Estimate of the incidence of PANDAS and PANS in 3 primary care populations.

Objective: Pediatric Autoimmune Neuropsychiatric Disorder Associated with Streptococcal infection (PANDAS) and Pediatric Acute-Onset Neuropsychiatric syndrome (PANS) are presumed autoimmune complications of infection or other instigating events. To determine the incidence of these disorders, we performed a retrospective review for the years 2017-2019 at three academic medical centers. Methods: We identified the population of children receiving well-child care at each institution. Potential cases of PANS and PANDAS were identified by including children age 3-12 years at the time they received one of five new diagnoses: avoidant/restrictive food intake disorder, other specified eating disorder, separation anxiety disorder of childhood, obsessive-compulsive disorder, or other specified disorders involving an immune mechanism, not elsewhere classified. Tic disorders was not used as a diagnostic code to identify cases. Data were abstracted; cases were classified as PANDAS or PANS if standard definitions were met. Results: The combined study population consisted of 95,498 individuals. The majority were non-Hispanic Caucasian (85%), 48% were female and the mean age was 7.1 (SD 3.1) years. Of 357 potential cases, there were 13 actual cases [mean age was 6.0 (SD 1.8) years, 46% female and 100% non-Hispanic Caucasian]. The estimated annual incidence of PANDAS/PANS was 1/11,765 for children between 3 and 12 years with some variation between different geographic areas. Conclusion: Our results indicate that PANDAS/PANS is a rare disorder with substantial heterogeneity across geography and time. A prospective investigation of the same question is warranted.

Telemedicine Use across Medical Specialties and Diagnoses.

BACKGROUND: The COVID-19 (coronavirus disease 2019) pandemic rapidly expanded telemedicine scale and scope. As telemedicine becomes routine, understanding how specialty and diagnosis combine with demographics to impact telemedicine use will aid in addressing its current limitations. OBJECTIVES: To analyze the relationship between medical specialty, diagnosis, and telemedicine use, and their interplay with patient demographics in determining telemedicine usage patterns. METHODS: We extracted encounter and patient data of all adults who scheduled outpatient visits from June 1, 2020 to June 30, 2021 from the electronic health record of an integrated academic health system encompassing a broad range of subspecialties. Extracted variables included medical specialty, primary visit diagnosis, visit modality (video, audio, or in-person), and patient age, sex, self-reported race/ethnicity and 2013 rural-urban continuum code. Six specialties (General Surgery, Family Medicine, Gastroenterology, Oncology, General Internal Medicine, and Psychiatry) ranging from the lowest to the highest quartile of telemedicine use (video and audio) were chosen for analysis. Relative proportions of video, audio, and in-person modalities were compared. We examined diagnoses associated with the most and least frequent telemedicine use within each specialty. Finally, we analyzed associations between patient characteristics and telemedicine modality (video vs. audio/in-person, and video/audio vs. in-person) using a mixed-effects logistic regression model. RESULTS: A total of 2,494,296 encounters occurred during the study period, representing 420,876 unique patients (mean age: 44 years, standard deviation: 24 years, 54% female). Medical diagnoses requiring physical examination or minor procedures were more likely to be conducted in-person. Rural patients were more likely than urban patients to use video telemedicine in General Surgery and Gastroenterology and less likely to use video for all other specialties. Within most specialties, male patients and patients of nonwhite race were overall less likely to use video modality and video/audio telemedicine. In Psychiatry, members of several demographic groups used video telemedicine more commonly than expected, while in other specialties, members of these groups tended to use less telemedicine overall. CONCLUSION: Medical diagnoses requiring physical examination or minor procedures are more likely to be conducted in-person. Patient characteristics (age, sex, rural vs. urban, race/ethnicity) affect video and video/audio telemedicine use differently depending on medical specialty. These factors contribute to a unique clinical scenario which impacts perceived usefulness and accessibility of telemedicine to providers and patients, and are likely to impact rates of telemedicine adoption.

Disparities in Telemedicine Access: A Cross-Sectional Study of a Newly Established Infrastructure during the COVID-19 Pandemic.

BACKGROUND: The COVID-19 pandemic led to dramatic increases in telemedicine use to provide outpatient care without in-person contact risks. Telemedicine increases options for health care access, but a "digital divide" of disparate access may prevent certain populations from realizing the benefits of telemedicine. OBJECTIVES: The study aimed to understand telemedicine utilization patterns after a widespread deployment to identify potential disparities exacerbated by expanded telemedicine usage. METHODS: We performed a cross-sectional retrospective analysis of adults who scheduled outpatient visits between June 1, 2020 and August 31, 2020 at a single-integrated academic health system encompassing a broad range of subspecialties and a large geographic region in the Upper Midwest, during a period of time after the initial surge of COVID-19 when most standard clinical services had resumed. At the beginning of this study period, approximately 72% of provider visits were telemedicine visits. The primary study outcome was whether a patient had one or more video-based visits, compared with audio-only (telephone) visits or in-person visits only. The secondary outcome was whether a patient had any telemedicine visits (video-based or audio-only), compared with in-person visits only. RESULTS: A total of 197,076 individuals were eligible (average age = 46 years, 56% females). Increasing age, rural status, Asian or Black/African American race, Hispanic ethnicity, and self-pay/uninsured status were significantly negatively associated with having a video visit. Digital literacy, measured by patient portal activation status, was significantly positively associated with having a video visit, as were Medicaid or Medicare as payer and American Indian/Alaskan Native race. CONCLUSION: Our findings reinforce previous evidence that older age, rural status, lower socioeconomic status, Asian race, Black/African American race, and Hispanic/Latino ethnicity are associated with lower rates of video-based telemedicine use. Health systems and policies should seek to mitigate such barriers to telemedicine when possible, with efforts such as digital literacy outreach and equitable distribution of telemedicine infrastructure.

Predictors of colorectal cancer screening variation among primary-care providers and clinics.

OBJECTIVES: Colorectal cancer (CRC) screening is underutilized. To effect change, we must understand reasons for underuse at multiple levels of the health-care system. We evaluated patient, provider, and clinic factors that predict variation in CRC screening among primary-care clinics and primary-care providers (PCPs). METHODS: We analyzed electronic medical record (EMR) data for 34,319 adults eligible for CRC screening, 19 clinics, and 97 PCPs in a large, academic physician group. Detailed data on potential patient, provider, and clinic predictors of CRC screening were obtained from the EMR. PCP perceptions of CRC screening barriers were measured via survey. The outcome was completion of CRC screening at the patient level. Multivariate logistic regression with clustering on clinics obtained adjusted odds ratios and 95% confidence intervals for potential predictors of CRC screening at each level. RESULTS: Seventy-one percentage of patients completed CRC screening. Variation in screening rates was seen among clinics (51-80%) and among PCPs (51-82%). Significant predictors of completing CRC screening were identified at all levels: patient (older age, white race, being married, primarily English-speaking, having commercial insurance plans vs. Medicare or Medicaid, and higher health-care resource utilization), provider (larger panel size of patients eligible for CRC screening), and clinic (hospital-owned, shorter distance to nearest optical colonoscopy center). CONCLUSIONS: Variation in CRC screening exists among primary-care clinics and providers within a single clinic. Predictors of variation can be identified at patient, provider, and clinic levels. Quality improvement interventions addressing CRC screening need to be directed at multiple levels of the health-care system.

Minority status and diabetes screening in an ambulatory population.

OBJECTIVE: Ethnicity has been identified as a risk factor not only for having type 2 diabetes but for increased morbidity and mortality with the disease. Current American Diabetes Association (ADA) guidelines advocate screening high-risk minorities for diabetes. This study investigates the effect of minority status on diabetes screening practices in an ambulatory, insured population presenting for yearly health care. RESEARCH DESIGN AND METHODS: This is a retrospective population-based study of patients in a large, Midwestern, academic group practice. Included patients were insured, had ≥1 primary care visit yearly from 2003 to 2007, and did not have diabetes but met ADA criteria for screening. Odds ratios (ORs), 95% confidence intervals (CI), and predicted probabilities were calculated to determine the relationship between screening with fasting glucose, glucose tolerance test, or hemoglobin A(1c) and patient and visit characteristics. RESULTS: Of the 15,557 eligible patients, 607 (4%) were of high-risk ethnicity, 61% were female, and 86% were ≥45 years of age. Of the eight high-risk factors studied, after adjustment, ethnicity was the only factor not associated with higher diabetes screening (OR = 0.90 [95% CI 0.76-1.08]) despite more primary care visits in this group. In overweight patients <45 years, where screening eligibility is based on having an additional risk factor, high-risk ethnicity (OR 1.01 [0.70-1.44]) was not associated with increased screening frequency. CONCLUSIONS: In an insured population presenting for routine care, high-risk minority status did not independently lead to diabetes screening as recommended by ADA guidelines. Factors other than insurance or access to care appear to affect minority-preventive care.

Effect of patient selection method on provider group performance estimates.

BACKGROUND: Performance measurement at the provider group level is increasingly advocated, but different methods for selecting patients when calculating provider group performance have received little evaluation. OBJECTIVE: We compared 2 currently used methods according to characteristics of the patients selected and impact on performance estimates. RESEARCH DESIGN, SUBJECTS, AND MEASURES: We analyzed Medicare claims data for fee-for-service beneficiaries with diabetes ever seen at an academic multispeciality physician group in 2003 to 2004. We examined sample size, sociodemographics, clinical characteristics, and receipt of recommended diabetes monitoring in 2004 for the groups of patients selected using 2 methods implemented in large-scale performance initiatives: the Plurality Provider Algorithm and the Diabetes Care Home method. We examined differences among discordantly assigned patients to determine evidence for differential selection regarding these measures. RESULTS: Fewer patients were selected under the Diabetes Care Home method (n=3558) than the Plurality Provider Algorithm (n=4859). Compared with the Plurality Provider Algorithm, the Diabetes Care Home method preferentially selected patients who were female, not entitled because of disability, older, more likely to have hypertension, and less likely to have kidney disease and peripheral vascular disease, and had lower levels of predicted utilization. Diabetes performance was higher under Diabetes Care Home method, with 67% versus 58% receiving >1 A1c tests, 70% versus 65% receiving ≥1 low-density lipoprotein (LDL) test, and 38% versus 37% receiving an eye examination. CONCLUSIONS: The method used to select patients when calculating provider group performance may affect patient case mix and estimated performance levels, and warrants careful consideration when comparing performance estimates.

Analysis of guidelines for screening diabetes mellitus in an ambulatory population.

OBJECTIVES: To compare the case-finding ability of current national guidelines for screening diabetes mellitus and characterize factors that affect testing practices in an ambulatory population. PATIENTS AND METHODS: In this retrospective analysis, we reviewed a database of 46,991 nondiabetic patients aged 20 years and older who were seen at a large Midwestern academic physician practice from January 1, 2005, through December 31, 2007. Patients were included in the sample if they were currently being treated by the physician group according to Wisconsin Collaborative for Healthcare Quality criteria. Pregnant patients, diabetic patients, and patients who died during the study years were excluded. The prevalence of patients who met the American Diabetes Association (ADA) and/or US Preventive Services Task Force (USPSTF) criteria for diabetes screening, percentage of these patients screened, and number of new diabetes diagnoses per guideline were evaluated. Screening rates were assessed by number of high-risk factors, primary care specialty, and insurance status. RESULTS: A total of 33,823 (72.0%) of 46,991 patients met either the ADA or the USPSTF screening criteria, and 28,842 (85.3%) of the eligible patients were tested. More patients met the ADA criteria than the 2008 USPSTF criteria (30,790 [65.5%] vs 12,054 [25.6%]), and the 2008 USPSTF guidelines resulted in 460 fewer diagnoses of diabetes (33.1%). By single high-risk factor, prediabetes (15.8%) and polycystic ovarian syndrome (12.6%) produced the highest rates of diagnosis. The number of ADA high-risk factors predicted diabetes, with 6 (23%) of 26 patients with 6 risk factors diagnosed as having diabetes. Uninsured patients were tested significantly less often than insured patients (54.9% vs 85.4%). CONCLUSION: Compared with the ADA recommendations, the new USPSTF guidelines result in a lower number of patients eligible for screening and decrease case finding significantly. The number and type of risk factors predict diabetes, and lack of health insurance decreases testing.