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Longitudinal Workforce Analysis using Routinely Collected Data: Challenges and Possibilities Shereen Hussein, BSc MSc PhD Kings College London.

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Presentation on theme: "Longitudinal Workforce Analysis using Routinely Collected Data: Challenges and Possibilities Shereen Hussein, BSc MSc PhD Kings College London."— Presentation transcript:

1 Longitudinal Workforce Analysis using Routinely Collected Data: Challenges and Possibilities Shereen Hussein, BSc MSc PhD Kings College London

2 Longitudinal Analysis General advantages Can control for individual heterogeneity Subject serve as own control Between-subject variation excluded from error Can better assess causality than cross- sectional data General challenges Conventional statistical methods require independence between observations Longitudinal data are likely to violate this assumption Missing data due to attrition Data availability 29/5/2012 2

3 Workforce Data Example: NMDS-SC Structure Design Coverage Time span Type of information collected Data collection and archiving size 29/5/2012 3

4 NMDS-SC data structure Social care providers in England Complete NMDS- SC returns Aggregate information on the workforce Detailed information on all or some individual workers Providers Database workers Database Linkable 29/5/2012 4

5 NMDS-SC longitudinal analysis: potential Data coverage Wide range of providers and individual workers information Sector specific- uniqueness Hierarchical structure Workforce development and business sustainability Timely –Demographics, austerity, unemployment Economics –Care costs, including turnover costs –Pay Linkable to local data characteristics 29/5/2012 5

6 Challenges in NMDS-SC longitudinal analysis No sampling framework No regular intervals for data collection Irregularities in data completion by different providers Additions/alterations of variables and fields Cumulative nature and consequences on data size and structure Archiving 29/5/2012 6

7 Challenges in NMDS-SC longitudinal analysis- continued Computational Data size –Innovation in system design and architecture Accumulative property –Scalability of the system Changes in data fields Variable additions and omissions Data over-ride and archiving –Software and hardware issues Methodological Unusual patterns of follow- up –Censoring Variability in the database over time Unbalanced cohort design Missing data –Update frequency –Attrition –True exit Other methodological issues 29/5/2012 7

8 Providers level longitudinal mapping From December 2007 to March 2011 Linked 18 separate databases on the providers level Each has records from 13,095 to 25, ,671 valid records included in the construction Number of updates ranged from 0 to 18 per provider Continuous process, more records added every 3 months 29/5/2012 8

9 Meta-data analysis: providers with different number of events 29/5/2012 9

10 Specific example 1: Providers with 18 updates 29/5/

11 29/5/ Specific example 2: Providers with 2 updates

12 Density distribution plot of providers with at least 2 updates during the period December 2007 to March /5/

13 density distributions of number of days elapsed between two updated providers events 29/5/

14 Simple example using providers database: workforce stability over time Longitudinal changes in care workers turnover and vacancy rates over time –From January 2008 to January 2010 Changes in reasons for leaving the sector, identified by employers –Differentiating between those with improved (reduced) turnover rates and those with worse (increased) turnover rates 29/5/

15 Pre analysis Selecting and constructing providers panel –Including those with at least two updates within +/- 3 months of T1 and T2 – 2953 providers with mean coverage duration of 602d Investigate sample representation Data quality checks Data manipulation/imputation 29/5/

16 Some findings: changes in turnover rates 29/5/

17 Reason for leaving and turnover rate changes 29/5/

18 Analysis expansion: next steps Consider changes over a longer period of time Examine other providers characteristics Different take on panel inclusion criteria Link to individual workers longitudinal databases to examine relations with detailed workforce structure –Pay, qualifications, profile etc. Build economic elements within analyses models, e.g. specific-turnover costs, within the longitudinal model 29/5/

19 Workers level longitudinal analysis A much larger database –Same period of time- over 11M records Providers not required to complete information for all workers –Structural/design missing data –True missing data Linkage issues –more data fields required for identification and linkage Considerably large number of variables and fields –Careful planning; analysis-tailored data retrieval Changes in database –Amendments, new variables etc. –Programming intensive and demanding models (may not be replicable for different databases) 29/5/

20 29/5/

21 Issues to consider Suitability of models –Longitudinal structure –Competing risks Measurement window –Late entry into risk sets Use proxies, other variables in the dataset Adopt suitable approach/model –Censoring (LHS and RHS) Assumptions –Guided by: Sector-specific knowledge Intelligence from other variables in the data 29/5/

22 Current longitudinal research Watch this space!! Workforce mobility within the sector Occupation durations Characteristic-specific probabilities of exiting or remaining in the sector Characteristic-specific probabilities of moving employer within the sector or having multiple jobs Career pathways within the sector 29/5/

23 Acknowledgments Thanks to the Department of Health for funding this workDepartment of Health Thanks to Skills for Care for providing the data on regular basisSkills for Care Thanks to Analytical Research Ltd for their technical and quantitative supportAnalytical Research Ltd 29/5/

24 Further information See: m/scwru/res/knowledge/nmdslong.aspx m/scwru/res/knowledge/nmdslong.aspx 29/5/

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