It is difficult, if not impossible, to conduct research on ageing without relying on reported age – if only to identify older people. Ageing research and policy are both therefore jeopardised by widespread age-misreporting, which is common among older people in low- and middle-income coutries (Randall and Coast, 2016). Here we discuss influences on the way that age is reported by and for older people in Uganda, Africa, using a pilot study of a peri-urban community.

Age reporting by older people: age-heaping, literacy and population registration

Age-heaping is the tendency to report ages in round numbers, usually numbers ending in zero or five. Many studies have found that age-heaping is higher in societies with low levels of education or literacy, and this has often been interpreted as a causal relationship, implying that uneducated people are unable to keep track of, or calculate, their age (Mba, 2014). This has led to the use of age-heaping as one of the most frequently used measures of numeracy and human capital (Crayen and Baten, 2010). 

In our study, people with high human capital (who could read, write, and had more education) were more likely than those with lower human capital to report a birth date with a day and month as well as a year. In addition, their birth date was more likely to be consistent with the age they reported. Contrary to expectation, however, they were more likely to report an age ending in a 0 or 5 than were those with lower human capital, as shown by Table 1. The relationship between human capital and age heaping, independent of other factors, was confirmed using a multivariate logistic regression (Reid et al, 2024). 

We suggest that the answer to this paradox is linked to the introduction of national ID cards five years before our survey took place. For many people these cards were the first document they had ever received with a date of birth, and this date was likely derived from a rounded age assigned when the card was issued. This date of birth therefore also yielded a round age during our survey. People with ID cards were more likely to report their birth date with high precision than those without, and were also more likely to report a date of birth consistent with the age they reported. People with high human capital were no more likely to have an ID card, but they may have been more likely to report a rounded age because they were quicker to sign up for ID cards and to have received one five years before the survey. Alternatively, they may have been more likely to consult or refer to their ID card and therefore to have learnt their assigned birth date.

Although we disagree that age heaping is an accurate indicator of numeracy, correlation between the two are common because the development of education and numeracy has often gone hand-in-hand with the introduction of registration and the growing requirement to report age or date of birth. However for today’s older generations in Uganda, and probably other societies too, we should be wary of interpreting age-heaping as an indicator of low social capital.

We argue that age reporting is a better measure of administrative modernisation (Spennemann, 2017; A’Hearn et al, 2022), than of human capital and that the registration of births and issuing of birth certificates is likely to be crucial. If large segments of the population receive identification at the same time, this can introduce patterns of age-heaping among those segments. The current push for much needed registration and identity documentation may not, therefore, be accompanied by immediate improvements in age reporting among the elderly.

Age reporting for older people: frailty as an indicator of perceived age

We also investigated factors which affected how others estimate the age of an older person. This is important because there are many circumstances where a person provides information on behalf of other household members. Table 1 also indicates that the ages of older people as reported by their carers are less precise than ages reported by the older people themselves. We investigated the presence of systematic patterns in this imprecision by comparing the age that an older person’s carer or companion thought they were with the age reported by the older person themself (discrepancies could be due to mis-reporting by either party). 

We found that, compared to the carers of older people in poor health (according to several measures), the carers of older people in better health were more likely to allocate an age at least five years younger than the age reported by the older person themself (Figure 1). Interestingly, the same pattern was true for the relatives or carers of people who could write (not shown in Figure 1). It is possible that those who can read and write appear more mentally agile to others than those who cannot. Compared to spouses (who are more likely to know how old their husband or wife is, or believes themself to be), all other carers were more likely to report a much older age than the older person themself, especially when the carer was not a relative or neighbour.

Our finding that proxy reports of age are likely to be informed by health or appearance can compromise comparisons of healthy life expectancies and differences between groups within places, between different places, and over time, as different cultures and norms as well as underlying health status may affect assessments of age.  

Our research also raises questions about the definitions of age. Peri-urban societies in Uganda currently use a primarily social-role or capability-based definition of old age although this may change in younger generations. Chronological age – although crucial for population modelling and forecasting – is not always a good indicator of biological age, which may be more important for defining vulnerability and related interventions.

References 

A’Hearn, B., Delfino, A. and Nuvolari, A. (2022) Rethinking age heaping: a cautionary tale from nineteenth‐century Italy†, The Economic History Review, 75(1), 111–137, doi: 10.1111/ehr.13087.

Crayen, D. and Baten, J. (2010) Global trends in numeracy 1820–1949 and its implications for long-term growth, Explorations in Economic History, 47(1), 82–99, doi: 10.1016/j.eeh.2009.05.004.

Mba, C.J. (2014) “Examining the Accuracy of Age-Sex Data: An Evaluation of Recent Sub-Saharan African Population Censuses.”, in Odimegwu, C.O. and Kekovole, J. (eds.), Continuity and Change in Sub-Saharan African Demography. 1st ed., Routledge. pp 12–36.

Randall, S. and Coast, E. (2016) The quality of demographic data on older Africans, Demographic Research, 34, 143–174, doi: 10.4054/DemRes.2016.34.5.

Reid, A., Kabagenyi, A., Wandera, S.O., et al (2024) Age reporting by and for older people in Uganda: relationships with frailty, human capital and population registration, Journal of Global Ageing, 1(1), 27–51, doi: 10.1332/29767202Y2023D000000003.

Spennemann, D.H.R. (2017) Age Heaping among Indian Hawkers in South-eastern Australia and their source communities in the Punjab, JSPS, 24(1 & 2), 149–202.

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Background 

Menopause is more than just a collection of unpleasant symptoms, such as hot flashes and brain fogs. The age when menstruation stops for good is an important marker for the woman’s future life and health. There is evidence that women reporting early menopause (before the global median age of 50), because of the drop in estrogen, are exposed to higher risks of heart and bone diseases (Golezar et al., 2019; Zhang et al., 2019) and of death (Shuster et al., 2010). 

The age at menopause (conventionally starting twelve months after the last menstruation) does not occur at random: it hinges on biocultural experiences of reproduction in adolescence and throughout childbearing years, with effects that accumulate over time (Mishra et al., 2010; Leidy Sievert et al., 2013). The phenomenon has been frequently studied in low-fertility countries, but much less in a high-fertility setting (for an exception, see OlaOlorun & Lawoyin, 2009).

Research approach

In a recent article (Batista et al., 2022), we explored the combinations of individual characteristics and social circumstances that were conducive to menopause appearing between ages 40 and 49 among ever-married heterosexual Ugandan women, using data from the 2016 Demographic and Health Survey (Uganda Bureau of Statistics & ICF International Inc., 2018). 

Women were asked numerous questions about their reproductive experiences, but the main factors that we explored were:

(a) if they ever used any method of modern or traditional contraception,

(b) the age when they gave birth to their first child, and 

(c) how many children they gave birth to. 

We expected reproductive factors to interact not only with each other, but also with sociodemographic factors such as education, wealth, rural-urban residence, marital status, and age at first marriage.

Main findings

About 17% of forty-year-olds in Uganda reported having experienced menopause when the survey was conducted in 2016. We studied their life course and found that never using contraceptives, having fewer children, and younger ages at first birth and marriage were associated with higher probabilities of menopause at ages 40-49.

In the pathways most likely to give rise to early menopause among Ugandan women, we found evidence of two interactive dynamics clustering around contraception. One emerged when no contraceptive use was linked to young age at first birth (Figure 1); the other, when lack of contraceptive use was linked to lack of education (Figure 2). 

Early menopause has thus far been studied almost exclusively in low-fertility, high income countries. Therefore, scholars have typically identified different types of risk factors, such as childlessness or smoking cigarettes. None of these applies to Uganda, given the very low occurrence of both: only 1.5% of the sample reported either. What seems to matter there is a different set of underlying causes, such as young age at motherhood and lack of education.

Conclusions

Menopause typically receives less attention in women’s health research than other reproductive issues, especially among African women. The early onset of menopause, i.e., before the global median age of 50 years, should become a policy concern, because of its connection to higher morbidity and mortality in older years. 

Increasing access to education for girls is better poised to increase menopausal age than simply increasing contraceptive use. Besides, increasing contraception would be most helpful in preventing early menopause for women with no education. For other women, reducing child marriage and delaying age at first birth seem to be more promising paths. These measures, which rank high in the comprehensive efforts already underway to improve women’s overall reproductive health, are also likely to result in older menopausal age.

References

Batista, M. S., Tsitsiou, Y., Dar, S., Ancillotti, G., Minhas, S., & Varshney, N. (2022). Life course reproductive dynamics associated with menopause in Ugandan women aged 40–49. American Journal of Human Biology. https://doi.org/10.1002/ajhb.23791 

Golezar, S., Ramezani Tehrani, F., Khazaei, S., Ebadi, A., & Keshavarz, Z. (2019). The global prevalence of primary ovarian insufficiency and early menopause: A meta-analysis. Climacteric, 22(4), 403–411. https://doi.org/10.1080/13697137.2019.1574738 

Leidy Sievert, L., Murphy, L., Morrison, L. A., Reza, A. M., & Brown, D. E. (2013). Age at menopause and determinants of hysterectomy and menopause in a multi-ethnic community: The Hilo Women’s Health Study. Maturitas, 76(4), Article 4. https://doi.org/10.1016/j.maturitas.2013.08.007 

Mishra, G. D., Cooper, R., & Kuh, D. (2010). A life course approach to reproductive health: Theory and methods. Maturitas, 65(2), 92–97. https://doi.org/10.1016/j.maturitas.2009.12.009 

OlaOlorun, F., & Lawoyin, T. (2009). Age at menopause and factors associated with attainment of menopause in an urban community in Ibadan, Nigeria. Climacteric, 12(4), 352–363. https://doi.org/10.1080/13697130802521282 

Shuster, L. T., Rhodes, D. J., Gostout, B. S., Grossardt, B. R., & Rocca, W. A. (2010). Premature menopause or early menopause: Long-term health consequences. Maturitas, 65(2), Article 2. https://doi.org/10.1016/j.maturitas.2009.08.003 

Uganda Bureau of Statistics & ICF International Inc. (2018). Uganda Demographic and Health Survey 2016.

Zhang, X., Liu, L., Song, F., Song, Y., & Dai, H. (2019). Ages at menarche and menopause, and mortality among postmenopausal women. Maturitas, 130, 50–56. https://doi.org/10.1016/j.maturitas.2019.10.009 

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