Publications
1.
Jasper, Paul; Jochem, Warren C; Lambert-Porter, Emma; Naeem, Umer; Utazi, Chigozie Edson
Mapping the prevalence of severe acute malnutrition in Papua, Indonesia by using geostatistical models Journal Article
In: BMC Nutrition, vol. 8, no. 13, 2022.
Abstract | Links | BibTeX | Tags: Asia, Bayesian geostatistics, Demographic and Health Surveys, Indonesia, malnutrition, Papua
@article{nokey,
title = {Mapping the prevalence of severe acute malnutrition in Papua, Indonesia by using geostatistical models},
author = {Jasper, Paul and Jochem, Warren C and Lambert-Porter, Emma and Naeem, Umer and Utazi, Chigozie Edson},
doi = {https://doi.org/10.1186/s40795-022-00504-z},
year = {2022},
date = {2022-02-14},
urldate = {2022-02-14},
journal = {BMC Nutrition},
volume = {8},
number = {13},
abstract = {Severe acute malnutrition (SAM) is the most life-threatening form of malnutrition, and in 2019, approximately 14.3 million children under the age of 5 were considered to have SAM. The prevalence of child malnutrition is recorded through large-scale household surveys run at multi-year intervals. However, these surveys are expensive, yield estimates with high levels of aggregation, are run over large time intervals, and may show gaps in area coverage. Geospatial modelling approaches could address some of these challenges by combining geo-located survey data with geospatial data to produce mapped estimates that predict malnutrition risk in both surveyed and non-surveyed areas.
Methods
A secondary analysis of cluster-level program evaluation data (n = 123 primary sampling units) was performed to map severe acute malnutrition (SAM) in Papuan children under 2 years (0–23 months) of age with a spatial resolution of 1 × 1 km in Papua, Indonesia. The approach used Bayesian geostatistical modelling techniques and publicly available geospatial data layers.
Results
In Papua, Indonesia, SAM was predicted in geostatistical models by using six geospatial covariates related primarily to conditions of remoteness and inaccessibility. The predicted 1-km spatial resolution maps of SAM showed substantial spatial variation across the province. By combining the predicted rates of SAM with estimates of the population under 2 years of age, the prevalence of SAM in late 2018 was estimated to be around 15,000 children (95% CI 10,209–26,252). Further tests of the predicted levels suggested that in most areas of Papua, more than 5% of Papuan children under 2 years of age had SAM, while three districts likely had more than 15% of children with SAM.
Conclusions
Eradication of hunger and malnutrition remains a key development goal, and more spatially detailed data can guide efficient intervention strategies. The application of additional household survey datasets in geostatistical models is one way to improve the monitoring and timely estimation of populations at risk of malnutrition. Importantly, geospatial mapping can yield insights for both surveyed and non-surveyed areas and can be applied in low-income country contexts where data is scarce and data collection is expensive or regions are inaccessible.},
keywords = {Asia, Bayesian geostatistics, Demographic and Health Surveys, Indonesia, malnutrition, Papua},
pubstate = {published},
tppubtype = {article}
}
Severe acute malnutrition (SAM) is the most life-threatening form of malnutrition, and in 2019, approximately 14.3 million children under the age of 5 were considered to have SAM. The prevalence of child malnutrition is recorded through large-scale household surveys run at multi-year intervals. However, these surveys are expensive, yield estimates with high levels of aggregation, are run over large time intervals, and may show gaps in area coverage. Geospatial modelling approaches could address some of these challenges by combining geo-located survey data with geospatial data to produce mapped estimates that predict malnutrition risk in both surveyed and non-surveyed areas.
Methods
A secondary analysis of cluster-level program evaluation data (n = 123 primary sampling units) was performed to map severe acute malnutrition (SAM) in Papuan children under 2 years (0–23 months) of age with a spatial resolution of 1 × 1 km in Papua, Indonesia. The approach used Bayesian geostatistical modelling techniques and publicly available geospatial data layers.
Results
In Papua, Indonesia, SAM was predicted in geostatistical models by using six geospatial covariates related primarily to conditions of remoteness and inaccessibility. The predicted 1-km spatial resolution maps of SAM showed substantial spatial variation across the province. By combining the predicted rates of SAM with estimates of the population under 2 years of age, the prevalence of SAM in late 2018 was estimated to be around 15,000 children (95% CI 10,209–26,252). Further tests of the predicted levels suggested that in most areas of Papua, more than 5% of Papuan children under 2 years of age had SAM, while three districts likely had more than 15% of children with SAM.
Conclusions
Eradication of hunger and malnutrition remains a key development goal, and more spatially detailed data can guide efficient intervention strategies. The application of additional household survey datasets in geostatistical models is one way to improve the monitoring and timely estimation of populations at risk of malnutrition. Importantly, geospatial mapping can yield insights for both surveyed and non-surveyed areas and can be applied in low-income country contexts where data is scarce and data collection is expensive or regions are inaccessible.
Methods
A secondary analysis of cluster-level program evaluation data (n = 123 primary sampling units) was performed to map severe acute malnutrition (SAM) in Papuan children under 2 years (0–23 months) of age with a spatial resolution of 1 × 1 km in Papua, Indonesia. The approach used Bayesian geostatistical modelling techniques and publicly available geospatial data layers.
Results
In Papua, Indonesia, SAM was predicted in geostatistical models by using six geospatial covariates related primarily to conditions of remoteness and inaccessibility. The predicted 1-km spatial resolution maps of SAM showed substantial spatial variation across the province. By combining the predicted rates of SAM with estimates of the population under 2 years of age, the prevalence of SAM in late 2018 was estimated to be around 15,000 children (95% CI 10,209–26,252). Further tests of the predicted levels suggested that in most areas of Papua, more than 5% of Papuan children under 2 years of age had SAM, while three districts likely had more than 15% of children with SAM.
Conclusions
Eradication of hunger and malnutrition remains a key development goal, and more spatially detailed data can guide efficient intervention strategies. The application of additional household survey datasets in geostatistical models is one way to improve the monitoring and timely estimation of populations at risk of malnutrition. Importantly, geospatial mapping can yield insights for both surveyed and non-surveyed areas and can be applied in low-income country contexts where data is scarce and data collection is expensive or regions are inaccessible.
2.
Wang, Li-Ping; Yuan, Yang; Liu, Ying-Le; Lu, Qing-Bin; Shi, Lu-Sha; Ren, Xiang; Zhou, Shi-Xia; Zhang, Hai-Yang; Zhang, Xiao-Ai; Wang, Xin; Wang, Yi-Fei; Lin, Sheng-Hong; Zhang, Cui-Hong; Geng, Meng-Jie; Li, Jun; Zhao, Shi-Wen; Yi, Zhi-Gang; Chen, Xiao; Yang, Zuo-Sen; Meng, Lei; Wang, Xin-Hua; Cui, Ai-Li; Lai, Sheng-Jie; and others,
Etiological and epidemiological features of acute meningitis or encephalitis in China: a nationwide active surveillance study Journal Article
In: The Lancet Regional Health-Western Pacific, vol. 20, no. 100361, 2022.
Abstract | Links | BibTeX | Tags: Asia, China, Demographic and Health Surveys, infectious disease
@article{nokey,
title = {Etiological and epidemiological features of acute meningitis or encephalitis in China: a nationwide active surveillance study},
author = {Wang, Li-Ping and Yuan, Yang and Liu, Ying-Le and Lu, Qing-Bin and Shi, Lu-Sha and Ren, Xiang and Zhou, Shi-Xia and Zhang, Hai-Yang and Zhang, Xiao-Ai and Wang, Xin and Wang, Yi-Fei and Lin, Sheng-Hong and Zhang, Cui-Hong and Geng, Meng-Jie and Li, Jun and Zhao, Shi-Wen and Yi, Zhi-Gang and Chen, Xiao and Yang, Zuo-Sen and Meng, Lei and Wang, Xin-Hua and Cui, Ai-Li and Lai, Sheng-Jie and and others},
doi = {https://doi.org/10.1016/j.lanwpc.2021.100361},
year = {2022},
date = {2022-01-03},
urldate = {2022-01-03},
journal = {The Lancet Regional Health-Western Pacific},
volume = {20},
number = {100361},
abstract = {Acute meningitis or encephalitis (AME) results from a neurological infection causing high case fatality and severe sequelae. AME lacked comprehensive surveillance in China.
Methods
Nation-wide surveillance of all-age patients with AME syndromes was conducted in 144 sentinel hospitals of 29 provinces in China. Eleven AME-causative viral and bacterial pathogens were tested with multiple diagnostic methods.
Findings
Between 2009 and 2018, 20,454 AME patients were recruited for tests. Based on 9,079 patients with all-four-virus tested, 28.43% (95% CI: 27.50%‒29.36%) of them had at least one virus-positive detection. Enterovirus was the most frequently determined virus in children <18 years, herpes simplex virus and Japanese encephalitis virus were the most frequently determined in 18−59 and ≥60 years age groups, respectively. Based on 6,802 patients with all-seven-bacteria tested, 4.43% (95% CI: 3.94%‒4.91%) had at least one bacteria-positive detection, Streptococcus pneumoniae and Neisseria meningitidis were the leading bacterium in children aged <5 years and 5−17 years, respectively. Staphylococcus aureus was the most frequently detected in adults aged 18−59 and ≥60 years. The pathogen spectrum also differed statistically significantly between northern and southern China. Joinpoint analysis revealed age-specific positive rates, with enterovirus, herpes simplex virus and mumps virus peaking at 3−6 years old, while Japanese encephalitis virus peaked in the ≥60 years old. As age increased, the positive rate for Streptococcus pneumoniae and Escherichia coli statistically significantly decreased, while for Staphylococcus aureus and Streptococcus suis it increased.
Interpretation
The current findings allow enhanced identification of the predominant AME-related pathogen candidates for diagnosis in clinical practice and more targeted application of prevention and control measures in China, and a possible reassessment of vaccination strategy.},
keywords = {Asia, China, Demographic and Health Surveys, infectious disease},
pubstate = {published},
tppubtype = {article}
}
Acute meningitis or encephalitis (AME) results from a neurological infection causing high case fatality and severe sequelae. AME lacked comprehensive surveillance in China.
Methods
Nation-wide surveillance of all-age patients with AME syndromes was conducted in 144 sentinel hospitals of 29 provinces in China. Eleven AME-causative viral and bacterial pathogens were tested with multiple diagnostic methods.
Findings
Between 2009 and 2018, 20,454 AME patients were recruited for tests. Based on 9,079 patients with all-four-virus tested, 28.43% (95% CI: 27.50%‒29.36%) of them had at least one virus-positive detection. Enterovirus was the most frequently determined virus in children <18 years, herpes simplex virus and Japanese encephalitis virus were the most frequently determined in 18−59 and ≥60 years age groups, respectively. Based on 6,802 patients with all-seven-bacteria tested, 4.43% (95% CI: 3.94%‒4.91%) had at least one bacteria-positive detection, Streptococcus pneumoniae and Neisseria meningitidis were the leading bacterium in children aged <5 years and 5−17 years, respectively. Staphylococcus aureus was the most frequently detected in adults aged 18−59 and ≥60 years. The pathogen spectrum also differed statistically significantly between northern and southern China. Joinpoint analysis revealed age-specific positive rates, with enterovirus, herpes simplex virus and mumps virus peaking at 3−6 years old, while Japanese encephalitis virus peaked in the ≥60 years old. As age increased, the positive rate for Streptococcus pneumoniae and Escherichia coli statistically significantly decreased, while for Staphylococcus aureus and Streptococcus suis it increased.
Interpretation
The current findings allow enhanced identification of the predominant AME-related pathogen candidates for diagnosis in clinical practice and more targeted application of prevention and control measures in China, and a possible reassessment of vaccination strategy.
Methods
Nation-wide surveillance of all-age patients with AME syndromes was conducted in 144 sentinel hospitals of 29 provinces in China. Eleven AME-causative viral and bacterial pathogens were tested with multiple diagnostic methods.
Findings
Between 2009 and 2018, 20,454 AME patients were recruited for tests. Based on 9,079 patients with all-four-virus tested, 28.43% (95% CI: 27.50%‒29.36%) of them had at least one virus-positive detection. Enterovirus was the most frequently determined virus in children <18 years, herpes simplex virus and Japanese encephalitis virus were the most frequently determined in 18−59 and ≥60 years age groups, respectively. Based on 6,802 patients with all-seven-bacteria tested, 4.43% (95% CI: 3.94%‒4.91%) had at least one bacteria-positive detection, Streptococcus pneumoniae and Neisseria meningitidis were the leading bacterium in children aged <5 years and 5−17 years, respectively. Staphylococcus aureus was the most frequently detected in adults aged 18−59 and ≥60 years. The pathogen spectrum also differed statistically significantly between northern and southern China. Joinpoint analysis revealed age-specific positive rates, with enterovirus, herpes simplex virus and mumps virus peaking at 3−6 years old, while Japanese encephalitis virus peaked in the ≥60 years old. As age increased, the positive rate for Streptococcus pneumoniae and Escherichia coli statistically significantly decreased, while for Staphylococcus aureus and Streptococcus suis it increased.
Interpretation
The current findings allow enhanced identification of the predominant AME-related pathogen candidates for diagnosis in clinical practice and more targeted application of prevention and control measures in China, and a possible reassessment of vaccination strategy.
3.
Jia, Peng; Sankoh, Osman; Tatem, Andrew J.
Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network Journal Article
In: Health & Place, vol. 36, pp. 88-96, 2015, ISSN: 1353-8292.
Abstract | Links | BibTeX | Tags: Africa, Asia, Demographic surveillance sites, Health, Remote sensing
@article{JIA201588,
title = {Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network},
author = {Peng Jia and Osman Sankoh and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S1353829215001379},
doi = {https://doi.org/10.1016/j.healthplace.2015.09.009},
issn = {1353-8292},
year = {2015},
date = {2015-01-01},
journal = {Health & Place},
volume = {36},
pages = {88-96},
abstract = {The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) has produced reliable longitudinal data about the lives of people in low- and middle-income countries (LMICs) through a global network of health and demographic surveillance system (HDSS) sites. Since reliable demographic data are scarce across many LMICs, we examine the environmental and socioeconomic (ES) similarities between existing HDSS sites and the rest of the LMICs. The HDSS sites were hierarchically grouped by the similarity of their ES conditions to quantify the ES variability between sites. The entire Africa and Asia region was classified to identify which regions were most similar to existing sites, based on available ES data. Results show that the current INDEPTH network architecture does a good job in representing ES conditions, but that great heterogeneities exist, even within individual countries. The results provide valuable information in determining the confidence with which relationships derived from present HDSS sites can be broadly extended to other areas, and to highlight areas where the new HDSS sites would improve significantly the ES coverage of the network.},
keywords = {Africa, Asia, Demographic surveillance sites, Health, Remote sensing},
pubstate = {published},
tppubtype = {article}
}
The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) has produced reliable longitudinal data about the lives of people in low- and middle-income countries (LMICs) through a global network of health and demographic surveillance system (HDSS) sites. Since reliable demographic data are scarce across many LMICs, we examine the environmental and socioeconomic (ES) similarities between existing HDSS sites and the rest of the LMICs. The HDSS sites were hierarchically grouped by the similarity of their ES conditions to quantify the ES variability between sites. The entire Africa and Asia region was classified to identify which regions were most similar to existing sites, based on available ES data. Results show that the current INDEPTH network architecture does a good job in representing ES conditions, but that great heterogeneities exist, even within individual countries. The results provide valuable information in determining the confidence with which relationships derived from present HDSS sites can be broadly extended to other areas, and to highlight areas where the new HDSS sites would improve significantly the ES coverage of the network.
