 individuals (84%) participated in a cross-sectional study. Individual 10-year absolute cardiovascular disease (CVD) risk was estimated using WHO/ISH charts. Using known effects of treatment, proportions of events that would be averted under different paradigms of BP lowering therapy were estimated. RESULTS: After imputation of pre-treatment BP levels for participants on existing treatment, 76·9% (95% confidence interval, 75.7-78.0%), 5·3% (4.9-5.6%), and 17·8% (16.9-18.8%) of individuals had a 10-year CVD risk defined as low (< 20%), intermediate (20-29%), and high (≥30%, established CVD, or BP > 160/100 mmHg), respectively. Compared to the 19.6% (18.4-20.9%) of adults treated with current practice, a slightly higher or similar proportion would be treated using an intermediate (23·2% (22.0-24.3%)) or high (17·9% (16.9-18.8%) risk threshold for instituting BP lowering therapy and this would avert 87·2% (85.8-88.5%) and 62·7% (60.7-64.6%) more CVD events over ten years, respectively. These strategies were highly cost-effective relative to the current practice. CONCLUSION: In a rural Indian community, a substantial proportion of the population has elevated CVD risk. The more efficient and cost-effective clinical approach to BP lowering is to base treatment decisions on an estimate of an individual's short-term absolute CVD risk rather than with BP based strategy. CLINICAL TRIAL REGISTRATION: Clinical Trials Registry of India CTRI/2013/06/003753 , 14 June 2013.

Engineering a mobile health tool for resource-poor settings to assess and manage cardiovascular disease risk: SMARThealth study.

BACKGROUND: The incidence of chronic diseases in low- and middle-income countries is rapidly increasing both in urban and rural regions. A major challenge for health systems globally is to develop innovative solutions for the prevention and control of these diseases. This paper discusses the development and pilot testing of SMARTHealth, a mobile-based, point-of-care Clinical Decision Support (CDS) tool to assess and manage cardiovascular disease (CVD) risk in resource-constrained settings. Through pilot testing, the preliminary acceptability, utility, and efficiency of the CDS tool was obtained. METHODS: The CDS tool was part of an mHealth system comprising a mobile application that consisted of an evidence-based risk prediction and management algorithm, and a server-side electronic medical record system. Through an agile development process and user-centred design approach, key features of the mobile application that fitted the requirements of the end users and environment were obtained. A comprehensive analytics framework facilitated a data-driven approach to investigate four areas, namely, system efficiency, end-user variability, manual data entry errors, and usefulness of point-of-care management recommendations to the healthcare worker. A four-point Likert scale was used at the end of every risk assessment to gauge ease-of-use of the system. RESULTS: The system was field-tested with eleven village healthcare workers and three Primary Health Centre doctors, who screened a total of 292 adults aged 40 years and above. 34% of participants screened by health workers were identified by the CDS tool to be high CVD risk and referred to a doctor. In-depth analysis of user interactions found the CDS tool feasible for use and easily integrable into the workflow of healthcare workers. Following completion of the pilot, further technical enhancements were implemented to improve uptake of the mHealth platform. It will then be evaluated for effectiveness and cost-effectiveness in a cluster randomized controlled trial involving 54 southern Indian villages and over 16000 individuals at high CVD risk. CONCLUSIONS: An evidence-based CVD risk prediction and management tool was used to develop an mHealth platform in rural India for CVD screening and management with proper engagement of health care providers and local communities. With over a third of screened participants being high risk, there is a need to demonstrate the clinical impact of the mHealth platform so that it could contribute to improved CVD detection in high risk low resource settings.

SMARThealth India: A stepped-wedge, cluster randomised controlled trial of a community health worker managed mobile health intervention for people assessed at high cardiovascular disease risk in rural India.

BACKGROUND: Cardiovascular diseases (CVD) are rising in India resulting in major health system challenges. METHODS: Eighteen primary health centre (PHC) clusters in rural Andhra Pradesh were randomised over three, 6-month steps to an intervention comprising: (1) household CVD risk assessments by village-based community health workers (CHWs) using a mobile tablet device; (2) electronic referral and clinical decision support for PHC doctors; and (3) a tracking system for follow-up care. Independent data collectors screened people aged ≥ 40 years in 54 villages serviced by the PHCs to create a high CVD risk cohort (based on WHO risk charts and blood pressure thresholds). Randomly selected, independent samples, comprising 15% of this cohort, were reviewed at each 6-month step. The primary outcome was the proportion meeting systolic blood pressure (SBP) targets (<140mmHg). FINDINGS: Eight-four percent of the eligible population (n = 62,254) were assessed at baseline (18.4% at high CVD risk). Of those at high risk, 75.3% were followed up over two years. CHWs screened 85.9% of the baseline cohort and doctors followed up 70.0% of all high risk referrals. There was no difference in the proportion of people achieving SBP targets (41.2% vs 39.2%; adjusted odds ratio (OR) 1.01 95% CI 0.76-1.35) or receiving BP-lowering medications in the intervention vs control periods respectively. There was a high discordance in risk scores generated by independent data collectors and CHWs, resulting in only 37.2% of the evaluation cohort exposed to the intervention. This discordance was mainly driven by fluctuating BP values (both normal variability and marked seasonal variations). In the pre-specified high risk concordant subgroup, there was greater use of BP-lowering medications in the intervention period (54.3% vs 47.9%, OR 1.22, 95% CI 1.03-1.44) but no impact on BP control. CONCLUSIONS: The strategy was well implemented with increased treatment rates among high risk individuals assessed by CHWs, however effects on BP were not demonstrated. Use of guideline-recommended BP thresholds for identifying high risk individuals substantially affected the reproducibility of risk assessment, and thus the ability to reliably evaluate the effectiveness of the intervention. In addition, unanticipated seasonal variation in BP in the context of a stepped-wedge trial highlights the inherent risks of this study design. TRIAL REGISTRATION: Clinical Trials Registry of India CTRI/2013/06/ 003753.

Healthcare choices in Mumbai slums: A cross-sectional study.

Background: Informal urban settlements, known as slums, are the home for a large proportion of the world population. Healthcare in these environments is extremely complex, driven by poverty, environmental challenges, and poor access to formal health infrastructures. This study investigated healthcare challenges faced and choices made by slum dwellers in Mumbai, India. Methods: Structured interviews with 549 slum dwellers from 13 slum areas in Mumbai, India, were conducted in order to obtain a population profile of health-related socio-economic and lifestyle factors, disease history and healthcare access. Statistical tools such as multinomial logistic regression were used to examine the association between such factors and health choices. Results: Private providers (or a mixture of public and private) were seen to be preferred by the study population for most health conditions (62% - 90% health consultations), apart from pregnancy (43% health consultations). Community-based services were also preferred to more remote options. Stark differences in healthcare access were observed between well-known conditions, such as minor injuries, pulmonary conditions, and pregnancy and emerging challenges, such as hypertension and diabetes. A number of socio-economic and lifestyle factors were found to be associated with health-related decisions, including choice of provider and expenditure. Conclusions: Better planning and coordination of health services, across public and private providers, is required to address mortality and morbidity in slum communities in India. This study provides insights into the complex landscape of diseases and health providers that slum dwellers navigate when accessing healthcare. Findings suggest that integrated services and public-private partnerships could help address demand for affordable community-based care and progress towards the target of universal health coverage.

Implications of Cardiovascular Disease Risk Assessment Using the WHO/ISH Risk Prediction Charts in Rural India.

Cardiovascular disease (CVD) risk in India is currently assessed using the World Health Organization/International Society for Hypertension (WHO/ISH) risk prediction charts since no population-specific models exist. The WHO/ISH risk prediction charts have two versions-one with total cholesterol as a predictor (the high information (HI) model) and the other without (the low information (LI) model). However, information on the WHO/ISH risk prediction charts including guidance on which version to use and when, as well as relative performance of the LI and HI models, is limited. This article aims to, firstly, quantify the relative performance of the LI and HI WHO/ISH risk prediction (for WHO-South East Asian Region D) using data from rural India. Secondly, we propose a pre-screening (simplified) point-of-care (POC) test to identify patients who are likely to benefit from a total cholesterol (TC) test, and subsequently when the LI model is preferential to HI model. Analysis was performed using cross-sectional data from rural Andhra Pradesh collected in 2005 with recorded blood cholesterol measurements (N = 1066). CVD risk was computed using both LI and HI models, and high risk individuals who needed treatment(THR) were subsequently identified based on clinical guidelines. Model development for the POC assessment of a TC test was performed through three machine learning techniques: Support Vector Machine (SVM), Regularised Logistic Regression (RLR), and Random Forests (RF) along with a feature selection process. Disagreement in CVD risk predicted by LI and HI WHO/ISH models was 14.5% (n = 155; p<0.01) overall and comprised 36 clinically relevant THR patients (31% of patients identified as THR by using either model). Using two patient-specific parameters (age, systolic blood pressure), our POC assessment can pre-determine the benefit of TC testing and choose the appropriate risk model (out-of-sample AUCs:RF-0.85,SVM-0.84,RLR:0.82 and maximum sensitivity-98%). The identification of patients benefitting from a TC test for CVD risk stratification can aid planning for resource-allocation and save costs for large-scale screening programmes.

Can noise make nonbursting chaotic systems more regular?

It has been known that noise can enhance the temporal regularity of dynamical systems that exhibit a bursting behavior--the phenomenon of coherence resonance. But can the phenomenon be expected for nonbursting chaotic systems? We present a theoretical argument based on the idea of time-scale matching and provide experimental evidence with a chaotic electronic circuit for coherence resonance in nonbursting chaotic systems.

SMARTHealth India: Development and Field Evaluation of a Mobile Clinical Decision Support System for Cardiovascular Diseases in Rural India.

BACKGROUND: Cardiovascular disease (CVD) is the major cause of premature death and disability in India and yet few people at risk of CVD are able to access best practice health care. Mobile health (mHealth) is a promising solution, but very few mHealth interventions have been subjected to robust evaluation in India. OBJECTIVE: The objectives were to develop a multifaceted, mobile clinical decision support system (CDSS) for CVD management and evaluate it for use by public nonphysician health care workers (NPHWs) and physicians in a rural Indian setting. METHODS: Plain language clinical rules were developed based on standard guidelines and programmed into a computer tablet app. The algorithm was validated and field-tested in 11 villages in Andhra Pradesh, involving 11 NPHWs and 3 primary health center (PHC) physicians. A mixed method evaluation was conducted comprising clinical and survey data and in-depth patient and staff interviews to understand barriers and enablers to the use of the system. Then this was thematically analyzed using NVivo 10. RESULTS: During validation of the algorithm, there was an initial agreement for 70% of the 42 calculated variables between the CDSS and SPSS software outputs. Discrepancies were identified and amendments were made until perfect agreement was achieved. During field testing, NPHWs and PHC physicians used the CDSS to screen 227 and 65 adults, respectively. The NPHWs identified 39% (88/227) of patients for referral with 78% (69/88) of these having a definite indication for blood pressure (BP)-lowering medication. However, only 35% (24/69) attended a clinic within 1 month of referral, with 42% (10/24) of these reporting continuing medications at 3-month follow-up. Physicians identified and recommended 17% (11/65) of patients for BP-lowering medications. Qualitative interviews identified 3 interrelated interview themes: (1) the CDSS had potential to change prevailing health care models, (2) task-shifting to NPHWs was the central driver of change, and (3) despite high acceptability by end users, actual transformation was substantially limited by system-level barriers such as patient access to doctors and medicines. CONCLUSIONS: A tablet-based CDSS implemented within primary health care systems has the potential to help improve CVD outcomes in India. However, system-level barriers to accessing medical care limit its full impact. These barriers need to be actively addressed for clinical innovations to be successful. TRIAL REGISTRATION: Clinical Trials Registry of India: CTRI/2013/06/003753; http://ctri.nic.in/Clinicaltrials/showallp.php?mid1=6259&EncHid=51761.70513&userName=CTRI/2013/06/003753 (Archived by WebCite at http://www.webcitation.org/6UBDlrEuq).