Impact of low birthweight on subsequent fertility: population-based register study

Petter Kristensen1, Lorentz M Irgens, Tor Bjerkedal

  • 1National Institute of Occupational Health, Oslo, Medical Birth Registry of Norway, University of Bergen, Norway. petter.kristensen@stami.no

Adverse birth outcomes may influence a family's wish for additional children. We investigated the influence of low birthweight in live births on subsequent fertility, and estimated secular trends of such an effect in a population-based cohort study of births arranged in consecutive sibship records in the Medical Birth Registry of Norway. We included births of order one to seven to all 587 785 mothers in Norway who had a first singleton birth in 1967-91. Associations between birthweight in 1 158 072 surviving index births of order one to six, 1967-91, and subsequent fertility (probability of another birth), 1967-97, were estimated as fertility ratios in Cox regression analysis. Giving birth to a live infant weighing < 3000 g had a negative effect on subsequent fertility, increasingly strong for decreasing birthweight. Low birthweight (<2500 g) was associated with a fertility ratio of 0.88 [95% confidence interval 0.87, 0.89]. This negative impact was stronger if the mother had also given birth to surviving children of low birthweight previously, particularly if combined with caesarean section in the most recent birth. The negative fertility effect of low birthweight grew slightly stronger between 1967 and approximately 1980, according to year of first birth. This trend paralleled reduced population fertility in the same period. The moderate negative impact of giving birth to a live infant of low birthweight on subsequent fertility could result from the combination of reduced wish for additional children and biological subfertility.

Related Concept Videos

Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Infertility in Males01:23

Infertility in Males

Male infertility affects millions of couples worldwide, arising from various factors that impact different stages of the reproductive process. An endocrine imbalance resulting from conditions like hypogonadism, Klinefelter syndrome, or pituitary disorders can disrupt hormone levels and reduce sperm production. Testicular defects, such as tumors, cryptorchidism, atrophic testes, abnormal sperm morphology, and low sperm count or motility, may arise due to genetic factors, structural...
Infertility in Females01:28

Infertility in Females

Female infertility is defined as the inability to conceive after a year of regular, unprotected intercourse and affects about 10–15% of couples worldwide. The primary cause of female infertility is ovulatory disorders, which hinder the release of eggs. These disorders can be classified as hypothalamic amenorrhea, polycystic ovarian syndrome (PCOS), premature ovarian failure, and hyperprolactinemic anovulation disorders.
Endometriosis, a condition characterized by abnormal growth of endometrial...
Life Histories01:29

Life Histories

Constrained by limited energy and resources, organisms must compromise between offspring quantity and parental investment. This trade-off is represented by two primary reproductive strategies; K-strategists produce few offspring but provide substantial parental support, whereas r-strategists produce much progeny that receives little care. These strategies are related to an organism’s survival likelihood across its lifespan, which is represented by a survivorship curve. Three general types of...
Testing a Claim about Mean: Known Population SD01:11

Testing a Claim about Mean: Known Population SD

A complete procedure of testing the hypothesis about a population mean is explained here.
Estimating a population mean requires the samples to be distributed normally. The data should be collected from the randomly selected samples having no sampling bias. The sample size needed to be higher than 30, and most importantly, the population standard deviation should be already known.
In most realistic situations, the population standard deviation is often unknown, but in rare circumstances, when it...