Chapter 8 Pregnancy and birth outcomes in Kerala · regard to foetal loss and neonatal mortality...

Chapter 8

Pregnancy and birth outcomes in Kerala

8.1 Introduction

The previous chapters (Chapters 5, 6 and 7) discussed the situation in the EME region with regard to foetal loss and neonatal mortality and the selected, adverse pregnancy and birth outcomes. The present chapter focuses on a region in transition, i.e. Kerala in South India. Its objectives are twofold: (1) to assess the situation in this region and (2) to place the selected risk factors in the framework of an epidemiologic transition. Data come from a hospital survey that was conducted in Sri Avittom Thirunal (SAT) Hospital, in Thiruvananthapuram. Chapter 4 has already explained the choice for a hospital survey in a case region, and the choice of SAT Hospital (SATH) in Thiruvananthapuram, Kerala. For a brief introduction to Kerala, please refer to Section 2.4.2. Overall, data were collected on 1,022 consecutive births between Monday October 9, 2000 and Monday October 30, 2000. The present chapter discusses and analyses data on 1,001 singleton births with birth weight ≥ 1,000 g and gestational age ≥ 28 completed weeks. At least 99% of women in the survey had received antenatal care, and 20% had been referred to SAT Hospital by other hospitals (see Chapter 4). To situate the results of the hospital survey within the context of the region they are compared to, and complemented by, data from the SRS (Sample Registration System), the NFHS (National Family and Health Survey), and other publications. Additional information was obtained from local informants (see Appendix D). In the present chapter, firstly, Section 8.2 analyses stillbirth and neonatal mortality, and constructs both a foetal life table and a neonatal life table. Subsequently, Section 8.3 discusses the selected pregnancy and birth outcomes and their prevalence in the population. The next section, Section 8.4, relates stillbirth and neonatal death to the pregnancy and birth outcomes (i.e. the risk factors). After a discussion on the associations between the risk factors themselves in Section 8.5, Section 8.6 summarises and discusses the results from the SATH survey. In addition, the results from the hospital survey are compared to the other data from Kerala. Finally, Section 8.7 compares the data from SAT Hospital and Kerala with the results for the EME region (from Chapters 5, 6, and 7) and secondary data from India. This makes it possible to place the risk factors under study within the framework of an epidemiologic transition and to study the changes that occur during this transition.

8.2 Foetal and neonatal life table

The present section analyses stillbirth and neonatal mortality in the population of the SATH survey. In addition, it constructs a foetal life table from 28 gestational weeks onward and a

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neonatal life table. The results are compared to other, secondary data from Kerala and to secondary data from India as a whole.

8.2.1 STILLBIRTH AND FOETAL LIFE TABLE

Stillbirth The SATH survey of 1,001 singleton births includes 988 (98.7%) live births and 13 (1.3%) stillbirths. These figures indicate a stillbirth (or foetal death) rate of 13.0 per 1,000 births and a stillbirth ratio of 13.2 per 1,000 live births. Of the stillbirths, 61.5% (8 cases) were antepartum, i.e. deaths before the onset of labour. The other 38.5% (5 cases) were intrapartum stillbirths, i.e. deaths during labour. How do the SATH data compare to other stillbirth rates observed in Kerala? Padmadas (2000) calculated stillbirth rate in Kerala on the basis of the first National Family Health Survey in 1992-1993 (NFHS-1). He estimated that during the five-year period preceding the survey 18.6 out of 1,000 births were stillbirths, which is higher than the result from the SATH survey. More recent estimates are available from the Sample Registration System (SRS). According to the SRS, the stillbirth rate in Kerala was only 6 per 1,000 births in 1998; 6 per 1,000 in rural areas and 7 per 1,000 in urban regions (Office of the Registrar General 2000). SRS data have been queried as to whether they represent Kerala figures correctly. Irudaya Rajan and Mohanachandran (1999a, 1999b) suspect that some deaths that occur immediately after birth are wrongly reported as ‘stillbirth’. This implies that the true stillbirth rate would even be lower than the figures from the SRS. Other data from SAT Hospital suggest even higher stillbirth rates. After excluding stillbirths ≤ 1,000 g, the stillbirth rate in 1990 in SAT Hospital was 23.7 per 1,000 births (Lalitha and Syamalan 1991). More recent labour room statistics from SAT Hospital (unpublished) provide rates that include births ≤ 28 weeks and/or ≤ 1,000 g. According to these figures, the stillbirth rate in SAT Hospital was 21.5 per 1,000 births in 1999 and 24.2 per 1,000 births in September 2000. Also in other hospitals in Kerala, the stillbirth rate seems to be relatively high. In Medical College Hospital in Kottayam, the stillbirth rate was 20.1 per 1,000 births during the period August 1992 to July 1993 (Pradeep et al. 1995). These additional hospital figures, however, include multiplets. In comparison, stillbirth rates and ratios are generally much lower in the EME region. In the early 1990s, late foetal death ratio, or stillbirth ratio, was 5.5 deaths per 1,000 live births in the Netherlands (in 1993), 4.5 in Italy (in 1992), 3.9 in Spain (1991), 3.4 in Sweden (1993), and 3.3 in Japan (in 1993) (UN, Demographic Yearbook, various years). In the hypothetical cohort that was constructed in Chapter 5 after the exclusion of induced abortion (see Table 5.6), the stillbirth rate was 4.9 per 1,000 births and the stillbirth ratio also 4.9 per 1,000 live births. In all-India, according to the SRS, the stillbirth rate was 9 per 1,000 births in 1998 (Office of the Registrar General 2000). In 1994, the lowest stillbirth rate in India was 4 per 1,000 in the state of Bihar, while the highest figures were found in Karnataka and Orissa at 15 per 1,000 births (Registrar General of India 1994 cited by Irudaya Rajan and Mohanachandran 1999a, 1999b). Overall stillbirth rate for all-India in 1994 was also 9 per

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1,000 births. The SRS figures for all-India are comparable to SRS figures for Kerala, both figures being lower than those observed in the SATH survey. In the SATH survey, the percentage of stillbirths (antepartum and intrapartum) was slightly higher in females than in males: 1.6% compared to 1.0% although the three antepartum stillbirths that were excluded from the sample on the basis of the inclusion criteria for weight and age, were two males and one female. With regard to cause of death, the assumed causes of the stillbirths included: abruptio placentae1 (3 cases), congenital anomalies (1 case), and non-immune hydrops foetalis2 (1 case). In the majority of cases the cause remained unknown. Moreover, results of possible autopsies were not available in the present survey. Several cases of stillbirth were complicated by hypertensive disorders, either pregnancy-induced hypertension (PIH) or pre-eclampsia. In addition, one of the pregnancies that resulted in antepartum stillbirth was complicated by PIH, herpes genitalis, and also 2nd trimester bleeding.

Foetal life table On the basis of the SATH survey, it is possible to construct a foetal life table as shown in Table 8.1. The table contains the events ‘live birth’ and ‘stillbirth’ as ‘exits’ or ways of leaving the uterus. An assumption in the table is that the events, on average, take place in the middle of the interval. The length of the intervals (h) equals one, i.e. one gestational week, and therefore, h is omitted from the equations and symbols below. Cases with unknown gestational age at birth have been excluded from the table. The life table does not include censored cases since the data on pregnancy have been collected retrospectively, for all women who completed the pregnancy and gave birth in SAT Hospital. In Table 8.1, column (1) indicates the gestational age interval in completed gestational weeks. For example, 28 weeks represents the 29th week of gestation. The lowest estimated age in the sample was 28 completed weeks and the highest was 44 completed weeks of gestation. Column (2) indicates the observed number of foetuses still in utero at the beginning of each interval (lx) which equals the risk set (Rx) since none of the cases are censored. Columns (3) and (4) represent the observed number of events in each interval; namely stillbirths and live births respectively. The total number of pregnancy terminations in each interval is shown in column (5). Subsequently, these data are the basis for calculating probabilities, rates, and in-utero expectancy. Columns (6) through (8) present the probabilities that were calculated using:

qx = Dx / lx (8.1)

or the number of events divided by the risk set. Column (9) shows the number of weeks lived in utero by the population within the indicated interval. The rates in columns (10) through

1 Abruptio placentae: the separation of the placenta from its site of implantation before the delivery of the foetus (Cunningham et al. 1993). See also Section 9.2.7. 2 Hydrops foetalis: the formation and accumulation of serous fluid in body cavities and subcutaneous oedema in the foetus (Cunningham et al. 1993).

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(12) were calculated as the number of events divided by the exposure time (in weeks). For example, column (12) was calculated using:

Mx = Dx / Lx (8.2)

Column (13) indicates the total number of gestational weeks that are remaining after having reached the indicated interval. Finally, column (14) presents the average number of weeks in utero that are remaining (cf. life expectancy). This ‘in-utero expectancy’ is calculated by dividing Tx in column (13) by lx in column (2). The resulting probabilities (columns 6 through 8) and rates (columns 10 through 12) in Table 8.1 are rather crude as the life table contains only the small numbers observed in the population of the SATH survey. Given the small sample size and the small number of events, the results are sensitive to random variation and show strong fluctuations. In the last interval (i.e. 44 completed gestational weeks), the combined probabilities of all events equal one since all foetuses will leave the uterus at one time or another. In SAT Hospital, the policy is to induce labour for postterm foetuses and, therefore, pregnancies of more than 44 completed gestational weeks are uncommon. In the table, ‘in-utero expectancy’ at 28 gestational weeks is 11.6 weeks, indicating an average gestational duration of 39.6 weeks for those who ‘survive’ up to 28 weeks. In comparison to the foetal life table of the hypothetical cohort in the EME region (Table 5.6), the weekly probability and rate of stillbirth are higher in the SATH population from gestational week 33 through to week 40. This may be a result of SAT Hospital’s status as a referral hospital with many compromised and high-risk cases. Remarkably, and similar to Table 5.6, the risk of stillbirth in the SATH population is nil during the last weeks (weeks 41 through 44) of the life table. Also in comparison to the hypothetical cohort from the EME region, the weekly probability and rate of live birth are higher in the SATH population from gestational week 28 through 30 and from gestational week 38 through 42. This suggests that there is a higher proportion of very preterm births (< 32 weeks) in the SATH survey when compared to the EME region.

8.2.2 NEONATAL DEATH AND NEONATAL LIFE TABLE

Neonatal and perinatal death In total, 13 of the 988 live births died in the neonatal period before discharge from SAT Hospital and at least one baby died during the neonatal period after discharge. On the basis of these data, the mortality rate until discharge from the hospital is 13.2 per 1,000 live births and the total neonatal mortality rate is 14.2 per 1,000 live births. However, these figures are minimums as information about neonatal survival is missing in 36% of cases. If one excludes the 359 cases that were lost to follow-up, the neonatal mortality rate becomes 22.3 per 1,000 live births. However, this figure is almost certainly an overestimation. The actual figure probably lies between the two extremes. According to the SRS, the neonatal mortality rate in Kerala was 11 per 1,000 live births in 1998; 11 deaths per 1,000 live births in rural regions and 12 per 1,000 in urban areas

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EARLY LIFE CHANGES

248

(Office of the Registrar General 2000). However, Irudaya Rajan and Mohanachandran (1999a, 1999b) believe the figures from the SRS to be underestimations. On the basis of NFHS-1, Padmadas (2000) estimated neonatal mortality rate in the 1988-1992 cohort in Kerala at 15.8 per 1,000 live births. More recently, on the basis of NFHS-2 held in 1998-1999, the neonatal mortality rate in the five-year period preceding the survey was 13.8 per 1,000 live births, made up of 12.5 per 1,000 live births in urban areas and 14.2 per 1,000 in rural regions (IIPS and ORC Macro 2001b). The lower estimate from the SATH survey (i.e. 14.2 per 1,000 live births) is comparable to these population-based figures from Kerala. This suggests that the majority of censored cases in the sample have survived the neonatal period and/or that the neonatal mortality rate in the sample does not reflect any overrepresentation of high-risk cases. In comparison, the neonatal mortality rate in the EME region ranges between 2 and 5 deaths per 1,000 live births (see Chapters 2 and 5). In the hypothetical cohort that was constructed in Chapter 5 (see Table 5.9), the neonatal mortality rate was 4.3 per 1,000 live births. Neonatal mortality rates in the EME region are thus lower than those in Kerala and the SATH population. Overall, on the basis of NFHS-2 held in 1998-1999, the neonatal mortality rate in all-India was as high as 43.4 deaths per 1,000 live births during the five-year period preceding the survey (IIPS and ORC Macro 2000). Within India, the highest rate was observed in the state of Madhya Pradesh at 54.9 per 1,000 live births, while Kerala had the lowest rate. On the basis of the SRS, the neonatal mortality rate in all-India was equally high at 45 per 1,000 live births in 1998 (Office of the Registrar General 2000). However, the difference between rural and urban areas was found to be considerable: the neonatal mortality rate was 47 per 1,000 live births in rural regions compared to only 27 per 1,000 in urban areas. Within the SATH survey, the majority of deaths took place during the early neonatal period, i.e. during the first week of life. In total, 11 of the 14 neonatal deaths occurred before 7 completed days of age. With the assumption that all censored cases survived, early neonatal mortality rate is then 11.1 per 1,000 live births. After excluding those cases that were lost to follow-up during the early neonatal period, the figure increases to 16.2 per 1,000 live births. The lower estimate (i.e. 11.1 per 1,000 live births) equals the early neonatal mortality rate that was observed in SAT Hospital during a survey ten years earlier, in 1990 (Lalitha and Syamalan 1991). Moreover, the early neonatal mortality rate in Kerala, according to the SRS, was 9.1 per 1,000 live births in 1998: 8.0 deaths per 1,000 live births in rural areas and 10.1 per 1,000 in urban regions (Office of the Registrar General 2000). On the basis of NFHS-1, Padmadas (2000) estimated the early neonatal mortality rate in the 1988-1992 birth cohort at 13.5 per 1,000 live births in Kerala. In Medical College Hospital in Kottayam, early neonatal mortality rate in 1992-1993 was higher at 18.7 per 1,000 live births (Pradeep et al. 1995). In comparison, the early neonatal mortality rate in the EME region is between 2 and 4 deaths per 1,000 live births (see Table 2.5). In the hypothetical cohort that was constructed in Chapter 5 (see Table 5.9), the early neonatal mortality rate was 3.4 per 1,000 live births. Early neonatal mortality rates in the EME region are thus lower than those in Kerala and the SATH population. Figures for all-India are much higher. The SRS estimated 33.3 early neonatal


249

deaths per 1,000 live births in 1998: 36.3 deaths per 1,000 live births in rural areas and 21.2 per 1,000 in urban regions (Office of the Registrar General 2000). When the number of early neonatal deaths and the number of stillbirths are added in the SATH survey, this results in a total of 24 perinatal deaths; 13 stillbirths (including antepartum deaths weighing at least 1,000 g) plus 11 early neonatal deaths. Consequently, the perinatal mortality rate is 24.0 deaths per 1,000 births and the perinatal mortality ratio is 24.3 deaths per 1,000 live births. After excluding cases that were lost to follow-up, these figures become 34.6 and 35.3 per 1,000 births or live births respectively. However, these high figures can be assumed to be overestimations since the vast majority of neonates who were lost to follow-up are likely to have survived the early neonatal period. In Medical College Hospital in Kottayam, the perinatal mortality rate was 38.5 per 1,000 births during the period August 1992 to July 1993 (Pradeep et al. 1995). However, on the basis of the SRS, the perinatal mortality rate in Kerala is much lower: 15 deaths per 1,000 births in 1998 (Office of the Registrar General 2000). In rural regions, the rate was 14 perinatal deaths per 1,000 births rising to 17 per 1,000 in urban areas. In comparison, the SRS-based figure for all-India was as high as 42 per 1,000 births in 1998; 45 perinatal deaths per 1,000 births in rural areas and 29 per 1,000 in urban regions (Office of the Registrar General 2000). In the EME region, perinatal mortality is much lower. In the early 1990s, the perinatal mortality ratio was 9.2 deaths per 1,000 live births in the Netherlands (in 1993), 8.6 in Italy (in 1992), 7.3 in Spain (in 1991), 5.9 in Sweden (in 1993), and 5.0 in Japan (in 1993) (UN, Demographic Yearbook, various years). In the SATH survey, the majority of neonatal deaths were males; ten males and only four females. Overall, 1.9% of male live births resulted in neonatal death, 60.4% survived the neonatal period, and 37.6% were lost to follow-up. For females, the corresponding figures are 0.9%, 64.3% and 34.9% respectively. Table 8.2 presents the possible causes of, and the health problems contributing to, neonatal death in those neonates who died during their stay in SAT Hospital after birth. Since most of these neonates had multiple health problems, the table adds up to more than the total of 13 deaths. The hospital records and death certificates were not always clear as to what was the single underlying cause of death. The most common contributing factors to neonatal death before discharge are preterm birth, septicaemia/sepsis, HMD/RDS, and birth asphyxia. All the cases of septicaemia/sepsis in Table 8.2 were also affected by at least one of the other health problems, usually preterm birth and RDS. In addition to the 13 neonatal deaths in hospital, one baby in the SATH survey died after discharge. This baby had major anomalies, i.e. hypospadias3 and absent anus. Thus, the pattern of causes of neonatal death is similar to that in the EME region: mainly perinatal conditions (including immaturity, RDS, and birth asphyxia) and congenital anomalies (see Chapter 2). That is unless there were additional neonatal deaths missed during the follow-up of the births in the SATH survey since such late neonatal deaths would more likely be the result of infections. 3 Hypospadias: a congenital defect characterised by the abnormal opening of the male urethra (see On-Line Medical Dictionary 1998-2002).

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Table 8.2: Health problems contributing to neonatal death before discharge,SAT Hospital, Trivandrum, Kerala, October 2000

Health problem Frequency

Preterm 7Septicaemia/sepsis 7Hyaline membrane disease/respiratory distress syndrome (HMD/RDS) 5Birth asphyxia / suspected birth asphyxia 5Aspiration [a] 3Congenital anomalies / suspected congenital anomalies [b] 3IUGR/SGA 2Congestive cardiac failure 2Intracranial haemorrhage 1Patent ductus arteriosus (PDA) 1Sclerema 1

Notes: [a] Meconium aspiration syndrome (1 case), massive aspiration (1), aspiration pneumonia (1);[b] Hydronephrosis, cleft palate and other suspected anomalies (1 case), suspected chromosomalanomalies (1), congenital heart disease (1).PDA - a condition where the channel between the pulmonary artery and aorta fails to close at birth;Sclerema - a disorder occurring chiefly in preterm or weaked infants suffering from an underlying illness andcharacterised by hardening of the skin and subcutaneous tissues (On-line Medical Dictionary 1998-2002).

Neonatal life table On the basis of the data from the SATH survey, it is possible to develop a neonatal life table as shown in Table 8.3. The length of the intervals (h) equals one, i.e. one day, and therefore, h is omitted from the equations and symbols below. Unfortunately, survival data for the complete neonatal period are missing in 359 cases (36.3%) out of 988 live births. These cases are so-called ‘censored’ cases and they are represented by column (4) in the table. The censored cases were lost to follow-up before the end of the neonatal period. During the survey, the main data sources on follow-up of newborns through the neonatal period were the discharge books from the hospital, the postnatal cards, and the hospital registers about check-ups after discharge. In Table 8.3, the censored cases are included up to the latest age (in days) that they were known to be alive. Some were already lost to observation during their stay in SAT Hospital (i.e. no discharge date known or any other information) while others were lost after discharge from the hospital. Table 8.3 assumes that both death and censoring occur in the middle of the interval. Here, the interval is one neonatal day. In Table 8.3, the age interval in completed days is shown in column (1). Column (2) presents the observed number of neonates still alive at the beginning of each interval. The initial live population consists of all singleton live births in the SATH survey, i.e. 988 liveborn neonates. Subsequently, columns (3) and (4) represent the observed number of events in each interval; i.e. deaths and censoring respectively. Column (5) shows for each interval the number of individuals at risk of the events at the beginning of the interval, i.e. the risk set. The risk set was calculated as:


251

Rx = lx – a * Cx (8.3)

where a = 0.5 since it is assumed that the events occur in the middle of the intervals. Column (6) shows the number of days lived by the population in question within the indicated interval. This was calculated as:

Lx = lx – a * Dx – a * Cx (8.4)

where a = 0.5 again. Death rate is shown in column (7) and was calculated as the number of deaths divided by the exposure time in days (cf. equation (8.2)). In column (8), the probability of death was calculated by dividing the number of deaths by the risk set. Column (9) indicates Table 8.3: Neonatal life table with censoring, SAT Hospital, Trivandrum, Kerala, October 2000

Age in No. alive No. of Lost to Risk Exposure Rate of Probability Total Neonatal lifedays at beginning deaths follow-up set time death of death person-days expectancy

x lx Dx Cx Rx Lx Mx qx Tx ex(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

0 988 3 37 969.5 968.0 0.0031 0.0031 18831.5 19.11 948 2 21 937.5 936.5 0.0021 0.0021 17863.5 18.82 925 2 110 870.0 869.0 0.0023 0.0023 16927.0 18.33 813 4 41 792.5 790.5 0.0051 0.0050 16058.0 19.84 768 0 37 749.5 749.5 0.0000 0.0000 15267.5 19.95 731 0 34 714.0 714.0 0.0000 0.0000 14518.0 19.96 697 0 28 683.0 683.0 0.0000 0.0000 13804.0 19.87 669 1 15 661.5 661.0 0.0015 0.0015 13121.0 19.68 653 0 12 647.0 647.0 0.0000 0.0000 12460.0 19.19 641 0 9 636.5 636.5 0.0000 0.0000 11813.0 18.4

10 632 0 2 631.0 631.0 0.0000 0.0000 11176.5 17.711 630 0 2 629.0 629.0 0.0000 0.0000 10545.5 16.712 628 0 0 628.0 628.0 0.0000 0.0000 9916.5 15.813 628 0 3 626.5 626.5 0.0000 0.0000 9288.5 14.814 625 0 0 625.0 625.0 0.0000 0.0000 8662.0 13.915 625 0 1 624.5 624.5 0.0000 0.0000 8037.0 12.916 624 0 0 624.0 624.0 0.0000 0.0000 7412.5 11.917 624 1 0 624.0 623.5 0.0016 0.0016 6788.5 10.918 623 0 2 622.0 622.0 0.0000 0.0000 6165.0 9.919 621 0 3 619.5 619.5 0.0000 0.0000 5543.0 8.920 618 0 1 617.5 617.5 0.0000 0.0000 4923.5 8.021 617 1 1 616.5 616.0 0.0016 0.0016 4306.0 7.022 615 0 0 615.0 615.0 0.0000 0.0000 3690.0 6.023 615 0 0 615.0 615.0 0.0000 0.0000 3075.0 5.024 615 0 0 615.0 615.0 0.0000 0.0000 2460.0 4.025 615 0 0 615.0 615.0 0.0000 0.0000 1845.0 3.026 615 0 0 615.0 615.0 0.0000 0.0000 1230.0 2.027 615 0 0 615.0 615.0 0.0000 0.0000 615.0 1.028 615

Total 14 359

Note: Assuming that, on average, the events and censoring take place in the middle of each interval.

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the total number of neonatal days remaining after having reached the indicated interval. Finally, column (10) estimates neonatal life expectancy in days by dividing Tx by lx. In Table 8.3, the neonatal life expectancy at birth is only 19.1 days. This can be explained by the inclusion of the relatively large number of censored cases. The censored cases are likely to differ from the neonates who were not lost to follow-up since complicated or high-risk cases generally stay in hospital for a longer period of time, and are more likely to come back for check-ups. In addition, the majority of known neonatal deaths occurred in hospital. In total, 975 neonates are known to have left the hospital alive and complete follow-up information is available in 615 of these cases. After discharge from the hospital, only one of the babies (0.16% of 615) died during the neonatal period. This implies an additional 0.6 neonatal deaths in the 359 censored cases (0.16% of 359). Table 8.4 assumes that all censored cases survived the neonatal period. The table was calculated in the same manner as Table 8.3. However, due to the absence of censoring, here the risk set (Rx) equals lx in column (2). The table shows that mortality rate and probability are highest during the first four days of life, but then drop to virtually zero during the remainder of the neonatal period. The neonatal life expectancy is 27.7 days at birth, which reflects the fact that the vast majority of infants survive the neonatal period. Additional life table calculations (which are not shown here) for the SATH population demonstrate that neonatal life expectancy for those who die during the neonatal period is only 5.0 days at birth. This refers to the fraction of the neonatal period lived by a newborn who dies during that same period (cf. Chiang’s a; Chiang 1968). The low life expectancy in this group reflects that most neonatal deaths take place early in the neonatal period (i.e. on average at the age of 5 completed days). Overall, 21.4% (3 out of 14) of neonatal deaths in the SATH population took place on the first day of life and 78.6% (11 out of 14) during first week. In comparison to the neonatal life table of the hypothetical cohort in the EME region (Table 5.9), neonatal life expectancy in the SATH population is slightly lower (27.7 vs. 27.9 days). The daily probability and rate of death are higher in the SATH population during the first few days, but lower during the rest of the neonatal period. The proportion of first-day deaths (21%) is low compared to figures from the EME region (35-60%, based on Table 2.5) and the hypothetical cohort in Chapter 5 (55%). The proportion of neonatal deaths during the first week (79%) is comparable to figures from the EME region (71-82%, based on Table 2.5) and the hypothetical cohort (78%).

Predicted survival function and hypothetical cohort In Tables 8.3 and 8.4, the resulting death rates and probabilities are rather crude. The number of deaths is relatively small and the pattern of attrition is based on the one observed in the small population of the SATH survey. For example, on most days the probability of death is zero whereas on days 7, 17, and 21 the probability increases ‘unexpectedly’. This is probably due to random variation since it seems unlikely that the risk of death is increased only on these exact days and to show such large differences with adjoining days. Therefore, to obtain the general pattern underlying the mortality, the Weibull function is fitted to the SATH data (also see Chapter 5).


253

Table 8.4: Neonatal life table, assuming the survival of censored cases, SAT Hospital,Trivandrum, Kerala, October 2000

Age in No. alive No. of Exposure Rate of Probability Total Neonatal lifedays at beginning deaths time death of death person-days expectancy

x lx Dx Lx Mx qx Tx ex(1) (2) (3) (4) (5) (6) (7) (8)

0 988 3 986.5 0.0030 0.0030 27342.0 27.71 985 2 984.0 0.0020 0.0020 26355.5 26.82 983 2 982.0 0.0020 0.0020 25371.5 25.83 981 4 979.0 0.0041 0.0041 24389.5 24.94 977 0 977.0 0.0000 0.0000 23410.5 24.05 977 0 977.0 0.0000 0.0000 22433.5 23.06 977 0 977.0 0.0000 0.0000 21456.5 22.07 977 1 976.5 0.0010 0.0010 20479.5 21.08 976 0 976.0 0.0000 0.0000 19503.0 20.09 976 0 976.0 0.0000 0.0000 18527.0 19.0

10 976 0 976.0 0.0000 0.0000 17551.0 18.011 976 0 976.0 0.0000 0.0000 16575.0 17.012 976 0 976.0 0.0000 0.0000 15599.0 16.013 976 0 976.0 0.0000 0.0000 14623.0 15.014 976 0 976.0 0.0000 0.0000 13647.0 14.015 976 0 976.0 0.0000 0.0000 12671.0 13.016 976 0 976.0 0.0000 0.0000 11695.0 12.017 976 1 975.5 0.0010 0.0010 10719.0 11.018 975 0 975.0 0.0000 0.0000 9743.5 10.019 975 0 975.0 0.0000 0.0000 8768.5 9.020 975 0 975.0 0.0000 0.0000 7793.5 8.021 975 1 974.5 0.0010 0.0010 6818.5 7.022 974 0 974.0 0.0000 0.0000 5844.0 6.023 974 0 974.0 0.0000 0.0000 4870.0 5.024 974 0 974.0 0.0000 0.0000 3896.0 4.025 974 0 974.0 0.0000 0.0000 2922.0 3.026 974 0 974.0 0.0000 0.0000 1948.0 2.027 974 0 974.0 0.0000 0.0000 974.0 1.028 974

Total 14

Notes: Assuming that all censored cases survived the neonatal period. Assuming that, on average, the events take place inthe middle of each interval.

With the assumption that all censored cases survived the neonatal period, the observed survival function S(x) was calculated by dividing lx by l0 and a linear regression was performed using SPSS. The result of the regression was: ln(-ln[S(x)]) = -5.337 + 0.359 ln(x) which was recalculated into a predicted survival function. Figure 8.1 compares the observed and the predicted survival probabilities. Observed survival up to one completed day is more

EARLY LIFE CHANGES

254

Figure 8.1: Observed and predicted survival probabilities, SAT Hospital, Trivandrum, Kerala, October 2000

0.98000

0.98500

0.99000

0.99500

1.00000

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

age (in days)

surv

ival

pro

babi

liy S

(x)

observed

predicted

favourable than would be expected or predicted. However, by the fourth day of life, observed survival drops well below the predicted survival function. On the basis of the predicted survival function, a neonatal life table (Table 8.5) was constructed for a hypothetical cohort of 100,000 live births. Neonatal life expectancy at birth is again 27.7 days (cf. Table 8.4). In total, 1,577 of 100,000 live births die during the neonatal period giving a neonatal mortality rate of 15.8 per 1,000 live births. This figure is somewhat higher than the observed rate in the SATH survey and the rates for Kerala estimated by the SRS and NFHS-2. Early neonatal mortality rate in Table 8.5 is 9.6 per 1,000 live births, which is slightly lower than the observed rate, but comparable to the SRS figure for Kerala in 1998. Overall, 30.4% of neonatal deaths occur during the first day and 61.0% during the first week. These figures are much closer to the figures for countries in the EME region (see Table 2.5) than the observed proportions in the SATH survey.

8.3 Pregnancy and birth outcome

This section discusses the selected pregnancy and birth outcomes and their prevalence in the population of the SATH survey. The results are compared to other, secondary, data from Kerala and to secondary data from India as a whole, where available. Only later, in Section 8.7, are the results from the SATH survey put alongside the figures from the EME region.


255

Table 8.5: Neonatal life table for a hypothetical cohort of 100,000 live births, Kerala

Age in Survival Number Number Probability Person- Rate Total Neonatal lifedays probability alive of deaths of death days lived of death person-days expectancy

x Sx lx dx qx Lx mx Tx ex(1) (2) (3) (4) (5) (6) (7) (8) (9)

0 1.00000 100,000 480 0.00480 99,760 0.00481 2,767,574 27.71 0.99520 99,520 135 0.00136 99,453 0.00136 2,667,814 26.82 0.99385 99,385 96 0.00097 99,337 0.00097 2,568,361 25.83 0.99289 99,289 77 0.00078 99,251 0.00078 2,469,024 24.94 0.99212 99,212 65 0.00066 99,180 0.00066 2,369,774 23.95 0.99147 99,147 58 0.00058 99,118 0.00059 2,270,594 22.96 0.99089 99,089 51 0.00051 99,064 0.00051 2,171,476 21.97 0.99038 99,038 47 0.00047 99,015 0.00047 2,072,413 20.98 0.98991 98,991 43 0.00043 98,970 0.00043 1,973,398 19.99 0.98948 98,948 41 0.00041 98,928 0.00041 1,874,429 18.9

10 0.98907 98,907 37 0.00037 98,889 0.00037 1,775,501 18.011 0.98870 98,870 36 0.00036 98,852 0.00036 1,676,613 17.012 0.98834 98,834 34 0.00034 98,817 0.00034 1,577,761 16.013 0.98800 98,800 32 0.00032 98,784 0.00032 1,478,944 15.014 0.98768 98,768 31 0.00031 98,753 0.00031 1,380,160 14.015 0.98737 98,737 29 0.00029 98,723 0.00029 1,281,407 13.016 0.98708 98,708 28 0.00028 98,694 0.00028 1,182,685 12.017 0.98680 98,680 27 0.00027 98,667 0.00027 1,083,991 11.018 0.98653 98,653 27 0.00027 98,640 0.00027 985,324 10.019 0.98626 98,626 25 0.00025 98,614 0.00025 886,685 9.020 0.98601 98,601 24 0.00024 98,589 0.00024 788,071 8.021 0.98577 98,577 24 0.00024 98,565 0.00024 689,482 7.022 0.98553 98,553 23 0.00023 98,542 0.00023 590,917 6.023 0.98530 98,530 23 0.00023 98,519 0.00023 492,376 5.024 0.98507 98,507 22 0.00022 98,496 0.00022 393,857 4.025 0.98485 98,485 21 0.00021 98,475 0.00021 295,361 3.026 0.98464 98,464 21 0.00021 98,454 0.00021 196,887 2.027 0.98443 98,443 20 0.00020 98,433 0.00020 98,433 1.028 0.98423 98,423

Total 1,577

Notes: Assuming that, on average, the events take place in the middle of each interval.

8.3.1 CONGENITAL ANOMALIES

Of the 1,001 births in the SATH survey, 13 births (1.3%) had one or more congenital anomalies and seven additional cases (0.7%) were suspected of having congenital anomalies. In total, 2.0% (20 cases) of all births, 1.8% (18 cases) of live births, and 15.4% (2 cases) of stillbirths were believed to have one or more congenital anomalies. Estimates of the prevalence of congenital anomalies thus become 16.0 per 1,000 births, 14.2 per 1,000 live births, and 153.8 per 1,000 stillbirths.

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In comparison, other studies in India on newborns have produced a wide range of prevalence estimates: from 0.25 to 5.5% of births (Mishra and Baveja 1989; Bhat and Babu 1998; Kaushik et al. 1999; Verma 2000). The prevalence figure in the SATH population is towards the lower end of this range. The differences between the findings may be due to genetic and environmental factors but may also be related to factors such as study design, sample size, detection, diagnostic methods, and autopsy rates. In the SATH survey, two stillbirth cases (both antepartum) were known or suspected of having congenital anomalies. In one of the two cases, the anomalies consisted of major exomphalos (i.e. navel rupture or umbilical hernia) and cystic hygroma (swelling) in the neck. Cystic hygroma have been associated with Roberts syndrome (single-gene disorder), and with Turner’s syndrome and Down’s syndrome (both chromosomal disorders) (On-Line Medical Dictionary 1998-2002). The second anomalous stillbirth was affected by non-immune hydrops foetalis. Though several causes may underlie non-immune hydrops foetalis, it was believed that in this case the likely cause was a congenital anomaly. In addition to these two stillborn cases, it is possible that other stillbirths in the survey may have been affected by anomalies but this could not be observed without an autopsy. In the live births, the most frequent anomalies were congenital heart disease, malformations of the foot (mainly congenital talipes equinovarus (CTEV) or clubfoot), facial anomalies, and renal anomalies (in particular hydronephrosis4). Table 8.6 presents these malformations together with the other anomalies detected in the live births. It should be noted that one of the cases registered by the hospital as congenital heart disease (CHD) is a very preterm neonate (< 32 weeks) who suffered from patent ductus arteriosus (PDA). PDA is “a

Table 8.6: Congenital anomalies in 988 live births, SAT Hospital, Trivandrum,Kerala, October 2000

Type of anomaly Frequency

Congenital heart disease / suspected congenital heart disease 5Abnormalities of the foot, incl. talipes equinovarus (CTEV) [a] 4Facial anomalies /suspected facial anomalies [b] 4Renal anomalies / suspected renal anomalies [c] 3Absent fore-arm (left) 1Cleft palate 1Hypospadias (abnormality of penis) 1Low anorectal malformations [d] 1Osteogenesis imperfecta 1Skeletal dysplasia 1Suspected chromosomal anomalies 1

Notes: [a] CTEV or clubfoot (3 cases), and abnormalities of the foot (1 case); [b] Facial asymmetry,hypoplastic mandible and congenital teeth (1 case), low set ears (1 case), and suspected facialdysmorphism (2 cases); [c] Hydronephrosis (2 cases), and suspected pyelectasis (1 case);[d] Absent anus (1 case).

4 Hydronephrosis: dilation of the pelvis and kidney resulting from obstruction to the flow of urine (Stedman’s Medical Dictionary 1995).


257

condition where the normal channel between the pulmonary artery and the aorta fails to close at birth” (On-Line Medical Dictionary 1998-2002). Among very preterm infants, the condition is generally regarded as being physiological rather than pathological and usually does not persist after therapy or maturation (Cornel et al. 1993b; Hoffman 1995b; Mantingh, personal communication in 2002). In Table 8.6, four newborns are included more than once since they were affected by more than one anomaly. The neonate with a cleft palate was also diagnosed as having hydronephrosis, the newborn with hypospadias is also the baby with the low anorectal malformations, and the newborn with foot abnormalities also had facial anomalies (facial asymmetry, hypoplastic mandible5, congenital teeth). Facial malformations may be an indication of the existence of additional anomalies or syndromes that go undetected. Finally, one child was affected by multiple anomalies (osteogenesis imperfecta6 and skeletal dysplasia7) and was also suspected of being chromosomally abnormal. According to informants in Thiruvananthapuram, the most common anomalies at birth include: neural tube defects, congenital heart disease, renal anomalies, CTEV or club foot, chromosomal anomalies, cleft lip/palate, and diaphragmatic hernia. Nearly all of these were observed in the SATH survey. However, it is noteworthy that no cases of NTDs were encountered in the sample. In comparison, the most prevalent anomalies in Indian studies in other regions include: hydrocephalus, cleft lip/palate, foot deformities and CTEV, polydactyly (i.e. too many fingers and/or toes), and hypospadias (Mishra and Baveja 1989; ICMR 1990; Bhat and Babu 1998). Overall, the prevalence of neural tube defects (NTDs) in India appears to range from 0.9 to 8.7 per 1,000 births (Shibuya and Murray 1998c). In developed countries, non-immune hydrops foetalis is estimated to occur in about 0.02-0.6% of foetuses and its most common causes are believed to be cystic hygroma, heart anomalies, chromosomal anomalies, and multiple malformations (Cunningham et al. 1993). Furthermore, in developed countries, urinary tract anomalies (including renal abnormalities) are estimated to occur in about 0.3-1.5% of foetuses, CTEV or clubfoot in about 1 per 1,000 births, and cleft lip/palate in 1.3 per 1,000 births (Cunningham et al. 1993). Compared to these figures, the prevalence of CTEV thus appears to be somewhat high in the population of the SATH survey. In general, causes of CTEV include mechanical factors that arise from low volumes of amniotic fluid and restrictions imposed by small size and inappropriate shape of the uterine cavity (Cunningham et al. 1993). High prevalence of clubfoot could thus be related to small size of mothers as generally observed in developing countries. However, the three CTEV cases in the SATH survey were born to mothers whose height was at least equal to the average height of 158.9 cm within the SATH population. Ultimately, five out of the 18 live births with (suspected) anomalies in the SATH survey are known to have died during the neonatal period, i.e. an incidence of 27.8%. Three

5 Hypoplastic mandible: underdeveloped jaw. 6 Osteogenesis imperfecta: a group of genetic diseases of the bones, which result in brittle and frail bones (On-Line Medical Dictionary 1998-2002). 7 Skeletal dysplasia: genetic disease in which the bony skeleton is abnormally formed during development (On-Line Medical Dictionary 1998-2002).

EARLY LIFE CHANGES

258

of them died during the early neonatal period, an incidence of 16.7%. For five anomalous cases, neonatal survival data were missing. Overall, 35.7% of neonatal deaths and 27.3% of early neonatal deaths had anomalies or was suspected of having anomalies.

8.3.2 BIRTH WEIGHT In the SATH survey, the mean birth weight for all births was 2,858 g (with a standard deviation of 497 g). The average birth weight for live births was slightly higher at 2,870 g (± 486 g). The smallest liveborn baby weighed 1,040 g and the heaviest 4,550 g. The mean birth weight for males was about 100 g greater than the average for females (2,918 g ± 482 g compared to 2,816 g ± 485 g). In the National Family Health Surveys, weight measured at birth was reported retrospectively by mothers. On the basis of NFHS-1, Padmadas (2000) estimated the mean birth weight of live births between January 1988 and February 1993 in Kerala at 2,875 g, which is in good agreement with the results for the SATH population. Hospital-based results from Kerala are provided by Kurup (1997) who studied data for the period 1992-1993 from two government hospitals in Trivandrum. The names of the hospitals were not mentioned, but the larger hospital of the two is likely to be SAT Hospital. In the larger hospital, the average birth weight was only 2,650 g, while in the other hospital it was somewhat higher at 2,730 g. Both hospital-based figures are lower than mean weight observed in the SATH survey. Compared to Indian standards, average birth weight in the SATH survey is relatively high but falls within the observed range. In all-India, mean birth weight is said to lie between 2,493 and 2,970 g (WHO 1980, 1984 cited by Kramer 1987a; Bhargava et al. 1991). In developed countries, mean birth weight is generally over 3,200 g (see Kramer 1987a). Table 8.7 presents birth weight categories by survival status at birth as observed in the SATH survey. The proportion of LBW births, i.e. birth weight less than 2,500 g, was much higher in stillbirths than in live births. After the exclusion of cases with unknown birth weight, 19.2% of all births, 18.4% of live births and 76.9% of stillbirths, were LBW. However, most of these births (75.9%) weighed between 2,000 and 2,500 g. The percentages

Table 8.7: Birth weight by survival status at birth (%),SAT Hospital, Trivandrum, Kerala, October 2000

Survival status at birthBirth weight live still- all(in grams) births births births

< 1,500 g 1.1 23.1 1.41,500-1,999 g 2.9 23.1 3.22,000-2,499 g 14.3 30.8 14.5

>= 2,500 g 81.3 23.1 80.5

unknown 0.4 0.0 0.4

Total (%) 100.0 100.0 100.0N 988 13 1,001


259

of births weighing less than 2,000 g were considerably lower: 4.6% (overall), 4.1% (live), and 46.2% (still). After excluding cases with unknown birth weight, the proportions of births weighing below 1,500 g remain the same as in the table. The NFHS provides additional data for Kerala, again based on birth weight as retrospectively reported by mothers. Padmadas (2000) calculated that 16.8% of live births in the 1988-1993 cohort weighed less than 2,500 g while only 4.2% weighed below 2,000 g. According to NFHS-2 held in 1998-99, 18% of babies born in Kerala during the three years preceding the survey weighed less than 2,500 g at birth (IIPS and ORC Macro 2001b). In addition to these figures, Radhakrishnan et al. (2000) studied birth weight in a non-coastal village in Thiruvananthapuram district, Kerala. They found that 17.9% of singleton live births had a birth weight of less than 2,500 g. To sum up, the results on LBW from the SATH survey seem to be in line with community-based prevalence figures from Kerala. Hospital-based results from Kerala indicate more variation. According to a sample study in SAT Hospital, 19.9% of births (including those < 1,000 g) in 1990 weighed below 2,500 g (Gopalakrishnan and Syamalan 1994). Yet, Radhakrishnan (1998 cited by Irudaya Rajan and Mohanachandran Nair 1999a) calculated prevalence on the basis of the total number of deliveries in 1990 in SAT Hospital and came to a prevalence as high as 25%. During the following years, the prevalence of LBW births in SAT Hospital declined to 19% in 1992 but then increased again to 27% in 1994 and in 1995. Kurup’s (1997) hospital study in Trivandrum (Thiruvananthapuram) for the period 1992-1993 resulted in lower percentages of LBW births: 17.0% in the larger hospital and only 12.2% in the smaller hospital. However, in Medical College Hospital in Kottayam (Kerala), 23.3% of births (≥ 1,000 g) in the period from August 1992 to July 1993 weighed less than 2,500 g, 6.4% < 2,000 g, and 2.0% < 1,500 g (Pradeep et al. 1995). Compared to birth weight figures from the EME region (see Chapter 7), the prevalence of LBW in the SATH survey is very high. However, elsewhere, the percentage has been estimated to be about 28% for all-India (De Onis et al. 1998, based on data from WHO Database on LBW). Results from various Indian studies (including Tibrewala et al. 1980, Hutter 1994, Antonisamy et al. 1994; WHO 1995; Mondal 1998; Shibuya and Murray 1998a; IIPS and ORC Macro 2000), both community-based and hospital-based, range from 11 to 56% of live births or all birth and tend to be higher than the prevalence proportion found in the SATH survey. Among the middle classes, the proportion of LBW births is as low as 5.7% (Gopalan 2002). Shibuya and Murray (1998a) believe that a reasonable estimate for all-India is around 35%. The figures for India suggest that Kerala is slowly moving towards a birth weight pattern that is closer to that of EME countries in the late stages of the epidemiologic transition. As was already discussed in Chapter 3, birth weight is, in general, normally distributed with a small excess of births in the lower tail (Wilcox and Russell 1983a; Wilcox and Russell 1986). Figure 8.2 presents a histogram for birth weight in live births in the SATH population compared to the normal curve. Indeed, the birth weight distribution appears to be close to normal. However, around the mode, there is a distortion to the right side, i.e. to higher birth weights. At the extremes, the number of live births with low birth weights is larger than anticipated, while the number with higher birth weights seems to be less.

EARLY LIFE CHANGES

260

Figure 8.2: Birthweight distribution in live births, SAT Hospital, October 2000

Birth weight (in grams)

46004200

38003400

30002600

22001800

14001000

Freq

uenc

y200

175

150

125

100

75

50

25

0

Std. Dev = 485.64 Mean = 2870

N = 984.00

In the SATH survey, neonatal mortality was highest among low birth weight infants. Overall, 71.4% of the 14 neonatal deaths weighed less than 2,500 g, 50.0% weighed less than 2,000 g, and 14.3% weighed less than 1,500 g. Among early neonatal deaths these prevalence proportions were 63.6%, 54.5%, and 9.1% respectively.

8.3.3 GESTATIONAL AGE AT BIRTH

As already noted in Table 8.1, the lowest estimated gestational age at birth in the SATH survey was 28 completed weeks, while the highest was 44 completed weeks of gestation. In live births, the mean gestational age at birth was 39 completed weeks (± 2 weeks). In stillbirths, the mean gestational age was lower at 36 completed weeks (± 3 weeks). Table 8.8 presents gestational age categories by survival status at birth. The proportion of preterm births, i.e. births before 37 completed gestational weeks, was higher in stillbirths than in live births. After excluding all the cases with unknown maturity status, 6.3% of all births, 5.7% of live births, and 54.5% of stillbirths were preterm. The prevalence of gestational age below 32 weeks was 2.1% in all births, 2.1% in live births, and 9.1% in stillbirths. On the basis of NFHS-1, Padmadas (2000) estimated that only 3.7% of live births in Kerala between January 1988 and February 1993 were premature. However, it should be noted that the data used were reported retrospectively by the mothers. Gopalakrishnan and Syamalan (1994) based their study on a 10% sample from the obstetric records of SAT Hospital, and estimated that, in 1990, 4.1% of the births in the hospital were preterm.


261

Prevalence proportions of preterm and very preterm birth in the SATH survey are similar to figures found in the EME region (see Chapter 7). Most studies in India provide higher estimates of the prevalence of preterm birth. For example, 13.5% of live births in a hospital in New Delhi (Singh et al. 1991), 17.9% of live births in rural Karnataka (Hutter 1994), 9.7% of births in urban and rural hospitals in Pune (WHO 1995), 15-16% of live births in rural and urban communities in Tamil Nadu (Antonisamy et al. 1996), and 13.4% of live births in a hospital in Shimla (Kaushik et al. 1999). In the SATH survey, preterm infants made up a relatively large share of neonatal deaths. Overall, the prevalence of preterm babies (< 37 weeks) in neonatal deaths was 50.0%, and 28.6% of neonatal deaths were < 32 weeks. Among early neonatal deaths, the prevalences were similar at 54.5% and 27.3% respectively.

8.3.4 SIZE FOR GESTATIONAL AGE It is natural that birth weight is affected by gestational age at birth. To have an indication as to whether a baby is small-for-gestational-age (SGA), the size of the foetus or newborn can be measured against a standard growth curve. However, the use of standard growth curves introduces several questions and choices that have no straightforward solutions (see Section 3.4.2). Issues include the choice of a standard curve and a standard population, the level of specificity or customisation, and the choice of cut-off points to define ‘too small’ and ‘too large’. The size of a baby depends partly on genetic and constitutional factors. For the live births in the SATH survey, Table 8.9a presents results based on several birthweight curves. The table also provides a brief description of these curves. Most of the curves have been based on singleton live births. The estimates of the proportion of SGA liveborns in the SATH population are rather high, i.e. all but one over 10%, while the proportion of LGA tends to be on the low side, i.e. all but one below 5%. The reference curve by Mathai et al. (1996; Mathai, personal communication in 2000) for the 5th/95th percentiles seems to provide the most reasonable figures and, indeed, this curve has been based on a South Indian population. Nevertheless, the application of the Indian curve still results in a

Table 8.8: Gestational age at birth by survival status at birth(%), SAT Hospital, Trivandrum, Kerala, October 2000

Survival status at birthGestational age live still- all

at birth births births births

< 32 weeks 2.0 7.7 2.132-36 weeks 3.6 38.5 4.137-41 weeks 90.9 38.5 90.2>= 42 weeks 1.5 0.0 1.5

unknown 1.9 15.4 2.1

Total (%) 100.0 100.0 100.0N 988 13 1001

Tab

le 8

.9a:

Siz

e fo

r ge

stat

iona

l age

in li

ve b

irth

s bas

ed o

n se

vera

l bir

th w

eigh

t cur

ves,

SAT

Hos

pita

l, T

riva

ndru

m, K

eral

a, O

ctob

er 2

000

Resu

lts S

ATH

sam

ple

Cur

ve%

%%

Sour

cePe

riod

Reg

ion

Perc

entil

esTy

pe-s

peci

fic fo

rU

nkno

wn

SGA

*LG

A*

Lubc

henc

o et

al.

(196

3)19

48-1

961

Den

ver,

U.S

.A.

10th

/ 90

thse

x2.

119

.42.

7Th

omso

n et

al.

(196

8)19

48-1

964

Abe

rdee

n, U

.K.

5th

/ 95t

hse

x, g

ravi

da4.

120

.91.

0Th

omso

n et

al.

(196

8)19

48-1

964

Abe

rdee

n, U

.K.

10th

/ 90

thse

x, g

ravi

da4.

133

.81.

9Y

udki

n et

al.

(198

7)19

78-1

984

Oxf

ord,

U.K

.3r

d / 9

7th

sex

4.8

10.5

1.3

Yud

kin

et a

l. (1

987)

1978

-198

4O

xfor

d, U

.K.

10th

/ 90

thse

x4.

828

.42.

7M

atha

i et a

l. (1

996)

, Mat

hai [

a]19

91-1

994

Vel

lore

(TN

), So

uth

Indi

a5t

h / 9

5th

sex,

par

ity, h

eigh

t mot

her [

b]10

.79.

04.

0M

atha

i et a

l. (1

996)

, Mat

hai [

a]19

91-1

994

Vel

lore

(TN

), So

uth

Indi

a10

th /

90th

sex,

par

ity, h

eigh

t mot

her [

b]5.

817

.86.

8

Not

es: S

GA

- sm

all-f

or-g

esta

tiona

l-age

; LG

A -

larg

e-fo

r-ge

stat

iona

l-age

; TN

- Ta

mil

Nad

u; *

Afte

r exc

ludi

ng th

e un

know

n an

d m

issi

ng c

ases

.[a

] pe

rson

al c

omm

unic

atio

n in

200

0; [b

] in

case

of m

issi

ng h

eigh

t, th

e m

ean

heig

ht in

the

SATH

surv

ey (1

59 c

m) w

as a

ssum

ed.

Tab

le 8

.9b:

Siz

e fo

r ge

stat

iona

l age

in li

ve b

irth

s bas

ed o

n he

ad c

ircu

mfe

renc

e cu

rves

, SA

T H

ospi

tal,

Tri

vand

rum

, Ker

ala,

Oct

ober

200

0

Resu

lts S

ATH

sam

ple

Cur

ve%

%%

Sour

cePe

riod

Reg

ion

Perc

entil

esTy

pe-s

peci

fic fo

rU

nkno

wn

SGA

*LG

A*

Lubc

henc

o et

al.

(196

6)19

48-1

961

Den

ver,

U.S

.A.

10th

/ 90

th-

3.4

9.0

3.6

Yud

kin

et a

l. (1

987)

1978

-198

4O

xfor

d, U

.K.

3rd

/ 97t

hse

x5.

925

.92.

0Y

udki

n et

al.

(198

7)19

78-1

984

Oxf

ord,

U.K

.10

th /

90th

sex

5.9

49.9

2.7

Not

e: S

GA

- sm

all-f

or-g

esta

tiona

l-age

; LG

A -

larg

e-fo

r-ge

stat

iona

l-age

; *A

fter e

xclu

ding

the

unkn

own

and

mis

sing

cas

es.


263

relatively high prevalence of SGA and a relatively low prevalence of LGA. The curve by Mathai and co-authors is customised for sex, parity, and height of the mother. For the analysis of the SATH data, the average height of 159 cm was assumed when information on the height of the mother was missing. Unfortunately, the number of cases that could not be categorised by this curve is quite high (10.7%). Moreover, the percentages of preterm and LBW liveborns in this ‘unknown’ category are high: 43.7 and 48.1% respectively. However, on the basis of the curves by Thomson et al. (1968) and Yudkin et al. (1987), the categories of unknown cases contain even higher proportions of preterm births (95.4% and 96.4% respectively) and LBW births (75.0% and 78.2% respectively). Overall, 9.0 to 33.8% of live births in the SATH survey are classified as SGA in Table 8.9a. Using the same weight curves, the proportion of SGA cases is higher among stillbirths (11.1 to 70.0%) but, not surprisingly, similar among all births combined (9.0 to 34.2%). A classification into size-for-age categories can also be made on the basis of measures other than birth weight, such as head circumference at birth. Lubchenco et al. (1966) and Yudkin et al. (1987) have provided curves based on head circumferences. However, their curves used data up to one decimal place while the data in the SATH survey on head circumference are rounded figures. Table 8.9b presents the results of the analysis for live births using these curves. Again, the results based on Yudkin et al. (1987) show a rather high prevalence of SGA newborns and a low proportion of LGA babies. The results based on Lubchenco et al. (1966) are more comparable to the results based on the curve by Mathai et al. (1996; Mathai, personal communication in 2000) for the 5th/95th birth weight percentiles. In other Indian regions, some studies suggest a lower prevalence of SGA in the population. For example, Kaushik et al. (1999) observed that 7.1% of live births in a hospital in Shimla were small-for-date. Similarly, Singh et al. (1991) found only 6.6% of neonates born at the All India Institute of Medical Sciences Hospital in New Delhi to be small-for-date. However, the exact criteria and definitions that were applied by these researchers are unknown. Moreover, the populations in these two hospitals may differ from the SATH population with regard to relevant characteristics such as socioeconomic status. A WHO multicentre study in 1990 in Pune indicated a far higher prevalence of IUGR/SGA: as many as 54.2% of births in urban and rural hospitals. The result was based on the 10th percentile of a sex-specific weight-for-gestational-age curve based on an international reference group (WHO 1995). Within the SATH survey, the proportion of SGA cases among deaths appears to be higher than that among births. On the basis of the various birthweight curves, 35.7 to 80.0% of neonatal deaths could be defined as SGA at birth. On the basis of head circumference, 14.3 to 44.4% of neonatal deaths were SGA. Among early neonatal deaths, the corresponding ranges were 27.3-75.0% and 18.2-42.9% respectively. The variation in the results is thus considerable and is probably explained by the variation in standard curves and their definitions.

EARLY LIFE CHANGES

264

8.3.5 BIRTH ASPHYXIA AND BIRTH TRAUMA/INJURY In the SATH survey, 17 cases were registered in the hospital records (labour records and/or IBN/SN patient files) as having suffered from birth asphyxia. Two additional cases were suspected to have been affected by birth asphyxia (BA). All of them were live births and, in total, 1.9% of live births experienced BA. None of the stillbirths were reported to have experienced birth asphyxia. Unfortunately, the definitions and criteria that were applied to diagnose BA are not known. Six of the 19 cases of BA or suspected BA were preterm and two had congenital anomalies. The Apgar score at one minute was ≤ 6 in most cases, apart from four cases in which the Apgar score was 9 at one minute. The Apgar score at five minutes was below 7 in six BA cases. In addition to the 19 cases, one newborn was suspected of suffering from hypoxic-ischaemic encephalopathy (HIE). It is the policy in SAT Hospital not to award Apgar scores of 10. Overall, the Apgar score at one minute was 9 in 93.3% (922 cases) of all live births, 6-8 in 4.6% (45 cases), below 6 in 1.4% (14 cases), and unknown in 0.7% (7 cases) of live births. The Apgar score at five minutes was recorded in only 51 cases where the score at one minute was less than 9 or unknown. Another 30 newborns (3.0% of live births) achieved a score of 9 at five minutes. Birth asphyxia has sometimes been defined on the basis of Apgar score, including criteria such as Apgar score ≤ 3 at one minute and ≤ 6 at five minutes (see Chapters 3 and 7). In the SATH survey, 11 live births (1.1%) scored ≤ 3 at one minute, and 7 of these were registered in the hospital records as suffering from BA. In addition, the Apgar score at five minutes was 6 or less in 10 live births (1.0%), of whom 6 were registered as BA cases. It is important to note that these cases of low Apgar scores include infants born preterm. Most of the data from Indian studies suggest a higher frequency of birth asphyxia. In New Delhi, Kumari et al. (1993) studied low Apgar scores (< 6 at one minute) during the years 1981, 1983, 1986, and 1988. The overall frequency of birth asphyxia was 7.6% of live births during the study period. The percentage was 5.8% in 1981 and increased during the following years to 8.9% in 1986, followed by a slight reduction to 7.2% in 1988. In a hospital-based study in Chandigarh during 1987-1989, the incidence of birth asphyxia – defined as an Apgar score below 6 at one minute – was 97.5 per 1,000 births (Bhakoo et al. 1989 cited by Shibuya and Murray 1998b). Singh et al. (1991) and Kaushik et al. (1999) studied live births in hospitals in New Delhi and Shimla respectively. In New Delhi, birth asphyxia of varying severity was observed in 5.9% of live births and in Shimla in 6.3%. However, information could not be obtained about the authors’ definitions of birth asphyxia. Chandra et al. (1997) identified asphyxia on the basis of both intrapartum criteria and neonatal criteria. They found that out of 2,371 births in a teaching hospital, 3.6% were asphyxiated and the frequency among live births was 2.2%. The percentage with asphyxia among stillbirths was as high as 63.6%. Kumar (1995) provides community-based data for a rural region in North India. In this study, two paediatricians retrospectively reviewed case histories and the frequency of birth asphyxia was estimated to be 2.0 to 2.5% of live births. In the SATH sample, 42.9% of neonatal deaths and 36.4% of early neonatal deaths were registered in the hospital records as cases of suspected or confirmed BA. On the basis of a low Apgar score, the prevalence among deaths was much lower. Only 28.6% of neonatal


265

deaths and 27.3% of early neonatal deaths had Apgar scores at one minute that were less than or equal to 3. As for an Apgar score ≤ 6 at five minutes, the corresponding figures were 21.4% and 18.2% respectively. Lastly, birth trauma or injuries were uncommon in the SATH survey. In total, only one liveborn infant was thought to have a birth injury: depressor anguli oris palsy8. The baby concerned was term and weighed 2,775 g. Nonetheless, the neonate was admitted to the IBN because of facial anomalies (hypoplastic mandible, congenital teeth, facial asymmetry) and abnormalities of the foot. The child is known to have survived the neonatal period.

8.4 Associations and risks

This section relates stillbirth and neonatal death to the pregnancy and birth outcomes (i.e. the risk factors). The relative risk and attributable risks are calculated when applicable.

8.4.1 STILLBIRTH In the SATH survey, elevated prevalence proportions of congenital anomalies, low birth weight, preterm birth, and small-for-gestational-age were observed among stillbirths (see Section 8.3). In crosstabulations, the associations appear to be strongest for preterm birth (< 37 weeks), low birth weight (< 1,500 g, < 2,000 g, and < 2,500 g), and congenital anomalies. Conversely, there appears to be no, or only a weak, association with very preterm birth (< 32 weeks), SGA, and birth asphyxia. The observed absence of association between birth asphyxia and stillbirth is questionable since asphyxia is generally believed to be an important cause of intrapartum stillbirth. This is probably explained by the small number of stillbirths in the survey (13) and possibly by the quality of the data, and the procedures and criteria that are applied in SAT Hospital to diagnose and define birth asphyxia. None of the stillbirths in the SATH survey were diagnosed as having experienced birth asphyxia. Among anomalous foetuses and IUGR/SGA cases, the proportion of stillbirths is a measure of incidence. The incidence, RR, AR(E), and EF of stillbirth are estimated for these two types of pregnancy outcomes and Table 8.10 presents the results. In the SATH survey, the incidence of stillbirth is 10.0% (2 out of 20) among anomalous foetuses compared to only 1.1% (11 out of 981) among their normal counterparts. As a result, the relative risk (RR) of stillbirth is 8.9 (95% confidence interval: 2.1-37.6). This means that anomalous foetuses alive or in utero at 28 gestational weeks are about 9 times as likely as foetuses without anomalies of the same age to result in a stillbirth. Consequently, the AR(E) or attributable risk among the exposed – calculated as (RR-1) divided by RR – has a value of 0.888. In other words, 88.8% of stillbirths in anomalous foetuses aged ≥ 28 weeks can be attributed to the anomalies. Lastly, the etiologic fraction (EF) is estimated on the basis of RR and the prevalence of anomalies within the total population. In the SATH survey, 2.0% of births ≥ 28 weeks and, therefore, 2.0% of foetuses at 28 gestational weeks were believed to have one or more congenital anomalies. As a result, the EF is: [0.020 * (8.9 – 1)] / [0.020 * (8.9 – 1) + 1] = 0.136

8 Depressor anguli oris palsy: paralysis of one of the muscles around the mouth.

EARLY LIFE CHANGES

266

Table 8.10: Incidence, RR, AR(E), and EF of stillbirth in anomalous and growth-retardedfoetuses, SAT Hospital, Trivandrum, Kerala, October 2000

Incidence RR AR(E) Prevalence EFPregnancy/birth outcome of SB (%) N 95% CI at 28 wks

Congenital anomaliesyes/suspected 10.0 20 8.9 2.1-37.6 0.888 0.020 0.136

no 1.1 981Weight by age

SGA, Lubchenco et al., 10th percentile 2.6 193 3.4 1.0-11.0* 0.706 0.197 0.321non-SGA 0.8 785

SGA, Thomson et al., 5th percentile 2.5 203 3.7 1.1-12.7 0.730 0.212 0.364non-SGA 0.7 754

SGA, Thomson et al., 10th percentile 2.1 327 4.5 1.2-17.3 0.778 0.342 0.545non-SGA 0.5 630

SGA, Yudkin et al., 3rd percentile 1.0 100 1.1 0.1-8.4* 0.091 0.105 0.010non-SGA 0.9 850

SGA, Yudkin et al., 10th percentile 2.2 273 5.0 1.2-19.7 0.800 0.287 0.534non-SGA 0.4 677

SGA, Mathai et al., 5th percentile 1.3 80 1.7 0.2-13.8* 0.412 0.090 0.059non-SGA 0.7 809

SGA, Mathai et al., 10th percentile 2.4 170 3.0 0.9-10.6* 0.667 0.181 0.266non-SGA 0.8 771

Notes: SB - stillbirth; CI - confidence interval. *95% confidence interval for RR includes 1.0. which means that 13.6% of stillbirths in the total population ≥ 28 weeks are attributable to congenital anomalies (see Table 8.10). In comparison to the estimates for the EME region in Chapter 6 (see Tables 6.25 through 6.28), the incidence of stillbirth among anomalous foetuses is higher in the SATH survey, i.e. 10% as compared to 2% in the EME population. This explains why the RR and the AR(E) are also higher in the SATH survey: 9 and 89% respectively as compared to an RR of 5 and an AR(E) of 81% in the EME region. However, the difference in EF is smaller (14% in SATH survey vs. 11% in EME population) since the prevalence of anomalies among births is higher in the EME region (3%) than in the population of the SATH survey (2%). With the assumption that every case of SGA was preceded by IUGR that was already present before gestational week 28, it is also possible to speak of the incidence of stillbirth in SGA cases. Table 8.10 presents incidence proportions, relative risks, and attributable risks of stillbirth for SGA cases (based on birthweight curves) in the SATH survey. The incidence of stillbirth in IUGR/SGA foetuses ranged from 1.0 to 2.6%, compared to 0.4 to 0.9% among normally grown foetuses. However, the differences between the two groups are not always significant. The relative risk varies from 1.1 (95% CI: 0.1-8.4; N.B. not significant) to 5.0 (95% CI: 1.2-19.7). However, using most SGA definitions, the 95% confidence interval of the relative risk includes the value of 1.0. This means that the null hypothesis, which poses that the incidence of stillbirth in the group with the risk factor is the same as the incidence in those without the risk factor should not be rejected. Only for SGA defined by Thomson et al.’s


267

(1968) percentiles and Yudkin et al.’s (1987) 10th percentile, does the confidence interval exclude the value of one. The AR(E) varies in these three cases from 73.0 to 80.0%. The prevalence of IUGR/SGA in the total population of foetuses ranged from 21.2 to 34.2% on the basis of these three curves. As a result, the EF varies between 36.4 and 54.5%. In comparison to the estimates for the EME region in Chapter 7 (see Tables 7.28 through 7.31), the incidence of stillbirth among growth-retarded foetuses in the SATH survey is similar, i.e. 1-3% as compared to 2-4% in the EME population. Despite this, the RR and AR(E) of stillbirth are clearly lower in the SATH population (1-5 vs. 7-44 and 9-78% vs. 86-98% respectively). The prevalence of SGA at birth is higher in the SATH survey (9-32%) than in the EME region (3-10%). However, the EF appears to be slightly lower in the SATH population than in the EME population (1-55% vs. 39-81%).

8.4.2 NEONATAL DEATH

Relative risk Table 8.11 presents the risks and relative risks of neonatal death by pregnancy/birth outcome in the population of the SATH survey. The table presents two versions that differ in their assumptions with regard to censored cases. In the first version, cases who are unknown to have survived the neonatal period are regarded as being censored and are not included in the analysis. The second version makes the assumption that all censored cases survived the neonatal period. As Table 8.11 shows, the incidence of neonatal death is clearly elevated for the majority of risk factors. For example, 27.8 to 38.5% of liveborn children with congenital anomalies die during the neonatal period compared to only 0.9 to 1.5% of non-anomalous neonates. In comparison to the estimates for the EME region (see Tables 6.25 and 7.28), the incidence of neonatal death in affected newborns is generally higher in the SATH population. The exceptions are the following groups of infants: those with LBW < 2,000 g, those with VLBW, and those SGA as based on the 10th percentile of Yudkin et al.’s (1987) head circumference curve. In addition to the incidence figures, Table 8.11 also presents relative risks of neonatal death and their accompanying 95% confidence intervals. Both the relative risks and the confidence intervals were calculated using SPSS (see Chapter 4). The value of the RR is over 1.0, i.e. the risk of neonatal death is elevated among those affected, in almost all risk groups. For instance, anomalous neonates are 26 to 30 times as likely as their non-anomalous counterparts to die during the neonatal period. Only in SGA cases defined by the 10th percentile of Yudkin et al.’s (1987) head circumference curve is the RR is less than 1.0. However, the 95% confidence intervals of the relative risks indicate large ranges, which are likely explained by the small numbers in the survey. In five of the risk groups, the 95% confidence interval of the relative risk includes the value of 1.0. Therefore, the probability that the incidence in the affected group equals the incidence in the non-affected group is regarded as too high in these cases to make any statements about associations between the pregnancy/birth outcome and neonatal death. These five risk groups include all SGA

T

able

8.1

1: In

cide

nce

and

RR

of n

eona

tal d

eath

, by

risk

fact

or, S

AT

Hos

pita

l, T

riva

ndru

m, K

eral

a, O

ctob

er 2

000

Vers

ion

1Ve

rsio

n 2

Inci

denc

eIn

cide

nce

Preg

nanc

y/bi

rth o

utco

me

of N

ND

(%)

NR

R95

% C

I o

f NN

D (%

)N

RR

95%

CI

Con

geni

tal a

nom

alie

sye

s/su

spec

ted

38.5

1326

.310

.2-6

7.7

27.8

1829

.911

.1-8

0.5

no1.

561

60.

997

0Bi

rth

wei

ght

< 2,

500

g8.

511

710

.93.

5-34

.25.

518

111

.13.

5-35

.02,

500

g or

mor

e0.

851

10.

580

3<

2,00

0 g

23.3

3019

.97.

5-53

.217

.540

23.6

8.7-

64.1

2,00

0 g

or m

ore

1.2

598

0.8

944

< 1,

500

g25

.08

12.9

3.4-

48.6

18.2

1114

.73.

7-58

.21,

500

g or

mor

e1.

962

01.

297

3G

esta

tiona

l age

< 37

wee

ks20

.035

16.6

6.2-

44.7

12.7

5516

.66.

0-45

.737

wee

ks o

r mor

e1.

258

10.

891

3<

32 w

eeks

28.6

1417

.26.

1-48

.220

.020

19.0

6.5-

55.4

32 w

eeks

or m

ore

1.7

602

1.1

948

Wei

ght b

y ag

eSG

A, L

ubch

enco

et a

l., 1

0th

perc

entil

e4.

112

32.

20.

8-6.

5*2.

718

82.

30.

8-6.

8*no

n-SG

A1.

849

31.

277

9SG

A, T

hom

son

et a

l., 5

th p

erce

ntile

3.8

131

3.6

1.1-

12.2

2.5

198

3.8

1.1-

12.9

non-

SGA

1.1

471

0.7

749

SGA

, Tho

mso

n et

al.,

10t

h pe

rcen

tile

4.0

201

8.0

1.7-

37.2

2.5

320

7.8

1.7-

36.7

non-

SGA

0.5

401

0.3

627

To b

e co

ntin

ued.

Tab

le 8

.11

(con

tinue

d)

Vers

ion

1Ve

rsio

n 2

Inci

denc

eIn

cide

nce

Preg

nanc

y/bi

rth o

utco

me

of N

ND

(%)

NR

R95

% C

Iof

NN

D (%

)N

RR

95%

CI

Wei

ght b

y ag

eSG

A, Y

udki

n et

al.,

3rd

per

cent

ile7.

071

9.3

2.6-

33.9

5.1

9910

.62.

9-38

.9no

n-SG

A0.

852

90.

584

2SG

A, Y

udki

n et

al.,

10t

h pe

rcen

tile

3.5

171

5.0

1.3-

19.8

2.2

267

5.0

1.3-

20.0

non-

SGA

0.7

429

0.4

674

SGA

, Mat

hai e

t al.,

5th

per

cent

ile7.

752

9.8

2.5-

38.1

5.1

7910

.22.

6-39

.9no

n-SG

A0.

851

10.

580

3SG

A, M

atha

i et a

l., 1

0th

perc

entil

e3.

611

03.

51.

0-12

.8*

2.4

166

3.7

1.0-

13.6

*no

n-SG

A1.

048

20.

776

5H

ead

circ

umfe

renc

e by

age

SGA

, Lub

chen

co e

t al.,

10t

h pe

rcen

tile

3.6

551.

70.

4-7.

3*2.

386

1.7

0.4-

7.4*

non-

SGA

2.2

556

1.4

868

SGA

, Yud

kin

et a

l., 3

rd p

erce

ntile

2.8

143

2.5

0.7-

9.3*

1.7

241

2.3

0.6-

8.4*

non-

SGA

1.1

452

0.7

689

SGA

, Yud

kin

et a

l., 1

0th

perc

entil

e1.

429

60.

80.

2-3.

0*0.

946

40.

80.

2-3.

0*no

n-SG

A1.

729

91.

146

6Bi

rth

asph

yxia

yes/

susp

ecte

d (h

ospi

tal r

ecor

ds)

42.9

1432

.913

.2-8

2.2

31.6

1938

.114

.7-9

9.2

no1.

361

40.

896

61

min

. Apg

ar sc

ore

=< 3

44.4

927

.410

.5-7

1.1

36.4

1135

.313

.0-9

5.5

1 m

in. A

pgar

scor

e >

31.

661

61.

097

05

min

. Apg

ar sc

ore

=< 6

[a]

42.9

724

.28.

6-68

.330

.010

26.7

8.8-

81.3

5 m

in. A

pgar

scor

e >

6 [a

]1.

862

21.

197

8

Not

es: N

ND

- ne

onat

al d

eath

. *95

% c

onfid

ence

inte

rval

for R

R in

clud

es 1

.0.

[a]

For t

hose

cas

es in

whi

ch th

e A

pgar

scor

e at

5 m

inut

es w

as n

ot re

cord

ed, i

t is a

ssum

ed th

at th

e sc

ore

was

> 6

.V

ersi

on 1

: in

the

pres

ence

of c

enso

ring;

Ver

sion

2: c

enso

red

case

s are

ass

umed

to h

ave

surv

ived

the

neon

atal

per

iod.

T

able

8.1

2: In

cide

nce

and

RR

of e

arly

neo

nata

l dea

th, b

y ri

sk fa

ctor

, SA

T H

ospi

tal,

Tri

vand

rum

, Ker

ala,

Oct

ober

200

0

Vers

ion

1Ve

rsio

n 2

Inci

denc

e of

Inci

denc

e of

Preg

nanc

y/bi

rth o

utco

me

ENN

D (%

)N

RR

95%

CI

ENN

D (%

)N

RR

95%

CI

Con

geni

tal a

nom

alie

sye

s/su

spec

ted

20.0

1516

.64.

9-56

.616

.718

205.

8-70

.0no

1.2

665

0.8

970

Birt

h w

eigh

t<

2,50

0 g

5.2

134

7.1

2.1-

23.9

3.9

181

7.8

2.3-

26.2

2,50

0 g

or m

ore

0.7

544

0.5

803

< 2,

000

g16

.237

20.8

6.7-

65.0

15.0

4028

9.0-

88.9

2,00

0 g

or m

ore

0.8

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320

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70.

584

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526

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52.

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241

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0.8

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0.6

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kin

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1133

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664

0.8

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d.

EARLY LIFE CHANGES

272

outcomes based on head circumference as well as SGA defined by the 10th percentiles of the birthweight curves by Mathai et al. (1996; Mathai, personal communication in 2000), and Lubchenco et al. (1963). The highest relative risks are observed in asphyxiated and anomalous neonates. In comparison to the estimates for the EME region (see Tables 6.26 and 7.29), the RRs of neonatal death are lower or similar in the SATH survey. The exception is the relative risk for anomalous neonates, which is higher. Since the incidence of death among affected neonates (I1) was observed to be generally higher, this indicates that the incidence of neonatal death among non-affected neonates (I0) is comparatively high in the SATH population. Out of the 14 neonatal deaths in the survey population, 11 occurred during the early neonatal period. The incidences and relative risks of early neonatal death by risk factor, i.e. adverse pregnancy/birth outcome, are presented in Table 8.12. The arrangement of the table is the same as Table 8.11. Again, all relative risks are over 1.0 except for SGA as based on the 10th percentile of Yudkin et al.’s (1987) head circumference curve. However, for as many as 9 of the risk groups (VLBW and SGA), the confidence intervals include the value of 1.0. The highest relative risks of early neonatal death are observed in asphyxiated newborns and babies who weighed less than 2,000 g at birth. Surprisingly, the relative risks in newborns < 1,500 g at birth are lower. In comparison to the estimates for the EME region (see Tables 6.25, 6.26, 7.28, and 7.29), the incidence of early neonatal death among affected infants is generally higher in the SATH population. The exceptions are the incidences among LBW and VLBW neonates. The relative risks of early neonatal death in the SATH survey are generally lower or equal to the figures for the EME population.

Attributable risk On the basis of the RR, it is now possible to calculate the attributable risk among the exposed (AR(E)), and the etiologic fraction (EF) or the population attributable risk. Table 8.13 presents the results for the AR(E) and they range widely from 41.2 to 97.4% for neonatal mortality and from 33.3 to 97.0% for early neonatal mortality. As the RR, the AR(E) of neonatal death is highest among asphyxiated and anomalous newborns. For example, as many as 96.2 to 96.7% of neonatal deaths in infants with congenital anomalies can be attributed to the anomalies. The AR(E) of early neonatal death is highest in asphyxiated newborns and babies below 2,000 g at birth. In comparison to the estimates for the EME region (see Tables 6.28 and 7.31), the AR(E) of neonatal death is generally similar or lower in the SATH population. The calculation of the EF not only involves the RR but also the prevalence of the risk factor in the population, i.e. the prevalence of the pregnancy/birth outcome among live births (see Chapter 4). Table 8.14 shows these prevalence proportions as well as the resultant EFs. The most frequent outcomes in the SATH population are SGA (9.0-33.8%) and LBW (18.4% are < 2,500 g). Consequently, the EFs of early and total neonatal mortality are high for SGA and LBW cases. For example, 64.6-65.0% of neonatal deaths in the population of the SATH survey can be attributed to LBW. In addition, preterm birth (< 37 weeks) is important. Overall, the EF of neonatal mortality ranges from 5.9 to 70.3% for the risk factors in Table


273

8.14. The EF of early neonatal mortality varies between 5.3 and 63.3%, being highest for SGA defined by the 10th percentile from Thomson et al. (1968), LBW, and preterm (< 37 weeks) birth. In comparison to the estimates for the EME region (see Tables 6.28 and 7.31), the EF of neonatal death is lower in the SATH survey for congenital anomalies, low birth weight, and preterm birth. The EF of neonatal death for SGA and birth asphyxia is higher or equal in the SATH population to that in the EME population.

Table 8.13: AR(E) of neonatal death, by risk factor, SAT Hospital, Trivandrum, Kerala,October 2000

AR(E)NND NND ENND ENND

Pregnancy/birth outcome Version1 Version 2 Version1 Version 2

Congenital anomaliesyes/suspected 0.962 0.967 0.940 0.950

Birth weight< 2,500 g 0.908 0.910 0.859 0.872< 2,000 g 0.950 0.958 0.952 0.965< 1,500 g 0.922 0.932 *0.836 0.886

Gestational age< 37 weeks 0.940 0.940 0.941 0.950< 32 weeks 0.942 0.947 0.922 0.944

Weight by ageSGA, Lubchenco et al., 10th percentile *0.545 *0.565 *0.333 *0.375SGA, Thomson et al., 5th percentile 0.722 0.737 *0.545 *0.565SGA, Thomson et al., 10th percentile 0.875 0.872 0.836 0.831SGA, Yudkin et al., 3rd percentile 0.892 0.906 0.821 0.844SGA, Yudkin et al., 10th percentile 0.800 0.800 *0.706 *0.706SGA, Mathai et al., 5th percentile 0.898 0.902 *0.792 *0.804SGA, Mathai et al., 10th percentile *0.714 *0.730 *0.412 *0.444

Head circumference by ageSGA, Lubchenco et al., 10th percentile *0.412 *0.412 0.524 0.545SGA, Yudkin et al., 3rd percentile *0.600 *0.565 0.565 0.524

Birth asphyxiayes/suspected (hospital records) 0.970 0.974 0.952 0.9661 min. Apgar score =< 3 0.964 0.972 0.956 0.9705 min. Apgar score =< 6 [a] 0.959 0.963 0.933 0.954

Notes: NND - neonatal death; ENND - early neonatal death. *95% confidence interval for RR includes 1.0.[a] For those cases in which the Apgar score at 5 minutes was not recorded, it is assumed that the score was > 6.Version 1: in the presence of censoring; Version 2: censored cases are assumed to have survived the neonatal period.

EARLY LIFE CHANGES

274

Table 8.14: Prevalence in live births and EF of neonatal death, by risk factor, SAT Hospital,Trivandrum, Kerala, October 2000

EFPrevalence NND NND ENND ENND

Pregnancy/birth outcome in LB Version 1 Version 2 Version 1 Version 2

Congenital anomaliesyes/suspected 0.018 0.313 0.342 0.219 0.257

Birth weight< 2,500 g 0.184 0.646 0.650 0.529 0.556< 2,000 g 0.041 0.437 0.481 0.448 0.528< 1,500 g 0.011 0.116 0.131 *0.053 0.079

Gestational age< 37 weeks 0.057 0.471 0.471 0.477 0.519< 32 weeks 0.021 0.254 0.274 0.199 0.261

Weight by ageSGA, Lubchenco et al., 10th percentile 0.194 *0.189 *0.201 *0.088 *0.104SGA, Thomson et al., 5th percentile 0.209 0.352 0.369 *0.201 *0.214SGA, Thomson et al., 10th percentile 0.338 0.703 0.697 0.633 0.624SGA, Yudkin et al., 3rd percentile 0.105 0.466 0.502 0.326 0.362SGA, Yudkin et al., 10th percentile 0.284 0.532 0.532 *0.405 *0.405SGA, Mathai et al., 5th percentile 0.090 0.442 0.453 *0.255 *0.270SGA, Mathai et al., 10th percentile 0.178 *0.308 *0.325 *0.111 *0.125

Head circumference by ageSGA, Lubchenco et al., 10th percentile 0.090 *0.059 *0.059 *0.090 *0.097SGA, Yudkin et al., 3rd percentile 0.259 *0.280 *0.252 *0.252 *0.222

Birth asphyxiayes/suspected (hospital records) 0.019 0.377 0.413 0.275 0.3481 min. Apgar score =< 3 0.011 0.225 0.274 0.192 0.2615 min. Apgar score =< 6 [a] 0.010 0.188 0.204 0.122 0.171

Notes: NND - neonatal death; ENND - early neonatal death. *95% confidence interval for RR includes 1.0.[a] For those cases in which the Apgar score at 5 minutes was not recorded, it is assumed that the score was > 6.Version 1: in the presence of censoring; Version 2: censored cases are assumed to have survived the neonatal period.

8.5 Overlap and associations

Following Sections 3.4.4, 6.6, and 7.6, the present section describes the interlinkages between the various adverse pregnancy/birth outcomes using data come from the survey in SAT Hospital. The various pregnancy and birth outcomes were analysed for associations on the basis of Pearson’s Chi-square and its p-value. The p-value indicates the probability that the model of the null hypothesis (i.e. no association or interaction) predicts the data in the population. The null hypothesis was rejected only when this p-value, or probability, was less than 5% (see Chapter 4). In addition, Cramer’s V was calculated to obtain an impression of the strength of the possible associations. The analysis was performed for all adverse pregnancy and birth outcomes. Only SGA as defined on the basis of head circumference was not included in the analysis. Generally, the


275

strength of the observed associations was weak. Moreover, it is of importance to note that the small numbers in the SATH survey require caution and hinder the formulation of strong statements.

8.5.1 LBW, PRETERM BIRTH, AND SGA In the SATH survey, 19.2% of births were LBW (< 2,500 g). According to Villar and Belizan (1982), a relatively large fraction of LBW births in regions where the percentage of LBW births exceeds 10% are the result of IUGR/SGA (see Chapter 7). The proportion of preterm births is believed to be almost the same (5-7%) as in regions where the prevalence of LBW is less than 10%. Indeed, only 6.2% of births in the population of the SATH survey were preterm (< 37 weeks). This suggests that the remainder of LBW births are growth retarded and that the prevalence of LBW-IUGR/SGA is comparatively high. Still, in the SATH survey, low birth weight was associated with both preterm birth and IUGR/SGA.

LBW and preterm birth In total, 29.1% of LBW births (< 2,500 g) in the survey were preterm (< 37 weeks) and 85.2% of preterm births were LBW (p < 0.01, V = 0.45). In live births, the corresponding figures were 26.9% and 83.6% (p < 0.01, V = 0.42) respectively. In addition, all live births weighing less than 1,500 g, and as many as 87.0% of live births weighing less than 1,750 g, were preterm. The overall percentage of live births that were both LBW (< 2,500 g) and preterm (< 37 weeks) was 4.8%. When those cases that were censored during the neonatal period are assumed to have survived, the incidence of neonatal death was highest in the group of infants who were both LBW and preterm, at 13.0%. Among preterm neonates ≥ 2,500 g, and among LBW neonates ≥ 37 weeks, the incidence proportions were 11.1% and 3.2% respectively. Unsurprisingly, neonates who were non-preterm and non-LBW had the best survival chances; for them, the incidence of neonatal death was only 0.4%.

LBW and SGA When considering only SGA based on birthweight curves, 56.3 to 90.7% of LBW births were SGA, and 41.6 to 95.0% of SGA births were LBW (p < 0.01 in all, V = 0.49-0.72). For live births the figures ranged from 57.3 to 91.0% and from 40.6 to 94.9% respectively (p < 0.01, V = 0.48-0.74). Overall, the percentage of live births that were both LBW and SGA ranged from 8.5% (based on 5th percentile of birthweight curve by Mathai et al. 1996) to 14.1% (based on 10th percentile of birthweight curve by Yudkin et al. 1987). When censored cases were assumed to have survived the neonatal period, the incidence of neonatal death among infants who were both LBW and SGA was between 3.6 and 5.6%. In comparison, among non-SGA infants ≥ 2,500 g the incidence was lower at only 0.2 to 0.6%.

SGA and preterm birth In the literature, IUGR/SGA has been associated with preterm delivery (see Chapters 3 and 7). However, in the SATH survey, the null hypothesis which states that there is no association

EARLY LIFE CHANGES

276

between SGA and preterm birth could only be safely rejected in the case of SGA defined by the 3rd percentile of Yudkin et al.’s (1987) birthweight curve (p = 0.04, V = 0.07). However, this finding could also indicate an inability of this curve to distinguish between SGA and preterm birth for Indian newborns. Overall, the percentage of live births that were both preterm and SGA was small and ranged between 0.1% (based on 5th percentile of birthweight curve by Mathai et al. 1996) and 1.6% (based on 10th percentile of birthweight curve by Thomson et al. 1968). The incidence of neonatal death in this group was between 0.0 and 16.7% (with the assumption that censored cases survived). Among preterm neonates, there appeared to be no survival advantage of SGA infants over non-SGA infants. Rather, the risk of neonatal death was increased in SGA infants for most classifications of SGA. However, none of these results had a p-value < 0.05, probably because of the very small numbers in the subcategories.

8.5.2 CONGENITAL ANOMALIES Congenital anomalies have been associated with low birth weight, preterm birth and IUGR/SGA (see Chapters 3 and 7). However, in the SATH survey, the presence of congenital anomalies was not associated with any of these pregnancy/birth outcomes, at least not for p < 0.05. Overall, 23.5% of live births with congenital anomalies were LBW (< 2,500 g), 11.1% were preterm (< 37 weeks), and 13.3 to 29.4% were SGA. Two preterm live births were registered as anomalous, having congenital heart disease and suspected facial dysmorphism. Both babies weighed less than 1,750 g and did not survive the neonatal period. The other two anomalous LBW cases were affected by hypospadias and low anorectal malformations and by osteogenesis imperfecta, skeletal dysplasia, and suspected chromosomal anomalies. They also died during the neonatal period and were both classified as SGA by all birthweight curves. In total, five live births with anomalies were classified as SGA by at least two different weight curves. Four of them did not survive the neonatal period.

8.5.3 BIRTH ASPHYXIA

In the SATH survey, birth asphyxia (as based on hospital records) is associated with LBW, preterm birth, SGA, and congenital anomalies. The percentages of live births that experienced birth asphyxia are: 5.9% of LBW births (p < 0.001, V = 0.14), 9.8% of preterm births (p < 0.001, V = 0.15), and 15.8% of anomalous births (p < 0.001, V = 0.14). Of SGA live births, 3.0-6.1% were affected by birth asphyxia (p < 0.04 in all, V = 0.07-0.12), although no association was observed with SGA as based on the 10th percentile of the birthweight curve by Thomson et al. (1968). The total percentage of live births that were both LBW (< 2,500 g) and asphyxiated was 1.1% (11 cases) in the SATH survey. When censored cases are assumed to have survived, the incidence of neonatal death in this group is as high as 45.5% compared to only 3.0% in non-asphyxiated LBW infants. Likewise, 0.6% (6 cases) of live births were both preterm (< 37 weeks) and asphyxiated. In total, 66.7% of asphyxiated preterm infants died during the neonatal period compared to only 6.1% of non-asphyxiated preterm neonates. The percentage of live births who were both SGA and asphyxiated varied from 0.5 to 0.9% (4 to 8 cases),


277

depending on the definition of SGA. The incidence of neonatal death in this group ranged from 11.1 to 50.0% compared to only 0.0 to 2.7% among non-asphyxiated SGA newborns. Finally, 3 cases, or 0.3%, of live births were anomalous as well as asphyxiated. These three infants all died, while 14.3% of non-asphyxiated neonates with anomalies died during the neonatal period. In conclusion, birth asphyxia increases the risk of neonatal death among high-risk infants even further.

8.6 Summary and discussion

The present chapter has discussed the results of the survey in SAT Hospital in Thiruvananthapuram, Kerala, India. Data were analysed for 1,001 consecutive singleton births with birth weight ≥ 1,000 g and gestational age ≥ 28 weeks. Complete neonatal follow-up data were available for 629 out of the 988 live births while 359 infants were lost to follow-up sometime during the neonatal period. The results from the hospital survey were compared with, and complemented by, other secondary, data from Kerala where available. Two life tables were constructed on the basis of the SATH survey: a foetal life table from 28 gestational weeks to termination of pregnancy and a neonatal life table (see Section 8.2). However, one of the main objectives was to assess the relative importance of congenital anomalies, low birth weight, preterm birth, intra-uterine growth retardation/small-for-gestational-age (IUGR/SGA), and birth asphyxia as causal factors or risk factors for stillbirth (if applicable) and neonatal death in the SATH population in Kerala. This importance was addressed at both the individual and the population level. The sections below summarise and discuss the results. The first section, Section 8.6.1, compares data from the SATH survey with other data from Kerala to situate the results within the context of the region. Subsequently, Section 8.6.2 discusses the results from the SATH survey. The focus of the discussion is on the relative importance of the selected pregnancy and birth outcomes as risk factors for stillbirth and neonatal death. The tables in both sections present results where those cases who were lost to follow-up are assumed to have survived the neonatal period. In the next section, Section 8.7, the results are put alongside the figures from the EME region (see Sections 6.7 and 7.7) and figures for all-India and for other Indian regions (presented in the current chapter).

8.6.1 WITHIN THE CONTEXT OF KERALA

SAT Hospital is a large teaching hospital in the public sector that treats a large number of referred cases. In our survey, 20% of the women had been referred by other hospitals in surrounding areas. Consequently, high-risk and complicated cases are likely to be over-represented. Moreover, government hospitals in Kerala are more likely to be visited by the lower socioeconomic classes than by the middle and higher classes (see Chapters 2 and 4). Throughout the present chapter, the results from the survey in SAT Hospital have been compared to other data from Kerala, both hospital data and community-based data. Table 8.15 presents an overview of how the population in the SATH survey compares to the overall population in Kerala with regard to mortality rates and frequency of adverse pregnancy and

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278

Table 8.15: Mortality rates and prevalence of adverse pregnancy/birth outcomesin the SATH survey compared with secondary data from Kerala

Other data from Keralahospital other

Mortality ratesStillbirth rate + –/+Perinatal mortality rate + –Early neonatal mortality rate =/+ –/+Neonatal mortality rate NA –/+

% of all births (live births)Congenital anomalies 2.0 (1.8) NA NALBW (< 2,500 g) 19.2 (18.4) –/+ –/=Preterm birth (< 37 wks) 6.3 (5.7) – –IUGR/SGA 9.0-34.2 (9.0-33.8) NA NABirth asphyxia 1.9 (1.9)** NA NA

Notes: NA - not available. *Survival assumed for cases censored during the neonatal period.**Based on diagnosis in the hospital records.The signs in the table indicate whether the secondary data from Kerala are lower (-), higher (+), or moreor less equal (=) to the results from the SATH survey.

13.024.0*11.2*14.2*

SATHsurvey

birth outcomes. This is to situate the results of the hospital survey within the context of the region. In the table, signs indicate whether the other figures from Kerala are lower (–), higher (+) or more or less equal (=) to the results from the SATH survey. The stillbirth rate, early neonatal mortality rate, and neonatal mortality rate in the SATH survey are comparable to the population-based figures from Kerala (see Table 8.15). The mortality rates in the SATH survey generally fall between the lower rates from the Sample Registration System (SRS) in 1998 and the higher rates from the National Family Health Survey (NFHS-1 and/or NFHS-2). It is important to note that perinatal mortality rates could not be obtained from the NFHS. The perinatal mortality rate in the SATH survey thus seems comparatively high in Table 8.15. Other hospital data from Kerala show mortality rates that are generally higher than those observed in our hospital survey. The differences could be due to year and time period of data collection, but also due to age and weight criteria. In addition, random factors and chance could play a role. For example, during the month prior to our survey, the stillbirth rate in SAT Hospital was as high as 24.2 deaths per 1,000 births. Unfortunately, figures from Kerala on the prevalence of adverse pregnancy/birth outcomes among births are limited. This finding in itself shows the added value of hospital surveys. Data were available only for low birth weight and preterm birth. The prevalence of low birth weight (< 2,500 g) in the SATH survey was comparable to other findings for Kerala. However, the proportion of preterm births was slightly higher in the SATH survey than in the data from the NFHS (5.7% versus 3.7%). In conclusion, the SATH survey did not seem to be strongly affected by high-risk cases. At least not in terms of the outcomes discussed here.


279

8.6.2 INDIVIDUAL LEVEL The relative importance at the level of the individual has been established in terms of risk of stillbirth or death for affected individuals, and in the relative risk of stillbirth or death (see also Chapter 4). Table 8.16 summarises incidence proportions of stillbirth (if applicable), early neonatal death, and neonatal death by risk factor. The incidence of stillbirth is much higher among anomalous children than among SGA infants (10% vs. 1-3%). The incidences of neonatal death are highest among infants who experienced birth asphyxia or who had a congenital anomaly. The lowest risks of neonatal death are observed among SGA infants. Within the five main risk categories (i.e. anomalies, LBW, SGA, preterm, and birth asphyxia), the ranking by highest incidence of neonatal death is: (1) birth asphyxia (I = 32), (2) congenital anomalies (I = 28), (3) preterm birth (I = 13), (4) LBW (I = 6), and (5) IUGR/SGA (I = 2-5). In addition, it is important to note that the incidence of neonatal death in infants who weighed less than 1,500 g at birth is relatively low, especially when compared with the figures for infants < 2,000 g. Most likely, this is explained by random variation. Overall, the majority of live-born infants affected by one of the risk factors in Table 8.16 do survive the neonatal period. However, almost one-third of asphyxiated newborns do not survive the neonatal period. Table 8.17 shows how the incidence proportions in Table 8.16 compare to the incidence of death in foetuses and neonates without the selected pregnancy/birth outcomes. The relative risks (RR) in the table indicate how many times more likely babies with the risk factor will experience stillbirth or neonatal death as compared to those without the risk factor. For example, anomalous foetuses are about 9 times as likely as non-anomalous ones to experience stillbirth. Most figures in the table reflect significantly increased risks, i.e. RR > 1 and 95% confidence interval excluding the value of 1.0. For some definitions of IUGR/SGA, the 95% confidence interval did include the value of 1.0. Indeed, the RR of stillbirth for IUGR/SGA foetuses ranges from 1 to 5, which indicates that the incidence of stillbirth is not, or mildly, elevated compared to non-SGA foetuses.

Table 8.16: Incidence of stillbirth and neonatal death, by risk factor, SATH survey

Incidence proportion (%)SB ENND* NND*

Congenital anomalies 10 17 28Birth weight < 2,500 g - 4 6Birth weight < 2,000 g - 15 18Birth weight < 1,500 g - 9 18Preterm birth (< 37 wks) - 11 13Very preterm birth (< 32 wks) - 15 20IUGR/SGA (based on birth weight) 1-3 1-3 2-5Birth asphyxia** - 21 32

Notes: SB - stillbirth (>= 28 wks); ENND - early neonatal death; NND - neonatal death.*Survival assumed for censored cases. **Based on diagnosis in the hospital records.Only including births >= 28 gestational weeks or >= 1,000 grams.Based on data from SATH survey, Trivandrum, Kerala, October 2000.

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Table 8.17: Relative risks of stillbirth and neonatal death, by risk factor,SATH survey

Relative risk (RR)

SB ENND* NND*


Notes: SB - stillbirth (>= 28 wks); ENND - early neonatal death; NND - neonatal death.*Survival assumed for censored cases. **Based on diagnosis in the hospital records.Only including births >= 28 gestational weeks or >= 1,000 grams.Based on data from SATH survey, Trivandrum, Kerala, October 2000.

An elevated risk of neonatal death is most distinct for asphyxiated newborns and infants with anomalies (see Table 8.17). Again, the relative risk of death is lowest for IUGR/SGA infants. The highest relative risks of neonatal death for SGA result from using the 3rd percentile of the weight curve by Yudkin et al. (1987) and the 5th percentile of the weight curve by Mathai et al. (1996; Mathai, personal communication in 2000): namely 10.6 (95% CI: 2.9-38.9) and 10.2 (95% CI: 2.6-39.9) respectively. These curves thus have the best ability to discern neonates at risk. Within the five main risk categories, the ranking by highest relative risk of neonatal death is: (1) birth asphyxia (RR = 38), (2) congenital anomalies (RR = 30), (3) preterm birth (RR = 17), (4) LBW (RR = 11), and (5) IUGR/SGA (RR = 2-11). As in Table 8.16, the results for newborns < 1,500 g appear to be too low in comparison to the relative risks experienced by neonates who weighed < 2,000 g, or even < 2,500 g, at birth. Similarly, the RR of early neonatal death among very preterm neonates (< 32 weeks) is lower than its equivalent among preterm neonates (< 37 weeks). It is unclear what causes these results, but it may simply be due to random variation. The SATH survey included only 20 very preterm infants (< 32 weeks) and only 11 infants who weighed less than 1,500 g at birth. In conclusion, the relative risks in Table 8.17 indicate elevated risks of stillbirth and neonatal death for foetuses and infants affected by one of the selected risk factors as compared with non-affected infants. Only for some definitions of IUGR/SGA, do the relative risks of stillbirth and death have a value of one, or close to one, indicating that there is no elevated risk as compared to non-SGA infants. Moreover, IUGR/SGA is, in general, the least important of the risk factors.

8.6.3 POPULATION LEVEL The relative importance at the level of the population has been established in terms of frequency (cf. prevalence) of the risk factor within the total population, and in the proportion of losses or deaths in a population that can be attributed to the risk factor (see also Chapter 4).


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Table 8.18 summarises the prevalence of the selected pregnancy/birth outcomes among stillbirths, births, and neonatal deaths. Among live births, the most prevalent outcomes are SGA (9-34%) and LBW (18%), while the most infrequent outcomes are birth weight < 1,500 g (1%), very preterm birth (2%), and birth asphyxia (2%). The average birth weight for live births is 2,870 g (± 486 g). The prevalence of low weight at birth drops sharply between 2,500 g and 2,000 g (from 18% to 4% in live births), which indicates that most LBW babies in the population of the SATH survey weigh between 2,000 and 2,499 g (76%). The prevalence among live births of SGA as based on the 3rd percentile of the weight curve by Yudkin et al. (1987), and the 5th percentile of the weight curve by Mathai et al. (1996; Mathai, personal communication in 2000), is 10.5% and 9.0% respectively. In general, the adverse pregnancy/birth outcomes are more frequently observed in stillbirths, early neonatal deaths, and neonatal deaths. Among stillbirths, the prevalence proportions are generally over 10% with LBW (77%), SGA (11-70%), and preterm birth (55%) being the most prevalent. However, the extremely low prevalence of birth asphyxia in stillbirths (no cases) seems highly unlikely and is probably explained by the small number of stillbirths in the survey, and possibly by the quality of the data, and the procedures and criteria that are applied in SAT Hospital to diagnose and define birth asphyxia. Among early neonatal and total neonatal deaths, prevalence proportions are generally over 27%, the only exception being birth weight < 1,500 g. The majority of neonatal deaths are affected by LBW and/or SGA, and half of the deceased neonates had a birth weight < 2,000 g and/or were preterm births. The ranking of the main risk categories by highest prevalence among neonatal deaths is: (1) LBW (71%), (2) preterm birth (50%), (3) birth asphyxia (43%), and (4) congenital anomalies (36%). The corresponding figures for SGA show a wide range from 36 to 80%. Table 8.19 summarises the findings for attributable risk, both among the exposed and in the total population, by risk factor. The attributable risk among the exposed, AR(E), indicates the proportion of deaths in the affected population that can be attributed to the risk Table 8.18: Prevalence of risk factors in stillbirths, live births, all births, and neonatal deaths,by type, SATH survey

Prevalence proportion (%)

SB LB all births ENND* NND*Congenital anomalies 15 2 2 27 36Birth weight < 2,500 g 77 18 19 64 71Birth weight < 2,000 g 46 4 5 55 50Birth weight < 1,500 g 23 1 1 9 14Preterm birth (< 37 wks) 55 6 6 55 50Very preterm birth (< 32 wks) 9 2 2 27 29IUGR/SGA (based on birth weight) 11-70 9-34 9-34 27-75 36-80Birth asphyxia** 0.0 2 2 36 43

Notes: SB - stillbirth (>= 28 wks); LB - live birth; ENND - early neonatal death; NND - neonatal death.*Survival assumed for censored cases. **Based on diagnosis in the hospital records.Only including births >= 28 gestational weeks or >= 1,000 grams.Based on data from SATH survey, Trivandrum, Kerala, October 2000.

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Table 8.19: Attributable risks of stillbirth and neonatal death, by risk factor,SATH survey

AR(E): Attributable risk among the 'exposed' (%)

SB ENND* NND*


EF: Etiologic fraction (%), in total population

SB ENND* NND*


Notes: SB - stillbirth (>= 28 wks); LB - live birth; ENND - early neonatal death; NND - neonatal death.*Survival assumed for censored cases. **Based on diagnosis in the hospital records.Only including births >= 28 gestational weeks or >= 1,000 grams.Based on data from SATH survey, Trivandrum, Kerala, October 2000.

factor. For example, 97% of neonatal deaths among anomalous infants are attributable to the anomaly in question. The table shows that the vast majority of deaths in the population affected by one of the selected risk factors can be attributed to that risk factor. Indeed, over 86% of stillbirths, early neonatal deaths, and total neonatal deaths in the affected populations are explained by the risk factors in question. The AR(E) of neonatal death is above 95% among infants with anomalies, birth asphyxia, birth weight < 2,000 g, or very preterm birth (< 32 weeks). The exception is IUGR/SGA that contributes to only 9-80% of stillbirths, 38-84% of early neonatal deaths, and 57-91% of neonatal deaths in the SGA population. A more interesting measure from a population point of view is the etiologic fraction (EF). The EF indicates the proportion of deaths in the total population that can be attributed to the risk factor and is calculated on the basis of prevalence in the population (see Table 8.18) and relative risk of death (see Table 8.17). Table 8.19 shows that congenital anomalies explain about 14% of stillbirths in the total population, while 1 to 55% of stillbirths can be attributed to IUGR/SGA. The most important contributors to early neonatal and neonatal death in the population of the SATH survey are birth weight < 2,500 g or < 2,000 g, and


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preterm birth (< 37 weeks). In the case of LBW, the high ranking is explained by the high prevalence among live births, while preterm children and infants < 2,000 g experience higher relative risks of neonatal death. The ranking of the five main risk categories by largest contribution to neonatal mortality in the total population is: (1) LBW (EF = 65%), (2) preterm birth (EF = 47%), (3) birth asphyxia (EF = 41%), and (4) congenital anomalies (EF = 34%). The rank of IUGR/SGA in this list is uncertain due to its wide range, i.e. the EF is 20 to 70% of neonatal deaths. For the population in the SATH survey, the 3rd percentile of the weight curve by Yudkin et al. (1987) and the 5th percentile of the weight curve by Mathai et al. (1996; Mathai, personal communication in 2000) seem to have the best ability to discern neonates at risk (see earlier discussion). The EF of neonatal death for SGA neonates on the basis of these curves is quite high at 50.2% and 45.3% respectively.

8.7 Transition

The original theory of the epidemiologic transition by Omran (1971) as well as most extensions to it (e.g. Olshansky and Ault 1986; Frenk et al. 1989a, 1989b) have no or hardly any reference to pregnancy, gestation, birth, and the newborn. Van der Veen’s (2001) recent review of a small epidemiologic transition (see Chapter 2) focused on contemporary changes in infant mortality in low-mortality countries. The present study aims to gain insights into the changes during earlier stages of the transition. The objective is to place some important risk factors for loss and death among foetuses and neonates within the framework of an epidemiologic transition while assessing the current situation in regions in transition that are approaching the later stages of the epidemiologic transition (see Chapter 1). The present chapter focused on Kerala as a region in transition, but its objectives are twofold: (1) to assess the situation in this region and (2) to place selected risk factors in the framework of an epidemiologic transition. The previous section, Section 8.6, has already discussed and summarised the situation in Kerala, i.e. with regard to the relative importance of congenital anomalies, low birth weight, preterm birth, IUGR/SGA, and birth asphyxia as causal factors or risk factors for stillbirth and neonatal death. The present section puts the results from our hospital survey in Kerala alongside the estimated figures for the EME region (see Chapters 2, 5, 6, and 7) and secondary data for all-India and other Indian regions (presented in the current chapter). This comparison makes it possible to place the risk factors under study within the framework of an epidemiologic or health transition, and to study the changes during this transition and the developments over time. In this, the EME region contains so-called low-mortality countries and is already strongly progressing through the later stages (i.e. the fourth stage) of the epidemiologic transition, while all-India appears to be only at the beginning of, what is called, the third stage. Kerala, as a region in transition that is approaching the later stages of the epidemiologic transition, is situated somewhere in-between (see Chapter 2). The present section discusses mortality rates (Section 8.7.1), the prevalence of the selected risk factors or adverse pregnancy/birth outcomes among births (Section 8.7.2), the relative importance of the risk factors at the individual level (Section 8.7.3), and the relative importance of the risk factors at the population level (Section 8.7.4).

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Table 8.20: Mortality rates from the SATH survey compared with figures forthe EME region and all-India

SATH EME all-IndiaMortality rates sample

Stillbirth rate** 13.0 – –Perinatal mortality rate** 24.0 – +Early neonatal mortality rate *11.1 – +Neonatal mortality rate *14.2 – +

Notes: *Survival assumed for cases censored during the neonatal period;**For the EME region: stillbirth ratio and perinatal mortality ratio (per 1,000 live births).The signs in the table indicate whether the figures for the EME region and all-India are lower (-),higher (+), or more or less equal (=) to the results from the SATH survey.Results for the EME region and all-India based on Chapters 2 and 8.

8.7.1 MORTALITY RATES Mortality figures for the EME region and India clearly show that mortality rates decline during the epidemiologic transition. For example, the neonatal mortality rate is around 43 to 45 deaths per 1,000 live births in all-India (IIPS and ORC Macro 2000; Office of the Registrar General 2000) while only about 2 to 6 per 1,000 live births in the EME region (UN, Demographic Yearbook, various years). However, the decline is less distinct for stillbirths (late foetal deaths). In the EME region, the stillbirth ratio is about 3 to 6 per 1,000 live births (UN, Demographic Yearbook, various years) whereas in all-India the stillbirth rate is 9 per 1,000 births (Office of the Registrar General 2000). The smaller difference in stillbirth rate may be explained by stillbirth registration, procedures, and criteria. Table 8.20 compares the stillbirth, perinatal, early neonatal, and neonatal mortality rates in the SATH survey with those observed in the EME region and in all-India. In the table, signs indicate whether rates observed in the EME region or in all-India are lower (–), higher (+), or more or less equal (=) to the rates in the SATH survey in Kerala. The rates in the SATH survey, as well as other published rates for Kerala, generally fall between lower figures for the EME region and higher figures for all-India. Only stillbirth rate is lower in all-India as based on the Sample Registration System (SRS). This may be due to the status of SAT Hospital as a referral hospital. Indeed, the stillbirth rate in Kerala as a whole, according to the SRS, is lower than its equivalent for all-India.

Conclusion To sum up, perinatal mortality and neonatal mortality rates decline during the transition. The decline is less distinct for stillbirth rate, but this may be a result of registration procedures and criteria.

8.7.2 PREVALENCE IN BIRTHS Below, a comparison of prevalence figures for the EME region to those for India indicates changes during the epidemiologic transition with regard to pregnancy and birth outcome. In addition, Table 8.21 compares the findings of the survey in SAT Hospital with frequency


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proportions observed in studies in the EME region and in Indian regions other than Kerala. As in Table 8.20, the signs in the table indicate whether proportions in the EME region or in Indian regions are lower (–), higher (+), or more or less equal (=) to figures from the SATH survey. The proportion of births that weigh < 2,500 g (LBW) clearly declines during the epidemiologic transition. The prevalence of LBW in the EME region is estimated to be 5-10% of births (see Chapter 7), while in all-India the estimated figure is as high as 28 to 35% of births (De Onis et al. 1998; Shibuya and Murray 1998a). These results are in accordance with generally held assumptions. Moreover, Table 8.21 indicates that the figures from the SATH survey fall between these two (18-19%). The higher prevalence in the SATH population, as compared to the EME population, is mainly explained by a large proportion of newborns who weigh between 2,000 and 2,499 g. The proportion of births < 2,000 g is only slightly higher in the SATH survey (4.6% vs. 2-3%) and the proportion < 1,500 g is more or less equal (0.9-1.2% vs. 1.4%). It is generally assumed that most LBW births in developing countries are caused by growth retardation as opposed to preterm birth. Villar and Belizan (1982)9 concluded that in those countries where the prevalence of LBW exceeds 10%, this is almost exclusively due to an increase in IUGR-LBW infants while the prevalence of preterm LBW births in the population remains almost unchanged at 5 to 7% (see earlier discussion). However, a comparison of prevalence figures from India with proportions from the EME region suggests that the proportion of preterm births does decline during the epidemiologic transition. In the EME region, 6 to 10% of births are preterm (see Chapter 7) whereas results from Indian studies indicate that about 10-14% of births in hospitals (Singh et al. 1991; WHO 1995; Kaushik et al. 1999), and 15-18% of live births in rural and urban communities (Hutter 1994;

Table 8.21: Prevalence of adverse pregnancy/birth outcomes in the SATH surveycompared with figures for the EME region and other Indian states

EME Other India% of births (not Kerala)

Congenital anomalies 2.0 (1.8) + –/+Birth weight < 2,500 g 19.2 (18.4) – –/+Birth weight < 2,000 g 4.6 (4.1) – NABirth weight < 1,500 g 1.4 (1.1) = NAPreterm birth (< 37 wks) 6.3 (5.7) =/+ +Very preterm birth (< 32 wks) 2.1 (2.1) – NAIUGR/SGA 9.0-34.2 (9.0-33.8) – –/+Birth asphyxia 1.9 (1.9) – =/+

Notes: NA - not available. *Proportions in live births between brackets.The signs in the table indicate whether the figures for the EME region and Indian regions (other thanKerala) are lower (-), higher (+), or more or less equal (=) to the results from the SATH survey.Results EME region based on Chapters 6 and 7. Results India based on Chapter 8.

SATHsample*

9 Villar and Belizan (1982) refer to incidence of LBW, IUGR-LBW, and preterm LBW.

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Antonisamy et al. 1996) are preterm. This observation is in spite of the generally lower gestational age limits that are applied in EME countries to distinguish between abortion and birth. Moreover, Antonisamy et al. (1996) observed that the proportion of preterm (< 37 weeks) newborns in urban Tamil Nadu (South India) declined from 20% during the period 1969-1973 to 15% during 1989-1993. In rural communities, the prevalence declined from 21 to 16% in the same period. In the SATH survey, the prevalence of preterm birth (< 37 weeks) among births is comparable to the proportions observed in the EME region (see Table 8.21). Only figures from the USA are higher at 10%, versus 5-7% in other EME countries (see Chapter 7) and 6.3% in the SATH population. In other regions in India, the prevalence of preterm birth is much higher. The decrease in the prevalence of preterm birth during the transition may be related to declines in causes and risk factors such as infections, uterine abnormalities, and IUGR. In addition, an improvement in the nutritional status of the mother might play a role. Gestational duration has been associated with low pre-pregnancy weight (Kramer 1987a, 1987b) and calcium supplementation has been suggested to help prevent hypertensive diseases in pregnancy as well as preterm labour and delivery (Ronsmans 2001). Nevertheless, it is important to note that accurate measurement of gestational duration is difficult, and that valid assessment of gestational age is often lacking in less developed regions (WHO 1991; De Onis et al. 1998). In comparison to preterm birth, figures on the anticipated and real decline in the prevalence of IUGR/SGA births during the epidemiologic transition are less straightforward. On the basis of two studies in hospitals, only around 7% of live births in India are small-for-date (Singh et al. 1991; Kaushik et al. 1999) which is comparable to the 3-10% observed in the EME region (see Chapter 7). However, these results are likely to be affected by the choice of study population (i.e. hospital-based) and even more strongly by the choice of a reference growth curve. Indeed, a WHO multicentre study in Pune observed as many as 54% of births in urban and rural hospitals to be IUGR/SGA (WHO 1995). This result was based on a weight-for-gestational-age curve from an international reference group. Since the elevated proportions of LBW births in the SATH survey and Kerala, as compared to the EME region, are not due to preterm birth, they are most likely the result of IUGR/SGA. Indeed, Table 8.21 shows that the prevalence of SGA among births is higher in the SATH survey than in the EME region, even when defined on the basis of the South Indian reference curves by Mathai et al. (1996; Mathai, personal communication in 2000). The above findings on LBW, preterm birth, and IUGR/SGA imply that during the epidemiologic transition the proportion of preterm births declines earlier and/or more rapidly than the proportion of IUGR/SGA births. It may be that the situation with regard to the underlying causes of preterm birth improves earlier and/or more rapidly than the situation with regard to the underlying causes of growth retardation. It may also be that intergenerational influence of foetal growth and size is stronger and more persistent than any possible intergenerational influence of preterm birth. Indeed, intergenerational links have only been documented for low birth weight caused by IUGR. Data from the USA have suggested that maternal birth weight has a stronger effect on intrauterine growth than on the duration of


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gestation (Klebanoff and Yip 1987 cited by Van der Veen 2001). Hennessy and Alberman (1998a) found that birth weight for gestational age in non-preterm babies is best explained by factors relating to the parent’s growth, primarily in utero but also up to adulthood. Parent’s own foetal growth accounted for nearly a third of the variability after adjustment for the sex of the child, smoking, parental height and weight, maternal age at menarche, and paternal age at birth. The authors were unable to find such a significant and independent intergenerational effect on preterm delivery (Hennessy and Alberman 1998b). Only a reported history of hypertension in mothers, fathers, and the maternal grandmother significantly and independently increased the risk of preterm birth. Finally, the findings of an earlier and/or more rapid decline in preterm birth than in IUGR/SGA could be specific for the situation in Kerala. In Kerala, mortality figures have declined and the population has achieved a comparatively high health status, despite levels of income and nutrition having been low (among the lowest in India) (Ratcliffe 1983; Panikar and Soman 1984; Bhat and Irudaya Rajan 1990; Zachariah et al. 1994; Kabir and Krishnan 1996). As with IUGR/SGA, results on congenital anomalies and birth asphyxia are also difficult to compare across regions, or even between studies within the same region. This is due to problems of definition, measurement, detection, and diagnosis. Studies in India have provided a wide range of prevalence estimates for congenital anomalies: from 0.25 to 5.5% of births (Mishra and Baveja 1989; Bhat and Babu 1998; Kaushik et al. 1999; Verma 2000). The estimated prevalence in the EME population is 3% (see Chapter 6). In the population of the SATH survey, the prevalence of congenital anomalies among births is lower than in the EME region (see Table 8.21). However, the ‘true’ prevalence is not necessarily lower. The difference might be explained by variations in rate of detection, available diagnostic methods, financial resources available for diagnosis, and length of follow-up but also by random variation. Birth asphyxia affects 2 to 10% of births in India (Bhakoo et al. 1989 cited by Shibuya and Murray 1998b; Singh et al. 1991; Kumari et al. 1993; Kumar 1995; Chandra et al. 1997; Kaushik et al. 1999) as compared to only 0.3 to 1.0% in the EME region (see Chapter 7). These findings indicate that there is a decline in the frequency of birth asphyxia cases during the epidemiologic transition. This is plausible since good-quality antenatal care and care at delivery are able to prevent severe birth asphyxia. Table 8.21 shows that the prevalence of birth asphyxia among live births (sometimes referred to as ‘incidence’) in the SATH survey lies between the values for the EME region and figures observed in other Indian regions. The high proportion found in the survey in SAT Hospital, when compared to the EME region, may be explained by the inclusion of suspected cases and/or by an elevated number of high-risk and complicated cases in the referral hospital. Moreover, the criteria applied in SAT Hospital to diagnose BA are not known.

Conclusion In conclusion, the frequency of certain adverse pregnancy and birth outcomes changes during the epidemiologic transition. In general, prevalence proportions among births (and among live births) decline. However, the figures on the prevalence of congenital anomalies among births are inconclusive.

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The proportion of birth asphyxia cases among births declines during the epidemiologic transition. The figures for the population in the SATH survey are slightly higher than those for the population in the EME region. The prevalence of low weight at birth (< 2,500 g and also < 2,000 g) declines strongly during the epidemiologic transition. The population in the SATH survey, and Kerala in general, still include a comparatively large group of infants who weigh between 2,000 g and 2,499 g at birth. However, the proportion of births weighing below 1,500 g is comparable to the proportion observed in the EME region. Thus, the distribution within the group of LBW births also appears to change during the transition. The prevalence of preterm birth (< 37 weeks and < 32 weeks) among births declines during the epidemiologic transition. Figures for the population in the SATH survey, and Kerala in general, match those observed in the EME region. The prevalence of SGA (small-for-gestational-age) also declines during the transition. The figures for the population in the SATH survey are, however, much higher than those for the population in the EME region. These findings suggest that during the epidemiologic transition the proportion of preterm births declines earlier and/or more rapidly than the proportion of IUGR/SGA births.

8.7.3 RELATIVE IMPORTANCE: INDIVIDUAL LEVEL

With regard to the relative importance of the selected risk factors, Table 8.22 compares the results from the SATH survey (from the current chapter) with the figures estimated for the EME region (see Chapters 6 and 7). The signs in the table indicate whether the figures in the SATH survey are lower (–), higher (+), or more or less equal (=) to the estimates for the EME region. The upper part of the table presents the measures that reflect relative importance at the level of the individual, while the lower part focuses on the population level. Above, in Section 8.7.1, it was already observed that stillbirth and mortality rates in the SATH survey and Kerala are higher than the figures for the EME region. Table 8.22 confirms that the incidence of stillbirth, early neonatal death, or total neonatal death for babies affected by one of the risk factors is generally higher in the SATH survey than in the EME region. However, among infants who weigh less than 2,500 g at birth and those < 2,000 g, the incidences of neonatal death in the SATH survey more or less equal the incidences in the EME region. This is probably due to a more favourable within-group distribution (see earlier discussion), with a comparatively large proportion of LBW births in SAT Hospital weighing over 1,500 g and even over 2,000 g. The low incidences of neonatal death among infants who weighed less than 1,500 g at birth in the SATH survey are most likely explained by random variation. Also within the SATH population itself, the incidence of death among those < 1,500 g was found to be unrealistically low (see Section 8.6.2). The relative risk (RR) indicates how many times more likely persons with a particular risk factor will experience stillbirth or neonatal death as compared to persons without the risk factor. According to Table 8.22, the relative risks of early neonatal and neonatal death in the SATH survey are more or less comparable to their equivalents in the EME region for the following risk factors: preterm birth (< 37 weeks), IUGR/SGA, and birth asphyxia. Since the


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Table 8.22: Relative importance in the SATH survey compared with results for the EME region,by risk factor

Congenital LBW LBW VLBW Preterm Preterm IUGR/ Birthanomalies < 2,500 g < 2,000 g < 1,500 g < 37 wks < 32 wks SGA asphyxia

Incidence ofSB + . . . . . = .ENND + = =/+ – + + NA +NND + = =/+ –/= + + + +

RR ofSB + . . . . . – .ENND –/= – – – –/+ – NA –/=NND + – – – –/= – –/= –/+

Prevalence inB – + + = –/= + + +LB – + + = –/= + + +

AR(E) ofSB + . . . . . – .ENND = – = – =/+ – NA =NND = – – – –/= – =/+ =

EF ofSB + . . . . . –/+ .ENND – – –/= – –/+ – NA +NND – –/+ – – – – =/+ +

Notes: SB - stillbirth (>= 28 wks); ENND - early neonatal death; NND - neonatal death; B - birth; LB - live birth;RR - relative risk; AR(E) - attributable risk among the exposed; EF - etiologic fraction; NA - not available.The signs in the table indicate whether the figures from the SATH survey are lower (-), higher (+), or more or less equal (=)to the results for the EME region.Based on Chapter 8 (Tables 8.16 through 8.19) in comparison with Chapters 6 and 7 (Tables 6.25 through 6.28 and Tables7.28 through 7.31). incidence of death among persons affected by these risk factors was found to be higher in the SATH population, the incidence of death among non-SGA, non-preterm, and non-asphyxiated neonates must be too, in order to achieve a similar ratio. For anomalous infants, the relative risks of stillbirth and neonatal death in the SATH survey exceed the relative risks in the EME region (see Table 8.22). It is important to note that the prevalence of congenital anomalies in births was found to be higher in the EME region. The higher prevalence may be explained by differences in rate of detection, available diagnostic methods, financial resources available for diagnosis, and length of follow-up. Moreover, the anomalies that were detected in the population of the SATH survey are likely to be those anomalies that are easiest to observe, such as those most severe and/or most lethal during early life. This would then explain the higher relative risk of death that was observed in the SATH survey.

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In contrast, the relative risks of neonatal death in the SATH survey are lower for all three low birth weight categories (< 2,500 g, < 2,000 g, and < 1,500 g), and for very preterm birth (< 32 weeks) when compared to the EME region. At first sight, this is not what would be expected in the light of a health transition. What explains these comparatively low relative risks? The low relative risk of death for neonates < 1,500 g is probably to a large extent explained by random variation (see earlier discussion) but we can speculate a little about the other three risk factors. First of all, it could be that, in Kerala, medical care is comparatively good and more intensive for the most compromised infants as compared to care for newborns who are not classified at birth as falling into one of the risk categories. After all, those infants who are compromised at birth are easier to identify as high-risk and problem children. However, the relative risks for SGA, preterm birth, and birth asphyxia do not seem to back this supposition and there is also no other evidence to support it. It could also be that there is a stronger antenatal selection effect in Kerala than in the EME region. Perhaps, those born alive very preterm and/or at low weights in Kerala are ‘survivors’ and therefore also survive comparatively well during the neonatal period. Another reason could be that the underlying causes of low weight at birth and very preterm birth differ between the two regions, and that they are less severe, and therefore less lethal, in the SATH population as compared to the EME population. Moreover, there may be racial/genetic variations involved concerning birth weight and, possibly, duration of gestation. Also within the UK and USA, babies of mothers of Indian origin have been found to have lower birth weights than white babies born to mothers of European or American origin (Wilcox et al. 1993; Moore et al. 1995; Fuentes Afflick and Hessol 1997). This may be the result of a continuing intergenerational effect of small size (i.e. hereditary), but it could also be due to differences in biology (i.e. genetic). Perhaps the optimal birth weight and/or gestational duration for Indian babies is lower. Indeed, Indian babies have been reported as having a lower incidence of hyaline membrane disease, which is related to preterm birth, than British and American babies (Webb et al. 1962 cited by Mathai et al. 1995). The most plausible explanation, however, seems to be found in the distribution of weights and gestational ages within the low-weight and preterm categories. In the SATH survey, a relatively large proportion of LBW births weighed between 1,500 and 1,999 g and an even larger proportion weighed between 2,000 and 2,499 g. The lowest birth weight was 1,040 g and the lowest gestational age was 28 weeks among live births. In contrast, in the EME region where the prevalences of LBW and preterm birth are lower, about 0.3 to 0.6% of live births weigh less than 1,000 g and 0.01 to 0.13% weigh even less than 500 g (see Table 7.5). In addition, 0.4% of live births in the USA during 1995 and 0.7% of live births in Canada during 1992-1994 were below 28 gestational weeks (Kramer et al. 2000). In our hypothetical cohort for the EME region, 0.3% of live births are born before 28 completed weeks (see Table 5.6). Births < 1,000 g and/or < 28 weeks in the SATH survey were excluded from further analysis, but, in total, only three cases had to be excluded and all three were antepartum stillbirths (none live births). SAT Hospital distinguishes births from abortions on the basis of the weight and age criteria of 1,000 g and 28 gestational weeks respectively. Abortions are


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registered in the so-called ‘abortion files’ and generally not in the Obstetric Register of births (see Chapter 4). It is not known by the author whether live births below the weight and age limits, especially those who died immediately after birth, were recorded in the abortion register. Since SAT Hospital has patients from a wide geographic area, it may also be that women are unable to reach the hospital in time in the unexpected event of extremely preterm labour (< 28 weeks). Nevertheless, the distributions of births by weight and gestational age within the low weight and preterm categories in the SATH survey differ clearly from the distribution as reported in the EME region, with fewer extremely low-birthweight births (< 1,000 g) and fewer extremely preterm births (< 28 weeks). The exclusion of these highest-risk groups is likely to reduce the relative risks of neonatal death. In the state of Alabama in the USA (i.e. EME region), Goldenberg, Phelan et al. observed a change between 1974 and 1994 in the weight distribution of reported births (Goldenberg et al. 1987; Phelan et al. 1998). According to Phelan et al. (1998), the percentage of total births weighing less than 500 g increased from 0.20% in 1974 to 0.51% in 1994. The increase was observed in both stillbirths and live births. The proportions of total births weighing 500-999 g and 1,000-1,499 g also increased during the same period. Moreover, within each of these three birth weight categories, the distribution between stillbirths and live births shifted in favour of live birth. The authors doubted that the disproportionate increase in infants < 500 g at birth was a real increase that could be explained by changes in the characteristics of the population of pregnant women (Goldenberg et al. 1987; Phelan et al. 1998). Rather, they suspected that the cause is to be found in increased and improved reporting/registration of very low-birthweight births. Phelan et al. (1998) concluded: “as the age of viability has moved downward to encroach on the under-500-g birthweight group, and as efforts to salvage very low-birthweight infants become more aggressive, it appears that more of the total births in the under-500-g group are recorded as live births rather than as spontaneous abortions or stillbirths” (p.1238). Following the findings in Alabama, comparing the SATH data to data from the EME region suggests that there is a shift in the birth-weight distribution of registered/reported births (both live and stillbirths) during the epidemiologic transition, towards the extremely low weight categories. In regions not so advanced in the transition as the EME region, such as Kerala, viability limits can be expected to lie at a higher birth weight and a higher gestational age. According to a local informant, the minimum weight and age at which babies can survive in SAT Hospital is a birth weight around 1,200 g and a gestational age of 32 weeks. In comparison, until recently, Groningen University Hospital used the limits of age ≥ 26 gestational weeks and weight ≥ 600 g in relation to life-saving efforts (Mantingh, personal communication in 2003). When not considered as viable, life-saving efforts during and before labour for extremely preterm and extremely low-birthweight infants are likely to be less aggressive. Subsequently, in less advanced regions such as Kerala, dead-born foetuses < 1,000 g and/or < 28 weeks, and probably also live-born foetuses < 1,000 g and/or < 28 weeks who die immediately after birth, are registered as spontaneous abortions. In addition, in these settings of the health transition, many women may not be able to reach high-level medical care in time in the event of sudden and rapidly progressing complications, or unexpected and extremely preterm labour.

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Conclusion In conclusion, the incidences of stillbirth and neonatal death among babies affected by anomalies, low weight at birth, preterm birth, intrauterine growth retardation/small-for-gestational-age, or birth asphyxia are generally higher – though sometimes equal – in less advanced regions than those in regions in the later stages of the epidemiologic transition. At least, this was the observation for the population of SAT Hospital in Kerala as compared to the population in the EME region. The incidences of stillbirth and death among non-affected infants are also likely to be higher in less advanced regions. The relative risks of stillbirth and neonatal death among anomalous babies in the SATH survey are higher than the corresponding risks in the more advanced EME region. This is probably explained by differences between the two regions in the detection of congenital anomalies after birth, with the anomalous group in Kerala containing mostly the easiest observable and most severe anomalies. On the other hand, low-birthweight and very preterm babies in the SATH survey experience lower relative risks of neonatal death than their counterparts in the EME region. The most plausible explanation for this is to be found in the differences in the distributions within the risk categories. During the epidemiologic transition, the birth-weight and gestational-age distributions of registered/reported births shift towards the extremely low-weight and preterm categories. More children born very preterm and/or with extremely low birth weight are reported or registered as births as a result of lower viability limits and more aggressive life-saving efforts at lower weights and younger ages. In addition, women in regions that are less advanced in the health transition may not be able to reach high-level medical care in time in the event of sudden and rapidly progressing complications, or unexpected and extremely preterm labour.

8.7.4 RELATIVE IMPORTANCE: POPULATION LEVEL The second part of Table 8.22 compares measures of prevalence, AR(E), and EF as indicators of the relative importance at the population level. Again, the signs in the table indicate whether the figures in the SATH survey are lower (–), higher (+), or more or less equal (=) to the estimates for the EME region. The results for prevalence among births were already discussed in Section 8.7.2. In addition, the attributable risk among the exposed, AR(E), is based on the relative risk, which has already been considered in Section 8.7.3. The AR(E) indicates the proportion of deaths in the affected population that can be attributed to the risk factor in question. Although based on RR, the results for AR(E) from the SATH survey are closer to the figures for the EME population (see Table 8.22). In both the SATH survey and the EME region, the vast majority (generally over 85%) of deaths in the affected populations can be attributed to the risk factor. Also, in both regions, IUGR/SGA is less important than the other risk factors, although results are inconclusive due to a wide variation in definitions and reference growth curves. The etiologic fraction (EF) is based on both the prevalence of the risk factor in the population and the relative risk. The EF indicates the proportion of deaths in the total population that can be attributed to the risk factor. Table 8.22 shows that the proportion of


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stillbirths in the total population that are attributable to congenital anomalies is larger in the SATH survey in Kerala than in the EME region. Since the prevalence at birth is lower, this is due to a higher relative risk of stillbirth. The contribution of birth asphyxia to early neonatal and neonatal mortality is also larger in the SATH survey. In this case, however, the finding is due to a higher prevalence among live births. For the following risk factors, the contribution to early neonatal death and total neonatal death in the population is smaller in the SATH survey than in the EME region: congenital anomalies (due to a lower prevalence), low weights at birth (due to a lower RR, despite a much higher prevalence), preterm birth (both a lower prevalence and a lower RR), and very preterm birth (due to a lower RR). In both populations, more than 40% of neonatal deaths can be attributed to LBW (< 2,500 g) or a birth weight of less than 2,000 g. In addition, over 40% of neonatal deaths in these populations are attributable to preterm birth.

Conclusion In conclusion, the attributable risks of stillbirth and neonatal death change during the epidemiologic transition following the changes in prevalence proportions among births and in relative risks. A comparison of the results from the SATH survey with estimates for the EME region suggest the following. Within the total population, the proportion of stillbirths that can be attributed to congenital anomalies, and the proportions of neonatal deaths that can be attributed to birth asphyxia decline during the epidemiologic transition. On the other hand, the proportions of neonatal deaths that are attributable to congenital anomalies, low weights at birth, preterm birth, and very preterm birth increase during the transition. Despite these changes, it is clear that the five types of pregnancy/birth outcome (i.e. congenital anomalies, low birth weight, preterm birth, IUGR/SGA, and birth asphyxia) account for a large share of neonatal deaths in both regions in transition and in regions more advanced in terms of the transition.

8.7.5 RANKING BY RELATIVE IMPORTANCE

In terms of relative importance, Table 8.23 presents rankings of the five main risk categories based on their prevalence in births, and RR and EF of neonatal death, in both SAT Hospital in Kerala and the EME region. It is important to note that one needs to be careful in the interpretation of these results since they represent rankings and are relative. The table suggests that the following changes may be expected to occur during the later stages of the epidemiologic transition. During the later stages of the epidemiologic transition, relative importance, as based on prevalence among births, remains strongest for LBW, increases for preterm birth, but decreases for IUGR/SGA and birth asphyxia. Relative importance, as based on relative risk of neonatal death, remains strongest for birth asphyxia, increases for LBW, and decreases for congenital anomalies. The strong increase in the relative importance of LBW is most likely due to changes in the registered weight distribution within this category, i.e. a shift towards lower and extremely low birth weights. The decrease in the relative importance of congenital anomalies is probably related to improvements in detection and diagnosis, which makes anomalies that are less severe and less lethal more visible. Finally, relative importance, as

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based on contribution to the total number of neonatal deaths in the population (i.e. the etiologic fraction or EF), remains strongest for LBW and preterm birth, increases for congenital anomalies, and decreases for birth asphyxia. The role of IUGR/SGA is generally unclear since its definition is very much dependent on the choices of a reference growth curve and a reference population. Moreover, when it comes to elevated risk (i.e. RR > 1) of neonatal death, low weight at birth seems to be a better indicator than size-for-gestational-age status as defined by a standard curve from a reference population. In addition, preterm birth is a better indicator of risk of neonatal death than IUGR/SGA. This finding has been supported by Alberman (1990 cited by Shibuya and Murray 1998a). Lastly, in the EME region, LBW is a better indicator of risk of neonatal death than preterm birth, while the opposite is observed in the SATH population in Kerala. This suggests that in the EME region preterm births have more favourable birth weights and/or that, in Kerala, LBW births are less likely to be preterm.

Table 8.23: Ranking by highest prevalence in births and highest RR and EF of neonatal death,for five major risk categories, SATH survey and EME region

Prevalence in births RR of neonatal deathRank SATH EME SATH EME

1 IUGR/SGA+LBW PT+LBW BA BA2 PT IUGR/SGA CONANO LBW3 CONANO+BA CONANO PT PT+CONANO4 - BA LBW IUGR/SGA5 - - SGA -

EF of neonatal deathRank SATH* EME

1 LBW LBW2 PT PT3 BA CONANO4 CONANO BA+IUGR/SGA5 - -

Notes: BA - birth ashyxia; CONANO - congenital anomalies; IUGR/SGA - intrauterine growth retardation/small-for-gestational-age; LBW - low birth weight (< 2,500 g); PT - preterm birth (< 37 wks).*Figures for IUGR/SGA are inconclusive on its position due to wide ranges.

Chapter 8 Pregnancy and birth outcomes in Kerala · regard to foetal loss and neonatal mortality...

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