Background In patients with type 2 diabetes, the effects of intensive glucose control on vascular outcomes remain uncertain.

BackgroundOne potential solution to limited healthcare access in low and middle income countries (LMIC) is task-shifting- the training of non-physician healthcare workers (NPHWs) to perform tasks traditionally undertaken by physicians. The aim of this paper is to conduct a systematic review of studies involving task-shifting for the management of non-communicable disease (NCD) in LMIC.MethodsA search strategy with the following terms “task-shifting”, “non-physician healthcare workers”, “community healthcare worker”, “hypertension”, “diabetes”, “cardiovascular disease”, “mental health”, “depression”, “chronic obstructive pulmonary disease”, “respiratory disease”, “cancer” was conducted using Medline via Pubmed and the Cochrane library. Two reviewers independently reviewed the databases and extracted the data.FindingsOur search generated 7176 articles of which 22 were included in the review. Seven studies were randomised controlled trials and 15 were observational studies. Tasks performed by NPHWs included screening for NCDs and providing primary health care. The majority of studies showed improved health outcomes when compared with usual healthcare, including reductions in blood pressure, increased uptake of medications and lower depression scores. Factors such as training of NPHWs, provision of algorithms and protocols for screening, treatment and drug titration were the main enablers of the task-shifting intervention. The main barriers identified were restrictions on prescribing medications and availability of medicines. Only two studies described cost-effective analyses, both of which demonstrated that task-shifting was cost-effective.ConclusionsTask-shifting from physicians to NPHWs, if accompanied by health system re-structuring is a potentially effective and affordable strategy for improving access to healthcare for NCDs. Since the majority of study designs reviewed were of inadequate quality, future research methods should include robust evaluations of such strategies.

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BackgroundVerbal autopsy methods are critically important for evaluating the leading causes of death in populations without adequate vital registration systems. With a myriad of analytical and data collection approaches, it is essential to create a high quality validation dataset from different populations to evaluate comparative method performance and make recommendations for future verbal autopsy implementation. This study was undertaken to compile a set of strictly defined gold standard deaths for which verbal autopsies were collected to validate the accuracy of different methods of verbal autopsy cause of death assignment.MethodsData collection was implemented in six sites in four countries: Andhra Pradesh, India; Bohol, Philippines; Dar es Salaam, Tanzania; Mexico City, Mexico; Pemba Island, Tanzania; and Uttar Pradesh, India. The Population Health Metrics Research Consortium (PHMRC) developed stringent diagnostic criteria including laboratory, pathology, and medical imaging findings to identify gold standard deaths in health facilities as well as an enhanced verbal autopsy instrument based on World Health Organization (WHO) standards. A cause list was constructed based on the WHO Global Burden of Disease estimates of the leading causes of death, potential to identify unique signs and symptoms, and the likely existence of sufficient medical technology to ascertain gold standard cases. Blinded verbal autopsies were collected on all gold standard deaths.ResultsOver 12,000 verbal autopsies on deaths with gold standard diagnoses were collected (7,836 adults, 2,075 children, 1,629 neonates, and 1,002 stillbirths). Difficulties in finding sufficient cases to meet gold standard criteria as well as problems with misclassification for certain causes meant that the target list of causes for analysis was reduced to 34 for adults, 21 for children, and 10 for neonates, excluding stillbirths. To ensure strict independence for the validation of methods and assessment of comparative performance, 500 test-train datasets were created from the universe of cases, covering a range of cause-specific compositions.ConclusionsThis unique, robust validation dataset will allow scholars to evaluate the performance of different verbal autopsy analytic methods as well as instrument design. This dataset can be used to inform the implementation of verbal autopsies to more reliably ascertain cause of death in national health information systems.

Non-communicable and chronic diseases are the leading causes of death in this part of rural India. The observed pattern of death is unlikely to be unique to these villages and provides new insight into the rapid progression of epidemiological transition in rural India.

BackgroundMonitoring progress with disease and injury reduction in many populations will require widespread use of verbal autopsy (VA). Multiple methods have been developed for assigning cause of death from a VA but their application is restricted by uncertainty about their reliability.MethodsWe investigated the validity of five automated VA methods for assigning cause of death: InterVA-4, Random Forest (RF), Simplified Symptom Pattern (SSP), Tariff method (Tariff), and King-Lu (KL), in addition to physician review of VA forms (PCVA), based on 12,535 cases from diverse populations for which the true cause of death had been reliably established. For adults, children, neonates and stillbirths, performance was assessed separately for individuals using sensitivity, specificity, Kappa, and chance-corrected concordance (CCC) and for populations using cause specific mortality fraction (CSMF) accuracy, with and without additional diagnostic information from prior contact with health services. A total of 500 train-test splits were used to ensure that results are robust to variation in the underlying cause of death distribution.ResultsThree automated diagnostic methods, Tariff, SSP, and RF, but not InterVA-4, performed better than physician review in all age groups, study sites, and for the majority of causes of death studied. For adults, CSMF accuracy ranged from 0.764 to 0.770, compared with 0.680 for PCVA and 0.625 for InterVA; CCC varied from 49.2% to 54.1%, compared with 42.2% for PCVA, and 23.8% for InterVA. For children, CSMF accuracy was 0.783 for Tariff, 0.678 for PCVA, and 0.520 for InterVA; CCC was 52.5% for Tariff, 44.5% for PCVA, and 30.3% for InterVA. For neonates, CSMF accuracy was 0.817 for Tariff, 0.719 for PCVA, and 0.629 for InterVA; CCC varied from 47.3% to 50.3% for the three automated methods, 29.3% for PCVA, and 19.4% for InterVA. The method with the highest sensitivity for a specific cause varied by cause.ConclusionsPhysician review of verbal autopsy questionnaires is less accurate than automated methods in determining both individual and population causes of death. Overall, Tariff performs as well or better than other methods and should be widely applied in routine mortality surveillance systems with poor cause of death certification practices.

BackgroundReliable data on the distribution of causes of death (COD) in a population are fundamental to good public health practice. In the absence of comprehensive medical certification of deaths, the only feasible way to collect essential mortality data is verbal autopsy (VA). The Tariff Method was developed by the Population Health Metrics Research Consortium (PHMRC) to ascertain COD from VA information. Given its potential for improving information about COD, there is interest in refining the method. We describe the further development of the Tariff Method.MethodsThis study uses data from the PHMRC and the National Health and Medical Research Council (NHMRC) of Australia studies. Gold standard clinical diagnostic criteria for hospital deaths were specified for a target cause list. VAs were collected from families using the PHMRC verbal autopsy instrument including health care experience (HCE). The original Tariff Method (Tariff 1.0) was trained using the validated PHMRC database for which VAs had been collected for deaths with hospital records fulfilling the gold standard criteria (validated VAs). In this study, the performance of Tariff 1.0 was tested using VAs from household surveys (community VAs) collected for the PHMRC and NHMRC studies. We then corrected the model to account for the previous observed biases of the model, and Tariff 2.0 was developed. The performance of Tariff 2.0 was measured at individual and population levels using the validated PHMRC database.ResultsFor median chance-corrected concordance (CCC) and mean cause-specific mortality fraction (CSMF) accuracy, and for each of three modules with and without HCE, Tariff 2.0 performs significantly better than the Tariff 1.0, especially in children and neonates. Improvement in CSMF accuracy with HCE was 2.5 %, 7.4 %, and 14.9 % for adults, children, and neonates, respectively, and for median CCC with HCE it was 6.0 %, 13.5 %, and 21.2 %, respectively. Similar levels of improvement are seen in analyses without HCE.ConclusionsTariff 2.0 addresses the main shortcomings of the application of the Tariff Method to analyze data from VAs in community settings. It provides an estimation of COD from VAs with better performance at the individual and population level than the previous version of this method, and it is publicly available for use.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-015-0527-9) contains supplementary material, which is available to authorized users.

IntroductionCommunity health workers (CHWs) are increasingly being tasked to prevent and manage cardiovascular disease (CVD) and its risk factors in underserved populations in low-income and middle-income countries (LMICs); however, little is known about the required training necessary for them to accomplish their role. This review aimed to evaluate the training of CHWs for the prevention and management of CVD and its risk factors in LMICs.MethodsA search strategy was developed in line with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, and five electronic databases (Medline, Global Health, ERIC, EMBASE and CINAHL) were searched to identify peer-reviewed studies published until December 2016 on the training of CHWs for prevention or control of CVD and its risk factors in LMICs. Study characteristics were extracted using a Microsoft Excel spreadsheet and quality assessed using Effective Public Health Practice Project’s Quality Assessment Tool. The search, data extraction and quality assessment were performed independently by two researchers.ResultsThe search generated 928 articles of which 8 were included in the review. One study was a randomised controlled trial, while the remaining were before–after intervention studies. The training methods included classroom lectures, interactive lessons, e-learning and online support and group discussions or a mix of two or more. All the studies showed improved knowledge level post-training, and two studies demonstrated knowledge retention 6 months after the intervention.ConclusionThe results of the eight included studies suggest that CHWs can be trained effectively for CVD prevention and management. However, the effectiveness of CHW trainings would likely vary depending on context given the differences between studies (eg, CHW demographics, settings and training programmes) and the weak quality of six of the eight studies. Well-conducted mixed-methods studies are needed to provide reliable evidence about the effectiveness and cost-effectiveness of training programmes for CHWs.