1,722,390 research outputs found
Modelled gridded population estimates for Mongala Province in the Democratic Republic of Congo version 4.4.
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Mongala province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC for 2023, settlement extent and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Mongala. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Krishnaveni KS with support from Ortis Yankey. Data processing was done by Krishnaveni KS and Tom Abbott with additional support from Heather Chamberlain. Project oversight was done by Attila Lazar, Chris Nnanatu and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns was collected, processed, anonymised and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 8.0) (CIESIN, 2025).
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Modelled gridded population estimates for Mongala Province in the Democratic Republic of Congo version 4.3
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Mongala province in the Democratic Republic of Congo (DRC), along with estimates of the number of people belonging to various age-sex groups. The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC for 2023, settlement extent and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. This time period corresponds to the PDRS survey date for Mongala. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Krishnaveni KS with support from Ortis Yankey. Data processing was done by Krishnaveni KS and Tom Abbott with additional support from Heather Chamberlain. Project oversight was done by Attila Lazar, Chris Nnanatu and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns was collected, processed, anonymised and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 6.0) (CIESIN, 2025).
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Anthropometry, glucose tolerance and insulin concentrations in South Indian children: relationships to maternal glucose tolerance during pregnancy
No full textEarlier studies have shown that individuals whose mothers were diabetic when they were in utero, have an increased risk of early obesity, and impaired glucose tolerance (lGT) and type 2 diabetes in adult life. This study was designed to test whether adiposity, glucose tolerance and insulin concentrations are altered in Indian children born to mothers with gestational diabetes (GDM), and are related to maternal glucose and insulin concentrations in pregnancy even in the absence of GDM.830 pregnant women attending the antenatal clinics of the Holdsworth Memorial Hospital (HMH), Mysore, India underwent an Oral Glucose Tolerance Test (OGTT) at 30±2 weeks. 674 of these women delivered at HMH. Detailed anthropometry was performed on the offspring at birth, and annually thereafter. 585 mothers returned with their offspring at 5 years of age for detailed investigations including OGTT for glucose and insulin concentrations, bio-impedance for fat estimation and blood pressure measurement. OGTT was administered to mothers and fasting plasma glucose and insulin concentrations were measured in fathers.The Mysore babies were small compared to UK neonates, but the deficit varied for different body measurements. While birthweight (-1.1 SD) was considerably lower, crown-heel length (-0.3 SD) and subscapular skinfold thickness (-0.2 SD) were relatively spared. At five years, subscapular skinfold thickness was larger than the UK standards (+0.23 SD, p<0.001) despite all other body measurements being significantly smaller. Findings at 5 years were similar in comparison with another standard, based on Dutch children. At 5 years, girls in the cohort had higher insulin concentrations and were more insulin resistant. Body fat was the strongest predictor of glucose and insulin concentrations independent of other body components and parental characteristics.Newborns of the mothers with gestational diabetes were larger in all body measurements than control neonates (born to non-GDM mothers and non-diabetic fathers). At one year, these differences had diminished and were not statistically significant. At five years, female, but not male offspring of diabetic mothers had larger subscapular and triceps skinfolds (P=0.01) and higher 30- and 120-minute insulin concentrations (P<0.05) than control females. Even in the control offspring maternal insulin area-under-the-curve was positively associated with 30-minute insulin concentrations, after adjusting for sex and maternal skinfolds (P<0.001). Offspring of diabetic fathers (n=41) were lighter at birth than controls; they showed no differences in anthropometry at five years.In conclusion, Maternal GDM is associated with adiposity and higher insulin concentrations in female offspring at 5 years. The absence of similar associations in offspring of diabetic fathers suggests a programming effect of the diabetic intra-uterine environment. With increasing levels of obesity and IGT among Indian mothers, these effects may be contributing to the rise of type 2 diabetes in India. Our continuing follow-up aims to study the long-term effects of higher maternal glucose concentrations in the absence of GDM
A principal components approach to parent-to-newborn body composition associations in South India
Full text linkBackground: size at birth is influenced by environmental factors, like maternal nutrition and parity, and by genes. Birth weight is a composite measure, encompassing bone, fat and lean mass. These may have different determinants. The main purpose of this paper was to use anthropometry and principal components analysis (PCA) to describe maternal and newborn body composition, and associations between them, in an Indian population. We also compared maternal and paternal measurements (body mass index (BMI) and height) as predictors of newborn body composition.Methods: weight, height, head and mid-arm circumferences, skinfold thicknesses and external pelvic diameters were measured at 30 ± 2 weeks gestation in 571 pregnant women attending the antenatal clinic of the Holdsworth Memorial Hospital, Mysore, India. Paternal height and weight were also measured. At birth, detailed neonatal anthropometry was performed. Unrotated and varimax rotated PCA was applied to the maternal and neonatal measurements.Results: rotated PCA reduced maternal measurements to 4 independent components (fat, pelvis, height and muscle) and neonatal measurements to 3 components (trunk+head, fat, and leg length). An SD increase in maternal fat was associated with a 0.16 SD increase (?) in neonatal fat (p < 0.001, adjusted for gestation, maternal parity, newborn sex and socio-economic status). Maternal pelvis, height and (for male babies) muscle predicted neonatal trunk+head (? = 0. 09 SD; p = 0.017, ? = 0.12 SD; p = 0.006 and ? = 0.27 SD; p < 0.001). In the mother-baby and father-baby comparison, maternal BMI predicted neonatal fat (? = 0.20 SD; p < 0.001) and neonatal trunk+head (? = 0.15 SD; p = 0.001). Both maternal (? = 0.12 SD; p = 0.002) and paternal height (? = 0.09 SD; p = 0.030) predicted neonatal trunk+head but the associations became weak and statistically non-significant in multivariate analysis. Only paternal height predicted neonatal leg length (? = 0.15 SD; p = 0.003).Conclusion: principal components analysis is a useful method to describe neonatal body composition and its determinants. Newborn adiposity is related to maternal nutritional status and parity, while newborn length is genetically determined. Further research is needed to understand mechanisms linking maternal pelvic size to fetal growth and the determinants and implications of the components (trunk v leg length) of fetal skeletal growt
Modelled gridded population estimates for Nord-Ubangi Province in the Democratic Republic of Congo version 4.4.
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Nord-Ubangi Province in the Democratic Republic of Congo (DRC). The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC between the periods 2021 to 2023, settlement extents and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Chris Nnanatu. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain, Assane Gadiaga and Krishnaveni KS. Project oversight was done by Attila Lazar, Chris Nnanatu and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 8.0) (CIESIN, 2025).
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Modelled gridded population estimates for Kinshasa Province in the Democratic Republic of Congo version 4.4.
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Kinshasa Province in the Democratic Republic of Congo (DRC). The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC between the periods 2021 to 2023, settlement extents and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Chris Nnanatu. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain, Assane Gadiaga and Krishnaveni KS. Project oversight was done by Attila Lazar, Chris Nnanatu and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 8.0) (CIESIN, 2025).
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Modelled gridded population estimates for Nord Kivu Province in the Democratic Republic of Congo version 4.4.
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Nord Kivu Province in the Democratic Republic of Congo (DRC). The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC between the periods 2021 to 2023, settlement extents and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Chris Nnanatu. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain, Assane Gadiaga and Krishnaveni KS. Project oversight was done by Attila Lazar, Chris Nnanatu and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 8.0) (CIESIN, 2025).
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Modelled gridded population estimates for Bas-Uele Province in the Democratic Republic of Congo version 4.4.
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Bas-Uele Province in the Democratic Republic of Congo (DRC). The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC between the periods 2021 to 2023, settlement extents and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Chris Nnanatu. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain, Assane Gadiaga and Krishnaveni KS. Project oversight was done by Attila Lazar, Chris Nnanatu and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 8.0) (CIESIN, 2025).
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Uptake Pattern of Training Programs over Two Decades at an International Ophthalmic Training Institute in India
No full textRecognizing the urgent need to improve eye care services to address the increasing eye care burden globally, the World Health Organization (WHO) and International Agency for the Prevention of Blindness (IAPB) together launched a global initiative “VISION 2020: The Right to Sight” in 1999 with the goal to eliminate avoidable blindness by the year 2020. This initiative identified disease control, human resource development and infrastructure development as the three pillars to achieve the desired outcomes. The global action plan of WHO (2014 – 2019) also has emphasized the need of trained workforce in all the WHO regions for ensuring comprehensive eye care services. The overall shortage of workforce warrants a robust strategy to ensure sustainable training programmes for all cadres of eye care professionals. Recognizing this, Aravind Eye Care System (AECS) in India has developed an extensive set of training programmes over the past 40 years, for supplementary skills development for all members of the eye care team. The aim of this study was to evaluate the uptake pattern of long term and short-term structured certificate training programs offered at AECS during the last two decades (2000 – 2019)
Modelled gridded population estimates for Kwango Province in the Democratic Republic of Congo version 4.4.
No full textThis data release provides gridded population estimates (spatial resolution of 3 arc-seconds, approximately 100-metre grid cells) for Kwango Province in the Democratic Republic of Congo (DRC). The project team used the Pre-Distribution Registration Survey (PDRS) data from the National Malaria Control Programme (PNLP) collected as part of anti-malarial campaigns in the DRC between the periods 2021 to 2023, settlement extents and geospatial covariates to model and estimate population numbers at grid cell level using a Bayesian statistical hierarchical modelling framework. The approach facilitated simultaneous accounting for the multiple levels of variability within the data. It also allowed the quantification of uncertainties in parameter estimates. These model-based population estimates can be considered as most accurately representing the year 2023. Although the methods were robust enough to explicitly account for key random biases within the datasets, it is noted that systematic biases, which may arise from sources other than random errors within the observed data collection process, are most likely to remain.
These data were produced by the WorldPop Research Group at the University of Southampton. This work was part of the GRID3 – Phase 2 Scaling project, with funding from the Gates Foundation (INV-044979). Project partners included GRID3 Inc, the Center for Integrated Earth System Information (CIESIN) within the Columbia Climate School at Columbia University, and WorldPop at the University of Southampton. The final statistical modelling was designed, developed, and implemented by Chris Nnanatu. Data processing was done by Ortis Yankey with additional support from Heather Chamberlain, Assane Gadiaga and Krishnaveni KS. Project oversight was done by Attila Lazar, Chris Nnanatu and Andy Tatem. The PDRS data from the malaria insecticide treated net (ITN) distribution campaigns were collected, processed, anonymised and shared by the PNLP and its implementing partners. The settlement extent data was prepared and shared by CIESIN (2024). The data has been clipped to GRID3-CIESIN health area extent (version 8.0) (CIESIN, 2025).
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