270 likes | 515 Views
State Epidemiological Data System (SEDS). Center for Substance Abuse Prevention (CSAP) Alcohol, Tobacco and Illicit Drug Epidemiological Data Web Resource for SPF SIG State Epidemiological Workgroups. Needs Assessment: SEW Tasks. Establish SEW Structure
E N D
State Epidemiological Data System (SEDS) Center for Substance Abuse Prevention (CSAP)Alcohol, Tobacco and Illicit DrugEpidemiological Data Web Resource for SPF SIG State Epidemiological Workgroups
Needs Assessment: SEW Tasks • Establish SEW Structure • Identify and select key constructs and indicators to assess burden of substance use in State • Establish a set of epidemiological dimensions for describing burden and define burden (Epi Profile) • Identify and apply criteria and process for defining priority problems
CSAP SEDS Overview • Data system of State (and where available county) level data on alcohol, tobacco and illicit drug consequences and consumption • Available on the web Http://www.epidcc.samhsa.gov • Developed for use by the SPF State Epidemiological Workgroups
Outcomes-Based Prevention Conceptual Framework for the Data System Substance-related consequences & Substance Use Risk and Protective Factors/ Causal Factors Programs/Policies/ Practices Strategic Prevention Framework Planning, Monitoring, Evaluation and Replanning
Two Categories of Data • Consequences • Undesired health, social and safety consequences of substance use • Scientific evidence to support alcohol, tobacco or illicit drug as contributing factor to consequence (acknowledging multiple causes) • Consumption • Patterns of substance use • Frequency, quantity • High risk situations • High risk populations
SEDS Development Process • Small group of technical experts • Identify key alcohol, tobacco and illicit drug related consequences and consumption patterns • For each consequence and consumption pattern, identify one or more indicators • For each indicator, identify one or more sources of data which meet a set of minimum standards
Criteria for Inclusion of Indicators • National source • Availability at the state level • Periodic collection over at least 3 to 5 past years • Validity • Consistency • Sensitivity
Application of Criteria Implications and Issues • Some relevant data not available from national sources and/or at state level • e.g., alcohol use by pregnant women, morbidity (burns, falls) • Measurement issues preclude use of some indicators of acknowledged important substance related consequences • e.g., work problems • Imperfect applications • e.g., crime reports
SEDS Data Organization • Constructs • Organized consumption patterns and consequences into grouping (constructs) • Indicators • Specific measure with definition and data source
Disaggregation by Demographics/Geography/Time • Age/Grade • Gender • Race/Ethnicity • State/County • Year
Use By States • Variation in need to access data • Additional data may be available in States • Of additional constructs, across time, for sub-state units • If data is added, consider…. • Placement in outcomes-based prevention model • Consequence with scientific evidence to support alcohol, tobacco or illicit drug as contributing factor • Substance use behavior • Meet criteria for validity, consistency, periodicity and sensitivity
Data Constructs/Indicators: Steps • Establish constructs • Establish criteria for selecting indicators • Choose indicators • Organize indicators to show relationships by construct
Constructs/Indicators: Lessons Learned • Start with what you want to know. Start with constructs rather than sources. • Time constraints, clarity • Establish set of measurement criteria for choosing indicators • Problem indicators vs. response indicators • Focus on choosing key constructs and indicators • Data quantity and limits to abilities to absorb • Ongoing monitoring has cost constraints • Sentinel indicators exist
Constructs/Indicators: Lessons Learned • Create an organizational framework to look at your data • Create sequential plan for examining the data • Start with consequences….then look at consumption • Keep expected relationships in mind and seek to understand them. Organize your data to reflect these relationships • Keep your various goals separate…don’t drop data that doesn’t fit ‘all’ needs • Be transparent and track your data selection decisions (People change/Processes continue)