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Model for Analysis of the Relationship Between Breastfeeding Data and Postpartum Anovulation Data

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Model for Analysis of the Relationship Between Breastfeeding Data and Postpartum Anovulation Data Maternal Nutrition and Lactational Infertility. edited by J. Dobbing. Nestld Nutrition, Vevey/ Raven Press, New York © 1985. Model for Analysis of the Relationship Between Breastfeeding Data and Postpartum Anovulation Data J-P. Habicht and K. M. Rasmussen Division of Nutritional Sciences, Cornell University, Ithaca, New York 14853 To identify likely determinants of postpartum infecundity and to understand their relative importance in free-living populations, data must be collected from many women. Under such circumstances, a battery of biochemical and endocrinological tests is not feasible. One must often be content to collect interview data, and the onset of menstruation after parturition, the onset of a new pregnancy, and the birth of a next child are the only data available for looking at the effects of age, nutritional intake, parity, and other possible determinants of postpartum infecundity. The data about the onset of menstruation are generally thought to relate more validly to ovulation and fecundity than are data about conception and birth of a new child, which are affected by many other factors. Breastfeeding is now known to be the most important influence in prolonging postpartum infecundity in populations where malnutrition is prevalent and severe enough to have a concomitant effect on fecundity. To study the effect of malnutrition on fecundity, one must then be able to control for differences in the breastfeeding characteristics which postpone the return of fecundity. The most common means of controlling for breastfeeding in the analysis of interview data from surveys has been by statistical methods, often by the use of ordinary least squares (OLS) linear multivariate regression analysis. This analysis as used is implicitly based on the assumption that the duration of postpartum amenorrhoea is determined by breastfeeding practices, even when menstruation returns before the lactation ceases. This seemed illogical and is in contradiction to present knowledge in endocrinology. These linear OLS statistical analyses intro- duce biases which make any interpretation about other determinants of menstrua- tion impossible. We therefore tried to develop a statistical procedure (1) which Editor's note: This was not one of the original papers commissioned for the workshop but arose from Dr. Habicht's precirculated commentary on the chapters by McNeilly et al., by Robyn et al., and by Lunn et al., this volume. The form in which it appears here—as an appendix—like the rest of the book, takes account of the discussion which occurred at the workshop itself. 119 120 APPENDICES was more compatible with modern endocrinological knowledge so as to be able to study the effect of determinants of postpartum amenorrhoea and anovulation other than breastfeeding in breastfeeding populations. MODEL Figure 1 (ref. 1, Table 3) depicts a model which relates breastfeeding to the duration of postpartum anovulation through an inhibiting influence (//).' At birth, H is at a higher level / than at conception. The inhibition falls more quickly when no breastfeeding occurs (slope n) than when partial (i.e., supplemented) breast- feeding occurs (slope p). This inhibition falls least rapidly when full (unsupple- mented) breastfeeding occurs (slope/). That is, we hypothesize that n<p<f. In fact, we find that/in Fig. 1 rises in the data discussed below. When the inhibition level falls below a certain threshold O, ovulation resumes. This model can be extended to cover more states of suckling intensity if the data are available for such analysis. Figure 1 embodies the simplifying hypothesis that the inhibition declines with slope n when no breastfeeding is taking place, no matter what the woman's previous breastfeeding history. Likewise, the slopes / and p, referring to full and partial breastfeeding, are independent of previous breastfeeding experience. Although the slope for each segment of inhibition decline is assumed to be independent of prior breastfeeding patterns, the duration of postpartum anovulation is not. This length of time depends on how long following the birth the woman fully breastfed, partially breastfed, and did not breastfeed, since the inhibition is pictured as declining at different rates during each type of breastfeeding. A woman who does not breast- H/n 1 2 S Months after Parturition FIG. 1. Behaviour of H. f, slope when full breastfeeding; p, slope when partial breastfeeding; n, slope when not breastfeeding = 1. 'H reveals our belief that the inhibition is mediated hormonally. APPLICATION OF MODELS 121 feed will ovulate sooner after a birth (at time 1.2 months in Fig. 1) because her H level declines at rate n and falls most quickly to the ovulatory threshold, O. A woman who first fully, then partially, breastfeeds will experience a longer period of postpartum anovulation, since her H rises with full breastfeeding, /, and then falls at a much slower rate, p, with partial breastfeeding than it does, n, with no breastfeeding. When the inhibition (H) falls below a threshold even lower than O, lactation ceases if it has not been previously stopped for other reasons unrelated to the inhibition. Restriction or cessation of lactation before this physiological limit can occur for many reasons: biological (e.g., maternal illness) and behavioural, vol- untary and involuntary. In this model, the relationships between breastfeeding and postpartum anovulation are independent of these reasons, and neither the physio- logical maximum duration of lactation nor the change in inhibition after ovulation is germane. They are therefore not depicted in Fig. 1. This model has implications for the statistical analysis of the relationships of measures of breastfeeding with those of postpartum anovulation and with birth spacing. The statistical techniques which evolve from these implications are devel- oped and illustrated in Habicht et al. (1). The following results are from this paper. EMPIRICAL RESULTS FROM MODEL Empirically, we find in Malaysian women interviewed in 1975 the mean behav- iour of the H (Habicht et al., ref. 1) as depicted in Fig. 1 for women who fully breastfed half a month and partially breastfed 2 months. These women resumed ovulation at 3.5 months with a 95% confidence limit of 3.2 to 3.8 months.2 The average duration of postpartum anovulation for women who did not breastfeed was 1.2 months with 95% confidence limits of 0.9 to 1.6 months. Empirically, we also find in these data that increasing age is associated with delayed resumption of ovulation, as depicted in Fig. 2. This is due either to an increased span (/ - O) between the H levels at birth and the threshold for ovulation (P<0.05) or to a slower fall of H during partial breastfeeding (/)<0.05). It is not due to a slower fall of H during full breastfeeding, and it is not due to a combination of any of the above (P>0.05). Further analyses show that it is in fact due to the increasing span (/ — O) with age. REVIEW OF ASSUMPTIONS UNDERLYING THE MODEL AND QUESTIONS ENTAILED (Where papers or discussions from the meeting answered the questions, this is noted.) 2For any single woman with this breastfeeding behaviour, the 95% confidence limits for postpartum amenorrhoea would be 0 to 10 months. 122 APPENDICES H/n - Months after Parturition FIG. 2. Changes in H at two ages according to two equations: (—) 25-year-old women; (...) 45-year-old women; Eq. I, intercept 1 changes with age; Eq. II, slope p changes with age; f, slope when full breastfeeding; p, slope when partial breastfeeding; n, slope when not breastfeeding = 1. 1. H can be thought of as a mean level of inhibition which declines in the absence of suckling (as in Figs. 1 and 2). We will use H in this sense. However, H could also be graphed upside down as a mean level of deinhibition, so that H rises in the absence of suckling. Does this model correspond to what endocrinol- ogistsfeel is the mechanism? Endocrinologists seem to agree that the mechanism appears to be a chain of suckling, nipple stimulation, neural impulse to the hypothalamus, suppressed pulses of gonadotrophin-releasing hormone, which suppresses the luteinizing hormone, which arrests follicular interaction, which in turn prevents ovulation and menstrua- tion. The links between arrival of the neural impulse in the hypothalamus and suppression of pulses of gonadotrophin-releasing hormone are uncertain. It may not be mediated through prolactin, even though blood levels of prolactin are determined in major part by nipple stimulation in lactating mothers. Our H could be thought of as an abstract integration of suckling influences. However our model would be more credible if either gonadotrophin-releasing hor- mone pulsatility, or whatever depresses that pulsatility, could be transformed math- ematically into a continuous variable with the characteristics which we describe for//. The above sequence describes a single chain of neuronal and hormonal events linking suckling to postpartum anovulation. This is compatible with our model. Another pathway is mentioned as possible by Robyn (this volume): prolactin, elevated by suckling, directly suppresses ovarian susceptibility to luteinizing hor- mone and thus prevents follicle maturation. But apparently this pathway is unlikely to be important physiologically even if it exists. Even if this pathway were to play an important role, so that ovulation is suppressed by two paths, our model could still be valid if both pathways were highly synchronized in their effects. APPLICATION OF MODELS 123 2. At any given moment, the presence or absence of ovulation occurring within a month depends solely on where H is relative to O, the threshold when ovulation resumes. If H is above O, there is no ovulation or it is less probable (/><50%) than if H is at or below O. What evidence is therefor this model? Our statistical analysis with OLS implies no ovulation until H reaches O, and then full ovulation. We believe that is physiologically unlikely but were unable to develop a satisfactory stochastic model.
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