The Infertility Trap

A colleague who happens to be a professor in New South Wales shared this video with me. I am tempted to just recapture the presented content here, but I feel everyone should just watch it for full impact. I intentionally used a cover image that is counter to the narrative. The challenge is not overpopulation. Rather, it’s the opposite. Find out why.

Video: RSNSW Clarke Memorial Lecture 2021: The changing tide of human populations: an infertility trap

I’ve cued the video beyond the introduction—feel free to rewind for context, but there is no material content to be missed—, and there are a couple of minutes of additional material at the end, making the content closer to 50 minutes (48.5) than an hour.

The Infertility Trap was published last month as a book. I’ve not read it, but it was referenced. Countdown, by Shanna Swan is also referenced.

Some highlights follow:

The Rise and Rise of Humankind

Geometric growth commenced after the Black Plague was driven by the discovery of how to harness fossil fuel. As with Malthusian predictions, The Population Bomb missed the mark—but not for all of the reasons you might be thinking.

Changing Pace of Population Growth

Population growth rates were already on the decline when The Population Bomb was published in 1968. This trend was a result of the fertility trend that became precipitous circa 1963.

The Demographic Transition: Population Momentum

Though birth rates may seem to be increasing, this is merely optics as this is a legacy of positive population momentum stemming from high birth rates a few decades prior to the impending decline in fertility.

The Malthusian Paradox

Thomas Malthus didn’t grasp the paradigmatic shift technology would provide nor the relationship between fertility and prosperity.

Charts: Prosperity, infant mortality, child mortality, and fertility rate

As prosperity (as measured by GDP) increases, infant and child mortality as well as total fertility rate, each decrease. (I’m calling out the poor statistical representation of the non-zero-based Y-axis, but I don’t believe this was done to exaggerate the slope. It’s apparently just out of index.)

Reproductive Patterns: Australia vs !Kung Hunter-Gatherers

Notable in the charts above, are the delays in reproduction by the average Australian woman to around 30 years effectively limits the delivery to about 2 (1.7) whereas the hunter-gatherers commence closer to 20 years, yielding them an average of 5 children.

Rapid decline in semen quality

Semen quality (motility) and count are down.

Projections: Countdown to sperm count of zero in Paris and New Zealand

If declining semen count trends remain unabated or unaltered, one might anticipate a point where male fertility (potency?) reaches zero. This is characterised as azoopermia and projects this on Parisian males just past 2030 and by 2026 for New Zealanders.

Secular trend in declining testosterone levels

This downward trend is not constrained by region.

Trends in Testicular Cancer (NSW)

A correlated trend in fertility rate is an increase in testicular cancer, as shown with NSW data, even as ovarian cancer remains steady and cervical cancers are decreasing.

Reproductive Cancers in New South Wales

Conversely, other reproductive cancers (in NSW)—uterine and breast cancers—are on the rise in sync with testicular cancers and the drop in fertility.

My intent with this post is to share rather than editorialise. The video speaks for itself. I’ve provided some excerpted content for those who can’t spare the time to view the source.

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Cancer

Without considering the veracity of this article or the underlying methodology employed, what if it were true that cancer is a random event?

Setting aside that it is only a co-factor and there are other dimensions such as the type and virulence, what does this inform us about research funding? Is this a veritable cash cow for researchers and ripe with charlatans?

After examining 32 different kinds, researchers determined that 66% of all cancers were driven by chance, 29% were due to environmental causes, and 5% inheriting a mutation.

Op. cit.

Sure, different cancers have different correlative percentages, whether 35% for lung cancer or 95% for prostate cancer, and there may be different covariant factors, but, as might be the case for prostate cancer, we only have about 5% to address systematically, and we don’t even know that tweaking within this 5% will have ant material benefits to the health outcome of the patient. At least we have 65% of leeway for lung cancer.

Is there an inverse relationship between the proportion of randomness and positive health outcomes?

I don’t know the answer to these questions. I am also aware that some randomness is probably due to methodology, approach, and simply not necessarily fully understanding the mechanisms behind cancer—or the cause of the seemingly random genetic mutation. I’m not sure how one selects a control group to limit other causes.