Presentation on theme: "The use of LFS data in the production of annual mortality rates Alaa Al-Hamad Centre for Health Analysis and Life Events."— Presentation transcript:
The use of LFS data in the production of annual mortality rates Alaa Al-Hamad Centre for Health Analysis and Life Events
Overview Reason and Aim Why the LFS Analytical approach adopted Results Conclusion & Further work
Reason for the work. The main work of the health inequalities team is; To contribute to the knowledge of the trends in health inequalities by producing high quality statistical information and analysis Main output: The production of mortality rates mainly for men and women of working age by occupation based socio-economic classification (NS-SEC). Use these estimates to assess the gap in health inequalities between the advantaged and the less advantaged in society. Required data: Deaths by NS-SEC using the death register; regularly produced. (Numerators) Population by NS-SEC using the Census of population ;produced every ten years. (Denominator)
The aim of this project To investigate the feasibility of using Survey data to provide population by occupation base Socio-economic indicator, on a more regular basis. Use this survey data to produce annual intercensal estimates of mortality rates. Contribute to the monitoring of health inequalities over time.
Why the LFS? The Labour Force Survey (LFS) Why, A comprehensive source of labour market data Collects detailed data on occupation for a representative sample of approximately 60,000 households (Approx:100,000 people age 16 and over) in the UK each quarter Available at national, regional and local authority levels Results from the survey are weighted to ONS estimates of private household population In 2004 the APS was introduced, The Annual Population Survey (APS) combines results from the Labour Force Survey (LFS) and the English, Welsh and Scottish Labour Force Survey boosts 170,000 households (Approx:360,000 people age 16 and over)
Comparison of LFS with Census 2001 LFS is a sample, while the census attempts to enumerate the whole population. LFS is not compulsory while census is. The LFS has an interviewer prompting for information, whereas the census usually does not. Only certain types of communal establishments are included in the LFS, but not all. Ex. NHS accommodation LFS has 2% more economically active people LFS has 7% fewer economically inactive.
How the LFS Feasibility was assessed Mortality rates by NS-SEC using LFS data as denominator was calculated for men aged 25–64 in England and Wales. The above was calculated using: –The LFS Spring Quarter 2001 –The 2001, 2002 and 2003 annual LFS dataset Compare results with the one produced using the Census (White et-al HSQ36, 2007). Assess the accuracy of LFS estimates at lower levels of geography
The analytical approach adopted to produce mortality rates. Used the NS-SEC as socio-economic indicator Prepared and adjusted the Deaths data Prepared and adjusted the LFS population Prepared the Midyear population Produced rates and variances by NS-SEC, Compared with the census-based mortality rate estimates, taken from the male mortality rates by NS-SEC for the period (2007, HSQ36).
The National Statistics Socio-economic Classification (NS-SEC) Occupationally based indicator of socio-economic position Used by the ONS in official statistics since 2001 Aim to replace both the Registrar General Social Class and the Socio-economic Groups. Due to its better conceptual and theoretical basis (Rose et-al) Developed from a sociological classification that has been widely used in pure and applied research, known as the Goldthorpe Schema (good predictor of health and educational outcomes) Occupations are differentiated in terms of reward mechanisms, promotion prospects, autonomy and job security. The information required to create NS-SEC is: – Occupations coded to the unit groups of SOC2000 – Employment status (eg. Employer, manager, employee). –The number of employees at a workplace. The operational categories can be derived in three ways: Full, reduced and Simplified functional categories. Furthermore it can be translated to analytical classes in number of ways.
Example of analytical classes
Preparing the Deaths data Deaths extracted for 2001, 2002 and 2003 from the death registration. Then transferred to Stata to tidy. –Limit the age bands to 8 categories between –Generate the 8 class NS-SEC plus classes 10 and 11, Full time students and others. The death data adjusted to correct for a misallocation of death from class 2 (lower managerial and professional) to class 3 (Intermediate). White et-al.
Preparing the LFS population by age and NS-SEC: 1- Used LFS data to derive reduced NS-SEC. –The LFS data has a variable relating to full NS-SEC, while the census uses reduced NS-SEC. –The Census classifies people by former occupation if they are not currently working, so we needed to take this into consideration when deriving reduced NS-SEC. –Those classified as full time students in the LFS kept as students regardless of any occupation data associated with them. 2- Adjusting the LFS population for Health Selection, using proportional adjustment. - This is to counteract a potential selection effect brought about because some of those in poor health may have been selected out of the labour market. See HSQ 45
LFS data used to derive REDUCED NS-SEC Personal and geographic data Age, gender, Country within UK, person weight and Govtof Data on occupation (There are ~375: coded: ) –Occupation main Job (SOC2KM) –Occupation in last job (SOC2KL) –Occupation made redundant from (last 3 month) (SOC2KR) Data on employment status (5 employment status) –Employment status in main job (STATR) –Employment status in last job (STATLR) –Self employed with or without employee (and in last job) (SOLOR & SOLOLR) –Managerial status (and in last job) (MANAGER & MANGLR) –Responsible for supervising (SUPVIS) –Whether full-time student (STUCUR) NS-SEC Class (NSECMMJ)
Example of adjusted LFS population by NS- SEC.
Results: Comparing NS-SEC population distributions using the Census and LFS
LFS Mortality rates by NS-SEC Mortality rate = deaths*100000/ LFS pops Adjusted to the European Standard population. Compared with the rates produced using the Census. White et-al estimates.
Results: Comparing mortality rates by NS-SEC using the Census and LFS
Mortality rates by NS-SEC, Men aged 25-64
Accuracies for different levels of geographies: Approximate estimates of the variances were calculated for lower levels of geographies and confidence intervals calculated.
Conclusion of the Feasibility study Although the mortality rates obtained using LFS are in part significantly different from the published census based estimates, the pattern is similar. The LFS estimates would have to be related to each other rather than to the census. The LFS as a population source can be used at the level of England and Wales and at regional level to measure health inequalities for men. Estimates based on one or three years deaths and LFS population should be capable of providing useful estimates for the study of trends in health inequalities. Similar approach can be adopted to produce mortality rates for women.
Progress and further work Following this analysis we have published trends in social inequalities Langford and Johnson, HSQ 47. Currently looking at the possibility of producing the same for female mortality. If achieved a number of other measures can be developed to help in the monitoring of cause specific health inequalities.
Preparing the mid year population by age and NS-SEC: Created from Census mid-year population. The main issue Population may change when updated over time. To illustrate : Population Old New