Dataset Information

Methodologic Considerations in Calculating and Analyzing Proportion of Time Covered as a Measure of Longitudinal Cancer Screening Adherence.

ABSTRACT: BACKGROUND:Proportion of time covered (PTC, or "covered time") is a longitudinal measure of adherence to preventive health services, the use of which has increased in recent years. This measure is helpful for evaluating the success of delivering screening interventions over time. However, there are challenges and nuances in computing and interpreting PTC. METHODS:In this manuscript, we describe some desired properties of PTC measures, challenges in achieving those, and potential solutions using hypothetical examples. RESULTS:We propose a modified PTC measure (mPTC) to complement the standard, existing PTC measure. The mPTC measure focuses on screening completion rather than initiation when a screening modality requires more than one step; is affected less by loss to follow-up, death, or cancer during covered time than the standard PTC measure; and is not sensitive to screening episode results. We propose weighting strategies to ensure that the average PTC and mPTC are more heavily influenced by individuals who were observed for longer and are thus more informative. We further describe how PTC and mPTC measures can incorporate test indication to focus specifically on screening. CONCLUSIONS:We recommend that studies of covered time present ample descriptive information, calculate both PTC and mPTC, describe how symptoms and indication are handled, and present multiple complementary measures, such as the proportion never screened and the proportion in need of screening. IMPACT:Common approaches, terminology, and reporting practices for covered time measures have the potential to improve the study of longitudinal cancer screening adherence.

SUBMITTER: Chubak J

PROVIDER: S-EPMC7415680 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Methodologic Considerations in Calculating and Analyzing Proportion of Time Covered as a Measure of Longitudinal Cancer Screening Adherence.

Chubak Jessica J Anderson Melissa L ML Cook Andrea J AJ Murphy Caitlin C CC Jackson Michael L ML Green Beverly B BB

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology 20200528 8

<h4>Background</h4>Proportion of time covered (PTC, or "covered time") is a longitudinal measure of adherence to preventive health services, the use of which has increased in recent years. This measure is helpful for evaluating the success of delivering screening interventions over time. However, there are challenges and nuances in computing and interpreting PTC.<h4>Methods</h4>In this manuscript, we describe some desired properties of PTC measures, challenges in achieving those, and potential s ...[more]

PMID: 32467346

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Project description:OBJECTIVE:Observational data facilitate examination of treatment-effect heterogeneity, but the risk of bias is substantial. The present study was undertaken to highlight methodologic considerations through an analysis of whether smoking affects response to tumor necrosis factor inhibitors (TNFi) in axial spondyloarthritis (SpA). METHODS:We used longitudinal data from the British Society for Rheumatology Biologics Register for Ankylosing Spondylitis. Participants fulfilling the Assessment of SpondyloArthritis international Society criteria for axial SpA who started their first TNFi were eligible for analysis. In comparing the impact of smoking status, weighted generalized estimating equations were used to examine changes in several continuous outcome measures, including the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and the Ankylosing Spondylitis Disease Activity Score (ASDAS). Inverse probability weights were used to account for differences in baseline covariates and excluded participants. We separately assessed response in the first 3 months to account for nonrandom dropout. RESULTS:For 840 participants who started on TNFi, 1,641 assessments from 627 individuals were analyzed (69% male, mean age 46 years). A total of 33% were current smokers and 30% ex-smokers. Ex-smokers and current smokers had worse disease than never smokers at baseline. Accounting for these differences, response did not differ according to smoking status. Compared to never smokers, ex-smokers (? = -0.6, 95% confidence interval [95% CI] -1.4, 0.3) and current smokers (? = -0.4, 95% CI -1.1, 0.4) had a similar response according to the BASDAI and ASDAS (ex-smokers ? = -0.1, 95% CI -0.5, 0.3; current smokers ? = -0.01, 95% CI -0.4, 0.4) at 3 months. CONCLUSION:TNFi response did not differ according to baseline smoking status in this UK cohort. Conflicting results from previous studies were likely due to methodologic differences. This analysis highlights potential sources of bias that should be addressed in future studies.

Project description:We established a clinical pharmacist adherence management system (CPAMS) led by clinical pharmacists to examine whether denosumab adherence could be improved. The results showed that CPAMS could effectively improve adherence to denosumab and the treatment of osteoporosis. However, this effect weakened during the spread of infectious diseases such as COVID-19.PurposeDenosumab is currently one of the drugs that can effectively reduce the risk of clinical fracture. However, as a drug requiring long-term subcutaneous injection, patient adherence to denosumab is the most important factor affecting its therapeutic efficacy. Therefore, we established a clinical pharmacist adherence management system (CPAMS) led by clinical pharmacists and examined whether denosumab adherence could be improved.MethodsData were collected from patients receiving denosumab in our hospital between March 2021 and May 2022. The patients who participated in the CPAMS were in the intervention group, and the rest were in the control group. We analysed the proportion of days covered (PDC) value of denosumab, distribution of subsequent visits, and proportion of patients who continued participating during the normal and coronavirus (COVID-19) periods.ResultsEighty-five patients were enrolled in this retrospective study: 32 in the intervention group and 53 in the control group. The PDC values were significantly higher in the intervention group (0.9875, 0.9025-1) than in the control group (0.5, 0.5-0.5) after 1 year. The subsequent visit rate in the intervention group was 93.80%. However, none of the patients in the control group returned. In the intervention group, the ratio of timely to delayed subsequent visits was 11:19. After the COVID-19 pandemic, the PDC value of the intervention group (0.957, 0.5-1) was lower than that before COVID-19, and the ratio of timely to delayed subsequent visits was 9:13.ConclusionsClinical pharmacist-led CPAMS could effectively improve adherence to denosumab and the treatment of osteoporosis.

Project description:BackgroundThe proportion of days covered (PDC) is used to estimate medication adherence by looking at the proportion of days in which a person has access to the medication, over a given period of interest. This study aimed to adapt the PDC algorithm to allow for plausible assumptions about prescription refill behaviour when applied to data from online pharmacy suppliers.MethodsThree PDC algorithms, the conventional approach (PDC1) and two alternative approaches (PDC2 and PDC3), were used to estimate adherence in a real-world dataset from an online pharmacy. Each algorithm has different denominators and increasing levels of complexity. PDC1, the conventional approach, is the total number of days between first dispensation and a defined end date. PDC2 counts the days until the end of supply date. PDC3 removes from the denominator specifically defined large gaps between refills, which could indicate legitimate reasons for treatment discontinuation. The distribution of the three PDCs across four different follow-up lengths was compared.ResultsThe dataset included people taking ACE inhibitors (n = 65,905), statins (n = 100,362), and/or thyroid hormones (n = 30,637). The proportion of people taking ACE inhibitors with PDC ≥ 0.8 was 50-74% for PDC1, 81-91% for PDC2, and 86-100% for PDC3 with values depending on drug and length of follow-up. Similar ranges were identified in people taking statins and thyroid hormones.ConclusionThese algorithms enable researchers and healthcare providers to assess pharmacy services and individual levels of adherence in real-world databases, particularly in settings where people may switch between different suppliers of medicines, meaning an individual supplier's data may show temporary but legitimate gaps in access to medication. Accurately identifying problems with adherence provides the foundation for opportunities to improve experience, adherence and outcomes and to reduce medicines wastage. Research with people taking medications and prescribers is required to validate the algorithms' assumptions.

Dataset Information

Methodologic Considerations in Calculating and Analyzing Proportion of Time Covered as a Measure of Longitudinal Cancer Screening Adherence.

Publications

Methodologic Considerations in Calculating and Analyzing Proportion of Time Covered as a Measure of Longitudinal Cancer Screening Adherence.

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