I. NOHS DATA PROCESSING

A. Extract NOHS Chemical Data for Chemicals Found in RTECS
Program: CNVI2E40

The purposes of this program are to: (1) produce a master file of
NOHS data to be used in the high risk identification model, (2)
eliminate ambiguous hazards from model consideration, (3) identify
and merge replicate chemical terms, (4) eliminate generically
resolved data from model consideration, (5) merge SIC codes, and
(6) merge occupation codes.

In order to produce NOHS data at the specific levels used for
input to the several model algorithms, it was necessary to manipu-
late several of the data elements from the NOHS Data Base Master
Load File and the NOHS Master Hazard Summary File.

Using Chemical Abstract System (CAS) numbers assigned to NOHS
chemicals existing in the NOHS Master Hazard Summary File, a file
of the chemicals common to RTECS and NOHS was established. Exami-
nation of this file indicated the presence of ambiguous and
replicate hazards as well as generically resolved data.

Twenty-one ambiguous hazards (e.g. decomposition products of
calcium hydroxide, decomposition products of ammonium chloride, and
products of combustion - naphtha petroleum) were identified. A
decision was made that such terms should be excluded from consid-
eration in the model. Accordingly, they were eliminated from
further consideration through use of a computer card deck program.

Seventy-two chemicals were identified as replicates. The term
replicate as used here means that two or more of these chemicals
were defined by CAS as synonymous. After examination of these
chemicals it was decided to combine synonymous chemicals under one
chemical name, and combine the NOHS data specific to each under
one preferred term. An example of these combinations is: 2 -
(2-methy - 4-chlorophenoxy) propionic acid and methyl chlorophenoxy
propionic acid combined as methyl chlorophenoxy propionic acid.
Combining the 72 replicates in this manner resulted in a final
definition of 34 composite chemical-specific sets of data. This
was accomplished through use of the card deck displayed at the top
of Figure 6. _

Additionally, chemicals were matched between RTECS and NOHS which
were present in the NOHS data base due only or in part to the
generic resolution of tradename products. It was decided that


670
671
672
673
674
680
681
690
692
694
695
703
705
706
710
713
714
715
740
750
751
753
755
760

CARDING, LAPPING, AND COMBING OPERATIVES
KNITTERS, LOOPERS, AND TOPPERS

SPINNERS, TWISTERS, AND WINDERS

WEAVERS

TEXTILE OPERATIVES, NEC

WELDERS AND FLAME-CUTTERS

WINDING OPERATIVES, NEC

MACHINE OPERATIVES, MISCELLANEOUS SPECIFIED
MACHINE OPERATIVES, NOT SPECIFIED
MISCELLANEOUS OPERATIVES

NOT SPECIFIED OPERATIVES

BUS DRIVERS

DELIVERYMEN AND ROUTEMEN

FORK LIFT AND TOW MOTOR OPERATIVES
MOTORMEN: MINE, FACTORY, LOGGING CAMP, ETC.
RAILROAD SWITCHMEN

TAXICAB DRIVERS AND CHAUFFEURS

TRUCK DRIVERS

ANIMAL CARETAKERS, EXC. FARM

CARPENTERS’ HELPERS

CONSTRUCTION LABORERS, EXC. CARPENTER HELPERS
FREIGHT AND MATERIAL HANDLERS

GARDENERS AND GROUNDSKEEPERS, EXC. FARM
LONGSHOREMEN AND STEVEDORES

0-8


@ Mutagenicity (MUT)
e@ Neoplastigenicity (NEO)
e Primary irritation (PI)
e Teratogenicity (TER)
© Other toxic effects (TFX)

It was decided that the algorithm should consider each of these
categories separately in the calculation of the overall Hazard
Risk Index Number (HRIN) for any given chemical. Accordingly,
these categories were designated sub-HRI's, and the numerical
value arising from the examination of the normalized dose data
within each sub-HRI became the sub-HRIN.

Calculation of the sub-HRIN is accomplished by averaging the
normalized dose data (one citation per test class) falling within
each sub-HRI for a given chemical. This results in the expression
of from one to eight sub-HRIN values for each chemical. (See
Appendix A and Figure 4 for computer program considerations.)

As expressed earlier, NIOSH intends that any of the algorithms in
this model be responsive to user needs. It was therefore decided
that the algorithm be designed so that any individual sub-HRIN
could be suppressed or enhanced in the calculation of the final
chemical-specific HRIN in accordance with user priorities (e.g.
increased emphasis on carcinogenic test results).

Finally, experimentation with various methods for utilizing the
sub-HRIN values to calculate the HRIN for each chemical indicated
that the best procedure was to simply sum the sub-HRIN'’s. This
decision was made on the basis of two observations:

a) Use of an averaging procedure for sub-HRIN's containing
data was observed to result in the expression of a value
biased toward chemicals with a single relatively high
sub-HRIN. Examination of trial listings, and subsequent
discussions with toxicologists confirmed that the averaging
procedure tended to assign relatively low HRIN values to
well-researched chemicals with broad spectrum toxicological
properties in comparison to the values assigned to single-
effect chemicals. This resulted in inappropriate ranking
for many chemicals.

b) While summing procedure equates no data in a sub-HRI with
no demonstrated toxic effect, this assumption is necessary
because of the limited nature of our data source. Although
it is not complete, we believe that the RTECS data source
contains as complete a listing of toxicity effects as is
currently available.

23


DISCLAIMER

Mention of company names or products does not constitute endorsement by
the National Institute for Occupational Safety and Health.

ACKNOWLEDGEMENT
The mathematical model discussed in this technical report was developed

by modifying an earlier model developed under NIOSH Contract
Number 210-78-0076 (Tracor Jitco, Inc.).

DHHS (NIOSH) Publication No. 83-117

For sale by the Superintendent of Documents, U.S. Government
Printing Office, Washington, D.C. 20402

di


395
402
403
404
405
008
412
413
ooc
421
422
424
oop
433
435
436

44]
442
443
446
452
453
454
455

NOT SPECIFIED CLERICAL WORKERS
BAKERS

BLACKSMITHS

BOILERMAKERS

BOOKBINDERS

BRICKLAYERS AND STONEMASONS
BULLDOZER OPERATORS

CABINETMAKERS

CARPENTERS

CEMENT AND CONCRETE FINISHERS
COMPOSITORS AND TYPESETTERS
CRANEMEN, DERRICKMEN, AND HOISTMEN
ELECTRICIANS

ELECTRIC POWER LINEMEN “AND CABLEMEN
ENGRAVERS, EXC. PHOTOENGRAVERS

EXCAVATING, GRADING, AND ROAD MACHINE OPERATORS:
EXC. BULLDOZER

FOREMEN, NEC

FORGEMEN AND HAMMERMEN

FURNITURE AND WOOD FINISHERS

HEAT TREATERS, ANNEALERS, AND TEMPERERS
INSPECTORS, NEC

JEWELERS AND WATCHMAKERS

JOB AND DIE SETTERS, METAL

LOCOMOTIVE ENGINEERS


b. weeks
¢. months
d. days other than 1 or 3
e. hours other than 24 or 72

4. Citations "NSE" or "RNS"
An "NSE" citation indicates a non-standard exposure, usually
resulting from such things-as accidental spills. Model cri-
teria defines this as an imprecise dose that cannot be compared
to laboratory testing.
An "RNS" citation means that the eyes were washed after dose
instillation. By model criteria, an irritant dose that was
washed immediately after instillation canmot be directly com-
pared to a continuous exposure.

Irritation Dose Amount

Dose amount starts in Position 22 and continues for a variable
number of positions.

Doses found to be acceptable were those:

1. Expressed as a whole number percentage, with or without decimal
numbers (i.e. 10.5% or 10%).

2. Expressed as a whole number, with or without decimal numbers,
in various gram notation (i.e. 10 MG or 10.5 MG).

NOTE: The program accepts only the first decimal digit.
Dose Types

This field is separated from dose amount by a blank.
Types accepted:

%, GM, GM/M3, MG, MG/M3, NG, NG/M3, UG, UG/M3, PPH, PPM, PPB,
PPT

Types rejected:

All per kilogram types (/KG).

A-10


ALR
AQTX
BCM
bdw
BLD
bmr
BPR
brd

CAR
cat
ec
chd
ckn
cL
CNS
COR
Cel
CRIT DOC
CUM
cvs
D
deck
DDP
DEF

allergenic effects
aquatic toxicity

blood clotting mechanism effects
wild bird species
blood effects

bone marrow

blood pressure effects
bird (domestic or lab)
continuous
carcinogenic effects
cat

cubic centimeter

child

chicken

ceiling concentration
central nervous system effects
corrosive effects
cattle

criteria document
cumulative effects
cardiovascular effects
day

duck

drug dependence effects

definition

B-1


where 24.45 is the number of liters occupied by one mole of an
ideal gas at 25°C and 760 mm Hg. All other conversion of units
followed standard metric system rules as specified in Appendix A.

The decision to use molar units was based on the way the data were
to be used, i.e., for systematic comparison of chemicals. Doses
expressed in moles per kilogram of body weight correspond directly
to the numbers of molecules which produced the observed effect and
were considered, therefore, more indicative of the toxicological
activity of the chemical. For example, if compound X has a gram-
molecular weight of 350 and compound Y has a gram-moleuclar weight
of 50, then any equal weight samples of the two compounds will con-
tain markedly different numbers of molecules, namely X will contain
about one~seventh as many molecules as Y. If the two chemicals act
similarly on the same target organ or cellular system, then a dose
of a given weight of Y would provide seven times as many molecules
to impact on receptor sites as would a dose of the same weight of X.

After the dose units were converted, the dose type (either ppm or
moles/kg) was compared against the route of administration to
assure that volumetric measurements (i.e., ppm) appear only in
inhalation studies and that per kilogram measurements (i.e., moles/
kg) appear only in non-inhalation studies. Any data record not
conforming to these guidelines is rejected to avoid direct compar-
ison of ppm and mole/kg dose units.

In certain cases, (in-vitro mutagenic studies and skin and eye
irritation studies performed under the Draize procedure) a known
volume is specified, and, with an assumption that the specific
gravity of the liquids is equal to 1.0, most units can be conver-
ted to millimole notation, making more data useable: (See the
computer program model criteria in Appendix A for specific lists
of converted and rejected dose units.)

Chronic Data Considerations

Chronic effect data in RTECS are expressed in terms of the unit or
daily dose administered and the frequency and/or duration of the
administration, but the data often are incomplete. After a review
of the RTECS data it was apparent that the only consistently avail-
able data entry was the unit or daily dose, and, for consistency in
treatment, it was decided that this parameter should be used in
this project without consideration of the frequency or duration of
administration.

Primary Irritation Data Considerations

The primary irritation data in RTECS are recorded in much the same
way as other toxicity data. The test animal, route of

13


number 3. This display exhibits the effect of utilizing
chemical-specific NEO sub-HRIN's as an estimator of carcino-
genic effect, as discussed in the text on page 15.

Microfiche No. 6 - HAZARD RISK INDEX - TRIAL 4

This microfiche displays the linked chemicals in descending
HRIN-order with both the CAR and NEO OPTIONS set at "YES".
All other settings are identical to those displayed on micro-
fiche number 3. This display exhibits the effects of simul-
taneously utilizing both options, thereby increasing the
amount of RTECS data available to the model's algorithms.

Microfiche No. 7 - HAZARD RISK INDEX - CARCINOGEN SELECTOR

This microfiche displays the capability of the model to rank
chemicals on the basis of carcinogenic potential only. Note
that the CAR OPTION was set at "NO".

Microfiche No. 8 - HAZARD RISK INDEX - MUTAGEN SELECTOR

This microfiche further displays the capability of the model
to select only certain effects. In this example, chemicals
are ranked only on the basis of mutagenic activity.

Microfiche No. 9 - HAZARD RISK INDEX - NEOPLASTIC SELECTOR

This microfiche displays the capability of the model to rank-

order the linked chemicals on the basis of reported and calcu-
lated neoplastic effect only. Note that, in this example,

the NEO OPTION is set at "YES", utilizing the model's ability

to generate surrogate neoplastic data on the basis of carcin-

ogen citations.

Microfiche No. 10 - HAZARD RISK INDEX - TERATOGEN SELECTOR

This microfiche displays the linked chemicals rank-ordered on
the basis of teratogenic potential only.

Microfiche No. 11 - HAZARD RISK INDEX - CARCINOGEN EMPHASIS

This microfiche shows the effect of the use of the "constant"
factors in the HRIN algorithm. This results is an HRIN listing
that considers all the selected data from RTECS, but emphasizes
carcinogenic potential instead of selecting only a single
effect as displayed on microfiche 7, 8, and 9

Microfiche No. 12 - HAZARD RISK INDEX - MUTAGEN EMPHASIS


TRIAL 3 - CAR OPTION = YES, NEO OPTION = NO
TRIAL 4 - CAR OPTION = YES, NEO OPTION = YES

Because the NEO OPTION is exercised in the CIORISTD program, pro-
duction of the four trials requires two CIORISTD runs from the
extract file (NEO OPTION = YES, and NEO OPTION = NO). The NEO
OPTION = NO run is used in creating TRIALS 1 and 3, and the NEO
OPTION = YES run is used to create TRIALS 2 and 4.

D. Calculate HRIN values
Program: CLIORISUM

The purposes of this program are to: (1) calculate HRIN values
based on user selection of NEO and CAR OPTIONS and user settings

for the multiplier and constant variables introduced in the HRI
algorithm format, (2) exercise the user option for "long" or "short"
print options, (3) create the sorted summary file (source HRI) used
to create the AHRI, IRI and ORI indices, (4) calculate AHRIN values
resulting from modifying source HRIN values with hazard-specific
NOHS exposure data.

As explained in the main text, calculation of the final HRIN value
is a simple summing of the eight sub-HRI values.

Following the algorithm format, CIORISUM modifies the calculated
sub-HRIN values derived by the CIORICAL program in accordance with
user settings of the multiplier and constant values. The original
sub-HRIN is either multiplied by the multiplier term or (if non-
zero) added to the constant value set. This modified number (where
applicable) is then substituted for the original sub-HRIN value,
and the sum of all sub-HRIN's computed. Note: the program will
accept only single-digit (0 - 9) whole number terms as input for
the constant and/or multiplier terms. However, the user may
simultaneously enter positive values in as many of the multiplier
or constant modifiers as is desired, and must specify some value
for each lower case a - p term in the algorithm.

User exercise of the "long" print option as illustrated on Micro-
fiche Card Nos. 1 and 2, allows examination of the data input to

the HRIN at the test class level, including specific test classes
and chemical specific qd, values within test class.

NOHS Summary file #1 data (hazard-specific) is used to modify
source HRIN values in accordance with the AHRI algorithm explained
in the text to create a specific summary file based on user exer-
cise of the options to this point.

A-16


NOHS
Data Base
Master

Load File

Eliminate
hazard cod

Mass change
hazard codes

CNVT2E49

Sort to Order
for highest

level summary

Sorted

Master

NOHS Computer Processing Considerations

File

FIGURE 6.

SUMEXTIO

NOHS
Detail
Summary

File #10

30


3611
3612
3613
362

3621
3622
3623
363

3632
3634
364

3641
3642
3643
3644
365

3651
3652
366

3661
3662
367

3673
3679
369

ELECTRIC MEASURING INSTRUMENTS

TRANS FORMERS.

SWITCHGEAR AND SWITCHBOARD APPARATUS
ELECTRICAL INDUSTRIAL APPARATUS
MOTORS AND GENERATORS

INDUSTRIAL CONTROLS

WELDING APPARATUS

HOUSEHOLD APPLIANCES

HOUSEHOLD REFRIGERATORS AND FREEZERS
ELECTRIC HOUSEWARES AND FANS
ELECTRIC LIGHTING AND WIRING EQUIPMENT
ELECTRIC LAMPS

LIGHTING FIXTURES

CURRENT-CARRYING WIRING DEVICES

NON CURRENT-CARRYING WIRING DEVICES
RADIO AND TV RECEIVING EQUIPMENT
RADIO AND TV RECEIVING SETS
PHONOGRAPH RECORDS

COMMUNICATION EQUIPMENT

TELEPHONE AND TELEGRAPH APPARATUS
RADIO AND TV COMMUNICATION EQUIPMENT
ELECTRONIC COMPONENTS AND ACCESSORIES
ELECTRON TUBES, TRANSMITTING
ELECTRONIC COMPONENTS, NEC

MISC. ELECTRICAL EQUIPMENT & SUPPLIES

C-17


Sorted
Summary

File

Sort AHRIN Ordey

File
(temporary)

AHRIN /
Sorted oA

|

NZ

PRTAHRIN

AHRIN

Sequence

FIGURE 7. AHRI Algorithm Computer Programs Flow Chart

37

Trial-Run
Header



5. Eliminate unusable records (in accordance with model
specifications).

6. Calculate the natural log of the dose and assure positive
value by adding 30.00 to the calculated log.

7. Generate minimum and maximum natural logs (+30.00) of
dose values for each test class.

8. Exercise the option to generate neoplasm data lines from
carcinogen data (see NEO OPTION).

9. Identify and link RTECS chemicals occurring in the NOHS
data base. (See NOHS Summary File #1 generation discussion
and Figure 6.)

NOTE: In addition to other specific conversion routines men-
tioned in this text, dose unit uniformity is achieved through
use of the first and second dose conversion routines. They
are:

First Conversion Routine:

The first routine converts everything that is not PPM, MG/KG,
or MG/M3, to these notations using the following equations:

pph to ppm = pph x 10,000
ppb to ppm = ppb x .001
ppt to ppm = ppt x .000001

UG/M3 to MG/M3 = UG/M3 x .001
NG/M3 to MG/M3 = NG/M3 x .000001
GM/M3 to MG/M3 = GM/M3 x 1,000

UG/KG to MG/KG = UG/KG x .001
NG/KG to MG/KG = NG/KG x .000001
GM/KG to MG/KG = GM/KG x 1,000
KG/KG to MG/KG = KG/KG x 1,000,000
PG/KG to MG/KG = PG/KG x .000000001

Second Conversion Routine:

This routine converts MG/KG to millimoles/KG (expressed as mm)
using: MG/KG divided by molecular weight, and; MG/M3 to ppm
using: (24.45 divided by molecular weight) x MG/M3.

The field resulting from these conversions is an 18-digit field
defined as 9(9)V9(9). (i.e. 9 digits, decimal, 9 digits.)


TABLE 4 - NOHS DATA FILES

Number
File Number of Records
1 2,092
2 20,060
3 42,125
4 56,206
5 48 ,768

Worker Group

Workers exposed to

without regard to Industry or occupa-

tion.

Workers exposed to
within an Industry
SIC level, without
pation.

Workers exposed to
within an industry
SIC level, without
pation.

Workers exposed to
within an industry
SIC level, without
pation.

Workers exposed to

a linked hazard

a |tnked hazard
at the 2-digit
regard to occu-

a linked hazard
at the 3-digit
regard to occu-

a linked hazard
at the 4-digit
regard to occu-

a linked hazard

within an occupation without regard

to Industry.

33


measurements to millimole equivalence (designated as MM).
a. MMOL/KG to MM = no conversion.
b. NMOL/KG to MM = NMOL/KG x .000001.

c. UMOL/KG to MM = UMOL/KG x .001.

S-line (irritation data)

RTECS record definitions are:

1.

Ox

Position 17 should always equal a dash (-). It is the separa-
tor between route and species.

Position 21 should always equal a blank. It is the separator
between species and dose amount.

Screening Process

RTECS S-line data is obtained from many sources. Model criteria
specifies use of only standard Draize Test procedure data. There-
fore, the following screening process was developed to eliminate
non-Draize data.

Delete data lines where:

ike

2

Dose type is expressed as per kilogram (/KG).

The chemical was applied intermittently (denoted as "-I"
following the test duration).

Test duration is other than:
a. 1 day (1D)

b. 3 days (3D)

,¢. 24 hours (24H)

d. 72 hours (72H)

Note: If a duration is not specified, it is assumed to be an
acceptable duration in accordance with RTECS statements.

Durations eliminated were:

a. seconds

A-9


3691
3694
3699
37
371
3711
3713
3714
3715
372
3721
3722
3729
373
3731
3732
374
3742
379
379]
3799
38
381
3811
382

STORAGE BATTERIES
ENGINE ELECTRICAL EQUIPMENT
ELECTRICAL EQUIPMENT, NEC
TRANSPORTATION EQUIPMENT

MOTOR VEHICLES AND EQUIPMENT

MOTOR VEHICLES

TRUCK AND BUS BODIES

MOTOR VEHICLE PARTS AND ACCESSORIES
TRUCK TRAILERS

AIRCRAFT AND PARTS

AIRCRAFT

AIRCRAFT ENGINES AND ENGINE PARTS
AIRCRAFT EQUIPMENT, NEC

SHIP AND BOAT BUILDING AND REPAIRING
SHIP BUILDING AND REPAIRING

BOAT BUILDING AND REPAIRING

RAILROAD EQUIPMENT

RAILROAD AND STREET CARS
MISCELLANEOUS TRANSPORTATION EQUIPMENT
TRAILER COACHES

TRANSPORTATION EQUIPMENT, NEC
INSTRUMENTS AND RELATED PRODUCTS
ENGINEERING & SCIENTIFIC INSTRUMENTS
ENGINEERING & SCIENTIFIC INSTRUMENTS

MECHANICAL MEASURING & CONTROL DEVICES


APPENDIX E

NIOSH RISK IDENTIFICATION MODEL OUTPUT


TABLE 7 - NOHS SIC CODES/TITLES
COMBINED OR CHANGED IN
PREPARATION OF THE INDUSTRIAL RISK
INDEX AT THE 4-DIGIT LEVEL

Combined/Original NOHS Code

0722,
0822,
0912,

1925,
1951,

4212,
4454,
4511,
4582,

4612,

4832,
4922,
5812,
5983

6023,
6026

6122,
6144,
6312,
6332,

6351

0729
0851
0913

1929, 1931, 1941
1999

4213
4459
4521
4583

4613, 4619

4833
4923, 4924

5813

6023, 6024, 6025

6123
6145, 6149
6313

6333, 6339

0720
0800

0900

1900

4210

4450

4500

4580

4610

4830

4920

5810

5900

6020
6120
6140
6310
6330

6350

43

Produced Code
Animal Husbandry Services
Forestry

Fisheries

Ordnance and Accessories
Trucking Local and Long Distance
Local Water Transportation
Transportation by Air

Air Transportation Services

Pipeline Transportation, Except
Natural Gas

Radio and Television Broadcasting
Gas Companies and Systems
Eating and Drinking Places

Fuel Oil Dealers

Commercial and Stock Savings Banks
Savings and Loan Associations
Personal Credit Institutions

Life Insurance

Fire, Marine, and Casualty Insurance

Surety Insurance


NUMBER

072
0720
073
0731
08
080
0800
09
090
0900
13
131
1311
138
1381
1382
1389
15
151
Voy
16
161
1611

STANDARD INDUSTRIAL CLASSIFICATIONS

ANIMAL HUSBANDRY SERVICES
ANIMAL HUSBANDRY SERVICES
HORTICULTURAL SERVICES
HORTICULTURAL SERVICES

FORESTRY

FORESTRY

FORESTRY

FISHERIES

FISHERIES

FISHERIES

OIL AND GAS EXTRACTION

CRUDE PETROLEUM AND NATURAL GAS
CRUDE PETROLEUM AND NATURAL GAS
OIL AND GAS FIELD SERVICES
DRILLING OIL AND GAS WELLS

OIL AND GAS EXPLORATION SERVICES
OIL AND GAS FIELD SERVICES, NEC
GENERAL BUILDING CONTRACTORS
GENERAL BUILDING CONTRACTORS
GENERAL BUILDING CONTRACTORS
HEAVY CONSTRUCTION CONTRACTORS
HIGHWAY AND STREET CONSTRUCTION
HIGHWAY AND STREET CONSTRUCTION

C-1


THE RTECS DATA SOURCE

The NIOSH Registry of Toxic Effects of Chemical Substances (RTECS)
is a compilation of published chemical toxicological and related
data. The RTECS data are derived from a comprehensive review of
the international professional literature, and are input to a
computer file which is used as the basis for the annual RTECS
publication, a quarterly microfiche edition of RTECS, and for
special purposes such as the one described in this report. The
January 1981 computer tape version of RTECS, which contains
positive effects data on 45,156 different chemicals, was the
source of toxicity data for this effort. The toxicity data in
RTECS are characteristically expressed in terms of:

The route of administration of the test chemical,

The species of animal and/or cell type used in the test,
The test procedure,

The toxic effect of the test, and

The dose at which the toxic effect was noted.

Figure 2 contains one example of the RTECS toxicity data entry
lines used in the model with each element defined. A total of 31
test animals and 26 routes of administration are found in RTECS
data entries. As newer versions of RTECS are accessed these

factors and the resultant outcomes of the model indices will change.

If the test endpoint is death, the dose given in RTECS is the
lowest published LD59, LCs59, lethal dose, or lethal concentration.
If the endpoint is manifestation of a toxic effect, the dose

given in the lowest published dose (or concentration) that

produced the toxic effect. Such toxic effects are recorded in
RTECS for non-human animal test subjects only if carcinogenic,
neoplastic, teratogenic, mutagenic, and skin and eye irritation
effects are observed; a variety of other toxic effects are recorded
for human exposures.

All toxic dose data in RTECS are derived from published reports of
the toxic effects produced in tests of the individual substances.
If the route of administration, test species, test procedure and
toxic effect are considered to define a "test class", RTECS general
publication policy can be stated as limiting the RTECS entries for
a chemical to the lowest published dose for each test class. Test
animal strain and sex, details of dose preparation and adminis-
tration, test chemical purity, and the relative scientific merit

of the tests reported in the literature are not considered when
data are assembled for inclusion in RTECS. Thus, the toxicity data
available for use in this project are made up of the unevaluated


10.
MM.
12.
13.
14.

Figures

Index Production Data Flow

A Typical Toxic Dose Entry From RTECS

RIECS Processing Computer Programs Flow Chart

HRI Algorithm Computer Programs Flow Chart

Example HRIN

NOHS Computer Processing Considerations

AHRI Algorithm Computer Programs Flow Chart

Example AHRI

Example 2-Digit IRI

Example 3-Digit IRI

Example 4-Digit IRI

IRI Algorithm Computer Programs Flow Chart

Example ORI

ORI Algorithm Computer Programs Flow Chart
Tables

Test Classes

Neoplastic Test Classes Showing the Effect of the

"NEO OPTION"

Test Classes Showing Dose Log Values, RTECS Chemical
Reference Cited, and Number of Records Per Test Class

NOHS Data Files

NOHS Occupation Codes Combined In Preparation of the

Occupational Risk Index

vi

ll
24
Fay
30
37
38
48
49
50
DZ
54
55

12

16

19

33

41


and all of the other factors are as previously defined. The sum-
mation is developed for each occupational worker group across all
of the matched chemicals to which workers in the group were
observed to be exposed in the NOHS survey. It should be recog-
nized that the increment is zero when no workers in a specific
group were observed to be exposed to a chemical in the NOHS survey,
i.e., when there is no data for a chemical in the NOHS data file
for a specific group.

Since the NOHS data are not directly applicable to the worker popu-
lation of the U.S. at other than the hazard specific or 2-digit SIC
level (see discussion on page 29), the need for additional data
became clear. Census Bureau data were located, converted to com-
puter form, and used optionally in the algorithm calculations as
the (N)* factor. It is interesting to note that while use of the
census data in the algorithm results in a risk expression for the
employee group as a whole, not using this data results in an ex-
pression of risk for an individual employed in that group.

A problem was encountered in coordination of the occupation codes
used in the Census Bureau data with those used in the NOHS data.

The NOHS data assigned separate codes to journeymen and apprentices
in the same occupation while the Census Bureau did not differentiate
between the two. Consequently, there are a number of NOHS occupation
codes for which there are no corresponding Census Bureau data. This
problem was resolved by combining some NOHS data categories to make
them correspond to the Census Bureau codes and titles. Unusual
(alphabetic) code numbers were assigned to each of the combined
categories. The NOHS codes involved and the resultant codes and
occupation titles are listed in Table 5.

As in the use of the Census Bureau occupation data, a problem was
encountered in coordination of the Census Bureau industry popula-
tion data with the NOHS data. In a number of cases no Census
Bureau data were available at the 3- and 4-digit SIC level, even
though NOHS data were presented at these levels. In these cases,
the NOHS data were combined to the extent necessary to match the
Census Bureau data. Codes ending in zero (a condition that does

* The N factor, number of workers in a specific occupation, was
obtained from Census Bureau Publication CBP-73-1, U.S. Summary,
County Business Patterns, 1973, Table 221. The N factor, number
of workers in a specific industry, was obtained from Census
Bureau Publication CBP-73-1, U.S. Summary, County Business
Patterns, 1973, Table 1B. Data from the 1973 edition of this
publication were used because they correspond in time with the
NOHS survey.

40


The purposes of the program are to: (1) eliminate duplicate
chemical-specific test class citations, (2) create minimum and
maximum natural log of dose value files (ranges) for each test
class, (3) exercise option to perform d_ calculation on all RTECS
or RTECS/NOHS linked chemicals only, (4p calculate d_ values for
each test class within chemical, (5) calculate sub-HRI's, (6)
exercise CAR OPTION.

Due to project-directed combination of species, routes, test and
toxic effects in the establishment of test classes, duplicate test
class citations within chemical were created. CIORICAL, in accor-
dance with RTECS policy, eliminates all but the most toxic test
class-specific citation within each chemical.

Based on the natural log values calculated in CIORISTD, CIORICAL
establishes range values for each test class by determining the
minimum and maximum natural log (+30.00) values for each test

class across all RTECS chemicals. As displayed in the text, these
tabulations also include the RTECS number citations for the minimum
and maximum log values, and the number of chemicals contained in
each test class citation.

Since all indices except the HRI depend on NOHS data input,
CLORICAL was designed to calculate d_ and sub-HRI values for linked
chemicals as a primary option. Sub-BRIN values are calculated by
averaging the d_ numbers within each of the sub-HRIN categories
(i.e. CAR, MUT, “TER) . At user discretion, these values can be
calculated for all RTECS chemicals, and an HRI produced for all
RTECS chemical listings containing toxicity data defined within

the 98 test classes.

At user discretion CIORICAL will utilize calculated NEO sub-HRIN's
as an estimator of the CAR sub-HRIN where no CAR data exists (CAR
sub-HRIN = 0.000) for the same chemical. This is accomplished by
moving the NEO sub-HRIN value into the CAR sub-HRIN field after all
qs and sub-HRIN values have been calculated.

At this point in the programming of the high risk model, only two
options (NEO and CAR) have been potentially exercised which affect
the outcome of the various risk indices. Therefore, it is necessary
to run the CIORICAL program only four times to cover all possibil-
ities. Each of these possibilities has a "TRIAL" designation,

which is repeated in the header data on the AHRI, IRI and ORI, and
mentioned in the main text as an “internal tracking system''. The
Trial numbers and associated CAR and NEO option settings are:

TRIAL 1 - CAR OPTION = NO, NEO OPTION = NO
TRIAL 2 - CAR OPTION = NO, NEO OPTION = YES

A-15


mg
mky
mi

MMI

mppcf
MSK
MTH
mul
mus
MUT
NEO
ng
ocu
ovr
orl
ORM
OSHA
par

Pg

pig
Pk
PNS

man

milligram (one thousandth of a gram: 10-3

gm)
monkey

milliliter

mucous membrane effects

mammary gland

million particles per cubic foot
musculo-skeletal effects

mouth effects

multiple routes

mouse

mutagenic effects

neoplastic effects

nanogram (one billionth of a gram: 10-2 grams )
ocular

ovary

oral

Other Regulated Materials (DOT)

Occupational Safety and Health Administration
parenteral

picogram (one trillionth of a gram: 10-12

gm)
Pigeon

pig

peak concentration

peripheral nervous system effects

B-4


NIOSH Technical Report

A MODEL FOR THE IDENTIFICATION
OF HIGH RISK OCCUPATIONAL GROUPS
USING RTECS AND NOHS DATA

David H. Pedersen, Randy 0. Young, and David S. Sundin

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES
Public Health Service
Centers for Disease Control
National Institute for Occupational Safety and Health
Division of Surveillance, Hazard Evaluations and Field Studies
Cincinnati, Ohio 45226

October, 1983


4580
46
461
4610
47
47)
4712
472
4721
48
481
4811
483
4830
489
4899
49
491
4911
492
4920
493
4931
4932
50

AIR TRANSPORTATION SERVICES

PIPE LINE TRANSPORTATION

PIPE LINES, EXCEPT NATURAL GAS
PIPELINE TRANSPORTATION, EXCEPT NATURAL GAS
TRANSPORTATION SERVICES

FREIGHT FORWARDING

FREIGHT FORWARDING

ARRANGEMENT OF TRANSPORTATION
ARRANGEMENT OF TRANSPORTATION
COMMUNICATION

TELEPHONE COMMUNICATION

TELEPHONE COMMUNICATION

RADIO AND TELEVISION BROADCASTING
RADIO AND TELEVISION BROADCASTING
COMMUNICATION SERVICES, NEC
COMMUNICATION SERVICES, NEC
ELECTRIC, GAS, AND SANITARY SERVICES
ELECTRIC COMPANIES AND SYSTEMS
ELECTRIC COMPANIES AND SYSTEMS

GAS COMPANIES AND SYSTEMS

GAS COMPANIES AND SYSTEMS
COMBINATION COMPANIES AND SYSTEMS
ELECTRIC AND OTHER SERVICES COMBINED
GAS AND OTHER SERVICES COMBINED

WHOLESALE TRADE


Microfiche No. 23 - 3-SIC INDUSTRIAL RISK INDEX - TERATOGEN
SELECTOR

This microfiche displays Standard Industrial Classifications

at the 3-digit level, rank-ordered by health risk due to po-
tential exposure to teratogens only. The explanations for data
input are the same as for microfiche No. 10.

Microfiche No. 24 - 4-SIC INDUSTRIAL RISK INDEX - ALL EFFECTS
EQUAL

This microfiche displays Standard Industrial Classifications

at the 4-digit level, rank-ordered by health risk due to all po-
tential chemical exposures. The explanations for data input are
the same as for microfiche No. 3.

Microfiche No. 25 - 4-SIC INDUSTRIAL RISK INDEX - CARCINOGEN
SELECTOR

This microfiche displays Standard Industrial Classifications
at the 4-digit level, rank-ordered by health risk due to po-
tential exposure to carcinogens only. The explanations for
data input are the same as for microfiche No. 7.

Microfiche No. 26 - 4-SIC INDUSTRIAL RISK INDEX - TERATOGEN
SELECTOR

This microfiche displays Standard Industrial Classifications

at the 4-digit level, rank-ordered by health risk due to po-
tential exposure to teratogens only. The explanations for data
input are the same as for microfiche No. 10.

Microfiche No. 27 - OCCUPATIONAL RISK INDEX - ALL EFFECTS EQUAL

This microfiche displays Bureau of the Census Standard Occupa-
tional Classifications rank-ordered by health risk due to all
potential chemical exposures. The explanations for data input
to the ORI algorithm (see page 39 in the text) are the same as
for microfiche No. 3.

Microfiche No. 28 - OCCUPATIONAL RISK INDEX - CARCINOGEN
SELECTOR

This microfiche displays Bureau of the Census Standard Occupa-
tional Classifications rank-ordered by health risk due to po-
tential exposure to carcinogens only. The explanations for
data input to the ORI algorithm are the same as for microfiche
No. 7.


Table of Contents

Introduction

Model Overview

The RTECS Data Source
Preparation of RTECS Data For Use In The Model
Selection of Specific Test Classes For The Model
Dose Unit Considerations
Chronic Data Considerations
Primary Irritation Data Considerations
Tumorigenic and Carcinogenic Data Considerations
Normalization of Dose Data
RTECS Work Record

The Hazard Risk Index
The HRI Algorithms

The NOHS Data Source

The Adjusted Hazard Risk Index
The AHRI Algorithms

Occupational Group Risk Indices
Occupational Group Risk Algorithms
Confidence Interval Calculations

The Industry Risk Index

The Occupational Risk Index

Appendix A - NIOSH Risk Identification Model Computer

Program. Considerations
Appendix B - RTECS Abbreviations

Appendix C - Standard Industrial Classifications Reported

In A Model For The Identification of High
Risk Occupational Groups Using RTECS and
NOHS Data
Appendix D - Occupations Reported In A Model For The
Identification of High Risk Occupational
Groups Using RTECS and NOHS Data
Appendix E - NIOSH Risk Idéntification Model Output

c-1

D-1
E-1


501
5013
502
504
5041
5047
5048
505
5053
506
5063
5065
507
5077
508
5081
5084
509
5091
5092
5099
52
521

MOTOR VEHICLES & AUTOMOTIVE EQUIPMENT
AUTOMOTIVE EQUIPMENT

DRUGS, CHEMICALS, AND ALLIED PRODUCTS
GROCERIES AND RELATED PRODUCTS
GROCERIES, GENERAL LINE

MEATS AND MEAT PRODUCTS

FRESH FRUITS AND VEGETABLES

FARM PRODUCT RAW MATERIALS

GRAIN

ELECTRICAL GOODS

ELECTRICAL APPARATUS AND EQUIPMENT
ELECTRONIC PARTS AND EQUIPMENT
HARDWARE, PLUMBING & HEATING EQUIPMENT
AIR CONDITIONING AND REFRIGERATION
MACHINERY, EQUIPMENT, AND SUPPLIES
COMMERCIAL MACHINES AND EQUIPMENT
INDUSTRIAL MACHINERY AND EQUIPMENT
MISCELLANEOUS WHOLESALERS

METALS & MINERALS, EXCEPT PETROLEUM
PETORLEUM AND PETROLEUM PRODUCTS
WHOLESALERS, NEC

BUILDING MATERIALS AND FARM EQUIPMENT
LUMBER AND OTHER BUILDING MATERIALS

G=23


which there are data within that test class. Next is a
number entry showing the normalized dose value for each
test class (to six decimals) which is used in the calcu-
lation of the sub-HRIN.

Printing of the third type of line in the HRI is an
option in the computer program, and is referred to as
the "long form".

In Figure 5, it can be noted that acetaldehyde (sequence number 1)
has RTECS data on acute toxicity, carcinogenicity, mutagenicity,
primary irritation, teratogenicity, and other toxic effects. Its
HRIN is derived from these six sub-HRIN's.

For details regarding the computer programs producing the HRI see
Figure 4, "HRI Algorithm Computer Programs Flow Chart", and the
HRI material in Appendix A.

For complete examples of the Hazard Risk Index, see the listing of
HRI's in Appendix E (pages E-1, E-2, and E-3) and the corresponding
microfiche copies of the computer printouts in the packet at the back
of this publication.

THE NOHS DATA SOURCE

The National Occupational Hazard Survey (NOHS) is a two-year study
conducted by the National Institute for Occupational Safety and
Health, starting in 1972. The survey was intended to describe the
health and safety conditions in the American work environment and,
more specifically, to determine the extent of worker exposure to
chemical and physical agents and the conditions under which such
exposure occurs. The sample of businesses in the survey, selected
by the Bureau of Labor Statistics, totaled approximately 5000 esta-
blishments in 67 metropolitan areas throughout the United States.
The sample represents all non-agricultural businesses covered under
the Occupational Safety and Health Act of 1970.

Survey data were collected by engineers specially trained in the
recognition of industrial health hazards. Worker exposures to
specific potential health hazards were recorded. Strong emphasis
was placed on uniformity in the data collection.

The exposure data used in this model are derived from the NOHS raw
data master files. Due to the design of this survey, in which

certain SIC groups (e.g., manufacturing) and certain facility sizes
(e.g., large) were oversampled, projections from the sample to the

28


1900
20
201
2011
2013
2015
202
2022
2026
203
2033
2035
2036
2037
204
2042
2044
2045
205
2051
2052
207
2071
208

ORDNANCE AND ACCESSORIES

FOOD AND KINDRED PRODUCTS

MEAT PRODUCTS

MEAT PACKING PLANTS

SAUSAGES AND OTHER PREPARED MEATS
POULTRY DRESSING PLANTS

DAIRY PRODUCTS

CHEESE, NATURAL AND PROCESSED
FLUID MILK

CANNED, CURED, AND FROZEN FOODS
CANNED FRUITS AND VEGETABLES
PICKLES, SAUCES, AND SALAD DRESSINGS
FRESH OR FROZEN PACKAGED FISH
FROZEN FRUITS AND VEGETABLES

GRAIN MILL PRODUCTS

PREPARED FEEDS FOR ANIMALS AND FOWLS
RICE MILLING

BLENDED AND PREPARED FLOUR

BAKERY PRODUCTS

BREAD, CAKE, AND RELATED PRODUCTS
COOKIES AND CRACKERS

CONFECTIONERY AND RELATED PRODUCTS
CONFECTIONERY PRODUCTS

BEVERAGES

c-3


trk
TWA
TXDS
ug
unk

UNS

WBC

turkey

time weighted average

qualifying toxic dose

microgram (One millionth of a gram: 10-6 gram)
unreported

toxic effects unspecified in source

week

white blood cell effects

woman

year

B-6


For greater detail regarding the computer program to access RTECS
data (including acceptable species, routes, tests and dose conver-
sions), see Figure 3 and the discussion in Appendix A, "High Risk
Model Computer Program Considerations"; CIORIEXT, CIORISTD, and
CIORICAL programs.

Selection of Specific Test Classes for the Model

In addition to the species combination decisions discussed earlier,
and based on the model approach to the HRIN algorithm, in which the
algorithm would use data from the different test classes to compare
and rank chemicals, it was obvious that test classes with small
populations of data would be of far less use than those that have
data on many chemicals. A threshold of 65 chemicals was selected
empirically, and data records within the test classes that met this
standard were extracted for use in the algorithm. It was found
that essentially all of the test classes with acute toxicity end-
points were included, but that a significant part of the chronic
data were excluded. To prevent this, the chronic test classes that
were excluded by the 65-chemical threshold were combined by grouping
all of the test species and routes into the "any" classification.
This produced the 98 test classes listed in Table 1. This set of
test classes was used in development of the HRIN algorithn.

Limitation of the RTECS data to these 98 test classes combined with
program data requirements for selected chemicals reduced the number
of matched chemicals to 1721 from the original NOHS-RTECS matched
total of 2092. A listing of these 1721 chemicals in RTECS
accession number order is attached in microfiche form. (See
listings in Appendix E.)

Dose Unit Consideration

The RTECS data use a number of different units to report the doses
that produced the stated toxic effect. In order to compare the
data from chemical to chemical the reported units were standardized
as follows:

a. All dose units on inhalation route were converted to parts
per million.

b. All dose units on data other than the inhalation route
were converted to milligrams per kilogram of unit body
weight, and then to molar form (i.e. millimoles/kg).

Conversion of RTECS data from milligrams per cubic meter (mg/m3)
to parts per million (ppm) was accomplished using the following
equation: 3
ppm = (mg/m )(24.45)/molecular weight

10


NOHS
Summary
File #2,

#3,or #4

Census Standard
ndustrial Clas-
ification Popu-
lation Totals

NOHS Standard
Industrial
Classification

B

IRIN SUMM

Sort
IRIN
Sequence

Sorted
IRI
Master

Trial-Run
Header Card

Print IRI

IRI Sequence

Census
Option

IRI Algorithm Computer Programs Flow Chart

FIGURE 12.

52


2831
2833
2834
284

2841
2842
2843
2844
285

2851
287

2871
289

2893
2899

301

BIOLOGICAL PRODUCTS

MEDICINALS AND BOTANICALS
PHARMACEUTICAL PREPARATIONS
SOAP, CLEANERS, AND TOILET GOONS
SOAP AND OTHER DETERGENTS
POLISHES AND SANITATION GOODS
SURFACE ACTIVE AGENTS

TOILET PREPARATIONS

PAINTS AND ALLIED PRODUCTS
PAINTS AND ALLIED PRODUCTS
AGRICULTURAL CHEMICALS
FERTILIZERS

MISCELLANEOUS CHEMICAL PRODUCTS
PRINTING INK

CHEMICAL PREPARATIONS, NEC
PETROLEUM AND COAL PRODUCTS
PETROLEUM REFINING

PETROLEUM REFINING

PAVING AND ROOFING MATERIALS
PAVING MIXTURES AND BLOCKS
ASPHALT FELTS AND COATINGS

MISC. PETROLEUM AND COAL PRODUCTS
LUBRICATING OILS AND GREASES
RUBBER AND PLASTICS PRODUCTS, NEC

TIRES AND INNER TUBES


Note: Subsets of the four TRIALS are created through user choice
of the options discussed previously. Any change in user option
selection creates a new summary file or "run", as referred to in
the internal tracking system. Additionally, all user option selec-
tions are displayed in the header information at the top of each
risk index page.

E. Create Adjusted Hazard Risk Printouts
Program: PRTAHRIN

The purpose of this program is to produce a listing of the linked
chemicals in AHRIN order.

The setting of the algorithm variables and calculation of the AHRIN
values is accomplished by the CIORISUM program. Since the sorted
summary file used as input to the AHRIN is maintained in RTECS #
sequence, an intermediate step orders the file in AHRIN sequence.

Use of a control card in this program provides the "Trial-run"
input data for the printout header needed for the internal tracking
system.

F. Create Industrial Risk Index (2-, 3-, or 4-digit SIC)
Program: CLIORIIRI

The purposes of this program are to: (1) calculate IRI values by
modifying source HRIN's produced by the CIORISUM program with
exposure data specific to NOHS Summary Files 2, 3, or 4 (2-digit,
3-digit, or 4-digit SIC), (2) input SIC descriptions from the NOHS
Standard Industrial Classification Descriptions File, (3) permit
optional input of census data for SIC-specific populations as an
algorithm factor, and (4) calculate a confidence interval for the
calculated IRIN.

As explained in the text on page 47, source HRIN values for every
linked chemical observed for the SIC are used. Each of these is
modified by the proportion of workers in the SIC (see PES/PEN,
page 32) observed to be exposed to it, as well as factors for
control and duration of that exposure. The modified HRIN values
are then summed across all observed chemical exposures specific to
an industrial group.

The resulting sum is optionally modified by the (N) or census
population factor for the SIC group.

A-17


chemical hazard as determined by direct observation or trade-
mame product resolution.

PC is defined as the percentage of observed exposures to the
given hazard where some valid type or types of exposure
control mechanism was employed.

PFT is defined as the percentage of observed exposures to the
given chemical hazard where the exposure normally occurred
more than 4 hours each working day.

THE ADJUSTED HAZARD RISK INDEX

The Adjusted Hazard Risk Index (AHRI) represents accomplishment of
the second major NIOSH goal in development of this model - to
provide a mechanism for the assessment of the relative workforce
health effect posed by specific chemical agents.

The AHRI algorithm provides a rank-ordering of the linked chemicals
based on both RTECS toxicological data and NOHS incidence, duration
and exposure control data.

An estimate of the relative health risk posed by any linked chem-
ical to the working population is provided in numerical form by
the AHRI algorithm. This number is used to rank-order all linked
chemicals in descending order of relative workforce health effect.
The resulting list provides the simultaneous evaluation of chem-
ical toxicity and worker exposure data needed to establish priori-
ties in the effort to control occupational hazards.

The AHRI Algorithm

The rationale for the Adjusted Hazard Risk Index algorithm is that
the overall risk posed by a chemical hazard is dependent upon both
the chemical's toxicity and the extent to which workers are exposed
to the chemical. Accordingly, a highly toxic chemical to which a
small number of persons are exposed is considered to pose less risk
than a chemical of similar toxicity to which many persons are ex-
posed, and a moderately toxic chemical to which many persons are
exposed poses more risk than one that is highly toxic but to which
only a few persons are exposed.

The AHRI is concerned only with the toxicity of each chemical and
the extent of worker exposure to that chemical. Therefore, data
from NOHS file number 1 (which provides exposure data on workers
without regard to their industry or occupation) was used in the

34


Using this equation one can calculate the 95 percent confidence
interval for the exposure ratio and, since the ORIN is directly
proportional to the exposure ratio, for the ORIN. In addition,
one can select an accuracy range and calculate the minimum sample
size that can be expected to yield an observed ratio within the
range 95 percent of the time. At an accuracy of + 0.10, the
equation becomes:

P %- 0.10 <r Fe 0.10 )« 95
and /I/n is equal to 0.10, which makes n equal to 100.

Based on these considerations, the algorithm computer programs were
modified to omit worker groups with sample sizes (i.e., PEN) of
less than 100. In addition, a routine was introduced into the pro-
grams to calculate the 95 percent confidence interval of the occu-
pational group risk index numbers based on the PEN value.

The occupational group risk index algarithm computer programs check
the PEN value for each worker group against the selected minimum
value, calculate the incremental risk involved in potential expo-
sure of workers in a group to each matched chemical, sum these
incremental risks over all chemicals to which workers in a group
are exposed to produce a risk index number for each occupational
group, calculate the 95 percent confidence interval limits for
each, sort the occupations into rank-order (descending), and print
the Risk Index Number.

For details regarding the computer programs which permit input of
Census data for occupations and 2-, 3-, or 4-digit SIC's as well
as selecting worker groups with PEN values of 100 or more and
calculate the confidence intervals, see Appendix A.

The occupational group indices are produced by employment within
standard occupations and within 2-, 3-, or 4-digit Standard
Industrial Classifications. A discussion of each follows:

THE INDUSTRY RISK INDEX

The objective of the Industry Risk Index (IRI) is to rank the
industries covered in the National Occupational Hazard Survey on
the basis of the potential risk to workers at the 2-, 3-, and
4-digit SIC levels, so that the IRI is produced in three parts
corresponding to the three SIC levels.

The IRI algorithm sums the risk associated with each of the toxic

chemicals to which workers: in an industry are potentially exposed.
The IRI algorithm used in production of the Industry Risk Index is:

46


ee DRAFT SMEN

153 ELECTRICIAL AND ELECTRONIC ENGINEERING TECHNICIANS
154 INDUSTRIAL ENGINEERING TECHNICIANS

155 MECHANICAL ENGINEERING TECHNICIANS

162 ENGINEERING AND SCIENCE TECHNICIANS, NEC
163 AIRPLANE PILOTS

170 FLIGHT ENGINEERS

71 RADIO OPERATORS

183 DESIGNERS

184 EDITORS AND REPORTERS

190 PAINTERS AND SCULPTORS

191 PHOTOGRAPHERS

195 RESEARCH WORKERS, NOT SPECIFIED

221 OFFICERS, PILOTS, AND PURSERS: SHIP

225 PURCHASING AGENTS AND BUYERS, NEC

230 RESTAURANT, CAFETERIA, AND BAR MANAGERS
233 SALES MANAGERS, EXCEPT RETAIL TRADE

ook MANAGERS AND ADMINISTRATORS, NEC

260 ADVERTISING AGENTS AND SALESMEN

280 SALESMEN AND SALES CLERKS, NEC

301 BANK TELLERS

303 BILLING CLERKS

305 BOOKKEEPERS

310 CASHIERS

0-2


consists of the number of linked chemicals (out of 1721)
to which workers in the industry group are exposed, and
the 95 percent confidence limits of the IRIN. Printing
of the second line is a program option.

For a listing of the subset of NOHS 2-, 3-, and 4-digit Standard
Industrial Classifications covered by this model, see Appendix C -
Standard Industrial Classifications Reported In A Model For the
Identification of High Risk Occupational Groups Using RTECS and
NOHS Data.

For details on the computer programs which produce the IRI, see
Figure 12 -IRI Algorithm Computer Programs Flowchart, and the IRI

material in Appendix A. Complete examples of IRI listings are provided
in microfiche form (See pages E-3, E-4, and E-5).

THE OCCUPATIONAL RISK INDEX

The objective of the Occupational Risk Index (ORI) is to rank occu-
pations on the basis of the potential risk to workers employed in
these occupations due to chemical exposure.

The ORI algorithm sums the risk associates with each of the toxic
chemicals to which wotkers in a specific occupation are potentially
exposed. The ORI algorithm used in production of the Occupational
Risk Index Number (ORIN) is:

ORIN = (BRIN) (PES/PEN) (1.0 - 0.9 PC)(0.5 + 0.5 PFT) (N)

where: ORIN = Occupational Risk Index Number,

N = Number of workers in the occupation (from Census
Bureau data),

and all other factors are as previously defined. The summation is
developed for each worker group across all the linked chemicals,
recognizing that the increment is zero when no workers in a specific
occupation are abserved to be exposed to a chemical in the NOHS data
file for a given occupation. In this algorithm, a worker group is
defined as all workers employed in the same occupation. The occu-
pation codes reported in this model are listed in Appendix D -
Occupation Reported In A Model For the Identification of High Risk
Occupational Groups Using RTECS and NOHS Data.

As in the AHRIN and IRIN, source HRIN's are obtained from the Hazard
Risk Index calculations, and all other factors except N are obtained
from the NOHS data. NOHS file number 5, as described in Table 4,
supplies exposure data on worker groups at the occupation level.

51


IRIN = (HRIN) (PES/PEN) (1.0 - 0.9 PC)(0.5 + 0.5 PFT) (N)

where: IRIN = Industrial Risk Index Number,
N = Number of workers in the Industry (from Census
Bureau data),

and all of the other factors are as previously defined. The sum-
mation is developed for each worker group across all of the matched
chemicals, recognizing that the increment is zero when no workers
in a specific SIC are observed to be exposed to a chemical in the
NOHS survey, i.e., when there is no data for a chemical in the NOHS
data file for a given SIC. In this algorithm a worker group is
defined as all workers in the NOHS survey that were employed in

the same industry. The NOHS industry codes reported in this model
are listed in Appendix C.

As in the AHRIN algorithm, source HRIN's are obtained from the
Hazard Risk Index calculations and all of the other factors except
N are obtained from the NOHS data. NOHS file numbers 2, 3, and 4,
as described in Table 4, supply exposure data on industrial worker
groups at the 2-, 3-, and 4-digit SIC levels, respectively.

A sample page from each of the three Industrial Risk Indices are
shown in Figures 9, 10, and 11. The description of format that
follows applies equally to all three.

As seen in Figures 9, 10, and 11, the IRI display format is in
several parts, as follows:

(1) The first line of the heading at the top of each page
identifies the index (2-, 3-, or 4-digit SIC level is
self-evident), the HRIN trial and run, the RTECS and
NOHS file update versions used, the "CENSUS OPTION"
("YES" meaning that census totals are used in the algor-
ithm), the date of the printout, and the sequential page
number within the IRI.

(2) The second line of the heading at the top of each page
identifies the settings of the multipliers and NEO OPTION
in the source HRI.

(3) The third line of the heading identifies the settings
for the constants and CAR OPTION in the source HRIN.

(4) The first line of the index entry for each SIC group
consists of a sequence number, SIC numerical designation
and title, and the calculated Industry Risk Index Number
(IRIN). The second line of the entry for each SIC group

47


APPENDIX B

RTECS ABBREVIATIONS


162
1621
17
71
W711
172
1721
173
1731
174
1741
1742
1743
175
1751
1752
176
1761
179
1791
1794
1799
19
190

HEAVY CONSTRUCTION, NEC

HEAVY CONSTRUCTION, NEC

SPECIAL TRADE CONTRACTORS

PLUMBING, HEATING, AIR CONDITIONING
PLUMBING, HEATING, AIR CONDITIONING
PAINTING, PAPER HANGING, DECORATING
PAINTING, PAPER HANGING, DECORATING
ELECTRICAL WORK

ELECTRICAL WORK

MASONRY, STONEWORK, AND PLASTERING
MASONRY AND OTHER STONEWORK
PLASTERING AND LATHING

TERRAZZO, TILE, MARBLE, MOSAIC WORK
CARPENTERING AND FLOORING
CARPENTERING

FLOOR LAYING AND FLOOR WORK, NEC
ROOFING AND SHEET METAL WORK
ROOFING AND SHEET METAL WORK

MISC. SPECIAL TRADE CONTRACTORS
STRUCTURAL STEEL ERECTION
EXCAVATING AND FOUNDATION WORK
SPECIAL TRADE CONTRACTORS, NEC
ORDNANCE AND ACCESSORIES

ORDNANCE AND ACCESSORIES

C-2


TABLE 5 - NOHS OCCUPATION CODES
COMBINED IN PREPARATION OF THE
OCCUPATIONAL RISK INDEX

ORIGINAL NOHS CODES NEW CODE
473, 474 00A
410, 411 008
415, 416 0oc
430, 431 000
561, 562 O0E
522, 523 OOF
423, 530, 531 006
371, 372 OOH
461, 462 001
491, 495, 571, 572 00d
220, 245 00K
535, 536 OOL

4]

OCCUPATION TITLE
Automobile Mechanics
Bricklayers and Stonemasons
Carpenters
Electricians
Tool and Die Makers
Plumbers and Pipefitters
Pressmen and Plate Printers
Secretaries, NEC
Machinists

Not Specified Mechanics and
Repai rmen

Managers and Administrators,
NEESC.

Sheet Metal Workers and
Tinsmiths


This microfiche displays use of the "constant" factor in the
HRIN algorithm. The list shown here rank-orders the linked
chemicals by descending HRIN. In this case the HRIN results
from considering all RTECS cited toxic effects with an
emphasis on mutagenesis.

Microfiche No. 13 - HAZARD RISK INDEX - TERATOGEN EMPHASIS

The list displayed on this microfiche is the final demonstra-
tion of the use of the "constant" factor in this series.
This HRIN emphasizes teratogenic effects.

Microfiche No. 14 - HAZARD RISK INDEX - CARCINOGENIC EFFECT
WEIGHTING

The list displayed on this microfiche is an example of the
multiplier factor as a toxic effect weighting device. Note
that while all data is considered in calculating the HRIN, the
CAR data is given a weight of 5.

Microfiche No. 15 - ADJUSTED HAZARD RISK INDEX - ALL EFFECTS
EQUAL

The AHRI displayed on this microfiche results from considering
all effects equal in weight (i.e., setting all multipliers at

1, and all constants at 0) and modifying the resulting HRIN's

by NOHS exposure data as detailed in the text on page 35.

Note that the NEO OPTION is set at "YES".

Microfiche No. 16 - ADJUSTED HAZARD RISK INDEX - ACUTE TOXICITY
SELECTOR

The AHRI on this microfiche is the result of considering only
the acute toxicity data cited in RTECS for the linked chemicals
to derive source HRIN's which are then modified by NOHS expo-
sure data as previously explained.

Microfiche No. 17 - ADJUSTED HAZARD RISK INDEX - CARCINOGEN
EMPHASIS

The AHRI displayed on this microfiche is a result of empha-
sizing carcinogenic effect, and then modifying the HRIN values
with NOHS exposure data.

Microfiche No. 18 - 2-SIC INDUSTRIAL RISK INDEX - ALL EFFECTS
EQUAL


734
7349
733
7391
7399
75
76
762
769
7699
78
72)
792
7929
793
7933
794
7946
7947
7949
80
801
8011
806
8061

SERVICES TO BUILDINGS

MISCELLANEOUS SERVICES TO BUILDINGS
MISCELLANEOUS BUSINESS SERVICES
RESEARCH & DEVELOPMENT LABORATORIES
BUSINESS SERVICES, NEC

AUTO REPAIR, SERVICES, AND GARAGES
MISCELLANEOUS REPAIR SERVICES
ELECTRICAL REPAIR SHOPS
MISCELLANEOUS REPAIR SHOPS

REPAIR SERVICES, NEC

MOTION PICTURES

AMUSEMENT & RECREATION SERVICES, NEC
PRODUCERS, ORCHESTRAS, ENTERTAINERS
ENTERTAINERS’ & ENTERTAINMENT GROUPS
BOWLING AND BILLIARD ESTABLISHMENTS
BOWLING ALLEYS

MISC. AMUSEMENT, RECREATION SERVICES
AMUSEMENT PARKS

GOLF CLUBS AND COUNTRY CLUBS
AMUSEMENT AND RECREATION, NEC
MEDICAL AND OTHER HEALTH SERVICES
OFFICES OF PHYSICIANS AND SURGEONS
OFFICES OF PHYSICIANS AND SURGEONS
HOSPITALS

HOSPITALS

C-27


626
630
631
633
634
635
640
641
642
643
644
645
650
651
652
653
656
660
661
662
663
664
665
666

HEATERS, METAL

LAUNDRY AND DRY CLEANING OPERATIVES, NEC
MEAT CUTTERS AND BUTCHERS, EXC. MANUFACTURING
MEAT CUTTERS AND BUTCHERS, MANUFACTURING
MEAT WRAPPERS, RETAIL TRADE

METAL PLATERS

MINE OPERATIVES, NEC

MIXING OPERATIVES

OILERS AND GREASERS, EXC. AUTO

PACKERS AND WRAPPERS, EXCEPT MEAT AND PRODUCE
PAINTERS, MANUFACTURED ARTICLES
PHOTOGRAPHIC PROCESS WORKERS

DRILL PRESS OPERATIVES

GRINDING MACHINE OPERATIVES

LATHE AND MILLING MACHINE OPERATIVES
PRECISION MACHINE OPERATIVES, NEC

PUNCH AND STAMPING PRESS OPERATIVES
RIVETERS AND FASTENERS

SAILORS AND DECKHANDS

SAWYERS

SEWERS AND STITCHERS

SHOEMAKING MACHINE OPERATIVES

SOLDERS

STATIONARY FIREMEN


APPENDIX E -

Selected Output On Microfiche

As an aid to potential users of this model, and as a demonstration
of the flexibility of the model, a sample of selected output has
been made a part of this report. Twenty-eight different types of
illustrative output listings were selected to serve as examples
and are presented in microfiche form.

Each microfiche contained in the pocket on the inside back cover
of this report has a two-line title. The first line reads "NIOSH
RISK IDENTIFICATION MODEL" and is printed on each microfiche as
the overall title. The second title is specific to the data on
the microfiche, and corresponds to the listing and explanations
that follow.

Microfiche Nos. 1 and 2 - LINKED CHEMICAL LIST IN RTECS ORDER

These microfiche display the 1721 chemicals common to both
RTECS and NOHS which were utilized in this model. The data
for each chemical is displayed in the "long" format discussed
on page 28 of the text. The chemicals are displayed in RTECS
order for ease of reference.

Microfiche No. 3 - HAZARD RISK INDEX - TRIAL 1

This microfiche displays the linked chemicals in descending
HRIN-order, with the CAR and NEO OPTIONS both set at "NO",
all multipliers set at 1, and constants set at 0. This is
an "all toxic effects considered equal" model. The Trial 1
identification is referred to in the text on page 36.

Microfiche No. 4 - HAZARD RISK INDEX - TRIAL 2

This microfiche displays the linked chemicals in descending
HRIN-order with the NEO OPTION set at "YES". All other
settings are identical to those displayed on microfiche
number 3. This display exhibits the effect of utilizing CAR
data as a surrogate for NEO data, as discussed in the text
on page 15.

Microfiche No. 5 - HAZARD RISK INDEX - TRIAL 3
This microfiche displays the linked chemicals in descending

HRIN-order with the CAR OPTION set at "YES". All other
settings are identical to those displayed on microfiche


ABSTRACT

The National Institute for Occupational Safety and Health (NIOSH)
has developed two data files, the Registry of Toxic Effects of
Chemical Substances (RTECS) and the National Occupational Hazard
Survey (NOHS), which contain data on chemical toxicity and
potential worker exposure to chemical agents, respectively.

This report describes an attempt to combine data from these two
files for the purpose of quantifying in a relative sense,
potential health risks associated with industries and occupations
covered in the NOHS. A model has been designed which permits

the user to make certain decisions concerning which toxic effects
or potential exposure conditions are to be emphasized in a given
computational cycle of the algorithms. The model thus produces
rank-ordered lists of exposure agents, industries, or occupations
which reflect the particular interests of the user.

The indices of potential health risk derived from the various
algorithms in this model are intended as an initial step in the
identification and prioritization of subjects for occupational
health research. The calculated indices do not provide absolute
measures of occupational health risk.

ifi


3951
3952
3953
3955
396
3961
3964
399
3991
3993
3994
3996
3999
4
aul
4
4119
412
4121
413
4131
415
4151

PENS AND MECHNICAL PENCILS

LEAD PENCILS AND ART GOODS
MARKING DEVICES

CARBON PAPER AND INKED RIBBONS
COSTUME JEWELRY AND NOTIONS
COSTUME JEWELRY

NEEDLES, PINS, AND FASTENERS
MISCELLANEOUS MANUFACTURES

BROOMS AND BRUSHES

SIGNS AND ADVERTISING DISPLAYS
MORTICIANS' GOODS

HARD SURFACE FLOOR COVERINGS
MANUFACTURES, NEC

LOCAL AND INTERURBAN PASSENGER TRANSIT
LOCAL AND SUBURBAN TRANSPORTATION
LOCAL AND SUBURBAN TRANSIT

LOCAL PASSENGER TRANSPORTATION, NEC
TAXICABS

TAXICABS

INTERCITY HIGHWAY TRANSPORTATION
INTERCITY BUS LINES

SCHOOL BUSSES

SCHOOL BUSSES

c-20


4. PPM (original or converted) are given an MG/KG designation.

5. MG/KG (converted) is changed to millimole (MM) designation
via the second conversion step.

6. The following are conversions of various per liter (/L)
measurements to millimole equivalence designated as (MM).

a. MMOL/L to MM = no conversion.

b. MOL/L to MM = MOL/L x 1,000.

c. UMOL/L to MM = UMOL/L x .001.

d. NMOL/L to MM = NMOL/L x .000001.

e. PMOL/L to MM = PMOL/L x .000000001.
In-vivo dose amount

Dose amount starts in position 26 and continues for a variable
number of positions.

In-vivo dose types
The dose type is separated from dose amount by one blank.
Dose amount types accepted:

All per kilogram (/KG) measurements, including: MG/KG, UG/KG,
GM/KG, NG/KG, NMOL/KG, MMOL/KG, UMOL/KG.

Types rejected:

All types that cannot be converted to equivalent millimole format.
Note that use of the constant volume assumption is not possible
for in-vivo data. Rejected types include: MMOL/L, UMOL/L, UG/L,
G/L, PPT, PPM, PPB, MG/M3, UG/M3.

In-vivo dose conversions (See first and second conversion steps).
Conversions are made in the following order:

1. UG/KG, GM/KG, and NG/KG to MG/KG via the first conversion step.

2. MG/KG (original or converted) to millimole (MM) equivalence via
the second conversion step.

3. The following are conversions of various mole per kilogram


{RI calculation based on HRIN Trial 02 Run 01 RTECS Date 01/81 NOHS Date 07/80 CENSUS OPTION=YES

Multipliers for
Constants for

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

1

10

AT=1

AT=0

SIC 8061

SIC 5810

SIC 5511

SIC 5541

SIC 5411

SIC 5311

SIC 7349

SIC 2834

SIC 6510

SIC 8092

CAR=1
CAR=0

ETA=1  MUT=1 NEQ=1 PI=1 ~~‘ TER=1
ETA=O MUT=0 NEO=O0 PI=0 TER=0
HOSPITALS
NUMBER OF CHEMICALS = 658

EATING AND DRINKING PLACES
NUMBER OF CHEMICALS = 95

NEW AND USED CAR DEALERS
NUMBER OF CHEMICALS = 162

GASOLINE SERVICE STATIONS
NUMBER OF CHEMICALS = 96

GROCERY STORES

NUMBER OF CHEMICALS = 75
DEPARTMENT STORES
NUMBER OF CHEMICALS = 238

MISCELLANEOUS SERVICES TO BUILDINGS
NUMBER OF CHEMICALS = 43

PHARMACEUTICAL PREPARATIONS
NUMBER OF CHEMICALS = 360

REAL ESTATE OPERATORS AND LESSORS
NUMBER OF CHEMICALS = 114

SANATORIA, CONVALESCENT & REST HOMES
NUMBER OF CHEMICALS = 135

TFX=1
TFX=0

Date: 09/02/81 page 1

NEO OPTION= YES
CAR OPTION= NO

IRIN = 422.822

4+/= 3.378

IRIN = 140.413

ve 2.603

IRIN = 137.032 7

4 4.761 8

IRIN = 120.240 a

Eps 6.284 q
tt

IRIN = 101.718 a

+/- 2.445
bd

IRIN = 97.368 a

+/- 1.103 Q
s

IRIN = 71.950 "3

+/- 2.898 &
iS

IRIN = 69.563 Ue

+/- 2.741

IRIN = 66.673

+/- 2.547

IRIN = 65.624

+/- 1.517

50


3493
3494
3496
3498
3499
35
351
3511
3519
352
3522
353
393)
3532
3233
3534
3535
3537

3541
3542
3544
3545
3548

355

STEEL SPRINGS

VALVES AND PIPE FITTINGS
COLLAPSIBLE TUBES

FABRICATED PIPE AND FITTINGS
FABRICATED METAL PRODUCTS, NEC
MACHINERY, EXCEPT ELECTRICAL
ENGINES AND TURBINES

STEAM ENGINES AND TURBINES
INTERNAL COMBUSTION ENGINES, NEC
FARM MACHINERY

FARM MACHINERY

CONSTRUCTION AND RELATED MACHINERY
CONSTRUCTION MACHINERY

MINING MACHINERY

GIL FIELD MACHINERY

ELEVATORS AND MOVING STAIRWAYS
CONVEYORS AND CONVEYING EQUIPMENT
INDUSTRIAL TRUCKS AND TRACTORS
METAL WORKING MACHINERY

MACHINE TOOLS, METAL CUTTING TYPES
MACHINE TOOLS, METAL FORMING TYPES
SPECIAL DIES, TOOLS, JIGS & FIXTURES
MACHINE TOOL ACCESSORIES
METALWORKING MACHINERY, NEC

SPECIAL INDUSTRY MACHINERY

C-k5


CALC

Minimum, max- Master Multipliers
imum Test File and Constants,
Class Values Trials

Long or Short

Trial 1,2,3,4 HRIN Print Option

(152535, or) 4)

CIORISUM

HRIN 7

Sequence

Summary
File
(temporary)

Sort RTECS #
Sequence

Input to AHRI, IRI, ORI Systems

FIGURE 4. HRI Algorithm Computer Programs Flow Chart

24



TABLE 3 (Continued)

TEST MINIMUM(+30.00) MINIMUM LOG VALUE MAXIMUM(+30.00) MAXIMUM LOG VALUE NO. OF TEST
CLASS NO. LOG VALUE RTECS ACCESSION NO. LOG VALUE RTECS ACCESSION NO CITATIONS
33. 14.911523 RT6475000 35.695938 PC1400000 2289
34. 21387405 0H6300000 34.891739 L.Q0525000 119
35. 25.041641 XX9625000 32.641083 YU0875000 66
36. 20. 983322 ED9770000 34.449814 KQ6300000 163
37 20.779022 FH5090000 32.877502 BG6125000 218
38. 17 .432083 BR7875000 34444733 NT8050000 271
39. 11.841690 RT6475000 35 .833664 TY2000000 217
40. 21.691162 GA0875000 33. 907898 VZ4725000 87
41. 17 .482758 VG4888000 33.647812 TS8750000 342
42. 13.354279 RT6475000 35.172318 MA1575000 10478
43. 18.488571 WR7940000 33808945 KW2975000 269
44, 12 .832092 RT6475000 34 .492493 TY2000000 1125
45. 20.381500 UL2100000 35.445465 AC5960000 508
46. 11.473969 RT6475000 34562103 LW5320000 539)
47. 20.684296 AR9548200 30.405457 EV6475000 208
48. 25.739029 YD0160000 32.092285 0v9100000 77
49. 22841568 TF3850000 35.034149 Pv6210000 320
50. 21.930389 GHO700000 34.869125 KQ6300000 107
Bila 21.166656 LZ0875000 34841537 GE8050000 72
52. 21 .930389 GHO700000 36023804 AL3150000 167
53. 22 ..389542 AY4375000 35.666611 TY2000000 181
54. 24 .332535 781700000 34594391 064375000 116
bon 16.806671 HP3500000 35.520004 TY2000000 420
56. 20.899017 GHO520000 34. 963333 NT8050000 152
57. 18.025192 ZG0700000 36.945023 TY2000000 148
58. 22.908752 J06300000 35.062012 HE4375000 458
59. 19.546204 13170000 38 .366623 TK7887000 6240
60. 23.071991 WJ5600000 40.013458 407030000 145
61. 25.839706 CW3850000 42.566772 $J3325000 136
62. 20.901321 HP3500000 38378464 AJ0175000 95
63. 20.719009 HP3500000 38443344 KQ6300000 69
64. 22 .436279 RQ2128000 35.189713 €z0240000 824
65. 20.253891 TF2780000 36.122849 GU9625000 5118

20


3821
3822
383
3831
384
3841
3842
3843
385
3851
386
3861
387
3871
39
39il
3911
3912
393
3931
394
3941
3942
3949
395

MECHANICAL MEASURING DEVICES
AUTOMATIC TEMPERATURE CONTROLS
OPTICAL INSTRUMENTS AND LENSES
OPTICAL INSTRUMENTS AND LENSES
MEDICAL INSTRUMENTS AND SUPPLIES
SURGICAL AND MEDICAL INSTRUMENTS
SURGICAL APPLICANCES AND SUPPLIES
DENTAL EQUIPMENT AND SUPPLIES
OPHTHALMIC GOODS

OPHTHALMIC GOODS

PHOTOGRAPHIC EQUIPMENT AND SUPPLIES
PHOTOGRAPHIC EQUIPMENT AND SUPPLIES
WATCHES, CLOCKS, AND WATCHCASES
WATCHES AND CLOCKS

MISCELLANEOUS MANUFACTURING INDUSTRIES
JEWELRY, SILVERWARE, AND PLATED WARE
JEWELRY, PRECIOUS METAL

JEWELERS' FINDINGS AND MATERIALS
MUSICAL INSTRUMENTS AND PARTS
MUSICAL INSTRUMENTS AND PARTS

TOYS AND SPORTING GOODS

GAMES AND TOYS

DOLLS

SPORTING AND ATHLETIC GOODS, NEC

PENS, PENCILS, OFFICE AND ART SUPPLIES

C-19


To eliminate such multiple counts, searches of the NOHS data base
were made by NIOSH and "people exposed" counts were produced.
These counts eliminated duplication within any "worker group" and
reported an individual only once for each identified hazard in the
relevant "worker group" cell. NOHS data tapes showing these data
were prepared by NIOSH and used in the high risk model. Successful
production of data for (1) the non-duplicative number of people
exposed (hazard specific) at the five worker group levels (see
below) used in this model and (2) the total number of people in
each worker group, allowed use of "worker exposed ratios" in com-
paring the relative risk for worker groups at the defined levels
of specificity. This ratio is expressed as:

Number of people in worker group X exposed to hazard Y
Total number of people in worker group X

With this ratio we compare, for example, the exposure of one out of
forty plumbers (1/40) to benzene with the exposure of twelve out of
ninety (12/90) electricians to benzene.

The NOHS data used in this model consists of five files in which
the NOHS data had been organized and accumulated by worker groups
at differing levels. These are described in Table 4. The files
presented data for each worker group as follows:

Industry code (2-, 3-, or 4-digit SIC).

Occupation code (OCC).

NOHS Hazard code and "linked" RTECS number.

Total number of people in the worker group (PEN).

Number of people in the worker group that were exposed to

the hazard (PES).

e@ Percentage of the exposures that were controlled in some
way (PC).

e@ Percentage of the exposures that were full-time (PFT).

A more exact definition of the above key factors is as follows:
SIC, see codes in Appendix C.
OCC, see codes in Appendix D.
PEN is defined as the number of people in a worker group noted
as potentially exposed to any chemical, physical or biological
agent in the NOHS data base as determined by direct obser-

vation or tradename product resolution.

PES is defined as the number of people in a worker group noted
in the NOHS data base as potentially exposed to a specific

32


APPENDIX D

OCCUPATION CODES REPORTED IN A
MODEL FOR THE IDENTIFICATION OF
HIGH RISK OCCUPATIONAL GROUPS
USING RTECS AND NOHS DATA


APPENDIX A

NIOSH RISK IDENTIFICATION MODEL

COMPUTER PROGRAM CONSIDERATIONS


631
6310
633
6330
64
641
6411
65
651
6510
653
6531
66
67
70
701
7011
72
721
7211
7213
7216
73
731
7311

LIFE INSURANCE

LIFE INSURANCE

FIRE, MARINE, AND CASUALTY INSURANCE
FIRE, MARINE, AND CASUALTY INSURANCE
INSURANCE AGENTS, BROKERS & SERVICE
INSURANCE AGENTS, BROKERS & SERVICE
INSURANCE AGENTS, BROKERS & SERVICE
REAL ESTATE

REAL ESTATE OPERATORS AND LESSORS
REAL ESTATE OPERATORS AND LESSORS
AGENTS, BROKERS, AND MANAGERS
AGENTS, BROKERS, AND MANAGERS
COMBINED REAL ESTATE, INSURANCE, ETC.
HOLDING AND OTHER INVESTMENT COMPANIES
HOTELS AND OTHER LODGING PLACES
HOTELS, TOURIST COURTS, AND MOTELS
HOTELS, TOURIST COURTS, AND MOTELS
PERSONAL SERVICES

LAUNDRIES AND DRY CLEANING PLANTS
POWER LAUNDRIES, FAMILY & COMMERCIAL
LINEN SUPPLY

DRY CLEANING PLANTS, EXCEPT RUG
MISCELLANEOUS BUSINESS SERVICES
ADVERTISING

ADVERTISING AGENCIES

C- 26


Calculation of the confidence interval (see text page numbers 45
and 46) is accomplished by machine essing of the equation
¥i/a (IRIN) which is expressed as ¥i/PEN ** (0.5)(IRIN). This
calculates a confidence interval for the IRIN based on the size
of the PEN or total number of workers observed in the SIC group
exposed to any NOHS hazard. (See text, page 51.)

G. Create Industrial Risk Index Printouts
Program: PRINTIRI

The purpose of this program is to produce a listing of 2-, 3-, or
4-digit SIC's in IRIN order.

The setting of the algorithm variables for source HRIN data is
accomplished in the CIORISUM program, and the calculation of the
TRIN's is accomplished in the CIORIIRI program.

Since the output from CIORIIRI is in S$IC-code sequence, an inter-
mediate step orders the SIC-listings in IRIN order.

Use of a control card in this program provides the "Trial-run"
input for the printout header exactly as in the PRTAHRIN program.

H. Create Occupational Risk Index
Program: CIORIORI

The purposes of this program are to: (1) calculate ORI values by
modifying source HRIN's produced by the CIORISUM program with
exposure data specific to NOHS Summary File 5 (Standard Occupations),
(2) input occupation titles from the NOHS Occupation Descriptions
File, (3) permit optional use of the odcupation-specific census
population total as an algorithm factor, and (4) calculate a con-
fidence interval for the calculated ORIN.

As illustrated in the main text on page 51, source HRIN values for
every linked chemical observed in the occupation are used. Each

of these is modified by the proportion of workers in the occupation:
(see PES/PEN, page 32) observed to be exposed to it, as well as
factors for control and duration of that exposure. The modified
HRIN values are then summed across all chemical exposures specific
to the occupation, and the resulting sum optionally modified by

the (N) or census population for the occupation.

Calculation of the confidence interval is performed in exactly the
same manner discussed in the CIORIIRI program.

A-18


to be zero a nominal value of 0.0001. Consequently, a zero sub-
HRIN is only encountered when there are no data in the sub-HRIN

category, and all such zero sub-HRIN's properly have no effect
on the final HRIN.

Computer programs to operate this algorithm and display the data
in standard format were developed and tested. A sample page from
a Hazard Risk Index is shown in Figure 5. As seen in Figure 5,
the data display format for the HRIN is in several parts, as
follows:

(1) The first line of the heading at the top of each page
identifies the index, the date of the RTECS data used
in its preparation, the date the index was run, and
the sequential page number.

(2) The second and third lines of the heading at the top of
each page identify the multiplier and constant values
used in calculation of the HRIN. They also identify the
setting of the "NEO OPTION” ("YES" indicating that RTECS
carcinogenic dose data were used to produce neoplastic
data citations in these cases where RTECS has a CAR
citation but no NEO citation within the same chemical/
test class), the setting of the "CAR OPTION" ("YES"
indicating that NEO sub-HRIN values were used as estima-
tors for the CAR sub-HRIN where RTECS. has a NEO cita-
tion(s) but no CAR citation within the same chemical).
(In this example, all multipliers are set at 1, all
constants are set at 0, and the "NEO OPTION" and "CAR
OPTION" set at "NO" - resulting in an HRIN value derived
from considering all sub-HRIN's equal in toxicological
importance; and with no estimate of CAR or NEO made.

(3) The entry for each chemical is made up of three types of
lines. The first line gives the sequence number of the
chemical (out of 1721), identifies it as a "linked"
chemical ("H"), cites the RTECS accession number, and
gives the RTECS primary name. The second line type gives
the calculated value for each sub-HRIN for which there is
data and the final HRIN. The third type of line presents
the RTECS toxicity data for the chemical; lines of this
type are entered in whatever number is needed to display
all the RTECS data within the selected 98 test classes
for the chemical. East test class is identified in
terms of route, species, test and outcome (e.g. SCU MUS
LD50 AT) using RTECS abbreviations. This is followed by
a numeric entry showing the number of chemicals for


2631
264
2641
2642
2643
2645
2647
2649
265
2651
2652
2653
2654
2655
266
2661
rah
271
2711
272
2721
273
2731
2732

PAPERBOARD MILLS

MISC. CONVERTED PAPER PRODUCTS
PAPER COATING AND GLAZING
ENVELOPES

BAGS, EXCEPT TEXTILE BAGS

DIE CUT PAPER AND BOARD
SANITARY PAPER PRODUCTS
CONVERTED PAPER PRODUCTS, NEC
PAPERBOARD CONTAINERS AND BOXES
FOLDING PAPERBOARD BOXES

SET-UP PAPERBOARD BOXES
CORRUGATED AND SOLID FIBER BOXES
SANITARY FOOD CONTAINERS

FIBER CANS, DRUMS, & RELATED MATERIAL
BUILDING PAPER AND BOARD MILLS
BUILDING PAPER AND BOARD MILLS
PRINTING AND PUBLISHING
NEWSPAPERS

NEWSPAPERS

PERIODICALS

PERIODICALS

BOOKS

BOOK PUBLISHING

BOOK PRINTING


TXDS: ihl-hmn TCLo:5000 mg/m3/7M TFX:CNS AHBAAM 116,131,36

d £

Abbreviations used in RTECS are listed in Appendix B.

An acronym which stands for "Toxic Dose”.

This is an abbreviation for the route of administration or
entry.

This is an abbreviation for the species.

This is the test procedure reported.

This is the dose which caused the toxic effect, including
period of administration (if applicable).

The first part of this notation, "TFX", is an acronym which
stands for "Toxic Effect". The last part of this notation
refers to the organ system affected by the dose administered,
and is the toxic effect portion of the test class.

This is a code denoting the reference from which the toxic
data was derived.

Volume number of the reference.

Page number of the reference.

These two digits stand for the year of publication, i.e. 1936.

FIGURE 2. A Typical Toxic Dose Entry From RTECS


I. Create Occupational Risk Index Printouts
Program: PRINTORI

The purpose of this program is to produce a listing of NOHS occu-
pations in ORIN order.

The setting of the algorithm variables for source HRIN data is
accomplished in the CIORISUM program, and the calculation of the
ORIN's is performed by the CIORIORI program.

Since the output from the CIORIORI program is in occupation-code
sequence, an intermediate step orders the occupation listings in
ORIN order.

Use of a control card in this program provides the "Trial run"
input for the printout header as in the PRTAHRIN program.

Note:

All software for this model will be on file with the Department

of Commerce, National Technical Information Service (NTIS), Federal
Software Exchange. Alternatively, questions relating to this model
may be addressed to the authors at NIOSH.

A-19


ppb parts per billion (v/v)

pph parts per hundred (v/v) (percent)
ppm parts per million (v/v)

ppt parts per trillion (v/v)

preg Pregnancy

PSY psychotropic effects

PUL pulmonary system effects

gal quail

rat rat

RBC red blood cell effects

rbt rabbit

rec rectal

ScP Standards Completion Program

scu subcutaneous

SKN skin effects

sql squirrel

SSSS skin/eye sensitivity (not RTECS, model definition)
sup superscript

SYS systemic effects

TCLo lowest published toxic concentration
TDLo lowest published toxic dose

TER teratogenic effects

TFX toxic effects

TLY threshold limit value

TOX REV toxicology review

B-5


toxicological risk they pose. The HRI algorithm calculates
index numbers, indicative of the relative toxicological
risk posed by each chemical, and uses these numbers to
sequence the chemicals in the index. The HRI is based
entirely on the unevaluated animal study toxicological

data available in the RTECS data base and does not include
any consideration of the extent of worker exposure.

Adjusted Hazard Risk Index (AHRI), a listing of the
chemicals common to both the RTECS and NOHS data bases
ranked by the toxicological risk they pose and the extent

of workér exposure. The algorithm that produces this

index combines the risk index numbers from the HRI

algorithm with data on worker exposure to chemical hazards
from the NOHS data file to produce "adjusted" risk index
numbers which are used to sequence the chemicals in the AHRI.

Industry Risk Index (IRI), a listing of industries at the
2-, 3-, and 4-digit Standard Industrial Classification
(SIC) levels which are included in the NOHS data base,
ranked by the potential risk due to chemical exposure of
workers in each industry. The algorithm that produces
this index calculates the incremental risk posed by each
chemical to which workers in an industry are potentially
exposed, and cumulates these incremental risks for all
the potential exposures in the industry to form an index
number for each industry, listing the industries in order
by these index numbers. 1973 Census Bureau data on
worker populations by industry are used in the algorithm.

Occupational Risk Index (ORI), a listing of the occupations
which are included in the NOHS data base, ranked by the
potential chemical risk to workers in each occupation. The
algorithm that produces this index calculates the incre-
mental risk posed by each chemical to which workers in an
occupation are potentially exposed, cumulates these
incremental risks for all the potential exposures in the
occupation to form an index number for each occupation,

and lists the occupations in order by these index numbers.
1973 Census Bureau data on worker population by occupation
are used in this algorithm.



5651
566
57
571
58
581
5810
59
591
5912
592
5921
590
5900
599
5999
60
602
6020
61
62
621
6211
63

FAMILY CLOTHING STORES

SHOE STORES

FURNITURE AND HOME FURNISHING STORES
FURNITURE AND HOME FURNISHINGS
EATING AND DRINKING PLACES

EATING AND DRINKING PLACES

EATING AND DRINKING PLACES
MISCELLANEOUS RETAIL STORES

DRUG STORES AND PROPRIETARY STORES
DRUG STORES AND PROPRIETARY STORES
LIQUOR STORES

LIQUOR STORES

FUEL OIL DEALERS

FUEL OIL DEALERS

RETAIL STORES, NEC

MISCELLANEOUS RETAIL STORES, NEC
BANKING

COMMERCIAL AND STOCK SAVINGS BANKS
COMMERCIAL AND STOCK SAVINGS BANKS
CREDIT AGENCIES OTHER THAN BANKS
SECURITY, COMMODITY BROKERS & SERVICES
SECURITY BROKERS AND DEALERS
SECURITY BROKERS AND DEALERS
INSURANCE CARRIERS

C- 25


ihl
imp
ims
inf
ipe
ipl
ipr
irn
IRR
isp
itr
ivg
ivn
kg
LC50
LCLo
LD50
LDLo
leu
lng

lvr

lym

inhalation

implant

intramuscular

infant

intraplacental

intrapleural

intraperitoneal

intrarenal

irritant effects

intraspinal

intratracheal

intravaginal

intravenous

kilogram (one thousand -grams )
lethal concentration 50 percent kill
lowest published lethal concentration
lethal dose 50 percent kill
lowest published lethal dose
leucocyte

Jung

liver

lymphocyte

minute

cubic meter

mammal (species unspecified)

B-3


42
421
4210
4214
422
4222
4225
4226
44
441
4411
442
4422
444
4441
445
4450
446
4463
45
450
4500
452
458

TRUCKING AND WAREHOUSING

TRUCKING, LOCAL AND LONG DISTANCE
TRUCKING, LOCAL AND LONG DISTANCE
LOCAL TRUCKING AND STORAGE

PUBLIC WAREHOUSING

REFRIGERATED WAREHOUSING, NEC
GENERAL WAREHOUSING AND STORAGE
SPECIAL WAREHOUSING AND STORAGE, NEC
WATER TRANSPORTATION

DEEP SEA FOREIGN TRANSPORTATION
DEEP SEA FOREIGN TRANSPORTATION
DEEP SEA DOMESTIC TRANSPORTATION
COASTWISE TRANSPORTATION
TRANSPORTATION ON RIVERS AND CANALS
TRANSPORTATION ON RIVERS AND CANALS
LOCAL WATER TRANSPORTATION

LOCAL WATER TRANSPORTATION

WATER TRANSPORTATION SERVICES
MARINE CARGO HANDLING
TRANSPORTATION BY AIR
TRANSPORTATION BY AIR
TRANSPORTATION BY AIR
NONCERTIFICATED AIR TRANSPORTATION

AIR TRANSPORTATION SERVICES

c-21


2337
2339
234
2341
2342
235
2352
236
2369
238
2381
239
2391
2392
2395
2399
24
242
2421
2429
243
2431
2432
2433

WOMEN'S AND MISSES' SUITS AND COATS
WOMEN'S AND MISSES' OUTERWEAR, NEC
WOMEN'S AND CHILDREN'S UNDERGARMENTS
WOMEN'S AND CHILDREN'S UNDERWEAR
CORSETS AND ALLIED GARMENTS

HATS, CAPS, AND MILLINERY

HATS AND CPAS, EXCEPT MILLINERY
CHILDREN'S OUTERWEAR

CHILDREN'S OUTERWEAR, NEC
MISCELLANEOUS APPAREL AND ACCESSORIES
FABRIC DRESS AND WORK GLOVES

MISC. FABRICATED TEXTILE PRODUCTS
CURTAINS AND DRAPERIES
HOUSEFURNISHING, NEC

PLEATING AND STITCHING

FABRICATED TEXTILE PRODUCTS, NEC
LUMBER AND WOOD PRODUCTS

SAWMILLS AND PLANING MILLS

SAWMILLS AND PLANING MILLS, GENERAL
SPECIAL PRODUCT SAWMILLS, NEC
MILLWORK, PLYWOOD & RELATED PRODUCTS
MILLWORK

VENEER AND PLYWOOD

PREFABRICATED WOOD STRUCTURES

C-6


generic resolution was imprecise, and should not be used in the
various algorithms of the model. Therefore, any generic data was
eliminated. This had the effect of deleting several chemicals
from any model consideration, as well as discarding those portions
of the NOHS data associated with other chemicals which were of
generic origin.

As mentioned in the text on page 40, and illustrated in Tables Sf
6, and 7, it was necessary to combine 3- and 4-digit SIC codes and
Standard Occupation Codes from the NOHS data in order to make it
possible to use census population data specific to industry and
occupation groups in the model algorithms.

B. Produce Summary Records at the Most Specific Possible Summary
Level

Program: SUMEXT1@

The purpose of this program is to produce non-duplicative records
of potential exposure to NOHS hazards, in order to avoid the
multiple counting of potentially exposed individuals discussed in
the text on pages 29 and 32.

NOHS Summary File #10 therefore contains non-duplicative records

of workers exposed to a linked hazard seen in a specific occupation
within each NOHS facility surveyed. This file can then be sorted
at the level necessary for input to the various algorithms without
multiple counting of individual potentially exposed workers.

C. Create Summary Files used as Input to the Model Algorithms

Programs: SUMEXT@2, SUMEXT@3, SUMEXT®@4, SUMEXT@5

The purpose of these programs is to utilize the sorted data sets
(e.g. hazard within occupation or hazard within 2-digit SIC) to
produce the various summary files used as input to the model algor-
ithms. The association of the various files and algorithms is
displayed on the figures illustrating computer program flow for the
HRI, AHRI, IRI and ORI programs. (See Figures 4, 7, 12, and 14)

Data included in these summary files includes number of workers
exposed to specific hazards, and the control and duration of expo-
sure observed during the NOHS survey expressed as percent exposure
controlled and percent full-time (more than 4 hours a day) exposure.
For each occupational group (see Table 4), these files also contain
the total number of workers observed exposed to any NOHS hazard.


ORI calculation based on HRIN Trial 02 Run O01 RTECS Date 01/81 NOHS Date 07/80 CENSUS OPTION=YES Date: 0-/03/81 page 1

Multipliers for
Constants for

SEQ#

SEQH

SEQH

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

1

10

AT=1

AT=0

occ

occ

occ

occ

occ

occ

occ

occ

occ

occ

944

075

903

280

OOA

925

510

OOH

690

441

ETA=1 MUT=1 NEO=1 PI=1 TER=1 TFX=1
ETA=0 MUT=0 NEO=O PI=O TER=O TFX=0

HAIRDRESSERS AND COSMETOLOGISTS
NUMBER OF CHEMICALS = 105

REGISTERED NURSES
NUMBER OF CHEMICALS = 300

JANITORS AND SEXTONS
NUMBER OF CHEMICALS = 627

SALESMEN AND SALES CLERKS, N.E.C.
NUMBER OF CHEMICALS = 163

AUTOMOBILE MECHANICS
NUMBER OF CHEMICALS = 264

NURSING AIDES, ORDERLIES, AND ATTENDANTS
NUMBER OF CHEMICALS = 153

PAINTERS, CONSTRUCTION AND MAINTENANCE
NUMBER OF CHEMICALS = 227

SECRETARIES, N.E.C.
NUMBER OF CHEMICALS = 204

MACHINE OPERATIVES, MISCELLANEOUS SPECIFIED
NUMBER OF CHEMICALS = 1086

FOREMEN, N.E.C.
NUMBER OF CHEMICALS = 931

NEO OPTION= YES
CAR OPTION= NO

ORIN
+/-

ORIN
1

ORIN
+/-

ORIN
+/-

ORIN
4/5

ORIN
+/-

ORIN
We

ORIN
+/-

ORIN
+/-

ORIN
+/-

3222
-836

22

215;
(5

165.

1

153.
824

1

137.
oe

120.
2.

100.
-548

83.
7731

77

74.
719

960

201
702

088

+530

046
280
177

208
175

437

692

cers)
Bessy

126

Example ORI

FIGURE 13.

54


TABLE 2 - NEOPLASTIC TEST CLASSES

SHOWING THE EFFECT OF THE "NEO OPTION"

MINIMUM(+30,00) MINIMUM LOG VALUE MAXIMUM(+30.00) MAXIMUM LOG VALUE NO. OF TEST
TEST CLASS LOG VALUE RTECS ACCESSION NO. LOG VALUE RTECS ACCESSION NO. CITATIONS
"NEO OPTION" OFF
3. ANY ANY ANY NEO 19,255035 HN2625000 37 .786942 XU7350000 253
30. IPR MUS TDLO NEO 25.502625 CW1795065 33. 985306 MU7700000 82
62. ORL MUS TDLO NEO 20.901321 HP3500000 38 .378464 AJ0175000 95
70. ORL RAT TDLO NEO 27683380 WU7675000 39.131470 AB4025000 95
84. SCU MUS TDLO NEO 21.820023 HN2625000 34937149 RQ8050000 125
89. SCU RAT TDLO NEO 19,291580 WJ5600000 36089828 RQ8050000 132
94. SKN MUS TDLO NEO 22.075012 CW1795050 39.639572 cy1400000 167
"NEO OPTION" ON
3. ANY ANY ANY NEO 19.255035 HN2625000 38 .845291 KX4550000 425
30 IPR MUS TDLO NEO 23.042969 ¥Q8925000 33. 985306 MU7700000 116
62 ORL MUS TDLO NEO 20.901321 HP3500000 40.013458 H07030000 240
70. ORL RAT TDLO NEO 23 .288483 GY1925000 39.131470 AB4025000 364
84. SCU MUS TDLO NEO 21820023 HN2625000 34.937149 RQ8050000 200
89. SCU RAT TDLO NEO 19.291580 WJ5600000 39.774017 Dv4900000 194
94. SKN MUS TDLO NEO 22.075012 CW1795050 39.639572 cv1400000 213

16


not exist in the SIC coding scheme), were assigned to the combined
NOHS data and the associated Census Bureau data. The codes that
were combined are listed in Tables 6 and 7.

Confidence Interval Calculations

In the occupational group risk index number calculations, the num-
ber of people in worker groups observed during the NOHS survey
(PEN, see page 32) differs from occupation to occupation and from
industry to industry. Review of the NOHS data indicated the magni-
tude of the PEN for occupational and industrial worker groups
varies widely, and that there are a number of instances where the
PEN was quite small (e.g., less than ten). It was also observed
that when the PEN was small, the PES/PEN-ratio was frequently quite
large; examples of PES/PEN (see page 32) ratios of 1.0 were ob-
served which were derived from calculations of 1/1, 2/2. etc. Such
sample statistics are not characteristic of random samples, and
would seem to be an artifact of a particular observation rather
than probability sampling.

It was decided that NOHS worker group data should only be used

when there is a reasonable probability that the observed proportion
is within some selected range of the Gaussian approximation of the
expected prapartion. This decision was implemented by first defin-
ing the confidence interval to.be used as:

P & - ofa <oc + fm) = 98

observed proportion of exposure

true proportion of exposure (unknown)

true proportion of non-exposure (unknown) = l-p
number of observations

where:

pov xl

This equation is based on the Gaussian approximation to the
binomial distribution with two standard deviations added and sub-
tracted from x.

Since the true proportion, p, in the above equation is unknown,
the worst case, (i.e., when pq/n is a maximum) should be used. The

value of p which achieves ‘this is 0.5. Substituting in the above
equation, one has:

2 (F- 2/035 Co¢ % + ofo.as )= .95
(5 Teed De 95

or

45


IRI calculation based on HRIN Trial 02 Run O1 RTECS Date 01/81 NOHS Date 07/80 CENSUS OPTION=YES Date: 09/02/81 page 1

Multipliers for AT=1 CAR=1

Constants for

SEQ#

SEQH

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

1

10

AT='

SIC

SIC

SIC

SIC

SIC

SIc

SIC

SIC

SIC

SIC

O  CAR=0

806

581

551

554

541

734

531

809

739

508

ETA=1 MUT=1 NEO=1 = PI=1

TER=1 = TFX=1

TER=0 TFX=0 CAR OPTION= NO

ETA=0 MUT=0 NEO=0 PI=0
HOSPITALS IRIN
NUMBER OF CHEMICALS = 658 +/-
EATING AND DRINKING. PLACES IRIN
NUMBER OF CHEMICALS = 95 +/-
NEW AND USED CAR DEALERS IRIN
NUMBER OF CHEMICALS = 162 +/-
GASOLINE SERVICE STATIONS IRIN
NUMBER OF CHEMICALS = 96 +/-
GROCERY STORES IRIN
NUMBER OF CHEMICALS = 75 +/-
SERVICES TO BUILDINGS IRIN
NUMBER OF CHEMICALS = 75 +/-
DEPARTMENT STORES IRIN
NUMBER OF CHEMICALS = 238 +/-
HEALTH AND ALLIED SERVICES, NEC IRIN
NUMBER OF CHEMICALS = 152 +/-
MISCELLANEOUS BUSINESS SERVICES IRIN
NUMBER OF CHEMICALS = 282 1/3
MACHINERY, EQUIPMENT, AND SUPPLIES IRIN
NUMBER OF CHEMICALS = 145 sf

0

1

"

422
3

140.
(hes

137
4

120.

NEO OPTION= YES

822
378

413
603

032
761

240
+284

-718
445

-429
-643

368
-103

642
-053

709d)
ETAL

639
794

Example 3-SIC IRI

FIGURE 10.

49


dog dog

dom domestic

DOT Department of Transportation
emb embryo

EPA Environmental Protection Agency
EYE eye effects

fbr fibroblast

frg frog

GIT gastrointestinal tract effects
GLN glandular effects

gm gram

gpg guinea

grb gerbil

H hour

ham hamster

hla HeLa cell

hmn human

I intermittent

TARC International Agency for Research on Cancer
jat intraarterial

jal intraaural

ice intracerebral

icv intracervical

idr intradermal

idu intraduodenal

B-2


FIGURE 3. RTECS Processing Computer Programs Flow Chart

RTECS
Master

File

CIORIEXT
NEO Extract
Option File

CIORISTD

Acute Standard
Toxicity Format
Rejects Master

i

Sort RTECS #,
Test Class, Log
Sequence

orted
Standard

CAR, all or match

chemicals options Format
RTECS & NOHS Dates File

Minimum and maxi-

NOHS

Summary
File #1
RTECS #

Order

Rejected
Records
(all)

Rejected Records
and min/max table

mum values for CIORICAL
each test class
y
CALC
input to HRI System Master
File

1

values

Lo” ;



A sample page from the ORI is shown in Figure 13. The ORI display
format is in several parts, as follows:

(2)

(2)

(3)

(4)

The first line of the heading at the top of each page
identifies the index, the HRIN trial and run, the RTECS
and NOHS file update versions used, the "CENSUS OPTION"
function (previously explained), the date of the print-
out, and the sequential page number within the ORI.

The second line of the heading at the top of each page
identifies the settings of the multipliers and NEO OPTION
in the source HRI.

The third line of the heading identifies the settings for
the constants and CAR OPTION in the source HRI.

The first line of the index entry for each occupational
group consists of a sequence number, occupation numerical
designation and title, and the calculated Occupational
Risk Index Number (ORIN). The second line of the entry
for each occupation consists of the number of linked
chemicals (out of 1721) to which workers in the occupa-
tional group are exposed and the 95 percent confidence
limits of the ORIN. Printing of the second line is a
program option.

For details on the computer programs which produce the ORI, see
Figure 14, ORI Algorithm Computer Programs Flow Chart, and Appendix A.
Complete examples of the ORI are provided in microfiche form (See
pages E-5 and E-6).

53


6. NOHS SIC Codes/Titles Combined or Changed In
Preparation of the Industrial Risk Index at the
3-Digit Level 42

7. NOHS SIC Codes/Titles Combined or Changed In

Preparation of the Industrial Risk Index at the
4-Digit Level 43

vil


After these assurances are made, the dose amount is passed to a
FORTRAN module called "ALOGX". This module calls the FORTRAN sub-
routine "ALOG" to obtain the natural log of the dose. A constant
of 30 is added to the calculated log to assure that all-log values
are positive. The calculated value is then returned to the
CIORISTD program.

Note:
Dose conversion assumptions:

1. A grams per kilogram (G/KG or Gm/KG) or grams per meter cubed
(G/m3 or Gm/M3) dose will be less than seven whole number digits.

2. A part per hundred (PPH) dose will be less than six whole number
digits.

3. A kilograms per kilogram (KG/KG) dose will be less than four
whole number digits.

4. A parts per million (PPM) or milligram per kilogram (MG/KG) dose
will be less than ten whole number digits.

Should these maximums be exceeded, the resulting high order (whole
number) digits would be truncated.

These conversion routines could possibly result in a zero value,
i.e. (400 PG/KG x .000000001) divided by molecular-weight (500).
The converted dose value is checked before the log value is calcu-
lated. If a dose value is zero, the minimum value (.000000001) is
inserted and the record in question is displayed. The displayed
records are manually reviewed for accuracy.

NEO option:

By RTECS definitions (page xxvii, 1979 RTECS), the difference
between a neoplasm and a carcinoma consists of cell cytology and
metastasizing of tumors. Carcinogens may therefore be viewed as a
subset of neoplastigens since both involve tumor formation. Noting
that the data lines within certain chemicals contained a test class
specific CAR citation, but no corresponding NEO citation, it was
decided that creation of a NEO citation based on CAR data (specific
to test class within chemical) was appropriate in such cases.

C. Calculate values for HRIN computation

Program: CIORICAL

A-14


On the basis of all these considerations, the final HRI algorithm
is expressed as:

HRIN = (aAT @ b) + (cCAR @ d) + (eETA @ £) + (gMUT @ h) +
(41NEO © j) + (KPI @ I) + (mTER © n) + (OTFX @ p)

where, for each test class within each chemical (See discussion on
Pages 17 and 18):
AT = The average of all acute toxicity normalized doses.
CAR = The average of all carcinogenic normalized doses.
ETA = The average of all equivocal tumorigenic agent
normalized doses.
MUT = The average of all mutagenic normalized doses.
NEO = The average of all neoplastigenic normalized doses.
PL = The average of all primary irritation normalized
doses.
TER = The average of all teratogenic normalized doses.
TFX = The average of all other toxic effect normalized
doses.

Lower case letters "a" through "p" indicate variable
numerical factors (values @ to 9) for the enhancement or
suppression of individual sub-HRIN values.

® = an addition which is performed only if the associated
sub-HRIN is not equal to zero.

The above HRI algorithm produces a hazard risk index number that
has a direct relationship to the overall observed toxicity of the
chemical; the higher the HRIN, the higher the toxicity. In
addition, an algorithm in this form allows the user great flexi-
bility in defining his toxicological priorities. He can increase
the impact of any sub-HRIN by using a multiplier (a, c, e, g, i,
k, m, arid o in the equation), he can eliminate any sub-HRIN from
the equation by using a zero multiplier, and he can cause chemicals
with data in any area, such as carcinogenesis, to head the Hazard
Risk Index by using sufficiently large constants (b, d, f, h, j,
1, n and p in the equation). In short, he can adapt the algorithm
to any toxicological priorities that apply in his/her situation.

It should be noted that the normalized dose for the least toxic
chemical in each test class (i.e., the chemical for which d, = d
produces a situation that must be handled separately by the com-
puter program. If the sub-HRIN were derived from a single RTECS
data entry of this type, the sub-HRIN would be zero and would not
be included in the process used to calculate the HRIN. This
situation is avoided, however, by a subroutine in the normalized
dose calculation program which gives any normalized dose calculated

25


Doses reported as PPH, PPB, PPT are converted to PPM via the
first conversion step.

Doses reported (original or converted) as MG/M3 are converted
to PPM via the second conversion step.

PPM doses (original or converted) are converted to MG via the
following equation:

PPM x (.5 for skin route) or (.1 for eye route) x 1000
10000

which reduces to:
PPM x .0005 or .0001 (depending on route) = MG

The resulting milligram dose is converted to millimoles via
the second conversion step.

Adjustment of moderate and severe dose amounts are performed
to permit direct comparison of doses by standardizing the out-
come, as follows:

a. Records with a "MLD" rating are held constant.

b. Records with a "MOD" rating are multiplied by .50.

c. Records with a "SEV" rating are multiplied by .25.

d. Records with no rating are assumed to be moderate.

T-line (toxicity data)

RTECS record definitions are:

Us

Position 27 should always equal a dash (-), (separator for
route and species).

Position 31 should always equal a blank (separator for species
and test end point).

Position 36 should always equal a colon (:), (separator for
test end point and dose amount).

Position 37 should always be numeric (first position of dose
amount) .

A-12


T-line (toxicity data)
T-line data is also selected according to model criteria.

Any route of administration (except "UNK") is accepted. In
addition, the program converts routes of administration "IDR"
and "IMS" to "Scu".

Any species (except "UNK") is accepted. Species "INF", "CHD",
"MAN", and "WMN" are converted to "HMN". In addition, species,
"BDW", "CKN", "DCK", "PGN", "QAL", and "TRK" are converted to
"BRD".

Additional notes:

1. It is possible, using the screening processes defined
above, that the only records selected for a given chemical
would be A & H line data. These chemicals are eliminated
in the CIORISTD program, which uses the output from the
CIORIEXT program.

2. This program converts all lower case characters to upper
case.

B. Standardize RTECS Data

Program: CIORISTD

The purpose of this program is to provide a standardized format
file from a file with many variable formats. This program performs
many functions against the output file of program CIORIEXT. It
serves to:

1. Identify RTECS data entry errors.

2. Identify variable fields and convert to a fixed position.

3. Identify various dose measurement units and convert to
either:

a. Parts per million (ppm), or
b. Millimole (MM) notation.

4. Assure uniformity of the dose units within test classes
(either ppm or millimole).


Thus, the difference between the observed dose in question and the
highest dose in the test class is divided by the range of doses in
the class. This normalization approach was selected because the
result is a positive number that ranges from zero for the highest
dose (i.e., the least toxic material) to 1.0 for the lowest dose
(i.e., the most toxic chemical) in a test class, and that is
directly proportional to the relative position of the observed
dose between the highest and the lowest observed doses. These
characteristics lend themselves to computer manipulation and to
generation of an index number that corresponds to the relative
risk resulting from exposure to specific chemicals.

The function shown above was used to produce normalized dose data
in each of the 98 test classes.

It was found that the wide variation in doses from the most toxic
chemicals to the least toxic within a specified test class involved
calculations that required as mich as fourteen significant figures
and masked the small dose differences that exist in the more toxic
chemicals. To bring the range of doses into more manageable terms,
the doses were converted to logarithmic scale. This produced dose
numerics that were all within two orders of magnitude and retained
the capability to recognize small differences that exist at the low
end of the dose scale. All algorithm calculations used data in
these units (in millimoles per kilogram body weight or in ppm), to
which a constant of 30.00 was added to assure that all values would
be positive. Examples of the calculated log values can be seen in
Table 3. In addition to displaying the minimum (most toxic dose)
log values for each test class, Table 3 cites the RTECS asseasion
number for the chemical where the minimum or maximum test class dose
is found, as well as the number of test citations in each test class.

RTECS Work Record
Accomplishment of the steps outlined in the preceding sections
produce a work record for each RTECS toxic data record that con-
tained the following separately addressable data elements:

e RTECS accession number

e Indicator for occurence in NOHS file

e@ RTECS chemical name (first 94 positions)

e Route of administration, test species, test procedure, and
toxic effect

e Dose in ppm or millimole/kg

18


AHRI calculations. The records in this file correspond to the
2,092 hazards (chemicals) that appear in both the RTECS and NOHS
data files. However, as explained on page 11, only 1721 of these
records were utilized in the model algorithms.

In examining the NOHS data, it was decided the extent of chemical-
specific worker exposure could best be described for use in the
AHRIN by utilizing data on: (1) the total number of workers seen
potentially exposed to any NOHS hazard, (2) the number of people
seen potentially exposed to a specific linked chemical, (3) the
percent of the chemical-specific exposures observed which were
controlled by some valid procedure, and (4) the percent of chem-
ical-specific exposures observed which were full-time by NOHS
criteria.

It was decided that the most logical approach in describing the
inter-relationship between chemical-specific toxicity and the
extent of worker exposure was to multiply the HRIN by those
factors describing the extent of worker exposure. The algorithm
for the Adjusted Hazard Risk Index Number (AHRIN) is therefore
as follows:

AHRIN = (HRIN) (PES/PEN) (1.0 - 0.9 PC)(0.5 + 0.5 PFT) (K)

where:
HRIN = Hazard Risk Index Number.

PES = Number of people potentially exposed to the
hazard.

PEN = Number of people in the worker group (In the
AHRIN, this is equal to the number of people
potentially exposed to any NOHS hazard. There-
fore, the PEN factor is a constant 545,569 in
all AHRIN calculations.)

PC = Percentage of controlled exposures.
PFT = Percentage of full-time exposures, and
K'= A constant used to remove leading zeroes.

In this algorithm the hazard risk index numbers (HRIN) come directly
from the Hazard Risk Index, the PES/PEN ratio indicates the per-
centage of all workers observed in the NOHS survey who were
potentially exposed to the hazard, the (1.0 - 0.9 PC) factor
reduces the significance of controlled exposures to ten percent*

* The ten percent factor for controlled exposures corresponds to
the minimum acceptable effect produced by use of a respirator,
and is considered to be indicative of the order of magnitude
protection that can be expected from the use of exposure controls.

35


Sorted NOHS nos
Sines SUanary Oeeub ata
iis File #5 Descriptions

Census
Occupation
Population

Totals

ORINSUMM

Sort
ORIN
Sequence

Sorted
ORL

Master

Print ORI

i

ORL
Sequence

Le

Trial-Run
Header

Census

ORI Algorithm Computer Programs Flow Chart

FIGURE 14.

55


TABLE 3 (Continued)

TEST MINIMUM(+30.00) MINIMUM LOG VALUE MAXIMUM( +30 .00) MAXIMUM LOG VALUE NO. OF TEST
CLASS NO. LOG VALUE RTECS ACCESSION NO. LOG VALUE RTECS ACCESSION NO. CITATIONS
66. 21.608261 HP3500000 38 .440323 JG8225000 68
67. 22 .237793 HP3500000 38 .776840 DE4550000 141
68. 23.288483 GY1925000 39.042648 PB2100000 269
69. 20.613647 HP3500000 41.909988 $J3325000 395
70. 27 .683380 WU7675000 39.131470 AB4025000 95
71. 19.802704 HP3500000 37 .041656 406300000 145
72. 19.620377 HP3500000 35.571304 TY2000000 3h
13% 23052094 GY1925000 34917923 KQ6300000 399
74. 23871597 7€2275000 34923645 YT7175000 155
Udo 22.613174 AR5960000 33.701080 AI9100000 115
76. 21676743 B04900000 34.869125 KQ6300000 121
Tilo 20.835098 WL2275000 35.779846 KI5775000 180
78. 20.533722 AR5960000 35.316406 TY2000000 224
79). 19.200699 XW5500000 34141830 025075000 98
80. 18 .078796 UL2100000 34766266 UY5715000 724
81. 16.777252 CR5250000 35.722839 Pc1400000 3581
82. 22.914780 CNO700000 34.172211 QL3675000 77
83. 21.591660 AU1575000 37 .979706 009625000 409
84. 21.820023 HN2625000 34.937149 RQ8050000 125
85. 21.226883 AR5950000 35389023 024550000 338
86. 13.820038 RT6475000 35571304 TY2000000 755
87. 23.572571 HN2625000 35.306015 FG4900000 67
88. 24.094467 MA1050000 36.906174 MN4725000 232
89. 19.291580 WJ5600000 36 .089828 RQ8050000 132
90. 20.427887 BR7875000 34.484070 KW2975000 316
GING 20.334595 781090000 34.700317 $A07 00000 108
92. 23.925781 CW3850000 37..517639 RN7700000 84
93 21.699829 HP3500000 38 .856308 EM7350000 307
94. 22.075012 CW1795050 39639572 CY1400000 167
Os 25.204819 UX6825000 34965195 FC1925000 253)
96. 23832809 4Q5270000 36.073105 KQ6300000 142
977 22.934586 HP3500000 36.436188 PC1400000 890
98. 12.321259 VR3545000 32.704666 AS6920000 1265

21


2082 MALT LIQUORS

2085 DISTILLED LIQUOR, EXCEPT BRANDY
2086 BOTTLED AND CANNED SOFT DRINKS
209 MISC. FOODS AND KINDRED PRODUCTS
2095 ROASTED COFFEE

2099 FOOD PREPARATION, NEC

21 TOBACCO MANUFACTURES

211 CIGARETTES

2111 CIGARETTES

212 CIGARS

2121 CIGARS

213 CHEWING AND SMOKING TOBACCO
2131 CHEWING AND SMOKING TOBACCO
214 TOBACCO STEMMING AND REDRYING
2141 TOBACCO STEMMING AND REDRYING
22 TEXTILE MILL PRODUCTS

221 WEAVING MILLS, COTTON

2211 WEAVING MILLS, COTTON

222 WEAVING MILLS, SYNTHETICS

2221 WEAVING MILLS, SYNTHETICS

224 NARROW FABRIC MILLS

2241 NARROW FABRIC MILLS

225 KNITTING MILLS

C4


TABLE 6 - NOHS SIC CODES/TITLES

COMBINED OR CHANGED IN PREPARATION

OF THE INDUSTRIAL RISK INDEX AT THE
3-DIGIT LEVEL

Combined/Original NOHS Codes

082, 085

091

192, 193, 194, 199
4ST, 452

598

42

Produced Codes

080 Forestry

090 Fisheries

190 Ordnance and Accessories
450 Transportation by Air

590 Fuel Oil Dealers


mnt
sit
dnd
dnr
dns
mre
CAS.
sce
sin
dit
msc
hma
spm
trn

otr

RTECS Mutation Test Systems

mutation in microorganisms

microsomal mutagenicity assay
micronucleus test

specific locus test

DONA damage

DNA repair

unscheduled DNA synthesis

gene conversion and mitotic recombination
cytogenic analysis

sister chromatid exchange

sex chromosome loss and nondisdinction
dominant lethal test

mutation in mammalian somatic cells
host-mediated assay

sperm morphology

heritable translocation test

oncogenic transformation

B-7


Accordingly, the program shifts these erroneous fields one
position to the right (shifting the decimal to position 5) and
inserts a leading zero or zeroes. If the RTECS number lacks

an H-line or the H-line contains a non-numeric entry (other
than blanks), all data lines for that RTECS number are rejected.

R-line (mutagenic data)

R-line data are selected in conformance to model criteria.

Accordingly, the program selects in-vivo and in-vitro records

which are project-restricted to "in-vivo studies on mammalian

species, or in-vitro studies on mammalian cells".

Acceptable in-vivo tests must:

1. Be performed on mammalian species CAT, CTL, CHD, DOG, DOM,
GRB, GBG, HAM, HMN, INF, MAM, MAN, MKY, MUS, PIG, RBT, RAT,
SQL, WMN. (See Appendix B for definitions.)

2. Be performed utilizing any of the seventeen tests listed
on page 6, Appendix B.

3. Be performed utilizing any route specified in RTECS, except
MUL, REC, and UNK.

Acceptable in-vitro tests must:

1. Be performed on specified cells originating from the
mammalian species specified for in-vivo studies.

2. Be performed utilizing cell types BMR, EMB, FBR, HLA, LEU,
LVR, LNG, LYM, MMR, OVR. (See Appendix B for definitions.)

3. Be performed utilizing any of the test types specified for
in-vivo studies.

R-line data not matching these criteria are rejected.
S-line (irritation data)

S-line data is selected in conformance to project criteria.
Therefore the program selects only those records where:

1. The route of administration is "EYE" or "SKN" (See
Appendix B)

2. The test species is "RBT".


APPENDIX C

STANDARD INDUSTRIAL CLASSIFICATIONS REPORTED
IN A MODEL FOR THE IDENTIFICATION OF HIGH
RISK OCCUPATIONAL GROUPS USING
RTECS AND NOHS DATA


540
545
550
551
552
554
560
OOE
563
575
601
602
604
610
611
612
613
614
615
620
621
622
623
624

SHIPFITTERS

STATIONARY ENGINEERS

STRUCTURAL METAL CRAFTSMEN

TAILORS

TELEPHONE INSTALLERS AND REPAIRMEN
TELEPHONE LINEMEN AND SPLICERS

TILE SETTERS

TOOL AND DIE MAKERS

UPHOLSTERERS

CRAFTSMEN AND KINDRED WORKERS, NEC
ASBESTOS AND INSULATION WORKERS
ASSEMBLERS

BOTTLING AND CANNING OPERATIVES

CHECKERS, EXAMINERS, AND INSPECTORS: MANUFACTURING
CLOTHING IRONERS AND PRESSERS

CUTTING OPERATIVES, NEC

DRESSMAKERS AND SEAMSTRESSES, EXCEPT FACTORY
DRILLERS, EARTH

DRY WALL INSTALLERS AND LATHERS

DYERS

FILERS, POLISHERS, SANDERS, AND BUFFERS
FURNACEMEN, SMELTERMEN, AND POURERS
GARAGE WORKERS AND GAS STATION ATTENDANTS
GRADERS AND SORTERS, MANUFACTURING

D-6


762
764
770
780
785
901
902
903
910
911
912
913
914
915
916
922
925
926
931
932
933
934
944
950

STOCK HANDLERS

VEHICLE WASHERS AND EQUIPMENT CLEANERS
WAREHOUSEMEN, NEC

MISCELLANEOUS LABORERS

NOT SPECIFIED LABORERS

CHAMBERMAIDS AND MAIDS, EXC. PRIVATE HOUSEHOLD
CLEANERS AND CHARWOMEN

JANITORS AND SEXTONS

BARTENDERS

BUSBOYS

COOKS, EXCEPT PRIVATE HOUSEHOLDS
DISHWASHERS

FOOD COUNTER AND FOUNTAIN WORKERS
WAITERS

FOOD SERVICE WORKERS, NEC, EXCEPT PRIVATE HOUSEHOLD
HEALTH AIDES, EXC. NURSING

NURSING AIDES, ORDERLIES, AND ATTENDANTS
PRACTICAL NURSES

AIRLINE STEWARDESSES

ATTENDANTS, RECREATION AND AMUSEMENT
ATTENDANTS, PERSONAL SERVICE, NEC
BAGGAGE PORTERS AND BELLHOPS
HAIRDRESSERS AND COSMETOLOGISTS
HOUSEKEEPERS, EXf. PRIVATE HOUSEHOLD


mational universe were found by NIOSH to be generally statistically
reliable only at the 2-digit SIC or hazard-specific levels. Pro-
jections at lower levels (e.g., 4-digit SIC) are relatively unre-
liable due to the high variance associated with the estimates.

Because of this, it was decided that it was not possible to pro-
duce sufficiently accurate projected estimates of the number of
persons exposed at the national level for all the various worker
groups* under consideration. Data at the hazard level Ges,

data that are irrespective of industry and occupation) as required
in the AHRI algorithm could be utilized directly, but data at the
occupation and at the 3-digit and 4-digit SIC levels could not

be used directly without introducing a great amount of uncertainty
in the calculations. Since one objective in this model is to per-
mit maximum algorithm input flexibility, it was decided that the
same set of data files should be used in all of the algorithms that
employ NOHS data. Mechanisms were considered, therefore, for the
examination and comparison of worker groups within only the NOHS
sample of people seen (actual observations), rather than within the
NOHS projections to the national universe.

Computer processing of the NOHS data base is illustrated in Figure 6.

Searches of the NOHS files demonstrated that the total number of
people surveyed in each worker group could be accurately determined
from the NOHS raw data master file. However, precautions had to
be taken to avoid multiple counting of individuals exposed to spe-
cific agents. This potential multiple counting of individuals is
due to the following characteristics of the NOHS data:

a. Potential exposure of an individual to a specific iden-
tified hazard in up to eight forms - dust, liquid, gas,
particles, fumes, mist, smoke, vapor.

b. Potential exposure of an individual to a specific chemical
substance as a component of several trade name compounds
present in his/her work environment.

c. Potential exposure of an individual to a specific substance,
singly or multiply, in pure form (actual) or as a trade
mame component, determined by resolution of the components
of trade name products.

* A worker group is defined as all workers observed in NOHS that
have the same occupation/industry characteristics. Different
worker groups are involved in each of the indexes. (See Table 4.)

29


o

Prepare RTECS
data for use

!

Normalize ‘oxicity

>

Produce HRIN
and HAI listing

HRIN
data

Produce AHA

AHRI

o. Census data
Extract data that Extract data
matches RTECS and keyboard
compounds (performed
by NIOSH)
Cansus
data
———
Adjust NOHS data
SIC and OCC
codes to match
Census data
R——~_~—_———’
Produce IRI
and OR!
IRI at
2-, 3-,
Adigit ORI
SIC leveis

FIGURE 1. Index Production Data Flow



(TABLE 7 Continued)

Combined/Original NOHS Code Produced Code

6512, 6513, 6514, 6519 6510 Real Estate Operators and Lessors
6552, 6553 6550 Subdividers and Developers

6724 6720 Investment Companies

44


MODEL OVERVIEW

The goal of the occupational health community is to eliminate
adverse health effects suffered by the worker as a consequence of
employment. However, a combination of finite resource commitment,
continual identification of new problem areas, and technological
change prevents the attainment of this goal.

What can be: done is to continually and systematically examine the
work place, and attempt to eliminate or control those situations
identifiable as high risk.

In order to label any occupational group as "high risk" due to
chemical exposure and to prioritize efforts to reduce the adverse
health effects associated with employment in such groups, it has
always been necessary to: (1) assess the toxicological effects of
individual chemicals known to be in the work place, (2) determine
the incidence of exposure to these individual chemicals, and (3)
estimate the aggregate risk due to the influence of multiple
chemical exposures.

In assessing current NIOSH computerized data base capabilities,

the authors decided that these three levels of necessary ‘information
could be at least partially met with a modeling technique. The
model reported here, therefore, is divided into three major areas:
(1) an assessment of the relative toxicity of chemicals based only
on RTECS data, (2) an assessment of potential work force health
effect based upon the calculated toxic potential of a chemical and
its incidence in the work place as reported in NOHS*, and (3) an
assessment of the health risk of occupational groups which considers
the chemical exposures specific to that group, the relative
incidence and conditions of exposure to each specific chemical
within that group, and the potential toxicity of these chemicals

to derive an estimate of aggregate risk.

The data flow resulting in the four risk indexes produced by this
model is shown in Figure 1.

* Any NOHS data relating to occupational exposure must be
considered to be "potential" due to the lack of environmental
level data associated with the observed chemical exposures.


AHRIN calculation based on HRIN Trial 02 Run 01 RTECS Date 01/81

Multipliers for AT=1 CAR=1 ETA=1 MUT=1 NEO=1 PI=1l  TER=1
Constants for AT=0 CAR=0 ETA=0 MUT=0 NEO=O PI=0 _ TER=0

SEQ# 1 KQ6300000/31500 Ethyl Alcohol
HRIN SEQ= 242 HRIN= .970 PES= 50539 PEN= 545569 PC= .27

SEQ# 2 NT8050000/40987_ _—Isopropyl Alcohol
HRIN SEQ= 584 HRIN= .575 PES= 70218  PEN= 545569 PC= .26

SEQ# 3 kJ2975000/46970 Ethane, 1,1,1-trichloro-
HRIN SEQ= 394 HRIN= .761 PES= 42605 PEN= 545569 PC= .26

SEQ# 4 XR2275000/A1358 Titanium Oxide
HRIN SEQ= 399 HRIN= .755 PES= 34656 PEN= 545569 PC= .43

SEQ# 5 £L6475000/13980 2-butanone
HRIN SEQ= 380 HRIN= .782 PES= 30314 PEN= 545569 PC= .34

SEQ# 6 KL9275000/M0260 Ethanol, 2,2',2''-nitrilotri-
HRIN SEQ= 348 HRIN= .814  PES= 31868 PEN= 545569 PC= .42

SEQ# 7 72€2100000/76720 Xylene
HRIN SEQ= 291 HRIN= .897 PES= 31029 PEN= 545569 C= .51

SEQ# 8 LP8925000/33640 Formaldehyde
HRIN SEQ= 21 HRIN= 2.223 PES= 10907 PEN= 545569 PC= .48

SEQ# 9 £01400000/13850 Butyl Alcohol
HRIN SEQ= 206 HRIN= 1.030 PES= 23069 PEN= 545569 PC= .47

SEQ# 10 KJ8575000/29930 Ethanol, 2-butoxy-
HRIN SEQ= 360 HRIN= .801 PES= 25711  PEN= 545569 PC= .41

NOHS Date 07/80 Date:

TFX=1

TFX=0
PFT= .02
PFT= .03
PFT= .01
PFT= .30
PFT= .04
PFT= .05
PFT= .04
PFT= .08
PFT= .00
PFT= .04

NEO OPTION=
CAR OPTION=

AHRIN=

AHRIN=

AHRIN=

AHRIN=

AHRIN=

AHRIN=

AHRIN=

AHRIN=

AHRIN=

AHRIN=

347.

292.

229%

191

156.

155.

143

136.

12S:

124.

08/31/81

YES
NO

241

226

964

-181

968

402

-634

337

652

008

page 1

Example AHRI

FIGURE 8.

38


001
470
47)
472
00A
481
482
434
485
492
00d
502
503
506
510
514
515
520
OOF
525
006
533
534
OOL

MACHINISTS

AIR CONDITIONING, HEATING, AND REFRIGERATION
AIRCRAFT

AUTOMOBILE BODY REPAIRMEN

AUTOMOBILE MECHANICS

HEAVY EQUIPMENT MECHANICS, INC. DIESEL
HOUSEHOLD APPLIANCE AND ACCESSORY INSTALLERS AND MECHANICS
OFFICE MACHINE

RADIO AND TELEVISION

MISCELLANEOUS MECHANICS AND REPAIRMEN

NOT SPECIFIED MECHANICS AND REPAIRMEN
MILLWRIGHTS

MOLDERS, METAL

OPTICIANS, AND LENS GRINDERS AND POLISHERS
PAINTERS, CONSTRUCTION AND MAINTENANCE
PATTERN AND MODEL MAKERS, EXC. PAPER
PHOTOENGRAVERS AND LITHOGRAPHERS
PLASTERERS

PLUMBERS AND PIPEFITTERS

POWER STATION OPERATORS

PRESSMEN AND PLATE PRINTERS

ROLLERS AND FINISHERS, METAL

ROOFERS AND SLATERS

SHEETMETAL WORKERS AND TINSMITHS

0-5


In addition, some RTECS tumorigentc data is classified as
“equivocal tumorigenic agent" (ETA). This classification is
utilized when "some evidence of tumorigenic activity is presented,
but one or more of the criteria listed" for neoplastic or carcino-
genic tumor formation is missing. This data is used in the model
algorithms because of the potential for tumor formation indicated
by existing data.

Normalization of Dose Data

If all of the chemicals in question had been tested in the same
way, i.e., in one test class, the doses of these tests could be
used directly to rank the chemicals. However, as noted pre-
viously, the chemicals have been tested in a: number of ways and
many individual chemicals have been tested in two or more test
classes. Furthermore, the test classes span a number of toxic
effects, including acute toxicity, primary irritation, carcino-
genicity, mutagenicity, etc., which are of significance in the
model algorithms. It was necessary, therefore, to devise a way to
compare dose data across test classes as well as from chemical to
chemical within a test class. The dose data had to be normalized
in some manner so that the effects of using specific test animals
and routes of administration would be removed.

To do this, one works with each of the test classes separately.
Each dose in a specific four element test class (d,) can be
expressed as a function of some characteristic of the class, such
as the lowest dose, the average dose, or the range of the doses
in the class. Each dose in a test class can thereby be expressed
as a function of the same base, and since each test class in
treated in the same way, doses from the various test classes
become more comparable.

The use of the range of doses within a test class was selected as
the most effective for purposes of the HRI algorithm. In this
approach the normalized dose is calculated as follows:

d= max i
n

where: qd = normalized dose
dy = observed dose*
a ex = maximum observed dose in the test class*

doin = minimum observed dose in the test class*

* All doses in this equation are expressed as the natural
logarithm of the dose.

17


TABLE 1 - TEST CLASSES*

ROUTE/SPECIES/TEST/TOXIC EFFECT ROUTE/SPECIES/TEST/TOXIC EFFECT ROUTE/SPECIES/TEST/TOXIC EFFECT
1. ANY ANY ANY CAR 34. IPR RAT TDLO TER 67. ORL RAT TD ETA
2. ANY ANY ANY ETA 35. IPR RBT LD5O AT 68. ORL RAT TDLO CAR
3. ANY ANY ANY NEO 36. IVN CAT LDLO AT 69. ORL RAT TDLO ETA
4. ANY ANY ANY TER 37. IVN CAT LDSO AT 70. ORL RAT TDLO NEO
5. ANY ANY ANY TFX 38. IVN DOG LDLO AT 71. ORL RAT TDLO TER
6. ANY ANY CYT MUT 39. IVN DOG LD50 AT 72. ORL RBT LDLO AT
7. ANY ANY — DLT MUT 40. IVN GPG LDLO AT 73. ORL RBT LD5O AT
8. ANY ANY  ODND MUT 41. IVN MUS LDLO AT 74. PAR MUS LDLO AT
9. ANY ANY MSC MUT 42. IVN MUS LD50 AT 75. SCU CAT LDLO AT
10. ANY ANY OTR MUT 43. IVN RAT LDLO AT 76. SCU DOG LDLO AT
11. ANY ANY SCE MUT 44. IVN RAT LD5O0 AT 77. SCU FRG LDLO AT
12. ANY ANY  VIT MUT 45. IVN RBT LDLO AT 78. SCU GPG LDLO AT
13. ANY ANY VIV MUT 46. IVN RBT LD50 AT 79. SCU GPG LD50 AT
14. ANY HAM CYT MUT 47. LYM MUS  DND MUT 80. SCU MUS LDLO AT
15. ANY HMN CYT MUT 48. ORL BRD LDLO AT 81. SCU MUS LD50 AT
16. ANY HMN DNS MUT 49. ORL BRD LD5O AT 82. SCU MUS TDLO CAR
17. EYE RBT SSSS_ PI 50. ORL CAT LDLO AT 83. SCU MUS TDLO ETA
18. IHL -GPG LCLO AT 51. ORL CAT LDSO AT 84. SCU MUS TDLO NEO
19. IHL HMN TCLO TFX 52. ORL DOG LDLO AT 85. SCU RAT LDLO AT
20. IHL MUS LCLO AT 53. ORL DOG LD50 AT 86. SCU RAT LD50 AT
21. IHL MUS LC50 AT 54. ORL GPG LDLO AT 87. SCU RAT TD ETA
22. IHL RAT LCLO AT 55. ORL GPG LDSO AT 88. SCU RAT DLO ETA
23. IHL RAT LC5O AT 56. ORL HMN LDLO AT 89. SCU RAT TDLO NEO
24. IPR GPG LDLO AT 57. ORL HMN TDLO TFX 90. SCU RBT LDLO AT
25. IPR GPG LD50 AT 58. ORL MUS LDLO AT 91. SCU RBT LDSO AT
26. IPR MUS LDLO AT 59. ORL MUS LD5O0 AT 92. SKN MUS TD ETA
27. IPR MUS LDIO AT 60. ORL MUS TDLO CAR 93. SKN MUS TDLO ETA
28. IPR MUS LD20 AT 61. ORL MUS DLO ETA 94. SKN MUS TDLO NEO
29. IPR MUS LD50 AT 62. ORL MUS TDLO NEO 95. SKN RAT LD50 AT
30. IPR MUS TDLO NEO 63. ORL MUS TDLO TER 96. SKN RBT LDLO AT
31. IPR MUS TDLO TER 64. ORL RAT LDLO AT 97. SKN RBT LD50 AT
32. IPR RAT LDLO AT 65. ORL RAT LD50 AT 98. SKN RBT SSSS_ PI
33. IPR RAT LD50 AT 66. ORL RAT 1D CAR

*RTECS Abbreviations - See Appendix B

12


INTRODUCTION

The process of prioritizing and selecting a discrete number of
occupational health research projects from the vast array of
potentially rewarding subject areas has always been difficult,
involving a variety of objective data and certain subjective
values. Based on evidence that certain chemicals are known to
cause specific illnesses or that certain agents are in widespread
occupational use, decision-makers assign a priority to the study
of a particular agent or occupational environment. These
priorities are then considered in light of available resources,

and decisions are made concerning which candidate projects should
be funded.

The National Occupational Hazard Survey* (NOHS) conducted during
1972-1974 in approximately 5,000 industrial facilities by the
National Institute for Occupational Safety and Health (NIOSH) has
developed data which can be used to estimate the number of workers
potentially exposed to particular compounds, and to describe the
conditions of those exposures in the workplace.

Similarly, the initial publication of the NIOSH Toxic Substances
List (now the Registry of Toxic Effects of Chemical Substances**)
in 1971 proved to be of value as a readily accessible compendium

of the (unevaluated) chemical-specific toxicity studies available
in the published technical literature.

Both of these data collections contain information which can be
used to identify potential occupational risk due to chemical
exposure through combination in a risk-ranking system. The risk-
ranking algorithms described herein are intended to provide
decision-makers with a technique for the initial assessment of
relative risk associated with potential occupational exposures to
chemical agents. The algorithms in this model are designed (1)

to rate and compare the relative potential health risk of exposure
to specific chemicals, and (2) to provide a measure of the overall
health risk faced by various occupational groups through the
computation of a single risk index number for an industry or an
occupation.

* National Occupational Hazard Survey, National Institute for
Occupational Safety and Health, DHEW Publications No. (NIOSH)
74-127, May 1974; (NIOSH) 77-213, July 1977; and (NIOSH)
78-114, December 1977.

ek

Registry of Toxic Effects of Chemical Substances, 1980 edition,
National Institute for Occupational Safety and Health, DHHS
(NIOSH), Publication No. 81-116, February 1982.


Microfiche No. 29 - OCCUPATIONAL RISK INDEX - TERATOGEN
SELECTOR

This microfiche displays Bureau of the Census Standard Occupa-
tional Classifications rank-ordered by health risk due to po-
tential exposure to teratogens only. The explanations for

data input to the ORI algorithm are the same as for micro-
fiche No. 10.

E-6 U5, OVERNMENT PAINTING OFFICE 1989 - 661-899


5211
525
53
531
5311
532
5321
533
5331
534
5341
539)
54
541
5411
55
551
5511
553
554
5541
56
562
5621
565

LUMBER AND OTHER BUILDING MATERIALS
HARDWARE AND FARM EQUIPMENT

RETAIL GENERAL MERCHANDISE
DEPARTMENT STORES

DEPARTMENT STORES

MAIL ORDER HOUSES

MAIL ORDER HOUSES

VARIETY STORES

VARIETY STORES

MERCHANDISING MACHINE OPERATORS
MERCHANDISING MACHINE OPERATORS
MISC. GENERAL MERCHANDISE STORES
FOOD STORES

GROCERY STORES

GROCERY STORES

AUTOMOTIVE DEALERS & SERVICE STATIONS
NEW AND USED CAR DEALERS

NEW AND USED CAR DEALERS

TIRE, BATTERY, AND ACCESSORY DEALERS
GASOLINE SERVICE STATIONS

GASOLINE SERVICE STATIONS

APPAREL AND ACCESSORY STORES
WOMEN'S READY-TO-WEAR STORES
WOMEN'S READY-TO-WEAR STORES

FAMILY CLOTHING STORES

C-24


Severity Rating

This field, if present, follows the dose type and duration,
if present, in no set position.

Acceptable values are mild (MLD), moderate (MOD), severe (SEV).
Records that do not contain a severity rating are arbitrarily
given a rating of moderate (MOD).

Dose Conversions (see first and second conversion steps)
Conversions are performed in the following order:

1. Percent records where route of administration is "SKN" are
multiplied by 5.

2. Percent records (expressed as a whole number) are converted to
milligrams on the basis of two assumptions.

a. Doses reported as percent are liquids.

b. The specific gravity is equal to 1.0.
Draize test states that the amount of solution is .5 ML for skin
studies, and .1 ML for eye studies; therefore the following con-
version steps are performed:

For skin studies using, for example, a 50% solution:

1. The dose volume is .5 ML, so a 50% solution = .25 ML of a 100%
solution.

2. Assume specific gravity of 1.0, therefore .25 ML
250 milligrams.

25 grams =

For eye studies using, for example, a 50% solution:
1. The dose is .1 ML, so a 50% solution = .05 ML of a 100% solution.

2. Assume specific gravity of 1.0, therefore .05 ML
50 milligrams.

r

-05 grams =
These records are assigned an MG/KG designation, and the record is
converted to millimoles via the second conversion step.

3. Doses reported as UG, NG, GM, etc. are converted to MG via the
first conversion step and given an MG/KG designation.

4. Volumetric doses (UG/M3, NG/M3, etc.) are converted to MG/M3
via the first conversion step.

A-11


© Dose jn natural log (In) millimole/kg or natural log (In) pom

The file of these records serves as the input used by the HRI
algorithm computer programs discussed in the next section of this
report.

THE HAZARD RISK INDEX

The Hazard Risk Index (HRI) represents accomplishment of the first
major NIOSH goal in the development of this model - to provide a
mechanism for the assessment of the toxicological effects of
chemicals known to be in the workplace.

The HRI algorithm provides a rank-ordering of the chemicals common
to both the RTECS and NOHS data sources ("linked" chemicals) based
only on their potential toxicological risk. Only RTECS data is
used, and without consideration of the conditions or extent of
worker exposure.

An estimate of the relative toxicological risk posed by any linked
chemical is provided in numerical form by the HRI algorithm. This
number is used to rank-order all linked chemicals in descending
order of toxicity. The resulting list provides the chemical
toxicity priorities needed for an assessment of potential workplace
hazard.

The HRI Algorithm

In producing an algorithm to assess the toxicological risk of a
chemical based solely on RTECS data, NIOSH realizes that use of the
results from this algorithm must be tempered by a recognition that:

a) The RTECS data are unevaluated; only the lowest dose pro-

ducing a specific effect for a qiven test class is reported
for any specific chemical, and coverage of chemicals and

test classes is incomplete; and

b) The criteria for selection of RTECS data, as detailed in
the preceding section, impose further data limitations.

Examination of the normalized dose and test class data derived in
preparation of the RTECS data source indicated that all the
selected data could be contained in the following eight categories:

@ Acute toxicity (AT)
e Carcinogenicity (CAR)
e@ Equivocal tumorigenic agents (ETA)

22


lowest doses producing a specific effect reported for a wide
variety of test animals and regimens.

In spite of these limitations, RTECS is an unequaled resource for
toxicity data. The January 1981 computer tape version of RTECS
used in this model contains 75,357 toxicity data measurements on
45,156 different chemical compounds. Each test is systematically
reported and all of the data are in machine-readable form and are
separately addressable. The RTECS file is reasonably current with
respect to the published literature and covers both domestic and
foreign publications.

Preparation of RTECS Data for Use in the Model

Even though the RTECS data are available in computer-readable form,
they are not in a form that permits their direct use in computer
algorithms. The available data are variable in format and subject
content. For example,

e The RTECS doses are expressed in a number of different units.

e RTECS presents data on tests using many different test
animals and routes of administration, and with many different
test endpoints.

e RTECS presents both acute and chronic test results, as well
as the results of skin and eye irritation tests.

In addition, problems related to machine processing of the data
base emerged. Primarily, these problems arose from the fact that
the majority of data in any given record is position and length
variable, requiring a character by character search of each record
to determine the presence of required data. This would probably
be true of any large scale toxicological data base. However, it
meant that the program to access selected RTECS data for the model
had to be designed to accomodate all the possible combinations of
data position and length.

General decisions defining the data to be extracted from the RTECS
file for use in model computer programs were:

e@ Model data needs are met with chemical name, molecular
weight, toxic effect (acute or chronic), and skin/eye
irritation data lines. (Defined by RTECS as of January
1981 as the "A", "H", "R", "S", and "T" lines.)

e@ In-vivo test procedures resulting in a mutagenic outcome
must be performed on specified mammalian species, utilizing
specified tests, and utilizing any route of dose adminis—
tration except "unknown", "multiple", or "rectal". This


HRIN calculations based on RTECS data as of 01/81 DATE: 08/17/81 page 1

Multipliers for AT=1  CAR=1 ETA=1 MUT=1 NEO=1 PI=1  TER=1 TFX=1 NEO OPTION= NO
Constants for AT=0 CAR=0 ETA=0 MUT=0 NEO=0 PI=0 TER=0 TFX=O0 CAR OPTION= NO

SEQ# 1 H &AR1925000 Acetaldehyde
AT= .186 CAR= .000 ETA= .000 MUT= .440 NEO= .000 PI= .120 TER= -131  TFX= .393. HRIN= 1.271

ANY ANY ANY TER 300 «131572
ANY ANY  DND MUT 189 -127994
ANY ANY = SCE MUT 88 -192107
ANY ANY VIV MUT 142 1.000000
EYE RBT SSSS_ PI 1454 -227582
IHL HMN TCLO TFX 89 -393606
IHL RAT LCLO AT 424 -340445
IPR RAT LDLO AT 865 - 162556
IVN MUS LD50 AT 10478 -165060
ORL RAT LDSO AT 5118 147655
scuU MUS LD5O AT 3581 -167873
scu RAT LDSO AT 755 133114
SKN RBT SSSS_ PI 1265 -013521

SEQ# 2 H AB2450000 Acetaldehyde, Chloro-
AT= .580 CAR= .000 ETA= .000 MUT= .000 NEO= .000 PI= .000 TER= .000 TFX= .000 HRIN= .580

IPR GPG LD5O0 AT 71 -809896
IPR MUS LD50 AT 12495 -459322
IPR RAT LD5O AT 2289 -450620
IPR RBT LD5O AT 66 -878338
ORL MUS LD5O0 AT 6240 -514611
ORL RAT LDSO AT 5118 -463197
SKN RBT LDS5O AT 890 -488431

FIGURE 5. Example HRI

27


323
3231
324
3241
325
3251
326
3269
327
3271
3272
3273
3274
328
3281
329
3291
3292
3295
3299
33
331
3312
3313
3315

PRODUCTS OF PURCHASED GLASS
PRODUCTS OF PURCHASED GLASS
CEMENT, HYDRAULIC

CEMENT, HYDRAULIC

STRUCTURAL CLAY PRODUCTS

BRICK AND STRUCTURAL CLAY TILE
POTTERY AND RELATED PRODUCTS
POTTERY PRODUCTS, NEC

CONCRETE, GYPSUM, AND PLASTER PRODUCTS
CONCRETE, BLOCK AND BRICK

CONCRETE PRODUCTS, NEC

READY-MIXED CONCRETE

LIME

CUT STONE AND STONE PRODUCTS

CUT STONE AND STONE PRODUCTS

MISC. NONMETALLIC MINERAL PRODUCTS
ABRASIVE PRODUCTS

ASBESTOS PRODUCTS

MINERALS, GROUND OR TREATED
NONMETALLIC MINERAL PRODUCTS, NEC
PRIMARY METAL INDUSTRIES

BLAST FURNACE AND BASIC STEEL PRODUCTS
BLAST FURNACES AND STEEL MILLS
ELECTROMETALLURGICAL PRODUCTS

STEEL WIRE AND RELATED PRODUCTS

C-12


2253
2256
2259
226
2262
2269
228
2281
2283
2284
229
2293
2294
2295
23
231
2311
232
2321
2323
2329
233
2331

2335

KNIT OUTERWEAR MILLS

KNIT FABRIC MILLS

KNITTING MILLS, NEC

TEXTILE FINISHING, EXCEPT WOOL
FINISHING PLANTS, SYNTHETICS
FINISHING PLANTS, NEC

YARN AND THREAD MILLS

YARN MILLS, EXCEPT WOOL

WOOL YARN MILLS

THREAD MILLS

MISCELLANEOUS TEXTILE GOODS
PADDINGS AND UPHOLSTERY FILLING
PROCESSED TEXTILE WASTE

COATED FABRICS, NOT RUBBERIZED
APPAREL AND OTHER TEXTILE PRODUCTS
MEN'S AND BOYS' SUITS AND COATS
MEN'S AND BOYS' SUITS AND COATS
MEN'S AND BOYS' FURNISHINGS

MEN'S AND BOYS' SHIRTS AND NIGHTWEAR
MEN'S AND BOYS' NECKWEAR

MEN'S AND BOYS' CLOTHING, NEC
WOMEN'S AND MISSES' OUTERWEAR
WOMEN'S AND MISSES' BLOUSES AND WAISTS
WOMEN'S AND MISSES' DRESSES


was done to restrict data to mammalian species tests
which were comparable to other RTECS data and to
approximate general mammalian response and exposure.

e In-vitro test procedures resulting in a mutagenic outcome
must have been performed on specified types of cells
originating from the same mammals specified for in-vivo
procedures, and performed utilizing specified test pro-
cedures. Such restriction was mandated for the same
reasons indicated for in-vivo mutagen studies.

e Skin and eye irritation data was restricted to rabbit
studies, with eye or skin routes in an effort to initially
extract only those studies meeting "Draize test" criteria.
This criteria was imposed for reasons of data comparability.

e In general toxicity studies, any route of administration
except "unknown" was accepted. Additionally, intradermal,
intramuscular and subcutaneous routes of administration
were project defined as synonymous to increase the number
of useable data lines which were considered directly
comparable and on the assumption that under occupational
conditions, any route of administration (other than
absorption) penetrating the skin occurs as the result of
an accident, involving all three routes simultaneously.
All such lines were converted via computer program to the
"subcutaneous" route of administration.

e As noted earlier, the RTECS data are reported in terms of
test animal species, routes of administration, test proce-
dures and toxic effects. The original developmental
contract showed (1) that the bulk of the RTECS data relates
to a relatively small number of test classes and (2) that
combination of some test classes through redefinition of
the aminal species vocabulary would produce a few addi-
tional test classes with useful data populations. In
general, any animal species utilized in test procedures
was acceptable, except citations of "unknown" species.
Species originally termed "infant", "child", "man", "woman",
and "human" were all converted to "human". Species
originally cited as "bird", "chicken", "duck", "pigeon",
"quail", "turkey", and "bird-wild bird" were all converted
to "bird". These steps were taken to increase the number
of directly comparable data lines.

These inital decisions on test class parameters resulted in the
creation of 393 test classes across RTECS. These initial test

classes contained a range of cited test results from 1 to 12,495
per test class.


3011
302
3021
306
3069
307
3079
31
311
3111
313
3131
314
3141
3142
316
3161
Silizé
3171
3172
32
321
3211
sed
3221

TIRES AND INNER TUBES

RUBBER FOOTWEAR

RUBBER FOOTWEAR

FABRICATED RUBBER PRODUCTS, NEC
FABRICATED RUBBER PRODUCTS, NEC
MISCELLANEOUS PLASTICS PRODUCTS
MISCELLANEOUS PLASTICS PRODUCTS
LEATHER AND LEATHER PRODUCTS
LEATHER TANNING AND FINISHING
LEATHER TANNING AND FINISHING
FOOTWEAR CT STOCK

FOOTWEAR CUT STOCK

FOOTWEAR, EXCEPT RUBBER

SHOES, EXCEPT RUBBER

HOUSE SLIPPERS

LUGGAGE

LUGGAGE

HANDBAGS AND PERSONAL LEATHER GOODS
WOMEN'S HANDBAGS AND PURSES
PERSONAL LEATHER GOODS

STONE, CLAY, AND GLASS PRODUCTS
FLAT GLASS

FLAT GLASS

GLASS AND GLASSWARE, PRESSED OR BLOWN
GLASS CONTAINERS


3551
3552
3553
3554
3559
356
3561
3562
3564
3565
3566
3567
3569
357
3573
3579
358
3581
3582
3585
3589
359
3599
36
361

FOOD PRODUCTS MACHINERY

TEXTILE MACHINERY

WOODWORKING MACHINERY

PAPER INDUSTRIES MACHINERY
SPECIAL INDUSTRY MACHINE, NEC
GENERAL INDUSTRIAL MACHINERY
PUMPS AND COMPRESSORS

BALL AND ROLLER BEARINGS

BLOWERS AND FANS

INDUSTRIAL PATTERNS

POWER TRANSMISSION EQUIPMENT
INDUSTRIAL FURNACES AND OVENS
GENERAL INDUSTRIAL MACHINERY, NEC
OFFICE AND COMPUTING MACHINES
ELECTRONIC COMPUTING EQUIPMENT
OFFICE MACHINES, NEC

SERVICE INDUSTRY MACHINES
AUTOMATIC MERCHANDISING MACHINES
COMMERCIAL LAUNDRY EQUIPMENT
REFRIGERATION MACHINERY

SERVICE INDUSTRY MACHINES, NEC
MISC. MACHINERY, EXCEPT ELECTRICAL
MISC. MACHINERY, ESCEPT ELECTRICAL
ELECTRICAL EQUIPMENT AND SUPPLIES

ELECTRIC TEST & DISTRIBUTING EQUIPMENT


312
314
315
321
323
325
332
343
344
345
355
360
362
364
OOH
374
375
376
381
385
390
391
392
394

CLERICAL SUPERVISORS, NEC

COUNTER CLERKS, EXCEPT FOOD
DISPATCHERS AND STARTERS, VEHICLE
ESTIMATORS AND INVESTIGATORS, NEC
EXPEDITERS AND PRODUCTION CONTROLLERS
FILE CLERKS

MAIL HANDLERS, EXCEPT POST OFFICE
COMPUTER AND PERIPHERAL EQUIPMENT OPERATORS
DUPLICATING MACHINE OPERATORS

KEY PUNCH OPERATORS

OFFICE MACHINE OPERATORS, NEC
PAYROLL AND TIMEKEEPING CLERKS
PROOFREADERS

RECEPTIONISTS

SECRETARIES, NEC

SHIPPING AND RECEIVING CLERKS
STATISTICAL CLERKS

STENOGRAPHERS

STOCK CLERKS AND STOREKEEPERS
TELEPHONE OPERATORS

TICKET, STATION, AND EXPRESS AGENTS
TYPISTS

WEIGHERS

MISCELLANEOUS CLERICAL WORKERS

D-3


R-line (mutagenic data)
RTECS record definitions are:

1. Position 17 equals a dash (-). It is the separator between
test and species.

2. Position 21 equals a colon (:) for in-vitro data, and a dash
(-) for in-vivo data. They are separators between species and
route or cell type.

3. Position 25 is a blank. It is the separator between route or
cell and dose amount.

If these conditions are not met, the record is rejected.
In-vitro dose amount

Dose amount starts in position 26 and continues for a variable
number of positions (see "dose amount" for T-line data).

In-vitro dose types
The dose type is separated from dose amount by one blank.

Dose amount types accepted:

GM/L, MG/L, UG/L, NG/L, MOL/L, MMOL/L, UMOL/L, NMOL/L,
PMOL/L, PPH, PPM, PPB.

Types rejected:

Per kilogram (/KG), per plate (/plate), per well (/well),
per disc (/disc), and doses expressed as NG, MG, UG, PG, GM.

In-vitro dose conversions (See first and second conversion steps).
Conversion of volumetric measurements to millimole notation is
possible because of assumptions for a constant volume of 1.0 liters
and a specific gravity of 1.0 permitting conversion to equivalent
MG/L notation. Conversions are made in the following order:

1. MG/L is given an MG/KG designation.

2.  GM/L, UG/L, and NG/L are changed to MG/L equivalence via the
first conversion step, and is given an MG/KG designation.

3. PPH and PPB are changed to PPM via the first conversion step.


Toxicity data dose amount:

Dose amount starts in position 37 and continues until a space is
found. The maximum accepted by the program is 9 whole number
digits. The maximum found in RTECS data was 5 whole number digits.
Some dose amounts have decimal parts (i.e. 10.5). The program
accepts only the first decimal digit.

Dose amount types:
This field is separated from dose amount by a blank.

Dose amount types accepted:

PPH, PPM, PPB, PPT, PG/KG, MG/KG, MG/M3, UG/KG, UG/M3, NG/KG,
NG/M3, G/KG, G/M3, GM/KG, GM/M3, KG/KG.

Types rejected:

MG(CA)KG, MG(LA)KG, MG(AS)KG, MG(SB)KG, MG(SE)KG, MG(MN)KG,
MG(MO)KG, IU/KG, PARTICLES/CC, MPPCF.

TFX Notation:

Following the dose type (no standard position), there may or may
not be a TFX value (expressed as TFX:). These values identify the
outcome of the test. If a TFX: is not present on a record, it is

assumed to be an acute effect.

Acceptable TFX notations: (model defined)

ALR, BCM, BLD, BPR, CAR, CNS, COR, CVS, ETA, GIT, GLN, IRR, MMI,
NSK, NEO, PNS, PSY, PUL, RBC, SYS, TER, WBC.

Unacceptable notations:

CUM, DDP, EYE, MLD, MOD, MUT, SEV, SKN, UNS.
Dose conversions: (See first and second conversion routines)

After the dose has been converted, the dose type (either ppm or mm)
is compared against the route of administration to assure that
volumetric measurements (ppm) appear only in inhalation studies
and per kilogram measurements (mm) appear only in routes of admin-
istration other than inhalation. If this check is not performed,
subsequent comparisons of data within test classes would be com-
paring PPM dose values with millimole dose values. Records that
do not conform to this criteria are rejected.

A-13


administration, and toxic effect are identified, as is the dose
that was administered. However, the duration of exposure, which
is highly significant in tests of this type, and the degree of
irritation observed are also usually reported. Consequently, the
dose could not be used as the single indicator of activity. It
was observed that the Draize procedure (J. Pharmacol. Exp. Ther.,
82:277-419, 1944) is very widely used in primary irritation testing.
In this test the amount of chemical (volume if liquid, weight if
solid) applied and the duration of application are fixed, and the
degree of irritation (if any) is expressed as mild, moderate, or
severe in terms of defined standards. Since a high percentage of
the RTECS primary irritation data records were from Draize tests,
it was decided to limit use of the RTECS primary irritation data
to results derived using this procedure. The doses were converted
to molar form and standard units, and then modified as follows to
correspond to the degree of irritation observed:

doses with mild effects were multiplied by 1.0

doses with moderate effects were multiplied by 0.5

doses with severe effects were multiplied by 0.25

doses with an irritant effect shown in other than Draize
notation were treated as moderate since examination of
the data indicated this to be the best approach

In this way the relatively complex primary irritation data were
expressed in dose terms in a way that was analogous to the
balance of the RTECS data used in the HRI algorithm.

Tumorigenic and Carcinogenic Data Considerations

RTECS differentiates between carcinogenic and neoplastic tumor
formation citations as follows:

e A citation is coded "TFX:NEO" (neoplastic) when "the
tumors consist of cells that closely resemble the tissue
of origin that are not grossly abnormal cytologically,
that may compress surrounding tissues, but that neither
invade tissues nor metastasize or when the tumors produced
cannot definitely be classified as either benign or
malignant."

e A citation is coded "TFX:CAR" (carcinogenic) when "the
tumors consist of autonomous populations of cells of ab-
normal cytology capable of invading and destroying normal
tissues, or the tumors metastasize as confirmed by histo-
pathology."

In working with the RTECS data, it was observed that many compounds

14


3316 COLD FINISHING OF STEEL SHAPES

3317 STEEL PIPE AND TUBES

332 IRON AND STEEL FOUNDRIES

3321 GRAY IRON FOUNDRIES

3322 MALLEABLE IRON FOUNDRIES

3323 STEEL FOUNDRIES

333 PRIMARY NONFERROUS METALS

3334 PRIMARY ALUMINUM

3339 PRIMARY NONFERROUS METALS, NEC

335 NONFERROUS ROLLING AND DRAWING

3351 COPPER ROLLING AND DRAWING

3352 ALUMINUM ROLLING AND DRAWING

3356 NONFERROUS ROLLING AND DRAWING, NEC
3357 NONFERROUS WIRE DRAWING AND INSULATING
336 NONFERROUS FOUNDRIES

3361 ALUMINUM CASTINGS

3362 BRASS, BRONZE, AND COPPER CASTINGS
3369 NONFERROUS CASTINGS, NEC

339 MISCELLANEOUS PRIMARY METAL PRODUCTS
3391 IRON AND STEEL FORGINGS

3399 PRIMARY METAL PRODUCTS, NEC

34 FABRICATED METAL PRODUCTS

341 METAL CANS

3411 METAL CANS

342 CUTLERY, HAND TOOLS, AND HARDWARE


TABLE 3 - TEST CLASSES SHOWING
DOSE LOG VALUES, RTECS CHEMICAL REFERENCE CITED, & NUMBER OF RECORDS PER TEST CLASS

TEST MINIMUM(+30.00) MINIMUM LOG VALUE MAXIMUM(+30,90) MAXIMUM LOG VALUE NO. OF TEST
CLASS NO.* LOG VALUE RTECS ACCESSION NO, LOG VALUE RTECS ACCESSION NO. CITATIONS
A 22566498 RP5950000 39.774017 Dv4900000 299
bo 22 ..090057 KG2975000 39.483154 K1I0175000 418
3. 19.255035 HN2625000 37.786942 XU7350000 253
4. 15.708064 HP3500000 34.117218 0J5800000 300
5. 14.211206 TJ2800000 37774139 2C0110000 360
6. 20.531967 KE4100000 33413162 EZ6475000 108
ifs 18.636520 KE4390000 35.916260 LZ6500000 83
(hs 13820038 HB7875000 40.296600 MU7175000 189
oF 17499817 FZ3675000 41.818069 BZ8580500 114
10. 17.499817 FZ3670000 31.169800 PB2100000 70
te 10.375351 TP2450000 33 . 688873 190525000 88
12. 19.724854 Ayv9800000 38, 070892 KU9625000 102
13, 15.105679 AB1925000 33493240 RC8965000 142
14. 17.754761 YY8050000 40.126617 RS2060000 150
15. 10.663040 DK7175000 35.562271 KQ6300000 139
16. 16.184479 UZ9850000 34605164 QH4560000 72
Wis 16.739517 EJ8225000 31987122 KL5600000 1454
18. 27723434 DT7000000 41.512924 PA8400000 102
19% 25.355911 RN1140000 41.091431 PC1400000 89
20. 27271454 DT7.000000 43 .458832 KI1100000 375
(Ake 19.947906 178524000 43 .764206 KU5340000 173
22, 27812057 017000000 43.704575 FG4920000 424
23% 27.908203 TAO700000 48 .927490 TE7000000 219
24. 20.140060 B08785750 34 .463669 KQ6300000 110
25. 23002609 WH6650000 34.479172 MA1575000 171
26. 20.081467 CB9459000 34.978333 ZF0800000 1760
(ahs, 11068497 VC3968770 30.315033 AF1710000 434
28. 22.160370 AU1490000 29.777191 1X7020000 83
(ale 14,339328 RT6475000 36.516541 QL2975000 12495
30. 25,502625 CW1795065 33985306 MU7700000 82
oils 20.898514 AU1575000 35331802 L.Q2100000 103
32. 20.939194 AR5950000 34659348 MN9275000 865

*See Table 1 for test class titles

19


IRI Calculation based on HRIN Trial 02 Run O01 RTECS Date 01/81 NOHS Date 07/80 CENSUS OPTION=YES Date: 09/01/81 page 1

Multipliers for
Constants for

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

SEQ#

1

10

AT=
AT=

SIc

SIC

SIC

SIC

SIC

SIC

SIC

SIC

SIC

SIC

1

0

80

55

73

50

28

30)

59

53

72

58

CAR=0

ETA=1 MUT=1 ~~ NEO=1 PI=1 ~—-TER=1
ETA=0 MUT=0 NEO=O PI=0  TER=0

MEDICAL AND OTHER HEALTH SERVICES
NUMBER OF CHEMICALS = 678

AUTOMOTIVE DEALERS & SERVICE STATIONS
NUMBER OF CHEMICALS = 197

MISCELLANEOUS BUSINESS SERVICES
NUMBER OF CHEMICALS = 325

WHOLESALE TRADE
NUMBER OF CHEMICALS = 259

CHEMICALS AND ALLIED PRODUCTS
NUMBER OF CHEMICALS = 1162

MACHINERY, EXCEPT ELECTRICAL
NUMBER OF CHEMICALS = 538

MISCELLANEOUS RETAIL STORES
NUMBER OF CHEMICALS = 205

RETAIL GENERAL MERCHANDISE
NUMBER OF CHEMICALS = 257

PERSONEL SERVICES
NUMBER OF CHEMICALS = 188

EATING AND DRINKING PLACES
NUMBER OF CHEMICALS = 95

TFX=1
TFX=0

TRIN
+/-

IRIN
+/-

IRIN
+/-

IRIN
+/-

IRIN
+/-

IRIN
+/-

IRIN
4/=
IRIN
+/-

IRIN
+/-

IRIN
+/-

"

"

716.
-320

5

308.
Sr

241

224.
3.

188.
=529:

i

150.
748

149.
4.

143.
451

1
141

140.
-603

NEO OPTION= YES
CAR OPTION= NO

138

163
606

- 982
ie

490

829
419

836

886

967

573
952

-854
Bp

376
413

Example 2-SIC IRI

FIGURE 9.

48


274 MISCELLANEOUS PUBLISHING

2741 MISCELLANEOUS PUBLISHING

27s COMMERCIAL PRINTING

2751 COMMERCIAL PRINTING, EXCEPT LITHOGRAPHIC
2752 COMMERCIAL PRINTING, LITHOGRAPHIC
276 MANIFOLD BUSINESS FORMS

2761 MANIFOLD BUSINESS FORMS

277 GREETING CARD PUBLISHING

Pal GREETING CARD PUBLISHING

278 BLANKBOOKS AND BOOKBINDING

2789 BOOKBINDING AND RELATED WORK

279 PRINTING TRADE SERVICES

2791 TYPESETTING

28 CHEMICALS AND ALLIED PRODUCTS

281 INDUSTRIAL CHEMICALS

2815 CYCLIC INTERMEDIATES AND CRUDES
2816 INORGANIC PIGMENTS

2818 INDUSTRIAL ORGANIC CHEMICALS, NEC
2819 INDUSTRIAL INORGANIC CHEMICALS, NEC
282 PLASTICS MATERIALS AND SYNTHETICS
2821 PLASTICS MATERIALS AND RESINS
2822 SYNTHETIC RUBBER

2823 CELLULOSIC MAN-MADE FIBERS

283 DRUGS

c-9


Sort by Sort by Sort by Sort by Sort
hazard hazard hazard hazard b
within within within within M
occupation h-digit SIC 3-digit SIC 2-digit SIC Hazard
Sorted Sorted Sorted Sorted
Detail Detail Detail Detail
Master Master Master Master
W y a2
SUMEXT®6 SUMEXT#5 SUMEXTB4 SUMEXTO3 SUMEXT#2

(Continued)

FIGURE 6.

31


reported to be carcinogenic did not also have neoplastigen records.
If the researcher studying a chemical observed a statistically
significant increase in the number of tumors but did not report
them as malignant or metastasized, the tumor-producing compound
would have been reported as a neoplastigen in RTECS. The
carcinogen category might then be regarded as a subset of the
neoplastigen category, at least for the purposes of this model.

If the data are considered in this light, then a neoplastigen
record could be considered to exist for any chemical with a
carcinogenic citation. Given this assumption, generation of a
neoplastigen record using the dose data of the existing carcinogen
record was made an option in the HRI algorithm computer programs.
Exercise of this option expands the data available on those
chemicals for which only a carcinogenic citation is present, and
standardizes the reporting of toxic effects across all chemicals
with carcinogenic properties. Under this option no record is
generated if the compound has a neoplastigen record in RTECS in
the same test class as the carcinogen record. In the HRIN
algorithm computer programs, this option is identified as the
"NEO OPTION” and is exercised if the "NEO OPTION" is set to "YES".
The effect of utilizing the "NEO OPTION" is illustrated in Table
2. Comparison of the data shows, for example, that for the ORAL-
RAT-TDLO-NEO test class, use of the "NEO OPTION" increases the
number of chemical-specific neoplastic citations from 95 to 364
and identifies a different chemical as the one producing a
neoplasm at the lowest dose level in the test class.

A second option was also developed in an effort to extend the RTECS
data. This option applies when RTECS has a neoplastigen record for
a compound but does not have a carcinogen record for the compound.
Since the observation of any tumor may have carcinogenic signifi-
cance, it was considered worthwhile to permit optional use of the
calculated neoplasm sub-HRIN (as discussed in the HRI algorithm
section) as an estimator of the carcinogen sub-HRIN. Note that
this procedure does not directly use the dose data from a neo-
plastic citation for the estimate of a dose to produce a carcino-
genic effect. This option is identified in the computer programs
as the "CAR OPTION" and is exercised when the "CAR OPTION" is set
to "YES" (See discussion on pages 26 and A~15).

Both of these options are controlled by the algorithm user through
control codes introduced into the computer programs. The programs
require the user to input codes for each option before the program
can be run, so the options must be considered each time the HRI
algorithm is used. It should be noted that when these options

are exercised in the HRI algorithm their effects are introduced
into all subsequent algorithms via use of the HRIN data produced
by the HRI algorithm (See Figure 1).

15


II. RTECS DATA PROCESSING

A. Extract RTECS Data Lines (Records)
Program: CIORIEXT

The purposes of this program are to: (1) Select only those RTECS
records meeting established project data parameters, (2) avoid the
necessity of scanning the entire RTECS base (over 600,000 records)
in subsequent programs, thereby shortening total system execution
time considerably.

Each RTECS number (chemical) may contain many separate data lines
(designated as line types A through Z - each containing different
data elements). For this model, line types A, H, R, S, and T are
selected.

Model data requirements, specifications and program functions for
each of these line types are as follows:

A-line (chemical name)

By model criteria, each RTECS number must have a name. If the
mame exceeds 94 characters, multiple A-lines will occur.
However, CIORIEXT uses only the first line. In those cases
where A-line data are missing, the program generates an A-line
and inserts "*** no name given in RTECS ***" into the name
field.

H-line (molecular weight)

By model criteria, each RTECS number must have a molecular
weight to be used for calculations in subsequent programs.
Molecular weight on the H-line is a seven-character field with
a decimal in position 5 (i.e., 9999.99, blank 999.99, or blank-
blank 99.99). Leading blank spaces are converted to a zero
designation.

Data entry errors in the insertion of a two or three whole
number molecular weight were occasionally noted (i.e. 999.99
or leading blank, 99.99 instead of the proper leading blank
999.99, or two leading blanks 99.99), which resulted in
shifting the entire field one position to the left and dis-
playing the decimal in position four rather than the formatted
position 5.

After questioning RTECS editorial personnel, it was determined
that the given values were correct.


This microfiche displays Standard Industrial Classifications

at the 2-digit level, rank-ordered by descending potential
health risk due to potential chemical exposure. The Industrial
Risk Index displayed on this microfiche results from consid-
ering all toxic effects equal in the source HRIN with the NEO
OPTION set at "YES". (See the explanations for microfiche 3
and 4.) Note that the CENSUS OPTION is also set at "YES" indi-
cating use of census population data as a modifier in the
algorithm, as discussed in this report on page 40.

Microfiche No. 19 - 2-SIC INDUSTRIAL RISK INDEX - CARCINOGEN
SELECTOR

This microfiche displays Standard Industrial Classifications
at the 2-digit level, rank-ordered by calculated potential
health risk due to potential exposure to carcinogens only.
The source HRIN for this IRI is displayed on microfiche No. 7,
and results from considering only carcinogenic data. Note
that the option to utilize census data in the IRI algorithm
has been exercised.

Microfiche No. 20 - 2-SIC INDUSTRIAL RISK INDEX - TERATOGEN
SELECTOR

This microfiche displays Standard Industrial Classifications
at the 2-digit level, rank-ordered by calculated health risk
due to potential exposure to teratogens only. The source HRIN
for this IRI is displayed on microfiche No. 10, and results
from considering only teratogenic data. Note that the CENSUS
OPTION is set at "YES", indicating use of census data in the
IRI algorithm.

Microfiche No. 21 - 3-SIC INDUSTRIAL RISK INDEX - ALL EFFECTS
EQUAL

This microfiche displays Standard Industrial Classifications
at the 3-digit level, rank-ordered by health risk due to all
potential chemical exposures. The explanations for data input
are the same as for microfiche No. 3.

Microfiche No. 22 - 3-SIC INDUSTRIAL RISK INDEX - CARCINOGEN
SELECTOR

This microfiche displays Standard Industrial Classifications
at the 3-digit level, rank-ordered by health risk due to po-
tential exposure to carcinogens only. The explanation for
data input are the same as for microfiche No. 7.


809
8092
8099
8]
811
8111
82
86
89
891
8911
893
8931

HEALTH AND ALLIED SERVICES, NEC
SANITORIES, CONVALESCENT & REST HOMES
HEALTH AND ALLIED SERVICES, NEC
LEGAL SERVICES

LEGAL SERVICES

LEGAL SERVICES

EDUCATIONAL SERVICES

NONPROFIT MEMBERSHIP ORGANIZATION
MISCELLANEOUS SERVICES

ENGINEERING & ARCHITECTURAL SERVICES
ENGINEERING & ARCHITECTURAL SERVICES
ACCOUNTING, AUDITING & BOOKKEEPING
ACCOUNTING, AUDITING & BOOKKEEPING


NUMBER

OCCUPATION

901
006
010
aya
012
013
014
O15
023
044
045
055
056
064
065
075
076
080
083
085
150

ACCOUNTANTS

AERONAUTICAL AND ASTRONAUTICAL ENGINEERS
CHEMICAL ENGINEERS

CIVIL ENGINEERS

ELECTRICAL AND ELECTRONIC ENGINEERS

INDUSTRIAL ENGINEERS

MECHANICAL ENGINEERS

METALLUGICAL AND MATERIALS ENGINEERS

ENGINEERS, NEC

BIOLOGICAL SCIENTISTS

CHEMISTS

OPERATIONS AND SYSTEMS RESEARCHERS AND ANALYSTS
PERSONNEL AND LABOR RELATIONS WORKERS
PHARMACISTS

PHYSICIANS, MEDICAL AND OSTEOPATHIC

REGISTERED NURSES

THERAPISTS

CLINICAL LABORATORY TECHNOLOGISTS AND TECHNICIANS
RADIOLOGIC TECHNOLOGISTS AND TECHNICIANS

HEALTH TECHNOLOGISTS AND TECHNICIANS, NEC
AGRICULTURE AND BIOLOGICAL TECHNICIANS, EXCEPT HEALTH
CHEMICAL TECHNICIANS

0-1


The identification of high risk chemicals and occupational groups
is accomplished by computer-based processing of the RTECS and NOHS
files using computational algorithms designed to incorporate data
from both sources simultaneously. The work, both in the develop-
mental contract and in the succeeding in-house effort, is
empirical, and uses only the data available from RTECS and NOHS

as well as selected data from the Bureau of the Census.*

All the model algorithm have the ultimate function of producing an
"index number" for the production of rank ordered indexes of
potential chemical and occupational group "risk". Accordingly,
RTECS toxicity data are accepted at face value, even though some
toxic effects are not universally accepted as dose dependent, and
the data is unevaluated.

The varying specialized interests of researchers were anticipated.
The model algorithms were, therefore, designed to permit user
emphasis and/or selection of specific toxicological outcomes
corresponding to researchers concerns.

The output from this model should be viewed as a pilot effort.

The algorithms provide indices of risk on the basis of potential
risk due to chemical exposure utilizing a limited chemical list,
and without considering actual environmental levels, since this
data is not available in either RTECS or NOHS. In addition,
animal study data is assumed to be an accurate predictor of human
response. Nevertheless, it is felt that the use of these model
indices as a surveillance mechanism for the identification of high
risk chemicals and occupational groups will be a valuable addition
to the process of determining occupational risk, and as input to
the identification and prioritization of possible research efforts.
Although both data collections used as the basis for these indices
have significant limitations in their coverage and in the consis-
tency of the data they contain, they appear to be the most
comprehensive data collections of their kind in the world.
Furthermore, the algorithms and software developed to make use of
the available RTECS and NOHS data should continue to prove useful
when better data are available for future algorithm development,
and for the machine processing of large scale toxicological data
bases.

The model produces four specific types of indices, which are:

e Hazard Risk Index (HRI), a listing of the chemicals common
to both the RTECS and NOHS data bases ranked by the

* Census Bureau Publication CBP-73-1, U.S. Summary, Country
Business Patterns, 1973.


244
2445
249
2499
25
251
2511
2512
2514
2515
252
2522
253
2531
254
2541
2542
259
2591
2599
26
262
2621
263

WOODEN CONTAINERS

COOPERAGE

MISCELLANEOUS WOOD PRODUCTS

WOOD PRODUCTS, NEC

FURNITURE AND FIXTURES.

HOUSEHOLD FURNITURE

WOOD HOUSEHOLD FURNITURE
UPHOLSTERED HOUSEHOLD FURNITURE
METAL HOUSEHOLD FURNITURE
MATTRESSES AND BEDSPRINGS

OFFICE FURNITURE

METAL OFFICE FURNITURE

PUBLIC BUILDING FURNITURE

PUBLIC BUILDING FURNITURE
PARTITIONS AND FIXTURES

WOOD PARTITIONS AND FIXTURES

METAL PARTITIONS AND FIXTURES
MISCELLANEOUS FURNITURE AND FIXTURES
VENETIAN BLINDS AND SHADES
FURNITURE AND FIXTURES, NEC

PAPER AND ALLIED PRODUCTS

PAPER MILLS, EXCEPT BUILDING PAPER
PAPER MILLS, EXCEPT BUILDING PAPER
PAPERBOARD MILLS

C-7


of that of uncontrolled exposures, and the (0.5 + 0.5 PFT) factor
reduces the significance of part-time exposures to one-half of
that of full-time exposures. The constant K is used to remove
leading zeroes in the resulting index number. The algorithm
produces an AHRIN that has a direct relationship to the potential
risk posed by the hazard: the higher the AHRIN the higher the
potential health risk to the workforce.

Computer programs to operate this algorithm and display the data in
standard format (flow-charted in Figure 7) were developed and tested.

A sample page from an Adjusted Hazard Risk Index is shown in Figure 8.
Complete examples of the AHRI are provided in microfiche form (See page E-3).

As shown in Figure 8, the data display format for the AHRI is in
several parts, as follows:

(1) The first line of the heading at the top of each page
identifies the index as the HRIN trial and run* from
which the HRIN input came, the date of the RTECS and
NOHS file update versions used, the date of the AHRIN
printout, and the page number within the specific AHRI
printout.

(2) The second line of the heading at the top of each page
repeats the multiplier values and "NEO OPTION" settings
used in the source HRIN.

(3) The third line of the heading at the top of each page
repeats the constant values and "CAR OPTION" settings
used in the source HRIN.

(4) The entry for each linked chemical consists of two lines
of data. The first line gives the sequence (rank-order)
number for the chemical, its RTECS and NOHS identifica-
tion codes (in RTECS/NOHS format), and the prime RTECS
name for the chemical. The second line gives the sequence
(rank-order) number for the chemical in the source HRIN,
the source HRIN value used, the exposure extent values
used (PES, PEN; PC, PFT), and the final AHRIN calculated.

For details on the computer programs which produce the AHRI, see
Appendix A.

* The words "trial" and "run" and attached numbers related to the
HRIN are internal tracking system references which further identify
the specific source HRIN and the "dial settings" used to produce
it. (See discussion in Appendix A for CIORICAL and CIORISUM
programs.)

36


961 FIREMEN, FIRE PROTECTION
962 GUARDS AND WATCHMEN

D-10


3421
3423
3429
343
3432
3433
344
3441
3442
3443
3444
3446
3449
345
3451
3452
346
3461
347
3471
3479
348
3481
349
3491

CUTLERY

HAND AND EDGE TOOLS, NEC

HARDWARE, NEC

PLUMBING AND HEATING, EXCEPT ELECTRIC
PLUMBING FITTINGS AND BRASS GOODS
HEATING EQUIPMENT, EXCEPT ELECTRIC
FABRICATED STRUCTURAL METAL PRODUCTS
FABRICATED STRUCTURAL STEEL

METAL DOORS, SASH, AND TRIM
FABRICATED PLATE WORK (BOILER SHOPS)
SHEET METAL WORK

ARCHITECTURAL METAL WORK
MISCELLANEOUS METAL WORK

SCREW MACHINE PRODUCTS, BOLTS, ETC
SCREW MACHINE PRODUCTS

BOLTS, NUTS, RIVETS, AND WASHERS
METAL STAMPINGS

METAL STAMPINGS

METAL SERVICES, NEC

PLATING AND POLISHING

METAL COATING AND ALLIED SERVICES
MISC. FABRICATED WIRE PRODUCTS

MISC. FABRICATED WIRE PRODUCTS

MISC. FABRICATED METAL PRODUCTS

METAL BARRELS, DRUMS, AND PAILS


OCCUPATIONAL GROUP RISK INDICES

The occupational group indices represent accomplishment of the final
NIOSH goal in development of this model - to provide a mechanism for
the assessment of the relative health risk borne by various occupa-
tional groups due to chemical exposure.

The occupational group risk algorithms provide a rank-ordering of
the relative risk associated with specific occupational groups on
the basis of the HRIN's of the chemicals to which each group is
exposed, and the incidence, duration and control of exposure data
specific to that group.

An estimate of the relative risk borne by an occupational group is
provided in numerical form by the occupational group algorithms.
This number is used to rank-order the occupational groups in
descending order of relative health risk. The resulting list pro-
vides the assessment of occupation group aggregate risk necessary
to establish priorities in the study and correction of health risks
associated with occupational groups.

Occupational Group Risk Algorithms

Algorithm Development

The objective of the Occupational Group Risk Indices are to rank
the occupational groups covered in the National Occupational Hazard
Survey on the basis of the potential toxicological risk to workers.
Since workers in a given group are exposed to a number of chemical
hazards the risk associated with each occupational group can best
be approximated by summing the risk associated with each of these
chemicals. Since the risk posed by one chemical is described in
the Adjusted Hazard Risk Index algorithm as:

AHRIN = (HRIN) (PES/PEN) (1.0 - 0.9 PC)(0.5 + 0.5 PFT)
the Occupational Risk Index algorithm should take the form:
IRIN or ORIN = (HRIN) (PES/PEN) (1.0 - 0.9 PC)(0.5 + 0.5 PFT) (N)

where: IRIN

Industrial Risk Index Number (at the 2-, 3-, or
4-digit level).

Occupational Risk Index Number.

Number of workers in the occupational group (from
Census Bureau data).

ORIN
N

39