The Hidden Factor in COVID-19 Mortality


“Population Age Structure: The Hidden Factor in COVID-19 Mortality”


Figure. Relative age-structural vulnerability to COVID-19 mortality
Applying New York City’s (NYC’s) COVID-19-related age-specific mortality rates to a country’s population age structure produces an estimate of the expected countrywide mortality, relative to NYC at a similar level of prevalence.

Until several months ago, demographers regarded a youthful age structure as an unequivocally detrimental demographic characteristic. Where more than half of the population is younger than age 25, countries are unable to attain high levels of economic and human capital development and face an increased risk of some forms of civil conflict. Yet, so far, during the ongoing pre-vaccine stage of the COVID-19 pandemic, the most age-structurally mature countries have been hardest hit by the disease. These countries are generally urbanized, wealthy, well-educated, and include a large proportion of seniors. And, somewhat surprisingly—despite being equipped with advanced medical technologies—these countries are experiencing the highest rates of mortality from complications related to COVID-19. See more …

To view the rest of this brief article, go to its New Security Beat site, or download the essay here.

Emulating Botswana’s Fertility Transition

Emulating Botswana’s Approach to Reproductive Health Services Could Speed Development in the Sahel

This new web essay (on the NSB website <click here>) reviews research that uses the relatively rapid changes in Botswana’s age-specific fertility rates to produce a “Botswana historic fertility variant” and applies this projection to the populations of countries in the Western Sahel—a contiguous cluster of states with populations that remain at the early stages of the fertility transition. For these high-fertility countries, projections of this variant (up to the year 2050) fall roughly around the TFR trajectory the UN low fertility variant.


Figure.  Declines in Fertility Across Women’s Age Groups in Botswana, Niger, and Mali from 1980–85 to 2015–20. In Botswana, girls’ education and a quality family planning program helped adolescents postpone childbearing and helped older women avoid the risks associated with unwanted middle-age pregnancy (Data source: UN Pop. Div., 2019 Rev.)

This exercise provides a glimpse of what could possibly be achieved in some states in tropical Africa by emulating Botswana’s efforts to ramp up access to family planning, to decrease the frequency of teen pregnancies, and to increase girls’ educational attainment. However, the essay notes how different the Sahelian countries are from Botswana—a well-governed, resource-endowed southern African country with a relatively small, urbanized (>65%) population (2.3 million)—and thus the formidable challenges those differences present in attempting to replicate the pace of Botswana’s fertility transition.



Figure.  UN Population Division’s High, Medium, and Low Fertility Variants for Niger and Mali vs. Botswana Historical Fertility Variant (BHFV). In both cases, applying Botswana’s age-specific pattern of fertility declines would produce a projection roughly similar to the current UN low fertility variant. (Data Source: UN Pop. Div., 2019 Rev. & author’s research)

For the full essay, go to the NSB website <click here>

Or, download the essay <click here to download>

Is demographic change the key to development?

This article is part of the World Economic Forum’s Geostrategy platform

Last year, 2018, marked the 60th anniversary of a landmark publication by a pair of academic social scientists who first recognized the close relationship between population age structure (the distribution of a country’s population, by age) and development, writes Richard Cincotta in the Wilson Center’s New Security BeatDoes demographic change set the pace of development?    [to read full article, click here on MORE]

[Graph on left]  The statistical method used to produce the age-structural timelines (logistic regression) shown here, works with categorical data, rather than individual data points. So, to show the pace of development on age-structural timelines, each of the three basic transitions—child survival, educational attainment, and income—are divided into a series of consecutive categories.

For example, the income transition (c., bottom graph) is represented by the World Bank’s low, lower-medium, upper-medium, and high-income categories. Similarly, the child survival transition (a.) is divided into five survival categories, and the educational attainment transition (b.) into five attainment categories.

Unlike historic timelines, each age-structural timeline is spanned by a series of curves. Each curve shows when—in terms of median age—countries are likely to achieve that category. The next curve (to the right in the series) shows how rapidly, in terms of progress in median age, countries are likely to move into the next higher category.

Attachments

Does Demography Set the Pace for Development

This year, 2018, marks the 60th anniversary of a landmark publication by a pair of academic social scientists who first recognized the close relationship between population age structure (the distribution of a country’s population, by age) and development. In Population Growth and Development in Low Income Countries (Princeton U. Press, 1958), demographer Ansley Coale (1917-2002) and economist Edgar M. Hoover (1907-1992) theorized that eventual declines in fertility would transform developing-country age structures. Coale and Hoover demonstrated that these newly transformed age structures would exhibit larger shares of citizens in the working ages, and smaller shares of dependent children and seniors. This transition, they argued, would someday help lift countries with youthful populations in Asia, Latin America, and Africa out of the low-income bracket. [read more … ]

To read the essay on New Security Beat, click here.   To download the essay, click here.

 

Attachments

Bangladesh & Pakistan: Demographic Twins Grow Apart

UN figures indicate that Bangladesh, a state once identified with natural catastrophes and rock-concert relief, is on the cusp of its demographic window (reflected in its age structure, shown in Figure 1, below)—a period of favorable age structures that researchers associate with an increased pace of development and a more stable political future. Bangladesh is already a solid member of the World Bank’s lower middle-income class. According to a set of statistical models that we have developed, by 2030 Bangladesh appears to have an even chance of reaching the Bank’s upper middle-income class (roughly US$4,000 to $12,000 per capita annually). For a country that Henry Kissinger famously dubbed “a basket case” at independence in 1971, that prospect is impressive.

Comparison of age structures, 1970 & 2015, following Bangladesh’s secession in 1971: East Pakistan Provincial Wing becomes Bangladesh; West Pakistan Provincial Wing becomes Pakistan.

 

 

This remarkable turnaround is not a big surprise to international health specialists. In 1975, the government in Dhaka began collaborating with the International Centre for Diarrheal Disease Research, Bangladesh (ICDDR/B) to initiate a program of community-based contraceptive distribution in Matlab subdistrict, a long-term health and demographic surveillance site.

 

Click here to read the rest of … “Bangladesh & Pakistan: Demographic Twins Grow Apart” on the New Security Beat, or download the .pdf here.