MEDICAL WASTE SMALL QUANTITY GENERATOR MODEL:
A model for estimating medical waste produced
by private practitioners at the state level.
By Jacob Schupak.
This report was researched and written at Hazardous
Waste Programs Branch in response to NNEPS question #3302
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EXECUTIVE SUMMARY.
This model is based primarily on total county population-
rather than an actual number of practitioners- that allows the
amounts of medical waste produced by physicians, dentists, and
veterinarians to be projected for any state. For physicians and
dentists, the calculations are done on the county level and all
counties are then summed up to obtain a total figure for the
state. A different methodology is used to calculate the amount
produced by veterinarians, with that figure being added to the
first to obtain a projection for the total amount of medical
waste produced yearly by all three types of practitioners.
Table A* Summary of all projected annual amounts of medical
waste for two states, New York and New Jersey.
NEW
YORK
NEW
JERSEY
Units
physician
dentist
vet.**
physician
dentist
vet.**
Ibs./year*
1,846
1,928
538
7,512
7,776
242
tons/year
923
964
269
376
389
121
tons/month
76.9
80.3
22.4
31.3
32.4
10. 1
of total
43
45
12
42
44
14
TOTALS— >
N.Y: 4,342,000 Ibs./Year
2,156 tons/year
179.6 tons/month
N.J; 1,772,000
886 tons/year
73.8 tons/month
(* = figure x 1000) (** = veterinarian)
In creating the model, it was first necessary to show that
using various population parameters (sex, race, age, ethnicity)
to calculate the number of visits yearly for any county, yields
figures that closely approximate those obtained by merely using
total population alone. This was done by applying available
national visit rates for all physicians and dentists (15-19, 33)
to all of the four parameters mentioned above, for five test
counties. Since rates were available for a number of physician
specialties- general & family practitioners, internists,
obstetrician/gynecologists and pediatricians- these were applied
to the aforementioned parameters at the county level to obtain a
projected number of annual visits to those particular
• i j *
specialties.
Age was the only parameter that was found to give a
significant difference (see table 8, page 15)- defined as a > 10
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change from the original number of visits based on total
population alone- but only for pediatricians, not for other
physician specialties or dentists. Age was therefore used as a
population parameter for calculating the annual number of visits
to this specialty, but should only be used in cases where the
segment of the population > 14 years old makes up 80% or more of
the total county population. Race and sex should also be used as
parameters in some situations.
Race should only be used as a parameter if the black
population is known to constitute 30% or more of the total
population in any county. Sex was found to generally have no
effect, this probably being due to the fact that all counties
tested have a proportion of males to females being an almost
exact 49:51 ratio. If the proportion of females begins to exceed
49% in any county, the difference obtained (in projected visits)
may then become significant since the female visit rate is
slightly higher than that for males. For all parameters, when
borderline cases (eg, with respect to the conditions above)
occur, the parameter in question should be applied to a number of
test counties (no more than 5) to test for any significance.
After the number of visits is obtained, the next step is to
multiply it by the amount of medical waste (in pounds)/patient
visit (representing the average amount of medical waste that each
patient brings with them) to obtain a figure for the amount of
medical waste generated annually. This is done for all physician
specialties (those listed above & a category including all the
rest), for dentists, and both are added to obtain the physician
and dentist total. It was shown that detailed calculations for
the largest (most populous) county can be "scaled down" to obtain
to obtain the total average amount for all other counties, thus
only one rigorous set of calculations is all that is required
(assuming all conditions concerning the population parameters
discussed above are met). The amounts of medical waste generated
from physician and dental visits, is projected by taking 49% and
51% out of the total figure for average waste respectively. The
amount of (average) waste generated by veterinarians was
calculated by multiplying the average medical waste/calendar
month (pounds monthly per vet.) by the number of practicing
veterinarians, since projecting patient visits in this case is
not possible.
Since the model calculated the amount of medical waste
independent of the actual numbers of office based physicians and
dentists in any given county, when comparing calculated amounts
between two counties (using this model) one must first make sure
that the counties being compared have similar numbers of both
types of practitioners. If this turns out not to be the case,
individuals in the underserved county may utilize private
(physician or dentist) health care in a neighboring county. With
the help of computer modeling- when used in conjunction with this
model- it may become possible to compare any two counties. Such
"migration" of medical waste across county lines may also come
about as a result of a number of factors that have little or no
predictive value (eg. personal preference).
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Table of contents.
Page,
I INTRODUCTION 1
1. Medical waste defined 1
2 . Need for a model 2
3. Need to show differences 2
4 . Inherent problems 3
II THE MODEL 3
1. Estimating the annual number of visits to office
based physicians at the county level 3
A. Number practicing and visits 3
B. Method 4
C. Example: Queens County 6
1) RATES METHOD 6
a) Total population 6
b) Sex 7
c) Race 8
d) Age 8
2 ) PROPORTIONING METHOD 10
a) Total population 10
b) Sex 11
c) Race 11
d) Age 12
3 ) RESULTS 13
D. All other specialties 17
2. Estimating annual visits to dentists at
the county level 19
A. Number practicing and visits 19
B. Example: Queens County 20
1) RATES METHOD 20
2 ) PROPORTIONING METHOD 20
3 ) CRUDE RATE METHOD 22
3 . Veterinarians 25
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4. Calculating amounts of medical waste 25
A. Inherent problems 25
1) Waste per patient visit 26
a) Physicians 26
b) Dentists 27
c) Veterinarians 27
2) EXAMPLE: Calculating the amounts of medical
waste produced by privately practicing
doctors,dentists and veterinarians,
in Queens county 28
5. Seal ing down 30
A. Showing no difference 30
1) Method 30
B. Use of average waste amounts 32
1) Method: physicians and dentists 33
2) Calculated amounts of medical waste for
New York: physicians and dentists 35
3) Veterinarian medical waste 36
4) Total medical waste: New York 37
III APPLYING THE MODEL: NEW JERSEY 37
1. Physicians and dentists 37
2 . Veterinarian 39
3 . total waste 39
IV DISCUSSION 39
1. Uses 39
2 . Improvements 41
BIBLIOGRAPHY 42
LIST OF CONTACTS 46
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MEDICAL WASTE SMALL-QUANTITY GENERATOR MODEL:
A model for estimating medical waste produced by
private practitioners at the state level.
I INTRODUCTION;
The rash of beach closings which occurred during the past year
has served to effectively focus public attention on the nations
waste management problems. Along with other material- such as wood,
municipal garbage and other solid wastes- various types of medical
wastes were discovered washed up on beaches in and New York, New
Jersey, and in several Northeast and Great Lakes states. The
medical waste included various syringes, tubings, and blood
encrusted vials. It was suggested at the time that some of the
syringes may have been discarded by I.V. drug users, but the
subsequent finding of unused syringes and marked prescription
bottles, as well as blood containing vials, seemed to implicate
medical facilities rather than any particular individual as the
source. In addition, the present widespread fear of AIDS served
only to fan rather that alleviate the fears of the beach going
public (1,2,3).
The washups began in 1987, but it was only after the most
recent occurrences during the 1988 beach season- one in which a
vast majority of bathers avoided the beaches completely -that local
legislatures and ultimately congress, took legislative
action. This was culminated on November 2, when President Ronald
Reagan signed the Medical Waste Tracking Act of 1988 (H.R.3515-
commonly referred to as MWTA '88) into law (4). The act mandates
that EPA set up a pilot program to track the disposal of medical
waste in New York, New Jersey and the Great Lakes states, marking
the first federal response to the much publicized beach washups
(4,5,6). Part of the information needed to implement such a
program is a knowledge of the amounts of medical waste that are
generated from various sources. The Medical Waste Generation and
Management Study was conducted by EPA in late 1988 to provide an
initial estimate of the medical waste problem in New York and New
Jersey (7).
1. Medical waste defined.
The survey enabled preliminary estimates to be made on the
amounts of medical waste produced in New York and New Jersey by
small quantity generators as defined in MWTA'88 as those producing
50 Ibs. per calendar month or less of medical waste. For the
purpose of the survey, medical wastes were defined as those which
fall into the following listed categories:
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A. Cultures and stocks of infectious agents and
associated biological.
B. Blood, blood products and body fluids (other than
urine) at least 20cc. of liquid volume per vessel.
C. Pathological wastes consisting of tissues, organs,
body parts (including products of conception) and
wastes discarded after surgery, obstetrical
procedures, autopsy, and laboratory procedures.
D. Needles and syringes or any other laboratory
articles (ie. sharps) that might cause punctures or
cuts including intravenous tubing with needles
attached, vacuum collection containers/tubes
containing blood, slides, etc..
E. Carcasses, body parts, and bedding of all research
animals that were intentionally exposed to
pathogens.
F. Solid wastes generated from rare, unusual or special
cases involving highly communicable diseases.
G. Wastes generated as a result of renal dialysis,
including tubing and needles.
H. Other discarded materials associated with patient
care, ie. disposable diagnostic supplies, cotton
balls, laboratory aids.
2. Need for a model.
Although the aforementioned survey did provide preliminary
estimates on medical waste production for Region II, the results
were based on a lengthy survey and numerous interviews. There was
still the need for a small generator model that can use available
sources of information to arrive at similar estimates
for the amounts of waste defined as medical generated by small
quantity generators, dentists and veterinarians in particular, in
New York and New Jersey. Ideally, the model should be applicable
to any state and be predictive using a wide variety of conditions.
3. Need to show differences.
In order to create such a model, there is an obvious need to
demonstrate that using one set of population parameters compared
to another (ie. age versus sex) will indeed affect/not affect the
outcome of the estimate. Unfortunately, many possible comparisons
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often cannot be made due either a lack of information, or
incompatibility of information from various sources. All such
differences, or a lack thereof, will be illustrated as they occur.
4. Inherent problems.
There are two main problems that need to be taken into account
when constructing a medical waste model for private practitioners:
The mobility of the practitioners under consideration, and the
variability in the amount of medical waste produced from one
practitioner to another of the same type (ie. specialty).
There is no ideal way of classifying private practitioners
(ie. doctors, dentists and veterinarians) according to geographic
region. Unlike hospitals- which must prove to the state that they
are needed in that area before opening- private practitioners can
set up practice anywhere they wish (8,9). The problem of
variability between the same type of practitioner, stems from the
fact that the number of patients seen in any period of time- when
comparing within the same specialty- may vary widely from one
practitioner to another. Using the "average" number of patients
as a factor in the model, may therefore not yield a realistic
estimate for medical waste produced (8).
Both these problems can be overcome by looking at the average
amount of medical waste each patient generates during an average
visit, since this is a measure that would vary much less from one
practitioner to another (within the same specialty) regardless of
location (8) . Of course it then becomes necessary to first
estimate the number of visits, and then multiply out both to
obtain an estimate for the amount of medical waste produced. By
applying these parameters at the county level (within a state), a
distribution within that state may then be obtained. The
methodology utilized for this project will be detailed in following
sections.
II THE MODEL:
1. Estimating the annual number of visits to office based
physicians at the county level.
A. Number practicing and visits.
According to the 1985 summary of the National Ambulatory
Medical Care Survey (or NAMCS) there was an estimated 636.4
million office visits made to non-federally employed office based
physicians in the U.S. between March 1985 and February 1986.
This represents a 60 million increase since 1980, the visit rates
have however remained fairly constant since then (10,11). This
includes visits to both doctors of medicine (M.D.'s) and
doctors of osteopathy (D.O.'s), with almost 95% of those visits
to the former (10). Physicians of osteopathic medicine are
designated as physicians and surgeons- they work within the same
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specialties and utilize the same treatments and techniques as
doctors of medicine. The only difference is that D.O.'s recognize
the importance of the musculoskeletal system in health and disease,
and often use their hands to diagnose/correct structural problems
relating this body system (12).
The numbers of private practicing physicians can be broken down as
follows (12,13,14):
Table 1. The number of practicing physicians in New York
and New Jersey.
TYPE
M D.
D.O.
TOTAL:
NEW YORK
29,118
1.074
30,192
NEW JERSEY
10,961
1.569
12,530
U.S. TOTAL
552,716
28.403
581,119
B. Method.
The number of estimated yearly visits was calculated (for
physicians) by using national visit rates for different groups
taken from summary reports on the various medical specialties,
published as part of the National Ambulatory Medical Survey (or
NAMCS) (15-18) . Rates were available for only five medical
specialties, but according to the 1985 NAMCS summary (10) these
made up almost 70% of all office visits to all physicians. The
rates of patient visits to all of the specialties used differ
widely when comparing among specialties (within the NAMCS), with
the highest total rates being those for General and Family
practitioners (85.7 visits/100 persons) and Internists (32.4
visits/100 persons)(15,16). It is obvious then that pooling much
of this specialty data would yield either an over, or under
inflated estimate of yearly office visits. It should also be taken
into consideration that one cannot assume that physicians
practicing in different specialties generate the same or similar
amounts of medical waste, since services rendered (eg. procedures
done) may differ greatly from one specialty to another. Indeed,
a review of physician data received from the Medical Waste
Management Study (7) done at EPA, seemed to support this
assumption. Unfortunately, due to the lack of quantity- as well
as quality- of the survey data (ie there were differences in the
way physicians applied the medical waste categories to their
respective practices) the amount of medical waste on a per-patient
per-visit basis could not be obtained for all specialties for which
information on visit rates was available.
Along with the total population visit rates- this referring
to the visit rate for a specialty that is adjusted for all ages and
sex- those for the parameters of various sub-populations were also
available. These rates were applied to the matching sub-
populations at the county level, and the resulting totals were
compared for differences. Out of New York States' 62 counties,
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five were selected at random, taking only population size into
consideration (eg. a county closest to 100,000 in population was
chosen on that basis alone, since a random selection might ensure
unbiased results). Within these five test-counties, the rates for
various medical specialties were applied to the various sub-
populations (also reffered to as population parameters). Total
county population, sex, race, ethnicity, and age were the sub-
populations used (when the rates for each were available).
Medical specialists are physicians who concentrate on certain
body systems, specific body structures and scientific techniques
to diagnose and/or treat certain disorders. A physician can be
certified as a specialist in a field of medicine after having
completed the training required by the associated specialty board
(eg. an internist would be certified to practice internal medicine
by The American Board of Internal Medicine). There are 23
specialty boards which are recognized by the American Board of
Medical Specialties that can grant such accreditation.
The medical specialties used in estimating patient visits to
physicians include (20):
General and Family Practitioner: The medical specialty that
provides health care within a family context. The only difference
between General practice and Family practice is that the latter
must have at least one year of residency training. General
practitioners are currently being phased out.
Internist: Similar to the above, except that general internists
also function as consultants to other specialists and are
competent to handle critically ill patients and non-surgical
disorders in an emergency room setting.
Pediatricians: Physicians trained in the care of individuals from
childhood to young adulthood.
Obstetrics and Gynecology: Physicians trained in the
medical and surgical care of the female reproductive
system and associated disorders.
General Surgeons: Physicians trained in the medical and surgical
care of the female reproductive system and associated disorders.
Applying available rates to the above five specialties will
be referred to as the RATES METHOD. Visits to all other
specialties, which comprised of about 30% of all total visits in
1985 (10) , were estimated by taking a proportion out of those
visits based on each counties total population. A variation of this
method (the second method) was used to calculate a new rate, which
was then compared to the one that was used to screen for large
variations (the rates method). This is necessary because the
assumption that the rates on both levels (county vs. national) are
similar, must be tested. The number of visits calculated using
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this second method was compared to that using the first method.
The second method will be referred to as the PROPORTIONING METHOD.
Both will be illustrated below for one county and specialty-
C. EXAMPLE: Queens County.
Specialty: General and Family Practitioners.
1) RATES METHOD.
This approach adjusts the 1980 county population figures for
each parameter (eg. age, sex, etc. . .) (21,22) , and multiplies the
adjusted figure by the appropriate rate to obtain a number of
visits based on that parameter. The 1980 population had to be used
as a base for calculating 1987 estimates since all updates on this
type of census data are done only on the national level and
sometimes regional (but not in this case), which leaves only total
population available at the county level. Therefore one
must rely on the assumption that the percentages of various
segments of the population used will be similar, since the amount
of change for the various parameters will not be fully known until
completion of forthcoming census (23).
The population parameters used were those for which rates
could be obtained for the five specialties used (15-19).
Those used for General and Family Practitioners (15) include:
total population, sex, race, and age. Ethnicity was used only for
the last three specialties listed (see page 5) . The number of
visits was calculated for each characteristic, and the total
population figure was used as the base for comparison. Only if the
number of visits- calculated using any of the population parameters
-differed from the total population estimate by more than 10%, was
it considered significant. As it turned out most variations were
closer to half that cutoff percentage difference.
For estimating visits based on total population, current available
total population estimates for each county were used (24).
a) Total population.
-of Queens County: 1,920,700 persons (1987) (24).
Visit rate/100 persons(per year)= .86 (or 86%) (15).
.86 x 1,920,700 persons= 1,651,802 visits/year.
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b) Sex.
Before applying the sex specific rates to the male and female
populations of Queens, the populations were first adjusted to 1987
levels.
This is done by:
First dividing the population of each characteristic (21,22)
by the total county population (both for 1980) to obtain the
proportion (percent out of the total population) for each separate
parameter (eg. the percent of the total Queens population that all
males in Queens represent). Then multiplying each proportion by the
total 1987 county population (21,22) to obtain the respective
amounts to be added to each, to make the adjustment.
Example: Adjusting 1980 male Queens population to
current levels:
1987 TOTAL Queens population= 1,920,700 persons (24).
1980 TOTAL Queens population= 1,891,325 persons (21).
1980 Male; 878,181 (21).
1980 Female: 1,013,144 (21).
Adjusting males:
(878,181 /I,891,825) X 1,920,700= 891,820 Males
for Queens, 1987.
Adjusting females:
(1,013,144 /I,891,325) X 1,920,700= 1,028,880 Females
for Queens, 1987.
The adjusted populations for each parameter (table 2: those
for sex in this case- column 4 below) are then multiplied by their
respective rates (column 5) to obtain an estimated number of
visits for each (column 6), which are then added to obtain a total
for all visits using that parameter.
Table 2. Summary of calculated physician visits,
calculated separately by sex, for Queens county.
SEX
MALE
FEMALE
1980 POPULATION
878,181
1,013,144
% OF TOTAL
54
54
1987 POPULATION
891,820
1,028,880
RATE
71
100
VISITS
633,194
1,028,880
TOTAL VISITS: 1,622,074
CHANGE FROM TOTAL POPULATION: 0.62%
RESULT: NOT SIGNIFICANT.
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c) Race.
All adjustments are made the same way except that three need to be
done (one each for the white, black, and other categories) instead of
the two (for each sex) above. All rates are for visits/100 persons (15)
unless otherwise stated.
Table 3; Summary of physicians calculated using race.
RACE
WHITE
BLACK
OTHER
1980 POPULATION
1,335,805
354,129
201,391
% OF TOTAL
71
19
11
1987 POPULATION
1,356,552
359,629
204,519
RATE
88.6
76.4
30.2
VISITS
1,201,905
247,757
61,765
TOTAL VISITS: 1,538,427
CHANGE FROM TOTAL POPULATION; 6.8%
RESULT: NOT SIGNIFICANT.
d) Age.
All calculations were made the same way as above, except thta the
rates for age were given for five age groups (15), so the rates for the
first two groups (see table 4 below) were averaged to simplify
calculations. Results using the five age groups are shown below, since
it was first necessary to show that no significant differences arise
from averaging the first three groups shown.
Table 4! Summary of physicians calculated using five age groups.
AGE
< 3
3-14
15-44
45-64
> 64
1980 POPULATION
68,454
280,602
816,458
444,482
281,328
% OF TOTAL
3.6
14.0
43.0
23.0
14.0
1987 POPULATION
69,
284,
517
960
829,139
451,385
285,697
RATE
90.2
42.0
77.0
109.0
151.0
VISITS
62,704
119,683
638,437
492,101
431,402
TOTAL VISITS: 1,744,236
CHANGE FROM TOTAL POPULATION: 5.6%
RESULT: NOT SIGNIFICANT.
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To combine the first three categories, the total number of visits
nationally had to be divided by the total number of persons (in the
U.S.) to give an averaged rate for both groups. This was done for all
specialties where age groups needed to be combined.
Example: Combining the first two age groups above.
# of national visits
(for the first three age groups combined)= 97,004,500
Total number of persons in the nation = 154,201,000
(15)
(15)
97,004,500/154,201,000= .63, or 63 visits per 100
persons in this age group
(all those < 45 years old).
The following is the result obtained by using three age groups:
Table 5; Summary of physician visits calculated using five age groups.
AGE
< 45
45-64
> 64
1980 POPULATION
912,947
444,482
281,328
% OF TOTAL
63.0
23.0
14.0
1987 POPULATION
1,183,616
451,385
285,697
RATE
63.0
109.0
151.0
VISITS
745,678
492,010
431,402
TOTAL VISITS: 1,669,061
CHANGE FROM TOTAL POPULATION: 1.0%
RESULT: NOT SIGNIFICANT.
CHANGE FROM USING FIVE AGE GROUPS: -4.3%
RESULT: NOT SIGNIFICANT.
It is therefore apparent that the combining the age groups had no
significant effect on the outcome.
Visits to all other specialties were calculated using the same
method with the rates used coming out of the same survey data (15-19).
Results of these calculations are shown in tables 7 and 8 (pages 15-17).
The only changes in the above calculations were minor, and involved
using different age grouping rather than any changes in method (see Top
ofr page 13).
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10
2) PROPORTIONING METHOD.
For the second method- taking an appropriate proportion out of the
national number of annual visits, by specialty, based on a
county population for the parameter being used- can be broken down into
the following three steps (25) :
Step 1: For the parameter being used, the percent of the national
population that the county population represents must first be
calculated (1980 population was used for both, since they were the only
ones available for all the parameters used) . This is accomplished by
dividing the county population (21,22) by the national population for
the parameter (26-30).
Step 2: The resulting proportion in then multiplied by the number of
visits nationally for the specialty (15-19) to obtain the number of
projected annual visits (to that specialty). The result is then
compared to the number of visits for the parameter that was previously
calculated using the rates method.
Step 3; The number of calculated visits is then divided by the
population of queens (both for the parameter under consideration) to
obtain a rate that can be compared to the national rate. This was done
because the assumption that the rates will be similar to the national
rates for parameters used had to be tested (25) . Note that what is
actually being compared are the 1980 visit rates for both the county and
the nation (see a-d below)
a) Total population (General practitioners)
Step 1: Queens population (1980)= 1,891,325 persons (21).
U.S. population (1980)= 222,674,000 persons (26).
[1,891,325 / 222,674,000]= proportion out of the national population
that the Queens population represents.
Step 2: Visits nationally (1980)= 190,850,000 total
visits (15).
[1,891,325 /222,674,000] x 190,850,000= 1,621,022 visits for
Queens(1980).
Change from original visit estimate: 3.5% NOT SIGNIFICANT
Step 3: number of visits*, Queens /Queens population
(* calculated in step 2 above) (both for 1980).
1,621,021 /I,891,325= .857 Change from rate used (.86)= .003 or .3%
NOT SIGNIFICANT
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11
b) Sex.
Calculations are made the same way as above, except that two sets
need to be made to compare the male and female rates separately.
For males; Male Queens population (1980)= 878,181. (21).
-Total U.S. population (1980)= 107,429,000 (26).
-Total U.S. visits = 76,132,500 (15).
[878,181 /107,429] x 76,132,500= 622,346 male visits (in Queens).
622,346 /878,181= .71 = Male visit rate for queens.
Change from rate used (.71)= .0% NOT SIGNIFICANT
For females: Female Queens population (1980)= 1,013,144. (21)
-Total U.S. population (1980)= 115,244,000 (26).
-Total U.S. visits = 76,132,500 (15).
[1,013,144 /115,244,000] x 114,720,000= 1,008,559 female visits
(Queens).
1,008,559 /1,013,144= 1.00 = Female visit rate for queens.
Change from rate used (1.00)= .0% NOT SIGNIFICANT
Total visits (male + female)= 1,645,079 visits
Change from original visit estimate: 1.0% NOT SIGNIFICANT
c) Race.
Three sets of calculations are needed here.
White; Queens population (1980)= 1,335,805 (21).
-Total U.S. population (1980)= 191,052,000 (26).
-Total U.S. visits = 169,230,000 (15) .
[1,335,805 /191,052,000] x 169,230,000= 1,184,677 white visits.
1,184,677 /1,133,5805= .886 = white visit rate for Queens.
Change from rate used (.886)= .0% NOT SIGNIFICANT
Black; Queens population (1980)= 354,129 (21)
-Total U.S. population (1980)= 26,107,000 (26).
-Total U.S. visits = 19,948,500 (15).
[354,129 726,107,000] x 19,948,500= 271,300 black visits.
271,300 /354,129= .77 = black visit rate for Queens.
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12
Change from rate used (.764)= .5% NOT SIGNIFICANT
Other: Queens population (1980)= 201,391 (21).
-Total U.S. population (1980)= 5,515,000 (26).
-Total U.S. visits = 1,667,000 (15).
[201,391 /5,515,000] x 1,667,000= 60,845 other visits.
60,845 /201,391= .320
Change from rate used (.302)= .0% NOT SIGNIFICANT
Total visits (white + black + other)= 516,822
Change from original visit estimate: 6.7% NOT SIGNIFICANT
d) Age.
Three sets of calculations are done here for the thee age groupings
used.
< 45 years old; Queens population (1980)= 1,813,616 (21).
-Total U.S. population (1980)= 154,201,000 (26).
-Total U.S. visits = 97,004,500 (15).
[1,183,616 /154,201,000] x 97,004,500= 746,935 visits, this age group.
746,935 /1,183,616= .63 = visit rate for this age group
Change from rate used (.63)= .0% NOT SIGNIFICANT
45-64 years old; Queens population (1980)= 444,482 (21).
-Total U.S. population (1980)= 43,963,000 (26).
-Total U.S. visits = 47,729,000 (15).
[444,482 /43,963,000] x 47,729,000= 447,290 visits, this age group.
447,290 /444,482= 1.07= visit rate for this group.
Change from rate used (.09)= -1.8% NOT SIGNIFICANT
> 64 years old; Queens population (1980)= 281,328 (21).
-Total U.S. population (1980)= 24,512,000 (26).
-Total U.S. visits = 36,933,500 (15).
[281,328 /24,512,000] x 36,933,500= 406,267 visits for this age group.
406,267 /281,328= 1.44 = visit rate for this age group.
Change from rate used (-.07)= -4.5% NOT SIGNIFICANT
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13
Change from original visit estimate: -4.1%
(see total population)
NOT SIGNIFICANT
Calculations for the other specialties were performed in the same
manner as for General and Family practitioners with few exceptions.
For Internists, Obstetrician/Gynecologists, and General Surgeons, the
age groups were divided up as: < 15, 15-44, and > 44 years old.
For Pediatricians: < 6, 6-14, and > 14 years old.
All age groups were congregated so as to keep the highest rates
intact, as was done for General and Family practitioners above (see
tables 3 and 4) . Information on ethnicity was only available for
internists, obstetrician/gynecologists, and general surgeons only. The
calculations were made by applying the available rates (as was done
above) to the two categories (hispanics & non hispanics) within that
parameter (16,18,19). For Obstetrician/Gynecologists only female
population was used, since they account for 99% of all visits to this
specialty (19).
3) Results.
For both methods used, the only significant difference (defined as
a > 10 % variation from the total population estimate) was observed
for pediatricians when using age. Since three out of five test counties
gave this result (see table 8) , age will therefore be used when
calculating visits for that specialty. It is interesting to note that
Kings County which has a larger population than Queens County (see table
7 page 35) had a much lower difference (8% versus 17% for Queens). This
may be because the two highest rates- 210 and 57 (both visits per 100
persons in the population) for the < 6 and 6-14 age groups respectively
-were applied to smaller segments of the population for Kings County.
Table 6; Summary of rates and percentage of black population for Kings
and Queens counties.
AGE*
< 6
6-14
> 14
RATE
visits/100 persons
210
57
3
% of total county population
KINGS COUNTY
7
11.1
81.9
QUEENS COUNTY
9.3
14.0
77.0
(* years old)
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14
By looking at Broome county ( -10.3 % significance), it was
observed that the highest age group ( > 14 years which was applied to
the highest rate) made up 80% of the total population, compared to 82%
for Queens county ( -18% significance). It would be prudent then to
consider using age in calculating county visits to pediatricians when
the >14 age group makes up 80% or more of the county population.
It was also observed that as the black population of a county shifts
from 19% of the total to 32%, the observed difference goes from 7.4% to
-9.4% for Queens and Kings counties respectively- It would therefore be
wise to consider race as a parameter when the percentage of the black
population begins to exceed 30% in any county- Ethnicity never reached
a significance level of more than 4%, which was observed for Kings
County (see table 8), which also had the highest hispanic population of
all the counties (18% of the total). It is obvious then that the ethnic
population will have to be a considerably larger percentage to make a
difference, possibly approaching as high as 40% (which is slightly more
than twice that of Kings County). It was also observed that the ratio
of male to female individuals from county to county varied remarkably
slight, being an almost constant 49% males to 51% females. This may of
course be due to the fact that only about one tenth of all counties in
the state (N.Y.) were sampled. If it is known (or discovered) that a
county has a ratio that differs appreciably from this- especially if the
number of females (who have the larger rate) is proportionately more-
sex should be used to calculate the number of visits for that county.
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15
Table 7; Summary of estimated physician visits based on
total population, for five test counties, using both methods.
KEY; GP/FP
OBGYN
GEN. SURG
= general and family practitioners.
= obstetrician/gynecologist.
= general surgery.
= not performed. PROPRTNING. = PROPORTIONING
m e
COUNTY
QUEENS
GP/FP
INTERNIST -
PEDIATRICS-
OBGYNN
GEN. SURG.-
BROOME
GP/FP
INTERNIST -
PEDIATRICS-
OBGYNN
GEN . SURG . -
EERIE
GP/FP
INTERNIST -
PEDIATRICS-
OBGYNN
GEN . SURG . -
ONANDAGA
GP/FP
INTERNIST -
PEDIATRICS-
OBGYNN
GEN . SURG . -
KINGS
GP/FP
INTERNIST -
PEDIATRICS -
OBGYNN
GEN . SURG . -
COUNTY POPULATION
1,920,700
209,000
958,300
460,200
2,309,600
RATES
(visits)
1,662,074
622,307
555,082
474,151
263,135
179,740
67,716
60,401
50,786
824,138
310,^89
276,948
235,062
131,436
360,354
149,105
132,998
112,340
64,047
2,001,187
748,310
667,474
563,443
316,415
PROPRTNING.
(visits)
1,593,639
601,197
537,581
485,514
254,424
171,770
64,877
57,949
50,936
-
-
-
CHANGE (%)
-3.5
-3.3
-3.1
2.3
-3.3
-0.04
-4.1
-1.8
0.3
-
-
-
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16
Cable 8; Summary of changes in estimated physician visits showing
observed differences from total population estimate for all
parameters used, differences between estimates for the same
parameter, and the largest rate deviation observed for the five
test counties used.
KEY: R = % change from total population estimate using the RATES
METHOD for a parameter.
P = % change (from the rate derived estimate) using the
PROPORTIONING METHOD.
CH= largest observed % change between corrected and
original rates.
CHART = Characteristic.
GP/FP = General and Family practice.
OBGYNN= Obstetrician/ Gynecologists.
GEN. SURG.= General Surgeons.
(-) = not performed.
ETHN = ethnicity
:OUNTY
:HART
(ueens
iex ---
tece --
.:thn.--
\ge ---
Jroome
Sex ---
lace --
Ethn.--
^ge.
Eerie
Sex ---
Race --
Ethn.-
Age ---
GP/FP
R
0.6
-7.4
1.0
0.0
1.9
2.6
0.2
0.3
-2.0
P
-3.4
-1.4
-4.1
1.9
3.3
-4.7
_
CH
1.8
0.8
4.5
0.2
1.4
-4.6
INTERNIST
R
0.4
-6.6
-5.2
8.1
6.1
3.2
2.4
5.6
0.0
0.7
2.0
6.0
P
-4.1
-1.4
-0.2
4.9
2.1
3.2
0.7
3.1
CH
-0.3
0.0
0.6
3.0
5.0
1.3
2.2
2.0
-
PEDIATRICS
R_
-0.2
-1.9
-18.0
0.0
0.0
-10.5
0.0
0.2
-12.0
P
-2.0
-1.5
2.9
6.5
3.2
0.0
CH
-7.0
0.0
1.0
4.4
0.9
-
1.9
OBGYNN
R
-2.0
-0.6
-4.4
1.6
0.6
-3.2
10.-
0.5
0.5
-0.5
P
-4.2
-4.3
-0.7
2.2
0.2
0.0
CH
-2.0
-2.5
-4.3
7.4
4.3
6.6
-
-
-
GEN. SURG.
R
-0.3
-6.4
2.5
-0.7
0.0
2.3
1.6
0.7
0.1
0.5
-1.3
-4.0
P
4.4
1.4
0.3
-4.1
2.3
3.4
6.5
1.3
CH
0.7
0.2
0.2
-2.0
0.4
1.1
2.3
5.1
-
-
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17
lUNTY
IART
londaga
>x ---
ice --
;hn.--
je ---
ings
5X ---
jce --
thn.--
3e ---
GP/FP
R
i
0.1
0.6
5.4
0.7
-9.4
-7.0
P
CH
-
INTERNIST
R
0.0
1.6
3.1
-0.4
0.0
-10.0
-
6.2
P
-
-
-
CH
-
-
PEDIATRICS
R_
0.0
0.0
-
-9.6
0.6
-0.5
-7.6
P
CH
-
-
-
OBGYNN
R
0.8
0.5
-0.5
-2.5
2.0
-0.6
P
-
CH
GEN. SURG.
R
0.7
1.0
2.3
1.0
0.4
-8.4
-4.0
1.2
P
-
-
CH
-
I>. All other specialties.
As previously mentioned, the above five specialties account
for almost 70% of all office visits to office based physicians in
1985. That leaves 30% or 191,500,000 visits (10) unaccounted for.
Visits to these remaining specialties will be calculated by taking
the appropriate proportion out of the number of national visits
based on total county population.
Example: Queens County.
county population (1987): 1,920,700
-TOTAL U.S. POPULATION (1987): 243,400,000
NATIONAL TOTAL FOR VISITS TO ALL SPECIALTIES
UNACCOUNTED FOR (in 1.) ABOVE:
(21) .
(31) .
191.500.000 VISITS (10).
Total county population is first divided by the total national
population to obtain the appropriate proportion:
[county population (1987)/ U.S. population (1987)]
[1,920,700/ 243,400,000]
The resulting proportion is then multiplied by the number of
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18
national visits to the remaining specialties (10) to obtain a
figure for visits in that county:
[Above] x National total for visits to all remaining specialties
= total visits to the remaining
specialties for the county.
For Queens:
[1,920,700/ 243,400,000] x 191,500,000 visits = 1,511,150 visits.
(nationally) (annual)
When added to the mimber of visits calculated for the other 5
categories (in D., above) the resulting figure is the total number
of visits to physicians for the county:
SPECIALTIES PHYSICIAN VISITS
For 5 specialties: 3,470,500
All other specialties: 1,511,150
TOTAL: 4,981,650 *
To check the total number of visits for the county, the number of
visits per person will be calculated and compared to the national
average. This is done by dividing the number of total county
visits by the number of persons:
Calculating average visit rate per person for Queens:
4,981,650 (Queens) visits*/ 1,920,700 persons** =
2.6 average visits/ per
person per year.
[*= see TOTAL above] [ ** = (32) ]
Calculating the average visit rate for the nation:
619,390,000 visits (national)(10)/ 238,149,000 persons (32)***
= 2.6 visits/per person per year.
[***NOTE: 1985 population was used to coincide with the year for
the number of national visits used].
From the results above, it can be seen that number of county visits
estimated using the rates method added to the "other" category
outlined above, correlates favorably with national data.
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19
2. Estimating annual visits to dentists at the county level.
A. Number practicing and visits.
There were an estimated 466,775,000 visits made to dentists
in 1986 (33) . Aside from those practicing General Dentistry (which
is not itself considered a dental specialty) there are seven
medical specialties that serve the public through private offices.
These include (35):
Oral & Maxillofacial Surgeons: Dentists who are oral surgeons.
Endodontists: Those who work with the root of the tooth
(perform root canal).
Orthodontists: Those dealing with dental work relating to braces.
Pediatric dentists: The dental care of children.
Periodontists: Treatment of gums.
Prosthodontists; False teeth, crowns, caps & bridges.
Oral pathologists; Pathology of the mouth.
Upon obtaining a license to practice dentistry from the state,
a dental graduate can practice exclusively within any of the above
specialties if they acquire the appropriate training to do so
(there are no specific requirements for this). The only limitation
is that they cannot call themselves a specialist, the titles for
dental specialties are reserved for those who complete post-
doctoral training. So a dentist who is a General Practitioner can
practice Endodontics only, but cannot call him/her self an
endodontist (34,35). This obviously complicates matters, since
there will be much overlap in terms of procedures performed.
Another problem is that there is no available information on
visits rates for any of the specialties, only total rates for all
dentists (33). But a specialty breakout is not really necessary,
since General Practitioners make up 85-90% of all practicing
dentists (36,37). A large majority of these (88%) practice
exclusively as part of a private practice (they are office based
dentists)(36). The rates given for dentistry in general, were
therefore applied to the various population parameters (breakdowns)
at the county level. A version of the proportioning method was
also used, as well as a method that applied crude rates available.
All of the three methods were compared for differences. Total
population, sex, race, age, ethnicity were the parameters used.
Although rates for family income and those having private dental
insurance were available, they could not be used because matching
data on the county level could not be found. This is unfortunate,
since both have been suggested to be strong factors in influencing
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20
whether someone will see a dentist or not (38).
B. EXAMPLE: Queens County
1) RATES METHOD.
The rates available for dentists (33) were applied similar to
the way the rates for physicians were applied to respective county
populations. The only difference is that the rates were given for
those persons making 1, 2, 3, 4, and 5-12 visits in the past year.
Each of these rates were multiplied by the respective population
(or parameter within the population) and totaled to give the number
of visits to dentists in the county.
a) Total population.
The rates for each of the number of visits (33) were
multiplied by the total population of the county and summed up to
give a total for the number of visits.
Table 9; Summary of calculated dentist visits for total
population using available visit rates.
VISITS
1
2
3
4
5-12
POPULATION*
1,920,700
VISIT RATE**
22.8
19.0
5.1
3.4
5.4
NUMBER OF VISITS
43,919
729,866
293,867
261,220
414,871***
TOTAL: 1,999,548 VISITS
**Visits/ 100 persons.
***Figure multiplied by 8, and the divided by 2 to give the
figure shown. This was necessary because the number of visits
will probably be closer to 5 than 12 (38).
Calculations for sex, race, and ethnicity were performed the same
way, with the only difference being that more than one set of
calculations needed to be done for each (eg. for sex: the male and
female rates were applied to each separately and the calculated
visits from each added). Not enough information was available on
visit rates for various ages to apply the RATES METHOD to the
county population.
2) PROPORTIONING METHOD.
The total number of national visits was multiplied by the
proportion of out of the national population that a county
represents, to obtain a number of dental visits for that county.
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21
TOTAL POPULATION - Queens (1987): 1,920,700 persons. (24)
- Nation (1986): 241,078,000 persons* (31)
TOTAL NUMBER OF NATIONAL VISITS: 466,775,000 (33)
[* National population for 1986 used because all rates used came
from a report of the same year (33)]
To obtain the proportion out of the national population that the
county population represents, the total county population (24) is
divided by the national population (31):
[1,920,700/ 241,078,000] = proportion out of the national
population that the county population
represents.
This proportion is then multiplied by the total number of visits
nationally (33) , to obtain a figure for the number of visits in
that county:
[1,920,700/ 241,078,000] x 466,775,000 national visits
= 3,718,843 visits to dentists in
this county-
CHANGE FROM ORIGINAL ESTIMATE [See 1) above]: 216%
RESULT: SIGNIFICANT
Calculations for sex, race, and ethnicity were performed the same
way, except that more than one set of calculations was needed for
each (eg. for sex, males: the proportion of males was calculated
by dividing the county male population by the national male
population, then multiplying by the total number of visits made by
males in the U.S. to get the number of visits made in the county
for males). For age, three age groupings were used: < 5, 5-44, and
> 44 years (to keep the highest rates intact).
[Note: The numbers of visits nationally (all persons, and broken
down within various population parameters) is given in the 1986
Report on the Use of Dental Services (33)].
The resulting change above is obviously very large. For all
other parameters used similar large differences were observed. Part
of the reason may be faulty recall of those surveyed (in 1986 by
NHIS) as well as a significant number of unknowns (33,38). The
national number of visits to dentists in 1986 (see above) was
almost twice the total U.S. population for that year. When the
two estimates above are compared to twice the Queens population
(3,841,400), the RATES derived estimate is 50% smaller, whereas
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22
the difference for the PROPORTIONING method is only 3% less.
The rates method should therefore not be used.
3) CRUDE RATE METHOD.
This method takes the crude rates available (eg. 2 visits per
person per year) and multiplies it by the county population to get
the number of visits:
CRUDE NATIONAL VISIT RATE: 2 visits per person/year (33) .
(for total population)
l,920,700(Queens population) x 2 per person/year= 3,841,400
visits/year
CHANGE FROM PROPORTIONING ESTIMATE [See 2) above]: 3.1%
RESULT:NOT SIGNIFICANT
The last two methods seem to agree very favorably- The same was
done for sex, race, ethnicity and age (using the same age groups
as in 2) above). All results for the five test counties- using all
methods- are summarized in tables 10 and 11 below. The base
comparison estimate for determining significant differances between
different population characteristics will be the one that was
computed using the proportioning method since the rates method (the
one that used the available rates) proved unreliable.
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23
Table 10; Summary of calculated dentist visits for total
population, showing differences for various methods
used, for five test counties.
KEY; Change 1 = % change from using RATES method.
Change 2 = % change between CRUDE RATE and
PROPORTIONING methods.
COUNTY
QUEENS
Visits —
Change 1-
Change 2-
BROOME
Visits —
Change 1-
Change 2 -
Eerie
Visits —
Change 1-
Change 2-
Onondaaa
Visits —
Change 1-
Change 2-
METHOD
RATES
1,999,458
206,283
1,066,587
512,201
PROPORTIONING
3,718,843
216
406,351
96
1,855,460
74
891,038
74
CRUDE RATE
3,841,400
227
3
418,000
102
3
1,916,600
79
3
1,916,600
80
3
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24
Table 11: Summary of observed changes for dental visits using all
methods, for the five test counties used.
KEY; 1 = % change from total population estimate
(see table 8) using RATES METHOD for each
characteristic.
2 = % change in the number of calculated visits
between the RATES and PROPORTIONING method,
for each characteristic.
3 = % change between number of calculated visits
between the RATES METHOD and the base
comparison estimate* for all characteristics
(-) = not performed.
Ethn = Ethnicity -
n
COUNTY
Queens
Sex -
Race -
Ethn -
Age -
Broome
Sex -
Race -
Ethn -
Age -
Eerie
Sex -
Race -
Ethn -
Age -
Onondaqa
Sex -
Race -
Ethn -
Age -
1
6.9
- 2.7
- 8.0
—
2.0
1.1
3.4
—
0.0
0.3
1.1
—
- 0.1
24.0**
- 3.5
—
2
5.9
4.6
- 0.4
4.6
6.0
4.6
- 3.6
4.7
6.0
3.5
- 1.4
8.4
0.7
3.4
3.6
2.7
3
0.5
- 4.4
- 2.8
1.5
1.9
8.0
3.4
- 3.9
0.6
0.9
- 0.7
3.5
0.1
2.3
2.3
2.5
see text, pg 22.
** was not considered significant
since all other differences are small
(note adjoining collumns).
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25
3. Veterinarians.
Veterinarians can practice in 41 different professional
activities, including large, small, and mixed animal practices,
which account for the activities of 97% of all self employed
veterinarians nationwide. Veterinarians in private practice make
up 75% of all those practicing. Out of those, 45% are self employed
and in private practice, and 30% work in other types of private
practice (40). This 75% (in private practice) is the proportion
that will be classified as small quantity generators under MWTA'88.
There are 2000 and 900 privately practicing veterinarians in New
York and New Jersey respectively (7).
It should be obvious that there is no real way "patient"
visits can be estimated for this group of private practitioners.
This made it necessary to use of an average amount of medical waste
per month, per practitioner. This figure was then multiplied by
the known number practicing in both states to obtain a figure for
the average amount of medical waste produced by this type of small
quantity generator each month for the respective states. But using
this method does not account for variability with respect to the
number of possible visits monthly (when comparing between
veterinarians engaged in similar practices).
**
4. Calculating amounts of medical waste.
A. Inherent problems.
Estimating the amount of medical waste produced by doctors,
dentists and veterinarians is inherently difficult because of two
main reasons:
o A wide range of possible patient loads is possible.
o The existence of numerous specialties within each of
the three disciplines.
A privately practicing physician, dentist or veterinarian may work
as many or as few hours as they choose. This may depend on the
physicians age (the older he or she is, the fewer hours they may
be able to work), his location (urban versus rural) or just the
preference of the individual (15-19). This problem is further
confounded by the fact that varying practice specialties (within
each of the three disciplines) perform a varying array of services.
For example, an orthopedic surgeon may see as many as 1000 patients
per week, whereas a typical internist would see closer to 500 per
week (usually less) (7) ; but, the internist may follow up with
treatment at the time of diagnosis, compared to the orthopedist
(whose is a surgeon, and the specialty is designated orthopedic
surgery) whose practice is mainly diagnostic in nature (41). As was
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26
mentioned previously different specialties may also produce varying
amounts of waste due to the inherent nature of the specialty being
considered, and there is no way of predicting where a private
practitioner will go; although there tends to be a concentration
of veterinarians that favors rural over urban areas nationwide
(40) .
Using patient visits in conjunction with the average amount
of medical waste per patient per visit (or per patient visit) will
therefore be the measure used in this model. This value, even
though there may still be a range, will prove to be more useful
since patient load ideally should have no effect on procedures
performed. The only exception may be at the extreme end (eg. a
situation where a practitioner of any type sees far more patients
than the average number) where it is conceivable that procedures
may not be performed as often because of time limitations. The
curve for medical waste should therefore be almost linear, with a
slight decrease possible at the high end (8) .
1) Waste per patient visit.
All information on the amounts of medical waste on a per
patient basis was gleaned from the Medical Waste Generation and
Management Study undertaken at EPA in late 1988. As part of the
study, medical waste questionnaires were mailed to physicians,
dentists, and veterinarians, out of which 482, 159, and 143 were
received in time to be used in the study. But unfortunately, the
information from the physician surveys was inadequate for
determining the differences (on a per-patient per-visit basis) in
the amounts of medical waste produced among various physician
specialties. One problem seemed to be differences in the way many
of the respondents applied the medical waste definition to their
respective practices (7). Another problem- which became obvious
after answering numerous phone inquiries shortly after the surveys
were mailed- was that they were never required to know this type
of information, and so many had to make first time educated guesses
(42) . It was possible in some cases to spot errors (by comparing
responses where the sample size was 10 or more) and therefore make
the necessary corrections.
a) Physicians.
For physicians, the only meaningful sample sizes (n=10 or
more) that could be obtained were those for General & Family
Practitioners (n=12) and Internists (n=13). The mean values for
both came out to .034 Ibs/patient (per visit). A range of .03 to
.05 Ibs/patient (per visit) was used (it was observed that
some values for both exceeded .04 even after all corrections were
made). Since the above two specialties- combined with the remaining
three used in calculating the number of patient visits -account for
almost 70% of all patient visits to physicians, it was decided to
apply the mean figure above to the 70% rather than to each
separately (this was also necessary because the sample size for the
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27
other specialties was too small).
For all other specialties (eg. orthopedic surgeons,
urologists..etc.) the sample sizes were again inadequate to obtain
a meaningful figure to be used in calculations. All available data
was therefore pooled, and the specialties treated as one. By
looking at the highest and lowest values, it was observed that the
lowest values rarely exceeded .05, and the highest .02 Ibs/patient
(per month). A range of .02-.05 Ibs./month was therefore applied
to the visits calculated for the "other" category (see page 17) to
obtain the amount of medical waste produced by those specialties.
b) Dentists.
Since there was no information available on rates for patient
visits to dental specialties, all dentists were included in one
broad category. This presents no problem, since (as was noted
previously) 80 to 90% of all dentists are in private practice. A
figure of .057 Ibs./patient (per visit) was obtained using the same
methodology as described above (for General & Family practitioners
and Internists) . This number appears to be high compared to the
average obtained for physicians (.034). But it must be taken into
account that some dental specialties (oral surgeons in particular)
may produce larger volumes of waste (eg. soiled cotton balls and
other wastes associated with patient treatment), as well as the
fact that a great many general dentists occasionally provide many
of the same services that dental specialists provide exclusively.
c) Veterinarians.
As previously stated, there is no information that is
available on specialties here- although it is well documented that
a majority of veterinarians (75%) work in a private practice (40) .
The average amounts of medical waste reported for New York and New
Jersey were 19.8 and 22.4 Ibs./month per practitioner . The average
of the two (21.1) will be the figure used- in conjunction with the
number practicing- to calculate the total amount medical waste
produced by veterinarians in each state separately.
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28
2) EXAMPLE: Calculating the amounts of medical waste produced
by doctors, dentists, and veterinarians in
Queens County.
Table 12; Summary of information needed to perform medical
waste calculations at the county level.
COUNTY: PARAMETER POPULATION
Queens Total population (1980)= 1,891,325. (21)
(1987)= 1,920,700. (24)
Female (1980)= 1,013,144
all adjusted
using 1980
population(24)
[see page 7]
Age (1987)
[< 6 ]= 134,716
[6-14]= 219,762
[ >14]= 1,566,221
NATION:
Total U.S population (1986)= 241,078,000 (31)
population (1987)= 243,915,000 (31)
."other"physician visits= 191,500,000 (10)
dental visits= 466,775,000 (33)
1) PHYSICIANS:
a. General & Family Practitioners.
POPULATION RATE VISITS
(Rounded)
1,920,700 persons x [86 visits = 1,651,802 1,652,000
per year/100 persons]
(or .86 visits per person per year)
b. Internists.
1,920,700 persons x [.324 visits]= 622,307 622,000
c. Pediatricians.
(age) (population) (rate) —
< 6 134,716 X 2.10
6-14 219,762 X .57
> 14 1,566,221 X .03
—Total= 456,172 456,000
d. General Surgeons.
1,920,700 persons x [.137 visits]* = 263,135 263,000
[* 137 visits per THOUSAND here]
e. Obstetrician-Gynecologists.
-------
29
1,920,700 persons x [.468 visits] = 474,151
474,000
f. OTHER
(all other specialties)
FIVE SPECIALTY TOTAL = 3,467,000
(a.-e.)
[1,920,700/243,915,000] x 191,500,000= 1,507,959 1,508,000
2) DENTISTS
466,775,000 X [1,920,700/ 241,078,000]= 3,718,843 3,718,000
CALCULATING MEDICAL WASTE: (all figures in Ibs./year)
I PHYSICIANS
a) [a-e above] Range= .03-.05 Ib/patient/visit
i)..visits x .05 = 173,000
ii)..visits x .03 = 104,000
b) [ f above ] Range= .02-.05 Ib/patient/visit
i).-visits x .05 = 75,000
ii).-visits x .02 = 30,000
II DENTISTS
Range= .05-.06 Ib/patient/visit
i).-visits x .06 = 223,000
ii).-visits x .05 = 180,000
UNITS*
pounds/year
tons/year
tons/month
PHYSICIANS
HIGH
248000
124.0
10.3
LOW
134000
67.0
6.0
DENTISTS
HIGH
223000
111.0
9.3
LOW
186000
93.0
7.8
TOTAL
HIGH
471000
134.0
19.6
LOW
320000
160.0
14.0
(* pounds/year is shown rounded to nearest thousand)
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30
5. Scaling down.
In order for the model to be successful, it should be easy to
use, and should be consistent from application to application (eg.
no matter what the county population, the error should be small
enough for a reasonably accurate estimation of the amount of
waste). Having to repeat the calculations above for every county
would be too tedious a method, especially in states with many
counties. A simpler method, is to perform one set of detailed
calculations for the most populous county, and then to scale down
the resulting waste amounts to project what the amounts for the
less populous counties will be.
A. Showing no difference.
In order to show that no appreciable differences exist between
the calculated (as detailed above) and scaled down waste amounts
over the total range of county (population) sizes, both types of
calculations were done and the differences compared. As shown
below, all counties were placed into one of eight groups based on
their population size. Group sizes were set so as to group
counties similar in population size together. Fifteen counties were
chosen, and their amounts of medical waste calculated. This amount
was then compared to a scaled down amount for any significant
differences.
1) Method.
Kings county, since it has the largest population, was used
as the base county for scaling down all estimates, the following
method was used for scaling down medical waste calculations to a
county:
o The population of the county being looked into is
first divided by the population of the base county
(Kings county) to obtain the percentage out of the base
counties population that the county represents.
o That proportion is then multiplied by the total
amount of medical waste (previously calculated) of the
base county, giving the total average amount of medical
waste for that county being looked at. This will be
referred to as the scaled down estimate.
EXAMPLE; Scaling down to project the amount of medical waste (high
and low) for Orleans county.
(Calculated)
Orleans- total medical waste-
HIGH = .41 tons/ month.
LOW = .28 tons/ month.
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31
population (1987) =
39,500
(24).
Kings- total medical waste-
HIGH = 24 tons/ month.
LOW = 16 tons/ month.
population (1987) = 2,309,600
Population Orleans/ Population kings = proportion.
(24).
[39,500/ 2,309,600] x 24 = .41 tons/ month. < scaled down high
estimate.
Change from calculated high (.41 tons) = 0.0% NOT SIGNIFICANT
[39,500/ 2,309,600] x 16 = .27 tons/ month. < scaled down low
estimate.
Change from calculated low (.28 tons) = - 2.1% NOT SIGNIFICANT
Table 13; Summary of categories used to show no differences
exist throughout a wide range or population sizes
for the amounts of medical waste produced by
counties for New York.
GROUP
I
II
III
IV
V
VI
VII
VIII
POPULATION RANGE
> 2 million
1-2 million
500,000- 1 million
250,000- 500,000
100,000- 250,000
50,000- 100,000
25,000- 50,000
< 25,000
# OF COUNTIES
2
4
3
5
11
18
16
3
# TESTED
1
2
2
3
3
1
2
1
TOTAL: 62 15
By looking at the summary table on the following page, it can
be seen that no significant changes were observed (between
calculated and scaled down amounts of medical waste) at all points
tested for all categories.
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32
Table 14; Summary shoving the resulting percent change
calculated and scaled down high and low amounts of
medical waste, for all test counties in all
categories.
%difference
between
CATEGORY
I
II
III
IV
V
VI
VII
VIII
COUNTY
KINGS
QUEENS
New York
Bronx
Eerie
Monroe
Onandaga
Richmond
Rockland
Oneida
Ulster
St . Lawrence
Sullivan
Orleans
Lewis
Yates
POPULATION
2,309,600
1,920,700
1,495,000
1,213,800
1,495,000
1,213,800
406,200
377,600
265,000
247,000
165,000
111,700
70,400
39,500
26,500
20,000
HIGH
_
1.8
1.3
0.0
0.0
0.0
- 0.4
2.6
1.5
2.8
0.7
0.8
0.7
0.0
1.8
0.0
LOW
_
- 4.9
0.0
1.2
- 2.9
0.0
- 0.6
0.0
3.2
0.6
0.0
1.2
1.0
2.1
0.6
- 1.4
B. Use of average waste amounts.
To further simplify calculations, an average amount of medical
waste for both physicians and dentists was used. The average amount
of waste means taking the simple average between the high and low
values for physicians and dentists
-------
33
respectively.
Table 151 An example of averaging the high and low waste
amounts for a county (Kings County).
n
s / m
n
PRACTITIONER
PHYSICIAN
DENTIST
HIGH
12.8
11.2
LOW
6.9
9.3
AVERAGE WASTE
> 9.9
> 10.3
TOTAL: 20.1
1) Method: Physicians and dentists.
It was observed during the course of doing calculations that
physicians and dentists consistently made up 49% and 51% of the
total medical waste calculated respectively. Taking an average of
the high and low amounts for each (as shown in the table above)
should ideally give a total average estimate out of which 49% and
51% could be taken to give the respective amounts for physicians
and dentists. This will only work if the changes from county to
county between the high and low estimates- for both physicians and
dentists -are consistent. As the table on the following page shows,
this did indeed prove to be the case.
-------
34
Table 16; Summary showing the ratio between physician and
dentist calculated waste amounts, and the percent difference
between the high and low amounts for each.
tons/month*
GROUP
I
a. Physician
b. Dentist
RATIO (a:b)**
II
a. Physician
b. Dentist
RATIO (a:b)**
III
a. Physician
b. Dentist
RATIO (a:b)**
IV
a. Physician
b. Dentist
RATIO (a:b)**
V
a. Physician
b. Dentist
RATIO (a:b)**
VI
a. Physician
b. Dentist
RATIO (a:b)**
VII
a. Physician
b. Dentist
RATIO (a:b)->
VIII
a. Physician
b. Dentist
RATIO (a:b)->
COUNTY
KINGS
NEW YORK
MONROE
ONONDAGA
ULSTER
SULLIVAN
ORLEANS
YATES
HIGH
12.8*
11.2*
1.14
8.0*
7.3*
1.11
3.8*
3.4*
1.12
2.5*
2.2*
1.14
0.88*
0.79*
1.11
0.38*
0.34*
1.11
0.22*
0.19*
1.05
0.11*
0.06*
1.10
LOW
6.9*
9.3*
.74
4.3*
6.0*
.74
2.6*
2.9*
.90
1.3*
1.9*
.68
0.50*
0.67*
0.74
0.20*
0.28*
0.71
0.12*
0.16*
0.75
0.10*
0.08*
0.75
CHANGE ( % )
46
17
48
16
47
18
48
14
43
15
47
18
45
16
45
20
(** dentists = 1)
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35
2) Calculated amounts of medical waste NEW YORK: Physicians
& dentists.
All medical waste amounts shown (see table 17) below were
scaled down using waste amounts calculated for Kings
County.
Table 17; Summary of medical waste calculations for physicians
and dentists, by county, for New York state.
tons/month
GROUP
I
II
III
IV
V
VI
COUNTY
Kings
Queens
New York
Suffolk
Nassau
Bronx
Eerie
Westchester
Monroe
Onondaga
Richmond
Orange
Albany
Rockland
Dutchess
Oneida
Niagara
Bregma
Saratoga
Ulster
Resselear
Chataqua
Schenectady
Otswego
St . Lawrence
Steuben
Jefferson
Ontario
Chemung
Wayne
Tomkins
Cattarugas
Putnam
Clinton
POPULATION
2,309,600
1,920,700
1,495,100
1,314,700
1,316,300
1,213,800
958,300
864,500
699,500
460,200
377,600
287,900
283,400
265,400
258,400
247,000
216,200
209,000
168,100
165,000
151,400
141,600
149,600
120,300
111,700
96,900
95,000
93,000
90,400
88,200
87,700
87,400
82,100
91,700
PHYSICIAN*
9.8
8.1
6.4
5.6
6.7
5.2
4.1
3.7
3.0
2.0
1.6
1.2
1.2
1.1
1.1
1.0
0.9
0.9
0.7
0.7
0.6
0.6
0.6
0.5
0.5
0.41
0.41
0.40
0.39
0.38
0.37
0.36
0.35
0.35
DENTIST*
10.3
8.6
6.6
5.8
7.0
5.4
4.2
3.8
3.1
2.0
1.7
1.3
1.3
1.2
1.1
1.1
1.0
0.9
0.8
0.7
0.7
0.6
0.7
0.5
0.5
0.43
0.42
0.41
0.40
0.39
0.39
0.38
0.36
0.36
TOTAL
20.1
16.7
13.0
11.4
13.7
10.6
8.3
7.5
6.1
4.0
3.3
2.5
2.5
2.3
2.2
2.1
1.9
1.8
1.5
1.4
1.3
1.2
1.3
1.0
1.0
0.84
0.83
0.81
0.79
0.77
0.76
0.74
0.71
0.71
-------
36
tons/month
GROUP
VI
VII
VIII
COUNTY
Cayuga
Sullivan
Herkimer
Madison
Columbia
Otswego
Livingston
Genesee
Washington
Warren
Fulton
Montgomery
Tioga
Chenango
Allegany
Cortland
Delaware
Franklin
Greene
Wyoming
Orleans
Essex
Seneca
Schoharie
Lewis
Yates
Schuyler
Hamilton
POPULATION
80,000
70,400
66,900
66,600
61,100
59,500
59,000
58,600
57,800
54,700
53,900
51,700
51,000
50,600
50,000
48,000
47,000
43,400
42,300
41,600
39,500
36,700
32,100
29,900
25,600
20,900
17,300
4,900
TOTAL:
PHYSICIAN*
0.34
0.30
0.28
0.28
0.26
0.25
0.25
0.25
0.24
0.24
0.24
0.22
0.22
0.22
0.22
0.21
0.20
0.19
0.18
0.18
0.17
0.16
0.14
0.13
0.11
0.09
0.07
0.02
76.90
DENTIST*
0.36
0.31
0.30
0.30
0.27
0.27
0.26
0.26
0.26
0.24
0.24
0.23
0.22
0.22
0.22
0.21
0.21
0.19
0.19
0.18
0.17
0.16
0.14
0.13
0.11
0.09
0.08
0.02
80.3
TOTAL
0.70
0.61
0.58
0.58
0.53
0.52
0.51
0.51
0.50
0.48
0.48
0.45
0.44
0.44
0.44
0.42
0.41
0.38
0.37
0.36
0.34
0.32
0.28
0.26
0.22
0.18
0.15
0.04
157.20
(* May not come out exactly to 49% and 51% due to rounding)
3) Veterinarian medical waste.
As was discussed previously, there is no was to calculate the
amounts of medical waste produced by veterinarians, and therefore
an average figure for pounds/month for the average veterinarian
will be used. This was found to be 22.4 pounds. Multiplying this
by the number of practicing veterinarians in the state (2,000)
yields a figure of 44,800 pounds or 22.4 tons per month for the
state.
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37
4) Total waste: New York.
Adding up the results for physicians , dentists, and
veterinarians gives a total figure of 179.6 tons/month for New York
state (see executive summary, table A).
III. APPLYING THE MODEL: NEW JERSEY.
1. Physicians and Dentists.
To double check the model, it was applied to New Jersey. As
was done for New York, the counties were grouped with other
counties having similar (size) populations. The amount of medical
waste was calculated for one county from each of the groups, which
was then compared to the amounts for those counties derived by
scaling down from the amount of medical waste calculated for the
most populous county (which for New Jersey is Essex). The groupings
used were the same as those used to group the New York counties
(see page 31), but only four were needed (since the county
populations vary much less over a range than do those for New
York).
Table 18; Summary of the groupings used to characterize the
populations of all counties in New Jersey.
GROUP
I
II
III
IV
POPULATION
500,000- 1 million
250,000- 500,000
100,000- 250,000
50,000- 100,000
# OF COUNTIES
6
6
5
5
# OF TRIALS
1
1
1
1
The results of all trials (summarized in table 19 below) show that
scaling down from the most populous county- when compared with the
amounts calculated for those counties -results in only small and
non-significant (< 10%) differences.
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38
Table 19; Summary of the trial county comparisons used, showing
that no appreciable differences result from previously
calculated amounts (of medical waste) when scaling
down the amounts from the most populous county, for New Jersey.
% difference betwee
calculated and sea]
high and low medica
GROUP &
COUNTY
I
ESSEX
MIDDLESEX
II
BURLINGTON
III
ATLANTIC
IV
WARREN
POP*
844,500
645,700
388,000
208,500
87,200
in
.ed down
il waste
amounts .
HIGH
2.3
0.0
-4.2
-1.7
LOW
0.0
-2.5
-2.3
-2.1
% difference between
average calculated and average
scaled down medical waste
amounts .
PHYSICIAN
0.0
1.5
1.1
-2.5
DENTIST
0.0
5.8
2.3
-2.6
TOTAL**
0.3
-2.8
0.0
-1.9
(* = population) (** = physician & dentist)
The amount of medical waste for both physicians and dentists
in the state is shown in table 20 below.
Table 20; Summary of medical waste calculations for physicians
and dentists, by county, for New Jersey.
tons/month
GROUP
I
II
COUNTY
Essex
Bergen
Middlesex
Monmouth
Hudson
Union
Camden
Passaic
Morris
Ocean
Burlington
Mercer
POPULATION
844,500
830,400
645,700
553,600
547,200
502,500
496,300
463,700
419,400
403,000
388,000
327,000
PHYSICIAN*
3.6
3.5
2.7
2.4
2.3
2.1
2.1
2.0
1.8
1.7
1.7
1.4
DENTIST*
3.7
3.7
2.9
2.4
2.4
2.2
2.2
2.0
1.8
1.8
1.7
1.4
TOTAL
7.3
7.2
5.6
4.8
4.7
4.3
4.3
4.0
3.6
3.5
3.4
2.8
-------
39
tons/month
GROUP
III
IV
COUNTY
Somerset
Glouchester
Atlantic
Cumberland
Sussex
Huntington
Cape May
Warren
Salem
POPULATION
221,600
213,000
208,500
137,600
124,300
98,900
94,200
87,200
65,400
TOTAL:
PHYSICIAN*
0.9
0.9
0.9
0.6
0.5
0.06
0.05
0.05
0.04
31.30
DENTIST*
1.0
0.9
0.9
0.6
0.6
0.06
0.06
0.05
0.04
32.40
TOTAL
1.9
1.8
1.8
1.2
1.1
0.12
0.11
0.10
0.08
63.70
(* May not come out exactly to 49% and 51% due to rounding)
2. Veterinarians.
As was done for New York, the amount of medical waste produced
by veterinarians was calculated by multiplying the average medical
waste/month by the number practicing in the state([22.4
pounds/month] x 900 practicing). The amount comes out to 20,160
pounds, or 10.1 tons, for the state per month.
3. Total Waste
Adding up the medical waste amounts for physicians, dentists,
and veterinarians gives a total amount of 73.7 tons/month for New
Jersey.
[**See Executive Summary, table A., for the summary results of all
state medical waste projections]
IV- DISCUSSION.
1. Uses.
The model outlined above was applied to two states, New York
& New Jersey, to obtain the amounts of medical waste produced by
physicians, dentists and veterinarians. The observation was made
that the (yearly) medical waste projected for a state can be scaled
down to give a one-time estimate for the whole state. If the amount
calculated for New York is scaled down to give and estimate for New
-------
40
Jersey, the resulting figure was found to deviate by only 5.4% from
the calculated figure (for total waste, using the methodology
outlined above). This small deviation is probably due to the fact
that the two states used are close enough geographically to keep
demographic differences to a minimum. But this can not be
guaranteed when applying this method nationally, especially as the
distance between the two states increases. The model above
minimizes demographic differences by using a set of conditions
(age, sex etc...) applied to a much smaller geographic area (the
county level).
Another important point is that the model serves the purpose
of projecting medical waste independent of the actual number of
practicing physicians, doctors and dentists in the state being
considered. This creates problems when trying to compare states,
or even counties, that have dissimilar numbers of these
practitioners. Since the model relies heavily on the number of
yearly patient visits it truly does reflect the (office based)
health care needs of the included population, whether they be in
a state or a county. But the smaller the land area, the larger the
probability that an individual will go outside their geographic
place of residence (eg. the county) to see a private physician,
dentist, or veterinarian. This presents problems since there is
really no way to know how large or small this phenomena may be.
If it is known that one county has half as many physicians as
another (but the same amount of physician waste is projected using
this model) it reasonable to assume that here too, is a case where
some migration from one county to another may occur. It does seem
feasible that this could be considered in any future medical waste
modeling, possibly being a random computer comparison (eg.
comparing the number of physicians with the number of projected
visits throughout the state, and to somehow weigh these into the
projection for each). Another reason for migration of visits may
be just plain old personal preference. Maybe you live close to the
county border, and the internist you prefer to attend has a
practice just outside county limits, or maybe the topography of
the area makes that internist more accessible (it's an easier trip
to make). Reasons relating to personal preference are obviously
impossible to account for, and this author doubts that any model
can ever take this into account.
Using this model to make county to county comparisons is
therefore not recommended, unless the actual number of practicing
physicians, dentists, and veterinarians in that county are compared
also. Such comparisons make this model ideal for health planning
uses, since it can be used to map out the needs of the state,
county by county, and thus help in identifying shortage areas.
Comparisons between states would be more valid, since a larger
geographical area is usually being considered, and an individual
is more likely to see an office based physician or dentist within
his state than outside of it (with the only exception being very
small states, of which there are very few) . The fact that the model
predicts possible yearly patient load makes it a stable predictor
for that amount of medical waste produced by all the sources
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41
discussed in this study, since the only way to significantly change
the estimate is to either increase or decrease a state's
population, or change it's composition (eg. male to female ratio
etc...).
2. Improvements.
One of the ways to improve the model is to try to develop a
methodology for including the number of practitioners actually
practicing in any county, since this would enable county
comparisons to readily be made (as discussed above). Another way,
would be to utilize the most recent survey (NAMCS for physicians
& NHIS for dentists) and census data. The more recent the survey
data used, the more confidence one has that the rates being used
are accurate. The more recent the last census year, better data
can be acquired for the various county parameters used in the
model. For the purposes of this model (as already mentioned
previously) it was assumed that the proportions within each county
(for a particular parameter used) are similar to what they were in
1980. The validity of this assumption will not be known fully until
the forthcoming census is complete. The fact that the census may
soon be taken every five years, would certainly help in obtaining
data needed for this model.
**********************************
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42
BIBLIOGRAPHY
1) News item; The New York Times, Aug. 12, 1988.
2) News item; The New York Times, Aug. 14, 1988.
3) News item; The Star Ledger, Newark New Jersey, Dec. 14, 1988.
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15) National Center for Health Statistics. Cypress, B.K.:
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44
24) Current Population Estimates. July 1987 & July 1986, Bureau
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Patterns of Ambulatory Care in Pediatrics.
The national Ambulatory Medical Care Survey. United States,
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Series 13, No.75. DHHS Pub. No.(84)1736. Public Health
Service. Washington. U.S. government printing office.
Oct. 1983. Appendix 1.
29) National Center for Health Statistics. Cypress, B.K.:
Patterns of Ambulatory Care in office visits to General
Surgeons. The national Ambulatory Medical Care Survey. United
States, Jan. 1980- Dec. 1981. Vital and Health Statistics.
Series 13, No.79. DHHS Pub. No.(84)1740. Public Health
Service. Washington. U.S. government printing office.
Sept. 1984. Appendix 1.
30) National Center for Health Statistics. Cypress, B.K.:
Patterns of Ambulatory Care in Obstetrics and Gynecology.
The national Ambulatory Medical Care Survey. United
States, Jan. 1980- Dec. 1981. Vital and Health Statistics.
Series 13, No.76. DHHS Pub. No.(84)1737. Public Health
Service. Washington. U.S. government printing office.
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32) Estimates of the population of the United States, to Nov. 1,
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33) National Center for Health Statistics, S. Jack and B. Bloom.
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39) Unpublished data, from Dental Economics. Park 80,
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visits to orthopedic surgeons. National Ambulatory Medical
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Hyattsville, Md., July 18, 1978.
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List of contacts.
1. Dr. Albertini
Chief, Associated Health Professions Branch.
U.S. Dept. of Health and Human Services, Public Health
Service, 5600 Fishers Lane, Hyattsville M.D.
(301) 443-6763
2. Dr. Canton Ash
Director, Bureau of Economic Research.
American Dental Association, Chicago 111.
(312) 440-2838
3. George Me Counahey
Co-publisher and national sales manager
for Dental Economics, Park 80 West/Plaza Two,
Saddlebrook N.J., 07662.
(201) 845-0800
4. James Delozier
Chief, Ambulatory Care Statistics Branch.
National Center for Health Statistics.
Rockville MD.
(301) 436-7132
5. George Estel
Program Research Specialist.
N.Y.S. Department of Health, Albany N.Y.
(518) 458-6402
6. Sylvia Etzel
Assistant Editor
Department of Directories and Publications.
American Medical Association,
535 north Dearborn St., Chicago, 111. 60610.
(312) 645-4693
1. Dr. Elmer Green
State Dental Director for N.Y.
N.Y.S. Department of Health, Albany N.Y.
(518) 474-1961
8. Ray Gagnon
Health Statistician.
Ambulatory Care Statistics Branch,
National Center for Health Statistics.
Hyattsville MD.
(301) 436-7132
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47
9. Jan Goldsmith
Regional Dental Officer (N.Y.- N.J. region)
Department of Health and Human Services.
26 Federal Plaza, New York N.Y., 10278.
(212) 264-2768
10. Maria Morales Harper
Information Services Specialist.
U.S. Dept. of Commerce, Bureau of Census.
26 Federal Plaza, New York N.Y., 10278.
(212) 264-4732
11. Suzan S. Jack
Health Statistician.
National Health Interview Survey,
National Center for Health Statistics,
3700 East-West Highway, Hyattsville MD. 20782.
(301) 436-7089
12. Ann Kahl
Statistician, Bureau of Labor Statistics.
(201) 648-5166
13. Hugo Koch
Survey Statistician.
Ambulatory Care Statistics Branch,
National Center for Health Statistics,
3700 East-West Highway, Hyattsville MD. 20782.
(301) 436-7132
14. Vincent Martiniano
Supervising Investigator.
N.Y.S. Department of Health,
Office of Professional Conduct,
Empire State Plaza, Albany.
(518) 474-8357
15. Mary Morris
Computer Specialist.
Office of Data Analysis and Management.
Bureau of Health Professions,
U.S. Dept. of Health and Human Services,
Parklawn Building, 5600 Fishers Lane, Rockville MD. 20857,
(301) 443-6936
16. Barbara Mortensen
Manager, Database Operations
Department of Membership,
American Dental Association, Chicago 111.
(312) 440-2613
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17. Melanie M. Neal
Research Analyst.
Bureau of Economic Research,
American Dental Association, Chicago 111.
(312) 440-2568
18. Judith Nicks
Assistant Secretary for Advanced Education.
Council on Dental Education,
American Dental Association, Chicago 111.
(312) 440-2825
19. Howard V.Stambler
Chief, Office of Data Analysis and Management.
Bureau of Health Professions,
U.S. Dept. of Health and Human Services,
Parklawn Building, 5600 Fishers Lane, Rockville MD. 20857
(301) 443-6936
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