Post-COVID Conditions in US Primary Care: A PRIME Registry Comparison of Patients With COVID-19, Influenza-Like Illness, and Wellness Visits.

PURPOSE: COVID-19 is a condition that can lead to other chronic conditions. These conditions are frequently diagnosed in the primary care setting. We used a novel primary care registry to quantify the burden of post-COVID conditions among adult patients with a COVID-19 diagnosis across the United States. METHODS: We used the American Family Cohort, a national primary care registry, to identify study patients. After propensity score matching, we assessed the prevalence of 17 condition categories individually and cumulatively, comparing patients having COVID-19 in 2020-2021 with (1) historical control patients having influenza-like illness in 2018 and (2) contemporaneous control patients seen for wellness or preventive visits in 2020-2021. RESULTS: We identified 28,215 patients with a COVID-19 diagnosis and 235,953 historical control patients with influenza-like illness. The COVID-19 group had higher prevalences of breathing difficulties (4.2% vs 1.9%), type 2 diabetes (12.0% vs 10.2%), fatigue (3.9% vs 2.2%), and sleep disturbances (3.5% vs 2.4%). There were no differences, however, in the postdiagnosis monthly trend in cumulative morbidity between the COVID-19 patients (trend = 0.026; 95% CI, 0.025-0.027) and the patients with influenza-like illness (trend = 0.026; 95% CI, 0.023-0.027). Relative to contemporaneous wellness control patients, COVID-19 patients had higher prevalences of breathing difficulties and type 2 diabetes. CONCLUSIONS: Our findings show a moderate burden of post-COVID conditions in primary care, including breathing difficulties, fatigue, and sleep disturbances. Based on clinical registry data, the prevalence of post-COVID conditions in primary care practices is lower than that reported in subspecialty and hospital settings.

FAIR enough: Building an academic data ecosystem to make real-world data available for translational research.

The Stanford Population Health Sciences Data Ecosystem was created to facilitate the use of large datasets containing health records from hundreds of millions of individuals. This necessitated technical solutions optimized for an academic medical center to manage and share high-risk data at scale. Through collaboration with internal and external partners, we have built a Data Ecosystem to host, curate, and share data with hundreds of users in a secure and compliant manner. This platform has enabled us to host unique data assets and serve the needs of researchers across Stanford University, and the technology and approach were designed to be replicable and portable to other institutions. We have found, however, that though these technological advances are necessary, they are not sufficient. Challenges around making data Findable, Accessible, Interoperable, and Reusable remain. Our experience has demonstrated that there is a high demand for access to real-world data, and that if the appropriate tools and structures are in place, translational research can be advanced considerably. Together, technological solutions, management structures, and education to support researcher, data science, and community collaborations offer more impactful processes over the long-term for supporting translational research with real-world data.

Population health science as a unifying foundation for translational clinical and public health research.

Separated both in academics and practice since the Rockefeller Foundation effort to "liberate" public health from perceived subservience to clinical medicine a century ago, research in public health and clinical medicine have evolved separately. Today, translational research in population health science offers a means of fostering their convergence, with potentially great benefit to both domains. Although evidence that the two fields need not and should not be entirely distinct in their methods and goals has been accumulating for over a decade, the prodigious efforts of biomedical and social sciences over the past year to address the COVID-19 pandemic has placed this unifying approach to translational research in both fields in a new light. Specifically, the coalescence of clinical and population-level strategies to control disease and novel uses of population-level data and tools in research relating to the pandemic have illuminated a promising future for translational research. We exploit this unique window to re-examine how translational research is conducted and where it may be going. We first discuss the transformation that has transpired in the research firmament over the past two decades and the opportunities these changes afford. Next, we present some of the challenges-technical, cultural, legal, and ethical- that need attention if these opportunities are to be successfully exploited. Finally, we present some recommendations for addressing these challenges.

Advancing primary care with Artificial Intelligence and Machine Learning.

Primary care is the largest healthcare delivery platform in the US. Facing the Artificial Intelligence and Machine Learning technology (AI/ML) revolution, the primary care community would benefit from a roadmap revealing priority areas and opportunities for developing and integrating AI/ML-driven clinical tools. This article presents a framework that identifies five domains for AI/ML integration in primary care to support care delivery transformation and achieve the Quintuple Aims of the healthcare system. We concluded that primary care plays a critical role in developing, introducing, implementing, and monitoring AI/ML tools in healthcare and must not be overlooked as AI/ML transforms healthcare.

Potential Reporting Bias in Neuroimaging Studies of Sex Differences.

Numerous functional magnetic resonance imaging (fMRI) studies have reported sex differences. To empirically evaluate for evidence of excessive significance bias in this literature, we searched for published fMRI studies of human brain to evaluate sex differences, regardless of the topic investigated, in Medline and Scopus over 10 years. We analyzed the prevalence of conclusions in favor of sex differences and the correlation between study sample sizes and number of significant foci identified. In the absence of bias, larger studies (better powered) should identify a larger number of significant foci. Across 179 papers, median sample size was n = 32 (interquartile range 23-47.5). A median of 5 foci related to sex differences were reported (interquartile range, 2-9.5). Few articles (n = 2) had titles focused on no differences or on similarities (n = 3) between sexes. Overall, 158 papers (88%) reached "positive" conclusions in their abstract and presented some foci related to sex differences. There was no statistically significant relationship between sample size and the number of foci (-0.048% increase for every 10 participants, p = 0.63). The extremely high prevalence of "positive" results and the lack of the expected relationship between sample size and the number of discovered foci reflect probable reporting bias and excess significance bias in this literature.

Patient and provider perspectives on the development of personalized medicine: a mixed-methods approach.

While genetic testing gains adoption in specialty services such as oncology, neurology, and cardiology, use of genetic and genomic testing has yet to be adopted as widely in primary care. The purpose of this study is to identify and compare patient and primary care provider (PCP) expectations of genetics services in primary care. Patient and PCP perspectives were assessed through a mixed-method approach combining an online survey and semi-structured interviews in a primary care department of a large academic medical institution. A convenience sample of 100 adult primary care patients and 26 PCPs was gathered. The survey and interview questions focused on perceptions of genetic testing, experience with genetic testing, and expectations of genetic services in primary care. Patients felt that their PCP was knowledgeable about genetic testing and expected their PCP to be the first to recognize a need for genetic testing based on family history. Nonetheless, patients reported that PCPs rarely used family history information to discuss genetic risks or order testing. In contrast, PCPs felt uncertain about the clinical utility and scientific value of genetic testing. PCPs were concerned that genetic testing could cause anxiety, frustration, discrimination, and reduced insurability, and that there was unequal access to testing. PCPs described themselves as being "gatekeepers" to genetic testing but did not feel confident or have the desire to become experts in genetic testing. However, PCPs were open to increasing their working knowledge of genetic testing. Within this academic medical center, there is a gap between what patients expect and what primary care providers feel they are adequately prepared to provide in terms of genetic testing services.

Smarter hospital communication: secure smartphone text messaging improves provider satisfaction and perception of efficacy, workflow.

BACKGROUND: Though current hospital paging systems are neither efficient (callbacks disrupt workflow), nor secure (pagers are not Health Insurance Portability and Accountability Act [HIPAA]-compliant), they are routinely used to communicate patient information. Smartphone-based text messaging is a potentially more convenient and efficient mobile alternative; however, commercial cellular networks are also not secure. OBJECTIVE: To determine if augmenting one-way pagers with Medigram, a secure, HIPAA-compliant group messaging (HCGM) application for smartphones, could improve hospital team communication. DESIGN: Eight-week prospective, cluster-randomized, controlled trial SETTING: Stanford Hospital INTERVENTION: Three inpatient medicine teams used the HCGM application in addition to paging, while two inpatient medicine teams used paging only for intra-team communication. MEASUREMENTS: Baseline and post-study surveys were collected from 22 control and 41 HCGM team members. RESULTS: When compared with paging, HCGM was rated significantly (P < 0.05) more effective in: (1) allowing users to communicate thoughts clearly (P = 0.010) and efficiently (P = 0.009) and (2) integrating into workflow during rounds (P = 0.018) and patient discharge (P = 0.012). Overall satisfaction with HCGM was significantly higher (P = 0.003). 85% of HCGM team respondents said they would recommend using an HCGM system on the wards. CONCLUSIONS: Smartphone-based, HIPAA-compliant group messaging applications improve provider perception of in-hospital communication, while providing the information security that paging and commercial cellular networks do not.

Systematic review and meta-analysis of opioid antagonists for smoking cessation.

OBJECTIVES: This meta-analysis sought to evaluate the efficacy of opioid antagonists in promoting long-term smoking cessation. Post-treatment abstinence was examined as a secondary outcome and effects on withdrawal symptoms, craving and reduced consumption were also explored. DESIGN: The search strategy for this meta-analysis included clinical trials (published and unpublished data) in the Cochrane Tobacco Addiction Group Specialized Register and MEDLINE. PARTICIPANTS: Adult smokers. INTERVENTIONS: We included randomised trials comparing opioid antagonists to placebo or an alternative therapy for smoking cessation and reported data on abstinence for a minimum of 6 months. PRIMARY AND SECONDARY OUTCOME MEASURES: Outcomes included smoking abstinence at long-term follow-up (primary); abstinence at end of treatment (secondary); and effects on withdrawal, craving and smoking consumption (exploratory). RESULTS: 8 trials with a total of 1213 participants were included. Half the trials examined the benefit of adding naltrexone versus placebo to nicotine replacement therapy (NRT). There was no significant difference between naltrexone and placebo alone (relative risk (RR) 1.00; 95% CI 0.66 to 1.51) or as an adjunct to NRT (RR 0.95; 95% CI 0.70 to 1.30), with an overall pooled estimate of RR 0.97; 95% CI 0.76 to 1.24. Findings for naltrexone effects on withdrawal, craving and reduced smoking were equivocal. CONCLUSIONS: The findings indicate no beneficial effect of naltrexone alone or as an adjunct to NRT on short-term or long-term smoking abstinence. While further trials may narrow the confidence limits, they are unlikely to appreciably alter the conclusion.

Process of care compliance is associated with fewer diabetes complications.

OBJECTIVE: To examine the association between processes measures of diabetes care and time to progression for 4 diabetes complications: coronary artery disease (CAD), stroke, heart failure (HF), and renal disease (RD). STUDY DESIGN: This retrospective study followed outcomes from 2003 through 2009 in a cohort of 1797 employees with diabetes who worked for a large US manufacturer and were enrolled in the same health insurance plan. METHODS: Quality of care was measured by consensus standards for testing glycated hemoglobin, lipids, and microalbuminuria. Employees with diabetes who received all 3 measures of care in the baseline year (2003) were compared with those who received less complete testing. Cox proportional hazard regression models were used to assess potential associations between diabetes care and time to complications, controlling for potential confounders. RESULTS: Observed differences between the 2 groups in time to event were significant for 2 of the 4 complications: HF (hazard ratio [HR] = 0.39, 95% confidence interval [CI], 0.19-0.81; P = .0117) and RD (HR = 0.48, 95% CI, 0.24-0.95; P = .0339) and any of the 4 complications (HR = 0.66, 95% CI, 0.48-0.91; P = .0101). Differences in time to complication for CAD (HR = 0.70, 95% CI, 0.49-1.02; P = .0635) and stroke (HR = 0.63, 95% CI, 0.38-1.07; P = .0891) showed the same trend but were not significant. CONCLUSIONS: Employees with diabetes who received all 3 quality measures experienced fewer complications, risk-adjusting for other factors. These results provide support for the importance of care quality.

Contribution of health status and prevalent chronic disease to individual risk for workplace injury in the manufacturing environment.

OBJECTIVES: An 'information gap' has been identified regarding the effects of chronic disease on occupational injury risk. We investigated the association of ischaemic heart disease, hypertension, diabetes, depression and asthma with acute occupational injury in a cohort of manufacturing workers from 1 January 1997 through 31 December 2007. METHODS: We used administrative data on real-time injury, medical claims, workplace characteristics and demographics to examine this association. We employed a piecewise exponential model within an Andersen-Gill framework with a frailty term at the employee level to account for inclusion of multiple injuries for each employee, random effects at the employee level due to correlation among jobs held by an employee, and experience on the job as a covariate. RESULTS: One-third of employees had at least one of the diseases during the study period. After adjusting for potential confounders, presence of these diseases was associated with increased hazard of injury: heart disease (HR 1.23, 95% CI 1.11 to 1.36), diabetes (HR 1.17, 95% CI 1.08 to 1.27), depression (HR 1.25, 95% CI 1.12 to 1.38) and asthma (HR 1.14, 95% CI 1.02 to 1.287). Hypertension was not significantly associated with hazard of injury. Associations of chronic disease with injury risk were less evident for more serious reportable injuries; only depression and a summary health metric derived from claims remained significantly positive in this subset. CONCLUSIONS: Our results suggest that chronic heart disease, diabetes and depression confer an increased risk for acute occupational injury.

Potential reporting bias in fMRI studies of the brain.

BACKGROUND: Functional magnetic resonance imaging (fMRI) studies have reported multiple activation foci associated with a variety of conditions, stimuli or tasks. However, most of these studies used fewer than 40 participants. METHODOLOGY: After extracting data (number of subjects, condition studied, number of foci identified and threshold) from 94 brain fMRI meta-analyses (k = 1,788 unique datasets) published through December of 2011, we analyzed the correlation between individual study sample sizes and number of significant foci reported. We also performed an analysis where we evaluated each meta-analysis to test whether there was a correlation between the sample size of the meta-analysis and the number of foci that it had identified. Correlation coefficients were then combined across all meta-analyses to obtain a summary correlation coefficient with a fixed effects model and we combine correlation coefficients, using a Fisher's z transformation. PRINCIPAL FINDINGS: There was no correlation between sample size and the number of foci reported in single studies (r = 0.0050) but there was a strong correlation between sample size and number of foci in meta-analyses (r = 0.62, p<0.001). Only studies with sample sizes <45 identified larger (>40) numbers of foci and claimed as many discovered foci as studies with sample sizes ≥ 45, whereas meta-analyses yielded a limited number of foci relative to the yield that would be anticipated from smaller single studies. CONCLUSIONS: These results are consistent with possible reporting biases affecting small fMRI studies and suggest the need to promote standardized large-scale evidence in this field. It may also be that small studies may be analyzed and reported in ways that may generate a larger number of claimed foci or that small fMRI studies with inconclusive, null, or not very promising results may not be published at all.

A predictive model to identify hospitalized cancer patients at risk for 30-day mortality based on admission criteria via the electronic medical record.

BACKGROUND: This study sought to develop a predictive model for 30-day mortality in hospitalized cancer patients, by using admission information available through the electronic medical record. METHODS: Observational cohort study of 3062 patients admitted to the oncology service from August 1, 2008, to July 31, 2009. Matched numbers of patients were in the derivation and validation cohorts (1531 patients). Data were obtained on day 1 of admission and included demographic information, vital signs, and laboratory data. Survival data were obtained from the Social Security Death Index. RESULTS: The 30-day mortality rate of the derivation and validation samples were 9.5% and 9.7% respectively. Significant predictive variables in the multivariate analysis included age (P < .0001), assistance with activities of daily living (ADLs; P = .022), admission type (elective/emergency) (P = .059), oxygen use (P < .0001), and vital signs abnormalities including pulse oximetry (P = .0004), temperature (P = .017), and heart rate (P = .0002). A logistic regression model was developed to predict death within 30 days: Score = 18.2897 + 0.6013*(admit type) + 0.4518*(ADL) + 0.0325*(admit age) - 0.1458*(temperature) + 0.019*(heart rate) - 0.0983*(pulse oximetry) - 0.0123 (systolic blood pressure) + 0.8615*(O2 use). The largest sum of sensitivity (63%) and specificity (78%) was at -2.09 (area under the curve = -0.789). A total of 25.32% (100 of 395) of patients with a score above -2.09 died, whereas 4.31% (49 of 1136) of patients below -2.09 died. Sensitivity and positive predictive value in the derivation and validation samples compared favorably. CONCLUSIONS: Clinical factors available via the electronic medical record within 24 hours of hospital admission can be used to identify cancer patients at risk for 30-day mortality. These patients would benefit from discussion of preferences for care at the end of life.

Hearing effects from intermittent and continuous noise exposure in a study of Korean factory workers and firefighters.

BACKGROUND: South Korea and surrounding countries in East Asia are believed to have the highest proportion in the world of high frequency hearing loss due to occupational noise exposure, yet there has been limited information published in international journals, and limited information for control of noise in local workplaces beyond strategies from western countries. We exploit medical surveillance information from two worker groups to enhance local knowledge about noise-induced hearing loss and explore the possible importance of shift work to risk. METHODS: Four-years of hearing data were evaluated for 81 male farm machine factory workers and 371 male firefighters who had successfully completed a health examination and questionnaires for the duration of the study period. The averages of hearing thresholds at 2, 3, and 4 kHz were used as the primary end-point for comparison. Repeat measure analysis adjusted for age, exposure duration and smoking status was used to measure the difference in hearing threshold between the two groups. RESULTS: Noise levels were measured in the factory at a mean of 82 dBA, with a range of 66-97. No concurrent measurements were taken for the firefighters, but historic comparison values showed a wider range but a similar mean of 76-79 dBA. Although losses during follow-up were negligible, the factory workers had significantly (P < 0.0001) more hearing loss at the baseline of the study than the firefighters in both ears at 2, 3, and 4 kHz, adjusted for age, duration of employment and smoking status. Among those with 10 years of employment, mean losses at these frequencies among the factory workers fell into the impairment range (> 25 dB loss). Firefighters also showed increased losses associated with longer exposure duration, but these were significantly less marked. Losses at lower frequencies (< or = 1 kHz) were negligible in both groups. CONCLUSIONS: Korean work environments with continuous noise exposure in the measured range should consider implementation of a hearing conservation program. Further evaluation of hearing loss in workers exposed to irregular or intermittent high noise levels, such as firefighters, is also warranted.