Safety and efficacy of nifedipine 20 mg tablets in hypertension using electronic data collection in general practice.

Electronic data collection was used in this open study to survey the safety and efficacy of nifedipine when used in the treatment of 3972 patients with mild to moderate essential hypertension. The safety and efficacy results are presented and discussed as well as the advantages, disadvantages and reliability of electronic data collection. The validity of data collected electronically has not previously been tested, such data having been assumed to be reliable. The pattern of adverse events reported in this study is compared with the pattern of reports to the Committee on Safety of Medicine (CSM), to Bayer UK and in a large paper-based study of nifedipine, in order to test these assumptions. Reported adverse medical events pre-treatment, prior to entry to the study and noted at visit 1, were compared with reports during treatment in the study at visits 2 and 3. The expected incidence of flushing and headache was seen which diminished with continued treatment. Reductions were seen in dyspnoea and impotence. Ankle oedema was observed and was not reduced by time alone. After one month of treatment with nifedipine 20 mg tablets twice daily, 66.5% of patients had a sitting phase V diastolic blood pressure of 90 mmHg or below and 79% of 95 mmHg or below.     

Deciphering Rashomon: An approach to verbal autopsies of maternal deaths

Abstract

The paper discusses an approach to verbal autopsies that engages with the Rashomon phenomenon affecting ex post facto constructions of death and responds to the call for maternal safety. This method differs from other verbal autopsies in its approach to data collection and its framework of analysis. In our approach, data collection entails working with and triangulating multiple narratives, and minimising power inequalities in the investigation process. The framework of analysis focuses on the missed opportunities for death prevention as an alternative to (or deepening of) the Three Delays Model. This framework assesses the behavioural responses of health providers, as well as community and family members at each opportunity for death prevention and categorises them into four groups: non-actions, inadequate actions, inappropriate actions and unavoidably delayed actions. We demonstrate the application of this approach to show how verbal autopsies can delve beneath multiple narratives and rigorously identify health system, behavioural and cultural factors that contribute to avoidable maternal mortality.     

A feasibility study on using smartphones to conduct short-version verbal autopsies in rural China

Background

Currently there are two main sources of mortality data with cause of death assignments in China. Both sources–the Ministry of Health-Vital Registration system and the Chinese Disease Surveillance Point system–present their own challenges. A new approach to cause of death assignment is a smartphone-based shortened version of a verbal autopsy survey. This study evaluates the feasibility and acceptability of this new method conducted by township health care providers (THP) and village doctors (VD) in rural China, where a large proportion of deaths occur in homes and cause of death data are inaccurate or lacking.

Methods

The Population Health Metrics Research Consortium mobile phone-based shortened verbal autopsy questionnaire was made available on an Android system-based application, and cause of death was derived using the Tariff method (Tariff 2.0); we called this set of tools “msVA.” msVA was administered to relatives of the deceased by six THPs and six VDs in 24 villages located in six townships of Luquan County, Hebei Province, China. Subsequently, interviews were conducted among 12 interviewers, 12 randomly selected respondents, and five study staff to assess the feasibility and acceptability of using msVA for mortality data collection.

Results

Between July 2013 and August 2013, 268 deaths took place in the study villages. Among the 268 deaths, 227 VAs were completed (nine refusals, 31 migrations and one loss of data due to breakdown of the smartphone). The average time for a VA interview was 21.5?±?3.4 min (20.1?±?3.5 min for THP and 23.2?±?4.1 min for VD). Both THPs and VDs could be successful interviewers; the latter needed more training but had more willingness to implement msVA in the future. The interviews revealed that both interviewers and relatives of the deceased found msVA to be feasible, acceptable, and more desirable than traditional methods. The cost of conducting a new VA was $8.87 per death.

Conclusions

Conduction of msVA by VDs in their own villages was feasible and acceptable in rural northern China. Broader implementation of msVA across rural China could potentially improve the coverage and quality of cause of death data, allowing for better national health evaluation and program planning.

     

Algorithms for verbal autopsies: a validation study in Kenyan children.

The verbal autopsy (VA) questionnaire is a widely used method for collecting information on cause-specific mortality where the medical certification of deaths in childhood is incomplete. This paper discusses review by physicians and expert algorithms as approaches to ascribing cause of deaths from the VA questionnaire and proposes an alternative, data-derived approach. In this validation study, the relatives of 295 children who had died in hospital were interviewed using a VA questionnaire. The children were assigned causes of death using data-derived algorithms obtained under logistic regression and using expert algorithms. For most causes of death, the data-derived algorithms and expert algorithms yielded similar levels of diagnostic accuracy. However, a data-derived algorithm for malaria gave a sensitivity of 71% (95% Cl: 58-84%), which was significantly higher than the sensitivity of 47% obtained under an expert algorithm. The need for exploring this and other ways in which the VA technique can be improved are discussed. The implications of less-than-perfect sensitivity and specificity are explored using numerical examples. Misclassification bias should be taken into consideration when planning and evaluating epidemiological studies.     

Evaluation of the quality of cause of death statistics in rural China using verbal autopsies

BACKGROUND: Death registration systems in rural China are in a developmental stage. The Disease Surveillance Points (DSP) system provides the only nationally representative information on causes of death. In this system, there are no standard procedures or instruments for ascertaining causes of death; hence available statistics require careful evaluation before use. AIM: To assess the reliability of data from the DSP. METHODS: 14 DSP sites were selected through stratified sampling, enrolling 2482 deaths registered during June-November 2002. Defined verbal autopsy (VA) procedures were used to derive underlying causes of death. kappa Measures of agreement between VA and registered diagnoses were computed. VA diagnoses were used as references to compute sensitivity and positive predictive values. Finally, patterns of cause-specific discordance between the two data sources were studied. RESULTS: kappa Scores indicate only moderate agreement for chronic obstructive pulmonary disease (COPD) and ischaemic heart disease (IHD). The results also suggest that COPD is undercounted in registration data. A significant degree of cross attribution of cause was found between the two data sources. Overall, the VA was successful in limiting deaths with ill-defined causes to <10%. CONCLUSIONS: Diagnoses based on structured symptom questionnaires in the VA approach seem plausible and reliable as compared with lay-reported diagnoses in registration data. Concerns with attribution of cause of death due to COPD, IHD and tuberculosis in registration data suggest caution in their use for research and health programme purposes. The VA methods tested in this study offer promise for implementation in the routine registration system.     

Collecting behavioural data using the world wide web: considerations for researchers

OBJECTIVE: To identify and describe advantages, challenges, and ethical considerations of web based behavioural data collection. METHODS: This discussion is based on the authors' experiences in survey development and study design, respondent recruitment, and internet research, and on the experiences of others as found in the literature. RESULTS: The advantages of using the world wide web to collect behavioural data include rapid access to numerous potential respondents and previously hidden populations, respondent openness and full participation, opportunities for student research, and reduced research costs. Challenges identified include issues related to sampling and sample representativeness, competition for the attention of respondents, and potential limitations resulting from the much cited "digital divide", literacy, and disability. Ethical considerations include anonymity and privacy, providing and substantiating informed consent, and potential risks of malfeasance. CONCLUSIONS: Computer mediated communications, including electronic mail, the world wide web, and interactive programs will play an ever increasing part in the future of behavioural science research. Justifiable concerns regarding the use of the world wide web in research exist, but as access to, and use of, the internet becomes more widely and representatively distributed globally, the world wide web will become more applicable. In fact, the world wide web may be the only research tool able to reach some previously hidden population subgroups. Furthermore, many of the criticisms of online data collection are common to other survey research methodologies.     

Methodological trends in studies based on verbal autopsies before and after published guidelines

Objective

To report on the uptake of guidelines published in the early 1990s with specific recommendations about the design of future studies based on verbal autopsy conducted for mortality surveillance.

Methods

We conducted a systematic literature search of all verbal autopsy studies published before January 2006 and extracted from the studies a standard set of data. We then compared studies designed before and after the recommendations were issued in terms of seven key methodological indicators.

Findings

We found 102 studies conducted in 39 countries; 60 were designed before and 42 after the guidelines were issued. The methods used in these 102 studies varied considerably. While some encouraging trends were noted, there is no evidence that the design recommendations have been systematically implemented. Specifically, there was no clear increase in the proportion of studies with a combined questionnaire (63% before recommendations versus 74% after; P = 0.3), a trained interviewer (70% versus 70%; P = 1.0), a suitable respondent (98% versus 100%; P = 1.0), an optimal recall period (84% versus 97%; P = 0.2), predefined algorithms (28% versus 38%; P = 0.4), an option for assigning multiple causes of death (30% versus 38%; P = 0.3), or a follow-up validation study (83% versus 72%; P = 0.7).

Conclusion

Expert recommendations for optimal design of verbal autopsy studies have been incompletely implemented to date. Better uptake of design recommendations through enhanced collaboration between research teams is likely to produce better mortality statistics from an increasing number of verbal autopsy studies.     

Diagnostic accuracy of physician review, expert algorithms and data-derived algorithms in adult verbal autopsies

BACKGROUND: The verbal autopsy (VA) is used to collect information on cause-specific mortality from bereaved relatives. A cause of death may be assigned by physician review of the questionnaires, or by an algorithm. We compared the diagnostic accuracy of physician review, an expert algorithm, and data-derived algorithms. METHODS: Data were drawn from a multicentre validation study of 796 adult deaths that occurred in hospitals in Tanzania, Ethiopia, and Ghana. A 'gold standard' cause of death was assigned using hospital records and death certificates. The VA interviews were carried out by trained fieldworkers 1-21 months after the subject's death. A cause of death was assigned by physician review and an expert algorithm. Data-derived algorithms that most accurately estimated the cause-specific mortality fraction (CSMF) for each cause of death were identified using logistic regression. RESULTS: The most common causes of death were tuberculosis/AIDS (CSMF = 18.6%), malaria (CSMF = 10.7%), meningitis (CSMF = 8.3%), and cardiovascular disorders (CSMF = 8.2%). The CSMF obtained using physician review was within +/-20% of the gold standard value for 12 causes of death including the four common causes. The CSMF obtained using the expert algorithm was within +/-20% of the gold standard for eight causes of death, including tuberculosis/AIDS, malaria, and meningitis. The CSMF obtained using the data-derived algorithms was within +/-20% of the gold standard for seven causes of death, including tuberculosis/ AIDS, meningitis, and cardiovascular disorders. All three methods yielded a specificity of at least 80% for all causes of death, and a sensitivity of at least 80% for deaths due to injuries and rabies. CONCLUSIONS: For those settings where physician review is not feasible, expert and data-derived algorithms provide an alternative approach for assigning many causes of death. We recommend that the algorithms proposed herein are validated further.     

Performance of the Tariff Method: validation of a simple additive algorithm for analysis of verbal autopsies

Background

Verbal autopsies provide valuable information for studying mortality patterns in populations that lack reliable vital registration data. Methods for transforming verbal autopsy results into meaningful information for health workers and policymakers, however, are often costly or complicated to use. We present a simple additive algorithm, the Tariff Method (termed Tariff), which can be used for assigning individual cause of death and for determining cause-specific mortality fractions (CSMFs) from verbal autopsy data.

Methods

Tariff calculates a score, or "tariff," for each cause, for each sign/symptom, across a pool of validated verbal autopsy data. The tariffs are summed for a given response pattern in a verbal autopsy, and this sum (score) provides the basis for predicting the cause of death in a dataset. We implemented this algorithm and evaluated the method's predictive ability, both in terms of chance-corrected concordance at the individual cause assignment level and in terms of CSMF accuracy at the population level. The analysis was conducted separately for adult, child, and neonatal verbal autopsies across 500 pairs of train-test validation verbal autopsy data.

Results

Tariff is capable of outperforming physician-certified verbal autopsy in most cases. In terms of chance-corrected concordance, the method achieves 44.5% in adults, 39% in children, and 23.9% in neonates. CSMF accuracy was 0.745 in adults, 0.709 in children, and 0.679 in neonates.

Conclusions

Verbal autopsies can be an efficient means of obtaining cause of death data, and Tariff provides an intuitive, reliable method for generating individual cause assignment and CSMFs. The method is transparent and flexible and can be readily implemented by users without training in statistics or computer science.     

What's behind the data: an examination of the processes and policies underlying the routine collection of clinical data in Ontario hospitals

This article surveyed the processes and policies underlying the routine collection of clinical data in acute care hospitals in Ontario, Canada. Although there is evidence of a small shortfall in the availability of human resources, most health records departments employ experienced staff with health records certification. However, there is much more important variation in the documented and undocumented processes used to generate routinely collected clinical data. Current guidelines and coding schedules are helpful but insufficient to guide the production of good quality data. The variations in the processes used to produce clinical data have important implications for the management, reimbursement, and planning of healthcare. This is particularly critical at a time when hospitals and other stakeholders, such as governments, are relying more and more on accurate, reliable, and comparable data.     

Selective recruitment designs for improving observational studies using electronic health records

Large-scale electronic health records (EHRs) present an opportunity to quickly identify suitable individuals in order to directly invite them to participate in an observational study. EHRs can contain data from millions of individuals, raising the question of how to optimally select a cohort of size n from a larger pool of size N. In this article, we propose a simple selective recruitment protocol that selects a cohort in which covariates of interest tend to have a uniform distribution. We show that selectively recruited cohorts potentially offer greater statistical power and more accurate parameter estimates than randomly selected cohorts. Our protocol can be applied to studies with multiple categorical and continuous covariates. We apply our protocol to a numerically simulated prospective observational study using an EHR database of stable acute coronary disease patients from 82 089 individuals in the U.K. Selective recruitment designs require a smaller sample size, leading to more efficient and cost-effective studies.     

Collecting sensitive data: the impact on researchers

The authors undertook an exploratory study to identify issues that arose for researchers while conducting fieldwork. Ten researchers engaged in "sensitive" inquiry were purposefully sampled and encouraged to talk about their experiences during the data collection process. Interviews were transcribed and common themes identified. The findings indicate that these researchers experienced a number of difficulties that centered around issues of lack of training, confidentiality, role conflict, costs to the participants, the desire for reciprocity, and feelings of isolation. The authors discuss the implications of the findings, including the need for support for researchers for further research.