[Reliability of clinical and paraclinical data: quality control].
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Clinical Laboratory Techniques↗
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Results for “data quality”
Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.
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Particle Total Exposure Assessment Methodology (PTEAM) 1990 study: method performance and data quality for personal, indoor, and outdoor monitoring.
The Particle Total Exposure Assessment Methodology (PTEAM) study provided the opportunity to test methodologies for measuring personal and microenvironmental PM10 and PM2.5 concentrations in a full-scale probability-based sample of 178 persons and homes in Riverside, California during the fall of 1990. The purpose of the study was to estimate frequency distributions of exposure to PM10, PM2.5, and selected elements in an urban population. Quality control samples and analyses were used to evaluate method performance. These included collocated sample collection, field and lab blank filters, sampler and balance field audits, and intra- and interlaboratory replicate elemental analyses. A portion of the study was also designed to include side-by-side operation of the personal and microenvironmental samplers with reference method (high-volume and dichotomous) samplers to provide an evaluation of method comparability. Over 95% of the approximately 2,900 scheduled samples were collected and analyzed, with very few losses due to equipment failure. The method limit of detection for the personal and microenvironmental monitor PM10 sampling was 8 micrograms/m3. Mean relative standard deviations (RSDs) of 2% to 8% were obtained for collocated personal and microenvironmental samples. Sampler flow rates were within the +/- 10% accuracy criterion during two field audits. Balances operated in a specially designed mobile laboratory were within specified tolerances for precision (+/- 4 micrograms) and accuracy (+/- 50 micrograms). Elemental analysis accuracy was measured with standard reference materials with biases ranging from 2% to 7%. Measurement precision for most elements ranged from 2.5% to 25% mean RSD. Personal and microenvironmental samplers gave median PM10 concentrations that were approximately 9% higher than the dichotomous sampler and 16% higher than the high-volume sampler across 96 monitoring periods at a fixed outdoor location.
Air Pollution↗
[Sentinel practice data quality. Denominator concepts, spatial and temporal frequency patterns. Special issue following the "1st Sentinel-Workshop Hannover" on 29 April 1992].
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Cross-Sectional Studies↗
Personal sampling of airborne particles: method performance and data quality.
A study of personal exposure to respirable particles (PM10) and fine particles (FP) was conducted in groups of 50-70 year-old adults and primary school children in the Netherlands. Four to eight personal measurements per subject were conducted, on weekdays only. Averaging time was 24 hours. Method performance was evaluated regarding compliance, flow, weighing procedure, field blanks and co-located operation of the personal samplers with stationary methods. Furthermore, the possibility that subjects change their behavior due to the wearing of personal sampling equipment was studied by comparing time activity on days of personal sampling with time activity other weekdays. Compliance was high; 95% of the subjects who agreed to continue participating after the first measurement, successfully completed the study, and, expect for the first two days of FP sampling, over 90% of all personal measurements were successful. All pre and post sampling flow readings were within 10% of the required flow rate of 4 L/min. For PM10 precision of the gravimetric analyses was 2.8 microgram/m3 and 0.7 micrograms/m3 for filters weighted on an analytical and a micro-balance respectively. The detection limit was 10.8 micrograms/m3 and 8.6 micrograms/m3 respectively. For FP, weighing precision was 0.4 micrograms/m3 and the detection limit was 5.3 micrograms/m3. All measurements were above the detection limit. Co-located operation of the personal sampler with stationary samplers gave highly correlated concentration (R > 0.90). Outdoor PM10 concentrations measured with the personal sampler were on average 4% higher compared to a Sierra Anderson (SA) inlet and 9% higher compared to a PM10 Harvard Impactor (HI). With the FP cyclone 6% higher classroom concentrations were measured compared to a PM2.5 HI. Adults spent significantly less time outdoor (0.5 hour) and more time at home (0.9 hour) on days of personal sampling compared to other weekdays. For children no significant differences in time activity were found.
Activities of Daily Living↗
Quality of data on subsequent events in a routine Medical Birth Register.
BACKGROUND: The maintenance of health registers has become routine. The main prerequisite for their use is that registers be complete and that their contents correspond to reality. METHODS: Data on all primiparous women who gave birth between 1987 and 1989 (N=73009) and on their second (N=55388) and third births (N=22904) in the 1987-1998 period were retrieved from the Finnish Medical Birth Register (MBR). The consistency of the MBR data on reproductive history and on previous Caesarean section was investigated by comparing the records on subsequent births. MAIN RESULTS: In total 98.5% of the information on reproductive history corresponded with the previous data in the MBR. Data quality decreased over time and with increasing parity. There were problems with the registration of rare cases, e.g. several extrauterine pregnancies or stillbirths. The quality deteriorated in the late 1990s, because no data on previous induced abortions and extrauterine pregnancies were collected between 1991 and 1995. The quality of data on previous Caesarean section was poor in 1987-1990, a period when the data were collected by using ICD-9 codes, but the quality improved after the introduction of a check-box format in 1991. CONCLUSIONS: Changes in question formats may change the quality of register data significantly. Check-boxes seem to improve quality compared to open-ended questions. The data on reproductive history and previous Caesarean sections could be combined routinely to improve the quality of the MBR.
Birth Rate↗
Evaluating Wearable Devices for Remote Monitoring in Psychosis: Pilot Study Nested Within the CONNECT Cohort Study.
BACKGROUND: Digital remote monitoring technologies, including smartphones and wearables, offer promising avenues for early detection of psychosis relapse. However, selecting devices that are acceptable to participants and produce high-quality data remains challenging. OBJECTIVE: The aim of this nested pilot study was to assess the acceptability and data quality of 3 commercially available wearable devices in people with psychosis recruited to the CONNECT cohort study. METHODS: Participants recruited to the CONNECT study before July 31, 2024, were included in the pilot study and selected 1 of 3 wearable devices: a Fitbit Charge 5, Samsung Galaxy Watch 5, or Apple Watch SE. Baseline demographics were compared between device groups. Acceptability of devices to participants was assessed through a Wearable Device Satisfaction Questionnaire after 3 months of use, with the proportion of positive responses to each question calculated and compared. Data completeness was also assessed by calculating the number (and percentage) of valid days of step count, heart rate, and sleep data, and comparing between groups. Data quality was assessed through summarizing the amount of troubleshooting required, additional metrics available from the wearables, and continuity of data completeness by calculating the proportion of participants with at least 3 days of heart rate data per week for the first 20 weeks of follow-up. Predefined criteria were used to determine the next steps for the wider CONNECT study: if one device was superior, this would be selected; if none were found to be superior and the Fitbit was found to be noninferior, then Fitbit would be retained. RESULTS: Of the first 107 participants recruited to CONNECT, 105 were included in the pilot study evaluation. The Samsung Galaxy Watch was selected most frequently by participants (46/105, 43.8%), followed by the Apple Watch (27/105, 25.7%), and Fitbit Charge (23/105, 21.9%). Differences in participant demographics were observed across device groups. Self-reported acceptability after use did not differ substantially between devices. However, in terms of data completeness, the median proportion of valid heart rate data days was significantly lower for Samsung Galaxy (median 31.2%, IQR 8.5%-46.0%) compared to Fitbit (median 80.1%, IQR 26.7%-95.0%; P=.003) and Apple Watch (median 49.3%, IQR 21.5%-86.0%; P=.02). There was no significant difference between Fitbit and Apple Watch. Similar patterns were observed for step count and sleep data. The Samsung Galaxy Watch required more frequent troubleshooting for data flow issues and lacked additional physiological metrics, available from the other devices. CONCLUSIONS: Due to comparatively lower data quality and technical performance, the Samsung Galaxy Watch was discontinued for use in the subsequent phase of the CONNECT study. The study highlights the importance of incorporating nested evaluations of devices in long-term research.
Humans↗
[Quality assurance of data collection and data processing in epidemiologic study data].
Quality assurance of the data generating processes in epidemiologic studies is a prerequisite for the internal validity of study results. This paper presents practical aspects of such a quality assurance system pertaining to the planning, data gathering, data entry and data processing phase of a study. It is concerned with data obtained in the framework of a project rather than with data accumulating continuously in private practices, research institutes or veterinary faculties. During the planning phase of a project, standard operating protocols should be developed that assure a reliable performance of observation, coding and data entry. The data base structure, consisting of tables, input validation rules and queries, should be predefined and well documented. A data safety concept will provide the necessary integrity, physical safety and availability of the data. The paper presents technical solutions to common data processing problems with emphasis on re-coding and relational data base facilities (Microsoft-ACCESS) using a hypothetical study on risk factors for mastitis.
Animals↗
Quality of age data in patients from developing countries.
BACKGROUND: Age misreporting is common in demographic studies but the prevalence and magnitude of age misreporting in clinical cohorts is unknown. We analysed single-year age distribution and terminal digit preference in cancer patients from developing countries. METHOD: Age distribution was analysed by plotting a single-year age of 3874 cancer patients from 72 different countries, mainly from the Indian subcontinent and the Middle East, who resided in the UAE at the time of cancer diagnosis. Preference for age ending with digits '0' and '5' was evaluated using Whipple's index (WI), which has value 100 in cohorts without preference. Preference for all 10 terminal digits was expressed as the difference between the found and expected frequencies using Myers blended method and was graphed. RESULTS: Age data quality was low in cancer patients from the Indian subcontinent (WI = 177) and Middle Eastern countries (WI = 113-204). Females of all nationalities supplied better quality of age data (lower WI) than males. Preference for age ending with digits '0' and '5' was found in all populations except the UAE male citizens who did not show preference for terminal digit '0'. CONCLUSION: Age data quality in this cohort of patients from developing countries was low. Preference for age ending with numbers '0' and '5' is common. In studies conducted in developing countries, age data quality should be analysed as it may bias results and weaken the power of the study.
Age Distribution↗
Applications of global statistics in analysing quality of life data.
Quality of life (QOL) instruments usually consist of a number of components, each of which deals specifically with a particular functionally related dysfunction. In a clinical trial whose primary aim is the evaluation of the treatment by means of QOL instruments, analysis of each of the components usually consists of either univariate analysis of variance (ANOVA) or some non-parametric methods. This multiple testing approach can produce an increase in false positive findings. One attempt to correct for this is the Bonferroni adjustment. Another approach is to apply global statistics (parametric or non-parametric) for the null hypothesis of no treatment difference versus the alternative hypothesis that one treatment is uniformly better than the other for QOL instruments as a whole. Data from a randomized double-blind trial of 111 congestive heart failure patients, which involved four QOL instruments, were analysed with univariate ANOVA, Bonferroni adjustment, parametric and non-parametric global statistics. The global statistics complemented the univariate methods and made the presentation of QOL data very effective. I recommend the general use of global statistics in analysis of QOL data.
Attitude to Health↗
Missing data in quality of life research in Eastern Cooperative Oncology Group (ECOG) clinical trials: problems and solutions.
Incorporation of quality of life (QOL) investigation into Eastern Cooperative Oncology Group (ECOG) multi-centre clinical trials has led to innovative strategies for protocol design and high quality data collection. A scientific advisory committee reviews protocol design components, measurement selection, timing of assessments and compliance issues. Extensive educational programmes provide information about the scientific and clinical relevance of QOL protocols, as well as practical strategies for data collection and management. Compliance with QOL data collection standards is prospectively monitored and evaluated. Preliminary results from eight ECOG-run protocols found overall compliance to be approximately 85 per cent (94 per cent at baseline and 73 per cent during treatment). Selected patient and institutional factors were evaluated for their association with compliance.
Adult↗
Verifying nursing home care quality using minimum data set quality indicators and other quality measures.
Researchers, providers and government agencies have devoted time and resources to the development of a set of Quality Indicators derived from Minimum Data Set (MDS) data. Little effort has been directed toward verifying that Quality Indicators derived from MDS data accurately measure nursing home quality. Researchers at the University of Missouri-Columbia have independently verified the accuracy of QI derived from MDS data using four different methods; 1) structured participative observation, 2) QI Observation Scoring Instrument, 3) Independent Observable Indicators of Quality Instrument, and 4) survey citations. Our team was able to determine that QIs derived from MDS data did differentiate nursing homes of good quality from those of poorer quality.
Data Collection↗
Computerized questionnaires and the quality of survey data.
STUDY DESIGN: A retrospective data quality analysis was conducted. OBJECTIVE: To compare missing response rates and internal consistency between computerized and paper surveys administered to spine patients. SUMMARY OF BACKGROUND DATA: Computerized patient surveys have been shown to offer numerous advantages over traditional paper surveys. It has been assumed that computerized surveys also improve data quality, but quantitative comparisons have not been made. METHODS: Between January 1998 and December 2000, approximately 3500 computerized questionnaires and 15,000 paper questionnaires containing the MOS 36-Item Short-Form Health Survey (SF-36) and the Oswestry Low Back Pain Disability Questionnaire were administered in the National Spine Network. Missing response rates and the Response Consistency Index (RCI) were compared between computerized and paper questionnaire data. RESULTS: Computer surveys had approximately half the missing response rate of paper surveys. For the SF-36, the computer survey had 1.7% missing, as compared with 3.3% missing on paper (P < 0.001). For the Oswestry, the computer survey had 2.9% missing, as compared with 6% missing on paper (P < 0.001). Whereas 84% of the SF-36 surveys and 85% of the Oswestry surveys collected by computer were completely filled out (no missing responses), only 68% of the SF-36 surveys (P < 0.001) and 77% of the Oswestry surveys (P < 0.001) collected on paper were completely filled out. The SF-36 data collected by computer had better internal consistency than the paper-form data, with average Response Consistency Index scores of 0.12 and 0.16, respectively (P = 0.001). CONCLUSIONS: Superior response rates and higher internal consistency suggest that computerized survey systems improve data quality, and may enhance instrument validity for commonly used measures of spine patient health.
Cohort Studies↗
Assessing the quality of clinical data in a computer-based record for calculating the pneumonia severity index.
OBJECTIVE: This study examined whether clinical data routinely available in a computerized patient record (CPR) can be used to drive a complex guideline that supports physicians in real time and at the point of care in assessing the risk of mortality for patients with community-acquired pneumonia. SETTING: Emergency department of a tertiary-care hospital. DESIGN: Retrospective analysis with medical chart review. PATIENTS: All 241 inpatients during a 17-month period (Jun 1995 to Nov 1996) who presented to the emergency department and had a primary discharge diagnosis of community-acquired pneumonia. METHODS/MAIN OUTCOME MEASURES: The 20 guideline variables were extracted from the CPR (HELP System) and the paper chart. The risk score and the risk class of the Pneumonia Severity Index were computed using data from the CPR alone and from a reference standard of all data available in the paper chart and the CPR at the time of the emergency department encounters. Availability and concordance were quantified to determine data quality. The type and cause of errors were analyzed depending on the source and format of the clinical variables. RESULTS: Of the 20 guideline variables, 12 variables were required to be present for every computer-charted emergency department patient, seven variables were required for selected patients only, and one variable was not typically available in the HELP System during a patient's encounter. The risk class was identical for 86.7 percent of the patients. The majority of patients with different risk classes were assigned too low a risk class. The risk scores were identical for 72.1 percent of the patients. The average availability was 0.99 for the data elements that were required to be present and 0.79 for the data elements that were not required to be present. The average concordance was 0.98 when all a patient's variables were taken into account. The cause of error was attributed to the nurse charting in 77 percent of the cases and to the computerized evaluation in 23 percent. The type of error originated from the free-text fields in 64 percent, from coded fields in 21 percent, from vital signs in 14 percent, and from laboratory results in 1 percent. CONCLUSION: From a clinical perspective, the current level of data quality in the HELP System supports the automation and the prospective evaluation of the Pneumonia Severity Index as a computerized decision support tool.
Algorithms↗
Paradigm shifts in clinical trials enabled by information technology.
The use of the world wide web for clinical trials changes the processes of performing clinical research in several fundamental ways. Greatly improved security, monitoring capability, and accuracy and timeliness of study conduct can be achieved while lowering cost. Data quality is enhanced while co-ordinating centre effort is reduced. The web provides a natural environment for linking the various components of clinical research, leading to new levels of simplicity and efficiency. It also enhances opportunities for recruitment of study investigators and patients. Other information technology tools and databases can be used to assist in this regard as well. Web-based trials change the relationship of the investigator site to the study and the site to the co-ordinating centre. Different roles and responsibilities lead to simplified processes and more and higher quality data. Many standard co-ordinating centre activities, such as randomization, protocol implementation and amending, document tracking, adverse event reporting, site monitoring, report generation and data analysis are all fundamentally changed in a web-based trial. Opportunities are enhanced to identify potential investigators and support their successful study conduct. As the role of investigator sites is changed in web-based research, more primary care medical providers can be attracted to research, providing more typical patients to studies than those sometimes available through more traditional research sites, especially those at academic study sites. Other activities can now be co-ordinated electronically with the advent of the web. The Institutional Review Board (IRB) can use online tools to control investigator participation, resulting in improved study efficiency and patient safety. A web-based research pharmacy provides tremendous efficiencies in managing and distributing study medications. Financial payments to the sites can be performed and recorded electronically, or even administered based on timeliness and quality of the data. Our early experience with web-based trials indicates that there can be tremendous gains in study efficiency and accuracy by restructuring processes, roles and responsibilities through a comprehensive centralized, web-based trial. The future appears bright for web-based clinical trials.
Clinical Trials as Topic↗
Spatial epidemiology: current approaches and future challenges.
Spatial epidemiology is the description and analysis of geographic variations in disease with respect to demographic, environmental, behavioral, socioeconomic, genetic, and infectious risk factors. We focus on small-area analyses, encompassing disease mapping, geographic correlation studies, disease clusters, and clustering. Advances in geographic information systems, statistical methodology, and availability of high-resolution, geographically referenced health and environmental quality data have created unprecedented new opportunities to investigate environmental and other factors in explaining local geographic variations in disease. They also present new challenges. Problems include the large random component that may predominate disease rates across small areas. Though this can be dealt with appropriately using Bayesian statistics to provide smooth estimates of disease risks, sensitivity to detect areas at high risk is limited when expected numbers of cases are small. Potential biases and confounding, particularly due to socioeconomic factors, and a detailed understanding of data quality are important. Data errors can result in large apparent disease excess in a locality. Disease cluster reports often arise nonsystematically because of media, physician, or public concern. One ready means of investigating such concerns is the replication of analyses in different areas based on routine data, as is done in the United Kingdom through the Small Area Health Statistics Unit (and increasingly in other European countries, e.g., through the European Health and Environment Information System collaboration). In the future, developments in exposure modeling and mapping, enhanced study designs, and new methods of surveillance of large health databases promise to improve our ability to understand the complex relationships of environment to health.
Bayes Theorem↗
[From quality assurance to quality management. Applying data from quality assurance studies].
The realization of internal and external surgical quality assurance is natural for hospital physicians. External influences on the hospital and a changed understanding of organisation demand a development of classical quality assurance. The primary aim is a patient orientated quality assurance related to the medical benefits and the patient individual needs. The active creation and establishing of functional organisation process and interprofessional and interdisciplinary structures in hospital setting is a requirement of time.
General Surgery↗
Quality of hospital data and DRGs.
Research on and application of DRG is frequently performed with the aid of computerized data. This study has been made to assess the importance of errors in hospital data bases for DRG research, and the financial consequences if DRG is used as a reimbursement system. For the same hospital stays information on the DRG grouping variables has been collected from medical records and a data base. Frequencies of errors in the data base for the DRG grouping variables (principal diagnosis, secondary diagnosis, complications, procedures, age and discharge status of death) and length of stay are given. DRG grouping is performed on the basis of both medical record and data base information. The analysis shows the DRG system to be robust towards errors in the data bases. The procedure used in many countries for testing the US DRG definitions with national data found in data bases is also considered to be robust towards errors. Data quality in the studied data base is sufficient for DRG research purposes. The factors giving the robustness are of a general nature and may also apply to other data bases. Quality of computerized data is a less critical factor for DRG than for other applications of such data. If the present data base had been used in financial connection the errors would have had almost no deteriorating effect on hospital incomes.
Abstracting and Indexing↗
A statistical test to determine the quality of accelerometer data.
Accelerometer data quality can be inadequate due to data corruption or to non-compliance of the subject with regard to study protocols. We propose a simple statistical test to determine if accelerometer data are of good quality and can be used for analysis or if the data are of poor quality and should be discarded. We tested several data evaluation methods using a group of 105 subjects who wore Motionlogger actigraphs (Ambulatory Monitoring, Inc.) over a 15 day period to assess sleep quality in a study of health outcomes associated with stress among police officers. Using leave-one-out cross-validation and calibration-testing methods of discrimination statistics, error rates for the methods ranged from 0.0167 to 0.4046. We found that the best method was to use the overall average distance between consecutive time points and the overall average mean amplitude of consecutive time points. These values gave us a classification error rate of 0.0167. The average distance between points is a measure of smoothness in the data, and the average mean amplitude between points gave an average reading. Both of these values were then normed to determine a final statistic, K, which was then compared to a cut-off value, K(C), to determine data quality.
Acceleration↗