Drivers of Geographic Patterns in Outpatient Antibiotic Prescribing in the United States.

In a retrospective, ecological analysis of US medical claims, visit rates explained more of the geographic variation in outpatient antibiotic prescribing rates than per-visit prescribing. Efforts to reduce antibiotic use may benefit from addressing the factors that drive higher rates of outpatient visits, in addition to continued focus on stewardship.

Spatiotemporal Trends in Group A Streptococcal Pharyngitis in the United States.

BACKGROUND: Group A Streptococcus (GAS) causes an estimated 5.2 million outpatient visits for pharyngitis annually in the United States (U.S.) with incidence peaking in winter, but the annual spatiotemporal pattern of GAS pharyngitis across the U.S. is poorly characterized. METHODS: We used outpatient claims data from individuals with private medical insurance between 2010-2018 to quantify GAS pharyngitis visit rates across U.S. census regions, subregions, and states. We evaluated seasonal and age-based patterns of geographic spread and the association between school start dates and the summertime upward inflection in GAS visits. RESULTS: The South had the most visits per person (yearly average 39.11 visits per 1000 people, 95% CI: 36.21-42.01), and the West had the fewest (yearly average 17.63 visits per 1000 people, 95% CI: 16.76-18.49). Visits increased earliest in the South and in school-age children. Differences in visits between the South and other regions were most pronounced in the late summer through early winter. Visits peaked earliest in central southern states, in December to January, and latest on the coasts, in March. The onset of the rise in GAS pharyngitis visits correlated with, but preceded, average school start times. CONCLUSIONS: The burden and timing of GAS pharyngitis varied across the continental U.S., with the South experiencing the highest overall rates and earliest onset and peak in outpatient visits. Understanding the drivers of these regional differences in GAS pharyngitis will help in identifying and targeting prevention measures.

Evaluation of deep learning-based depression detection using medical claims data.

Human accuracy in diagnosing psychiatric disorders is still low. Even though digitizing health care leads to more and more data, the successful adoption of AI-based digital decision support (DDSS) is rare. One reason is that AI algorithms are often not evaluated based on large, real-world data. This research shows the potential of using deep learning on the medical claims data of 812,853 people between 2018 and 2022, with 26,973,943 ICD-10-coded diseases, to predict depression (F32 and F33 ICD-10 codes). The dataset used represents almost the entire adult population of Estonia. Based on these data, to show the critical importance of the underlying temporal properties of the data for the detection of depression, we evaluate the performance of non-sequential models (LR, FNN), sequential models (LSTM, CNN-LSTM) and the sequential model with a decay factor (GRU-Δt, GRU-decay). Furthermore, since explainability is necessary for the medical domain, we combine a self-attention model with the GRU decay and evaluate its performance. We named this combination Att-GRU-decay. After extensive empirical experimentation, our model (Att-GRU-decay), with an AUC score of 0.990, an AUPRC score of 0.974, a specificity of 0.999 and a sensitivity of 0.944, proved to be the most accurate. The results of our novel Att-GRU-decay model outperform the current state of the art, demonstrating the potential usefulness of deep learning algorithms for DDSS development. We further expand this by describing a possible application scenario of the proposed algorithm for depression screening in a general practitioner (GP) setting-not only to decrease healthcare costs, but also to improve the quality of care and ultimately decrease people's suffering.

Characteristics of pediatric patients claimed with acute upper respiratory infection during otorhinolaryngology consultations: A descriptive study of a large Japanese medical claims database.

This study aimed to clarify other diseases claimed simultaneously with acute upper respiratory infection (URI), antibiotic prescriptions, and examinations associated with infectious diseases in pediatric patients with acute URI insurance claims at otorhinolaryngology outpatient visits. Pediatric patients who visited an otolaryngology department between 2019 and 2021 and were definitively diagnosed with URI were selected using a large Japanese medical claims database. Patient backgrounds, antibiotic use, and examinations were descriptively evaluated. In total, 8010 patients were included in the analysis. The median number (interquartile range) of diseases claimed in the same month as acute URI was 4 (3-6). Only 519 (6.5 %) patients were claimed as acute URI alone. Regardless of the prescription of antibiotics, the most commonly redundantly claimed disease in these patients was allergic rhinitis, followed by acute bronchitis, acute sinusitis, and earwax impaction. The frequently prescribed antibiotics were third-generation cephalosporins, macrolides, and penicillins with extended-spectrum, including amoxicillin which was recommended by the Japanese manual; the proportion of patients with examinations was low (2.9-21.7 %). Among patients with acute URI, diagnoses requiring antibiotics were also claimed; therefore, when evaluating acute URI using the Japanese medical claims database, care must be taken in patient selection. Moreover, the implementation rate of examinations necessary for diagnosis was low, so there is an urgent need to develop an environment where examinations can be conducted in outpatient settings.

Comparing the Strength of Associations Between Male Genital Problems and Mental Illnesses and Sleep Disorders.

This study compares the rate of selected types of mental illnesses (stress, anxiety, depression) and sleep disorders (insomnia, sleep apnea) according to the status of eight male genital problems. Analyses utilize medical claims data for male employees aged 18 to 64 years of a large corporation, 2017 to 2021. Approximately 1,076 (7.3%) men per year have one or more genital problems. The most common being benign prostatic hyperplasia (BPH; 3.8%) and then erectile dysfunction (ED; 1.7%). For BPH patients, the rate experiencing stress, anxiety, depression, or a combination of these is 0.96%, 6.2%, 5.3%, and 5.1%, respectively. Corresponding rates for ED are 1.5%, 7.2%, 5.9%, and 7.5%. For BPH patients, the rate experiencing insomnia, sleep apnea, or both is 3.1%, 22.7%, and 2.0%, respectively. Corresponding rates for ED are 1.2%, 20.6%, and 2.2%. Male genital problems positively associate with having one or more mental illnesses (stress, anxiety, depression), except for hydrocele, with ED and penis disorder having the strongest associations. Male genital problems also positively associate with having insomnia and/or sleep apnea, except for infertility and orchitis, with BPH and ED having the strongest associations. The positive associations involving BPH and ED with mental illnesses are each more pronounced in the younger age group (18-49 vs. 50-64). Similar results are seen in the models involving sleep disorders. Thus, comorbid male genital problems, mental illnesses, and sleep disorders exist, with the strength of associations unique to the male genital problem and sometimes modified by age.

Early Prediction of COVID-19 Associated Hospitalization at the Time of CDC Contact Tracing using Machine Learning: Towards Pandemic Preparedness.

Objective: To develop and validate machine learning models for predicting COVID-19 related hospitalization as early as CDC contact tracing using integrated CDC contact tracing and South Carolina medical claims data. Methods: Using the dataset (n=82,073, 1/1/2018 - 3/1/2020), we identified 3,305 patients with COVID-19 and were captured by contact tracing. We developed and validated machine learning models (i.e., support vector machine, random forest, XGboost), followed by multi-level validations and pilot statewide implementation. Results: Using 10-cross validation, random forest outperformed other models (F1=0.872 for general hospitalization and 0.763 for COVID-19 related hospitalization), followed by XGBoost (F1=0.845 and 0.682) and support vector machine (F1=0.845 and 0.644). We identified new self-reported symptoms from contact tracing (e.g., fatigue, congestion, headache, loss of taste) that are highly predictive of hospitalization. Conclusions: Our study demonstrated the feasibility of identifying individuals at risk of hospitalization at the time of contact tracing for early intervention and prevention. Policy implications: Our findings demonstrate existing promise for leveraging CDC contact tracing for establishing a cost-effective statewide surveillance and generalizability for nationwide adoption for enhancing pandemic preparedness in the US.

Risk of Cancer After Diagnosis of Cardiovascular Disease.

Background: Cardiovascular disease (CVD) and cancer share several risk factors. Although preclinical models show that various types of CVD can accelerate cancer progression, clinical studies have not determined the impact of atherosclerosis on cancer risk. Objectives: The objective of this study was to determine whether CVD, especially atherosclerotic CVD, is independently associated with incident cancer. Methods: Using IBM MarketScan claims data from over 130 million individuals, 27 million cancer-free subjects with a minimum of 36 months of follow-up data were identified. Individuals were stratified by presence or absence of CVD, time-varying analysis with multivariable adjustment for cardiovascular risk factors was performed, and cumulative risk of cancer was calculated. Additional analyses were performed according to CVD type (atherosclerotic vs nonatherosclerotic) and cancer subtype. Results: Among 27,195,088 individuals, those with CVD were 13% more likely to develop cancer than those without CVD (HR: 1.13; 95% CI: 1.12-1.13). Results were more pronounced for individuals with atherosclerotic CVD (aCVD), who had a higher risk of cancer than those without CVD (HR: 1.20; 95% CI: 1.19-1.21). aCVD also conferred a higher risk of cancer compared with those with nonatherosclerotic CVD (HR: 1.11; 95% CI: 1.11-1.12). Cancer subtype analyses showed specific associations of aCVD with several malignancies, including lung, bladder, liver, colon, and other hematologic cancers. Conclusions: Individuals with CVD have an increased risk of developing cancer compared with those without CVD. This association may be driven in part by the relationship of atherosclerosis with specific cancer subtypes, which persists after controlling for conventional risk factors.

Pneumoconiosis incidence and prevalence among US Medicare beneficiaries, 1999-2019.

BACKGROUND: Pneumoconiosis is a group of occupational lung diseases caused by dust and fiber exposure. This study analyzes Medicare claims to estimate the burden of pneumoconiosis among fee-for-service (FFS; Medicare Parts A and B) Medicare beneficiaries during 1999-2019 in the United States. METHODS: Claim and enrollment information from 81 million continuously enrolled FFS Medicare beneficiaries were analyzed. Beneficiaries with any pneumoconiosis and cause-specific pneumoconiosis (e.g., asbestosis, silicosis) were identified using three case definitions (broad, intermediate, and narrow) with varying diagnostic criteria based on claim International Classification of Diseases, Clinical Modification (ICD-CM) diagnosis codes and Healthcare Common Procedure Coding System codes. Results are presented as ranges of values for the three case definitions. RESULTS: The 21-year prevalence range for any pneumoconiosis was 345,383-677,361 (412-833 per 100,000 beneficiaries) using the three case definitions. The highest prevalence was among those ≥75 years of age, males, Whites, and North American Natives. Most claims (70.0%-72.5%) included an ICD-CM diagnosis code for asbestosis. The broad pneumoconiosis prevalence rate increased significantly (p < 0.001) during 2002-2009 by 3%-10% annually and declined significantly by 3%-5% annually starting in 2009. The average annual broad incidence rate declined significantly by 7% annually during 2009-2019. CONCLUSIONS: Despite the decline in rate for any pneumoconiosis among Medicare beneficiaries, which is primarily attributed to a decline in asbestosis, pneumoconiosis is prevalent among FFS Medicare beneficiaries.

Prediction of Future Medical Costs by Modifiable Measures of Health.

Introduction: Strategies to mitigate rising health-care costs are a priority for patients, employers, and health insurers. Yet gaps currently exist in whether health risk assessment can forecast medical claims costs. This study examined the ability of a health quotient (HQ) based on modifiable risk factors, age, sex, and chronic conditions to predict future medical claims spending. Methods: The study included 18,695 employees and adult dependents who participated in health assessments and were enrolled in an employer-sponsored health plan. Linear mixed effect models stratified by chronic conditions and adjusted for age and sex were utilized to evaluate the relationship between the health quotient (score of 0-100) and future medical claims spending. Results: Lower baseline health quotient was associated with higher medical claims cost over 2 years of follow up. For participants with chronic condition(s), costs were $3628 higher for those with a low health quotient (<73; N = 2673) compared to those with high health quotient (>85; N = 1045), after adjustment for age and sex (P value = 0.004). Each one-unit increase in health quotient was associated with a decrease of $154 (95% CI: 87.4, 220.3) in average yearly medical claims costs during follow up. Discussion: This study used a large employee population with 2 years of follow-up data, which provides insights that are applicable to other large employers. Results of this analysis contribute to our ability to predict health-care costs using modifiable aspects of health, objective laboratory testing and chronic condition status.

Comparison of Machine Learning Algorithms for Predicting Hospital Readmissions and Worsening Heart Failure Events in Patients With Heart Failure With Reduced Ejection Fraction: Modeling Study.

BACKGROUND: Heart failure (HF) is highly prevalent in the United States. Approximately one-third to one-half of HF cases are categorized as HF with reduced ejection fraction (HFrEF). Patients with HFrEF are at risk of worsening HF, have a high risk of adverse outcomes, and experience higher health care use and costs. Therefore, it is crucial to identify patients with HFrEF who are at high risk of subsequent events after HF hospitalization. OBJECTIVE: Machine learning (ML) has been used to predict HF-related outcomes. The objective of this study was to compare different ML prediction models and feature construction methods to predict 30-, 90-, and 365-day hospital readmissions and worsening HF events (WHFEs). METHODS: We used the Veradigm PINNACLE outpatient registry linked to Symphony Health's Integrated Dataverse data from July 1, 2013, to September 30, 2017. Adults with a confirmed diagnosis of HFrEF and HF-related hospitalization were included. WHFEs were defined as HF-related hospitalizations or outpatient intravenous diuretic use within 1 year of the first HF hospitalization. We used different approaches to construct ML features from clinical codes, including frequencies of clinical classification software (CCS) categories, Bidirectional Encoder Representations From Transformers (BERT) trained with CCS sequences (BERT + CCS), BERT trained on raw clinical codes (BERT + raw), and prespecified features based on clinical knowledge. A multilayer perceptron neural network, extreme gradient boosting (XGBoost), random forest, and logistic regression prediction models were applied and compared. RESULTS: A total of 30,687 adult patients with HFrEF were included in the analysis; 11.41% (3184/27,917) of adults experienced a hospital readmission within 30 days of their first HF hospitalization, and nearly half (9231/21,562, 42.81%) of the patients experienced at least 1 WHFE within 1 year after HF hospitalization. The prediction models and feature combinations with the best area under the receiver operating characteristic curve (AUC) for each outcome were XGBoost with CCS frequency (AUC=0.595) for 30-day readmission, random forest with CCS frequency (AUC=0.630) for 90-day readmission, XGBoost with CCS frequency (AUC=0.649) for 365-day readmission, and XGBoost with CCS frequency (AUC=0.640) for WHFEs. Our ML models could discriminate between readmission and WHFE among patients with HFrEF. Our model performance was mediocre, especially for the 30-day readmission events, most likely owing to limitations of the data, including an imbalance between positive and negative cases and high missing rates of many clinical variables and outcome definitions. CONCLUSIONS: We predicted readmissions and WHFEs after HF hospitalizations in patients with HFrEF. Features identified by data-driven approaches may be comparable with those identified by clinical domain knowledge. Future work may be warranted to validate and improve the models using more longitudinal electronic health records that are complete, are comprehensive, and have a longer follow-up time.

Susceptibility to Colorectal Cancer Based on HSD17B4 rs721673 and rs721675 Polymorphisms and Alcohol Intake among Taiwan Biobank Participants: A Retrospective Case Control Study Using the Nationwide Claims Data.

Colorectal cancer (CRC) is a major public health issue, and there are limited studies on the association between 17ß-hydroxysteroid dehydrogenase type 4 (HSD17B4) polymorphism and CRC. We used two national databases from Taiwan to examine whether HSD17B4 rs721673, rs721675, and alcohol intake were independently and interactively correlated with CRC development. We linked the Taiwan Biobank (TWB) participants' health and lifestyle information and genotypic data from 2012 to 2018 to the National Health Insurance Database (NHIRD) to confirm their medical records. We performed a genome-wide association study (GWAS) using data from 145 new incident CRC cases and matched 1316 healthy, non-CRC individuals. We calculated the odds ratios (OR) and 95% confidence intervals (CI) for CRC based on multiple logistic regression analyses. HSD17B4 rs721673 and rs721675 on chromosome 5 were significantly and positively correlated with CRC (rs721673 A > G, aOR = 2.62, p = 2.90 × 10-8; rs721675 A > T, aOR = 2.61, p = 1.01 × 10-6). Within the high-risk genotypes, significantly higher ORs were observed among the alcohol intake group. Our results demonstrated that the rs721673 and rs721675 risk genotypes of HSD17B4 might increase the risk of CRC development in Taiwanese adults, especially those with alcohol consumption habits.

Long Short-Term Memory-Based Deep Learning Models for Screening Parkinson's Disease Using Sequential Diagnostic Codes.

BACKGROUND AND PURPOSE: It is challenging to detect Parkinson's disease (PD) in its early stages, which has prompted researchers to develop techniques based on machine learning methods for detecting PD. However, previous studies did not fully incorporate the slow progression of PD over a long period of time nor consider that its symptoms occur in a time-sequential manner. Contributing to the literature on PD, which has relied heavily on cross-sectional data, this study aimed to develop a method for detecting PD early that can process time-series information using the long short-term memory (LSTM) algorithm. METHODS: We sampled 926 patients with PD and 9,260 subjects without PD using medical-claims data. The LSTM algorithm was tested using diagnostic histories, which contained the diagnostic codes and their respective time information. We compared the prediction power of the 12-month diagnostic codes under two different settings over the 4 years prior to the first PD diagnosis. RESULTS: The model that was trained using the most-recent 12-month diagnostic codes had the best performance, with an accuracy of 94.25%, a sensitivity of 82.91%, and a specificity of 95.26%. The other three models (12-month codes from 2, 3, and 4 years prior) were found to have comparable performances, with accuracies of 92.27%, 91.86%, and 91.81%, respectively. The areas under the curve from our data settings ranged from 0.839 to 0.923. CONCLUSIONS: We explored the possibility that PD specialists could benefit from our proposed machine learning method as an early detection method for PD.

Peritoneal Dialysis-Associated Peritonitis Trends Using Medicare Claims Data, 2013-2017.

RATIONALE & OBJECTIVE: The occurrence and consequences of peritoneal dialysis (PD)-associated peritonitis limit its use in populations with kidney failure. Studies of large clinical populations may enhance our understanding of peritonitis. To facilitate these studies we developed an approach to measuring peritonitis rates using Medicare claims data to characterize peritonitis trends and identify its clinical risk factors. STUDY DESIGN: Retrospective cohort study of PD-associated peritonitis. SETTING & PARTICIPANTS: US Renal Data System standard analysis files were used for claims, eligibility, modality, and demographic information. The sample consisted of patients receiving PD treated at some time between 2013 and 2017 who were covered by Medicare fee-for-service (FFS) insurance with paid claims for dialysis or hospital services. EXPOSURES/PREDICTORS: Peritonitis risk was characterized by year, age, sex, race, ethnicity, vintage of kidney replacement therapy, cause of kidney failure, and prior peritonitis episodes. OUTCOME: The major outcome was peritonitis, identified using ICD-9 and ICD-10 diagnosis codes. Closely spaced peritonitis claims (30 days) were aggregated into 1 peritonitis episode. ANALYTICAL APPROACH: Patient-level risk factors for peritonitis were modeled using Poisson regression. RESULTS: We identified 70,271 peritonitis episodes from 396,289 peritonitis claims. Although various codes were used to record an episode of peritonitis, none was used predominantly. Peritonitis episodes were often identified by multiple aggregated claims, with the mean and median claims per episode being 5.6 and 2, respectively. We found 40% of episodes were exclusively outpatient, 9% exclusively inpatient, and 16% were exclusively based on codes that do not clearly distinguish peritonitis from catheter infections/inflammation ("catheter codes"). The overall peritonitis rate was 0.54 episodes per patient-year (EPPY). The rate was 0.45 EPPY after excluding catheter codes and 0.35 EPPY when limited to episodes that only included claims from nephrologists or dialysis providers. The peritonitis rate declined by 5%/year and varied by patient factors including age (lower rates at higher ages), race (Black > White>Asian), and prior peritonitis episodes (higher rate with each prior episode). LIMITATIONS: Coding heterogeneity indicates a lack of standardization. Episodes based exclusively on catheter codes could represent false positives. Peritonitis episodes were not validated against symptoms or microbiologic data. CONCLUSIONS: PD-associated peritonitis rates decline over time and were lower among older patients. A claims-based approach offers a promising framework for the study of PD-associated peritonitis.

Impact of Respiratory Infection and Chronic Comorbidities on Early Pediatric Antibiotic Dispensing in the United States.

BACKGROUND: In the United States, children aged <5 years receive high volumes of antibiotics, which may contribute to antibiotic resistance. It has been unclear what role preventable illnesses and chronic comorbidities play in prompting antibiotic prescriptions. METHODS: We conducted an observational study with a cohort of 124 759 children aged <5 years born in the United States between 2008 and 2013 with private medical insurance. Study outcomes included the cumulative number of antibiotic courses dispensed per child by age 5 and the proportion of children for whom at least 1 antibiotic course was dispensed by age 5. We identified which chronic medical conditions predicted whether a child would be among the top 20% of antibiotic recipients. RESULTS: Children received a mean of 6.8 (95% confidence interval [CI]: 6.7-6.9) antibiotic courses by age 5, and 91% (95% CI: 90%-92%) of children had received at least 1 antibiotic course by age 5. Most antibiotic courses (71%; 95% CI: 70%-72%) were associated with respiratory infections. Presence of a pulmonary/respiratory, otologic, and/or immunological comorbidity substantially increase a child's odds of being in the top 20% of antibiotic recipients. Children with at least 1 of these conditions received a mean of 10.5 (95% CI: 10.4-10.6) antibiotic courses by age 5. CONCLUSIONS: Privately insured children in the United States receive many antibiotics early in life, largely due to respiratory infections. Antibiotic dispensing varies widely among children, with more antibiotics dispensed to children with pulmonary/respiratory, otologic, and/or immunological comorbidities.

Specialized Versus Generic Allied Health Therapy and the Risk of Parkinson's Disease Complications.

BACKGROUND: Specialized versus generic physiotherapy (PT) reduces Parkinson's disease (PD)-related complications. It is unclear (1) whether other specialized allied heath disciplines, including occupational therapy (OT) and speech and language therapy (S<), also reduce complications; (2) whether there is a synergistic effect among multiple specialized disciplines; and (3) whether each allied health discipline prevents specific complications. OBJECTIVES: To longitudinally assessed whether the level of expertise (specialized vs. generic training) of PT, OT, and S< was associated with the incidence rate of PD-related complications. METHODS: We used claims data of all insured persons with PD in the Netherlands between January 1, 2010, and December 31, 2018. ParkinsonNet-trained therapists were classified as specialized, and other therapists as generic. We used mixed-effects Poisson regression models to estimate rate ratios adjusting for sociodemographic and clinical characteristics. RESULTS: The population of 51,464 persons with PD (mean age, 72.4 years; standard deviation 9.8) sustained 10,525 PD-related complications during follow-up (median 3.3 years). Specialized PT was associated with fewer complications (incidence rate ratio [IRR] of specialized versus generic = 0.79; 95% confidence interval, [0.74-0.83]; P < 0.0001), as was specialized OT (IRR = 0.88 [0.77-0.99]; P = 0.03). We found a trend of an association between specialized S< and a lower rate of PD-related complications (IRR = 0.88 [0.74-1.04]; P = 0.18). The inverse association of specialized OT persisted in the stratum, which also received specialized PT (IRR = 0.62 [0.42-0.90]; P = 0.001). The strongest inverse association of PT was seen with orthopedic injuries (IRR = 0.78 [0.73-0.82]; P < 0.0001) and of S< with pneumonia (IRR = 0.70 [0.53-0.93]; P = 0.03). CONCLUSIONS: These findings support a wider introduction of specialized allied health therapy expertise in PD care and conceivably for other medical conditions. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Identifying long-term effects of SARS-CoV-2 and their association with social determinants of health in a cohort of over one million COVID-19 survivors.

BACKGROUND: Despite an abundance of information on the risk factors of SARS-CoV-2, there have been few US-wide studies of long-term effects. In this paper we analyzed a large medical claims database of US based individuals to identify common long-term effects as well as their associations with various social and medical risk factors. METHODS: The medical claims database was obtained from a prominent US based claims data processing company, namely Change Healthcare. In addition to the claims data, the dataset also consisted of various social determinants of health such as race, income, education level and veteran status of the individuals. A self-controlled cohort design (SCCD) observational study was performed to identify ICD-10 codes whose proportion was significantly increased in the outcome period compared to the control period to identify significant long-term effects. A logistic regression-based association analysis was then performed between identified long-term effects and social determinants of health. RESULTS: Among the over 1.37 million COVID patients in our datasets we found 36 out of 1724 3-digit ICD-10 codes to be statistically significantly increased in the post-COVID period (p-value < 0.05). We also found one combination of ICD-10 codes, corresponding to 'other anemias' and 'hypertension', that was statistically significantly increased in the post-COVID period (p-value < 0.05). Our logistic regression-based association analysis with social determinants of health variables, after adjusting for comorbidities and prior conditions, showed that age and gender were significantly associated with the multiple long-term effects. Race was only associated with 'other sepsis', income was only associated with 'Alopecia areata' (autoimmune disease causing hair loss), while education level was only associated with 'Maternal infectious and parasitic diseases' (p-value < 0.05). CONCLUSION: We identified several long-term effects of SARS-CoV-2 through a self-controlled study on a cohort of over one million patients. Furthermore, we found that while age and gender are commonly associated with the long-term effects, other social determinants of health such as race, income and education levels have rare or no significant associations.

Influencia de las reclamaciones en la gestión asistencial de un servicio de cirugía ortopédica y traumatología / Influence of claims on the management of an orthopaedic surgery and traumatology service

Introducción: Las reclamaciones constituyen una de las principales fuentes de información para evaluar la calidad percibida en los centros asistenciales, siendo la cirugía ortopédica y traumatología (COT) una de las especialidades con mayor probabilidad de recibirlas por su elevada demanda quirúrgica que genera importantes listas de espera. Objetivos: Mostrar la evolución de las reclamaciones presentadas en un servicio de COT, clasificar los motivos expuestos e identificar las oportunidades de mejora derivadas de las mismas. Metodología: Estudio epidemiológico descriptivo, observacional y de orientación temporal retrospectiva. Revisión de los registros correspondientes a las reclamaciones presentadas durante el periodo 2014-2018 en el servicio de COT de un hospital universitario. Para la clasificación de las reclamaciones se han utilizado los motivos establecidos por la aseguradora pública CatSalut. El análisis estadístico se ha realizado mediante el programa Excel® y el software R-Project (versión 4.0.2), considerándose un nivel de significación estadística de p<0,05. Resultados: El servicio de COT ha recibido un total de 424 reclamaciones durante el periodo 2014-2018, mostrando una tasa global de 3,18 reclamaciones por cada 1.000 episodios asistenciales considerados. Los principales motivos de reclamación han sido los organizativos (73%) y los asistenciales (20%). A partir del año 2016 se evidencia un descenso en el número de las reclamaciones presentadas. Conclusión: La implantación de procedimientos informativos estables para modular las expectativas de los pacientes incorporados a las listas de espera, y una comunicación más empática que facilite una buena relación paciente-profesional son acciones de mejora identificadas para reducir su frecuencia de presentación.(AU)

Introduction: Claims constitute one of the main sources of information to evaluate the perceived quality in healthcare centres, being Orthopaedic and Traumatology Surgery (OTS) one of the specialties with greater probability of receiving them due to its high surgical demand generating long waiting lists. Objectives: To display the evolution of the filed claims addressed to the OTS department, to classify the reasons stated in the complaint, and to identify the opportunities for improvement derived from the forementioned. Methodology: Descriptive, observational and retrospective epidemiological study. The target population has been configured by those citizens who have submitted a claim addressed to the OTS Service of a University Hospital of Barcelona from 2014 to 2018. In reference with the classification of claims, it has been used the reasons established by the public service CatSalut: assistance, treat, information, organisation, documentation and hospitality/habitability/comfort. Results: OTS service received a total of 424 claims during the study period, showing an overall rate of 3.18 claims per 100 assistance episodes considered. The main reasons for claiming were organizational (73%) and assistance (20%). No claims regarding dissatisfaction of hospitality/habitability/comfort were registered. A noticeable decrease in the number of claims submitted is observed since 2016. Conclusion: Actions in the management of waiting lists and standardised information procedures that improve the doctor–patient relationship have been identified as measures of improvement to reduce the claim presentation rate.(AU)

[Translated article] Influence of claims on the management of an orthopaedic surgery and traumatology service / Influencia de las reclamaciones en la gestión asistencial de un servicio de cirugía ortopédica y traumatología

Introducción: Las reclamaciones constituyen una de las principales fuentes de información para evaluar la calidad percibida en los centros asistenciales, siendo la cirugía ortopédica y traumatología (COT) una de las especialidades con mayor probabilidad de recibirlas por su elevada demanda quirúrgica que genera importantes listas de espera. Objetivos: Mostrar la evolución de las reclamaciones presentadas en un servicio de COT, clasificar los motivos expuestos e identificar las oportunidades de mejora derivadas de las mismas. Metodología: Estudio epidemiológico descriptivo, observacional y de orientación temporal retrospectiva. Revisión de los registros correspondientes a las reclamaciones presentadas durante el periodo 2014-2018 en el servicio de COT de un hospital universitario. Para la clasificación de las reclamaciones se han utilizado los motivos establecidos por la aseguradora pública CatSalut. El análisis estadístico se ha realizado mediante el programa Excel® y el software R-Project (versión 4.0.2), considerándose un nivel de significación estadística de p<0,05. Resultados: El servicio de COT ha recibido un total de 424 reclamaciones durante el periodo 2014-2018, mostrando una tasa global de 3,18 reclamaciones por cada 1.000 episodios asistenciales considerados. Los principales motivos de reclamación han sido los organizativos (73%) y los asistenciales (20%). A partir del año 2016 se evidencia un descenso en el número de las reclamaciones presentadas. Conclusión: La implantación de procedimientos informativos estables para modular las expectativas de los pacientes incorporados a las listas de espera, y una comunicación más empática que facilite una buena relación paciente-profesional son acciones de mejora identificadas para reducir su frecuencia de presentación.(AU)

Introduction: Claims constitute one of the main sources of information to evaluate the perceived quality in healthcare centres, being Orthopaedic and Traumatology Surgery (OTS) one of the specialties with greater probability of receiving them due to its high surgical demand generating long waiting lists. Objectives: To display the evolution of the filed claims addressed to the OTS department, to classify the reasons stated in the complaint, and to identify the opportunities for improvement derived from the forementioned. Methodology: Descriptive, observational and retrospective epidemiological study. The target population has been configured by those citizens who have submitted a claim addressed to the OTS Service of a University Hospital of Barcelona from 2014 to 2018. In reference with the classification of claims, it has been used the reasons established by the public service CatSalut: assistance, treat, information, organisation, documentation and hospitality/habitability/comfort. Results: OTS service received a total of 424 claims during the study period, showing an overall rate of 3.18 claims per 100 assistance episodes considered. The main reasons for claiming were organizational (73%) and assistance (20%). No claims regarding dissatisfaction of hospitality/habitability/comfort were registered. A noticeable decrease in the number of claims submitted is observed since 2016. Conclusion: Actions in the management of waiting lists and standardised information procedures that improve the doctor–patient relationship have been identified as measures of improvement to reduce the claim presentation rate.(AU)

Human Papillomavirus and Risk of Colorectal Cancer: An Analysis of Nationwide Claims Data.

Background and Objectives: Although human papillomavirus (HPV) is a major etiology of cervical and anogenital cancers, whether it is associated with colorectal carcinogenesis is yet undetermined. Materials and Methods: The longitudinal association of HPV infection with colorectal cancer (CRC) was evaluated using 2000-2013 data from a nationwide Taiwanese claims database. In this retrospective cohort study, 358 patients with primary HPV diagnoses (HPV-infected cohort) and 1432 patients without such a diagnosis (HPV-uninfected cohort) were recruited between 2000 and 2006. Both cohorts were followed up to identify CRC incidences from 2006 to 2013. Hazard ratios (HRs) and their 95% confidence intervals (CIs) derived from Cox proportional hazards models were used to estimate the association between HPV and CRC risk. Results: The HPV-infected cohort had a significantly higher cumulative incidence of CRC than the HPV-uninfected cohort. The presence of HPV was associated with an increased risk of CRC (adjusted HR, 1.63; 95% CI, 1.02-3.62). Furthermore, the significant HPV-CRC risk association was evident in both sexes. Conclusions: This population-based cohort study reveals longitudinal evidence that HPV is associated with an increased risk of CRC. Further studies are required to verify the role of HPV in colorectal carcinogenesis.