56 research outputs found
Introgression progress for phenotypic traits and parent-progeny diversity at advanced segregation population from Oryza barthii and O. glaberrima/O. sativa crosses
Oryza barthii has candidature for some significant economic traits but its utilization in rice breeding is rare. This study traced introgression of heritable traits in the offspring of O. barthii with an Africa-Asian progenitor to F8 and assessed diversity between the parents and the F8 population. Significant (P<0.05) genotypic variation existed for some traits. Grains per panicle and days to 50% flowering had respective least (3.34%) and highest (96.32%) broad sense heritability. Genotypic Coefficient of Variation (GCV) was lower than Phenotypic Coefficient of Variation (PCV) in all traits. Grains per panicle and tiller number had respective least (5.28% and 8.05%) and highest (90.8% and 98.1%) GCV and PCV. Progenies significantly differ in panicles and grains sizes, shapes, colours, presence or absence of awns. Five principal components explained 80.1% of the total variance. Plant height at maturity was the only trait with significant (p ≤ 0.01) correlation and regression between F6 and F7. Progenies resemblance to P1 retrogressively declined while offspring similarity to P2 progressively increased from F6 to F8. The present diversity study discovered three heterotic groups: the O. barthii (11%), O. sativa (67%) and the intermediate group (22%). This research has added to rice genetic resources, making investigation of the nutritional status of the different progenies interesting research for further studies
Diversity of indigenous arbuscular mycorrhizal fungi in rhizosphere of upland rice (Oryza sativa L.) varieties in Southwest Nigeria
Article Details: Received: 2020-02-05 | Accepted: 2020-05-07 | Available online: 2020-06-30https://doi.org/10.15414/afz.2020.23.02.42-48 Arbuscular mycorrhizal fungi (AMF) have the potential to increase crop productivity and play a key role in the functioning and sustainability of most agroecosystems. However, limited information is available on the divervisity of AMF associated with upland rice varieties in Southwest Nigeria. Field survey was conducted to investigate colonization and diversity of AMF in 13 upland rice varieties commonly grown in Southwest Nigeria. Root and soil samples were collected from rice fields in 2012. The results showed natural root colonization of all the rice varieties by AMF with highest root colonization in ITA 157and Ofada. The spore densities retrieved from the different rhizospheres were relatively high, varying from 13 spores in UORW 111 to 174 spores in Ofada with a mean of 67.6 spores per 20 g dry soil. Glomus was observed to be the most abundant AMF genus. Funneliformis mosseae was the most frequently occurring AMF species (96.2%) with relative density (RD) of 32.2%, followed by Glomus intraradices, Claroideoglomus etunicatum, and Glomus clareium. This study showed that AMF naturally colonized the roots of these rice varieties and diversity of different AMF genera in rice rhizosphere. This study will help draw attention to natural colonization of AMF in rice producing areas of Nigeria that can influence future possibility of using inocula of the dominant AMF species in upland rice cultivation.Keywords: Arbuscular mycorrhizal fungi, community structure, diversity, upland rice, spore densityReferences ADEYEMI, N.O. et al. (2020). Effect of commercial arbuscular mycorrhizal fungi inoculant on growth and yield of soybean under controlled and natural field conditions. Journal of Plant Nutrition, 43(4), 487–499, DOI: https://doi.org/10.1080/019041 67.2019.1685101 ADEYEMI, N.O. et al. (2019). Identification and relative abundance of native arbuscular mycorrhizal fungi associated with oil-seed crops and maize (Zea mays L.) in derived savannah of Nigeria. Acta fytotechn zootechn, 22(3), 84–89. DOI: https://doi.org/10.15414/afz.2019.22.03.84-89 ADEYEMI, N. et al. (2017). Yield and yield attributes responses of soybean (Glycine max L. Merrill) to elevated CO2 and arbuscular mycorrhizal fungi inoculation in the humid transitory rainforest. Notulae Scientia Biologicae, 9(2), 233–241. DOI: https://doi.org/10.15835/nsb9210002 BARBER, N.A. et al. (2013). Linking agricultural practices, mycorrhizal fungi, and traits mediating plant-insect interactions. Ecol Appl, 23(7), 1519–1530.BŁASZKOWSKI, J. (2012) Glomeromycota. Kraków: W. Szafer Institute of Botany, Polish Academy of Sciences. BOUYOUCOS, G.H. (1951). A recalibration of the hydrometer method for testing mechanical analysis of soils. Agronomy Journal, 43,434–438.BRUNDRETT, M.C. and TEDERSOO, L. (2018) Evolutionary history of mycorrhizal symbioses and global host plant diversity. New Phytol, 220,1108–1115. CAMPOS-SORIANO, L. et al. (2010). Activation of basal defense mechanisms of rice plants by Glomus intraradices does not affect the arbuscular mycorrhizal symbiosis. New Phytol, 188(2), 597–614. CHEN, M. et al. (2018) Beneficial services of arbuscular mycorrhizal fungi – from ecology to application. Frontiers in Plant Science, 9. DOI: https://doi.org/10.3389/fpls.2018.01270DAVISON, J. et al. (2015). Global assessment of arbuscular mycorrhizal fungus diversity reveals very low endemism. Science, 349, 970–973. DE ANDRADE-JÚNIOR, J.A. et al. (2018) Fixação de carbono em sistemas agroecológicos na região do Vale do São Patrício, Goiás. Científica – Multidiscip J, 5, 85–98. DE MOURA, J.B. et al. (2018) Taxa de colonização micorrízica sob diferentes sistemas de cultivo no cerrado em cana-deaçúcar. Diálogos & Ciência, 2, 60–66. GIANINAZZI, S. et al. (2010). Agroecology: The key role of arbuscular mycorrhizas in ecosystem services. Mycorrhiza, 20(8), 519–530. INVAM (2018). International culture collection of (vesicular) arbuscular mycorrhizal fungi. Morgantown: West Virginia University. HAZARD, C. et al. (2013). The role of local environment and geographical distance in determining community composition of arbuscular mycorrhizal fungi at the landscape scale. The ISME Journal, 7, 498–508. JIANG, Y.N. et al. (2017). Plants transfer lipids to sustain colonization by mutualistic mycorrhizal and parasitic fungi. Science, 356, 1172–1175. JOHNSON, N.C. (2010). Resource stoichiometry elucidates the structure and function of arbuscular mycorrhizas across scales. New Phytol, 185(3), 631–647. LEKBERG, Y. and KOIDE, R.T. (2005). Is plant performance limited by abundance of arbuscular mycorrhizal fungi? A metaanalysis of studies published between 1988 and 2003. New Phytol, 168(1). LIN, X. et al. (2012). Long-term balanced fertilization decreases arbuscular mycorrhizal fungal diversity in an arable soil in north China revealed by 454 pyrosequencing. Environmental Science & Technology, 46, 5764–5771. LUGINBUEHL, L.H. et al. (2017). Fatty acids in arbuscular mycorrhizal fungi are synthesized by the host plant. Science, 356, 1175–1178. LUMINI, E. et al. (2011). Different farming and water regimes in Italian rice fields affect arbuscular mycorrhizal fungal soil communities. Ecol Appl, 21(5), 1696–1707.OEHL, F. et al. (2010). Soil type and land use intensity determine the composition of arbuscular mycorrhizal fungal communities. Soil Biology and Biochemistry, 42, 724–738. OEHL, F. et al. (2017) Diversity and biogeography of arbuscular mycorrhizal fungi in agricultural soils. Biol Fertil Soils, (53), 777–797. PEYRET-GUZZON, M. et al. (2016). Arbuscular mycorrhizal fungal communities and Rhizophagus irregularis populations shift in response to short term ploughing and fertilisation in a buffer strip. Mycorrhiza, 26, 33–46. PHILLIPS, J.M. and HAYMAN, D.S. (1970). Improved procedures for clearing roots and staining parasitic and vesicular-arbuscular mycorrhizal fungi for rapid assessment of infection. Trans Br Mycol Soc., 55,158–IN18. PIVATO, B. et al. (2007). Medicago species affect the community composition of arbuscular myccorhizal fungi associated with roots. New Phytologist 176, 197–210. RILLIG, M.C. and MUMMEY, D.L. (2006). Mycorrhizas and soil structure. New Phytol, 171(1), 41–53. SILVA-FLORES, P. et al. (2019) Factors affecting arbuscular mycorrhizal fungi spore density in the Chilean Mediterraneantype ecosystem. J Soil Sci Plant Nutr, 19, 42–50. SMITH, S.E. and READ, D.J. (2008). Mycorrhizal symbiosis. 3rd ed., New York: Academic Press. SNOECK, D. et al. (2010). Temporal changes in VAM fungi in the cocoa agroforestry systems of central Cameroon. Agroforestry Syst., 78, 323–328. SOUZA, B.R. et al. (2016) Arbuscular mycorrhizal fungi as indicative of soil quality in conservation systems in the region of vale do São Patrício, Goiás. Int J Curr Res, 8, 43307–43311.VALLINO, et al. (2014). Rice flooding negatively impacts root branching and arbuscular mycorrhizal colonization, but not fungal viability. Plant Cell Environ, 37(3), 557–572. VAN Der HEIJDEN, M.G. et al. (2015) Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol 205, 1406–1423. VENTURA, M.V.A, et al. (2018) Influence of arbuscular mycorrhizal fungi in the establishment of pre-broken sugar cane. Poljoprivreda i Sumarstvo, 64,149–157. WALKLEY, A., and BLACK, I.A. (1934). An examination of Degtjareff method for determining soil organic matter and proposed modification of the chromic acid in soil analysis.1. Experimental soil science, 79, 459–465. WANG, Y.T. et al. (2015). Community dynamics of arbuscular mycorrhizal fungi in high-input and intensively irrigated rice cultivation systems. Appl Environ Microbiol, 81(8), 2958–2965. ZHANG, S.J. et al. (2015). Is resource allocation and grain yield of rice altered by inoculation with arbuscular mycorrhizal fungi? J Plant Ecol, 8(4), 436–448
PHYSIOLOGICAL AND YIELD RESPONSE OF SOME UPLAND RICE VARIETIES TO RE-WATERING AFTER IMPOSED SOIL MOISTURE STRESS
A pot experiment was conducted in the Screen house of Federal University of Agriculture, Abeokuta, October, 2011 (late dry season) to study drought recovery ability of 13 upland rice varieties exposed to soil moisture stress (20 days) at three growth stages (vegetative, reproductive and grain filling stage). The experiment was in completely randomized design, with three replicates. Under moisture stress significantly higher growth recovery, more erect canopy and flatter leaf surface were obtained in all the rice varieties at vegetative growth stage than other growth stages with increasing duration of re-watering. Under stress condition NERICA 4 maintained a significantly higher leaf area (27.50 cm2 and 40.18 cm2), plant height (53.45 cm and 67.62 cm) and number of tillers (1.67 and 1.67), but with a depressed number of leaf, slanted leaf posture and curved leaf especially during the later stage of its growth (Reproductive and grain filling stage respectively). It could be concluded that NERICA 4 had higher recovery ability than other rice varieties in drought prone upland ecology
ROOT RESPONSE OF SOME SELECTED RICE VARIETIES TO SOIL MOISTURE STRESS AT DIFFERENT PHENOLOGICAL STAGES
Physiological adjustment in plant root system is a determinant for survival and crop productivity in situation of moisture stress. A screen house experiment was conducted to access response of rice roots to moisture stress. Thirteen varieties of rice comprising six NERICAs, WAB 56-104, CG 14, ART26-3-1-B, AC 103549, MOROBEREKAN, ART19-25-1-B and a local check (OFADA) were subjected to twenty-day moisture stress once at each phenological stage. Results indicated that root growth generally showed preference over shoot growth. Moisture stress did not affect root volume (RV), deep root numbers (DRN), root dry weight (RDW) and root depth (RD) of all the rice varieties at reproductive stage. CG14 however recorded 67.6% increase in RD at this stage while NERICA 3, CG14 and OFADA recorded an increase in root depth: shoot length. At vegetative and grain filling stages, RV, DRN, RDW, RD, and RMC were significantly (p< 0.05) increased by moisture stress in most rice varieties. NERICA2, NERICA7, ART26-3-1-B, MOROBEREKAN and WAB56-104 however recorded 54%, 76.5%, 72.7%, 57.1%, and 56.3% significant reduction in DRN respectively at vegetative stage. Correlation analysis showed that plant height, leaf area, and number of tillers depend highly on, RD, RV, RDW and deep root weight. Therefore, attention should be focused on these parameters in selection for moisture stress tolerance in rice
GADSA: Decision Support App for Antibiotics Prescribing in Nigeria
GADSA (Gamified Antimicrobial Stewardship Decision Support App) is a decision support tool to improve evidence-based prescribing, designed to be used at the point-of-care to help clinicians comply with guidelines in their everyday practice. The app represents a novel cross-platform, mobile decision support tool, integrating principles from serious games and gamification, to improve compliance with prescription guidelines of Surgical Antibiotic Prophylaxis (SAP) in Nigeria. This paper focuses on the decision support component of the mobile application, integrating the World Health Organisation and Sanford guidelines for SAP prescriptions
Track D Social Science, Human Rights and Political Science
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd
Track E Implementation Science, Health Systems and Economics
Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138412/1/jia218443.pd
Global, regional, and national prevalence and mortality burden of sickle cell disease, 2000-2021: a systematic analysis from the Global Burden of Disease Study 2021
BACKGROUND: Previous global analyses, with known underdiagnosis and single cause per death attribution systems, provide only a small insight into the suspected high population health effect of sickle cell disease. Completed as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021, this study delivers a comprehensive global assessment of prevalence of sickle cell disease and mortality burden by age and sex for 204 countries and territories from 2000 to 2021. METHODS: We estimated cause-specific sickle cell disease mortality using standardised GBD approaches, in which each death is assigned to a single underlying cause, to estimate mortality rates from the International Classification of Diseases (ICD)-coded vital registration, surveillance, and verbal autopsy data. In parallel, our goal was to estimate a more accurate account of sickle cell disease health burden using four types of epidemiological data on sickle cell disease: birth incidence, age-specific prevalence, with-condition mortality (total deaths), and excess mortality (excess deaths). Systematic reviews, supplemented with ICD-coded hospital discharge and insurance claims data, informed this modelling approach. We employed DisMod-MR 2.1 to triangulate between these measures-borrowing strength from predictive covariates and across age, time, and geography-and generated internally consistent estimates of incidence, prevalence, and mortality for three distinct genotypes of sickle cell disease: homozygous sickle cell disease and severe sickle cell β-thalassaemia, sickle-haemoglobin C disease, and mild sickle cell β-thalassaemia. Summing the three models yielded final estimates of incidence at birth, prevalence by age and sex, and total sickle cell disease mortality, the latter of which was compared directly against cause-specific mortality estimates to evaluate differences in mortality burden assessment and implications for the Sustainable Development Goals (SDGs). FINDINGS: Between 2000 and 2021, national incidence rates of sickle cell disease were relatively stable, but total births of babies with sickle cell disease increased globally by 13·7% (95% uncertainty interval 11·1-16·5), to 515 000 (425 000-614 000), primarily due to population growth in the Caribbean and western and central sub-Saharan Africa. The number of people living with sickle cell disease globally increased by 41·4% (38·3-44·9), from 5·46 million (4·62-6·45) in 2000 to 7·74 million (6·51-9·2) in 2021. We estimated 34 400 (25 000-45 200) cause-specific all-age deaths globally in 2021, but total sickle cell disease mortality burden was nearly 11-times higher at 376 000 (303 000-467 000). In children younger than 5 years, there were 81 100 (58 800-108 000) deaths, ranking total sickle cell disease mortality as 12th (compared to 40th for cause-specific sickle cell disease mortality) across all causes estimated by the GBD in 2021. INTERPRETATION: Our findings show a strikingly high contribution of sickle cell disease to all-cause mortality that is not apparent when each death is assigned to only a single cause. Sickle cell disease mortality burden is highest in children, especially in countries with the greatest under-5 mortality rates. Without comprehensive strategies to address morbidity and mortality associated with sickle cell disease, attainment of SDG 3.1, 3.2, and 3.4 is uncertain. Widespread data gaps and correspondingly high uncertainty in the estimates highlight the urgent need for routine and sustained surveillance efforts, further research to assess the contribution of conditions associated with sickle cell disease, and widespread deployment of evidence-based prevention and treatment for those with sickle cell disease. FUNDING: Bill & Melinda Gates Foundation
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The burden of bacterial antimicrobial resistance in the WHO African region in 2019: a cross-country systematic analysis
Background
A critical and persistent challenge to global health and modern health care is the threat of antimicrobial resistance (AMR). Previous studies have reported a disproportionate burden of AMR in low-income and middle-income countries, but there remains an urgent need for more in-depth analyses across Africa. This study presents one of the most comprehensive sets of regional and country-level estimates of bacterial AMR burden in the WHO African region to date.
Methods
We estimated deaths and disability-adjusted life-years (DALYs) attributable to and associated with AMR for 23 bacterial pathogens and 88 pathogen–drug combinations for countries in the WHO African region in 2019. Our methodological approach consisted of five broad components: the number of deaths in which infection had a role, the proportion of infectious deaths attributable to a given infectious syndrome, the proportion of infectious syndrome deaths attributable to a given pathogen, the percentage of a given pathogen resistant to an antimicrobial drug of interest, and the excess risk of mortality (or duration of an infection) associated with this resistance. These components were then used to estimate the disease burden by using two counterfactual scenarios: deaths attributable to AMR (considering an alternative scenario where infections with resistant pathogens are replaced with susceptible ones) and deaths associated with AMR (considering an alternative scenario where drug-resistant infections would not occur at all). We obtained data from research hospitals, surveillance networks, and infection databases maintained by private laboratories and medical technology companies. We generated 95% uncertainty intervals (UIs) for final estimates as the 25th and 975th ordered values across 1000 posterior draws, and models were cross-validated for out-of-sample predictive validity.
Findings
In the WHO African region in 2019, there were an estimated 1·05 million deaths (95% UI 829 000–1 316 000) associated with bacterial AMR and 250 000 deaths (192 000–325 000) attributable to bacterial AMR. The largest fatal AMR burden was attributed to lower respiratory and thorax infections (119 000 deaths [92 000–151 000], or 48% of all estimated bacterial pathogen AMR deaths), bloodstream infections (56 000 deaths [37 000–82 000], or 22%), intra-abdominal infections (26 000 deaths [17 000–39 000], or 10%), and tuberculosis (18 000 deaths [3850–39 000], or 7%). Seven leading pathogens were collectively responsible for 821 000 deaths (636 000–1 051 000) associated with resistance in this region, with four pathogens exceeding 100 000 deaths each: Streptococcus pneumoniae, Klebsiella pneumoniae, Escherichia coli, and Staphylococcus aureus. Third-generation cephalosporin-resistant K pneumoniae and meticillin-resistant S aureus were shown to be the leading pathogen–drug combinations in 25 and 16 countries, respectively (53% and 34% of the whole region, comprising 47 countries) for deaths attributable to AMR.
Interpretation
This study reveals a high level of AMR burden for several bacterial pathogens and pathogen–drug combinations in the WHO African region. The high mortality rates associated with these pathogens demonstrate an urgent need to address the burden of AMR in Africa. These estimates also show that quality and access to health care and safe water and sanitation are correlated with AMR mortality, with a higher fatal burden found in lower resource settings. Our cross-country analyses within this region can help local governments to leverage domestic and global funding to create stewardship policies that target the leading pathogen–drug combinations.
Funding
Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care using UK aid funding managed by the Fleming Fund
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Global fertility in 204 countries and territories, 1950–2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021
Background
Accurate assessments of current and future fertility—including overall trends and changing population age structures across countries and regions—are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios.
Methods
To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10–54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values—a metric assessing gain in forecasting accuracy—by comparing predicted versus observed ASFRs from the past 15 years (2007–21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline.
Findings
During the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63–5·06) to 2·23 (2·09–2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137–147), declining to 129 million (121–138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1—canonically considered replacement-level fertility—in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7–29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59–2·08) in 2050 and 1·59 (1·25–1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6–43·1) in 2050 and 54·3% (47·1–59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions—decreasing, for example, in south Asia from 24·8% (23·7–25·8) in 2021 to 16·7% (14·3–19·1) in 2050 and 7·1% (4·4–10·1) in 2100—but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40–1·92) in 2050 and 1·62 (1·35–1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction.
Interpretation
Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world
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