Leadership, Management and Workforce Development
Sharing Session - 60 Minutes
The public health workforce needs to address community problems rooted in behaviors and complex associations involving the environment, income, education, and other social determinants of health. According to the Public Health 3.0 concept, these challenges require a robust and diverse workforce trained in social indicators, systems thinking, community engagement and leadership, use of disparate community data sources, and partnership development with government and nongovernment sectors. To adequately train and prepare the public health workforce to respond to complex problems, CDC’s Population Health Workforce Branch (PHWB) developed the Population Health Workforce Initiative (PHWI). This presentation provides an overview of PHWI, and initial evaluation results from two pilot sites. PHWB piloted this Initiative with interdisciplinary fellowship teams at one local and one state health department. Through the Initiative’s activities, the fellowship teams and their health departments were able to collaboratively define complex population health problems and design intervention plans to improve the community health outcomes. PHWB conducted key informant interviews to understand the Initiatives strengths and weaknesses. PHWI findings will be presented and next steps discussed. PHWI’s evaluation findings provide valuable information for potential program expansion and improvement.
How Big is the opioid crisis? A data Immersion:
Measuring the prevalence and burden of opioid-related problems in health care systems is challenging because of a lack of standard definitions, coding, and documentation practices.
As part of the Population Health Workforce Initiative (PHWI), we employed a multidisciplinary and multimethods strategy involving clinical and public health researchers, subject matter experts, informaticians, health economists, and data scientists, to gain a deep understanding of documentation and data coding processes and develop operational definitions of opioid use disorder (OUD), opioid misuse (OM), and opioid poisoning (OP). After this data immersion phase, we applied the developed definitions in Denver Health (DH), a large, safety-net healthcare system, to measure burden of opioid-related problems.
We developed two operational definitions containing different markers of OUD, OM, and OP in DH electronic health records. The markers used in each definition were guided by previous work and refined through engagement with DH stakeholders and examination of attributes of electronic health records. The first operational definition of OUD, OM, and OP was based on International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes. The second operational definition was based on ICD-10-CM codes and included the following set of additional markers: presence of opioid withdrawal assessments, structured opioid-related admission notes, and prescriptions for medications to treat opioid use disorder .
As a first case study, we applied the definitions to the inpatient setting to quantify number of people with OUD, OM, and OP and estimated direct costs for opioid-related problems for DH in 2017. We compared costs associated with each definition to illustrate significant differences in estimated burden of opioid-related problems when using different definitions.
As a second case study, we analyzed DH paramedics’ data during August 2017–April 2018 to illustrate limitations of hospital-based information to assess burden of OUD, OM, and OP in our community. In this setting, we compared number of cases where “narcan” or “naloxone” was included in the treatment section of trip reports to the number of cases where “heroin” was mentioned in trip reports. Two paramedics then independently reviewed results to assess the accuracy of both approaches. Discrepancies were solved by an independent arbiter, a senior paramedic. Based on the review, we determined which cases showed evidence of opioid misuse and attempted to pair them with DH records meeting the criteria of the diagnosis-based definition of OUD, OM, or OP.
By applying the first operational definition for DH’s inpatient setting during January–December 2017, we identified 220 hospitalizations representing $9.8 million in total charges. By applying the second operational definition of OUD, OM, and OP, we identified 739 hospitalizations representing $35,033,157 in total charges. Total costs were estimated using cost-to-charge ratios and were $13,346,099 for 739 hospitalizations; whereas, the mean all-payer charges per hospitalization were $47,406 with mean reimbursement of $9,751 (20.6%).
During August 2017–April 2018, we identified 1298 cases where paramedics administered narcan (n = 704), where narratives mentioned heroin (n = 414), or where paramedics administered narcan and narratives mentioned heroin (n = 180). A substantial majority (90%) of trip reports that mentioned “heroin” in the narratives vs. 55% of cases where paramedics administered narcan showed evidence of opioid misuse. In total, 1039 cases showed evidence of opioid misuse, 577 of them resulting in transportation to DH. These cases represented 360 distinct persons; just over one half (52%, 187/360) had an ICD-10-CM diagnosis code related to the diagnosis-based definition of OUD, OM, or OP in their DH electronic health records.
Recommendations/Practical Applications/Future Goals:
A multidisciplinary and multimethods strategy allowed us to determine that use of ICD-10-CM codes to identify OUD, OM and OP leads to an underestimation of the burden of opioid-related problems. We were also able to apply the developed definitions to integrate and summarize information from different clinical settings. As part of PHWI, we sought to apply the same methods to compare the burden of opioid-related problems in other contexts of DH (i.e., the emergency department and the outpatient settings). Future studies may be able to reproduce our study in a different healthcare setting to validate relevance of different definitions.
Cluster of Persons with Chronic and Behavioral Health Comorbidities Among Medicaid Beneficiaries — Utah, 2017
Background: In August of 2017, CDC fellows assigned to the Utah Population Health Workforce Initiative received notification that a limited proportion of Medicaid beneficiaries accounted for a substantial expenditure of resources. This pattern mirrored Medicaid nationally. These beneficiaries were possibly a heterogeneous group with complex needs. We investigated to identify clusters and recommend mitigation strategies.
Methods: The analysis included fiscal year 2017 Medicaid beneficiaries aged greater than equal to18 years. We geocoded beneficiary addresses, assigned them to census block groups and designated an area deprivation index (ADI) measuring socioeconomic status. We defined cases, based on prior analysis, as beneficiaries with ≥2 chronic and ≥1 behavioral health conditions using Medicaid chronic conditions warehouse definitions. Spearman rank correlation assessed census block group association of ADI quintiles and case proportion. Spatial mapping of prevalence was overlaid with Medicaid covered behavioral health facilities. Local Moran’s I statistic evaluated spatial autocorrelation of prevalence among census block groups.
Results: Geocoding was possible for 93.9% of beneficiaries (n = 157,739); 18.9% met the case definition. Among cases (n = 29,742), common chronic conditions included hypertension (56.0%), hyperlipidemia (35.5%), and diabetes (30.7%); common behavioral health conditions were depression (69.4%) and anxiety disorders (56.8%). 16.2% and 15.4% had drug and alcohol use disorders, respectively. Significant positive association between ADI and prevalence of cases (P <0.001) was noted. Spatial mapping revealed the highest number of cases (>4 times the mean) in 97 of 1690 census tracts comprising 11 counties; 6 census block groups with highest number of cases had no behavioral health facilities.
Conclusion: Approximately 1 of 5 Utah Medicaid beneficiaries has physical and behavioral health needs. Areas with greater socioeconomic deprivation have greater proportion of cases and less access to behavioral health facilities. Utah Medicaid could consider developing programs to that address population health and integrate services focusing on socioeconomically deprived areas.