Purpose Opioid overdose death rates rose 36% from 2015 to 2016 in Missouri, indicating a worsening of the opioid overdose epidemic. To better understand urban and rural differences in nonfatal opioid overdoses treated in Missouri emergency departments, this paper analyzed hospital billing data from emergency departments due to opioid overdose from 2012 to 2016. Methods Emergency department records meeting the opioid overdose case definition were aggregated into 6 progressively rural groups using the National Center for Health Statistics (NCHS) urban‐rural county classification from 2013. These data were analyzed to determine significant trends amongst and between the geographic groups. Findings Generally, the magnitude of opioid overdose morbidity decreased as levels of rurality increased, using annual percentage change as the metric of change. Over the study period, Missouri's most urban counties had significantly higher rates of opioid overdose and saw larger percentage increases in rates compared to more rural areas. Statewide, all rural‐urban classifications experienced increases in heroin overdose morbidity; however, there was extreme variation in the trajectory of those increases. Heroin overdose rates were much higher in urban areas than rural areas. Conversely, rural and urban areas saw relatively similar patterns for non‐heroin opioid overdoses, though overall magnitude of these increases was more modest across all geographic groups. Conclusions The results from this analysis can help inform prioritization of strategies and resources to implement activities addressing the opioid overdose epidemic. Using a rich hospital discharge database could allow for further analysis of subpopulations to enhance personalization and customization of care.
ObjectiveIn this analysis we examine Missouri NAS discharge rates with special focus on the ICD-9-CM/ICD-10-CM transition and changes in code descriptions.IntroductionNeonatal Abstinence Syndrome (NAS) rates have tripled for Missouri residents in the past three years. NAS is a condition infants suffer soon after birth due to withdrawal after becoming opioid-dependent in the womb. NAS has significant immediate health concerns and can have long term effects on child development and quality of life.2 The Missouri Department of Health and Senior Services (MODHSS) maintains the Patient Abstract System (PAS), a database of inpatient, emergency room, and outpatient records collected from non-federal hospitals and ambulatory surgical centers throughout the state. PAS records contain extensive information about the visit, patient, and diagnosis. When examining 2015 annual PAS data for NAS-associated discharges, Missouri analysts noticed a greater than 50% increase in discharges, even larger than anticipated in light of the opioid epidemic. Provisional 2016 data produced similar high rates, dispelling the notion that the trend was a transitional problem. In fact, provisional 2016 rates are 115% higher than NAS rates in 2015. In contrast, percentage change of opioid misuse emergency department visits (as defined by MODHSS) for Missouri women age 18-44 was +13% in 2015 and -12% in 2016.MethodsNAS discharges for Missouri residents under the age of 1 were identified using all available diagnosis fields of the PAS record, using finalized data from 2014 and 2015 and provisional data from 2016. Results were stratified by quarter and ICD-CM code. Rates for each of these stratifications were calculated using Missouri resident live births as the denominator. Adhering to methodology used by MODHSS to calculate significance on its public data query tool, 95% confidence intervals were used to determine statistical significance. Depending on numerator size, either Poisson or the inverse gamma methodology was utilized to analyze changes in discharge rates over time. Two ICD-9-CM codes and four ICD-10-CM codes (identified as equivalents using an in-house crosswalk system) were used as NAS indicators (Figure 1).ResultsAn exploration of the data by quarter and diagnosis code (ICD-9-CM or ICD-10-CM), as well as supporting information from the Centers for Medicare & Medicaid Services, show that definitional changes to ICD-10-CM codes P044 and P0449, (previously 76072 in ICD-9-CM coding), was responsible for the majority of the NAS rate increase in Missouri. Annual rates for 76072 and its equivalents jumped significantly from a rate of 3.82 (per 1,000) to 8.22 Q3 to Q4-2015 (95% CI: 3.39-4.29, 7.57-8.87), while ICD-9-CM code 7795 and its equivalents had a more modest rise, from 5.57 to 6.17, which was not statistically significant (95% CI: 5.04-6.13, 5.62-6.76). Once this anomaly was identified, examination of the code’s description was conducted. This exposed a change in definition, with the words ‘suspected to be’ added to the ICD-10-CM long description, which were not present in the ICD-9-CM equivalent. Further complicating matters is a 2017 revision (effective Q3-2016) deleting the ‘suspected’ language from the description. This reversion to language more closely aligning with prior descriptions may be the reason for the slight decrease in discharges coded to P044 in the provisional Q4-2016 PAS data. Though this dataset is not finalized, there was a decrease in discharges that included code P044 from 27.50 in Q3-2016 to 23.15 in Q4-2016 (Figure 2, Figure 3).ConclusionsWhile NAS discharge rates are undoubtedly increasing in Missouri in tune with the opioid epidemic, the extreme escalation from 2014 to 2016 is, at least partially, the result of a definitional change that came with the transition from ICD-9-CM to ICD-10-CM and not a true indication of profound intensification. Indeed, the definitional change of a single ICD-CM code was responsible, in part, for a greater than three-fold increase in NAS discharge rates in Missouri. This analysis will allow public health program planners to better understand NAS trends and adjust intervention strategies accordingly. Further analysis exploring quarterly trends associated with the 2017 ICD-10-CM revision are ongoing.References1. Centers for Medicare & Medicaid Services. ICD-9-CM and ICD-10. https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/index.html.2. Stanford Children’s Health. Neonatal Abstinence Syndrome. http://www.stanfordchildrens.org/en/topic/default?id=neonatal-abstinence-syndrome-90-P02387.
ObjectiveCompare rate changes over time for Emergency Department (ED) visits due to opioid overdose in urban versus rural areas of the state of Missouri.IntroductionLike many other states in the U.S., Missouri has experienced large increases in opioid abuse resulting in hundreds dying each year and thousands of ED visits due to overdose. Missouri has two major urban areas, St. Louis and Kansas City and a few smaller cities, while the remainder of the state is more rural in nature. The opioid epidemic has impacted all areas in the state but the magnitude of that impact varies as well as the type of opioid used. Missouri Department of Health and Senior Services (MODHSS) maintains the Patient Abstract System (PAS) which contains data from hospitals and ambulatory surgical centers throughout the state. PAS includes data from ED visits including information on diagnoses, patient demographics, and other information about the visit. MODHSS also participates in the Enhanced State Surveillance of Opioid-involved Morbidity and Mortality project (ESOOS). One major aspect of this surveillance project is the collection of data on non-fatal opioid overdoses from ED visits. Through this collection of data, MODHSS analyzed opioid overdose visits throughout the state, how rates compare across urban and rural areas, and how those rates have changed over time.MethodsThe 115 counties in Missouri were organized into the six-level urban-rural classification scheme developed by the National Center for Health Statistics (NCHS). The attached table shows the breakout of counties into the six different categories. The data years analyzed were 2012 through 2016. ED visits due to opioid overdose were identified using case definitions supplied by ESOOS. Overdoses were analyzed in three different categories—all opioids, heroin, and non-heroin opioids. The all opioid category combines heroin and non-heroin opioids. Non-heroin opioids includes prescription drugs such as oxycodone, hydrocodone, fentanyl, and fentanyl analogues. Annual rates per 10,000 were calculated for each county classification using population estimates. Confidence intervals (at 95%) were then calculated using either inverse gamma when the number of ED visits was under 500, or Poisson when the number was 500 or more. Changes over time were calculated using both a year over year method and a 5 year change method.ResultsOverall opioid rates have increased in all geographic areas during the 5 year period analyzed. Large Central Metro and Large Fringe Metro counties had the highest rates of ED visits due to opioid overdose. These two classifications also saw the largest increases in rates. The Large Central Metro counties collectively increased over 125%, while the Large Fringe Metro area increased 130%. Both areas experienced statistically significant increases year-to-year between 2014 and 2016 in addition to the overall 5 year period of 2012-2016.Analysis was also conducted for heroin and non-heroin subsets of opioid abuse. There were important differences in these two groups. For heroin ED visits, the highest rates were found in the Large Central Metro and Large Fringe Metro regions. However, the largest increase in percentage terms were found in the Medium Metropolitan, Micropolitan and Noncore regions which all saw increases of over 300%. Notably, every region experienced increases of over 150%. The Medium Metro had two consecutive years (2013/2014 and 2014/2015) where the heroin ED rate more than doubled.In contrast, non-heroin ED visits did not experience such a large increase over time. Most areas saw small fluctuations year-to-year with moderate overall increases over the 5-year time period. The exception to this trend is the Large Fringe Metro area, which saw increases every year most notably between 2014 and 2015 and had by far the largest 5 year increase at 82%.ConclusionsThe urban areas in Missouri continue to have the highest rates of opioid overdose, however all areas within the state have experienced very large increases in heroin ED visits within the past five years. The increase in heroin ED visits in the rural areas suggests the abuse of heroin has now spread throughout the state, as rates were much lower in 2012. The steady increase in non-heroin opioids unique to the Large Fringe Metro may be due to the availability of fentanyl in urban areas especially the St. Louis area. This possible finding would correspond with the increased deaths due to fentanyl experienced in and around the St. Louis urban area that has been identified through analysis of death certificate data.
ObjectiveBy the end of this session, users will be able to describe the innovative and multilayered suppression rules that are applied to Missouri's homegrown health data web query system. They will also be able to use the lessons learned and user feedback described in the session to facilitate discussions surrounding the application of suppression to their specific data systems.IntroductionIn Spring 2017, the Missouri Department of Health and Senior Services (MODHSS) launched the Missouri Public Health Information Management System (MOPHIMS) web-based health data platform. Missouri has supported a similar data system since the 1990s, allowing the public, local public health departments, and other stakeholders access to community level birth, death, and hospitalization data (among other datasets). The MOPHIMS system is composed of two separate pieces. Community Data Profiles are topic-, disease-, or demographic-specific reports that contain 15-10 indicators relevant to the report. Because these static reports are developed in-house a multilayered suppression rule is not required. The second piece of MOPHIMS, the Data MICAs, or Missouri Information for Community Assessent, can be used to create customized datasets that slice and dice up to a dozen demographic and system-specific variables to answer complex research questions.The MOPHIMS interface features, among other things, a new and innovative method for addressing confidentiality concerns through the suppression of health data. This pioneering approach integrates multi-level logic that uses inner and outer cell analytics, the use of exempt and conditionally exempt variables, and multiple levels of user access. Moving beyond a simple model of suppressing any values below a certain threshold, MOPHIMS takes a bold step in providing users exceptionally granular data while still protecting citizen privacy.MethodsIn order to implement this new suppression methodology, MODHSS worked with both internal information technology resources (OA-ITSD) and outside contractors to develop the suppression rules utilized in the Data MICAs. Before these meetings began, MODHSS analysts met weekly to determine the overall goals and frames for the rule, knowing that writing the code to implement the complicated and comprehensive vision would be a collaborative and iterative process. Because the MOPHIMS system is homegrown and this specific confidentiality process is not currently utilized (to our knowledge) elsewhere, all of those at the discussion table were required to be innovative, open to criticism, and willing to engage in extremely detailed explanations. A team of users from Missouri’s local public health departments provided feedback throughout this process.A basic description of the process flow that occurs before suppression is applied in MOPHIMS follows. To begin, de-identified record-level data are loaded into online analytical processing (OLAP) cubes and relational databases. No suppression is applied to these back end databases. The information is then aggregated for display on the front end screens of the Data MICAs based on customized user selections. Depending upon which level of access a user has logged in, suppression is then applied to the data output generated using these customized selections. Not only are the rules applied to data tables but also to the MOPHIMS data visualization tools, which include multiple types of charts and maps.ResultsIn addition to the rules themselves, MOPHIMS contains a mechanism that allows users to log in at different levels of access. Public and Registered user levels are free and available to all operators with a valid e-mail address. Partner level access is reserved for epidemiologists at the state and local level who are using the Data MICAs for program planning, evaluation, and grant writing. Because these individuals are required to adhere to the same data dissemination policies as those who create the MOPHIMS system, Partner level access turns off suppression in the MOPHIMS system. Values that would be suppressed at the Public or Registered user levels are shown in italicized, red font. A multi-level approval process is required for individuals to obtain Partner level access to MOPHIMS.ConclusionsMODHSS created an innovative suppression system that allows public health planners to access granular data through customizable queries without risking a confidentiality breach. Users have indicated this is highly preferable to a blanket suppression rule that hides any value under a certain threshold. Additionally, approved MOPHIMS users can view specially formatted values that would otherwise have been suppressed. The flexibility associated with creating a homegrown web query system has allowed the formation and implementation of this multilayered rule, which likely would not have been possible if using an off-the-shelf product. Data disseminators are encouraged to review current confidentiality and suppression rules to determine whether they might be modified to provide more granular data users while still protecting the privacy of citizens.
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