ABSTRACT
Introduction: While an estimated 50% of adult women experience urinary incontinence (UI), the majority will never receive treatment. Most studies of incontinence care delivery have been limited to administrative (billing) data following treatment. Much less is known about earlier steps in evaluation, including primary care intentions to refer to specialty care. Objective: To better understand the gaps and barriers to receiving care, we examined referral patterns from primary care providers for patients with new diagnoses of urinary incontinence between 2018-2020 and the extent to which such referrals changed during the COVID-19 pandemic. Methods: Electronic health records (EHR) from 24 primary care practices within a single academic medical system were queried to identify a cohort of adult (18 - 90-year-old) female patients first diagnosed with urinary incontinence during primary care (family or general internal medicine) outpatient visits between January 2018 and December 2020. Demographics were determined from appropriate EHR fields, and diagnoses pulled from problem lists, past medical histories, and office visit diagnosis fields. EHR referral fields were utilized to ascertain referral dates, types, and associated diagnoses. Electronic prescription fields were used to record treatment information including medication class, name, and prescription dates. Subjects were excluded if there was EHR evidence of urinary tract infection at diagnosis, UI in the prior year based on diagnosis or medication usage (anticholinergic, B3 agonists), or presence of conditions for which incontinence management might differ substantially in the prior year (pregnancy, spinal cord injury). Referrals to specialty physicians (urology/urogynecology) and pelvic floor physical therapy (PFPT) were examined for the year after UI diagnosis. Logistic regression was then used to assess for associations between referrals and patient demographics, comorbidity, and diagnosis dates (pre-vs during-COVID-19). Results: The study identified 514 women with a newly diagnosed urinary incontinence diagnosis (Table 1). In the year following UI diagnosis, 31.91% were referred to specialty care for management -29.0% to urology/urogynecology and 3.5% to pelvic floor physical therapists. Women diagnosed with UI during the COVID-19 pandemic, starting January 2020, were less likely to be referred with an odds ratio of 0.29 (95% CI 0.19, 0.45) compared to those diagnosed before (Table 2). There was no association of referrals with patient age, race, or number of comorbidities (Elixhauser Comorbidity Index), but confidence intervals were wide. Patterns were similar for models that examined specialty physician or PFPT referral separately. Conclusions: Less than 1 in 3 women were referred to specialty care for UI by their primary care provider with less than 1 in 25 referred to PFPT. There was a significant decrease in likelihood of referrals during 2020 suggesting that the COVID-19 pandemic interfered with UI patients receiving quality care. Future studies aiming to improve incontinence care should examine other aspects of nonsurgical UI care delivery, including barriers to behavioral self-management, medication use, and completion of specialty referrals.
ABSTRACT
Background: CDC recommended increased social distancing in order to reduce virus transmission during the COVID-19 pandemic. This included physical isolation for older adults at elevated risk for COVID-19 due to age and chronic medical conditions such as lung disease. While social distancing is effective at reducing the spread of COVID-19, the secondary negative impact of isolation and reduction in social resources is likely to impact vulnerable older adults with medical risk factors such as a history of heavy smoking and chronic lung disease. In this study we will examine the impact of pandemic on longitudinal change in SF-36 Mental Component Score. Methods: COPDGene is a longitudinal study of current and former smokers with at least a 10 pack-year smoking history. The study has included 3 visits (Baseline, Phase 2 at 5-years, and Phase 3 at 10-years), during which quality of life was assessed using the SF-36. Phase 3 in-person visits were interrupted by COVID-19 in March 2020. SF-36 was included in virtual visits conducted from March-October of 2020 using telephony and online surveys. We examined vectors of change in MCS across the 3 timepoints employing group-based trajectory models (SAS Proc Traj) to identify group membership and the probability of the observed MCS given group memberships. Each model used baseline and 5-year data in the same way. The first model used phase 3 data collected in-person (PRE) and the second used phase 3 data collected during the pandemic (DURING). Trajectory membership was compared using demographic profiles of participants pre- vs post-pandemic. Results: Figure 1 shows the trajectories identified for each group. The PRE group (n=2,242) included 4 trajectories: 1) 68% began high (MCS=56.1) and continued to be high across 10 years, 2) 15% began low (MCS=38.8) and improved, 3) 11.0% began high (MCS=51.4) and decreased and 4) 6.4% began low (MCS=30.0%) and remained low. The DURING group included three trajectories with the majority of observations beginning high (MCS=54.5) and remaining high, and two other groups that duplicated the pattern of PRE groups 2 and 3. The most consistent predictors of group membership in both PRE and DURING were age, MMRC and 6 minute walk at both baseline and year 10 follow-up (p<0.0001 in all cases). Conclusions: Trajectories of change in mental-health related quality of life do not reflect a large negative impact of the COVID- 19 pandemic in this large sample of older current and former smokers with and without COPD.
ABSTRACT
Rationale: COPD360social is a global online community forum including ∼35,000 members from the United States, serving a critical role in supporting the community, promoting patient-provider relationships, and addressing specific needs and risks faced by individuals with COPD. This qualitative analysis aimed to inform the COPDF programs addressing community needs during the COVID-19 pandemic. Methods: 5,180 unique posts were extracted from COPD360social between 1/1/2020 and 9/30/2020 utilizing Beautiful Soup, a Python library, and used to identify discussion themes and word clouds. Extracted posts were linked to user information such as Type (Medical Professional, Family Member, Friend, Patient), Country, and State/Region of residence (if voluntarily provided) using SAS 9.4. Posts by Medical Professionals and the COPDF were removed (N=4,727 remaining). A subset of posts that contained at least one COVID-19 related keyword (“covid”, “coronavirus”, “pandemic”, “C-19”, and 10 variations) was created (N=316). Posts from only within the United States were kept and manually scanned for coronavirus relevance, and an additional 3 posts were removed for unrelated content (N=268). Data cleaning and word tokenization following standard procedures (removal of stop words, cases, punctuation, non-letters, and extra characters) was performed in Python. The Natural Language ToolKit (NLTK) library in Python was used to create lists of unigrams and bigrams. The top 200 bigrams were used in the creation of word clouds based on data from (A) 268 COVID-19-only posts and (B) 4,031 US posts. Themes were identified based on the top 50 n-grams and calculated from word frequencies. Results: Themes identified were COPD, symptoms, smoking, positive attitudes, worry, COVID-19 lifestyle, and COVID-19 contact/testing. Many discussions included data sharing keywords representing test results, medications, and physician feedback. Others described general feelings (“feeling good”) but also detailed specific symptoms associated with COPD or COVID-19. Many community members encouraged others to stay safe, wear masks, social distance, and disinfect, while other members expressed deep concerns and fear. Users appeared grateful for the support they have on COPD360social. Word clouds (Figure 1) demonstrate the similarities and differences in discussion themes among users. Conclusions: COPD360social discussions regarding COVID-19 reflect community fear and impact on daily lives but also highlight the positivity spread among users of this forum confirming its role in supporting one of the most vulnerable populations that is susceptible to negative outcomes from COVID-19. This powerful analytical approach will inform future COPDF community support programming and guide directions of future COPDF community support for the population.