1209. The Evolving Nature of Syndromic Surveillance During the COVID-19 Pandemic in Massachusetts

Abstract Background We developed a syndromic algorithm for COVID-19 like illness (CLI) to provide supplementary surveillance data on COVID-19 activity. Methods The CLI algorithm was developed using the Electronic Medical Record Support for Public Health platform (esphealth.org) and data from five clinical practice groups in Massachusetts that collectively care for 25% of the state’s population. Signs and symptoms of CLI were identified using ICD-10 diagnosis codes and measured temperature. The algorithm originally included three categories: Category 1 required codes for coronavirus infection and lower respiratory tract infections (LRTI); Category 2 required an LRTI-related diagnosis and fever; Category 3 required an upper or lower RTI and fever. The three categories mirrored statewide laboratory-confirmed case trends during spring and summer 2020 but did not detect the increase in late fall. We hypothesized this was due to the requirements for fever and LRTI. Therefore, we added three new categories defined by milder symptoms without fever: Category 4 requires LRTI-related diagnoses only; Category 5 requires upper or lower RTI or olfactory/taste disorders; and Category 6 requires at least one sign of CLI not identified by another category. Results The six-category algorithm detected the initial surge in April 2020, the summer lull, and the second surge in late fall (see figure). Category 1 cases were not identified until mid-March, which coincides with the first laboratory-confirmed cases in Massachusetts. Categories 2 and 3, which required fever, were prominent during the initial surge but declined over time. Category 5, the broadest category, declined during February and March 2020, likely capturing the end of the influenza season, and successfully detected the spring surge and fall resurgence. Weekly number of COVID-19 like illnesses by category, February 2, 2020 through May 8, 2021 Conclusion A syndromic definition that included mild upper RTI and olfactory/taste disorders, with or without fever or LRTI, mirrored changes in laboratory-confirmed COVID-19 cases better than definitions that required fever and LRTI. This suggests a shift in medically attended care and/or coding practices during initial vs subsequent surges of COVID-19, and the importance of using a broad definition of CLI for ongoing surveillance. Disclosures Michael Klompas, MD, MPH, UpToDate (Other Financial or Material Support, Chapter Author)

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Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings

Antibiotics ◽

10.3390/antibiotics9090536 ◽

2020 ◽

Vol 9 (9) ◽

pp. 536

Author(s):

George Germanos ◽

Patrick Light ◽

Roger Zoorob ◽

Jason Salemi ◽

Fareed Khan ◽

...

Keyword(s):

Urinary Tract ◽

Urinary Tract Infections ◽

Chart Review ◽

International Classification Of Diseases ◽

Primary Care Settings ◽

Diagnosis Codes ◽

Classification Of Diseases ◽

Icd 10 ◽

Tract Infections ◽

Outpatient Settings

Objective: To validate the use of electronic algorithms based on International Classification of Diseases (ICD)-10 codes to identify outpatient visits for urinary tract infections (UTI), one of the most common reasons for antibiotic prescriptions. Methods: ICD-10 symptom codes (e.g., dysuria) alone or in addition to UTI diagnosis codes plus prescription of a UTI-relevant antibiotic were used to identify outpatient UTI visits. Chart review (gold standard) was performed by two reviewers to confirm diagnosis of UTI. The positive predictive value (PPV) that the visit was for UTI (based on chart review) was calculated for three different ICD-10 code algorithms using (1) symptoms only, (2) diagnosis only, or (3) both. Results: Of the 1087 visits analyzed, symptom codes only had the lowest PPV for UTI (PPV = 55.4%; 95%CI: 49.3–61.5%). Diagnosis codes alone resulted in a PPV of 85% (PPV = 84.9%; 95%CI: 81.1–88.2%). The highest PPV was obtained by using both symptom and diagnosis codes together to identify visits with UTI (PPV = 96.3%; 95%CI: 94.5–97.9%). Conclusions: ICD-10 diagnosis codes with or without symptom codes reliably identify UTI visits; symptom codes alone are not reliable. ICD-10 based algorithms are a valid method to study UTIs in primary care settings.

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A Descriptive Analysis of Outpatient Antimicrobial Use for Urinary Tract Infections in Virginia

Infection Control and Hospital Epidemiology ◽

10.1017/ice.2020.1125 ◽

2020 ◽

Vol 41 (S1) ◽

pp. s453-s454

Author(s):

Hasti Mazdeyasna ◽

Shaina Bernard ◽

Le Kang ◽

Emily Godbout ◽

Kimberly Lee ◽

...

Keyword(s):

Urinary Tract ◽

Urinary Tract Infections ◽

Private Insurance ◽

Geographic Region ◽

Study Cohort ◽

Antibiotic Prescribing ◽

Prescribing Trends ◽

Icd 10 ◽

Tract Infections ◽

Insurance Payer

Background: Data regarding outpatient antibiotic prescribing for urinary tract infections (UTIs) are limited, and they have never been formally summarized in Virginia. Objective: We describe outpatient antibiotic prescribing trends for UTIs based on gender, age, geographic region, insurance payer and International Classification of Disease, Tenth Revision (ICD-10) codes in Virginia. Methods: We used the Virginia All-Payer Claims Database (APCD), administered by Virginia Health Information (VHI), which holds data for Medicare, Medicaid, and private insurance. The study cohort included Virginia residents who had a primary diagnosis of UTI, had an antibiotic claim 0–3 days after the date of the diagnosis and who were seen in an outpatient facility in Virginia between January 1, 2016, and December 31, 2016. A diagnosis of UTI was categorized as cystitis, urethritis or pyelonephritis and was defined using the following ICD-10 codes: N30.0, N30.00, N30.01, N30.9, N30.90, N30.91, N39.0, N34.1, N34.2, and N10. The following antibiotics were prescribed: aminoglycosides, sulfamethoxazole/trimethoprim (TMP-SMX), cephalosporins, fluoroquinolones, macrolides, penicillins, tetracyclines, or nitrofurantoin. Patients were categorized based on gender, age, location, insurance payer and UTI type. We used χ2 and Cochran-Mantel-Haenszel testing. Analyses were performed in SAS version 9.4 software (SAS Institute, Cary, NC). Results: In total, 15,580 patients were included in this study. Prescriptions for antibiotics by drug class differed significantly by gender (P < .0001), age (P < .0001), geographic region (P < .0001), insurance payer (P < .0001), and UTI type (P < .0001). Cephalosporins were prescribed more often to women (32.48%, 4,173 of 12,846) than to men (26.26%, 718 of 2,734), and fluoroquinolones were prescribed more often to men (53.88%, 1,473 of 2,734) than to women (47.91%, 6,155 of 12,846). Although cephalosporins were prescribed most frequently (42.58%, 557 of 1,308) in northern Virginia, fluoroquinolones were prescribed the most in eastern Virginia (50.76%, 1677 of 3,304). Patients with commercial health insurance, Medicaid, and Medicare were prescribed fluoroquinolones (39.31%, 1,149 of 2,923), cephalosporins (56.33%, 1,326 of 2,354), and fluoroquinolones (57.36%, 5,910 of 10,303) most frequently, respectively. Conclusions: Antibiotic prescribing trends for UTIs varied by gender, age, geographic region, payer status and UTI type in the state of Virginia. These data will inform future statewide antimicrobial stewardship efforts.Funding: NoneDisclosures: Michelle Doll reports a research grant from Molnlycke Healthcare.

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136. Don’t Sweat the Small Stuff: Solutions for Large-Scale Stewardship Obstacles

Open Forum Infectious Diseases ◽

10.1093/ofid/ofaa439.181 ◽

2020 ◽

Vol 7 (Supplement_1) ◽

pp. S79-S80

Author(s):

Joanne Huang ◽

Zahra Kassamali Escobar ◽

Rupali Jain ◽

Jeannie D Chan ◽

John B Lynch ◽

...

Keyword(s):

Urgent Care ◽

Complex Data ◽

Open Communication ◽

University Of Washington ◽

Data Set ◽

Constructive Feedback ◽

Diagnostic Coding ◽

Icd 10 ◽

Tract Infections ◽

The University

Abstract Background In an effort to support stewardship endeavors, the MITIGATE (a Multifaceted Intervention to Improve Prescribing for Acute Respiratory Infection for Adult and Children in Emergency Department and Urgent Care Settings) Toolkit was published in 2018, aiming to reduce unnecessary antibiotics for viral respiratory tract infections (RTIs). At the University of Washington, we have incorporated strategies from this toolkit at our urgent care clinics. This study aims to address solutions to some of the challenges we experienced. Challenges and Solutions Methods This was a retrospective observational study conducted at Valley Medical Center (Sept 2019-Mar 2020) and the University of Washington (Jan 2019-Feb 2020) urgent care clinics. Patients were identified through ICD-10 diagnosis codes included in the MITIGATE toolkit. The primary outcome was identifying challenges and solutions developed during this process. Results We encountered five challenges during our roll-out of MITIGATE. First, using both ICD-9 and ICD-10 codes can lead to inaccurate data collection. Second, technical support for coding a complex data set is essential and should be accounted for prior to beginning stewardship interventions of this scale. Third, unintentional incorrect diagnosis selection was common and may require reeducation of prescribers on proper selection. Fourth, focusing on singular issues rather than multiple outcomes is more feasible and can offer several opportunities for stewardship interventions. Lastly, changing prescribing behavior can cause unintended tension during implementation. Modifying benchmarks measured, allowing for bi-directional feedback, and identifying provider champions can help maintain open communication. Conclusion Resources such as the MITIGATE toolkit are helpful to implement standardized data driven stewardship interventions. We have experienced some challenges including a complex data build, errors with diagnostic coding, providing constructive feedback while maintaining positive stewardship relationships, and choosing feasible outcomes to measure. We present solutions to these challenges with the aim to provide guidance to those who are considering using this toolkit for outpatient stewardship interventions. Disclosures All Authors: No reported disclosures

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Multisite medical record review of emergency department visits for traumatic brain injury

Injury Prevention ◽

10.1136/injuryprev-2019-043510 ◽

2021 ◽

Vol 27 (S1) ◽

pp. i42-i48

Author(s):

Barbara A Gabella ◽

Jeanne E Hathaway ◽

Beth Hume ◽

Jewell Johnson ◽

Julia F Costich ◽

...

Keyword(s):

Traumatic Brain Injury ◽

Emergency Department ◽

Brain Injury ◽

Skull Fracture ◽

Signs And Symptoms ◽

International Classification Of Diseases ◽

Emergency Department Visits ◽

Intracranial Injury ◽

Imaging Results ◽

Icd 10

BackgroundIn 2016, the CDC in the USA proposed codes from the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for identifying traumatic brain injury (TBI). This study estimated positive predictive value (PPV) of TBI for some of these codes.MethodsFour study sites used emergency department or trauma records from 2015 to 2018 to identify two random samples within each site selected by ICD-10-CM TBI codes for (1) intracranial injury (S06) or (2) skull fracture only (S02.0, S02.1-, S02.8-, S02.91) with no other TBI codes. Using common protocols, reviewers abstracted TBI signs and symptoms and head imaging results that were then used to assign certainty of TBI (none, low, medium, high) to each sampled record. PPVs were estimated as a percentage of records with medium-certainty or high-certainty for TBI and reported with 95% confidence interval (CI).ResultsPPVs for intracranial injury codes ranged from 82% to 92% across the four samples. PPVs for skull fracture codes were 57% and 61% in the two university/trauma hospitals in each of two states with clinical reviewers, and 82% and 85% in the two states with professional coders reviewing statewide or nearly statewide samples. Margins of error for the 95% CI for all PPVs were under 5%.DiscussionICD-10-CM codes for traumatic intracranial injury demonstrated high PPVs for capturing true TBI in different healthcare settings. The algorithm for TBI certainty may need refinement, because it yielded moderate-to-high PPVs for records with skull fracture codes that lacked intracranial injury codes.

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Cardiovascular disease prevalence in type 2 diabetes – an analysis of a large German statutory health insurance database

BMC Public Health ◽

10.1186/s12889-021-10381-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Maximilian Gabler ◽

Silke Geier ◽

Lukas Mayerhoff ◽

Wolfgang Rathmann

Keyword(s):

Type 2 Diabetes ◽

Cardiovascular Disease ◽

Health System ◽

Cardiovascular Complications ◽

Population Based ◽

Stratified Sample ◽

Icd 10 ◽

Definition Of ◽

Observation Period

Abstract Background The aim of this study was to determine the prevalence of cardiovascular disease in persons with type 2 diabetes mellitus (T2D) in Germany. Methods A claims database with an age- and sex-stratified sample of nearly 4 million individuals insured within the German statutory health system was used. All patients aged ≥18 years with T2D documented between 1 January 2015 and 31 December 2015 and complete retrospective documentation of ≥5 years (continuous enrollment in the German statutory health system) before 2015 were selected based on a validated algorithm. Cardiovascular disease (CVD) events were identified based on ICD-10 and OPS codes according to a previous clinical study (EMPA-REG OUTCOME trial). Results The prevalence of T2D in Germany in 2015 was 9.9% (n = 324,708). Using a narrow definition of CVD, the 6-year observation period prevalence of CVD was estimated as 46.7% [95% CI: 46.52%;46.86%]. Applying a wider CVD definition, the proportion of T2D patients who showed a history of CVD was 57.1% [95% CI: 56.9%;57.24%]. The prevalence of CVD in patients with T2D ranged from 36.3 to 57.1%, depending on the observation period and definition of CVD. Conclusions The results underline the need for a population-based registration of cardiovascular complications in T2D.

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The association between training load indices and upper respiratory tract infections (URTIs) in elite soccer players

International Journal of Sports Science & Coaching ◽

10.1177/17479541211020479 ◽

2021 ◽

pp. 174795412110204

Author(s):

Caoimhe Tiernan ◽

Thomas Comyns ◽

Mark Lyons ◽

Alan M Nevill ◽

Giles Warrington

Keyword(s):

Respiratory Tract ◽

Signs And Symptoms ◽

Training Load ◽

Soccer Players ◽

Upper Respiratory Tract ◽

Increased Risk ◽

Upper Respiratory ◽

Tract Infections ◽

Load Indices ◽

And Training

This study aimed to investigate the association between training load indices and Upper Respiratory Tract Infection (URTI) across different lag periods in elite soccer players. Internal training load was collected from 15 elite soccer players over one full season (40 weeks). Acute, chronic, Acute:Chronic Workload Ratio (ACWR), Exponentially Weighted Moving Averages (EWMA) ACWR, 2, 3 and 4-week cumulative load, training strain and training monotony were calculated on a rolling weekly basis. Players completed a daily illness log, documenting any signs and symptoms, to help determine an URTI. Multilevel logistic regression was used to analyze the associations between training load indices and URTIs across different lag periods (1 to 7-days). The results found a significant association between 2-week cumulative load and an increased likelihood of a player contracting an URTI 3 days later (Odds Ratio, 95% Confidence Interval: OR = 2.07, 95% CI = 0.026-1.431). Additionally, a significant association was found between 3-week cumulative load and a players’ increased risk of contracting an URTI 4 days later (OR = 1.66, 95% CI = 0.013–1.006). These results indicate that accumulated periods of high training load (2- and 3-week) associated with an increased risk of a player contracting an URTI, which may lead to performance decrements, missed training sessions or even competitions.

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Dispersion in total cost of care for Medicare fee-for-service (FFS) patients with metastatic pancreatic cancer receiving FDA-approved/NCCN Category 1 regimens at 340B verus non-340B institutions.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e18843 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e18843-e18843

Author(s):

Helen Latimer ◽

Samantha Tomicki ◽

Gabriela Dieguez ◽

Paul Cockrum ◽

George P. Kim

Keyword(s):

Pancreatic Cancer ◽

Cost Of Care ◽

Metastatic Pancreatic Cancer ◽

Fee For Service ◽

Total Cost ◽

Diagnosis Codes ◽

Heavily Pretreated ◽

Icd 10 ◽

Liposomal Irinotecan ◽

Line Of Therapy

e18843 Background: The Department of Health and Human Services (HHS) designed the 340B drug pricing program to allow institutions that service specialty populations to acquire drugs at lower prices. Objective: To analyze the dispersion in total cost of care (TCOC) for Medicare FFS patients (pts) with metastatic pancreatic cancer (m-PANC) treated at 340B or non-340B institutions, by NCCN Category 1 regimen. Methods: We identified pts with m-PANC using ICD-10 diagnosis codes in the 2016-18 Medicare Parts A/B/D 100% Research Identifiable Files. Study pts had 2+ claims with a pancreatic cancer diagnosis and Medicare FFS coverage for 6 months pre- and 3 months post-metastasis diagnosis. Study pts were treated with NCCN Category 1 regimens: 1L gemcitabine monotherapy (gem-mono), 1L gemcitabine/nab-paclitaxel (gem-nab), 1L FOLFIRINOX (FFX), and 2L liposomal irinotecan-based regimen (nal-IRI). Pts were attributed to 340B or non-340B institutions based on plurality of chemotherapy claims. TCOC reflects insurer-paid services per line of therapy (LOT) for 3 categories: chemotherapy/supportive drugs (chemo/Rx), inpatient care (IP), and other outpatient care (OP). We grouped pts by quartile (qrt) and evaluated drivers of TCOC and mean rates of admissions (admits/pt). Results: We identified 2,697 (340B) and 3,839 (non-340B) pts taking NCCN Category 1 regimens. Gem-mono represented 1% and 4% of all pts in 340B and non-340B institutions, respectively. Gem-nab accounted for 72% of pts in both cohorts. For gem-nab, FFX, and nal-IRI pts, median TCOC was similar in both cohorts, although mean TCOC by qrt was lower at 340B institutions than non-340B institutions, except for gem-nab in the 1st qrt. The components of TCOC were similar between 340B and non-340B institutions in all qrts. In both cohorts, % IP costs increased between the 1st and 4th qrt (340B:15% to 23%, non-340B:14% to 25%). From the 1st to the 4th qrt, admits/pt increased in both cohorts. In the 340B cohort, nal-IRI pts had the lowest admits/pt while gem-nab pts had the highest in all qrts. In the non-340B cohort, nal-IRI pts had the lowest admits/pt except for in the 1st qrt. Conclusions: Median TCOC was lower at 340B institutions than non-340B institutions for all regimens, and the range of TCOC dispersion was also smaller at 340B institutions. Across qrts, chemotherapy accounted for approximately half the TCOC; however, IP costs were proportionally higher in the 4th qrt. Comparing regimens, despite 2L nal-IRI pts being more heavily pretreated, median costs in each cohort were similar to 1L gem-nab and 1L FFX, while admits/pt were generally lower than 1L gem-nab and 1L FFX across qrts and cohorts.

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Abstract P250: A Strategy for Reliable Identification of Ischemic Stroke, Thrombolytics, and Thrombectomy in Large Administrative Databases

Stroke ◽

10.1161/str.52.suppl_1.p250 ◽

2021 ◽

Vol 52 (Suppl_1) ◽

Author(s):

Kori S Zachrison ◽

Sijia Li ◽

Mathew J Reeves ◽

Opeolu M Adeoye ◽

Carlos A Camargo ◽

...

Keyword(s):

Ischemic Stroke ◽

Administrative Data ◽

Administrative Databases ◽

Stroke Patients ◽

Inpatient Hospitalization ◽

Accurate Identification ◽

Stroke Research ◽

Diagnosis Codes ◽

Icd 10 ◽

Icd Codes

Background: Administrative data are frequently used in stroke research. Ensuring accurate identification of ischemic stroke patients, and those receiving thrombolysis and endovascular thrombectomy (EVT) is critical to ensure representativeness and generalizability. We examined differences in patient samples based on different modes of identification, and propose a strategy for future patient and procedure identification in large administrative databases. Methods: We used nonpublic administrative data from the state of California to identify all ischemic stroke patients discharged from an emergency department or inpatient hospitalization from 2010-2017 based on ICD-9 (2010-2015), ICD-10 (2015-2017), and MS-DRG discharge codes. We identified patients with interhospital transfers, patients receiving thrombolytics, and patients treated with EVT based on ICD, CPT and MS-DRG codes. We determined what proportion of these transfers and procedures would have been identified with ICD versus MS-DRG discharge codes. Results: Of 365,099 ischemic stroke encounters, most (87.7%) had both a stroke-related ICD-9 or ICD-10 code and stroke-related MS-DRG code; 12.3% had only an ICD-9 or ICD-10 code, and 0.02% had only a MS-DRG code. Nearly all transfers (99.9%) were identified using ICD codes. We identified32,433 thrombolytic-treated patients (8.9% of total) using ICD, CPT, and MS-DRG codes; the combination of ICD and CPT codes identified nearly all (98%). We identified 7,691 patients treated with EVT (2.1% of total) using ICD and MS-DRG codes; both MS-DRG and ICD-9/-10 codes were necessary because ICD codes alone missed 13.2% of EVTs. CPT codes only pertain to outpatient/ED patients and are not useful for EVT identification. Conclusions: ICD-9/-10 diagnosis codes capture nearly all ischemic stroke encounters and transfers, while the combination of ICD-9/-10 and CPT codes are adequate for identifying thrombolytic treatment in administrative datasets. However, MS-DRG codes are necessary in addition to ICD codes for identifying EVT, likely due to favorable reimbursement for EVT-related MS-DRG codes incentivizing accurate coding.

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Reasons that made aged people seek care at a basic health unit

Revista Latino-Americana de Enfermagem ◽

10.1590/s0104-11692009000500012 ◽

2009 ◽

Vol 17 (5) ◽

pp. 670-676

Author(s):

Mary Rosane Quirino Polli Rosa ◽

Zuleica Maria Patrício ◽

Maria Regina Silvério ◽

Davi Rumel

Keyword(s):

Health Care ◽

Signs And Symptoms ◽

Study Region ◽

Health Team ◽

Basic Health ◽

Aged People ◽

Medicine Prescription ◽

Icd 10 ◽

Initial Reading ◽

Care Demands

This quantitative study aimed to get to know the reasons that made aged people seek care at a basic health care outpatient clinic in the State of Santa Catarina, Brazil. The data was collected in the patient files of 401 aged people attended by the health team. Initial reading of these records evidenced 4634 reasons that, after qualitative analysis, were grouped under complaints and requests for attention. In a second analysis, these data were classified as R and Z, according to ICD-10. The R category - complaints expressed by signs and symptoms- equals 64% of the reasons, with "pain" as the most common one. The other reasons, 36%, corresponded to the Z category, called requests for attention, represented by medicine prescription requests and attendance for health control. The study evidenced the complexity of this population's health care demands in the study region, showing the need for that service to develop specific and interdisciplinary care.

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