Dataset Information

Assessing the Burden of Neonatal Abstinence Syndrome: Validation of ICD-9-CM Data, Florida, 2010-2011.

ABSTRACT:

Context

On October 1, 2015, the United States transitioned from using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to ICD-10-CM. Continuing to monitor the burden of neonatal abstinence syndrome (NAS) after the transition presently requires use of data dependent on ICD-9-CM coding to enable trend analyses. Little has been published on the validation of using ICD-9-CM codes to identify NAS cases.

Objective

To assess the validity of hospital discharge data (HDD) from selected Florida hospitals for passive NAS surveillance, based on ICD-9-CM codes, which are used to quantify baseline prevalence of NAS.

Design

We reviewed infant and maternal data for all births at 3 Florida hospitals from 2010 to 2011. Potential NAS cases included infants with ICD-9-CM discharge codes 779.5 and/or 760.72 in linked administrative data (ie, HDD linked to vital records) or in unlinked HDD and infants identified through review of neonatal intensive care unit admission logs or inpatient pharmacy records. Confirmed infant cases met 3 clinician-proposed criteria. Sensitivity and positive predictive value were calculated to assess validity for the 2 ICD-9-CM codes, individually and combined.

Results

Of 157 confirmed cases, 134 with 779.5 and/or 760.72 codes were captured in linked HDD (sensitivity = 85.4%) and 151 in unlinked HDD (sensitivity = 96.2%). Positive predictive value was 74.9% for linked HDD and 75.5% for unlinked HDD. For either HDD types, the single 779.5 code had the highest positive predictive value (86%), lowest number of false positives, and good to excellent sensitivity.

Conclusions

Passive surveillance using ICD-9-CM code 779.5 in either linked or unlinked HDD identified NAS cases with reasonable validity. Our work supports the use of ICD-9-CM code 779.5 to assess the baseline prevalence of NAS through 2015.

SUBMITTER: Phillips-Bell GS

PROVIDER: S-EPMC6956845 | biostudies-literature | 2020 Jan/Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assessing the Burden of Neonatal Abstinence Syndrome: Validation of ICD-9-CM Data, Florida, 2010-2011.

Phillips-Bell Ghasi S GS Holicky Abigail A Lind Jennifer N JN Sappenfield William M WM Hudak Mark L ML Petersen Emily E Anjorhin Suzanne S Watkins Sharon M SM Creanga Andreea A AA Correia Jane A JA

Journal of public health management and practice : JPHMP 20200101 1

<h4>Context</h4>On October 1, 2015, the United States transitioned from using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) to ICD-10-CM. Continuing to monitor the burden of neonatal abstinence syndrome (NAS) after the transition presently requires use of data dependent on ICD-9-CM coding to enable trend analyses. Little has been published on the validation of using ICD-9-CM codes to identify NAS cases.<h4>Objective</h4>To assess the validity of h ...[more]

PMID: 31765350

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Project description:ImportanceSubstantial increases in both neonatal abstinence syndrome (NAS) and maternal opioid use disorder have been observed through 2014.ObjectiveTo examine national and state variation in NAS and maternal opioid-related diagnoses (MOD) rates in 2017 and to describe national and state changes since 2010 in the US, which included expanded MOD codes (opioid use disorder plus long-term and unspecified use) implemented in International Classification of Disease, 10th Revision, Clinical Modification.Design, setting, and participantsRepeated cross-sectional analysis of the 2010 to 2017 Healthcare Cost and Utilization Project's National Inpatient Sample and State Inpatient Databases, an all-payer compendium of hospital discharge records from community nonrehabilitation hospitals in 47 states and the District of Columbia.ExposuresState and year.Main outcomes and measuresNAS rate per 1000 birth hospitalizations and MOD rate per 1000 delivery hospitalizations.ResultsIn 2017, there were 751 037 birth hospitalizations and 748 239 delivery hospitalizations in the national sample; 5375 newborns had NAS and 6065 women had MOD documented in the discharge record. Mean gestational age was 38.4 weeks and mean maternal age was 28.8 years. From 2010 to 2017, the estimated NAS rate significantly increased by 3.3 per 1000 birth hospitalizations (95% CI, 2.5-4.1), from 4.0 (95% CI, 3.3-4.7) to 7.3 (95% CI, 6.8-7.7). The estimated MOD rate significantly increased by 4.6 per 1000 delivery hospitalizations (95% CI, 3.9-5.4), from 3.5 (95% CI, 3.0-4.1) to 8.2 (95% CI, 7.7-8.7). Larger increases for MOD vs NAS rates occurred with new International Classification of Disease, 10th Revision, Clinical Modification codes in 2016. From a census of 47 state databases in 2017, NAS rates ranged from 1.3 per 1000 birth hospitalizations in Nebraska to 53.5 per 1000 birth hospitalizations in West Virginia, with Maine (31.4), Vermont (29.4), Delaware (24.2), and Kentucky (23.9) also exceeding 20 per 1000 birth hospitalizations, while MOD rates ranged from 1.7 per 1000 delivery hospitalizations in Nebraska to 47.3 per 1000 delivery hospitalizations in Vermont, with West Virginia (40.1), Maine (37.8), Delaware (24.3), and Kentucky (23.4) also exceeding 20 per 1000 delivery hospitalizations. From 2010 to 2017, NAS and MOD rates increased significantly for all states except Nebraska and Vermont, which only had MOD increases.Conclusions and relevanceIn the US from 2010 to 2017, estimated rates of NAS and MOD significantly increased nationally and for the majority of states, with notable state-level variation.

Project description:BackgroundWe aimed to estimate the impact of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding transition on traumatic injury-related hospitalization trends among young adults across a geographically and demographically diverse group of U.S. states.MethodsInterrupted time series analyses were conducted using statewide inpatient databases from 12 states and including traumatic injury-related hospitalizations in adults aged 19-44?years in 2011-2017. Segmented regression models were used to estimate the impact of the October 2015 coding transition on external cause of injury (ECOI) completeness (percentage of hospitalizations with a documented ECOI code) and on population-level rates of injury-related hospitalizations by nature, intent, mechanism, and severity of injury.ResultsThe transition to ICD-10-CM was associated with a drop in ECOI completion in the transition month (-?3.7%; P?<?.0001), but there was no significant change in the positive trend in ECOI completion from the pre- to post-transition periods. There were significant increases post-transition in the measured rates of hospitalization for traumatic brain injury (TBI), unintentional injury, mild injury (injury severity score (ISS) <?9), and injuries caused by drowning, firearms, machinery, other pedestrian, suffocation, and unspecified mechanism. Conversely, there were significant decreases in October 2015 in the rates of hospitalization for assault, injuries of undetermined intent, injuries of moderate severity (ISS 9-15), and injuries caused by fire/burn, other pedal cyclist, other transportation, natural/environmental, and other specified mechanism. A significant increase in the percentage of hospitalizations classified as resulting from severe injury (ISS?>?15) was observed when the general equivalence mapping maximum severity method for converting ICD-10-CM codes to ICD-9-CM codes was used. State-specific results for the outcomes of ECOI completion and TBI-related hospitalization rates are provided in an online supplement.ConclusionsThe U.S. transition from ICD-9-CM to ICD-10-CM coding led to a significant decrease in ECOI completion and several significant changes in measured rates of injury-related hospitalizations by injury intent, mechanism, nature, and severity. The results of this study can inform the design and analysis of future traumatic injury-related health services research studies that use both ICD-9-CM and ICD-10-CM coded data.Level of evidenceII (Interrupted Time Series).

Project description:BackgroundImplementation 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.ResultsThe 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.ConclusionsThe 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.

Project description:BACKGROUND:The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). OBJECTIVE:The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. METHODS:We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. RESULTS:We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). CONCLUSIONS:This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.

Dataset Information

Assessing the Burden of Neonatal Abstinence Syndrome: Validation of ICD-9-CM Data, Florida, 2010-2011.

Context

Objective

Design

Results

Conclusions

Publications

Assessing the Burden of Neonatal Abstinence Syndrome: Validation of ICD-9-CM Data, Florida, 2010-2011.

Similar Datasets

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