The Impact of Occupational Injury And Illness The Impact of - - PowerPoint PPT Presentation

Commission on Commission on Health and Safety and Health and Safety and Workers’ Compensation Workers’ Compensation

The Impact of Occupational Injury And Illness The Impact of Occupational Injury And Illness

• n Non
• n Non-
• occupational Disability Benefits
• ccupational Disability Benefits

October 13, 2006 October 13, 2006 NASI Conference NASI Conference— —Washington, D.C. Washington, D.C.

Frank Neuhauser Frank Neuhauser Anita Mathur Anita Mathur

Survey Research Center/ Survey Research Center/ UC Data Archive and Technical Assistance UC Data Archive and Technical Assistance University of California, Berkeley University of California, Berkeley

Overview Overview

• Background

Background

• Data

Data

• Adjustments

Adjustments

• Results

Results

• Implications

Implications

• Future Work

Future Work

Background Background

California workers’ compensation California workers’ compensation

• Paid for by

Paid for by employers employers

• Average premiums have ranged from 3%

Average premiums have ranged from 3%-

• 6% (1999

6% (1999-

• 2004)

2004) – – Range is 0.4% to 60% across industry classes Range is 0.4% to 60% across industry classes

• Includes medical, temporary and long

Includes medical, temporary and long-

• term disability

term disability

• California

California--

• -Temporary disability up to 730 days

Temporary disability up to 730 days California one of 5 states with near universal non California one of 5 states with near universal non-

• occupational
• ccupational

disability system disability system

• Paid for by

Paid for by employees employees

• California rate 1.1% of payroll, with maximum contribution

California rate 1.1% of payroll, with maximum contribution

• Covers disability lasting 7

Covers disability lasting 7-

• 365 days

365 days

• No medical or long

No medical or long-

• term disability benefits

term disability benefits

Background Background

Policy concerns Policy concerns

• Internalizing occupational injury costs to

Internalizing occupational injury costs to give employers and employees proper give employers and employees proper incentive for investments in prevention incentive for investments in prevention

• Proper employee costs for SDI signals

Proper employee costs for SDI signals appropriate benefit breadth and level appropriate benefit breadth and level – –Paid “Family Leave” Paid “Family Leave”

• Frequent litigation over correct

Frequent litigation over correct payor payor, , leads to substantial legal and admin costs leads to substantial legal and admin costs

Background Background

• This is a truly unique set of research

This is a truly unique set of research

– –Only research SDI in any state Only research SDI in any state – –Only research comparing two, Only research comparing two, separate short to medium term separate short to medium term disability systems disability systems

Data Data— —State Disability Insurance (SDI) State Disability Insurance (SDI)

• We obtained a 20% sample of all claimants,

We obtained a 20% sample of all claimants, the “Single Client File” (SCF) for 1991 the “Single Client File” (SCF) for 1991-

• 2002

2002

• Many employers can opt out of SDI if they

Many employers can opt out of SDI if they are: are: – –State government State government – –Large employers that elect self Large employers that elect self-

• insurance

insurance – –Self Self-

• employed workers

employed workers

Data Data— —SDI SDI

• From Employment Development Department

From Employment Development Department (EDD) “employer file” we obtained a specially (EDD) “employer file” we obtained a specially constructed data that constructed data that

– – Defined all workers that were eligible for SDI benefits by Defined all workers that were eligible for SDI benefits by number of unique number of unique SSNs SSNs – – By 2 By 2-

• digit SIC

digit SIC – – By contribution and wage By contribution and wage

• Allowed us to construct denominators for injury,

Allowed us to construct denominators for injury, illness, and total rates by 2 illness, and total rates by 2-

• digit industry

digit industry

• Numerators:

Numerators:

– –Excluded several ICD Excluded several ICD-

• 9 codes

9 codes (pregnancy) (pregnancy) – –Defined each claim as injury or illness Defined each claim as injury or illness based on ICD based on ICD-

• 9 codes

9 codes

DATA DATA— —Bureau of Labor Statistics (BLS) for Bureau of Labor Statistics (BLS) for California California

• Survey of Occupational injuries and

Survey of Occupational injuries and Illnesses (SOII) for 2000 Illnesses (SOII) for 2000-

• 2002

2002

• Data are incidence/(100 FTEs)

Data are incidence/(100 FTEs)

• Separately for injuries and illnesses

Separately for injuries and illnesses

• By 2

By 2-

• digit industry codes

digit industry codes

• Differs from SDI data which are incidence

Differs from SDI data which are incidence relative to unique relative to unique SSNs SSNs/year /year

DATA DATA-

• Current Population Survey (CPS)

Current Population Survey (CPS)

Basic Monthly File Basic Monthly File

• Allows us to translate unique

Allows us to translate unique SSNs SSNs into into Full Full-

• time equivalents (FTEs)

time equivalents (FTEs)

• Allows us to identify characteristics of

Allows us to identify characteristics of workers that might affect probability of workers that might affect probability of disability disability – –Age, gender, race, ethnicity, etc. Age, gender, race, ethnicity, etc.

Data Data— —National Health Interview Survey National Health Interview Survey

• Injuries/Illnesses may be correlated with both

Injuries/Illnesses may be correlated with both industry and worker demographics for industry and worker demographics for example, example, – –young workers have fewer non young workers have fewer non-

• ccupational illnesses (but maybe more
• ccupational illnesses (but maybe more

non non-

• occupational injuries)
• ccupational injuries)

– –Female workers might have more illnesses, Female workers might have more illnesses, but fewer injuries but fewer injuries – –Construction has mostly younger, male Construction has mostly younger, male workers workers

Data Data— —National Health Interview Survey National Health Interview Survey

• Constructed estimates for a range of worker

Constructed estimates for a range of worker characteristics characteristics

• Adjusted each California industry group to

Adjusted each California industry group to reflect injury/illness risk of workforce reflect injury/illness risk of workforce

• After adjustment, each industry should have

After adjustment, each industry should have the same non the same non-

• occupational injury/illness rate
• ccupational injury/illness rate

– –Except Except, if occupational injury/illness rates , if occupational injury/illness rates affect non affect non-

• occupational injury/illness rates
• ccupational injury/illness rates

Occupational and Non-Occupational Incidence Rates for Injuries by Industry, 2000-2001

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0 Non-Occupational (SDI) Rates Occupational (BLS) Rates

Occupational and Non-Occupational Incidence Rates for Illnesses by Industry, 2000-2001

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.0 2.0 4.0 6.0 8.0 10.0 Non-Occupational (SDI) Rates Occupational (BLS) Rates

Occupational and Non-Occupational Incidence Rates for Injuries and Illnesses by Industry, 2000-2002

0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 0.0 2.0 4.0 6.0 8.0 10.0 Non-Occupational (SDI) Rates Occupational (BLS) Rates

Correlations Between Correlations Between Occupational and Non Occupational and Non-

• Occupational

Occupational Incidence Rates Incidence Rates

Injury Injury Illness Illness Injury or Injury or Illness Illness Pearson Pearson Correlation Correlation .374** .374** .394** .394** .268** .268** N N 105 105 105 105 161 161

**Correlation is significant at the 0.01 level (2-tailed)

Average Incidence Rate All Industries Average Incidence Rate All Industries (incidence/100 FTE) (incidence/100 FTE)

Injury Injury Rate Rate Illness Illness Rate Rate Injury or Injury or Illness Illness Rate Rate Non Non-

• Occupational

Occupational (SDI) (SDI) Occupational Occupational (BLS) (BLS) 0.84 0.84 3.04 3.04 4.08 4.08 2.95 2.95 0.20 0.20 3.20 3.20

Regressions Predicting Regressions Predicting Non Non-

• Occupational Incidence Rates

Occupational Incidence Rates from Occupational Incidence Rates from Occupational Incidence Rates

Injury Injury Illness Illness Injury or Illness Injury or Illness Year Year BLS Rate BLS Rate 0.064** 0.064** (0.016) (0.016) 2.849** 2.849** (0.647) (0.647) 0.217** 0.217** (0.063) (0.063) 0.014 0.014 (0.056) (0.056) 0.198 0.198 (0.209) (0.209) 0.257 0.257 (0.143) (0.143)

**Significant at the .01 level of confidence

Percentage of Non Percentage of Non-

• Occupational

Occupational Incidence Rates Explained by Incidence Rates Explained by Occupational Incidence Rates Occupational Incidence Rates

Injury Injury Illness Illness Injury or Injury or Illness Illness 25% 25% 20% 20% 19% 19%

Implications Implications

• Substantial subsidization of employer

Substantial subsidization of employer supported workers’ compensation by supported workers’ compensation by employee financed State Disability Insurance employee financed State Disability Insurance

• Approximately 20

Approximately 20-

• 25% of injuries/illnesses may

25% of injuries/illnesses may be misclassified as non be misclassified as non-

• occupational
• ccupational
• Integration could save substantial

Integration could save substantial administrative costs administrative costs

• Employers might pick up larger percentage of

Employers might pick up larger percentage of combined program with costs offset by combined program with costs offset by administrative savings administrative savings

Implications Implications

• Impact on employer cost would be 0.13% of

Impact on employer cost would be 0.13% of payroll, on average payroll, on average

• High

High-

• risk industries might pay substantially

risk industries might pay substantially more more

• Cross

Cross-

• subsidization may also imply substantial

subsidization may also imply substantial misclassification in both directions misclassification in both directions

• Any cross

Any cross-

• subsidization and/or

subsidization and/or misclassification will lead to under investment misclassification will lead to under investment in safety in safety – –Applies to both parties Applies to both parties

Further Study Necessary Further Study Necessary

• Do these data accurately reflect final disposition of

Do these data accurately reflect final disposition of disputed cases? disputed cases?

– –Check by matching SDI Check by matching SDI WCAB WCAB

• Do these data accurately reflect longer

Do these data accurately reflect longer-

• term

term

• verlap between SDI and Workers’ Compensation
• verlap between SDI and Workers’ Compensation

– –Recent changes in benefit levels Recent changes in benefit levels – –Recent changes in premium levels Recent changes in premium levels – –Long Long-

• term trends in illness,

term trends in illness, apportionment, causation standards etc. apportionment, causation standards etc.

Future Work Future Work— —Some Requirements Some Requirements

• Extend SDI data through 2005

Extend SDI data through 2005

• Extend EDD employment data for full period,

Extend EDD employment data for full period, 1993 1993-

• 2005

2005

• Link EDD and WCAB

Link EDD and WCAB

• Link WCIS and other data systems

Link WCIS and other data systems – –First effort, First effort, MediCal MediCal/SSI /SSI

• This model could be come standard for

This model could be come standard for California and example for other states California and example for other states