The data from the Bwamanda study, which are still of high relevance today, and the newly published WHO growth velocity standards offered a unique opportunity to study longitudinal growth over up to five successive 3-month periods of 2223 children between 0–2 years in relation to seasonal and spatial factors, which are of increasing interest.
In order to study longitudinal growth of children, we described patterns for age- and sex-dependent growth velocity Z-scores for weight and length. We find that growth velocity Z-scores for weight and length were strongly related to season while no index was related to the selected spatial parameters in our sample. The seasonal impact could not be demonstrated in attained growth. This and the fact that a restriction in growth showed much earlier when studying growth velocity Z-scores, argue for longitudinal growth as an important tool in the timely detection of growth faltering. It also points to a limitation of many previous studies of the relation between geographical factors and growth.
Patterns of longitudinal growth
Poor nutritional status was common in our sample, reflected by around 52% stunted and 6% wasted children in each survey round. This was also observable in the percentage of children with velocity Z-scores under what is usually defined as the normal range (Z-score < −2). 16.5% and 24.1% had a slow growth in weight and length respectively. Analyses of child growth in low- and middle-income countries show that growth faltering starts early in life and worsens up to the age of two years (Victora et al. [2010]). In our sample we also see continuous deteriorating indices of attained growth despite increasing velocities with age. This finding underlines the importance of early interventions.
Length velocities of successive periods were more strongly negatively correlated than weight velocities and when looking at two consecutive periods, we found that periods with slow length velocities were more often followed by rapid growth (indicating catch-up growth) and vice versa (catch-down growth) than in ponderal growth. This suggests that length velocity is more variable than weight velocity. Nevertheless, the corresponding index for attained growth (LAZ) still deteriorated over time. Catch-up growth can be defined as growth that statistically exceeds normal growth (i.e. > + 2 Z-scores). The periods with rapid growth following those with slow growth were probably not strong enough, and there were still many more children who after a period with slow growth had growth velocities within the normal growth range and therefore no catch-up growth, as defined above. It has to be mentioned that this description does not take into account any confounding factors and cannot therefore suggest any causes of the longitudinal behavior. Physiological growth is characterized by variation within the normal range in growth velocities in subsequent periods (WHO [2009]), so it is difficult to interpret only a single growth velocity of an individual. It is thus necessary to study further patterns of growth velocity for individual children in consecutive growth periods to gain insight and guidance in the evaluation of longitudinal growth for clinical use and research.
Longitudinal growth according to sex, breastfeeding status and birth rank
When comparing mean growth velocities no differences were seen between boys and girls. After stratifying for age, boys aged 7–12 month had a significantly lower mean weight velocity Z-score than girls. This difference was not observed in the older age group. This could either illustrate a better ability for the boys to catch-up or a survivor bias. But neither mortality nor catch-up growth showed a difference between the sexes in this sample. We do not have any information about intra-household and gender disparities in food and dietary composition for our sample. Olusanya and Renner ([2011]) found in their study sample composed of 658 Nigerian children 0–8 weeks old that male gender is an independent predictor of higher weight velocity. They argue that boys usually have larger growth increments than the same aged girls (WHO [2009]; Guo et al. [1991]; van't Hof et al. [2000]), but since weight and length gains were not scored according to the WHO-[2009] standards, this argument is invalid for our sample.
Breastfeeding status was significantly associated with growth velocities, but did not improve the multivariate model. This could be explained by almost universal exclusive breastfeeding up to the age of four months in our study sample and a weaning period thereafter as described previously (Van den Broeck et al. [1996]). Therefore breastfeeding status might not add to the fit of the model as there is too little variation and the mechanism is already captured by the age variable.
Birth-rank was not associated with growth velocity Z-scores and did not mitigate the effect of season in our study. In Bwamanda large household usually divide into three groups during the major meals: (i) children below the age of three, who eat with their mothers and other females in the household, (ii) those children above three years, who eat with other children, and (iii) adult males. A problem could arise in the group of children eating together (group ii), where there is competition and some discrimination against younger children, but for the age span we analyzed (0–24 months) this is not considered to be likely. Even if the younger family members did lose out during family meals, this is likely to be constant throughout the year independent of the total amount of food available and would therefore not affect the relation between season and growth.
Associations with season
The analysis of mean weight velocities shows that Z-scores decreased in pre-harvest and increased in post-harvest season. The same is true for length velocities, only peak velocity lagged by about two months. This lag is also described by Maleta et al. ([2003]) and Xu et al. ([2001]), who find that height gains peaked three months after weight gain. Some authors explained the lag as an effect of wasting on subsequent stunting, i.e. that a certain cut-off in weight-for-height has to be reached before compensatory linear growth appears (Costello [1989]; Walker and Golden [1988]). Although weight-for-height is also associated with weight gain in the study of Maleta et al. ([2003]), it only explains very little of the variation in height gain. Therefore other factors might play a more central role in linear growth.
We could not demonstrate a different impact of season in the age three groups 0–6, 7–12 months and 13–24 months. This is in accordance with the study by Hauspie and Pagezy ([1989]) in Zaire, who saw the same seasonal pattern in all age groups of 0–4 years. Age-dependency is reported by other studies (Maleta et al. [2003]; Jalil et al. [1989]; Karlberg et al. [1993]; Miller et al. [2013]; Rosetta [1988]; McGregor et al. [1968]), but with inconsistent results. The lack of differences related to age in our study might be due to the typical early weaning of children at around four month of age on average, resulting in less protection from environmental vectors and less mediation through maternal health.
Seasonality was not seen in indices of attained growth (WAZ, LAZ, WLZ). Our analysis suggests that it is important to study longitudinal growth and to associate factors when the growth of a child is actually faltering. This is supported by the work of Kismul et al. ([2014]) who find a seasonality in the incidence of wasting and stunting in children under five years in the same area. The lower responsiveness to season of length-for-age in comparison to length gains has been identified by others (Brown et al. [1982]; Miller et al. [2013]; Tomkins et al. [1986]), but not for weight-based indices.
Associations with spatial parameters
Spatial parameters selected for this analysis were village size and distance from the village to health center, to the hospital, to a market, to fishing grounds and to the forest. None of them contributed essentially to differences in growth velocities in our sample, although they differed substantially for individual participants and could be expected to be of importance, especially in areas like the study setting, where only restricted motorized transport is available for local people.
Other studies, have found that population density has a close relationship with child malnutrition, explained by lower market penetration, and more limited access to health facilities and nutrition-related information (De Sherbinin [2011]; Nikoi and Anthamatten [2013]). In our setting, the scale was very different with a relatively homogenous village size, ranging from about 900 inhabitants to about 2000. Nevertheless, accessibility was different with some villages near the main road and local markets and others far off and difficult to reach, due to almost impassible roads, especially during the rains. Nevertheless, the whole region mainly comprises subsistence farmers producing only a minimal portion of cash crops. Therefore distance to market as well as size of village as an indication of connectivity or food availability, seem less important than size of the extended family, forming the working capacity to cultivate the fields. Quantity of cultivatable land is only of relevance if adequate working capacity exist, also having an impact on the quality of the harvested foods.
Low cash income may be one of the reasons why distance to the hospital, which varied greatly, did not have an impact on growth velocities, for even if the hospital was accessible, it is of no benefit if one cannot afford it. Local health centers, which where more accessible for all children and less expensive, did not show any impact on child growth velocities either. From our own experience in this region, health centers have variable resources in terms of medical equipment, medication, and training of their staff. Also the use of traditional healers has not been adjusted for in this analysis.
Similar arguments could be used for the association of distance to fishing grounds, which we were not able to show. Distance to fishing grounds was included in the analysis as a proxy for the availability of fish and therefore a source of animal protein-rich food. Although distance might determine the possibility for fishing, informal talks to people in the area indicated that even if people lived near fishing grounds, the lack of money to purchase fishing equipment stopped them from fishing.
Using distance to forests as a proxy for bush food did not generate correlations. Since fallow fields and other areas may be important sources of nutrient-dense supplementary foods, other measures of access to these resources should be developed.
Limitations
In addition to the limitations already discussed above, some uncertainties in measuring age, date and climatic data have to be reported. The exact age was documented for 90% of the 5657 children; in the remaining 10% it was obtained through careful interviews with the help of local calendars. Even if some inaccuracy persists in this small portion of the children, it is not expected to substantially influence the results.
Data on the date of measuring was derived from adding the age in days to the birthdate, assuming that every month has 30.43 days. This will add some minor inaccuracies, but measuring date was only used to categorize the timing of measurements into seasons. Season is not a construct with clear borders, going from dry season to rainy in one day, and therefore only a few misclassifications with no substantial effects are expected.
Precipitation data was derived from triangulation of data from a local weather station, satellite data and published data from a nearby meteorological station. Nevertheless, no data for the specific study years were available, only long-term data from 1941–2005 and detailed data for the years 2001–2005. Weather conditions deviating from the normal in the study years could not therefore be accounted for.
The measure of harvest to define sub-seasons also has to be evaluated critically. Data on harvest was obtained by asking the interviewee if they had harvested a specific food item in the past two months. This rather long recall period is susceptible to recall bias. Further the possibility of information bias exists, since only two months of the average 3-month period between survey rounds are covered. However, the estimate still gives a good idea of the timing of harvest, since different households were interviewed every day and the purpose was not to assess the exact amount of harvest, but the timing, which we believe can be adequately recalled for the last two months. In addition, timing was confirmed by informal talks to local specialists. Analysis of the amount harvested compared to the amount eaten or sold assessed with a 24-hour recall, confirmed that it is an adequate indicator of food availability, because neither storing nor selling was common (data not shown).
A weakness in our study is the fact, that we did not include birth weight in our analysis. As mentioned in previous work (Van den Broeck et al. [1996]), the majority of births took place at home with the help of a traditional birth attendant and it was therefore impossible to record birth weight for our study sample. Birth weight deviating from the normal is known to affect early child growth rates, representing catch-up or catch-down growth (WHO [2009]). Births were almost evenly distributed throughout the year in our study, so varying growth rates in early childhood are not expected to change the relationship between season and growth velocities to a substantial extent.