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This paper reports on the findings of an international workshop organised by the UK-France+ Genomics and Ethics Network (UK-FR + GENE) in 2022. The focus of the workshop were the ethical and social issues raised by public-private partnerships in the context of large-scale genomics initiatives in France, Germany, the United Kingdom and Israel, i.e. collaborations where commercial entities are given access to publicly held genomic data. While the public sector relies on partnerships with commercial entities to exploit the full potential of the data it holds, such collaborations may have an impact on the return of benefits to the public sector and on public trust, and subsequently challenge the social contract. The first part of this paper explores the ways in which the four countries examined respond to the challenges posed to the social contract, and what safeguards they put in place to secure public trust. The second part presents three approaches to address the challenges of private-public partnerships in secondary data use. In conclusion, this paper offers a set of minimum requirements for these partnerships within solidarity-based publicly funded healthcare systems. These include the necessity of public-private partnerships to (1) contribute to the public benefit and minimise harm produced by the use of publicly held data; (2) avoid prioritisation of commercial interests over robust governance structures to guarantee benefits to the public and protect donors, especially marginalised groups; (3) side-step the pitfalls of the rhetoric of solidarity and be transparent about the challenges to return the benefits to ‘all’.

Periodic demographic health surveillance and surveys are the main sources of health information in developing countries. Conducting a survey requires extensive use of paper-pen and manual work and lengthy processes to generate the required information. Despite the rise of popularity in using electronic data collection systems to alleviate the problems, sufficient evidence is not available to support the use of electronic data capture (EDC) tools in interviewer-administered data collection processes. This study aimed to compare data quality parameters in the data collected using mobile electronic and standard paper-based data capture tools in one of the health and demographic surveillance sites in northwest Ethiopia. A randomized controlled crossover health care information technology evaluation was conducted from May 10, 2016, to June 3, 2016, in a demographic and surveillance site. A total of 12 interviewers, as 2 individuals (one of them with a tablet computer and the other with a paper-based questionnaire) in 6 groups were assigned in the 6 towns of the surveillance premises. Data collectors switched the data collection method based on computer-generated random order. Data were cleaned using a MySQL program and transferred to SPSS (IBM SPSS Statistics for Windows, Version 24.0) and R statistical software (R version 3.4.3, the R Foundation for Statistical Computing Platform) for analysis. Descriptive and mixed ordinal logistic analyses were employed. The qualitative interview audio record from the system users was transcribed, coded, categorized, and linked to the International Organization for Standardization 9241-part 10 dialogue principles for system usability. The usability of this open data kit-based system was assessed using quantitative System Usability Scale (SUS) and matching of qualitative data with the isometric dialogue principles. From the submitted 1246 complete records of questionnaires in each tool, 41.89% (522/1246) of the paper and pen data capture (PPDC) and 30.89% (385/1246) of the EDC tool questionnaires had one or more types of data quality errors. The overall error rates were 1.67% and 0.60% for PPDC and EDC, respectively. The chances of more errors on the PPDC tool were multiplied by 1.015 for each additional question in the interview compared with EDC. The SUS score of the data collectors was 85.6. In the qualitative data response mapping, EDC had more positive suitability of task responses with few error tolerance characteristics. EDC possessed significantly better data quality and efficiency compared with PPDC, explained with fewer errors, instant data submission, and easy handling. The EDC proved to be a usable data collection tool in the rural study setting. Implementation organization needs to consider consistent power source, decent internet connection, standby technical support, and security assurance for the mobile device users for planning full-fledged implementation and integration of the system in the surveillance site.

We sought to assess the current status of global data on death registration and to examine several indicators of data completeness and quality. We summarized the availability of death registration data by year and country. Indicators of data quality were assessed for each country and included the timeliness, completeness and coverage of registration and the proportion of deaths assigned to ill-defined causes. At the end of 2003 data on death registration were available from 115 countries, although they were essentially complete for only 64 countries. Coverage of death registration varies from close to 100% in the WHO European Region to less than 10% in the African Region. Only 23 countries have data that are more than 90% complete, where ill-defined causes account for less than 10% of total of causes of death, and where ICD-9 or ICD-10 codes are used. There are 28 countries where less than 70% of the data are complete or where ill-defined codes are assigned to more than 20% of deaths. Twelve high-income countries in western Europe are included among the 55 countries with intermediate-quality data. Few countries have good-quality data on mortality that can be used to adequately support policy development and implementation. There is an urgent need for countries to implement death registration systems, even if only through sample registration, or enhance their existing systems in order to rapidly improve knowledge about the most basic of health statistics: who dies from what?

Background Verbal autopsy (VA) has been proposed to determine the cause of death (COD) distributions in settings where most deaths occur without medical attention or certification. We develop performance criteria for VA-based COD systems and apply these to the Registrar General of India’s ongoing, nationally-representative Indian Million Death Study (MDS). Methods Performance criteria include a low ill-defined proportion of deaths before old age; reproducibility, including consistency of COD distributions with independent resampling; differences in COD distribution of hospital, home, urban or rural deaths; age-, sex- and time-specific plausibility of specific diseases; stability and repeatability of dual physician coding; and the ability of the mortality classification system to capture a wide range of conditions. Results The introduction of the MDS in India reduced the proportion of ill-defined deaths before age 70 years from 13% to 4%. The cause-specific mortality fractions (CSMFs) at ages 5 to 69 years for independently resampled deaths and the MDS were very similar across 19 disease categories. By contrast, CSMFs at these ages differed between hospital and home deaths and between urban and rural deaths. Thus, reliance mostly on urban or hospital data can distort national estimates of CODs. Age-, sex- and time-specific patterns for various diseases were plausible. Initial physician agreement on COD occurred about two-thirds of the time. The MDS COD classification system was able to capture more eligible records than alternative classification systems. By these metrics, the Indian MDS performs well for deaths prior to age 70 years. The key implication for low- and middle-income countries where medical certification of death remains uncommon is to implement COD surveys that randomly sample all deaths, use simple but high-quality field work with built-in resampling, and use electronic rather than paper systems to expedite field work and coding. Conclusions Simple criteria can evaluate the performance of VA-based COD systems. Despite the misclassification of VA, the MDS demonstrates that national surveys of CODs using VA are an order of magnitude better than the limited COD data previously available.

Background Data collection consumes a large proportion of clinical trial resources. Each data item requires time and effort for collection, processing and quality control procedures. In general, more data equals a heavier burden for trial staff and participants. It is also likely to increase costs. Knowing the types of data being collected, and in what proportion, will be helpful to ensure that limited trial resources and participant goodwill are used wisely. Aim The aim of this study is to categorise the types of data collected across a broad range of trials and assess what proportion of collected data each category represents. Methods We developed a standard operating procedure to categorise data into primary outcome, secondary outcome and 15 other categories. We categorised all variables collected on trial data collection forms from 18, mainly publicly funded, randomised superiority trials, including trials of an investigational medicinal product and complex interventions. Categorisation was done independently in pairs: one person having in-depth knowledge of the trial, the other independent of the trial. Disagreement was resolved through reference to the trial protocol and discussion, with the project team being consulted if necessary. Key results Primary outcome data accounted for 5.0% (median)/11.2% (mean) of all data items collected. Secondary outcomes accounted for 39.9% (median)/42.5% (mean) of all data items. Non-outcome data such as participant identifiers and demographic data represented 32.4% (median)/36.5% (mean) of all data items collected. Conclusion A small proportion of the data collected in our sample of 18 trials was related to the primary outcome. Secondary outcomes accounted for eight times the volume of data as the primary outcome. A substantial amount of data collection is not related to trial outcomes. Trialists should work to make sure that the data they collect are only those essential to support the health and treatment decisions of those whom the trial is designed to inform.

This study explored the economic costs and response rate of mail and web-based surveys with practicing dentists. A random sample of 6,000 practicing dentists was randomly assigned into three groups of 2,000: choice (mail or web-based), postal mail, or web-based. The Florida Tobacco Control Survey 2009, which is composed of 28 questions (including subject demographic questions), served as the survey instrument. A total of 1,232 surveys were returned by the three different groups (21% overall response rate). Response rates were best for the mail (26%) with the worst response rate coming from the Web group (11%). However, a cost-effectiveness analysis revealed that web surveys were 2.68 times more cost effective.

The Disablement in the Physically Active (DPA) scale is a patient-reported outcome instrument recommended for use in clinical practice and research. Analysis of the scale has indicated a need for further psychometric testing. To assess the model fit of the original DPA scale using a larger and more diverse sample and explore the potential for a short-form (SF) version. Observational study. Twenty-four clinical settings. Responses were randomly split into 2 samples: sample 1 (n = 690: 353 males, 330 females, and 7 not reported; mean age = 23.1 ± 9.3 years, age range = 11-75 years) and sample 2 (n = 690: 351 males, 337 females, and 2 not reported; mean age = 22.9 ± 9.3 years, age range = 8-74 years). Participants were physically active individuals who were healthy or experiencing acute, subacute, or persistent musculoskeletal injury. Confirmatory factor analysis was conducted to assess the factor structure of the original DPA scale. Exploratory factor, internal consistency, covariance modeling, correlational, and confirmatory factor analyses were conducted to assess potential DPA scale SFs. The subdimensions of the disablement construct were highly correlated (≥0.89). The fit indices for the DPA scale approached recommended levels, but the first-order correlational values and second-order path coefficients provided evidence for multicollinearity, suggesting that clear distinctions between the disablement subdimensions cannot be made. An 8-item, 2-dimensional solution and a 10-item, 3-dimensional solution were extracted to produce SF versions. The DPA SF-8 was highly correlated ( = 0.94, ≤ .001, = 0.88) with the DPA scale, and the fit indices exceeded all of the strictest recommendations. The DPA SF-10 was highly correlated ( = 0.97, ≤ .001, = 0.94) with the DPA scale, and its fit indices values also exceeded the strictest recommendations. The DPA SF-8 and SF-10 are psychometrically sound alternatives to the DPA scale.

Background For complex diseases like cancer, pooled-analysis of individual data represents a powerful tool to investigate the joint contribution of genetic, phenotypic and environmental factors to the development of a disease. Pooled-analysis of epidemiological studies has many advantages over meta-analysis, and preliminary results may be obtained faster and with lower costs than with prospective consortia. Design and methods Based on our experience with the study design of the Melanocortin-1 receptor ( MC1R ) gene, SKin cancer and Phenotypic characteristics (M-SKIP) project, we describe the most important steps in planning and conducting a pooled-analysis of genetic epidemiological studies. We then present the statistical analysis plan that we are going to apply, giving particular attention to methods of analysis recently proposed to account for between-study heterogeneity and to explore the joint contribution of genetic, phenotypic and environmental factors in the development of a disease. Within the M-SKIP project, data on 10,959 skin cancer cases and 14,785 controls from 31 international investigators were checked for quality and recoded for standardization. We first proposed to fit the aggregated data with random-effects logistic regression models. However, for the M-SKIP project, a two-stage analysis will be preferred to overcome the problem regarding the availability of different study covariates. The joint contribution of MC1R variants and phenotypic characteristics to skin cancer development will be studied via logic regression modeling. Discussion Methodological guidelines to correctly design and conduct pooled-analyses are needed to facilitate application of such methods, thus providing a better summary of the actual findings on specific fields.

Objectives Inaccuracies in cause-of-death information in death certificates can reduce the validity of national death statistics and result in poor targeting of resources to reduce morbidity and mortality in people with HIV. Our objective was to measure the sensitivity, specificity, and agreement between multiple causes of deaths from death certificates obtained from the National Death Index (NDI) and causes determined by expert physician review. Methods Physician specialists determined the cause of death using information collected from the medical records of 50 randomly selected HIV-infected people who died in San Francisco from July 1, 2016, through May 31, 2017. Using expert review as the gold standard, we measured sensitivity, specificity, and agreement. Results The NDI had a sensitivity of 53.9% and a specificity of 66.7% for HIV deaths. The NDI had a moderate sensitivity for non–AIDS-related infectious diseases and non–AIDS-related cancers (70.6% and 75.0%, respectively) and high specificity for these causes (100.0% and 94.7%, respectively). The NDI had low sensitivity and high specificity for substance abuse (27.3% and 100.0%, respectively), heart disease (58.3% and 86.8%, respectively), hepatitis B/C (33.3% and 97.7%, respectively), and mental illness (50.0% and 97.8%, respectively). The measure of agreement between expert review and the NDI was lowest for HIV (κ = 0.20); moderate for heart disease (κ = 0.45) and hepatitis B/C (κ = 0.40); high for non–AIDS-related infectious diseases (κ = 0.76) and non–AIDS-related cancers (κ = 0.72); and low for all other causes of death (κ < 0.35). Conclusions Our findings support education and training of health care providers to improve the accuracy of cause-of-death information on death certificates.

The quality of interventions can affect the results of clinical trials. Reviews of complex interventions need to take this into account