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Optimising medication data collection in a large-scale clinical trial.

ABSTRACT: OBJECTIVE:Pharmaceuticals play an important role in clinical care. However, in community-based research, medication data are commonly collected as unstructured free-text, which is prohibitively expensive to code for large-scale studies. The ASPirin in Reducing Events in the Elderly (ASPREE) study developed a two-pronged framework to collect structured medication data for 19,114 individuals. ASPREE provides an opportunity to determine whether medication data can be cost-effectively collected and coded, en masse from the community using this framework. METHODS:The ASPREE framework of type-to-search box with automated coding and linked free text entry was compared to traditional method of free-text only collection and post hoc coding. Reported medications were classified according to their method of collection and analysed by Anatomical Therapeutic Chemical (ATC) group. Relative cost of collecting medications was determined by calculating the time required for database set up and medication coding. RESULTS:Overall, 122,910 participant structured medication reports were entered using the type-to-search box and 5,983 were entered as free-text. Free-text data contributed 211 unique medications not present in the type-to-search box. Spelling errors and unnecessary provision of additional information were among the top reasons why medications were reported as free-text. The cost per medication using the ASPREE method was approximately USD $0.03 compared with USD $0.20 per medication for the traditional method. CONCLUSION:Implementation of this two-pronged framework is a cost-effective alternative to free-text only data collection in community-based research. Higher initial set-up costs of this combined method are justified by long term cost effectiveness and the scientific potential for analysis and discovery gained through collection of detailed, structured medication data.

SUBMITTER: Lockery JE

PROVIDER: S-EPMC6934269 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Optimising medication data collection in a large-scale clinical trial.

Lockery Jessica E JE Rigby Jason J Collyer Taya A TA Stewart Ashley C AC Woods Robyn L RL McNeil John J JJ Reid Christopher M CM Ernst Michael E ME

PloS one 20191227 12

<h4>Objective</h4>Pharmaceuticals play an important role in clinical care. However, in community-based research, medication data are commonly collected as unstructured free-text, which is prohibitively expensive to code for large-scale studies. The ASPirin in Reducing Events in the Elderly (ASPREE) study developed a two-pronged framework to collect structured medication data for 19,114 individuals. ASPREE provides an opportunity to determine whether medication data can be cost-effectively collec ...[more]

PMID: 31881040

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Project description:It is estimated that 190 million individuals are at risk of blindness from trachoma, and that control by mass drug administration (MDA) is reducing this risk in many populations. Programs are monitored using prevalence of follicular trachoma disease (TF) in children. However, as programs progress to low prevalence there are challenges interpreting this indirect measure of infection. PCR and sero-surveillance are being considered as complementary tools to monitor low-level transmission, but there are questions on how they can be most effectively used. We use a previously-published, mathematical model to explore the dynamic relationship between TF and PCR throughout a control program and a sero-catalytic model to evaluate the utility of two cross-sectional sero-surveys for estimating sero-conversion rates. The simulations show that whilst PCR is more sensitive than TF at detecting infection, the probability of detecting at least one positive individual declines during an MDA program more quickly for PCR than for TF (for the same sample size). Towards the end of a program there is a moderate chance of a random sample showing both low PCR prevalence and higher TF prevalence, which may contribute to the lack of correlation observed in epidemiological studies. We also show that conducting two cross-sectional sero-surveys 10 years apart can provide more precise and accurate estimation of epidemiological parameters than a single survey, supporting previous findings that whilst serology holds great promise, multiple cross-sections from the same community are needed to generate the most valuable information about transmission. These results highlight that the quantitative dynamics of infection and disease should be included alongside the many logistical and practical factors to be considered in designing a monitoring and evaluation strategy at the operational research level, in order to help subsequently inform data collection for individual country programs. Whilst our simulations provide some insight, they also highlight that some level of longitudinal, individual-level data on reinfection and disease may be needed to monitor elimination progress.

Project description:BackgroundLarge cross-sectional household surveys are common for measuring indicators of neglected tropical disease control programs. As an alternative to standard paper-based data collection, we utilized novel paperless technology to collect data electronically from over 12,000 households in Ethiopia.MethodologyWe conducted a needs assessment to design an Android-based electronic data collection and management system. We then evaluated the system by reporting results of a pilot trial and from comparisons of two, large-scale surveys; one with traditional paper questionnaires and the other with tablet computers, including accuracy, person-time days, and costs incurred.Principle findingsThe electronic data collection system met core functions in household surveys and overcame constraints identified in the needs assessment. Pilot data recorders took 264 (standard deviation (SD) 152 sec) and 260 sec (SD 122 sec) per person registered to complete household surveys using paper and tablets, respectively (P = 0.77). Data recorders felt a lack of connection with the interviewee during the first days using electronic devices, but preferred to collect data electronically in future surveys. Electronic data collection saved time by giving results immediately, obviating the need for double data entry and cross-correcting. The proportion of identified data entry errors in disease classification did not differ between the two data collection methods. Geographic coordinates collected using the tablets were more accurate than coordinates transcribed on a paper form. Costs of the equipment required for electronic data collection was approximately the same cost incurred for data entry of questionnaires, whereas repeated use of the electronic equipment may increase cost savings.Conclusions/significanceConducting a needs assessment and pilot testing allowed the design to specifically match the functionality required for surveys. Electronic data collection using an Android-based technology was suitable for a large-scale health survey, saved time, provided more accurate geo-coordinates, and was preferred by recorders over standard paper-based questionnaires.

Project description:BackgroundPatient characteristics are associated with adherence, which has implications for planning clinical research or designing payment systems that reward superior outcomes. It is unclear to what extent clinician efforts to improve adherence can attenuate these associations.MethodsTo identify factors predicting visit and medication adherence in settings designed to optimize adherence, we did a retrospective analysis of participants in the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT). ALLHAT recruited participants at 632 sites in North America, Puerto Rico, and the U.S. Virgin Islands for random assignment to antihypertensive treatment with amlodipine, chlorthalidone, or lisinopril. Site investigators reported clinic characteristics at the time they applied to participate in the study and research coordinators used standardized methods to measure patient characteristics. We defined adequate visit adherence as attending at least 80 % of scheduled visits; adequate medication adherence was defined as taking 80 % or more of the randomly assigned medication at all study visits.ResultsThe 31,250 ALLHAT participants eligible for the visit adherence analysis attended 78.5 % of scheduled study visits; 68.9 % attended more than 80 % of scheduled visits. Clinic setting was predictive of both forms of adherence; adherence was worst at private clinics; clinics that enrolled more study participants had superior adherence. Adjusting for clinic characteristics and clinical factors, women, younger participants, Blacks and smokers were less likely to have adequate visit adherence. Among the 28,967 participants eligible for the medication adherence analysis, 21,261 (73.4 %) reported adequate medication adherence. In adjusted analyses, younger and less educated participants, Blacks, and smokers were less likely to report adequate adherence.ConclusionsParticipant demographics were associated with adherence despite strenuous efforts to optimize adherence. Our results could inform decisions by researchers planning trials and policymakers designing payment systems.Trial registrationNCT00000542 . Registered 27 October 1999.

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Optimising medication data collection in a large-scale clinical trial.

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Optimising medication data collection in a large-scale clinical trial.

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