Comparative analysis of patterns of survival by season of birth in rural Bangladeshi and Gambian populations

Sophie E Moore1, Anthony JC Fulford1, P Kim Streatfield2, Lars Åke Persson2,3 and Andrew M Prentice1

1 MRC International Nutrition Group, Public Health Nutrition Unit, London School of Hygiene and Tropical Medicine, 49–51 Bedford Square, London, WC1B 3DP, UK and MRC Keneba, The Gambia
2 Health and Demographic Surveillance Programme, Public Health Science Division, ICDDR,B Centre for Health and Population Research, Dhaka, Bangladesh
3 Current address: Department of Women's and Children's Health, International Maternal and Child Health (IMCH), University Hospital, SE-751 85, Uppsala, Sweden

Correspondence: Dr Sophie E Moore, MRC International Nutrition Group, Public Health Nutrition Unit, London School of Hygiene and Tropical Medicine, 49–51 Bedford Square, London, WC1B 3DP, UK. E-mail: sophie.moore{at}lshtm.ac.uk


    Abstract
 Top
 Abstract
 Methods
 Results
 Discussion
 References
 
Background Analysis of data from rural Gambia has previously shown that being born during the annual hungry season strongly influences susceptibility to mortality from infectious disease in young adulthood, possibly through an influence on immune function. In rural Bangladesh pregnancies are exposed to similar seasonality. The current paper uses data from a large demographic survey in the Matlab region of Bangladesh to retest the Gambian-derived hypothesis that early life exposures correlated with season of birth predict later patterns of mortality.

Methods Since 1966, a continuous demographic surveillance system has been in operation in the rural Matlab region of Bangladesh. The current analysis is based on 172 228 births and 24 697 deaths between 1974 and 2000. Season of birth was defined as ‘harvest’ (January–June) and ‘hungry’ (July–December), based on monthly variations in rates of conception and neonatal mortality within the same dataset.

Results Birth during the hungry season resulted in excess mortality during the first year of life. However, for adult mortality (deaths >15 years), there was no excess in individuals born during the annual hungry season: ratio of hazard July–December versus January–June = 1.12; 95% CI: 0.87, 1.45.

Conclusions The current study found no excess mortality in young adults born during the ‘hungry’ season in rural Bangladesh. This differing pattern in survival when compared with The Gambia may be a consequence of the greatly reduced incidence of young adult deaths in Bangladesh (0.1%) compared with The Gambia (3%). Under such conditions possible differences in immune function may not be detectable with early adult death as the outcome. However, it also remains possible that our Gambian observation could be a highly discrete phenomenon localized in either time or place, and as such, will not be replicated in other populations.


Keywords Mortality, seasonality, Bangladesh, infections, maternal nutrition, intrauterine growth retardation

Accepted 29 July 2003

In 1999 we published in this journal the finding that, in rural Gambia, birth during or shortly after the annual hungry season (July–December) predicts excess mortality in young adulthood.1 The hazard ratio for early death in the hungry season births rose from 3.7 for deaths over 14.5 years (P = 0.000013) to 10.3 for deaths over the age of 25 years (P = 0.00002) when compared with the harvest season (January–June) (Figure 1). Cause of death data indicated that the majority of these deaths were from infectious diseases, or had a likely infectious aetiology. None of the deaths over this period were from chronic degenerative diseases.



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Figure 1 Kaplan–Meier survival plots by season of birth in rural Gambia

 
In rural Gambia, there is a variety of early-life exposures correlated with season of birth that could influence later mortality. These include seasonal infections in pregnancy (especially malaria),2 pre- and post-natal exposure to environmental toxins (e.g. aflatoxins),3,4 and postnatal exposure to seasonal infections (particularly diarrhoeal disease).5 However, a review of the evidence suggests that nutritionally mediated intrauterine growth retardation during the annual hungry season seems the most likely cause.6 This suggestion that immune function and susceptibility to infectious disease may be programmed during early life adds a novel dimension to the fetal and infant origins of adult disease hypothesis.7,8

In order to test whether the Gambian finding could be replicated elsewhere, we searched for long-term demographic datasets from other seasonally exposed regions of the world. The Demographic Surveillance System (DSS) from the rural area of Matlab in Bangladesh met the necessary criteria of meticulously recorded births and deaths within an area of high nutritional seasonality. This paper reports patterns of mortality analysed according to season of birth and presented alongside our previous Gambian analysis.


    Methods
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 Abstract
 Methods
 Results
 Discussion
 References
 
Study populations
Bangladesh
Since 1966, the International Centre for Diarrhoeal Disease Research, Bangladesh (ICDDR,B), formerly the Cholera Research Laboratory, has been conducting a demographic surveillance and health-related research programme in Matlab, a rural but densely populated riverine area of Bangladesh (Figure 2). The Matlab field research area forms part of the low-lying deltaic plain of Bangladesh, situated about 70 km southeast of Dhaka.



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Figure 2 Map of Bangladesh

 
A DSS was initiated in Matlab in 1966 for the registration of vital events (initially births, deaths, in- and out-migration, but later extended to include additional information on marriage and divorce). In addition, periodic censuses have been carried out in the DSS area (1974, 1982, and 1996) to collect demographic and socioeconomic information. Data on childhood illnesses, nutritional status, health status of women of reproductive age, contraceptive use, and use of health care have also been collected. In 1966, the surveillance population was 112 711 and this had increased to a total population of 219 884 by 2001.

Prior to 1974, the village Dais (traditional birth attendants) were responsible for visiting households and recording all vital events (births, deaths, migration in or out, marriage or divorce). This information was then passed to the male Health Assistants who would visit the households to collect all the details of each event on to specific health forms. From 1974, Community Health Workers took over the responsibility of recording and reporting all events, and visited each household every 2 weeks. In addition, the Health Assistants continued to visit each household every month to complete the detailed health forms. This system continued for the duration of the period used in the current analysis (1974–2000). The type of identification used prior to 1974 was a VTS (vaccine trial number) which changed with any change of residence. As a consequence, this has made it very difficult to follow individuals beyond 1974, and for this reason the current analysis runs from 1974 only. From 1974 onwards, there have been no interruptions in the data collection process. This rigorous data collection protocol coupled with multiple levels of supervision ensures that no important events are missed, and hence the demographic data can be considered as complete.

In rural Bangladesh, three seasons define its subtropical climate; the monsoon season (June–September), the cool-dry ‘winter’ season (October–February), and the hot-dry season (March–May). Traditionally, the major agricultural crop has been deep-water aman rice grown during the monsoon season, with lesser acreage during the winter season devoted to boro rice.9 Since agriculture provides the livelihood for most families in this region, their existence is highly dependent on this seasonal pattern and, as in The Gambia, it has consequences on the nutritional status of both adults and children. Figure 3 shows the seasonal pattern in maternal weight in Matlab. This data is taken from a longitudinal study of over 2000 women between 1975 and 1978.10 As illustrated, weight fluctuated throughout the study period, with lowest mean weights observed during the months of September and October. These weight fluctuations were observed to correspond with seasonal food shortages. Previous work in Matlab has also demonstrated a seasonal pattern in the proximate determinants of infant and child mortality and malnutrition,11 with the period of worst nutritional status coinciding with the highest rice prices and the highest risk of mortality.9



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Figure 3 Seasonality of maternal weights in rural Bangladesh, 1976–1977. (Adapted from ref. 10)

 
In Bangladesh, the average birthweight is low, with between 25% and 48% of babies estimated to have a birthweight below 2.5 kg.12 Analysis of birthweights of 1772 singleton babies born at Kumundini Hospital in rural Bangladesh over two consecutive years showed a consistent variation correlated with the seasonal availability of food, with the highest birthweights occurring in the period March–May, and the lowest in September–November.12 The well-documented effects of the seasonal pattern in nutritional status of pregnant women and their infants in rural Bangladesh, together with the large-scale long-term demographic database provides the opportunity to retest the hypothesis that season of birth predicts adult survival.

The Gambia
The Gambian data used in this comparative study come from the analysis, by season of birth, of all deaths between 1949 and 1998 in three isolated rural villages (Keneba, Kanton Kunda, and Manduar) in the West Kiang region of The Gambia. In this region, the subsistence farming existence is heavily influenced by an annual rainy season (July–October). The annual rains coincide with a ‘hungry’ season when food crops from the previous year's harvest become depleted, and adults are engaged in heavy agricultural labour prior to the next harvest.13 As a consequence, a chronic negative energy balance is observed in all adults, including pregnant women. Birthweights are around 200 g lower during the hungry season, a deficit that can be reversed by maternal dietary supplementation.14,15 Most maternal and infant diseases also peak during the hungry season, especially malaria and diarrhoea.5,16 Season of birth can therefore be used as an indicator of fetal and early infant exposure to malnutrition and infectious diseases.

Since 1949, a demographic record of all births and deaths has been continuously maintained in this region. This record was established by Professor Sir Ian McGregor to provide demographic data for his early studies of parasitology and nutrition.17 The system instituted consisted of village recorders whose task it was to accurately record all births and deaths in each village. At registration, each villager was assigned a unique identification number, and this system has been maintained continuously to date. Although rates of migration have not been tracked using this recorder system, the fate of each individual in the survey at the time of follow-up was established through discussion with family members.

Using a database of 3162 individuals aged up to 48 years, for whom month of birth and current fate (dead or alive) were known with certainty, a survival analysis by season of birth found a profound bias in adult mortality in individuals born during the annual hungry season.1 In the current paper, the same data will be analysed in conjunction with the data from Bangladesh.

Statistical analysis
The seasonal patterns observed in neonatal mortality rate and birth rate (taken as a proxy measure of maternal energy status 9 months previously) in Matlab, demonstrate that the first 6 months of the year (January–June) appear considerably better than the latter 6 months (Figures 4a and b). Therefore for the purpose of analysis, month of birth has been divided into two seasons; January–June and July–December. By coincidence, the same division of the year was found to be appropriate in the original analysis of data from The Gambia. The similarity in the seasonality of fertility rates between Matlab and Keneba is illustrated in Figure 4b. In order to check whether post-adolescent mortality might follow another pattern with month of birth, four other seasonal divisions of the year were defined: two with 6-month-long seasons (March–August/September–February and May–October/November–April) and two with 4-month-long seasons (January–April/May–August/September–December and March–June/July–October/November–February).



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Figure 4 Seasonal variation in neonatal mortality in Matlab. (a) The neonatal mortality curve plots the percentage dying in the first month of life against the month of birth. Two cycles are plotted. (b) Seasonal variation in fertility in Matlab and Keneba. The fertility curves plot the percentage of births born versus the month of conception. Two cycles are plotted

 
Cox proportional hazards models were fitted and checked using Stata 7 (Stata Corporation, Texas, USA). Estimates of the hazard ratio and its standard error for the seasonal effect were derived from separate analyses of the two datasets. In order to compare the seasonal effects between the populations, the datasets were pooled and the population—season interaction fitted. In this last model allowance was made for the possibility that the underlying pattern of hazard with age differed between the populations or sexes by fitting a separate baseline hazard function for each group. Analysis of the Schoenfeld residuals revealed no evidence for deviation from the proportional hazards assumption in any of the four sex—site groups after the age of 15 years.


    Results
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 Abstract
 Methods
 Results
 Discussion
 References
 
The current analysis is based on 172 228 births and 24 697 deaths in the Matlab population between June 1974 and December 2000 and the subset of 59 834 births and 252 deaths followed beyond the age of 15 years. These are compared with the 1842 Keneba births followed beyond the age of 15 years among whom 58 deaths were subsequently observed. Figure 5 shows survival according to season of birth in both populations. Mortality among young adults in rural Bangladesh is considerably lower than in The Gambia, with a cumulative mortality of only 0.1% between the ages of 15 and 25 years compared with approximately 3% for the same age span in The Gambia.



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Figure 5 Kaplan-Meier survival estimates by country and season. (a) Survival from birth, (b) Survival from 15 years of age

 
In Matlab birth during the hungry season resulted in excess mortality during the first year of life. However, following those known to survive beyond 15 years of age, there was no excess mortality in individuals born during the annual hungry season: ratio of hazard during the second half of the year compared with the first half = 1.12 (95% CI: 0.87, 1.45). Furthermore, no other stratification of the year into two or three equal-length seasons correlated significantly with the post-adolescent mortality rates. Stratifying the analysis by sex made little difference to this result and produced no evidence for a gender difference in the effect of season of birth on adult deaths. In contrast, the hazard ratio for season among over 15 year olds in Keneba is 3.80 (95% CI: 1.97, 7.34). In an analysis of the pooled datasets, the term for the interaction between season and country was highly significant (P = 0.001) indicating that the seasonal effect is genuinely smaller in Matlab.


    Discussion
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 Abstract
 Methods
 Results
 Discussion
 References
 
The possibility that human immune function may be programmed by early-life exposure to nutritional or other insults could have major implications for our understanding of disease processes in relation to infections, auto-immune disease, and cancer immuno-surveillance. Our original Gambian observation is remarkable in terms of the size of the effect on mortality in young adulthood.1 The quality of the data collection, together with the high level of statistical significance, leaves little room to doubt its validity. Subsequently we have demonstrated evidence of seasonal effects on infant thymic development in this population18 and on T-cell subset profiles.19 Furthermore, a study of adolescents participating in an ongoing longitudinal study in the Philippines has shown that prenatal undernutrition is significantly associated with reduced thymopoietin production, and growth in length during the first year of life was shown to be positively associated with adolescent thymopoietin production.20 In the same cohort, the predicted probability of mounting an adequate antibody response to a typhoid vaccine was lower among adolescents who were currently undernourished and born with a low birthweight than those who were currently undernourished but born with an adequate birthweight (0.32 versus 0.70).21

However, in spite of these findings, it remains possible that our Gambian observation could be a highly discrete phenomenon localized in either time or place. We are therefore collaborating with a number of other studies worldwide to test whether the Gambian mortality pattern occurs in other seasonally stressed populations.

The Matlab cohort has many strengths. The population data are collected using a highly intensive and rigorously audited DSS that captures all births and deaths. We are unaware of any systematic source of error that might lead to a bias in relation to survival analysis based on season of birth. The Matlab cohort is much larger than our Gambian cohort although, due to the much lower adult mortality rate, the Bangladesh analysis covers only 252 deaths beyond the age of 15 years. Most importantly there is good evidence for nutritional seasonality. In common with The Gambia, life in rural Bangladesh revolves around the annual rains through the impact that this has on agriculture, rice prices, agricultural wage rates, household food stocks, and in turn, on the nutritional status of both adults and children.9 Although the total weight loss in Matlab is less than the amount observed during the annual hungry season in The Gambia, the loss in these women who start with such low weights would be as critical in terms of the effect on reproductive success. Analyses of neonatal mortality rates and birth rates clearly show a seasonal swing at least as severe as that observed in The Gambia. Therefore season of birth in this environment can also act as a proxy for early-life exposures. In spite of these strengths, the current analysis has been unable to demonstrate any effect of season of birth on adult survival in this region of rural Bangladesh.

The fact that the season of birth division in Matlab was classified according to the same monthly definition as used in the analysis of data from The Gambia (January–June versus July–December) is purely coincidental, and was based on internal evidence relating to neonatal mortality and conception patterns from within the Matlab dataset. The question of whether the profound seasonality in conception rates represents a true biological phenomenon, mediated by the Frisch fertility hypothesis,22 requires scrutiny as there could be an alternative sociological explanation. In rural Bangladesh, marriage is most common in the first months of the year, and could theoretically account for the peak in births during September. However, the seasonal pattern of fertility is independent of birth order, thus implying that the seasonality is most unlikely to reflect the timing of weddings.

We therefore believe that our pre hoc selection of seasonal boundaries is robust and optimal. Nonetheless, we repeated the mortality analysis using other divisions of the year into two or three equal periods to search for any possible effect. No other post hoc division (either 6- or 4-monthly) gave a larger (or significant) difference in mortality rates among young adults than the January–June versus July–December split.

There are several possible explanations for the discordant results between Gambia and Bangladesh. Although the Bangladesh results ostensibly represent a negative finding, a careful examination of the source of the discordance may help in interpreting the Gambian data and in informing future studies into the biological mechanisms involved.

Reference to Figure 5 suggests that one explanation for the lack of an association between season of birth and adult survival in Matlab lies in the fact that, once the age of 5 years has been reached, survival rates in this population are very high, with only 0.1% mortality between the age of 15 and 25 years. In the Gambian cohort, mortality in the harvest season births was 20-fold higher at approximately 2% between these ages, and in the hungry season births this increased to approximately 50-fold higher. Under such relatively benign circumstances it is possible that underlying differences in immune function could exist without becoming apparent in relation to mortality. Unfortunately morbidity data have not been collected on a systematic basis in Matlab and therefore an analysis of less severe end-points is not possible. Future studies might include morbidity surveillance or measures of in vivo immune function (possibly using vaccine challenges as has been successfully employed elsewhere).21 Longer term follow-up of the Matlab cohort might eventually reveal differences in mortality as the normal immunosenescence associated with ageing erodes the function reserve of immune defences in the whole population. However, it must be acknowledged that by the age of 30 years there is no hint of an incipient divergence (Figure 5).

Another possible explanation is that the original seasonal exposure responsible for impaired adult immunity in The Gambia does not relate to fetal undernutrition. Various studies are in progress in The Gambia and prospectively in Matlab to investigate possible associations between size at birth and later immune function. Those completed so far have found limited evidence in relation to early-life measures of immunity in infancy19 and at age 8 years.23 A large study in young men aged 18–24 years for whom birthweight is known is currently in progress in rural Gambia. This will represent a more definitive test since it relates to an age when the survival curves have started to separate.

Other exposures need to be considered. The most notable difference in environmental exposures between The Gambia and Bangladesh is in the exposure to malaria. In The Gambia, malaria is endemic, with exposure peaking during and shortly after the annual rains, and mortality is significantly increased during this time.16 In Matlab, there is no malaria. Malaria can have a profound effect on placental function during pregnancy which might influence the early ontogeny of immunity. However, we have argued elsewhere1,6 that the timing of peak malaria transmission does not fit well with the observed pattern of affected individuals, and also that malaria infection is most virulent in primiparous mothers. This would predict an excess of risk in first-born offspring that has not been observed. Other seasonal infections might also influence either the pre-natal or post-natal development of immunity if there are critical windows in which immunity can be strongly influenced by infectious stimuli. Evidence in favour of this comes from the observation that the precise timing of neonatal BCG vaccination (a strong Th1 booster) has a profound effect on the incidence of atopy.24 It may be that such infections occurred in The Gambia and not in Bangladesh.

Finally, it is possible that the phenomenon observed in The Gambia is confined to individuals born during the 1950s and 1960s and is not observed in younger cohorts. Indeed a similar analysis from rural Senegal has found that individuals born during the hungry season showed no greater mortality than those born during the harvest season.25 Whilst environmental conditions in rural Senegal are almost parallel to those observed in The Gambia, the main primary difference in the two analyses is the age of the Gambian dataset compared with that from Senegal. This would provide further evidence that the effect observed in The Gambia might be a cohort effect, and is thus not observed in more recent populations. Possible candidates for a time-dependent seasonal exposure which has now diminished in intensity might be the consumption of certain bush foods to ward off famine in the hungry season, or the use of early forms of insecticide on stored crops. We continue to try and investigate such possibilities.


KEY MESSAGES

  • Analysis of data from rural Gambia has shown that being born during the annual ‘hungry’ season strongly influences susceptibility to mortality from infectious diseases in young adulthood. It was hypothesized that this effect could be mediated by an early-life insult to the developing immune system.
  • The current study compared survival by season of birth in a large cohort of subjects from the rural Matlab region of Bangladesh.
  • No excess mortality was found in young adults born during the ‘hungry’ season in rural Bangladesh, despite a strong seasonality in indicators of maternal and infant health.
  • It is possible that the Gambian finding is a highly discrete phenomenon that will not be replicated in other populations.

 


Matlab newborn with parents and grandmother. Photograph: Asem Ansari


    References
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 Abstract
 Methods
 Results
 Discussion
 References
 
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17 Billewicz WZ, McGregor IA. The demography of two West African (Gambian) villages, 1951–75. J Biosoc Sci 1981;13:219–40.[ISI][Medline]

18 Collinson AC, Moore SE, Cole TJ, Prentice AM. Environmental and nutritional factors influencing the tracking of thymic size in rural Gambian children. Acta Paediatr 2002;(in press).

19 Collinson AC. Early Nutritional and Environmental Influences on Immune Function in Rural Gambian Infants. 2002, University of Bristol.

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21 McDade TW, Beck MA, Kuzawa C, Adair LS. Prenatal undernutrition, postnatal environments, and antibody response to vaccination in adolescence. Am J Clin Nutr 2001;74:543–48.[Abstract/Free Full Text]

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23 Moore SE, Collinson AC, Prentice AM. Immune function in rural Gambian children is not related to season of birth, birth size, or maternal supplementation status. Am J Clin Nutr 2001;74:840–47.[Abstract/Free Full Text]

24 Aaby P, Shaheen SO, Heyes CB et al. Early BCG vaccination and reduction in atopy in Guinea-Bissau. Clin Exp Allergy 2000;30:644–50.[CrossRef][ISI][Medline]

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