Data and Information Management in Public Health Data and

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Data and Information Management in Public Health Adrienne S. Ettinger, Sc.D., M.P.H. Environmental Public Health Tracking Methods Course July 2004

Outline • Information Management in Public Health – Information – Infrastructure – Informatics

• Database Design – Relational Model – Data Linkage

• Data Mining and Data Warehousing

J2

Information Management – why? • Data needs – Need for good record-keeping and documentation – Need for program evaluation – Need high quality data to support valid inference

• Data vs. Information – Public health tradition of generating data – Staff time and skills not being spent on analysis – Possibility of automating analyses

Slide 3 J2

Perhaps need to clarify the difference between data and information JHerbstman, 2/2/2005

Information Integration • Lack of existing data standards • Incompatible systems • Paper systems • Categorical stand-alone systems • Inability to identify (link) individuals being served in multiple systems • Integration of individual multiple records

J1

Rates of IT Failure: High • 16.2% were “project successful” (software projects that are completed on-time and on-budget among American companies and governments)

• 52.7% were “project challenged” (they were completed and operational but over-budget, over the time estimate, and offers fewer features and functions than originally scheduled)

• 31.1% were “project impaired” (cancelled) Source: Charting the Seas of Information Technology The Standish Group 1994

Slide 5 J1

What is the context for this slide? What aspect of IT? JHerbstman, 2/2/2005

Barriers to IT in Public Health 1. Information 2. Infrastructure 3. Informatics

1. Information • Surveillance data – Only 15-20% of reportable cases reported – Delays of days to weeks – Not typically in electronic form

• Other relevant data not electronically available – – – –

Environment, Environment injury, etc. Guidelines Contacts Training materials

J3

Information in Progress • NEDSS = National Electronic Disease Surveillance System – Architectural elements – Public health conceptual data model

• Knowledge management – Preventioneffects.net – Encoding of clinical guidelines • Disseminate • Point-of-care reminders

Slide 8 J3

Maybe an intro bullet point about how NEDSS and other efforts are being made to remedy some of the problems about public health 'Information" JHerbstman, 2/2/2005

2. Infrastructure • Information technology – Only 48.9% of local health departments have high-speed continuous internet connections (NACCHO, 1999)

• Workforce – 83% of local health departments indicate that computer training is a key need (NACCHO, 1996)

Desktop Web Access Among Minnesota Public Health Staff by Job Class, February 2000

60% 50% 40% 30%

All Managers All Support Staff All Professional Staff

20% 10% 0%

Source: Minnesota Health Alert Network (HAN) Project, 2000

J4

Infrastructure Development • INPHO = Information Network for Public Health Officials (state) – Ending in FY2001

• HAN = Health Alert Network (local) • Frist-Kennedy authorization – Infrastructure standards/ assessment – Preparedness of public health system

Slide 11 J4

Perhaps a bit more information describing these efforts. . .maybe Frist-Kennedy bullet ok JHerbstman, 2/2/2005

J6

3. Informatics • The systematic application of computer & information science and technology to public health practice, research, and learning...

Slide 12 J6

need to integrate this slide and the next slide (12 and 13) JHerbstman, 2/2/2005

3. Informatics • The systematic application of computer & information science and technology to public health practice, research, and learning…through integrated information resource management planning; assembling and managing teams with diverse skill sets; managing tasks to complete projects, etc.

J7

Management Skills – IT projects expensive and high risk – Interdisciplinary teams required – New skills needed by public health managers

Slide 14 J7

not sure the point of this slide. . .perhaps can be combined with the next one? JHerbstman, 2/2/2005

Specific roles - public health managers • Specify requirements (minimum) • Facilitate integrated, coordinated IT development (through advice, leadership) • Manage specific IT projects; assemble and manage development teams • Translate program vision for technical staff, and vice versa • Appropriately procure IT products & services • Resolve inevitable tensions

Informatics in Public Health • Information Access – Databases – Knowledge management

• Information Systems – Effective management – Improved productivity

• Surveillance integrated with EMR • Feedback to providers

Why gather data? Determine the magnitude of the problem • Data is the connection between the problem and how to solve the problem • Describe what is known about the problem: person, place, time • Place the problem in context • Describe what already exists (prevention and intervention programs) • Compare data to what should exist, identify gaps • Identify populations or areas at high-risk • Learn more about your community

Why gather data? Monitor trends over time • Provides a source of baseline information • Progress can be measured against baseline benchmark

Why gather data? Provides information and a basis for decisionmaking • Set priorities • Develop program based on current information • Needs • Resources • Inform and convince decision-makers • Need a roadmap to know where you are going and when you have arrived

How to gather data? ƒ Define the problem or question to be addressed • Number of people affected • Place that is affected • Time period of analysis

ƒ Generate a hypothesis (educated guess) about the reason for the problem ƒ Identify sources of data to answer the question posed ƒ Define variables to measure problem or question ƒ Identify methods to be used to analyze data collected

What data to collect? •

Types of Data • •



Levels of Data / Unit of Observation • •



Primary Secondary Individual-level data on persons or houses Aggregate data at the community-level

Sources of Data • • • • •

Demographic characteristics (ex: vital statistics) Geographic characteristics (ex: census data) Socioeconomic Characteristics (ex: labor, education) Health (ex: health department) Environment (ex: state environmental protection)

Hazard Data Sources ™ ™ ™ ™ ™ ™

Ambient Air Concentrations Air Emissions and Inspections Toxic Release Inventory Ground Water Sampling Drinking Water Databases Meteorology

Exposure Data Sources ™ Human Biomonitoring ™ Personal Sampling ™ Exposure Surrogates ⎯ Survey Data ⎯ Modeled Exposures

Health Data Sources ™ ™ ™ ™ ™ ™ ™ ™

Notifiable diseases Laboratory specimens Vital records Sentinel surveillance Disease registries Periodic surveys Special studies Administrative data systems

What data to collect? Logistical considerations – Budget constraints – Staffing time and expertise – Available technology – Planning for future updates – Linkage and integrations of existing systems – Security concerns – User-friendliness – Can the system be maintained

Planning for Data Collection • • • • • •

Identify public health needs Identify users Identify purpose: Why build the system? Define objectives: How will the data be used? Establish case definitions and standards Integration with existing systems – functional – technical

Data Management Protocol Data management protocol defines: – Standard operating procedures – Data sources – Data collection procedures – Data file structure – Data dictionary/code book – Documentation and archiving

Evaluation of Data Sources • Availability of data (format, access, approvals needed, cost) • Comparability (across geographic areas) • Coverage (local, state, national; missing data) • Relevance for tracking (timeliness, etc.) • Misclassification • Ability to control confounding, individual level data • Size, complexity, and format of data files (technology)

Additional Considerations • Legal requirements • Confidentiality & security • Analysis plan – Who – Table shells – Statistics – Periodicity

• Dissemination plan

Database • An organized collection of information (nowadays almost invariably electronic). • In relational databases, the table is a fundamental building block. • A database consists of one or more tables, which are related (conceptually linked) to each other.

Table • A structure that consists of rows and columns. • The rows are also called records, the columns are also called fields. • Example - a table of Students will have the fields: – – – –

Social Security Number First name Last name Date of birth, etc.

There will be one row (record) for each student.

Types of Data Configuration • Wide (one record per person) – One line for every individual (name, date of birth, gender, race…)

• Long (many records per person) – Multiple lines for every individual • Fixed (visit 1, visit 2, visit 3…) • Variable (prescription drug utilization, number of diagnoses per hospitalization, number of procedures per visit…)

Data Linkage ™ “Linkage” is defined as the physical integration of different databases resulting from a merge that utilizes a common variable ™ Integration of health surveillance and environmental monitoring systems for hazards and exposures

Key field(s) • A combination of one or more common variables (fields) that are used for indexed search whose value uniquely identifies a record in a table • Therefore, no two records in a table can have the same key value.

Entities and Relationships • When planning a database, one needs to identify Entities (the things about which we want to capture information) and the Relationships between them. • Relationships between entities are one-to-one, oneto-many, or many-to-many.

1-to-1

1-to Many

Many-to-1

Many-toMany

Relational Database • Store data in tables • Variables are grouped in logical units – By data source – By visit or interaction with system – By type of data (i.e. laboratory test)

• Normalize the tables • Make prudent choice of primary key(s) • It implies "logical" (proper) design of a database with minimal redundancy of data.

Data Dictionary/Code Book • Define and name the variables • Data attribute, format, and range of permissible values • Range and logic checks performed • Coding scheme – Use standard coding scheme – Be consistent – Anticipate missing values

Metadata • “Data that describes other data” • Technical vs. Descriptive – Process-related or technical metadata supports software efforts – Descriptive metadata, which supports users concerned with the software’s application domain/s (e.g., medicine, business).

Enterprise Architecture • The guiding structure and integrating framework for the design and development of information systems (IS) • Encompasses broad decisions that must be made by an organization as it creates its organizational information support system

Variable Attributes • Number – Integer (whole), real (decimal) – Leading zeros

• Character/alphanumeric/text/string • Logical value (yes/no, male/female) • Date/time – different formats (MMDDYYYY, MMDDYY)

• Missing values – “special” missing

Data Coding Standards Diagnosis codes (ICD-10) Medical procedures codes (CPT) National drug codes (U.S. FDA) Logical Observations Identifier Names and Codes (LONIC) • Systematized Nomenclature of Medicine (SNOMED) • Health Level 7 standard (HL-7) • • • •

From data to analytic files • Raw data stored in data files should not be altered • Quality “cleaning” of data – Range checks – Consistency

• Derived variables • Merged files or variables from other files

Data Linkage • A unique identifier is needed to link data from different sources

Common Problems • Duplicate records • Merging of data files – 1-to-1 merge – 1-to-many merge

• • • •

Errors in programming logic for derived variables Inadequate documentation De-identification of records Version control – protocols, computer programs and reports

J8

Historical Perspectives • • • •

Hierarchical Databases (mid 60s) Network Databases (late 60s) Relational Databases (late 60s to present) Object-Oriented Databases and ObjectRelational Databases (late 80s to present)

Slide 45 J8

I'm not sure it is clear what these are or how they are different JHerbstman, 2/2/2005

Why Study the Relational Model? • Most widely used model. Vendors: IBM, Informix, Microsoft, Oracle, Sybase, etc. • “Legacy systems” in older models –

e.g., IBM’s IMS (hierarchical model) • Recent competitor: object-oriented model –



ObjectStore, Versant, Ontos, O2



A synthesis emerging: object-relational model • Informix UDS, UniSQL, Oracle, DB2

SQL and file manipulation • Structured Query Language (SQL) – Implemented in relational database management systems

• Frequently used SQL commands – Select variable(s) – Combine tables (merge) – Apply selection criteria (view, query)

Data Mining • The process of secondary data analysis of large databases aimed at finding suspected relationships which are of interest or value to the database owners. – Hand DJ. Am Statistician 1998; 52:112-8.

• Also known as: “Knowledge discovery” • Keeping a watchful eye for unsuspected relationships by evaluating large datasets with many diseases and many variables of potential interest without a specific hypothesis

Data Mining: Issues • No a priori hypothesis • No pre-specified model form • Multiple comparisons • Expected counts • Granularity • Data mining tools create analytical models that are predictive, descriptive or both.

Data Warehousing • The act of gathering data from distributed locations in a single store, usually in some aggregated form for further analysis. • A data warehouse is a collection of data gathered and organized so that it can easily by analyzed, extracted, synthesized, and otherwise be used for the purposes of further understanding the data. • It may be contrasted with data that is gathered to meet immediate objectives.

Information and Data Systems Challenges • Electronic communication gaps & fragmentation • Many disparate systems • Slow adoption of standards • Technology just arriving on scene for many agencies • Lack of financial resources

Competing agendas – Build simple systems ⇔ address complex problems – Solve immediate problem ⇔ build an integrated IT environment – Program specialists ⇔ IT specialists – “Get it done” ⇔ “Do it right” – Build application today ⇔ Build foundation for tomorrow

Data Linkage – the details • Data collected for different purposes • Level of specificity or reporting may not be sufficient (aggregate data) • Access or permission to use data difficult to obtain, cost or fees associated with use • Information needed to conduct an epidemiologic study can vary greatly from what is needed for surveillance • Inadequate variable(s) for indexing • Methodological limitations

Critical Questions ™ Is it possible to “retro-fit” existing data systems for environmental public health tracking? ™ How can we use the lessons learned to move forward with recommendations for new data collection for tracking?

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Data and Information Management in Public Health Data and

Data and Information Management in Public Health Adrienne S. Ettinger, Sc.D., M.P.H. Environmental Public Health Tracking Methods Course July 2004 O...

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