NATIONAL CENTER Series 2 

For HEALTH STATISTICS INumber 11 

PROPERW OF~~E


I?IJBL\CA~l~~~ @A~c~ “ 

EDITORIALLIBWRY 

VITAL and HEALTH STATISTICS 

DATA EVALUATION AND METHODS RESEARCH 

Design of SampleSurveys 

to Estimate the Prevalence 

of IRareDiseases: 

Three Unbiased Estimates 

Three formu Ias, varying in the extent that they 

utilize all the information COIIected by a survey 

based on a stratified sample of medical sources, 

are presented to estimate the number of diagnosed 

cases of a rare disease in the population. 

Washington, D.C. October 1965 

U.S. DEPARTMENT OF I. 

HEALTH, EDUCATION, AND WELFARE Public Health Serv,ice 

John W. Gardner Luther L. Terry 

Secretary Surgeon General 
Public Health Service publicationNo. loofj.serie~” Z.No. 11 

For sale by the Superintendent of Documents, U.S. Government Printing Office 

Washington, D. C., 20402 - Price 15 cents 
NATIONAL CENTER FOR HEALTH STATISTICS 

FORREST E. LINDER, PH. D., Director 

‘1’HEODORE D. WOOLSEY, ~ejwy Director 

0. K. SAGEX, PH. D., zfwbant Director 

WALT R. SIMMOITS, M. A., Stat&tical Advi~or 

.lLICE M. WATERHOUSE, M. D., Medical Advbor 

JAMES E. KELLY, D. D. S., Dental Advbor 

LOUS R. STOLCIS, MA., Executive Oj$cer 

OFFICE OF HEALTH STATISTICS ANALYSIS 

IWAO M. MORXYAMA,PH. D., C&f 

DIVISION OF VITAL STATISTICS 

ROBERTD. GROVE, PH. D., Chief 

DIVISION OF HEALTH INTERVIEW STATISTICS 

PMILIP S. LAWRENCE)Sc. D., Chief 

DIVISION OF HEALTH RECORDS STATISTICS 

MOXROE G. SIRKEN,PH. D., Chiej 

DIVISION OF HEALTH EXAMINATION STATISTICS 

ARTHURJ. XICDOWELL, Chicj 

DIVISION OF DATA PROCESSING 

SIDNEYBINDER, Chirj 

Public Health Service Publication No. 1000-Series2-No. 11 

Library of Congress Catalog Card Number 65-61845 
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CONTENTS 

1. Introduction


2. Description ofthePopulation


3. ~e`SamplingSurvey


4, An Unbiased EstimateforN


5. Another Unbiased Estimate forN


6.	 An Unbiased Estimat&Based onStratawithPriorities


Priority Rule ----------------------------------------------------


7.	 Some Gen~alObsen~ions


References


1 

2 

3 

3 

4 

6 

6 

7 

8 
IN THIS REPORT a stratified vandom samp~e design is p~oposed foY 

a survey of medical scnmces to estimate the prevalence of diagnosed 

cases of a rare disease in the population. The medical sources are 

stratified by cyiteria, such as specialty of physician and service and 

size of hospital, which are presumed to be Yelated to the probability of 

the sowce .tYeating patients with the particular disease. Deriving an unbiased estimate of the number of diagnosed cases based on patients Yeported by a sample of medical sources, hawever, presents a special 

problem since it is not uncommon fov patients with YaYe diseases to 

have been treated by more than one medical souvce. ThYee foymulas aye 

presented fov deriving unbiased estimates under these circumstances. 

The foymulas difler in the extent that they utilize information collected 

in the sample survey about multiple vepoyting of patients. 

I 
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DESIGN OF SAMPLE SURVEYSTO ESTIMATE


THE PREVALENCEOF RARE DISEASES:


THREE UNBIASED ESTIMATES 

‘Z.W. 13irnbautn, Ph.D. Department of Mathematics, University of Washington 

Monroe G. Sirken, Ph. D., Division of Health Records Statistics 

1. INTRODUCTION 

The National Center for Health Statistics has 

been concerned with the problem of developing 

survey procedures for estimating the occurrence 

of diagnosed cases of rare diseases in the population and, during the past few years, has conducted several sample surveys to obtain information on diagnosed cases of cystic fibrosis.1 2 

The incidence of this genetic disease in the population had been variously estimated as occurring 

in from 1 in 3,700 live births 3 to 1 in 600 live 

births.~ On the basis of a survey conducted in 

three New England States,2 it was estimated that 

Ml cystic fibrosis patients had received medical 

care during the 8-year period 1952-59, indicating 

an incidence of 1 diagnosed case per 2,300 live 

births and a high mortality rate during the early 

vears of life, Although a relatively rare disease, 

cystic fibrosis has been increasingly recognized 

as one of the major childhood diseases and as 

representing an important health problem since 

it requires intensive medical care and expensive 

treatment and nearly always results in premature 

death, 

Historically, the design of sample surveys to 

estimate the occurrence of diagnosed cases of 

rme diseases in the population has presented 

two major difficulties: (a) large sampling errors 

usually associated with survey estimates of rarely 

occurring events and (b) potentially large non: 

sampling errors associated with diagnostic information reported in surveys. To minimize these 

difficulties, a stratified random sample design 

of medical sources has been used in cystic 

fibrosis surveys conducted by the Center. Medical 

sources, the primary sampling units in these surveys, were stratified according to the likelihood 

of their treating cases of this disease of children. 

Thus, pediatricians and hospitals with a pediatric 

residency were selected with certainty, while in 

other strata varying fractions of physicians and 

hospitals were selected for the sample. In order 

to control errors of diagnosis, medical sources 

were asked to provide the results of diagnostic 

tests for each reported cystic fibrosis case. This 

information was subsequently reviewed by clinicians to substantiate or contraindicate the diagnosis. Where the reported diagnostic information 

was incomplete, the medical source was frequently 

asked to provide the missing information if it was 

available. 

Deriving an estimate of the number of diagnosed cases of a rare disease from the sample 

survey of medical sources presents a unique 

statistical problem because it is not uncommon 

for the patient to have been treated and hence to 

be reported by several medical sources. For 

example, the results of a cystic fibrosis survey 

in three New England States conducted by the 

Center indicated that each of the 651 patients had 

been treated, on the average, by 1.6 sources. The 

percentage distribution of these patients by the 

number of medical sources who reported them was 

as follows: 

Percentage 

distribution 

Total patients 100.0 

Patients reported by 1 source 53.6 

Patients reported by 2 sources 32.5 

Patients reported by 3 sources 10.8 

Patients reported by 4 or 5 sources-- 3.1 

1 
Under these circumstances, it appears to be 

necessary to obtain information about the’’multipIicity” of medical sources that treated the” 

patients reported in the sample survey in order 

to derive unbiased estimates of the number of 

diagnosed cases. 

Recent studies conducted by the Center 

suggest the feasibility of a particular survey procedure to obtain this kind of information for 

cases of cystic fibrosis, and it appears very 

likely that this same procedure could be readily 

adapted to surveys for other rare diseases. 

According to this survey procedure, each 

medical source in the sample survey reports 

separately each of the cystic fibrosis patients 

that he has treated. For each patient reported, he 

identifies other medical sources that have also 

treated the patient. Subsequently, the coverage of 

the survey is expanded to include those medical 

sources not originally selected in the sample 

(nonsample medical sources) that have been 

identified as having treated patients reported by 

the medical sources in the original sample. The 

purpose of the survey of nonsample medical 

sources is to verify that they in fact did treat and 

do report the patient(s) in question, and if they do 

report the patients, to have them identify other 

medical sources that also treated these patients. 

In this manner, the survey presumably is 

able to identify all of the medical sources, whether 

or not they are included in the original sample, 

that treated patients reported by one or more of 

the original sample medical sources. The success 

of this survey procedure hinges ultimately on the 

extent to which medical sources that have treated 

the same patients for a specified disease know of 

each other and report one another in the survey. 

In view of the seriousness and infrequent occurrence of the kind of diseases being considered 

here, this assumption seems reasonable. More 

developmental work is needed, however, to test 

the survey method for completeness in identifying the medical sources treating the same 

patient and to determine the size of the bias, due 

to incomplete identification of medical sources, 

in the estimate of the number of patients with the 

disease. 

Three unbiased estimates of the number of 

diagnosed cases of a rare disease in the population 

based on a stratified sample .of medical sources 

are presented in this report. Each of them makes 

use of information on “multiplicity” of sources 

reporting a patient obtained in the manner described above from cystic fibrosis surveys of 

medical sources. The first estimate (see formula 4.6) utilizes the least information on 

“multiplicity; ‘‘ it requires only the total number 

of medical sources that treated each patient reported by a medical source in the original sample. 

The second estimate (see formulas 5.4 and 5.5) 

requires information on the number of medical 

sources within each stratum that treated the same 

patient reported by a sample source. The third 

estimate (see formulas 6.7 and 6.8) appears to 

utilize the most detailed “multiplicity” information. The second and third estimates require the 

matching of patients reported by sample sources 

to assure that each patient is counted only once. 

For the first estimate, each patient is counted as 

many times as he is reported by sample sources. 

This is a report of some preliminary results 

from research work now in progress. Besides the 

estimates presented in this report, still other estimates may exist which use in more detail the 

“multiplicity” information collected in the surve y 

than do the estimates presented here. The variances of the three proposed estimates are not 

compared although the variances of the first and 

second estimates are derived in this report. These 

results are preliminary to work on the problem 

of optimum allocation of sample size among the 

strata. Existing statistical theor ys 6 on optimum 

allocation in stratified random sampling does not 

consider the possibility of overlapping of elements 

(patients) among the primary sample units (meclical sources) either in the same stratum or in 

different strata. 

2. DESCRIPTION OF THE POPULATION 

The sampling units are “medical sources” 

such as physicians in individual practice, clinics’, 

or hospitals. The population of all such medical 

sources consists of strata, for example, large 

hospitals, small hospitals, pediatricians, and 

other physicians. To describe this population we 

use the following notations: 

II = population of all medical sources, 

E,= r–th stratum, r=1,2, . . . ..R. (2.1) 

Sr,i = the i - th source of the stratum i, 

i=l,2 ,. ..., M,. 

2 
The population H is therefore the union of the The totality of the Ua,,,i, for ~=l,. . . ..N. 

R strata ~1, ~z,. . . . . ~R; the r-th stratum ~, r=l, . . . ..l?. i=l ,. ..., M,, represents a comconsists of the llf~ sources S~,l, S~,2, . ..-, S~,M[; plete description of the way in which every 

and 11 consists of all the sources S1 ~ S, * patient occurs or does not occur in the records >> . . . . . 

s l, M1’S2,1’ S2,2’ . . . .. S2. M29 . ..!sR. &f ,, ~ . . . . . of each of the sources. Clearly in view of (2.6) 

‘R,MR . the knowledge of all the ~~,,, i would imply the 

The number of sources in ~, is M,, and the knowledge of N. 

total number of sources in H is 

3. THE SAMPLING SURVEY 

(2.2) The sampling design used consists of a onestage stratified sample from H, and the survey 

consists of the following steps. 

The different individual patients are denoted by a. From the Mr sources of the stratum t r 

11,12, . . . ..IN a number mr (S Mr) is drawn at random 

without replacements for r = ],2, . . . . . R, 

where The samples from different strata are 

independent. The total sample size is 

N = number of different patients (2.3) R 

m=r~, mr. 

is the quantity we wish to estimate. When a


patient IQ appears in the records of a medical b. If a source S,, i is included in the sample


source S~,l , we shall say that he is “contained” from &r then all its records are re-


in that source. Since each patient maybe contained viewed, and the record for every patient
 in several sources, we introduce the indicator 

10 suffering from the disease under
 vari; dew study and contained in S ~,i is obtained. 

c. This record of 1~ contained in a source 

1 when Za is contained in S~,i S~, i which occurs in the sample is used 

‘a, t,i = O when Ia is not contained in S,.i. (2.4) 1 to determine all other sources which

contain the same patient Ia, no matter 

whether these other sources occur in the 

These indicator variables are defined for ~= 1,2, sample or not. 

. . . . . N;r=l,2, . . . .. R.i=l., M,, ... M,, and The description of step c nmy be paraphrased 

with their aid we define the auxiliary quantities by saying that when patient Ia is contained in a 

list ed below together with their intuitive m~anings source S~,i which occurs in the sample, we not 

which are easy to verify: only learn that Ma,r,i = 1, but we also find out 

N from the record of Za in S,,i the values of all ~ W,r, i - ivr, i = number of different (2.5.1) ,.. -1 Pa, t,j for this patient Z& and all sources L.—. patients in Sr, i, st, j# Sr, i . 

M, The information obtained in step c is the 

x ~ff,r,l ‘Sa,, = number of sources essential feature of the design, and by utilizing 1=1 containing la . this information, more or less completely, we R M, P are able to construct the estimates for N de­

,:1 L ~:’r,’ -r~l- Sal, = Sa = number of (2.5.3) scribed and discussed in the following sections. 

sources in II 

containing Ia. 4. AN UNBIASED ESTIMATE FOR N 

M is easiIy seen that 

According to (2.5.3) patient Ia belongs to 

sa sources in It, where Sa may be any of the 

integers 1,2 ,. ... M. When Sa= s, we shall say 

3 
that Za has “multiplicity S“ in 11, or that he is 

an “S -fold patient” in II. We now define 

‘s, r,i = number of s -fold patients in S ~,i (4.1) 

and 

Zlr, i= Y $ Ii,, r,i. (4.2)

S=l 

One verifies easily that 

Mr 

N= ; Z 17r, i . (4. 3) r=l i=l 

We furthermore introduce the random 

variables 

1 when S r,i occurs in the 

Ur,i= sample (4.4) 

{ O when S,, i does not occur 

in the sample 

with the probabilities 

(4. 5) 

for r=] ,. ..., R; i= ],...., M,. The probabilities (4.5) are determined by the sample design. 

We now consider the statistic 

G= ,:, ig, % Ur,i. (4.6) 

P~, i 

While the right-hand side of (4.6) formally is a 

sum over all sources in n, it can be computed 

by using only the information obtained from those 

sources which occur in the sample, since for all 

other sources one has Ur i = O and the corresponding term in (4.6) vanishes. When SF,i occurs 

in the sample, we can determine for every patient 

contained in it his multiplicity, hence we can 

compute the n ~ ~ i and nr, i , and thus the coefficient of Ur,; ‘ in (4.6). In view of (4.5) and

(4.3) one has immediately 

(4. 7) 

hence N is an unbiased estimate for N. 

Whenever source Sr, i occurs in the sample, 

we can according to step c of our survey (Section 

3) determine not only the multiplicity of every 

patient in S, ,j —which is all that is needed to 

compute n ~,i, and hence & —but we can also tell 

for every other source in II whether or not it 

contains any one patient contained in S‘, i . This 

more detailed information derived from the sur ­

vey is not utilized in the statistic N. 

To compute the variance of R, we first note 

that n ~.i is a variate ascribed to the source 

nr i M, Sr, i, andsoisd= ‘r.l. —mr . The statistic R is therefor~’~he sum of the values of the 

variate Zlr,i. ~ Mr obtained by taking a stratified 

sample without replacemer,ts from H. Applying 

the classical theory of such samples one obtains 

R Mr-mr Mr 

var (E)= Z

r=l ~r(~r-l) [‘r i~l (nr,i)2 - (~1 Z7~,i)2]. (4.8) 

For given total sample size m =r~l mr the right 

side in (4.8) is minimum when 

Ar= ~+1 Mr Y (nr,l)z -(i~l nr,l)2 

r [ i=l 1 

for r-1 . . . . . . R. 

5. ANOTHER UNBIASED 

ESTIMATE FOR N 

We now consider for each patient Ia the two 

complementary events: (a) he is contained in at 

least one of the sources occurring in the sample, 

and (b) he is not contained in any of the sources 

of the sample. The following indicator variable 

tells which of these two events has occurred: 

1 when la is m a source 

w.= included in the sample (5.1) 

{ O otherwise. 

4 
The random variable Wa has the probability distribution 

Pr {Wa” O}”i (Mr7a) M ‘q~, r=l r 

()mr 

(5. 2) 

Pr {Wa=l}=l-qa= pa, 

A linear estimate of N in terms of the Wa 

(5.3) 

is unbiased if and only if 

N N 

which is an identity in the pa if and only if ~a =-& 

for a=l *. ..., N. We thus arrive at a unique 

unbiased linear estimate for N of the form (5.3) 

(5.4) 

where 

R (MrXa”r) ,55, Pa-l -c?a= l-n ‘=l M, “ 

() mr 

As in (4.6), the right-hand side of (5.4) can 

again be computed from the information provided 

in our survey. It utilizes, however, a different 

aspect of this information, since in computing the 

coefficients * in (5.4) according to formula 

(S.5) one needs, for each Za contained in a source 

occurring in the sample, all the quantities 

Sal,= multiplicity of Za in t r 

for r=] . . . . .. R, while for computing the coefficients in (4.6) one needed for every source 

S,,, occurring in the sample all quantities 

n ~,~,, = number of s-fold patients in Sr, i 

for S- 1,2 ,, ..., M. 

The variance of ; can be computed as 

follows: 

where, for a # B 

cov (Wa, WB)=E (Wa Wp) -E (Wa) E (Wp)= 

. Pr {Wa=Wfl=l\-Pa PB. 

To write Pr { Wa = we = I } one needs the auxiliary quantities 

= number of sources containing both 

la and ID. 

One has 

Pa,~=Pr {J%= Wp=l}= 

. l-Pr {Wa=O}– Pr {W6=O}+ 

+Pr {Wa=WB= O}=, 

= I-qa -clB+ (5. 7) 

(

M,-sa ,- ‘f9, r + ‘cc,~, r 

mr ) +fi~=1 M, 

() ‘r 

and 

Cov (Wa, Wp) = pa, ~ - PaPp . (5.8) 

. 
For ~=p obviously if Ia is contained in S~. i , and S~.i occurs in 

the sample, and S,, i has’ highest priority among 

cov (W&, Wa) = var (Wa) = pa qa the sources occurring in the sample which contain Za. We have therefore 

and with the conventions 

P= {~,,,,=l}=E {Va,,,,}= 

Sa,m,r = Sa, r 

= ‘a, r,i . Pr { .Sr,i, is in the sample}. 

m= Pa 

.Pr{Sr, i has highest priority among sources 

one obtains in the sample which contain Ia , given 

that S, ,i is in the sample}. 

(5.9) Denoting the last of the three factors by ma,,,,, 

we write this in the form 

Pff,


6. AN UNBIASED ESTIMATE BASED Pr{V~,f,i ‘1}= #~,~,i , ~ . ~~,~ i . (6.3) r ON STRATA WITH PRIORITIES 

All the sources .$. i may be ordered in a Of the three factors ‘in (6.3) neither ~ 

sequence by ascribing them priorities in some 

nor ma ~ i depend on whether Ia is contained ;n arbitrary manner. We choose the following con- Sr,i. ‘ ‘ 

vention. To evaluate ma,,,i we introduce the auxiliary 

Priority Rule quantities 

i-1 Source s~, i has higher priority than source 

]=1, A%,r,j – ~ - Sgr, = number, ofi sources iii kr (6.4)

S,, j when either q<r or q=r and i<j. containing Za. and having 

Since every patient Za may be contained in higher priority than S,,i 

several sources, and we wish to account. for this 

in our estimate of N, we now decide to count for a-l ,. ...> N;r=l ,. ..., R; i=.l, . . . .. Mr. 

him not more than once (and later on to attach a 

weight to this count) according to the following One verifies that 

counting rule. 

If patient Ia is contained in some sources (v:%) 

occurring in the sample, he is counted only in 

that one among them which has the highest Ta,l, i = Ml-]’” (6 .5) 

priority. ml– 1 

We now define the indicator variables () 

I

1 when Ia is counted in S,, i vct, r,i = (6.1) and 

O otherwise 

and consider linear estimates- of the form 

(6. 2) 

To compute the probabilities of the random 

variables (6. 1) we note that Va, r,i = ] if and only for r ?2. 

. 6 
In view of (2.6), for (6.2) to be an unbiased statistics, i.e., they can be computed from 

estimate for N it is necessary and sufficient the information provided in the survey. 

that However, to compute T&,P,~ for given ~ 

one actually needs to know a large number NRM, of the Ma, r i; hence the information obtained in the survey appears to be utilized 

here in considerable detail. 

The variance of N can be written out by using 

=; ; !4. L.P i auxiliary quantities similar to (6.4) but referring 

a=l r=l i=l S(Y a,r, 

to pairs of patients Za, 16. The resulting expressions are unwieldy and we have not succeeded 

identically in Ya, ~,I , hence in putting them in a form which would lend itself 

to some intuitive interpretation. 

mr 1 

7~,~,i , — . ‘a, r,i .— 

Mr Sa 7. SOME GENERAL OBSERVATIONS 

In the sampling design of section 3, it is posand the unique unbiased estimate of the form (6.2) sible that for some strata of medical sources 

is one decides to carry out a complete census, i.e., 

to set the sample size equal to the number of 

L!ll ~=1 ~~.1 

Mr: 

~i.1 

1 

‘rx, r,i 

~ 

a,r, i. (6. 7) sources. If this is done, one may lump all such 

strata into one stratum $0 and call it the certainty 

stratum. This convention would imply 

The following comments on this estimate 

may be in order. m. ~ M. 

a. For every ~, at most one term of the mr<jlfr for r=l,2, . . . ..R. 

double sum 

It would also assure a complete enumeration of 

Iv 

r=l 

;5; Mr i=l ‘a, r,l a,r, i — patients who belong to sources in Lo, and any

effect of random sampling would be limited to 

those patients who do not belong to any sources 

can be 1, and all others must vanish, in ~O. One can, therefore, assume without loss 

since Ia can be counted in at most one of generality that the developments of the presource, One can therefore write (6.7) in ceding sections were made for designs which did 

the form not provide for a certainty stratum. 

We do not know at this time how the variances 

$=!! A.-24?. ~=1 % mp — 

1 

~cr,p,l 

. v cl,,,’ 65.0 of the three estimates proposed in sections 4, 5, 

and 6 compare. In fact, we do not know whether 

there are other estimates which would utilize the 

information obtained in the survey described in 

where (P, L) are the subscripts of that Section 3 in more detail than the estimates prosource in which Za is counted. posed here. Similarly, the problem of OP~$ 

b.	 As in the estimates ~ and i, the coef- allocation of sample sizes for the estimates N 

ficients in (6.7) or (6.8) are themselves and N remains open. 

7 
REFERENCES 

lSirken,.M. G., Crane, M. M., Brown, M. L:, and Kramm, 

E. R.: A national hospital survey of cystic fibrosis. Pub. 

Health Rep. 74764-770, Sept. 1959. 

2Krmm, E. R., Crane, M. M., Sirken, M. G., and Brown, 

M. L.: A cystic fibrosis pilot survey in three New England 

States. An. J. Pub. Health 52:2041-2057, Dec. 1962. 

3Steinberg, A. G., and Brown, D. C.: On the incidence of 

cystic fibrosis of the pancreas. Am. J. Human Genet. 12(4): 

416-424, Dec. 1960. 

4,4nders~n, D. H+,and Hodges, R. G.: Celiac syndrom~~v> 

Genetics of cystic fibrosis of the pancreas with a consideration of etiology. Am. J. LMs. Child. 72(1):62-80, July 1946. 

5Hansen, M.H., Hurwitz, W.N., and Madow, W.G,: Samp2e 

Survey Methods and Theory, Volumes I and II. New York. John 

Wiley and Sons, Inc., 1963. 

6Cochran, W:G.: Sarnp2ing Techniques, New York. John 

Wiley and Sons, Inc., 1953. 

000 

8 

* U.S. GovERNUEMT PRINTING OFFICE : !S65 0-78 S-! !4 
OUTLINE OF REPORT SERIES FOR VITAL AND HEALTH STATISTICS 

Public Health Service Publication No. 1000 

SeVies 1. 

Setves 2. 

SeYies 3. 

Ativlf.?s ./. 

Sc)tes 10. 

Series 11. 

SeVies 12. 

Se vies 20. 

Series 21. 

k ries 22. 

Programs and collection procedures. —Reports which describe the general programs of the National 

Center for Health Statistics and its offices and divisions, data collection methods used, definitions, and 

other material necessary for understanding the data. 

Reports number 1-4 

Oata evaluation and metitods reseat-clt.— Studies of new statistical methodology including: experiment 

tests of new survey methods, studies of vital statistics collection methods, new analytical techniques, 

objective evaluations of reliability of collected data, contributions to statistical theory. 

Reports number 1-11 

Analytlcul studies. — Reports presenting analytical or interpretive studies based on viral and health statistics, carrying the ~nalysis further than the expository types of reports in the other series. 

Reports number 1-4 

.!)ocu)nents and commlttce reports .—Final reports of major committees concerned with vital and he~lth 

st.lt istics, md documents such as recommended model vital registration laws and revised birth and 

de,]th certificates. 

Reports number 1 and 2 

t.)ata
 F)’otn tile Health Interview SuYtJey.—Statistics on illness, accidental injuries, disability, use of 

hospit.il, medical, dent~l, and ather services. and other health-related topics, based on d~ta collected in 

LIcontinuing national household interview survey. 

Reports number 1-22 

L)atuFrom the Hcaltlt L“.rumination Su?wey.- Statistics based on the direct examination, testing, and 

measurement of nationnl s~mples of the population, including the medically defined prevalence of specific disc.lses, and distributions of the population with respect to various physical and physiological 

measurements. 

Reports number 1-10 

LJatuFrom the Health Iiecovds Survey. —Statistics from records of hospital discharges and statistics 

relating to the health characteristics of persons in institutions, and on hospital, medical, nursing, and 

personal care received, based on national samples of establishments providing these services and 

samples of the residents or patients. 

Reports number I and 2 

Data on mortality. —Various statistics on mortality other than as included in annual or monthly reportsspecial analyses by cause of death, age, and other demographic variables, also geographic and time 

series analyses. 

No reports to date 

Data on natality, marriage, and diuorce. — Various statistics on natality, marriage, and divorce other 

than .1s included in annual or monthiy reports— special analyses by demographic variables, also geographic and time series analyses, studies of fertility. 

Reports number 1-6 

Data From the National Natality and Mortality Surueys. —Statistics on characteristics of births and 

deaths not available from the vital records, based on sample surveys stemming from these records, 

including such topics as mortality by sczioeconomic class, medical experience in the last year of life, 

characteristics of pregnancy, etc. 

Reports number 1 

For a list of titles of reports published in these series, write to:	 National Center for Health Statistics 

U.S. Public Health Service 

Washinjgon, D.C. 20201