T H E ASSESSM ENT O F TH E Q U A LITY O F G ESTA TIO N A L A G E AND B IR TH W E IG H T DATA
Gestational age has been found to be important in determining infant survival. A number of studies have indicated that gestational age, together with birth weight, serves as a powerful predictor for perinatal survival (Wilcox and Skjaerven, 1992; Golding and Shenton, 1990; WHO, 1984).
An accurate determination of gestational age is a key process in making decisions about when to conduct certain tests and interventions and allows for the identification and management of pregnancy complications. For clinicians the information may help in making decisions about induction, dysfunctional labor and caesarean section. For epidemiologists, such information will be important in distinguishing between prematurity and retarded foetal growth. For the present longitudinal study, gestational age data are used as a basis for determining the values of variables which use gestational age as one component in their calculations, for example, in calculating inter-pregnancy interval, the timing of antenatal care, maternal age at the beginning of pregnancy and the classification of preterm delivery. Thus, an assessment of the quality of gestational age data will be important in the study of factors bearing on pregnancy outcomes and, in turn, may provide information for the improvement of obstetric practices and indirectly support efforts to lower perinatal mortality.
This chapter raises issues about the need to obtain good quality gestational age data. In doing so, it presents a comparison of the results of univariate and bivariate analysis of the Indramayu gestational age data with the results of other studies. It explains the stages involved in the assessment of gestational age data, including finding a way to select good quality gestational age data. The results of the analysis of the edited and original gestational age data are also compared.
4.1 The Need for Assessing Gestational Data Quality.
The determination of gestational age is essential in pregnancy management. The duration of gestation is measured from the first day of the last normal menstrual period (LMP) and is expressed in completed days or weeks (WHO ICD 9, 1977:764). A number of methods have been used to ascertain the length of gestation. These include the use of ultrasound estimates of foetal size, the use of menstrual histories together with other physical measurements, such as fundal height, and laboratory examination, and a physical and neurological examination of the newborn baby to estimate the length of gestation at delivery (Lubchenco, 1976:10-57). For epidemiologists, a commonly used method for estimating gestational age is the LMP-based estimate of gestational age. This assumes an invariant 28-day menstrual cycle with ovulation occurring at midcycle (Berg, 1991:585).
The use of women's recollection of their last normal menstrual period for ascertaining the length of gestation is fraught with potential error. This is mainly due to uncertainty about the accuracy of the date provided by the pregnant woman. Despite this, menstrual dating is still used as an important tool in determining the duration of gestation (Lubchenco, 1976:10). An accurate date of the last menstrual period provides the most reliable estimate of delivery date. In the pre-ultrasonographic era, which still exists in most developing countries, gestation based on LMP proved to have greater statistical and biological validity than gestation based on physicians' estimates (Kiely and Susser, 1992:343). Some hospital-based studies that have been conducted revealed that, in some circumstances, the estimate of gestational age based on a mother's recall of the LMP is valid (Kramer et al., 1988; Hakim et al., 1992). These studies used ultrasonographic estimates of the foetal biparietal diameter and medical record-based gestational age as the benchmark in validating gestational data based on the last normal menstrual period.
In the Indramayu community-based study, neither ultrasound estimates of foetal growth nor medical records were available. The gestational age data collected were based solely on the women's recall of the date of the last menstrual period prior to pregnancy. Considering the inadequacies that might be found in the use of LMP-based
gestational data and the importance of gestational age in the analysis of factors bearing on pregnancy outcomes, an examination of the quality of gestational age data gathered in the Indramayu study is essential. The next two sections are univariate and bivariate analyses of gestational age which provide some evidence about why the validity of such data needs to be assessed.
4.1.1 Results of Univariate Analysis.
As described in Chapter One, the gestational timing of infant birth can be classified into preterm, term, and post-term. The validity of the Indramayu gestational age data is tested indirectly by examining the proportion of preterm deliveries, using gestational age as a means of categorizing preterm cases. The incidence of preterm delivery found in some other studies is used as a relative standard in examining the quality of the Indramayu data.
Table 4.1 shows the incidence of preterm deliveries found in studies conducted in some South-East Asian countries. In these studies, the definition of preterm delivery was in accordance with the WHO standard.
Table 4.1 Incidence of Preterm Deliveries in Various Research Centres
Country Area Proportion delivered preterm (%)
Burma North Okalapa 12.7
Hlebu 13.2
Hmawbi 12.9
China Shanghai 5.1
Thailand Bang Pa-in 22.4
Ubon (1) 10
Ubon (2) 13.6
Ubon (3) 16
Vietnam Phu Xuyen 14.7
Hanoi 8.3
The proportion of preterm deliveries found in the Indramayu study is presented in Table 4.2.
Table 4.2. The proportion of preterm deliveries in the Indramayu study, 1990-1993.
Total Total valid Proportion of
Study Area cases cases preterm (%)
Gabus Wetan 656 612 21.7
Sliyeg 816 800 28.3
Source: Data on preterm delivery is extracted from the MotherCare database file.
Compared to the data on preterm deliveries found in South-East Asian countries, it seems that the proportion of LMP-based preterm deliveries in the Indramayu, in general, tends to be higher.
Forty weeks of gestation is the norm and about 80 per cent of deliveries occur within the two weeks on either side of that (Lubchenco, 1976:3). The distribution of live births by gestational age from the Indramayu and other studies is shown in Table 4.3.
Table 4.3 Births by gestation (percentage)
Gestational age (in weeks)
V A J U I I U J O I U Uy M I C < 3 7 3 7 - 3 8 3 9 - 4 0 41 -4 2 above 42 Burma: Rural 12.5 1 0 76.8 0.4 0 . 2 Urban 4 2.5 93.2 — 0.3 Hospital 8 . 2 5.1 8 6 . 6 - 0 . 1 India: Rural 7.7 - 89 - 3.3 Thailand: Rural 2.5 1 2 64.7 20.3 0.5 Hospital 5.7 20.7 57.1 16 0 . 6
Indramayu Gabus Wetan 21.7 15.4 39.5 13.4 1 0
Sliyeg 28.3 17.5 34.3 12.4 7.6
Data source: a. Data on Burma, India, Thailand was extracted from the WHO (1984:22). b. Data on the Indramayu study was extracted from the MotherCare