NAME:  LOW BIRTH WEIGHT DATA [1-3 MATCH] (MLOWBWT.DAT)
KEYWORDS:  Logistic Regression (for a matched 1-3 case-control study)
SIZE:  116 observations (29 cases, 87 controls), 9 variables

SOURCE: Hosmer and Lemeshow (1989) Applied Logistic Regression, Appendix 4.
	These data are copyrighted by John Wiley & Sons Inc. and must 
	be acknowledged and used accordingly.  Data were collected at Baystate 
	Medical Center, Springfield, Massachusetts.



DESCRIPTIVE ABSTRACT:

The goal of this study was to identify risk factors associated
with giving birth to a low birth weight baby (weighing less than 2500
grams). Data were originally collected on 189 women, 59 of which had low
birth weight babies and 130 of which had normal birth weight babies.  For
29 low birth weight cases, 3 controls were selected matched on age.  Four
variables which were thought to be of importance were age, weight of the
subject at her last menstrual period, race, and the number of physician
visits during the first trimester of pregnancy.



NOTE: This data set consists of the 1-3 matched data.  The complete original
data set for this low birth weight data may be found in lowbwt.dat and
a 1-1 paired data set created from the low birth weight data may be
found in lowbwtm11.dat.


LIST OF VARIABLES:

        Variable                                              Abbreviation
-----------------------------------------------------------------------------
        Stratum Number                                          STRATUM

        Observation Type (1 = Case, 2,3,4 = Controls)           OBS 
   
        Age of the Mother in Years                              AGE

        Low Birth Weight (0 = Birth Weight ge 2500g,            LOW
                          l = Birth Weight < 2500g)
  
        Weight in Pounds at the Last Menstrual Period           LWT
     
        Smoking Status During Pregnancy (1 = Yes, 0 = No)       SMOKE
          
        History of Hypertension (1 = Yes, 0 = No)               HT
     
        Presence of Uterine Irritability (1 = Yes, 0 = No)      UI
     
        History of Premature Labor (0 = None, 1 = Yes)          PTD

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PEDAGOGICAL NOTES:
        
These data have been used as an example of fitting a logistic
regression model for 1-M matched studies.


STORY BEHIND THE DATA:
        
Low birth weight is an outcome that has been of concern to physicians
for years. This is due to the fact that infant mortality rates and birth
defect rates are very high for low birth weight babies. A woman's behavior
during pregnancy (including diet, smoking habits, and receiving prenatal care)
can greatly alter the chances of carrying the baby to term and, consequently,
of delivering a baby of normal birth weight.
        
The variables identified in the code sheet given in the table have been
shown to be associated with low birth weight in the obstetrical literature. The
goal of the current study was to ascertain if these variables were important
in the population being served by the medical center where the data were
collected.


References:

1. Hosmer and Lemeshow, Applied Logistic Regression, Wiley, (1989).