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Some methodological issues in estimating demographic parameters in Southern Africa

Centre for Actuarial Research (CARe) A Research Unit of the University of Cape Town. Some methodological issues in estimating demographic parameters in Southern Africa. Overview. Child mortality Adult mortality Orphanhood method Methods using deaths and population GGB vs SEG methods

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Some methodological issues in estimating demographic parameters in Southern Africa

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  1. Centre for Actuarial Research (CARe) A Research Unit of the University of Cape Town Some methodological issues in estimating demographic parameters in Southern Africa

  2. Overview • Child mortality • Adult mortality • Orphanhood method • Methods using deaths and population • GGB vs SEG methods • Deaths reported by households vs vital registration • Use of deaths reported by households • Fertility • Brass P/F and variants • Relational Gompertz models

  3. Child mortality: 5q0 • The rule of 60 • If … • 30% prevalence among women 15-49 • 1/3 maternal transmission • 60% of children infected die before age 5 • Then … • Increase in child mortality must be > 60 per mille • So… • Can’t use CEB/CS

  4. Orphanhood • Problems • Correlation of mortality of child with mother/parents • Life table • AIDS • Not a single table • Bias apparently not too significant (Timæus and Nunn – 4%?) • Confine to older age women – but then of limited use

  5. Generalized Growth Balance and Synthetic Extinct Generations • Methods based on census counts alone can’t be used (inaccuracies in counts, no model life tables) • Constant-r methods (Growth Balance (Brass) and Preston-Coale method) not applicable • GGB (Hill), SEG (Bennett and Horiuchi) • Hill and Choi recommended GGB+SEG, BUT • SEG+delta on their dataset better, BUT • Not necessarily in Africa with AIDS, BUT • The real problem…

  6. Proportionate error in estimate of 45q15: African hypothetical data set

  7. Deaths reported by households vs vital registration of deaths • Virtually no countries with adequate vital registration systems (South Africa = 85% adult completeness) • Otherwise ask households to report on deaths in household in “the last 12 months” • Problem: Potential biases • Problem: How complete is the reporting? Only 1 year deaths but censuses 10+ years apart

  8. Potential biases • Two underlying assumptions of methods of estimating rates adjusted for completeness • deaths represent the population • completeness the same for all ages • Deaths reported by households represent only those in households – the bulk of the population • Completeness is unlikely to be the same for children as adults, and may not be the same for all adult ages

  9. Potential for bias • Under-reporting (as a result of): • failure to report a recent death; • confusion around the length of the reference period; • *non-coverage of specific areas/populations; • *disintegration of the household on the death; or, for completeness, • *the institutionalising of segments of the population (e.g. the aged) • Over-reporting (as a result of): • confusion around the length of the reference period; or • people seen as belonging to more than one household) • Age misreporting (either age heaping or *age exaggeration)

  10. Method – Data situations • Two censuses and data on deaths from both censuses • Estimate the deaths in the intercensal period by assuming exponential growth/change in the number of deaths over time • GGB and SEG+delta to intercensal period • Two censuses and data on deaths from the latter census • Estimate the population one year prior to the second census • Apply GGB and SEG+delta to the year prior to the second census • Single census with data on deaths • Use Growth Balance as a diagnostic

  11. Zimbabwe – males (1992-2002)

  12. Zimbabwe – females (1992-2002)

  13. Zimbabwean adult mortality (45q15)

  14. Best of both vital registration + deaths reported by households • Problem with vital registration sub-nationally • Dorrington, Moultrie and Timaeus monograph • Use vital registration to estimate completeness and hence expected number of deaths by sex and age nationally • Derive factors by sex and age to adjust the deaths reported by households for misreporting • Assume misreporting by households in the census is independent by sub-population • Correct deaths reported by households sub-nationally

  15. Fertility: Brass P/F methods • Results from a simulation exercise • 200 year projections starting / ending with stable populations; systematically introducing fertility decline; mortality decline; mortality rise due to HIV (with associated fertility impacts) • What is the best of the existing methods for adapting the P/F method for declining fertility?

  16. Declining fertility and declining mortality

  17. Declining fertility and rising AIDS mortality

  18. Fertility: Relational Gompertz models • Model is designed for medium-high fertility populations • Booth standard is based on 33 Coale-Trussell high fertility schedules • What if the schedules are not appropriate for use in Africa? • Fertility patterns no longer typical of the 33 • Particular problem at the oldest age group, where the C-T force estimated fertility to be low • … ongoing research work with Reinier van Gijsen (M student)

  19. Relational Gompertz models

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