Publications
1.
Utazi, C Edson; Pannell, Oliver; Aheto, Justice MK; Wigley, Adelle; Tejedor-Garavito, Natalia; Wunderlich, Josh; Hagedorn, Brittany; Hogan, Dan; and Tatem, Andrew J.
Assessing the characteristics of un- and under-vaccinated children in low- and middle-income countries: A multi-level cross-sectional study Journal Article
In: PLoS Global Public Health, vol. 2, no. 4, pp. e0000244, 2022.
Abstract | Links | BibTeX | Tags: Demographic and Health Surveys, LMICs, vaccination
@article{nokey,
title = {Assessing the characteristics of un- and under-vaccinated children in low- and middle-income countries: A multi-level cross-sectional study},
author = {Utazi, C Edson and Pannell, Oliver and Aheto, Justice MK and Wigley, Adelle and Tejedor-Garavito, Natalia and Wunderlich, Josh and Hagedorn, Brittany and Hogan, Dan and and Tatem, Andrew J. },
doi = {https://doi.org/10.1371/journal.pgph.0000244},
year = {2022},
date = {2022-04-27},
urldate = {2022-04-27},
journal = {PLoS Global Public Health},
volume = {2},
number = {4},
pages = {e0000244},
abstract = {Achieving equity in vaccination coverage has been a critical priority within the global health community. Despite increased efforts recently, certain populations still have a high proportion of un- and under-vaccinated children in many low- and middle-income countries (LMICs). These populations are often assumed to reside in remote-rural areas, urban slums and conflict-affected areas. Here, we investigate the effects of these key community-level factors, alongside a wide range of other individual, household and community level factors, on vaccination coverage. Using geospatial datasets, including cross-sectional data from the most recent Demographic and Health Surveys conducted between 2008 and 2018 in nine LMICs, we fitted Bayesian multi-level binary logistic regression models to determine key community-level and other factors significantly associated with non- and under-vaccination. We analyzed the odds of receipt of the first doses of diphtheria-tetanus-pertussis (DTP1) vaccine and measles-containing vaccine (MCV1), and receipt of all three recommended DTP doses (DTP3) independently, in children aged 12–23 months. In bivariate analyses, we found that remoteness increased the odds of non- and under-vaccination in nearly all the study countries. We also found evidence that living in conflict and urban slum areas reduced the odds of vaccination, but not in most cases as expected. However, the odds of vaccination were more likely to be lower in urban slums than formal urban areas. Our multivariate analyses revealed that the key community variables–remoteness, conflict and urban slum–were sometimes associated with non- and under-vaccination, but they were not frequently predictors of these outcomes after controlling for other factors. Individual and household factors such as maternal utilization of health services, maternal education and ethnicity, were more common predictors of vaccination. Reaching the Immunisation Agenda 2030 target of reducing the number of zero-dose children by 50% by 2030 will require country tailored analyses and strategies to identify and reach missed communities with reliable immunisation services.},
keywords = {Demographic and Health Surveys, LMICs, vaccination},
pubstate = {published},
tppubtype = {article}
}
Achieving equity in vaccination coverage has been a critical priority within the global health community. Despite increased efforts recently, certain populations still have a high proportion of un- and under-vaccinated children in many low- and middle-income countries (LMICs). These populations are often assumed to reside in remote-rural areas, urban slums and conflict-affected areas. Here, we investigate the effects of these key community-level factors, alongside a wide range of other individual, household and community level factors, on vaccination coverage. Using geospatial datasets, including cross-sectional data from the most recent Demographic and Health Surveys conducted between 2008 and 2018 in nine LMICs, we fitted Bayesian multi-level binary logistic regression models to determine key community-level and other factors significantly associated with non- and under-vaccination. We analyzed the odds of receipt of the first doses of diphtheria-tetanus-pertussis (DTP1) vaccine and measles-containing vaccine (MCV1), and receipt of all three recommended DTP doses (DTP3) independently, in children aged 12–23 months. In bivariate analyses, we found that remoteness increased the odds of non- and under-vaccination in nearly all the study countries. We also found evidence that living in conflict and urban slum areas reduced the odds of vaccination, but not in most cases as expected. However, the odds of vaccination were more likely to be lower in urban slums than formal urban areas. Our multivariate analyses revealed that the key community variables–remoteness, conflict and urban slum–were sometimes associated with non- and under-vaccination, but they were not frequently predictors of these outcomes after controlling for other factors. Individual and household factors such as maternal utilization of health services, maternal education and ethnicity, were more common predictors of vaccination. Reaching the Immunisation Agenda 2030 target of reducing the number of zero-dose children by 50% by 2030 will require country tailored analyses and strategies to identify and reach missed communities with reliable immunisation services.
2.
Boo, Gianluca; Darin, Edith; Leasure, Douglas R; Dooley, Claire A; Chamberlain, Heather R; and Lázár, Attila N; Tschirhart, Kevin; Sinai, Cyrus; Hoff, Nicole A; Fuller, Trevon
High-resolution population estimation using household survey data and building footprints Journal Article
In: Nature Communications, vol. 13, no. 1330, 2022.
Abstract | Links | BibTeX | Tags: Bayesian inference, Demographic and Health Surveys, Population
@article{nokey,
title = {High-resolution population estimation using household survey data and building footprints},
author = {Boo, Gianluca and Darin, Edith and Leasure, Douglas R and Dooley, Claire A and Chamberlain, Heather R and and Lázár, Attila N and Tschirhart, Kevin and Sinai, Cyrus and Hoff, Nicole A and Fuller, Trevon},
doi = {https://doi.org/10.1038/s41467-022-29094-x},
year = {2022},
date = {2022-03-14},
urldate = {2022-03-14},
journal = {Nature Communications},
volume = {13},
number = {1330},
abstract = {The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.},
keywords = {Bayesian inference, Demographic and Health Surveys, Population},
pubstate = {published},
tppubtype = {article}
}
The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.
3.
Pezzulo, Carla; Alegana, Victor A; Christensen, Andrew; Bakari, Omar; Tatem, Andrew
Understanding factors associated with attending secondary school in Tanzania using household survey data Journal Article
In: PLoS ONE, vol. 17, no. 2, 2022.
Abstract | Links | BibTeX | Tags: Africa, Demographic and Health Surveys, education, SDG4, Tanzania
@article{nokey,
title = {Understanding factors associated with attending secondary school in Tanzania using household survey data},
author = {Carla Pezzulo and Victor A Alegana and Andrew Christensen and Omar Bakari and Andrew Tatem},
doi = {http://dx.doi.org/10.1371/journal.pone.0263734},
year = {2022},
date = {2022-02-25},
urldate = {2022-02-25},
journal = {PLoS ONE},
volume = {17},
number = {2},
abstract = {Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3.1). Here we aim to understand drivers of school attendance using one country in East Africa as an example.
Methods
Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework.
Results
Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance.
Conclusions
Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.},
keywords = {Africa, Demographic and Health Surveys, education, SDG4, Tanzania},
pubstate = {published},
tppubtype = {article}
}
Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3.1). Here we aim to understand drivers of school attendance using one country in East Africa as an example.
Methods
Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework.
Results
Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance.
Conclusions
Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.
Methods
Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework.
Results
Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance.
Conclusions
Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.
4.
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.
5.
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.
6.
Utazi, C. Edson; Thorley, Julia; Alegana, Victor A.; Ferrari, Matthew J.; Takahashi, Saki; Metcalf, C. Jessica E.; Lessler, Justin; Tatem, Andrew J.
High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries Journal Article
In: Vaccine, vol. 36, no. 12, pp. 1583-1591, 2018, ISSN: 0264-410X.
Abstract | Links | BibTeX | Tags: Bayesian geostatistics, Coverage heterogeneities, Demographic and Health Surveys, Measles vaccine
@article{UTAZI20181583,
title = {High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries},
author = {C. Edson Utazi and Julia Thorley and Victor A. Alegana and Matthew J. Ferrari and Saki Takahashi and C. Jessica E. Metcalf and Justin Lessler and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S0264410X18301944},
doi = {https://doi.org/10.1016/j.vaccine.2018.02.020},
issn = {0264-410X},
year = {2018},
date = {2018-01-01},
journal = {Vaccine},
volume = {36},
number = {12},
pages = {1583-1591},
abstract = {Background
The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and ‘coldspots’ of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized.
Methods
Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods.
Results
Measles vaccination coverage was found to be strongly predicted by just 4–5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets.
Conclusion
The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.},
keywords = {Bayesian geostatistics, Coverage heterogeneities, Demographic and Health Surveys, Measles vaccine},
pubstate = {published},
tppubtype = {article}
}
Background
The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and ‘coldspots’ of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized.
Methods
Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods.
Results
Measles vaccination coverage was found to be strongly predicted by just 4–5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets.
Conclusion
The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.
The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and ‘coldspots’ of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized.
Methods
Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods.
Results
Measles vaccination coverage was found to be strongly predicted by just 4–5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets.
Conclusion
The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.
