Publications
21.
Liu, Xiaobo; Guo, Pi; Liang, Ying; Chen, Chuanwei; Sun, Jince; Wu, Haisheng; Su, Tianyun; Lai, Shengjie; Liu, Qiyong
Lessons from failure to success on malaria elimination in the Huai River Basin in China Journal Article
In: bmj, vol. 389, 2025.
Abstract | Links | BibTeX | Tags:
@article{liu2025lessons,
title = {Lessons from failure to success on malaria elimination in the Huai River Basin in China},
author = {Xiaobo Liu and Pi Guo and Ying Liang and Chuanwei Chen and Jince Sun and Haisheng Wu and Tianyun Su and Shengjie Lai and Qiyong Liu},
url = {https://doi.org/10.1136/bmj-2024-080658},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {bmj},
volume = {389},
publisher = {British Medical Journal Publishing Group},
abstract = {Malaria is still a significant public health threat. After generations of control efforts, China was certified as a malaria-free country by the World Health Organization in June 2021. Not without its challenges, China’s experience of eliminating malaria is informative for elimination strategies in other countries and regions.
The Huai River Basin (HRB) in central China was the epicentre of two epidemics in the 1960s and 1970s, accounting for 93.1% and 91.2% of total reported cases in China, respectively.5 A comprehensive control strategy focused on eliminating infection sources, supplemented by integrated mosquito control, was adopted, such that by 1987 most regions in the HRB had achieved “basic malaria elimination”—with incidence rates below 1/10 000.
Unfortunately, malaria resurged in the HRB in 2003 and peaked in 2006. At that time, 62.45% of China’s total cases (60 193 cases) were in the HRB.5 As a result, the government’s leadership in malaria control was re-enforced through the implementation of comprehensive measures, such as mass drug administration, case management,6 and sustainable vector management.7 Consequently, the incidence of malaria in the HRB decreased significantly. No indigenous malaria has occurred in the HRB since the end of 2012.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Malaria is still a significant public health threat. After generations of control efforts, China was certified as a malaria-free country by the World Health Organization in June 2021. Not without its challenges, China’s experience of eliminating malaria is informative for elimination strategies in other countries and regions.
The Huai River Basin (HRB) in central China was the epicentre of two epidemics in the 1960s and 1970s, accounting for 93.1% and 91.2% of total reported cases in China, respectively.5 A comprehensive control strategy focused on eliminating infection sources, supplemented by integrated mosquito control, was adopted, such that by 1987 most regions in the HRB had achieved “basic malaria elimination”—with incidence rates below 1/10 000.
Unfortunately, malaria resurged in the HRB in 2003 and peaked in 2006. At that time, 62.45% of China’s total cases (60 193 cases) were in the HRB.5 As a result, the government’s leadership in malaria control was re-enforced through the implementation of comprehensive measures, such as mass drug administration, case management,6 and sustainable vector management.7 Consequently, the incidence of malaria in the HRB decreased significantly. No indigenous malaria has occurred in the HRB since the end of 2012.
The Huai River Basin (HRB) in central China was the epicentre of two epidemics in the 1960s and 1970s, accounting for 93.1% and 91.2% of total reported cases in China, respectively.5 A comprehensive control strategy focused on eliminating infection sources, supplemented by integrated mosquito control, was adopted, such that by 1987 most regions in the HRB had achieved “basic malaria elimination”—with incidence rates below 1/10 000.
Unfortunately, malaria resurged in the HRB in 2003 and peaked in 2006. At that time, 62.45% of China’s total cases (60 193 cases) were in the HRB.5 As a result, the government’s leadership in malaria control was re-enforced through the implementation of comprehensive measures, such as mass drug administration, case management,6 and sustainable vector management.7 Consequently, the incidence of malaria in the HRB decreased significantly. No indigenous malaria has occurred in the HRB since the end of 2012.
22.
Liu, Qiyong; Wang, Yiguan; Liu, Xiaobo; Hay, Simon I; Lai, Shengjie
Stratified sustainable vector control strategies and measures for malaria control and elimination in China: a 70 year journey Journal Article
In: bmj, vol. 389, 2025.
Abstract | Links | BibTeX | Tags:
@article{liu2025stratified,
title = {Stratified sustainable vector control strategies and measures for malaria control and elimination in China: a 70 year journey},
author = {Qiyong Liu and Yiguan Wang and Xiaobo Liu and Simon I Hay and Shengjie Lai},
url = {https://doi.org/10.1136/bmj-2024-080656},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {bmj},
volume = {389},
publisher = {British Medical Journal Publishing Group},
abstract = {Malaria is a mosquito-borne infectious disease that significantly threatens global health. Considerable efforts and investments have led to a steady decline in incidence and mortality over recent decades. However, 249 million cases were reported from 85 countries and areas in 2022, resulting in 608 000 deaths.1 Notably, approximately 95% of these cases and deaths occurred in the African region. China has had a heavy disease burden of malaria for more than 3000 years, evidenced by the Chinese character for malaria—疟 or nüè—discovered on oracle bone and bronze inscriptions from between 1562 and 1066 BC.2 Chinese medicine has historically been used to treat people with malaria. However, in the 1940s, before the foundation of the People’s Republic of China, the burden of malaria was still immense, with an estimated 30 million annual cases, more than 90% of the population at risk, and a fatality rate of approximately 1%.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Malaria is a mosquito-borne infectious disease that significantly threatens global health. Considerable efforts and investments have led to a steady decline in incidence and mortality over recent decades. However, 249 million cases were reported from 85 countries and areas in 2022, resulting in 608 000 deaths.1 Notably, approximately 95% of these cases and deaths occurred in the African region. China has had a heavy disease burden of malaria for more than 3000 years, evidenced by the Chinese character for malaria—疟 or nüè—discovered on oracle bone and bronze inscriptions from between 1562 and 1066 BC.2 Chinese medicine has historically been used to treat people with malaria. However, in the 1940s, before the foundation of the People’s Republic of China, the burden of malaria was still immense, with an estimated 30 million annual cases, more than 90% of the population at risk, and a fatality rate of approximately 1%.
23.
Seidler, Valentin; Utazi, Edson C; Finaret, Amelia B; Luckeneder, Sebastian; Zens, Gregor; Bodarenko, Maksym; Smith, Abigail W; Bradley, Sarah EK; Tatem, Andrew J; Webb, Patrick
Subnational variations in the quality of household survey data in sub-Saharan Africa Journal Article
In: Nature Communications, vol. 16, no. 1, pp. 3771, 2025.
Abstract | Links | BibTeX | Tags:
@article{seidler2025subnational,
title = {Subnational variations in the quality of household survey data in sub-Saharan Africa},
author = {Valentin Seidler and Edson C Utazi and Amelia B Finaret and Sebastian Luckeneder and Gregor Zens and Maksym Bodarenko and Abigail W Smith and Sarah EK Bradley and Andrew J Tatem and Patrick Webb},
url = {https://doi.org/10.1038/s41467-025-58776-5},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Nature Communications},
volume = {16},
number = {1},
pages = {3771},
publisher = {Nature Publishing Group UK London},
abstract = {Nationally representative household surveys collect geocoded data that are vital to tackling health and other development challenges in sub-Saharan Africa. Scholars and practitioners generally assume uniform data quality but subnational variation of errors in household data has never been investigated at high spatial resolution. Here, we explore within-country variation in the quality of most recent household surveys for 35 African countries at 5 × 5 km resolution and district levels. Findings show a striking heterogeneity in the subnational distribution of sampling and measurement errors. Data quality degrades with greater distance from settlements, and missing data as well as imprecision of estimates add to quality problems that can result in vulnerable remote populations receiving less than optimal services and needed resources. Our easy-to-access geospatial estimates of survey data quality highlight the need to invest in better targeting of household surveys in remote areas.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Nationally representative household surveys collect geocoded data that are vital to tackling health and other development challenges in sub-Saharan Africa. Scholars and practitioners generally assume uniform data quality but subnational variation of errors in household data has never been investigated at high spatial resolution. Here, we explore within-country variation in the quality of most recent household surveys for 35 African countries at 5 × 5 km resolution and district levels. Findings show a striking heterogeneity in the subnational distribution of sampling and measurement errors. Data quality degrades with greater distance from settlements, and missing data as well as imprecision of estimates add to quality problems that can result in vulnerable remote populations receiving less than optimal services and needed resources. Our easy-to-access geospatial estimates of survey data quality highlight the need to invest in better targeting of household surveys in remote areas.
24.
Chamberlain, Heather R; Pollard, Derek; Winters, Anna; Renn, Silvia; Borkovska, Olena; Musuka, Chisenga Abel; Membele, Garikai; Lazar, Attila N; Tatem, Andrew J
In: International Journal of Health Geographics, vol. 24, no. 1, pp. 13, 2025.
Abstract | Links | BibTeX | Tags:
@article{chamberlain2025assessing,
title = {Assessing the impact of building footprint dataset choice for health programme planning: a case study of indoor residual spraying (IRS) in Zambia},
author = {Heather R Chamberlain and Derek Pollard and Anna Winters and Silvia Renn and Olena Borkovska and Chisenga Abel Musuka and Garikai Membele and Attila N Lazar and Andrew J Tatem},
url = {https://doi.org/10.1186/s12942-025-00398-7},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {International Journal of Health Geographics},
volume = {24},
number = {1},
pages = {13},
publisher = {Springer},
abstract = {The increasing availability globally of building footprint datasets has brought new opportunities to support a geographic approach to health programme planning. This is particularly acute in settings with high disease burdens but limited geospatial data available to support targeted planning. The comparability of building footprint datasets has recently started to be explored, but the impact of utilising a particular dataset in analyses to support decision making for health programme planning has not been studied. In this study, we quantify the impact of utilising four different building footprint datasets in analyses to support health programme planning, with an example of malaria vector control initiatives in Zambia.
Using the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.
We show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.
The results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The increasing availability globally of building footprint datasets has brought new opportunities to support a geographic approach to health programme planning. This is particularly acute in settings with high disease burdens but limited geospatial data available to support targeted planning. The comparability of building footprint datasets has recently started to be explored, but the impact of utilising a particular dataset in analyses to support decision making for health programme planning has not been studied. In this study, we quantify the impact of utilising four different building footprint datasets in analyses to support health programme planning, with an example of malaria vector control initiatives in Zambia.
Using the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.
We show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.
The results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.
Using the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.
We show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.
The results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.
25.
Nnanatu, Chibuzor Christopher; Bonnie, Amy; Joseph, Josiah; Yankey, Ortis; Cihan, Duygu; Gadiaga, Assane; Voepel, Hal; Abbott, Thomas; Chamberlain, Heather R; Tia, Mercedita; others,
Estimating small area population from health intervention campaign surveys and partially observed settlement data Journal Article
In: Nature Communications, vol. 16, no. 1, pp. 4951, 2025.
Abstract | Links | BibTeX | Tags:
@article{nnanatu2025estimating,
title = {Estimating small area population from health intervention campaign surveys and partially observed settlement data},
author = {Chibuzor Christopher Nnanatu and Amy Bonnie and Josiah Joseph and Ortis Yankey and Duygu Cihan and Assane Gadiaga and Hal Voepel and Thomas Abbott and Heather R Chamberlain and Mercedita Tia and others},
url = {https://doi.org/10.1038/s41467-025-59862-4},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Nature Communications},
volume = {16},
number = {1},
pages = {4951},
publisher = {Nature Publishing Group UK London},
abstract = {Effective governance requires timely and reliable small area population counts. Geospatial modelling approaches which utilise bespoke microcensus surveys and satellite-derived settlement maps and other spatial datasets have been developed to fill population data gaps in countries where censuses are outdated and incomplete. However, logistics and costs of microcensus surveys and tree canopy or cloud cover obscuring settlements in satellite images limit its wider applications in tropical rural settings. Here, we present a two-step Bayesian hierarchical modelling approach that can integrate routinely collected health intervention campaign data and partially observed settlement data to produce reliable small area population estimates. Reductions in relative error rates were 32–73% in a simulation study, and ~32% when applied to malaria survey data in Papua New Guinea. The results highlight the value of demographic data routinely collected through health intervention campaigns or household surveys for improving small area population estimates, and how biases introduced by satellite data limitations can be overcome.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Effective governance requires timely and reliable small area population counts. Geospatial modelling approaches which utilise bespoke microcensus surveys and satellite-derived settlement maps and other spatial datasets have been developed to fill population data gaps in countries where censuses are outdated and incomplete. However, logistics and costs of microcensus surveys and tree canopy or cloud cover obscuring settlements in satellite images limit its wider applications in tropical rural settings. Here, we present a two-step Bayesian hierarchical modelling approach that can integrate routinely collected health intervention campaign data and partially observed settlement data to produce reliable small area population estimates. Reductions in relative error rates were 32–73% in a simulation study, and ~32% when applied to malaria survey data in Papua New Guinea. The results highlight the value of demographic data routinely collected through health intervention campaigns or household surveys for improving small area population estimates, and how biases introduced by satellite data limitations can be overcome.
26.
Wariri, Oghenebrume; Utazi, Chigozie Edson; Okomo, Uduak; Dotse-Gborgbortsi, Winfred; Sogur, Malick; Fofana, Sidat; Murray, Kris A.; Grundy, Chris; Kampmann, Beate
Multi-level determinants of timely routine childhood vaccinations in The Gambia: Findings from a nationwide analysis Journal Article
In: Vaccine, vol. 43, 2025.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Multi-level determinants of timely routine childhood vaccinations in The Gambia: Findings from a nationwide analysis},
author = {Oghenebrume Wariri and Chigozie Edson Utazi and Uduak Okomo and Winfred Dotse-Gborgbortsi and Malick Sogur and Sidat Fofana and Kris A. Murray and Chris Grundy and Beate Kampmann },
url = {https://doi.org/10.1016/j.vaccine.2024.126500},
year = {2025},
date = {2025-01-01},
journal = {Vaccine},
volume = {43},
abstract = {Achieving the ambitious goals of the Immunisation Agenda 2030 (IA2030) requires a deeper understanding of factors influencing under-vaccination, including timely vaccination. This study investigates the demand- and supply-side determinants influencing the timely uptake of key childhood vaccines scheduled throughout the first year of life in The Gambia.
We used two nationally-representative datasets: the 2019–20 Gambian Demographic and Health Survey and the 2019 national immunisation facility mapping. Using Bayesian multi-level binary logistic regression models, we identified key factors significantly associated with timely vaccination for five key vaccines: birth dose of hepatitis-B (HepB0), first, second, and third doses of the pentavalent vaccine (Penta1, Penta2, Penta3), and first-dose of measles-containing vaccine (MCV1) in children
We found that demand-side factors, such as ethnicity, household wealth status, maternal education, maternal parity, and the duration of the household's residency in its current location, were the most common drivers of timely childhood vaccination. However, supply-side factors such as travel time to the nearest immunisation clinic, availability of cold-storage and staffing numbers in the nearest immunisation clinic were also significant determinants. Furthermore, the determinants varied across specific vaccines and the timing of doses. For example, delivery in a health facility (aOR = 1.58, 95 %CI: 1.02–2.53), living less than 30 min (aOR = 2.11, 95 %CI: 1.2–8.84) and living between 30 and 60 min (aOR = 3.68, 95 %CI: 1.1–14.99) from a fixed-immunisation clinic was associated with timely HepB0, a time-sensitive vaccine that must be administered within 24 h of birth. On the other hand, children who received Penta1 and Penta2 on time were three- to five-fold more likely to receive subsequent doses on time (Penta2 and Penta3, respectively). Finally, proximity to an immunisation facility with functional vaccine cold-storage was a significant supply-side determinant of timely MCV1 (aOR = 1.4, 95 %CI: 1.09–1.99).
These findings provide valuable insights for programme managers and policymakers. By prioritising interventions and allocating scarce resources based on these identified determinants, they can maximize their impact and ensure children in The Gambia receive timely vaccinations throughout their first year of life, contributing to IA2030 goals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Achieving the ambitious goals of the Immunisation Agenda 2030 (IA2030) requires a deeper understanding of factors influencing under-vaccination, including timely vaccination. This study investigates the demand- and supply-side determinants influencing the timely uptake of key childhood vaccines scheduled throughout the first year of life in The Gambia.
We used two nationally-representative datasets: the 2019–20 Gambian Demographic and Health Survey and the 2019 national immunisation facility mapping. Using Bayesian multi-level binary logistic regression models, we identified key factors significantly associated with timely vaccination for five key vaccines: birth dose of hepatitis-B (HepB0), first, second, and third doses of the pentavalent vaccine (Penta1, Penta2, Penta3), and first-dose of measles-containing vaccine (MCV1) in children
We found that demand-side factors, such as ethnicity, household wealth status, maternal education, maternal parity, and the duration of the household's residency in its current location, were the most common drivers of timely childhood vaccination. However, supply-side factors such as travel time to the nearest immunisation clinic, availability of cold-storage and staffing numbers in the nearest immunisation clinic were also significant determinants. Furthermore, the determinants varied across specific vaccines and the timing of doses. For example, delivery in a health facility (aOR = 1.58, 95 %CI: 1.02–2.53), living less than 30 min (aOR = 2.11, 95 %CI: 1.2–8.84) and living between 30 and 60 min (aOR = 3.68, 95 %CI: 1.1–14.99) from a fixed-immunisation clinic was associated with timely HepB0, a time-sensitive vaccine that must be administered within 24 h of birth. On the other hand, children who received Penta1 and Penta2 on time were three- to five-fold more likely to receive subsequent doses on time (Penta2 and Penta3, respectively). Finally, proximity to an immunisation facility with functional vaccine cold-storage was a significant supply-side determinant of timely MCV1 (aOR = 1.4, 95 %CI: 1.09–1.99).
These findings provide valuable insights for programme managers and policymakers. By prioritising interventions and allocating scarce resources based on these identified determinants, they can maximize their impact and ensure children in The Gambia receive timely vaccinations throughout their first year of life, contributing to IA2030 goals.
We used two nationally-representative datasets: the 2019–20 Gambian Demographic and Health Survey and the 2019 national immunisation facility mapping. Using Bayesian multi-level binary logistic regression models, we identified key factors significantly associated with timely vaccination for five key vaccines: birth dose of hepatitis-B (HepB0), first, second, and third doses of the pentavalent vaccine (Penta1, Penta2, Penta3), and first-dose of measles-containing vaccine (MCV1) in children
We found that demand-side factors, such as ethnicity, household wealth status, maternal education, maternal parity, and the duration of the household's residency in its current location, were the most common drivers of timely childhood vaccination. However, supply-side factors such as travel time to the nearest immunisation clinic, availability of cold-storage and staffing numbers in the nearest immunisation clinic were also significant determinants. Furthermore, the determinants varied across specific vaccines and the timing of doses. For example, delivery in a health facility (aOR = 1.58, 95 %CI: 1.02–2.53), living less than 30 min (aOR = 2.11, 95 %CI: 1.2–8.84) and living between 30 and 60 min (aOR = 3.68, 95 %CI: 1.1–14.99) from a fixed-immunisation clinic was associated with timely HepB0, a time-sensitive vaccine that must be administered within 24 h of birth. On the other hand, children who received Penta1 and Penta2 on time were three- to five-fold more likely to receive subsequent doses on time (Penta2 and Penta3, respectively). Finally, proximity to an immunisation facility with functional vaccine cold-storage was a significant supply-side determinant of timely MCV1 (aOR = 1.4, 95 %CI: 1.09–1.99).
These findings provide valuable insights for programme managers and policymakers. By prioritising interventions and allocating scarce resources based on these identified determinants, they can maximize their impact and ensure children in The Gambia receive timely vaccinations throughout their first year of life, contributing to IA2030 goals.
27.
Kostandova, Natalya; Schluth, Catherine; Arambepola, Rohan; Atuhaire, Fatumah; Bérubé, Sophie; Chin, Taylor; Cleary, Eimear; Cortes-Azuero, Oscar; García-Carreras, Bernardo; Grantz, Kyra H.; Hitchings, Matt D. T.; Huang, Angkana T.; Kishore, Nishant; Lai, Shengjie; Larsen, Soren L.; Loisate, Stacie; Martinez, Pamela; Meredith, Hannah R.; Purbey, Ritika; Ramiadantsoa, Tanjona; Read, Jonathan; Rice, Benjamin L.; Rosman, Lori; Ruktanonchai, Nick; Salje, Henrik; Schaber, Kathryn L.; Tatem, Andrew J.; Wang, Jasmine; Cummings, Derek A. T.; Wesolowski, Amy
A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission Journal Article
In: Nature Communications, vol. 15, no. 10504, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission},
author = {Natalya Kostandova and Catherine Schluth and Rohan Arambepola and Fatumah Atuhaire and Sophie Bérubé and Taylor Chin and Eimear Cleary and Oscar Cortes-Azuero and Bernardo García-Carreras and Kyra H. Grantz and Matt D. T. Hitchings and Angkana T. Huang and Nishant Kishore and Shengjie Lai and Soren L. Larsen and Stacie Loisate and Pamela Martinez and Hannah R. Meredith and Ritika Purbey and Tanjona Ramiadantsoa and Jonathan Read and Benjamin L. Rice and Lori Rosman and Nick Ruktanonchai and Henrik Salje and Kathryn L. Schaber and Andrew J. Tatem and Jasmine Wang and Derek A. T. Cummings and Amy Wesolowski },
url = {https://doi.org/10.1038/s41467-024-54895-7},
year = {2024},
date = {2024-12-03},
journal = {Nature Communications},
volume = {15},
number = {10504},
abstract = {The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.
28.
Tian, Ya; Zhang, Junze; Li, Zonghan; Wu, Kai; Cao, Min; Lin, Jian; Pradhan, Prajal; Lai, Shengjie; Meng, Jia; Fu, Bojie; Chen, Min; Lin, Hui
Trade-offs among human, animal, and environmental health hinder the uniform progress of global One Health Journal Article
In: iScience, vol. 27, iss. 12, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Trade-offs among human, animal, and environmental health hinder the uniform progress of global One Health},
author = {Tian, Ya and Zhang, Junze and Li, Zonghan and Wu, Kai and Cao, Min and Lin, Jian and Pradhan, Prajal and Lai, Shengjie and Meng, Jia and Fu, Bojie and Chen, Min and Lin, Hui },
url = {https://doi.org/10.1016/j.isci.2024.111357},
doi = {10.1016/j.isci.2024.111357 },
year = {2024},
date = {2024-11-22},
journal = {iScience},
volume = {27},
issue = {12},
abstract = {The One Health (OH) approach, integrating aspects of human, animal, and environmental health, still lacks robustly quantified insights into its complex relationships. To fill this knowledge gap, we devised a comprehensive assessment scheme for OH to assess its progress, synergies, trade-offs, and priority targets. From 2000 to 2020, we find evidence for global progress toward OH, albeit uneven, with its average score rising from 61.6 to 65.5, driven primarily by better human health although environmental health lags. Despite synergies prevalent within and between the three health dimensions, over half of the world’s countries, mainly low-income ones, still incur substantial trade-offs impeding OH’s advancement, especially between animal and environmental health. Our in-depth analysis of synergy and trade-off networks reveals that maternal, newborn, and child health are critical synergistic targets, whereas biodiversity and land resources dominate trade-offs. We provide key information for the synergetic and uniform development of global OH and policymaking.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The One Health (OH) approach, integrating aspects of human, animal, and environmental health, still lacks robustly quantified insights into its complex relationships. To fill this knowledge gap, we devised a comprehensive assessment scheme for OH to assess its progress, synergies, trade-offs, and priority targets. From 2000 to 2020, we find evidence for global progress toward OH, albeit uneven, with its average score rising from 61.6 to 65.5, driven primarily by better human health although environmental health lags. Despite synergies prevalent within and between the three health dimensions, over half of the world’s countries, mainly low-income ones, still incur substantial trade-offs impeding OH’s advancement, especially between animal and environmental health. Our in-depth analysis of synergy and trade-off networks reveals that maternal, newborn, and child health are critical synergistic targets, whereas biodiversity and land resources dominate trade-offs. We provide key information for the synergetic and uniform development of global OH and policymaking.
29.
Utazi, C. Edson; Olowe, Iyanuloluwa D.; Chan, H. M. Theophilus; Dotse-Gborgbortsi, Winfred; Wagai, John; Umar, Jamila A.; Etamesor, Sulaiman; Atuhaire, Brian; Fafunmi, Biyi; Crawford, Jessica; Adeniran, Adeyemi; Tatem, Andrew J.
In: Vaccines, vol. 12, no. 1299, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Geospatial Variation in Vaccination Coverage and Zero-Dose Prevalence at the District, Ward and Health Facility Levels Before and After a Measles Vaccination Campaign in Nigeria},
author = {C. Edson Utazi and Iyanuloluwa D. Olowe and H. M. Theophilus Chan and Winfred Dotse-Gborgbortsi and John Wagai and Jamila A. Umar and Sulaiman Etamesor and Brian Atuhaire and Biyi Fafunmi and Jessica Crawford and Adeyemi Adeniran and Andrew J. Tatem},
url = {https://doi.org/10.3390/vaccines12121299},
year = {2024},
date = {2024-11-21},
journal = {Vaccines},
volume = {12},
number = {1299},
abstract = {Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies. Here, we undertake geospatial analyses of the 2021 measles PCCS in Nigeria to map indicators of coverage measuring the individual and combined performance of the campaign and routine immunization (RI) at 1 × 1 km resolution and the ward and district levels in 13 states. Using additional geospatial datasets, we also produced estimates of numbers of unvaccinated children during the campaign and numbers of measles-containing vaccine (MCV) zero-dose children before and after the campaign at these levels and within health facility catchment areas. Our study revealed that although the campaign reduced the numbers of MCV zero-dose children in all the districts, areas of suboptimal campaign and RI performance with considerable numbers of zero-dose children remained. Our analyses further identified wards and health facility catchment areas with higher numbers of unvaccinated children within these areas. Our outputs provide a robust evidence base to plan and implement follow-up RI strategies and to guide future campaigns at flexible and operationally relevant spatial scales.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies. Here, we undertake geospatial analyses of the 2021 measles PCCS in Nigeria to map indicators of coverage measuring the individual and combined performance of the campaign and routine immunization (RI) at 1 × 1 km resolution and the ward and district levels in 13 states. Using additional geospatial datasets, we also produced estimates of numbers of unvaccinated children during the campaign and numbers of measles-containing vaccine (MCV) zero-dose children before and after the campaign at these levels and within health facility catchment areas. Our study revealed that although the campaign reduced the numbers of MCV zero-dose children in all the districts, areas of suboptimal campaign and RI performance with considerable numbers of zero-dose children remained. Our analyses further identified wards and health facility catchment areas with higher numbers of unvaccinated children within these areas. Our outputs provide a robust evidence base to plan and implement follow-up RI strategies and to guide future campaigns at flexible and operationally relevant spatial scales.
30.
Yankey, Ortis; Utazi, Chigozie E.; Nnanatu, Christopher C.; Gadiaga, Assane N.; Abbot, Thomas; Lazar, Attila N.; Tatem, Andrew J.
Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model Journal Article
In: Applied Geography, vol. 174, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model},
author = {Ortis Yankey and Chigozie E. Utazi and Christopher C. Nnanatu and Assane N. Gadiaga and Thomas Abbot and Attila N. Lazar and Andrew J. Tatem},
url = {https://doi.org/10.1016/j.apgeog.2024.103416},
year = {2024},
date = {2024-09-14},
journal = {Applied Geography},
volume = {174},
abstract = {Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.
31.
Duan, Qianwen; Steele, Jessica; Cheng, Zhifeng; Cleary, Eimear; Ruktanonchai, Nick; Voepel, Hal; O'Riordan, Tim; Tatem, Andrew J.; Sorichetta, Alessandro; Lai, Shengjie; Eigenbrod, Felix
Identifying counter-urbanisation using Facebook's user count data Journal Article
In: Habitat International, vol. 150, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Identifying counter-urbanisation using Facebook's user count data},
author = {Qianwen Duan and Jessica Steele and Zhifeng Cheng and Eimear Cleary and Nick Ruktanonchai and Hal Voepel and Tim O'Riordan and Andrew J. Tatem and Alessandro Sorichetta and Shengjie Lai and Felix Eigenbrod},
url = {https://doi.org/10.1016/j.habitatint.2024.103113},
doi = {10.1016/j.habitatint.2024.103113},
year = {2024},
date = {2024-06-04},
journal = {Habitat International},
volume = {150},
abstract = {Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.
32.
Mekonnen, W.; Dechassa, W.; Melesse, D. Y.; Tejedor-Garavito, N.; Nilsen, K.; Getachew, T.; Mulu, S.; Wondrad, N
Inter-district and Wealth-related Inequalities in Maternal and Child Health Service Coverage and Child Mortality within Addis Ababa City Journal Article
In: Journal of Urban Health, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Inter-district and Wealth-related Inequalities in Maternal and Child Health Service Coverage and Child Mortality within Addis Ababa City},
author = {Mekonnen, W. and Dechassa, W. and Melesse, D.Y. and Tejedor-Garavito, N. and Nilsen, K. and Getachew, T. and Mulu, S. and Wondrad, N},
url = {https://doi.org/10.1007/s11524-024-00836-0},
doi = {doi.org/10.1007/s11524-024-00836-0},
year = {2024},
date = {2024-03-27},
journal = {Journal of Urban Health},
abstract = {In sub-Saharan Africa, urban areas generally have better access to and use of maternal, newborn, and child health (MNCH) services than rural areas, but previous research indicates that there are significant intra-urban disparities. This study aims to investigate temporal trends and geographic differences in maternal, newborn, and child health service utilization between Addis Ababa’s poorest and richest districts and households. A World Bank district-based poverty index was used to classify districts into the top 60% (non-poor) and bottom 40% (poor), and wealth index data from the Ethiopian Demographic and Health Survey (EDHS) was used to classify households into the top 60% (non-poor) and bottom 40% (poor). Essential maternal, newborn, and child health service coverage was estimated from routine health facility data for 2019–2021, and five rounds of the EDHS (2000–2019) were used to estimate child mortality. The results showed that service coverage was substantially higher in the top 60% than in the bottom 40% of districts. Coverage of four antenatal care visits, skill birth attendance, and postnatal care all exceeded 90% in the non-poor districts but only ranged from 54 to 67% in the poor districts. Inter-district inequalities were less pronounced for childhood vaccinations, with over 90% coverage levels across all districts. Inter-district inequalities in mortality rates were considerable. The neonatal mortality rate was nearly twice as high in the bottom 40% of households' as in the top 60% of households. Similarly, the under-5 mortality rate was three times higher in the bottom 40% compared to the top 60% of households. The substantial inequalities in MNCH service utilization and child mortality in Addis Ababa highlight the need for greater focus on the city’s women and children living in the poorest households and districts in maternal, newborn, and child health programs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In sub-Saharan Africa, urban areas generally have better access to and use of maternal, newborn, and child health (MNCH) services than rural areas, but previous research indicates that there are significant intra-urban disparities. This study aims to investigate temporal trends and geographic differences in maternal, newborn, and child health service utilization between Addis Ababa’s poorest and richest districts and households. A World Bank district-based poverty index was used to classify districts into the top 60% (non-poor) and bottom 40% (poor), and wealth index data from the Ethiopian Demographic and Health Survey (EDHS) was used to classify households into the top 60% (non-poor) and bottom 40% (poor). Essential maternal, newborn, and child health service coverage was estimated from routine health facility data for 2019–2021, and five rounds of the EDHS (2000–2019) were used to estimate child mortality. The results showed that service coverage was substantially higher in the top 60% than in the bottom 40% of districts. Coverage of four antenatal care visits, skill birth attendance, and postnatal care all exceeded 90% in the non-poor districts but only ranged from 54 to 67% in the poor districts. Inter-district inequalities were less pronounced for childhood vaccinations, with over 90% coverage levels across all districts. Inter-district inequalities in mortality rates were considerable. The neonatal mortality rate was nearly twice as high in the bottom 40% of households' as in the top 60% of households. Similarly, the under-5 mortality rate was three times higher in the bottom 40% compared to the top 60% of households. The substantial inequalities in MNCH service utilization and child mortality in Addis Ababa highlight the need for greater focus on the city’s women and children living in the poorest households and districts in maternal, newborn, and child health programs.
33.
Chamberlain, Heather R.; Darin, Edith; Adewole, Wole Ademola; Jochem, Warren C.; Lazar, Attila N.; Tatem, Andrew J.
Building footprint data for countries in Africa: To what extent are existing data products comparable? Journal Article
In: Computers, Environment and Urban Systems, vol. 110, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Building footprint data for countries in Africa: To what extent are existing data products comparable?},
author = {Heather R. Chamberlain and Edith Darin and Wole Ademola Adewole and Warren C. Jochem and Attila N. Lazar and Andrew J. Tatem},
url = {https://doi.org/10.1016/j.compenvurbsys.2024.102104},
doi = {10.1016/j.compenvurbsys.2024.102104},
year = {2024},
date = {2024-03-22},
journal = {Computers, Environment and Urban Systems},
volume = {110},
abstract = {Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data from multiple sources. Building footprint data is particularly useful in a range of applications including mapping population distributions, planning resource distribution campaigns and in humanitarian response. In settings with well-developed geospatial data systems, such datasets may complement existing authoritative sources, but in data-scarce settings, they may be the only source of data. However, knowledge on the degree to which building footprint data products are comparable and can be used interchangeably, and the impact of selecting a particular dataset on subsequent analyses remains limited. For all countries in Africa, we review the available multi-country building footprint data products and analyse their similarities and differences in terms of building area and count metrics. We explore the variation between building footprint data products across a range of spatial scales, including sub-national administrative units and different settlement types. Our results show that the available building footprint data products are not interchangeable. There are clear differences in counts and total area of building footprints between the assessed data products, as well as considerable spatial heterogeneity in building footprint coverage and completeness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data from multiple sources. Building footprint data is particularly useful in a range of applications including mapping population distributions, planning resource distribution campaigns and in humanitarian response. In settings with well-developed geospatial data systems, such datasets may complement existing authoritative sources, but in data-scarce settings, they may be the only source of data. However, knowledge on the degree to which building footprint data products are comparable and can be used interchangeably, and the impact of selecting a particular dataset on subsequent analyses remains limited. For all countries in Africa, we review the available multi-country building footprint data products and analyse their similarities and differences in terms of building area and count metrics. We explore the variation between building footprint data products across a range of spatial scales, including sub-national administrative units and different settlement types. Our results show that the available building footprint data products are not interchangeable. There are clear differences in counts and total area of building footprints between the assessed data products, as well as considerable spatial heterogeneity in building footprint coverage and completeness.
34.
Buckman, Tonnies Abeku; Sakyi, Samuel Asamoah; Yeboah-Mensah, Kwame; Antwi, Maxwell Hubert; Darban, Isaac; Owusu-Brenya, Lawrence; Yorke, Joseph; Boateng, Andy Opoku; Senu, Ebenezer; Dompreh, Albert; Addei, Akwasi Minnah; Boateng, Richard; Yankey, Ortis; Tandoh, Samuel
Demographic, Clinical Profile of Rheumatoid Arthritis Patients and Their Association with Disease Severity in Ghana Journal Article
In: International Journal of Rheumatology, 2024.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Demographic, Clinical Profile of Rheumatoid Arthritis Patients and Their Association with Disease Severity in Ghana},
author = {Tonnies Abeku Buckman and Samuel Asamoah Sakyi and Kwame Yeboah-Mensah and Maxwell Hubert Antwi and Isaac Darban and Lawrence Owusu-Brenya and Joseph Yorke and Andy Opoku Boateng and Ebenezer Senu and Albert Dompreh and Akwasi Minnah Addei and Richard Boateng and Ortis Yankey and Samuel Tandoh},
url = {https://doi.org/10.1155/2024/6639079},
doi = {10.1155/2024/6639079},
year = {2024},
date = {2024-01-12},
journal = {International Journal of Rheumatology},
abstract = {Rheumatoid arthritis (RA) is one of the frequent chronic, systemic, inflammatory autoimmune disorders with an estimated global prevalence of 1%. RA leads to joint destruction and disability if left untreated. Ghana has seen very few studies on RA, and little is known about the disease’s severity and related variables. This study sought to characterize the clinical presentation and determine disease severity and associated risk factors with disease severity among RA patients in a tertiary hospital in Ghana.
This cross-sectional study was conducted between September 2020 and August 2021. This study included 56 consecutively consenting RA patients from the Komfo Anokye Teaching Hospital orthopaedic unit. Diagnosis of RA was based on the updated American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) 2022 rheumatoid arthritis classification criteria by a rheumatologist. A study questionnaire was used to gather participant demographics and clinical features, and results from the laboratory were taken from the patients’ charts and medical records. The patients’ disease severity was evaluated based on the rheumatoid arthritis disease activity score, which is based on a 28-joint count (DAS28), and their functioning was evaluated using the modified health assessment questionnaire.
The participants’ mean age was years. Out of the total participants, 46 were females, and 10 were males (female-to-male ratio 4.6 : 1). Moreover, 37.50% had arthritis of the hand; 5.30% had severe disease, and 94.60% were not severe. A majority (76.80%) were on methotrexate medication. The most frequently involved joints were the knee (42.90%), wrist (32.10%), and elbow (12.50%). There was no statistically significant association with disease severity and a functional status score of >0.5 (cOR: 10.60, 95% CI (0.52-217.30); ). In addition, marital status (), disease duration (), family complaints (), and ESR () were significantly associated with disease severity.
RA is predominant among elder populations and females. Disease duration, family complaints, and ESR are associated with disease severity. The findings of this study call for interventions towards ensuring early diagnosis of RA among high-risk populations to enhance good management practices.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Rheumatoid arthritis (RA) is one of the frequent chronic, systemic, inflammatory autoimmune disorders with an estimated global prevalence of 1%. RA leads to joint destruction and disability if left untreated. Ghana has seen very few studies on RA, and little is known about the disease’s severity and related variables. This study sought to characterize the clinical presentation and determine disease severity and associated risk factors with disease severity among RA patients in a tertiary hospital in Ghana.
This cross-sectional study was conducted between September 2020 and August 2021. This study included 56 consecutively consenting RA patients from the Komfo Anokye Teaching Hospital orthopaedic unit. Diagnosis of RA was based on the updated American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) 2022 rheumatoid arthritis classification criteria by a rheumatologist. A study questionnaire was used to gather participant demographics and clinical features, and results from the laboratory were taken from the patients’ charts and medical records. The patients’ disease severity was evaluated based on the rheumatoid arthritis disease activity score, which is based on a 28-joint count (DAS28), and their functioning was evaluated using the modified health assessment questionnaire.
The participants’ mean age was years. Out of the total participants, 46 were females, and 10 were males (female-to-male ratio 4.6 : 1). Moreover, 37.50% had arthritis of the hand; 5.30% had severe disease, and 94.60% were not severe. A majority (76.80%) were on methotrexate medication. The most frequently involved joints were the knee (42.90%), wrist (32.10%), and elbow (12.50%). There was no statistically significant association with disease severity and a functional status score of >0.5 (cOR: 10.60, 95% CI (0.52-217.30); ). In addition, marital status (), disease duration (), family complaints (), and ESR () were significantly associated with disease severity.
RA is predominant among elder populations and females. Disease duration, family complaints, and ESR are associated with disease severity. The findings of this study call for interventions towards ensuring early diagnosis of RA among high-risk populations to enhance good management practices.
This cross-sectional study was conducted between September 2020 and August 2021. This study included 56 consecutively consenting RA patients from the Komfo Anokye Teaching Hospital orthopaedic unit. Diagnosis of RA was based on the updated American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) 2022 rheumatoid arthritis classification criteria by a rheumatologist. A study questionnaire was used to gather participant demographics and clinical features, and results from the laboratory were taken from the patients’ charts and medical records. The patients’ disease severity was evaluated based on the rheumatoid arthritis disease activity score, which is based on a 28-joint count (DAS28), and their functioning was evaluated using the modified health assessment questionnaire.
The participants’ mean age was years. Out of the total participants, 46 were females, and 10 were males (female-to-male ratio 4.6 : 1). Moreover, 37.50% had arthritis of the hand; 5.30% had severe disease, and 94.60% were not severe. A majority (76.80%) were on methotrexate medication. The most frequently involved joints were the knee (42.90%), wrist (32.10%), and elbow (12.50%). There was no statistically significant association with disease severity and a functional status score of >0.5 (cOR: 10.60, 95% CI (0.52-217.30); ). In addition, marital status (), disease duration (), family complaints (), and ESR () were significantly associated with disease severity.
RA is predominant among elder populations and females. Disease duration, family complaints, and ESR are associated with disease severity. The findings of this study call for interventions towards ensuring early diagnosis of RA among high-risk populations to enhance good management practices.
35.
Zhang, Die; Ge, Yong; Wang, Jianghao; Liu, Haiyan; Zhang, Wen-Bin; Wu, Xilin; Heuvelink, Gerard BM; Wu, Chaoyang; Yang, Juan; Ruktanonchai, Nick W; others,
Optimizing the detection of emerging infections using mobility-based spatial sampling Journal Article
In: International Journal of Applied Earth Observation and Geoinformation, vol. 131, pp. 103949, 2024.
Abstract | Links | BibTeX | Tags:
@article{zhang2024optimizing,
title = {Optimizing the detection of emerging infections using mobility-based spatial sampling},
author = {Die Zhang and Yong Ge and Jianghao Wang and Haiyan Liu and Wen-Bin Zhang and Xilin Wu and Gerard BM Heuvelink and Chaoyang Wu and Juan Yang and Nick W Ruktanonchai and others},
url = {https://doi.org/10.1016/j.jag.2024.103949},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {131},
pages = {103949},
publisher = {Elsevier},
abstract = {Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals’ movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Timely and precise detection of emerging infections is imperative for effective outbreak management and disease control. Human mobility significantly influences the spatial transmission dynamics of infectious diseases. Spatial sampling, integrating the spatial structure of the target, holds promise as an approach for testing allocation in detecting infections, and leveraging information on individuals’ movement and contact behavior can enhance targeting precision. This study introduces a spatial sampling framework informed by spatiotemporal analysis of human mobility data, aiming to optimize the allocation of testing resources for detecting emerging infections. Mobility patterns, derived from clustering point-of-interest and travel data, are integrated into four spatial sampling approaches at the community level. We evaluate the proposed mobility-based spatial sampling by analyzing both actual and simulated outbreaks, considering scenarios of transmissibility, intervention timing, and population density in cities. Results indicate that leveraging inter-community movement data and initial case locations, the proposed Case Flow Intensity (CFI) and Case Transmission Intensity (CTI)-informed spatial sampling enhances community-level testing efficiency by reducing the number of individuals screened while maintaining a high accuracy rate in infection identification. Furthermore, the prompt application of CFI and CTI within cities is crucial for effective detection, especially in highly contagious infections within densely populated areas. With the widespread use of human mobility data for infectious disease responses, the proposed theoretical framework extends spatiotemporal data analysis of mobility patterns into spatial sampling, providing a cost-effective solution to optimize testing resource deployment for containing emerging infectious diseases.
36.
Li, Yanming; Tong, Xunliang; Jiang, Mingyue; Wu, Zeni; Duan, Yuping; Zhang, Li; Lai, Shengjie; Yang, Weizhong; Feng, Luzhao
Chronic disease and infection in China: lessons from the covid-19 pandemic Journal Article
In: bmj, vol. 387, 2024.
Abstract | Links | BibTeX | Tags:
@article{li2024chronic,
title = {Chronic disease and infection in China: lessons from the covid-19 pandemic},
author = {Yanming Li and Xunliang Tong and Mingyue Jiang and Zeni Wu and Yuping Duan and Li Zhang and Shengjie Lai and Weizhong Yang and Luzhao Feng},
url = {https://doi.org/10.1136/bmj.q2000},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
journal = {bmj},
volume = {387},
publisher = {British Medical Journal Publishing Group},
abstract = {The harms from covid-19 during the pandemic extended beyond the disease itself. Older people with a chronic or non-communicable disease were among the most vulnerable to SARS-CoV-2 and at the highest risk of death. The interplay between chronic disease and infection can exacerbate each condition, highlighting the importance of preventing infection in people with chronic disease and increasing the challenges of caring for patients with comorbidity. For instance, during the omicron outbreak in Hong Kong excess deaths occurred not only from covid-19 infections but from chronic diseases, including cerebrovascular disease and kidney disease. This may be because the healthcare system was heavily overwhelmed, leading to low quality healthcare services and longer waits for some interventions for non-covid illness. Moreover, patients with chronic diseases may face increased risks of complications and severe outcomes after covid-19 infection.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The harms from covid-19 during the pandemic extended beyond the disease itself. Older people with a chronic or non-communicable disease were among the most vulnerable to SARS-CoV-2 and at the highest risk of death. The interplay between chronic disease and infection can exacerbate each condition, highlighting the importance of preventing infection in people with chronic disease and increasing the challenges of caring for patients with comorbidity. For instance, during the omicron outbreak in Hong Kong excess deaths occurred not only from covid-19 infections but from chronic diseases, including cerebrovascular disease and kidney disease. This may be because the healthcare system was heavily overwhelmed, leading to low quality healthcare services and longer waits for some interventions for non-covid illness. Moreover, patients with chronic diseases may face increased risks of complications and severe outcomes after covid-19 infection.
37.
Aheto, Justice Moses K.; Olowe, Iyanuloluwa Deborah; Chan, Ho Man Theophilus; Ekeh, Adachi; Dieng, Boubacar; Fafunmi, Biyi; Setayesh, Hamidreza; Atuhaire, Brian; Crawford, Jessica; Tatem, Andrew J.; Utazi, Chigozie Edson
In: Vaccines, vol. 11, iss. 12, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria},
author = {Justice Moses K. Aheto and Iyanuloluwa Deborah Olowe and Ho Man Theophilus Chan and Adachi Ekeh and Boubacar Dieng and Biyi Fafunmi and Hamidreza Setayesh and Brian Atuhaire and Jessica Crawford and Andrew J. Tatem and Chigozie Edson Utazi},
url = {https://doi.org/10.3390/vaccines11121830},
doi = {10.3390/vaccines11121830 },
year = {2023},
date = {2023-12-08},
journal = {Vaccines},
volume = {11},
issue = {12},
abstract = {The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.
38.
Luo, Wei; Liu, Qianhuang; Zhou, Yuxuan; Ran, Yiding; Liu, Zhaoyin; Hou, Weitao; Pei, Sen; Lai, Shengjie
Spatiotemporal variations of “triple-demic” outbreaks of respiratory infections in the United States in the post-COVID-19 era Journal Article
In: BMC Public Health, vol. 23, no. 2452, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Spatiotemporal variations of “triple-demic” outbreaks of respiratory infections in the United States in the post-COVID-19 era},
author = {Wei Luo and Qianhuang Liu and Yuxuan Zhou and Yiding Ran and Zhaoyin Liu and Weitao Hou and Sen Pei and Shengjie Lai},
url = {https://doi.org/10.1186/s12889-023-17406-9},
doi = {10.1186/s12889-023-17406-9},
year = {2023},
date = {2023-12-07},
journal = {BMC Public Health},
volume = {23},
number = {2452},
abstract = {The US confronted a “triple-demic” of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies.
We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively.
Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023.
Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The US confronted a “triple-demic” of influenza, respiratory syncytial virus (RSV), and COVID-19 in the winter of 2022, leading to increased respiratory infections and a higher demand for medical supplies. It is urgent to analyze these epidemics and their spatial-temporal co-occurrence, identifying hotspots and informing public health strategies.
We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively.
Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023.
Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.
We employed retrospective and prospective space-time scan statistics to assess the situations of COVID-19, influenza, and RSV in 51 US states from October 2021 to February 2022, and from October 2022 to February 2023, respectively. This enabled monitoring of spatiotemporal variations for each epidemic individually and collectively.
Compared to winter 2021, COVID-19 cases decreased while influenza and RSV infections significantly increased in winter 2022. We found a high-risk cluster of influenza and COVID-19 (not all three) in winter 2021. In late November 2022, a large high-risk cluster of triple-demic emerged in the central US. The number of states at high risk for multiple epidemics increased from 15 in October 2022 to 21 in January 2023.
Our study offers a novel spatiotemporal approach that combines both univariate and multivariate surveillance, as well as retrospective and prospective analyses. This approach offers a more comprehensive and timely understanding of how the co-occurrence of COVID-19, influenza, and RSV impacts various regions within the United States. Our findings assist in tailor-made strategies to mitigate the effects of these respiratory infections.
39.
Cheng, Qu; Jing, Qinlong; Collender, Philip A.; Head, Jennifer R.; Li, Qi; Yu, Hailan; Li, Zhichao; Ju, Yang; Chen, Tianmu; Wang, Peng; Cleary, Eimear; Lai, Shengjie
In: Frontiers in Public Health, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Prior water availability modifies the effect of heavy rainfall on dengue transmission: a time series analysis of passive surveillance data from southern China},
author = {Qu Cheng and Qinlong Jing and Philip A. Collender and Jennifer R. Head and Qi Li and Hailan Yu and Zhichao Li and Yang Ju and Tianmu Chen and Peng Wang and Eimear Cleary and Shengjie Lai},
url = {https://doi.org/10.3389/fpubh.2023.1287678},
doi = {10.3389/fpubh.2023.1287678},
year = {2023},
date = {2023-12-01},
journal = {Frontiers in Public Health},
abstract = {Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.
In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.
Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days.
These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Given the rapid geographic spread of dengue and the growing frequency and intensity of heavy rainfall events, it is imperative to understand the relationship between these phenomena in order to propose effective interventions. However, studies exploring the association between heavy rainfall and dengue infection risk have reached conflicting conclusions, potentially due to the neglect of prior water availability in mosquito breeding sites as an effect modifier.
In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.
Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days.
These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.
In this study, we addressed this research gap by considering the impact of prior water availability for the first time. We measured prior water availability as the cumulative precipitation over the preceding 8 weeks and utilized a distributed lag non-linear model stratified by the level of prior water availability to examine the association between dengue infection risk and heavy rainfall in Guangzhou, a dengue transmission hotspot in southern China.
Our findings suggest that the effects of heavy rainfall are likely to be modified by prior water availability. A 24–55 day lagged impact of heavy rainfall was associated with an increase in dengue risk when prior water availability was low, with the greatest incidence rate ratio (IRR) of 1.37 [95% credible interval (CI): 1.02–1.83] occurring at a lag of 27 days. In contrast, a heavy rainfall lag of 7–121 days decreased dengue risk when prior water availability was high, with the lowest IRR of 0.59 (95% CI: 0.43–0.79), occurring at a lag of 45 days.
These findings may help to reconcile the inconsistent conclusions reached by previous studies and improve our understanding of the complex relationship between heavy rainfall and dengue infection risk.
40.
Dwomoh, Duah; Iddi, Samuel; Afagbedzi, Seth Kwaku; Tejedor-Garavito, Natalia; Dotse-Gborgbortsi, Winfred; Wright, Jim; Tatem, Andrew J; Nilsen, Kristine
In: Journal of Urban Health, 2023.
Abstract | Links | BibTeX | Tags:
@article{nokey,
title = {Impact of Urban Slum Residence on Coverage of Maternal, Neonatal, and Child Health Service Indicators in the Greater Accra Region of Ghana: an Ecological Time-Series Analysis, 2018-2021},
author = {Duah Dwomoh and Samuel Iddi and Seth Kwaku Afagbedzi and Natalia Tejedor-Garavito and Winfred Dotse-Gborgbortsi and Jim Wright and Andrew J Tatem and Kristine Nilsen },
url = {https://doi.org/10.1007/s11524-023-00812-0
},
doi = {10.1007/s11524-023-00812-0},
year = {2023},
date = {2023-11-16},
journal = {Journal of Urban Health},
abstract = {Among other focus areas, the global Sustainable Development Goals (SDGs) 3 and 11 seek to advance progress toward universal coverage of maternal, neonatal, and child health (MNCH) services and access to safe and affordable housing and basic services by 2030. Governments and development agencies have historically neglected the health and well-being associated with living in urban slums across major capital cities in sub-Saharan Africa since health policies and programs have tended to focus on people living in rural communities. This study assessed the trends and compared inequities in MNCH service utilization between slum and non-slum districts in the Greater Accra region of Ghana. It analyzed information from 29 districts using monthly time-series Health Management Information System (HMIS) data on MNCH service utilization between January 2018 and December 2021. Multivariable quantile regression models with robust standard errors were used to quantify the impact of urban slum residence on MNCH service utilization. We assessed the inequality of MNCH coverage indicators between slum and non-slum districts using the Gini index with bootstrapped standard errors and the generalized Lorenz curve. The results indicate that rates of vaccination coverage and antenatal care (ANC) attendance have declined significantly in slum districts compared to those in non-slum districts. However, skilled birth delivery and postnatal care (PNC) were found to be higher in urban slum areas compared to those in non-urban slum areas. To help achieve the SDGs’ targets, it is important for the government of Ghana and other relevant stakeholders to prioritize the implementation of effective policies, programs, and interventions that will improve access to and utilization of ANC and immunization services among urban slum dwellers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Among other focus areas, the global Sustainable Development Goals (SDGs) 3 and 11 seek to advance progress toward universal coverage of maternal, neonatal, and child health (MNCH) services and access to safe and affordable housing and basic services by 2030. Governments and development agencies have historically neglected the health and well-being associated with living in urban slums across major capital cities in sub-Saharan Africa since health policies and programs have tended to focus on people living in rural communities. This study assessed the trends and compared inequities in MNCH service utilization between slum and non-slum districts in the Greater Accra region of Ghana. It analyzed information from 29 districts using monthly time-series Health Management Information System (HMIS) data on MNCH service utilization between January 2018 and December 2021. Multivariable quantile regression models with robust standard errors were used to quantify the impact of urban slum residence on MNCH service utilization. We assessed the inequality of MNCH coverage indicators between slum and non-slum districts using the Gini index with bootstrapped standard errors and the generalized Lorenz curve. The results indicate that rates of vaccination coverage and antenatal care (ANC) attendance have declined significantly in slum districts compared to those in non-slum districts. However, skilled birth delivery and postnatal care (PNC) were found to be higher in urban slum areas compared to those in non-urban slum areas. To help achieve the SDGs’ targets, it is important for the government of Ghana and other relevant stakeholders to prioritize the implementation of effective policies, programs, and interventions that will improve access to and utilization of ANC and immunization services among urban slum dwellers.
