Multisite medical record review of emergency department visits for unspecified injury of head following the ICD-10-CM coding transition.

INTRODUCTION: In 2016, a proposed International Classification of Diseases, Tenth Edition, Clinical Modification surveillance definition for traumatic brain injury (TBI) morbidity was introduced that excluded the unspecified injury of head (S09.90) diagnosis code. This study assessed emergency department (ED) medical records containing S09.90 for evidence of TBI based on medical documentation. METHODS: State health department representatives in Maryland, Kentucky, Colorado and Massachusetts reviewed a target of 385 randomly sampled ED records uniquely assigned the S09.90 diagnosis code (without proposed TBI codes), which were initial medical encounters among state residents discharged home during October 2015-December 2018. Using standardised abstraction procedures, reviewers recorded signs and symptoms of TBI, and head imaging results. A tiered case confirmation strategy was applied that assigned a level of certainty (high, medium, low, none) to each record based on the number and type of symptoms and imaging results present in the record. Positive predictive value (PPV) of S09.90 by level of TBI certainty was calculated by state. RESULTS: Wide variation in PPV of sampled ED records assigned S09.90: 36%-52% had medium or high evidence of TBI, while 48%-64% contained low or no evidence of a TBI. Loss of consciousness was mentioned in 8%-24% of sampled medical records. DISCUSSION: Exclusion of the S09.90 code in surveillance estimates may result in many missed TBI cases; inclusion may result in counting many false positives. Further, missed TBI cases influenced by incidence estimates, based on the TBI surveillance definition, may lead to inadequate allocation of public health resources.

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

BACKGROUND: In 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. METHODS: Four 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). RESULTS: PPVs 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%. DISCUSSION: ICD-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.

Descriptive exploration of overdose codes in hospital and emergency department discharge data to inform development of drug overdose morbidity surveillance indicator definitions in ICD-10-CM.

BACKGROUND: In October 2015, discharge data coding in the USA shifted to the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM), necessitating new indicator definitions for drug overdose morbidity. Amid the drug overdose crisis, characterising discharge records that have ICD-10-CM drug overdose codes can inform the development of standardised drug overdose morbidity indicator definitions for epidemiological surveillance. METHODS: Eight states submitted aggregated data involving hospital and emergency department (ED) discharge records with ICD-10-CM codes starting with T36-T50, for visits occurring from October 2015 to December 2016. Frequencies were calculated for (1) the position within the diagnosis billing fields where the drug overdose code occurred; (2) primary diagnosis code grouped by ICD-10-CM chapter; (3) encounter types; and (4) intents, underdosing and adverse effects. RESULTS: Among all records with a drug overdose code, the primary diagnosis field captured 70.6% of hospitalisations (median=69.5%, range=66.2%-76.8%) and 79.9% of ED visits (median=80.7%; range=69.8%-88.0%) on average across participating states. The most frequent primary diagnosis chapters included injury and mental disorder chapters. Among visits with codes for drug overdose initial encounters, subsequent encounters and sequelae, on average 94.6% of hospitalisation records (median=98.3%; range=68.8%-98.8%) and 95.5% of ED records (median=99.5%; range=79.2%-99.8%), represented initial encounters. Among records with drug overdose of any intent, adverse effect and underdosing codes, adverse effects comprised an average of 74.9% of hospitalisation records (median=76.3%; range=57.6%-81.1%) and 50.8% of ED records (median=48.9%; range=42.3%-66.8%), while unintentional intent comprised an average of 11.1% of hospitalisation records (median=11.0%; range=8.3%-14.5%) and 28.2% of ED records (median=25.6%; range=20.8%-40.7%). CONCLUSION: Results highlight considerations for adapting and standardising drug overdose indicator definitions in ICD-10-CM.

Development and evaluation of a standardized research definition for opioid overdose outcomes.

Background: Increasing epidemiologic and intervention research is being conducted on opioid overdose, a serious and potentially fatal outcome. However, there is little consensus on how to verify opioid overdose outcomes for research purposes. To ensure reproducibility, minimize misclassification, and permit data harmonization across studies, standardized and consistent overdose definitions are needed. The aims were to develop a case criteria classification scheme based on information commonly available in medical records and to compare it with reviewing physician clinical impression and simple encounter documentation. Methods: In 2 large health systems, we developed a case criteria classification scheme for opioid overdose based on prior literature, expert opinion, and pilot testing with sample medical records. We then identified emergency department and hospital encounters (n = 259) with at least 1 International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) code representing a broad range of opioid and non-opioid related poisonings. Physicians conducted structured medical record reviews to identify the proposed case criteria and generate a clinical impression, and trained abstractors verified documentation. We then compared the case criteria classification scheme with clinical impression and encounter documentation. Results: We developed a quantitative opioid overdose case criteria classification scheme that included 3 sets of major criteria and 9 minor criteria (supporting documentation). For the encounters identified using poisoning codes, the proportion verified as opioid overdoses using the case criteria classification scheme, clinical impression, and encounter documentation ranged from 50.4% to 52.7% at one site and 55.5% to 67.2% at the second site. Discrepancies across approaches and sites related to differences in available records and documentation of clinical signs of overdose. Conclusions: We propose a novel case criteria classification scheme for opioid overdose that could be used to rigorously and consistently define overdose across multiple research settings. However, prior to widespread use, further refinement and validation are needed.

Evaluating Behavioral Health Surveillance Systems.

In 2015, more than 27 million people in the United States reported that they currently used illicit drugs or misused prescription drugs, and more than 66 million reported binge drinking during the previous month. Data from public health surveillance systems on drug and alcohol abuse are crucial for developing and evaluating interventions to prevent and control such behavior. However, public health surveillance for behavioral health in the United States has been hindered by organizational issues and other factors. For example, existing guidelines for surveillance evaluation do not distinguish between data systems that characterize behavioral health problems and those that assess other public health problems (eg, infectious diseases). To address this gap in behavioral health surveillance, we present a revised framework for evaluating behavioral health surveillance systems. This system framework builds on published frameworks and incorporates additional attributes (informatics capabilities and population coverage) that we deemed necessary for evaluating behavioral health-related surveillance. This revised surveillance evaluation framework can support ongoing improvements to behavioral health surveillance systems and ensure their continued usefulness for detecting, preventing, and managing behavioral health problems.

Overcoming inertia: increasing public health departments' access to evidence-based information and promoting usage to inform practice.

In 2010, the New England Region-National Network of Libraries of Medicine at University of Massachusetts Medical School received funding to improve information access for public health departments in 6 New England states and Colorado. Public health departments were provided with desktop digital access to licensed e-resources available through special pricing. In January through mid-April 2012, we evaluated the effectiveness of providing access to and training for using e-resources to public health department staff to motivate usage in practice. We found that additional strategies are needed to accomplish this.

Tools for identifying and prioritizing evidence-based obesity prevention strategies, Colorado.

Colorado's adult obesity rate has more than doubled since 1995, prompting its Department of Public Health and Environment to list obesity as its top prevention priority. To initiate comprehensive and effective action, the department used a well-known evidence-based public health framework developed by Brownson and others. This article describes the tools and process developed to conduct 2 of the 7 stages in this framework that challenge public health organizations: reviewing the literature and prioritizing effective strategies from that literature. Forty-five department staff participated in an intensive literature review training to identify physical activity and nutrition strategies that effectively address obesity and worked with external stakeholders to prioritize strategies for the state. Divided into 8 multidisciplinary teams organized by the setting where public health could exert leverage, they scanned the scientific literature to identify potential strategies to implement. These teams were trained to use standardized tools to critique findings, systematically abstract key information, and classify the evidence level for each of 58 identified strategies. Next, departmental subject matter experts and representatives from local public health and nonprofit health agencies selected and applied prioritization criteria to rank the 58 strategies. A team charter, group facilitation tools, and 2 web-based surveys were used in the prioritization stage. This process offered the staff a shared experience to gain hands-on practice completing literature reviews and selecting evidence-based strategies, thereby enhancing Colorado's obesity prevention efforts and improving public health capacity. Practitioners can use these tools and methodology to replicate this process for other health priorities.

Tools for implementing an evidence-based approach in public health practice.

Increasing disease rates, limited funding, and the ever-growing scientific basis for intervention demand the use of proven strategies to improve population health. Public health practitioners must be ready to implement an evidence-based approach in their work to meet health goals and sustain necessary resources. We researched easily accessible and time-efficient tools for implementing an evidence-based public health (EBPH) approach to improve population health. Several tools have been developed to meet EBPH needs, including free online resources in the following topic areas: training and planning tools, US health surveillance, policy tracking and surveillance, systematic reviews and evidence-based guidelines, economic evaluation, and gray literature. Key elements of EBPH are engaging the community in assessment and decision making; using data and information systems systematically; making decisions on the basis of the best available peer-reviewed evidence (both quantitative and qualitative); applying program-planning frameworks (often based in health-behavior theory); conducting sound evaluation; and disseminating what is learned.

Multi-site medical record review for validation of intentional self-harm coding in emergency departments.

BACKGROUND: Codes in the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), are used for injury surveillance, including surveillance of intentional self-harm, as they appear in administrative billing records. This study estimated the positive predictive value of ICD-10-CM codes for intentional self-harm in emergency department (ED) billing records for patients aged 10 years and older who did not die and were not admitted to an inpatient medical service. METHODS: The study team in Maryland, Colorado, and Massachusetts selected all or a random sample of ED billing records with an ICD-10-CM code for intentional self-harm (specific codes that began with X71-X83, T36-T65, T71, T14.91). Positive predictive value (PPV) was determined by the number and percentage of records with a physician diagnosis of intentional self-harm, based on a retrospective review of the original medical record. RESULTS: The estimated PPV for the codes' capture of intentional self-harm based on physician diagnosis in the original medical record was 89.8% (95% CI 85.0-93.4) for Maryland records, 91.9% (95% CI 87.7-95.0) for Colorado records, and 97.3% (95% CI 95.1-98.7) for Massachusetts records. CONCLUSION: Given the high PPV of the codes, epidemiologists can use the codes for public health surveillance of intentional self-harm treated in the ED using ICD-10-CM coded administrative billing records. However, these codes and related variables in the billing database cannot definitively distinguish between suicidal and non-suicidal intentional self-harm.

Interrupted time series design to evaluate the effect of the ICD-9-CM to ICD-10-CM coding transition on injury hospitalization trends.

BACKGROUND: Implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) in the U.S. on October 1, 2015 was a significant policy change with the potential to affect established injury morbidity trends. This study used data from a single state to demonstrate 1) the use of a statistical method to estimate the effect of this coding transition on injury hospitalization trends, and 2) interpretation of significant changes in injury trends in the context of the structural and conceptual differences between ICD-9-CM and ICD-10-CM, the new ICD-10-CM-specific coding guidelines, and proposed ICD-10-CM-based framework for reporting of injuries by intent and mechanism. Segmented regression analysis was used for statistical modeling of interrupted time series monthly data to evaluate the effect of the transition to ICD-10-CM on Kentucky hospitalizations' external-cause-of-injury completeness (percentage of records with principal injury diagnoses supplemented with external-cause-of-injury codes), as well as injury hospitalization trends by intent or mechanism, January 2012-December 2017. RESULTS: The segmented regression analysis showed an immediate significant drop in external-cause-of-injury completeness during the transition month, but returned to its pre-transition levels in November 2015. There was a significant immediate change in the percentage of injury hospitalizations coded for unintentional (3.34%) and undetermined intent (- 3.39%). There were immediate significant changes in the level of injury hospitalization rates due to poisoning, suffocation, struck by/against, other transportation, unspecified mechanism, and other specified not elsewhere classifiable mechanism. Significant change in slope after the transition (without immediate level change) was identified for assault, firearm, cut/pierce, and motor vehicle traffic injury rates. The observed trend changes reflected structural and conceptual features of ICD-10-CM coding (e.g., poisoning and suffocations are now captured via diagnosis codes only), new coding guidelines (e.g., requiring coding of injury intent as "accidental" if it is unknown or unspecified), and CDC proposed external-cause-of-injury code groupings by injury intent and mechanism. Researchers can replicate this methodology assessing trends in injuries or other ICD-10-CM-coded conditions using administrative billing data sets. CONCLUSIONS: The CDC 's Proposed Framework for Presenting Injury Data Using ICD-10-CM External Cause of Injury Codes provided a logical transition from the ICD-9-CM-based matrix on injury hospitalization trends by intent and mechanism. Our findings are intended to raise awareness that changes in the ICD-10-CM coding system must be understood to assure accurate interpretation of injury trends.

Comparison of nursing home residents with and without traumatic brain injury: use of the Minimum Data Set.

OBJECTIVE: To describe the magnitude of the population with traumatic brain injury (TBI) in Colorado living in nursing homes and compare these residents to the nursing home residents with neither TBI nor dementia. METHODS: The standardized Minimum Data Set of resident assessments was used to describe the behavior, cognitive performance, activities of daily living, and discharge potential of residents. RESULTS: There were 16,478 nursing home residents in 2005, of whom 1.4% had TBI but not dementia, 0.7% had both TBI and dementia, and 50.2% had neither diagnosis. The prevalence of TBI in this population was 2.1%. TBI residents without dementia were younger (median age 53 years). A larger proportion consisted of men (64%), from a racial/ethnic minority (24%), and needed greater assistance with eating, toileting, and hygiene. The percent with severe cognitive impairment was greater for individuals with TBI (22%) compared to those with neither TBI nor dementia (5%). Fewer TBI residents expressed a preference to return to the community. CONCLUSION: These differences suggest the need for increased training and staffing to care for nursing home residents with TBI.

Sex differences in mortality after traumatic brain injury, Colorado 1994-1998.

PRIMARY OBJECTIVE: The purpose of this study was to assess the relationship between sex and traumatic brain injury (TBI) mortality. METHODS AND PROCEDURES: A total of 20,465 persons with TBI were identified from a Colorado population-based surveillance system for 1994-1998. Case fatality ratios were calculated to identify sex differences for selected risk factors. Unconditional logistic regression was used to determine the relationship between TBI mortality and sex controlling for risk factors. MAIN OUTCOMES AND RESULTS: Adjusting for age, race, metropolitan residence and penetrating injury, the estimated odds of TBI mortality for males compared to females was 1.21 (95% CI 1.10, 1.34) for pre-hospital fatalities and 1.19 (95% CI 1.05, 1.37) for hospital fatalities. CONCLUSION: Results indicate differences in TBI mortality comparing males and females. Future studies are warranted to identify if behaviour and physiological responses are associated with TBI outcomes among males and females.