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Bayesian population reconstruction of female populations for less developed and more developed countries.

ABSTRACT: We show that Bayesian population reconstruction, a recent method for estimating past populations by age, works for data of widely varying quality. Bayesian reconstruction simultaneously estimates age-specific population counts, fertility rates, mortality rates, and net international migration flows from fragmentary data, while formally accounting for measurement error. As inputs, Bayesian reconstruction uses initial bias-reduced estimates of standard demographic variables. We reconstruct the female populations of three countries: Laos, a country with little vital registration data where population estimation depends largely on surveys; Sri Lanka, a country with some vital registration data; and New Zealand, a country with a highly developed statistical system and good quality vital registration data. In addition, we extend the method to countries without censuses at regular intervals. We also use it to assess the consistency of results between model life tables and available census data, and hence to compare different model life table systems.

SUBMITTER: Wheldon MC

PROVIDER: S-EPMC4798897 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Bayesian population reconstruction of female populations for less developed and more developed countries.

Wheldon Mark C MC Raftery Adrian E AE Clark Samuel J SJ Gerland Patrick P

Population studies 20160223 1

We show that Bayesian population reconstruction, a recent method for estimating past populations by age, works for data of widely varying quality. Bayesian reconstruction simultaneously estimates age-specific population counts, fertility rates, mortality rates, and net international migration flows from fragmentary data, while formally accounting for measurement error. As inputs, Bayesian reconstruction uses initial bias-reduced estimates of standard demographic variables. We reconstruct the fem ...[more]

PMID: 26902913

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Project description:ObjectiveTo compare treatment effects from randomised trials conducted in more developed versus less developed countries.DesignMeta-epidemiological study.Data sourcesCochrane Database of Systematic Reviews (August 2012).Data extractionMeta-analyses with mortality outcomes including data from at least one randomised trial conducted in a less developed country and one in a more developed country. Relative risk estimates of more versus less developed countries were compared by calculating the relative relative risks for each topic and the summary relative relative risks across all topics. Similar analyses were performed for the primary binary outcome of each topic.Results139 meta-analyses with mortality outcomes were eligible. No nominally significant differences between more developed and less developed countries were found for 128 (92%) meta-analyses. However, differences were beyond chance in 11 (8%) cases, always showing more favourable treatment effects in trials from less developed countries. The summary relative relative risk was 1.12 (95% confidence interval 1.06 to 1.18; P<0.001; I(2)=0%), suggesting significantly more favourable mortality effects in trials from less developed countries. Results were similar for meta-analyses with nominally significant treatment effects for mortality (1.15), meta-analyses with recent trials (1.14), and when excluding trials from less developed countries that subsequently became more developed (1.12). For the primary binary outcomes (127 meta-analyses), 20 topics had differences in treatment effects beyond chance (more favourable in less developed countries in 15/20 cases).ConclusionsTrials from less developed countries in a few cases show significantly more favourable treatment effects than trials in more developed countries and, on average, treatment effects are more favourable in less developed countries. These discrepancies may reflect biases in reporting or study design as well as genuine differences in baseline risk or treatment implementation and should be considers when generalising evidence across different settings.

Project description:IntroductionThe human malaria parasite, Plasmodium vivax, is proving more difficult to control and eliminate than Plasmodium falciparum in areas of co-transmission. Comparisons of the genetic structure of sympatric parasite populations may provide insight into the mechanisms underlying the resilience of P. vivax and can help guide malaria control programs.Methodology/principle findingsP. vivax isolates representing the parasite populations of four areas on the north coast of Papua New Guinea (PNG) were genotyped using microsatellite markers and compared with previously published microsatellite data from sympatric P. falciparum isolates. The genetic diversity of P. vivax (He = 0.83-0.85) was higher than that of P. falciparum (He = 0.64-0.77) in all four populations. Moderate levels of genetic differentiation were found between P. falciparum populations, even over relatively short distances (less than 50 km), with 21-28% private alleles and clear geospatial genetic clustering. Conversely, very low population differentiation was found between P. vivax catchments, with less than 5% private alleles and no genetic clustering observed. In addition, the effective population size of P. vivax (30353; 13043-69142) was larger than that of P. falciparum (18871; 8109-42986).Conclusions/significanceDespite comparably high prevalence, P. vivax had higher diversity and a panmictic population structure compared to sympatric P. falciparum populations, which were fragmented into subpopulations. The results suggest that in comparison to P. falciparum, P. vivax has had a long-term large effective population size, consistent with more intense and stable transmission, and limited impact of past control and elimination efforts. This underlines suggestions that more intensive and sustained interventions will be needed to control and eventually eliminate P. vivax. This research clearly demonstrates how population genetic analyses can reveal deeper insight into transmission patterns than traditional surveillance methods.

Dataset Information

Bayesian population reconstruction of female populations for less developed and more developed countries.

Publications

Bayesian population reconstruction of female populations for less developed and more developed countries.

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