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BMJ Open Quality Jul 2020
Review
Topics: Anthropology, Cultural; Data Collection; Focus Groups; Humans; Interviews as Topic; Patient-Centered Care; Patients; Qualitative Research
PubMed: 32699082
DOI: 10.1136/bmjoq-2020-000912 -
Value in Health : the Journal of the... Dec 2011Response burden is often defined as the effort required by the patient to answer a questionnaire. A factor that has been proposed to affect the response burden is... (Meta-Analysis)
Meta-Analysis Review
BACKGROUND
Response burden is often defined as the effort required by the patient to answer a questionnaire. A factor that has been proposed to affect the response burden is questionnaire length, and this burden is manifested in, for example, response rate. Even though response burden is frequently mentioned as a reason for abridging questionnaires, evidence to support the notion that shorter instruments are preferable is limited.
OBJECTIVES
This study aimed to accumulate, analyze, and discuss evidence regarding the association between response burden, as measured by response rate, and questionnaire length.
METHODS
A systematic literature review and meta-analysis of studies reporting response rates in relation to questionnaire length was performed. A Cochran-Mantel-Haenszel test stratified by study using the Breslow-Day test was undertaken to investigate homogeneity of the odds ratios.
RESULTS
Thirty-two reports were identified, of which 20 were eligible for inclusion in the meta-analysis. Three studies used patient input as main outcome when evaluating response burden. In the meta-analysis, a general association between response rate and questionnaire length was found (P ≤ 0.0001). Response rates were lower for longer questionnaires, but because the P value for test of homogeneity was P = 0.03, this association should be interpreted with caution because it is impossible to separate the impact of content from length of the questionnaires.
CONCLUSION
Given the inherently problematic nature of comparing questionnaires of various lengths, it is preferable to base decisions on use of instruments on the content rather than the length per se.
Topics: Clinical Trials as Topic; Data Collection; Humans; Outcome Assessment, Health Care; Surveys and Questionnaires
PubMed: 22152180
DOI: 10.1016/j.jval.2011.06.003 -
Computers in Biology and Medicine Jun 2014Utilizing electronic data capture (EDC) systems in data collection and management allows automated validation programs to preemptively identify and correct data errors....
Utilizing electronic data capture (EDC) systems in data collection and management allows automated validation programs to preemptively identify and correct data errors. For our multi-center, prospective study we chose to use TeleForm, a paper-based data capture software that uses recognition technology to create case report forms (CRFs) with similar functionality to EDC, including custom scripts to identify entry errors. We quantified the accuracy of the optimized system through a data audit of CRFs and the study database, examining selected critical variables for all subjects in the study, as well as an audit of all variables for 25 randomly selected subjects. Overall we found 6.7 errors per 10,000 fields, with similar estimates for critical (6.9/10,000) and non-critical (6.5/10,000) variables-values that fall below the acceptable quality threshold of 50 errors per 10,000 established by the Society for Clinical Data Management. However, error rates were found to widely vary by type of data field, with the highest rate observed with open text fields.
Topics: Data Collection; Electronic Data Processing; Humans; Medical Informatics Computing; Obesity; Prospective Studies; Software
PubMed: 24709056
DOI: 10.1016/j.compbiomed.2014.03.002 -
Malaria Journal Dec 2019Routine health information systems can provide near real-time data for malaria programme management, monitoring and evaluation, and surveillance. There are widespread...
BACKGROUND
Routine health information systems can provide near real-time data for malaria programme management, monitoring and evaluation, and surveillance. There are widespread concerns about the quality of the malaria data generated through routine information systems in many low-income countries. However, there has been little careful examination of micro-level practices of data collection which are central to the production of routine malaria data.
METHODS
Drawing on fieldwork conducted in two malaria endemic sub-counties in Kenya, this study examined the processes and practices that shape routine malaria data generation at frontline health facilities. The study employed ethnographic methods-including observations, records review, and interviews-over 18-months in four frontline health facilities and two sub-county health records offices. Data were analysed using a thematic analysis approach.
RESULTS
Malaria data generation was influenced by a range of factors including human resource shortages, tool design, and stock-out of data collection tools. Most of the challenges encountered by health workers in routine malaria data generation had their roots in wider system issues and at the national level where the framing of indicators and development of data collection tools takes place. In response to these challenges, health workers adopted various coping mechanisms such as informal task shifting and use of improvised tools. While these initiatives sustained the data collection process, they also had considerable implications for the data recorded and led to discrepancies in data that were recorded in primary registers. These discrepancies were concealed in aggregated monthly reports that were subsequently entered into the District Health Information Software 2.
CONCLUSION
Challenges to routine malaria data generation at frontline health facilities are not malaria or health information systems specific; they reflect wider health system weaknesses. Any interventions seeking to improve routine malaria data generation must look beyond just malaria or health information system initiatives and include consideration of the broader contextual factors that shape malaria data generation.
Topics: Data Accuracy; Data Collection; Data Interpretation, Statistical; Epidemiological Monitoring; Health Facilities; Health Information Systems; Humans; Kenya; Malaria
PubMed: 31842872
DOI: 10.1186/s12936-019-3061-y -
International Journal of Environmental... Aug 2016Information and communications technologies (ICTs) such as mobile survey tools (MSTs) can facilitate field-level data collection to drive improvements in national and... (Comparative Study)
Comparative Study
Evaluating Mobile Survey Tools (MSTs) for Field-Level Monitoring and Data Collection: Development of a Novel Evaluation Framework, and Application to MSTs for Rural Water and Sanitation Monitoring.
Information and communications technologies (ICTs) such as mobile survey tools (MSTs) can facilitate field-level data collection to drive improvements in national and international development programs. MSTs allow users to gather and transmit field data in real time, standardize data storage and management, automate routine analyses, and visualize data. Dozens of diverse MST options are available, and users may struggle to select suitable options. We developed a systematic MST Evaluation Framework (EF), based on International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) software quality modeling standards, to objectively assess MSTs and assist program implementers in identifying suitable MST options. The EF is applicable to MSTs for a broad variety of applications. We also conducted an MST user survey to elucidate needs and priorities of current MST users. Finally, the EF was used to assess seven MSTs currently used for water and sanitation monitoring, as a validation exercise. The results suggest that the EF is a promising method for evaluating MSTs.
Topics: Data Collection; Environmental Monitoring; Humans; Rural Population; Sanitation; Surveys and Questionnaires; Water Pollutants
PubMed: 27563916
DOI: 10.3390/ijerph13090840 -
Trials Jun 2020Data collection consumes a large proportion of clinical trial resources. Each data item requires time and effort for collection, processing and quality control...
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.
Topics: Clinical Trials as Topic; Data Collection; Data Interpretation, Statistical; Humans
PubMed: 32546192
DOI: 10.1186/s13063-020-04388-x -
Journal of Medical Entomology Jul 2013Capture of surveillance data on mobile devices and rapid transfer of such data from these devices into an electronic database or data management and decision support...
Capture of surveillance data on mobile devices and rapid transfer of such data from these devices into an electronic database or data management and decision support systems promote timely data analyses and public health response during disease outbreaks. Mobile data capture is used increasingly for malaria surveillance and holds great promise for surveillance of other neglected tropical diseases. We focused on mosquito-borne dengue, with the primary aims of: 1) developing and field-testing a cell phone-based system (called Chaak) for capture of data relating to the surveillance of the mosquito immature stages, and 2) assessing, in the dengue endemic setting of Mérida, Mexico, the cost-effectiveness of this new technology versus paper-based data collection. Chaak includes a desktop component, where a manager selects premises to be surveyed for mosquito immatures, and a cell phone component, where the surveyor receives the assigned tasks and captures the data. Data collected on the cell phone can be transferred to a central database through different modes of transmission, including near-real time where data are transferred immediately (e.g., over the Internet) or by first storing data on the cell phone for future transmission. Spatial data are handled in a novel, semantically driven, geographic information system. Compared with a pen-and-paper-based method, use of Chaak improved the accuracy and increased the speed of data transcription into an electronic database. The cost-effectiveness of using the Chaak system will depend largely on the up-front cost of purchasing cell phones and the recurring cost of data transfer over a cellular network.
Topics: Animal Distribution; Animals; Cell Phone; Culicidae; Data Collection; Dengue Virus; Geographic Information Systems; Insect Vectors; Larva; Mexico; Mosquito Control; Population Surveillance; Pupa
PubMed: 23926788
DOI: 10.1603/me13008 -
PloS One 2019Multi-mode data collection is widely used in surveys. Since several modes of data collection are successively applied in such design (e.g. self-administered...
BACKGROUND
Multi-mode data collection is widely used in surveys. Since several modes of data collection are successively applied in such design (e.g. self-administered questionnaire after face-to-face interview), partial nonresponse occurs if participants fail to complete all stages of the data collection. Although such nonresponse might seriously impact estimates, it remains currently unexplored. This study investigates the determinants of nonresponse to a self-administered questionnaire after having participated in a face-to-face interview.
METHODS
Data from the Belgian Health Interview Survey 2013 were used to identify determinants of nonresponse to self-administered questionnaire (n = 1,464) among those who had completed the face-to-face interview (n = 8,133). The association between partial nonresponse and potential determinants was explored through multilevel logistic regression models, encompassing a random interviewer effect.
RESULTS
Significant interviewer effects were found. Almost half (46.6%) of the variability in nonresponse was attributable to the interviewers, even in the analyses controlling for the area as potential confounder. Partial nonresponse was higher among youngsters, non-Belgian participants, people with a lower educational levels and those belonging to a lower income household, residents of Brussels and Wallonia, and people with poor perceived health. Higher odds of nonresponse were found for interviews done in the last quarters of the survey-year. Regarding interviewer characteristics, only the total number of interviews carried out throughout the survey was significantly associated with nonresponse to the self-administered questionnaire.
CONCLUSIONS
The results indicate that interviewers play a crucial role in nonresponse to the self-administered questionnaire. Participant characteristics, interview circumstances and interviewer characteristics only partly explain the interviewer variability. Future research should examine further interviewer characteristics that impact nonresponse. The current study emphasises the importance of training and motivating interviewers to reduce nonresponse in multi-mode data collection.
Topics: Adolescent; Adult; Age Factors; Belgium; Data Collection; Effect Modifier, Epidemiologic; Female; Health Surveys; Humans; Male; Middle Aged; Multilevel Analysis; Socioeconomic Factors; Young Adult
PubMed: 31026300
DOI: 10.1371/journal.pone.0215652 -
Genome Research Mar 2000
Topics: Data Collection; Humans; Models, Theoretical; Research Design
PubMed: 10720567
DOI: 10.1101/gr.10.3.271 -
Journal of Clinical Epidemiology Sep 2006
Topics: Clinical Medicine; Costs and Cost Analysis; Data Collection; Epidemiologic Research Design; Epidemiology; Evaluation Studies as Topic; Evidence-Based Medicine; Humans; Reproducibility of Results
PubMed: 16895808
DOI: 10.1016/j.jclinepi.2006.06.006