Department of Health  and Human  Services 
OFFICE  OF 
INSPECTOR  GENERAL 
DRG  296  VALIDATION  UPDATE: 
NUTRITIONAL  AND  MISCELLANEOUS 
METABOLIC  DISORDERS 
OCTOBER  1992  OFFICE  OF INSPECTOR  GENERAL 
The  mission  of the  Office  of Inspector  General  (OIG),  as mandated  by Public  Law 95-
452, as amended,  is to protect  the  integrity  of the  Department  of Health  and  Human 
Services  (HHS)  programs  as well as the  health  and  welfare  of beneficiaries  served  by 
those  programs.  This  statutory  mission  is carried  out  through  a nationwide  network  of 
audits,  investigations,  and  inspections  conducted  by three  OIG  operating  components: 
the  Office  of Audit  Services,  the  Office  of Investigations,  and  the  Office  of Evaluations 
and  Inspections.  The  OIG  also informs  the  Secretary  of HHS  program  and 
management  problems  and  recommends  courses  to correct  them. 
OFFICE  OF AUDIT  SERVICES 
The  OIGS Office  of Audit  Services  (OAS)  provides  all auditing  semices  for  HHS, 
either  by conducting  audits  with its own audit  resources  or by overseeing  audit  work 
done  by others.  Audits  examine  the  performance  of HHS  programs  and/or  its 
grantees  and  contractors  in carrying  out  their  respective  responsibilities  and  are 
intended  to provide  independent  assessments  of HHS  programs  and  operations  in 
order  to reduce  waste,  abuse,  and  mismanagement  and  to promote  economy  and 
efficiency  throughout  the  Department. 
OFFICE  OF  INVESTIGATIONS 
The  OIGS Office  of Investigations  (01)  conducts  criminal,  civil, and  administrative 
investigations  of allegations  of wrongdoing  in HHS  programs  or to HHS  beneficiaries 
and  of unjust  enrichment  by providers.  The  investigative  efforts  of 01  lead  to criminal 
convictions,  administrative  sanctions,  or civil money  penalties.  The  01  also  oversees 
State  Medicaid  fraud  control  units which investigate  and  prosecute  fraud  and  patient 
abuse  in the  Medicaid  program. 
OFFICE  OF EVALUATION  AND  INSPECTIONS 
The  OIGS Office  of Evaluation  and  Inspections  (OEI)  conducts  short-term 
management  and  program  evaluations  (called  inspections)  that  focus  on issues  of 
concern  to the  Department,  the  Congress,  and  the  public.  The  findings  and 
recommendations  contained  in these  inspection  reports  generate  rapid,  accurate,  and 
up-to-date  information  on the  efficiency,  vulnerability,  and  effectiveness  of 
departmental  programs. 
Amy L. Lockwood  of BOTEC  Analysis  Corporation  prepared  this report  with 
direction  from  Janet  W. Knight,  BOTEC  Project  Director,  and  David  C. Hsia,  OIG 
Project  Officer.  Contract  information  and  project  participants  are  listed  in Appendix 
A to this inspection.  Department of Health  and Human  Services 
OFFICE  OF 
INSPECTOR  GENERAL 
DRG  296  VALIDATION  UPDATE: 
NUTRITIONAL  AND  MISCELLANEOUS 
METABOLIC  DISORDERS 
OCTOBER  1992  OEI-12-89-00195  EXECUTIVE  SUMMARY

PURPOSE 
This inspection  reabstracted  on a blinded  basis,  the  International  Classification  of 
Diseases,  9th Edition,  Clinical  Modification  codes  from  a sample  of Medicare 
discharges  billed  as diagnosis-related  group  (DRG)  296, nutritional  and  miscellaneous 
metabolic  disorders.  It compared  the  reabstracted  DRG  to the  hospital-billed  DRG 
for reimbursement  changes.  The  sample  was nationally  representative  and  covered  all 
of 1988, the  most  recent  data  available. 
This  inspection  updated  a previous  Office  of Inspector  General  (OIG)  study.  For 
1985, the  OIG  found  19.3 percent  errors  among  173 reabstractions,  improperly  over-
reimbursing  hospitals  by a projected  $19.9 million.  This  inspection  used  a parallel 
methodology  to make  these  studies  statistically  comparable. 
FINDINGS 
DRG  296 errom not reduced 
Of  106 discharges  reabstracted  for this inspection,  10.4 percent  had  billing  errors.  This 
proportion  did not  differ  statistically  from  the  19.3 percent  errors  for DRG  296 
discharges  in 1985.  It also did not  differ  statistically  from  the  14.7 percent  errors  for 
all discharges  in 1988. 
DRG  296 erronrrandom 
Of  11 bills with DRG  errors,  45.5 percent  over-reimbursed  the  hospital.  This 
proportion  differed  statistically  from  the  75.0 percent  over-reimbursement  in  1985. 
DRG  296 erron cause no over-reimbursement 
The  DRG  296 billing errors  under-reimbursed  hospitals  $14.7 million.  While  not  itself 
statistically  different  from  zero  (no under-reimbursement),  this result  does  differ 
statistically  from  the  $19,9 million  over-reimbursement  in 1985. 
i  TABLE  OF  CONTENTS

EXECUTIVE  SUMMARY  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  i

TABLE  OF  CONTENTS  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ii

INTRODU(XION  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  1

FINDINGS  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  4

Errorsnotreduced  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  4

Errors  occur  randomly  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  4

Errors  cause  no over-reimbursement  . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5

Reasons  for errors  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  5

ENDNOTES  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  6

APPENDICES  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  7

A: Project  participants  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  8

B: ICD-9-CM  codes  in DRG  296.....  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  10

C: Sample  representativeness.  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  11

D: Proportion  of DRG  296 billing errors,  1985 and  1988 . . . . . . . . . . . . . . . . . . .  12

E: DRG296case-mix  index change,  1985 and  1988  . . . . . . . . . . . . . . . . . . . . . .  13
 INTRODUCTION

Background 
Diagnosis-related  group  (DRG)  296 accepts  58 International  Classification  of Diseases, 
%hEdition,  Clinical  Modification  (ICD-9-CM)  codes  asptincipal  diagnoses.  These 
ICD-9-CM  codes  identify  metabolic  disorders  principally  associated  with  dehydration 
and  fluid-electrolyte  imbalances.  DRG  297 accepts  the  same  principal  diagnoses,  but 
requires  a complication.  DRGs  296 and  297 do not  include  admissions  for diabetes, 
the  commonest  metabolic  order.  [Appendix  B]. 
DRG  296s weight  increased  from  0.8886 in 1985 to 0.9259 in 1988.  Beginning  in FY 
1988, the  HCFA  altered  DRG  296 to drop  age  over  69 as a complication  code.  This 
change  shifted  billings  in one  quarter  of the  data  collection  period  for this inspection. 
In a previous  study, the  OIG  found  that  DRG  296 had  an unusually  high  proportion  of 
billing  errors.1  Correct  ICD-9-CM  coding would  have  grouped  19.3 percent  of its 173 
reabstractions  to different  DRGs  in 1985.  These  billing errors  over-reimbursed  the 
hospitals  a projected  $19.9 million. 
This  inspection  updated  the  previous  study using  1988 data,  the  most  recent  available. 
It used  a parallel  methodology  to make  these  inspections  statistically  comparable. 
Methodology 
This  inspection  randomly  selected  106 DRG  296 discharges.  The  study population

consisted  of the  191,509 Medicare-reimbursed  DRG  296 discharges  during  calendar

year  1988.  The  design  excluded  discharges  from  specialty  institutions  such as

childrens  hospitals,  tuberculosis  units,  and  psychiatric  facilities.  It also  excluded

discharges  in Maryland  and  New Jersey,  which the  PPS still exempted  in 1988.  Finally,

it excluded  bills for pediatric,  obstetric,  and  psychiatric  DRGs  (principally  drug  and

alcohol  rehabilitation  performed  by a general  hospital).2  Unlike  its  1985 predecessor,

it included  hospitals  established  since  the  advent  of the  PPS in 1983.

The  OIG  requested  that  hospitals  send  complete  copies  of the  sampled  medical

records  to the  OIGS contractor,  Baxter-Health  Data  Institute  (HDI)  of Lexington,

MA.  The  OIG  followed-up  missing records  and  issued  subpoenas  to compel  the

cooperation  of four  hospitals.

The  OIG  contracted  with the  American  Medical  Record  Association  (AMRA)  to

reabstract  the  charts.  The  AMRA  selected  ICD-9-CM  codes  supported  by the  record,

determined  the  principle  diagnosis,  and  grouped  to select  the  correct  DRG.  To  assure

that  the  original  ICD-9-CM  codes  and  DRGs  did not  effect  the  reabstraction,  the

AMRA  coders  conducted  their  work without  knowledge  of the  original  ICD-9-CM

codes  and  DRGs.  The  coders  had  instructions  not  to treat  marginal  problems  or

1  honest  differences  injudgement  about  appropriate  coding  as DRG  errors.  This 
standard  should  have  produced  a conservative  estimate  of the  proportion  of discharges 
having  DRG  errors.  A series  of reliability  checks  verified  the  reproducibility  and 
accuracy  of the  AMRA  coding.  The  AMRA  also identified  the  reasons  why a 
hospitals  bill differed  from  the  correct  codes. 
BOTEC  Analysis  Corporation  of Cambridge,  Massachusetts  (BOTEC)  edited  the 
AMRA  database,  checked  the  samples  representativeness,  and  conducted  statistical 
analyses  of the  correlates  and  financial  consequences  of DRG  296 miscoding.  It also 
reweighted  the  1985 data  to improve  comparability  with this inspection.  The  t-test 
determined  whether  numeric  differences  between  the  1985 results  and  1988 results 
were  real  (statistically  significant)  or could  be attributed  to random  error. 
Representativeness 
To test  the  samples  representativeness,  the  OIG  compared  the  distribution  of sample 
bills to the  distribution  of the  underlying  population  of DRG  296.  The  sample  came 
disproportionately  from  small,  nonteaching,  and  for-profit  hospitals.  It did not  differ 
statistically  from  the  underlying  population  with respect  to patient  characteristics. 
[Appendix  C]. 
Bedsize  I  Teachingstatus 
lW-299  Taaching
<I(M 
100-299 
@l!!5* 
~<m  ,on.w.~  ~ 
Nonteachhg 
Sample  Population  Sample  Population 
Looation  Controi 
,mm@prNm.(=Jm
Q:_QNm.. 
Sample  Population  Sample  Population 
2
 Age 
Sex 
Race 
3  FINDINGS

Errors not reduced 
Of the  106 sample  discharges,  10.4

percent  had  billing errors  that  changed  +  
20

their  reimbursement  from  DRG  296.

Year 
The  difference  between  this result  and

1 1985 
the  19.3 percent  error  rate  the  OIG

 
1988 
previously  reported  for  1985 was not

10

statistically  significant.a  This  trend 
applied  across  most  hospital  and  patient

characteristics.  Small  and  large

hospitals,  urban  hospitals,  and  teaching

institutions  showed  more  improvement.  0i  

Patients  aged  65-74 years  and  85+

years,  and  males  also had  better  coding  Figure  3: Coding  errors,  1985 & 1988

accuracy.  [Appendix  D].

This  nonsignificant  1985-1988 decrease  in the  proportion  of DRG  296 billing errors

exceeded  the  improvement  for all DRGs.  However,  the  latter  change  from  20.8

percent  in 1985 to  14.7 percent  in 1988 comparison  attained  statistical  significance

because  of their  larger  sample  sizes.  The  difference  between  this inspections  10.4

percent  DRG  296 e~rors and  the  14.7 percent  errors  for  all DRGs  in-1988 was not

statistically  significant.3 
Percent 
randomly  30- Errordkectlon 
&Ol_S  occur 
  Over-reimbursed 
Of the  11 billing  errors,  45.5 percent  20    Under-reimbursed 
over-reimbursed  the  hospital.  The  errors 
lo-
do not  have  any statistical  directionality. 
In  1985, a statistically  significant  75.0 
0-
percent  of the  37 coding  errors  over-
reimbursed  the  hospital.  Curiously,  the 
-1o-
randomness  of this inspections  error 
1988 
Year 1~ 
direction  does  not  differ  statistically  from 
the  previous  inspections  definite 
Figure  4: Direction  of DRG  296 coding 
directionality,  presumably  because  of 
errors,  1985 & 1988 
their  relatively  small  sample  sizes. 
a. Because  of the  smaller  sample  size used  in this DRG-specific  analysis,  estimates  are 
less precise  than  the  OIGS  national  estimate.  Statistical  testing  determined  whether 
apparent  differences  were  real  (statistically  significant)  or could  be  attributed  to 
random  error. 
4  Errors  cause  no over-reimbursement 
$million  2 
The  106 sample  discharges  originally 
carried  Relative  Weights  of 0.9259 
equivalent  to an average  payment  of 
$2,829.  The  AMRA  reabstraction 
increased  the  case-mix  index  (CMI)  to 
0.9499, a nonstatistically  significant 
difference  of 0.0240.  This trend 
persisted  across  all hospital  and  patient 
demographic  categories.  However,  the 
net  CMI  change  in 1988 did differ 
statistically  from  the  net  CMI  change  for 
DRG  296 in 1985.  [Appendix  E]. 
. 
Fim.we 5: Financial  imn1a. ct.  1985 &  1988
Extrapolation  of the  CMI  change  in   
1988 to all 191,509 DRG  296 discharges  from  the  hospital  categories  included  in this 
sample  projected  that  billing errors  under-reimbursed  hospitals  a nonstatistically 
significant  $14.7 million.  However,  this under-reimbursement  did differ  statistically 
from  the  $19.9 million  over-reimbursement  the  OIG  previously  reported  for  1985. 
Reasons  for errors 
The AMRA  identified  only two types  of errors  in the  sample,  mis-specification  and 
resequencing  in  1988.  Mis-specification  caused  36.4 percent  of the  11 billing  errors, 
not  statistkafiy  different  from its  37.2 percent  for  1985;  Resequencing  cause~  the 
balance  of 1988 coding  errors  (63.4 percent),  compared  to 50.7 percent  for  1985. 
Miscoding  and  other  errors  caused  the  remainder  of errors  for  1985. 
5

  ENDNOTES

1. Stone  D, Kleiman  M, Meyers  M, Schutte  J, Lee  F, Hsia  D, & Krushat  M.  DRG 
296: Nutritional  and  metabolic  disorders.  Washington,  DC:  HHS  Office  of Inspector 
General,  1989.  Publication  no. 0AI-12-88-01150. 
2. Knight  J W & Hsia  D C, eds.  National  DRG  Validation  Study Update:  Technical 
Report.  Washington,  DC:  HHSOffice  of Inspector  General,  1992.  Inspection  no. 
OEI-12-90-00191. 
3. Knight J W & Hsia  D C, eds.  National  DRG  Validation  Study Update:  Summary 
Report.  Washington,  DC  HHS  Ofilce  of Inspector  General,  1992.  Inspection  no. 
OEI-12-90-00190. 
6
 APPENDICES 
7  Appendix #k Project participants 
~ 
Cathaleen  A. Ahern,  B.A.

Evan  J. Buckingham,  B.A.

David  C. Hsia,  J.D.,  M.D.,  M.P.H.

Thomas  F. Komaniecki,  M.P.A.

W. Mark  Krushat,  M.P.H.

Linda  M. Moscoe,  B.A.

Brian  P. Ritchie,  B.A.

Barry  L. Steeleyb

John  M. Traczyk,  B.A.

HCFA

Timothy  F. Greene,  M.A., M.B.A.

Stephen  F. Jencks,  M.D.

Michael  R. McMullan,  M.B.A.

Harry  L. Savitt,  Ph.D.

Jeanette  M. Smith,  M.D.,  M.P.H.

Malcolm  A. Sneen,  B.S.

RAND  Cor~oration

Haya  P. Rubin,  M.D.,  Ph.Dd

Baxter-Health  Data  Institutee

Patricia  J. Baxter,  R.N.

Patricia  Cassidy-Tsnosas,  R.N.

Annette  M. Delaney,  R.N.,  M.A.

Ellen  B. Inghilleri,  R.N.

Janet  Mathews,  A.R.T.

Laurie  H. Moore,  R.R.A.

Claire  Shannon,  A.R.T.

Michele  A. Wiese,  B.A.

AMRA 
Margret  K. Amatayakul,  M.B.A.,  R.R.A.

Mary  Converse,  R.R.A.

Nicholas  J. Cotsonas,  M.D.f

Linda  Ertl,  R.R.A.

b. Now at Health  Audit  Services,  Ellicott  City, MD. 
c. Now at the  Journal  of the  American  Medical  Association,  Chicago,  IL. 
d. Now at Johns  Hopkins  Medical  Institutions. 
e. Ceased  operations  February  16, 1990. 
f. Outside  contractor. 
8  Rita  M. Finnegan,  R.R.A.

Desla  Mantilla,  A.R.T.

Barbara  Manny,  R.R.A.

Sonia  Martyniuk,  R.R.A.

Toula  Nicholas,  A.R.T.

Charlotte  Razor,  R.R.A.

LouAnn  Schraffenberger,  R.R.A.

Lynn  Smetko,  R.R.A.

Dawn  Smith,  A.R.T.

Joan  Zacharias,  A.R.T.

BOTEC  Analvsis  Corporation

Geraldine  M. Berenhol~  R.R.A.

Andrew  H. Chalsma,  B.A.

David  P. Cavanagh,  M.A., Ph.D.

Janet  W. Knight,  R.N.,  Ph.D.

Amy L. Lockwood,  B.A.

Contract  information 
Contractor

BOTEC  Analysis  Corporation

1698 Massachusetts  Avenue

Cambridge,  MA  02138

Proiect  Officer

David  Hsia,  J.D.,  M.D.,  M.P.H.

Office  of Inspector  General

330 Independence  Avenue

Washington,  D.C.  20201

Contract

HHS-1OO-9O-OO23

Firm-fixed  price  contract

$203,257

9
 Appendix B: ICD-9-CM  codes in DRG  2% 
251  hypoglycemia 
260  kwashiorkor 
261  marasmus 
262  other  severe  malnutrition 
263  other  malnutrition 
264  vitamin  A deficiency 
265  beriberi 
266  vitamin  B deficiencies 
267  vitamin  C deficiency 
268  vitamin  D deficiency 
269  vitamin  Kand  other  nutritional  deficiencies 
275  magnesium-calcium  metabolism  diseases 
276  fluid and  electrolyte  disorders 
277  cystic fibrosis 
278  obesity 
781.7  tetany 
783  weight  change 
790.2  abnormal  glucose  tolerance  test 
791.6  acetonuria 
10
 Appendix C  Sample representativeness 
Number  [percent]  Population  Sample  Chi-square 
Hos~ital  demoma~hv 
1-99 beds  32,071 [16.7]  25 [23.6]  9.54, 2 df, P= O.991 
100-299 beds  70,929 [37.0]  48 [45.3] 
300+  beds  88,474 [46.2]  33 [31.1] 
Metropolitan  143,417 [74.9]  72 [67.9]  2.58, 1 df, P= O.888 
Nonmetropolitan  48,092 [25.1]  34 [32.1] 
Teaching  76,999 [40.2]  29 [27.4]  6.87, 1 df, P= O.991 
Nonteaching  114,510 [59.8]  77 [72.6] 
Profit  20,273 [10.8]  18 [17.1]  4.10, 1 df, P= O.955 
Nonprofit  166,626 [89.2]  87 [82.9] 
Patient  demoma~hy 
c 65 years  16,914 [8.8]  6 [5.7]  2.99, 3 df, P= O.606 
65-74 years  55,985 [29.2]  36 [34.0] 
75-84 years  70,772 [37.0]  34 [32.1] 
85+  years  47,838 [25.0]  30 [28.3] 
Male  70,471 [36.8]  34 [32.1]  0.96, 1 df, P=0.668 
Female  121,038 [63.2]  72 [67.9] 
White  155,215 [81.0]  88 [83.0]  0.47, 3 df, P= O.076 
Black  28,005 [14.6]  14 [13.2] 
Other  3,104 [1.6]  1 [0.9] 
Unknown  5,185 [2.7]  3 [2.8] 
Total  191.., 509 [~10.0-.01  106 [100.0]
J 
11
 Appendix D: Proportion of DRG  2%  billingerrors,  1985  & 1988 
Number  [percent]  1988  1985  t-test 
HosRital  demo~ra~hy 
1-99 beds  3 [12.0 t  6.6]  21 [28.8 ~ 5.3]  3.18 
100-299 beds  6 [12.5 ~ 4.8]  9 [16.1 ~ 5.0]  0.82 
300+  beds  2 [6.1 f  4.2]  7 [15.9 ~ 5.6]  2.48 
Metropolitan  5 [6.9 f  3.0]  14 [20.0 t  5.0]  2.82 
Nonmetropolitan  6 [17.6 ~ 6.6]  23 [28.6 ~ 7.5]  1.39 
Teaching  2 [6.9 ~ 4.8]  5 [15.1 A 6.3]  2.02 
Nonteaching  9 [11.7 t  3.7]  32 [22.0 f  5.2]  1.61 
Profit  3 [9.2 ~ 3.1]  2 [15.1 f  11.0]  1.57 
Nonprofit  8 [16.7 t  9.0]  35 [18.3 f  3.4]  0.31 
Patient  demo~raphv 
<65  years  2 [33.3 f  21.1]  5 [5.0 f  2.8]  8.85 
65-74 years  3 [8.3 ~ 4.7]  13 [28.2 f  7.6]  3.92 
75-84 years  3 [8.8*  4.9]  10 [18.9 ~ 6.1]  1.83 
85+  years  3 [10.0 ~ 5.6]  9 [18.0 f  5.0]  2.08 
Male  4 [11.8 ?  5.6]  17 [24.2 ~ 6.4]  2.31 
Female  7 [9.7 * 3.5]  20 [13.5 * 3.4]  0.90 
White  10 [11.4 * 3.4] 
Black  o [0.0 * 0.0] 
Other  o [0.0 * 0.0] 
Unknown  1 [33.3 t  33.3] 
Total  11 [10.4 * 3.0]  37 [18.0 f  3.3]  1.59 
*. Reweighted  for comparability  to  1988. 
12  Appendix E  DRG  2%  case-mix index change, 1985  & 1988 
Relative  weight  ~  1988  1985  Difference  t-test 
standard  error 
Hospital  demoma~hv 
1-99 beds  -0.0123  ~ 0.0121  -0.0429 f  0.0153  0.0306  2.12 
100-299 beds  0.0654  * 0.0500  -0.0172 ~ 0.0129  0.0826  2.73 
300+  beds  -0.0084  ~ 0.0118  -0.0237 f  0.0135  0.0153  1.53 
Metropolitan  0.0412  & 0.0331  -0.0207 ~ 0.0142  0.0619  2.35 
Nonmetropolitan  -0.0123  ~ 0.0171  -0.0557 ~ 0.0214  0.0434  1.94 
Teaching  -0.0096  t  0.0135  -0.0226 ~ 0.0134  0.0130  1.36 
Nonteaching  0.0367  ~ 0.0315  -0.0267 & 0.0137  0.0634  2.46 
Profit  0.0046  ~ 0.0114  0.0209 i  0.0443  -0.0163  1.16 
Nonprofit  0.1190  t  0.1259  -0.0284 ~ 0.0080  0.1474  6.23 
patient  demomaphy 
<65  years  0.0445  t  0.1345  -0.0066 ~ 0.0060  0.0511  2.72 
65-74 years  0.0667  * 0.0634  -0.0621 k 0.0189  0.1288  3.84 
75-84 years  -0.0110  t  0.0102  -0.0349 f  0.0178  0.0239  1.52 
85+  years  0.0084  t  0.0143  -0.0065 * 0.0144  0.0149  1.41 
Male  0.0597  t  0.0684  -0.0295 k 0.0152  0.0892  2.80 
Female  0.0072  ~ 0.0117  -0.0204 f  0.0093  0.0276  2.25 
White  0.0313 ~ 0.0279 
Black  O.0000 * O.0000 
Other  O.0000 * O.0000 
Unknown  -0.0678  f  0.0678 
Total  0.0240 ~ 0.0232  -0.0243 k 0.0083  0.0483  2.30 
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