-
Journal of Medical Internet Research Jan 2022Potential is seen in web data collection for population health surveys due to its combined cost-effectiveness, implementation ease, and increased internet penetration....
BACKGROUND
Potential is seen in web data collection for population health surveys due to its combined cost-effectiveness, implementation ease, and increased internet penetration. Nonetheless, web modes may lead to lower and more selective unit response than traditional modes, and this may increase bias in the measured indicators.
OBJECTIVE
This research assesses the unit response and costs of a web study versus face-to-face (F2F) study.
METHODS
Alongside the Belgian Health Interview Survey by F2F edition 2018 (BHISF2F; net sample used: 3316), a web survey (Belgian Health Interview Survey by Web [BHISWEB]; net sample used: 1010) was organized. Sociodemographic data on invited individuals was obtained from the national register and census linkages. Unit response rates considering the different sampling probabilities of both surveys were calculated. Logistic regression analyses examined the association between mode system and sociodemographic characteristics for unit nonresponse. The costs per completed web questionnaire were compared with the costs for a completed F2F questionnaire.
RESULTS
The unit response rate is lower in BHISWEB (18.0%) versus BHISF2F (43.1%). A lower response rate was observed for the web survey among all sociodemographic groups, but the difference was higher among people aged 65 years and older (15.4% vs 45.1%), lower educated people (10.9% vs 38.0%), people with a non-Belgian European nationality (11.4% vs 40.7%), people with a non-European nationality (7.2% vs 38.0%), people living alone (12.6% vs 40.5%), and people living in the Brussels-Capital (12.2% vs 41.8%) region. The sociodemographic characteristics associated with nonresponse are not the same in the 2 studies. Having another European (OR 1.60, 95% CI 1.20-2.13) or non-European nationality (OR 2.57, 95% CI 1.79-3.70) compared to a Belgian nationality and living in the Brussels-Capital (OR 1.72, 95% CI 1.41-2.10) or Walloon (OR 1.47, 95% CI 1.15-1.87) regions compared to the Flemish region are associated with a higher nonresponse only in the BHISWEB study. In BHISF2F, younger people (OR 1.31, 95% CI 1.11-1.54) are more likely to be nonrespondents than older people, and this was not the case in BHISWEB. In both studies, lower educated people have a higher probability of being nonrespondent, but this effect is more pronounced in BHISWEB (low vs high education level: Web, OR 2.71, 95% CI 2.21-3.39 and F2F OR 1.70, 95% CI 1.48-1.95). The BHISWEB study had a considerable advantage; the cost per completed questionnaire was almost 3 times lower (€41 [US $48]) compared with F2F data collection (€111 [US $131]).
CONCLUSIONS
The F2F unit response rate was generally higher, yet for certain groups the difference between web and F2F was more limited. Web data collection has a considerable cost advantage. It is therefore worth experimenting with adaptive mixed-mode designs to optimize financial resources without increasing selection bias (eg, only inviting sociodemographic groups who are keener to participate online for web surveys while continuing to focus on increasing F2F response rates for other groups).
Topics: Aged; Cross-Sectional Studies; Data Collection; Health Surveys; Home Environment; Humans; Internet; Surveys and Questionnaires
PubMed: 34994701
DOI: 10.2196/26299 -
American Journal of Public Health Dec 2021While underscoring the need for timely, nationally representative data in ambulatory, hospital, and long-term-care settings, the COVID-19 pandemic posed many challenges...
While underscoring the need for timely, nationally representative data in ambulatory, hospital, and long-term-care settings, the COVID-19 pandemic posed many challenges to traditional methods and mechanisms of data collection. To continue generating data from health care and long-term-care providers and establishments in the midst of the COVID-19 pandemic, the National Center for Health Statistics had to modify survey operations for several of its provider-based National Health Care Surveys, including quickly adding survey questions that captured the experiences of providing care during the pandemic. With the aim of providing information that may be useful to other health care data collection systems, this article presents some key challenges that affected data collection activities for these national provider surveys, as well as the measures taken to minimize the disruption in data collection and to optimize the likelihood of disseminating quality data in a timely manner. (. 2021;111(12):2141-2148. https://doi.org/10.2105/AJPH.2021.306514).
Topics: Ambulatory Care; COVID-19; Data Collection; Electronic Health Records; Health Care Surveys; Hospitalization; Humans; Long-Term Care; Pandemics; SARS-CoV-2; Time Factors; United States
PubMed: 34878878
DOI: 10.2105/AJPH.2021.306514 -
Current Oncology (Toronto, Ont.) Dec 2021Prognostic factors have important utility in various aspects of cancer surveillance, including research, patient care, and cancer control programmes. Nevertheless, there...
Prognostic factors have important utility in various aspects of cancer surveillance, including research, patient care, and cancer control programmes. Nevertheless, there is heterogeneity in the collection of prognostic factors and outcomes data globally. This study aimed to investigate perspectives on the utility and application of prognostic factors and clinical outcomes in cancer control programmes. A qualitative phenomenology approach using expert interviews was taken to derive a rich description of the current state and future outlook of cancer prognostic factors and clinical outcomes. Individuals with expertise in this work and from various regions and institutions were invited to take part in one-on-one semi-structured interviews. Four areas related to infrastructure and funding challenges were identified by participants, including (1) data collection and access; (2) variability in data reporting, coding, and definitions; (3) limited coordination among databases; and (4) conceptualization and prioritization of meaningful prognostic factors and outcomes. Two areas were identified regarding important future priorities for cancer control: (1) global investment and intention in cancer surveillance and (2) data governance and exchange globally. Participants emphasized the need for better global collection of prognostic factors and clinical outcomes data and support for standardized data collection and data exchange practices by cancer registries.
Topics: Data Collection; Humans; Prognosis; Registries; Research Design
PubMed: 34940071
DOI: 10.3390/curroncol28060432 -
PharmacoEconomics Dec 2017A conceptual model framework and an initial literature review are invaluable when considering what health state utility values (HSUVs) are required to populate health... (Review)
Review
A conceptual model framework and an initial literature review are invaluable when considering what health state utility values (HSUVs) are required to populate health states in decision models. They are the recommended starting point early within a research and development programme, and before development of phase III trial protocols. While clinical trials can provide an opportunity to collect the required evidence, their appropriateness should be reviewed against the requirements of the model structure taking into account population characteristics, time horizon and frequency of clinical events. Alternative sources such as observational studies or registries may be more appropriate when evidence describing changes in HSUVs over time or rare clinical events is required. Phase IV clinical studies may provide the opportunity to collect additional longitudinal real-world evidence. Aspects to consider when designing the collection of the evidence include patient and investigator burden, whom to ask, the representativeness of the population, the exact definitions of health states within the economic model, the timing of data collection, sample size, and mode of administration. Missing data can be an issue, particularly in longitudinal studies, and it is important to determine whether the missing data will bias inferences from analyses. For example, respondents may fail to complete follow-up questionnaires because of a relapse or the severity of their condition. The decision on the preferred study type and the particular quality of life measure should be informed by any evidence currently available in the literature, the design of data collection, and the exact requirements of the model that will be used to support resource allocation decisions (e.g. reimbursement).
Topics: Clinical Trials as Topic; Data Collection; Decision Support Techniques; Health Status; Humans; Models, Economic; Quality of Life; Reimbursement Mechanisms; Research Design; Surveys and Questionnaires
PubMed: 29052159
DOI: 10.1007/s40273-017-0549-6 -
BMJ Open Feb 2024The widespread use of electronic health records (EHRs) has led to a growing number of large routine primary care data collection projects globally, making these records... (Review)
Review
BACKGROUND
The widespread use of electronic health records (EHRs) has led to a growing number of large routine primary care data collection projects globally, making these records a valuable resource for health services and epidemiological and clinical research. This scoping review aims to comprehensively assess and compare strengths and limitations of all German primary care data collection projects and relevant research publications that extract data directly from practice management systems (PMS).
METHODS
A literature search was conducted in the electronic databases in May 2021 and in June 2022. The search string included terms related to general practice, routine data, and Germany. The retrieved studies were classified as applied studies and methodological studies, and categorised by type of research, subject area, sample of publications, disease category, or main medication analysed.
RESULTS
A total of 962 references were identified, with 241 studies included from six German projects in which databases are populated by EHRs from PMS. The projects exhibited significant heterogeneity in terms of size, data collection methods, and variables collected. The majority of the applied studies (n = 205, 85%) originated from one database with a primary focus on pharmacoepidemiological topics (n = 127, 52%) including prescription patterns (n = 68, 28%) and studies about treatment outcomes, compliance, and treatment effectiveness (n = 34, 14%). Epidemiological studies (n = 77, 32%) mainly focused on incidence and prevalence studies (n = 41, 17%) and risk and comorbidity analysis studies (n = 31, 12%). Only 10% (n = 23) of studies were in the field of health services research, such as hospitalisation.
CONCLUSION
The development and durability of primary care data collection projects in Germany is hindered by insufficient public funding, technical issues of data extraction, and strict data protection regulations. There is a need for further research and collaboration to improve the usability of EHRs for health services and research.
Topics: Humans; Electronic Health Records; Data Collection; Comorbidity; Cross-Sectional Studies; Primary Health Care
PubMed: 38382948
DOI: 10.1136/bmjopen-2023-074566 -
The Lancet. Digital Health Aug 2019
Topics: Data Collection; Electronic Health Records; European Union; Humans; Information Dissemination; Privacy; United Kingdom
PubMed: 33323179
DOI: 10.1016/S2589-7500(19)30090-1 -
Systematic Reviews Apr 2015Key performance indicators (KPIs) are used to identify where organisational performance is meeting desired standards and where performance requires improvement. Valid... (Review)
Review
BACKGROUND
Key performance indicators (KPIs) are used to identify where organisational performance is meeting desired standards and where performance requires improvement. Valid and reliable KPIs depend on the availability of high-quality data, specifically the relevant minimum data set ((MDS) the core data identified as the minimum required to measure performance for a KPI) elements. However, the feasibility of collecting the relevant MDS elements is always a limitation of performance monitoring using KPIs. Preferably, data should be integrated into service delivery, and, where additional data are required that are not currently collected as part of routine service delivery, there should be an economic evaluation to determine the cost of data collection. The aim of this systematic review was to synthesise the evidence base concerning the costs of data collection in hospitals for performance monitoring using KPI, and to identify hospital data collection systems that have proven to be cost minimising.
METHODS
We searched MEDLINE (1946 to May week 4 2014), Embase (1974 to May week 2 2014), and CINAHL (1937 to date). The database searches were supplemented by searching for grey literature through the OpenGrey database. Data was extracted, tabulated, and summarised as part of a narrative synthesis.
RESULTS
The searches yielded a total of 1,135 publications. After assessing each identified study against specific inclusion exclusion criteria only eight studies were deemed as relevant for this review. The studies attempt to evaluate different types of data collection interventions including the installation of information communication technology (ICT), improvements to current ICT systems, and how different analysis techniques may be used to monitor performance. The evaluation methods used to measure the costs and benefits of data collection interventions are inconsistent across the identified literature. Overall, the results weakly indicate that collection of hospital data and improvements in data recording can be cost-saving.
CONCLUSIONS
Given the limitations of this systematic review, it is difficult to conclude whether improvements in data collection systems can save money, increase quality of care, and assist performance monitoring of hospitals. With that said, the results are positive and suggest that data collection improvements may lead to cost savings and aid quality of care.
SYSTEMATIC REVIEW REGISTRATION
PROSPERO CRD42014007450 .
Topics: Cost-Benefit Analysis; Data Collection; Databases, Factual; Hospitals; Humans; Quality Indicators, Health Care
PubMed: 25875828
DOI: 10.1186/s13643-015-0013-7 -
Value in Health : the Journal of the... May 2023
Topics: Humans; Data Collection; Patient Reported Outcome Measures
PubMed: 36990208
DOI: 10.1016/j.jval.2023.03.010 -
International Journal of Medical... Jan 2015Usage of data from electronic health records (EHRs) in clinical research is increasing, but there is little empirical knowledge of the data needed to support multiple...
BACKGROUND AND OBJECTIVE
Usage of data from electronic health records (EHRs) in clinical research is increasing, but there is little empirical knowledge of the data needed to support multiple types of research these sources support. This study seeks to characterize the types and patterns of data usage from EHRs for clinical research.
MATERIALS AND METHODS
We analyzed the data requirements of over 100 retrospective studies by mapping the selection criteria and study variables to data elements of two standard data dictionaries, one from the healthcare domain and the other from the clinical research domain. We also contacted study authors to validate our results.
RESULTS
The majority of variables mapped to one or to both of the two dictionaries. Studies used an average of 4.46 (range 1-12) data element types in the selection criteria and 6.44 (range 1-15) in the study variables. The most frequently used items (e.g., procedure, condition, medication) are often available in coded form in EHRs. Study criteria were frequently complex, with 49 of 104 studies involving relationships between data elements and 22 of the studies using aggregate operations for data variables. Author responses supported these findings.
DISCUSSION AND CONCLUSION
The high proportion of mapped data elements demonstrates the significant potential for clinical data warehousing to facilitate clinical research. Unmapped data elements illustrate the difficulty in developing a complete data dictionary.
Topics: Biomedical Research; Data Collection; Database Management Systems; Delivery of Health Care; Electronic Health Records; Humans; Information Storage and Retrieval; Patient Selection; Research Design; Retrospective Studies
PubMed: 25453276
DOI: 10.1016/j.ijmedinf.2014.10.004 -
Journal of Medical Internet Research Nov 2023As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these...
As wearable devices, which allow individuals to track and self-manage their health, become more ubiquitous, the opportunities are growing for researchers to use these sensors within interventions and for data collection. They offer access to data that are captured continuously, passively, and pragmatically with minimal user burden, providing huge advantages for health research. However, the growth in their use must be coupled with consideration of their potential limitations, in particular, digital inclusion, data availability, privacy, ethics of third-party involvement, data quality, and potential for adverse consequences. In this paper, we discuss these issues and strategies used to prevent or mitigate them and recommendations for researchers using wearables as part of interventions or for data collection.
Topics: Humans; Data Accuracy; Data Collection; Privacy; Research Personnel; Wearable Electronic Devices
PubMed: 37988147
DOI: 10.2196/52444