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Global incidence, prevalence, years lived with disability (YLDs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00757-8. [PMID: 38642570 DOI: 10.1016/s0140-6736(24)00757-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 03/07/2024] [Accepted: 04/12/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Detailed, comprehensive, and timely reporting on population health by underlying causes of disability and premature death is crucial to understanding and responding to complex patterns of disease and injury burden over time and across age groups, sexes, and locations. The availability of disease burden estimates can promote evidence-based interventions that enable public health researchers, policy makers, and other professionals to implement strategies that can mitigate diseases. It can also facilitate more rigorous monitoring of progress towards national and international health targets, such as the Sustainable Development Goals. For three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has filled that need. A global network of collaborators contributed to the production of GBD 2021 by providing, reviewing, and analysing all available data. GBD estimates are updated routinely with additional data and refined analytical methods. GBD 2021 presents, for the first time, estimates of health loss due to the COVID-19 pandemic. METHODS The GBD 2021 disease and injury burden analysis estimated years lived with disability (YLDs), years of life lost (YLLs), disability-adjusted life-years (DALYs), and healthy life expectancy (HALE) for 371 diseases and injuries using 100 983 data sources. Data were extracted from vital registration systems, verbal autopsies, censuses, household surveys, disease-specific registries, health service contact data, and other sources. YLDs were calculated by multiplying cause-age-sex-location-year-specific prevalence of sequelae by their respective disability weights, for each disease and injury. YLLs were calculated by multiplying cause-age-sex-location-year-specific deaths by the standard life expectancy at the age that death occurred. DALYs were calculated by summing YLDs and YLLs. HALE estimates were produced using YLDs per capita and age-specific mortality rates by location, age, sex, year, and cause. 95% uncertainty intervals (UIs) were generated for all final estimates as the 2·5th and 97·5th percentiles values of 500 draws. Uncertainty was propagated at each step of the estimation process. Counts and age-standardised rates were calculated globally, for seven super-regions, 21 regions, 204 countries and territories (including 21 countries with subnational locations), and 811 subnational locations, from 1990 to 2021. Here we report data for 2010 to 2021 to highlight trends in disease burden over the past decade and through the first 2 years of the COVID-19 pandemic. FINDINGS Global DALYs increased from 2·63 billion (95% UI 2·44-2·85) in 2010 to 2·88 billion (2·64-3·15) in 2021 for all causes combined. Much of this increase in the number of DALYs was due to population growth and ageing, as indicated by a decrease in global age-standardised all-cause DALY rates of 14·2% (95% UI 10·7-17·3) between 2010 and 2019. Notably, however, this decrease in rates reversed during the first 2 years of the COVID-19 pandemic, with increases in global age-standardised all-cause DALY rates since 2019 of 4·1% (1·8-6·3) in 2020 and 7·2% (4·7-10·0) in 2021. In 2021, COVID-19 was the leading cause of DALYs globally (212·0 million [198·0-234·5] DALYs), followed by ischaemic heart disease (188·3 million [176·7-198·3]), neonatal disorders (186·3 million [162·3-214·9]), and stroke (160·4 million [148·0-171·7]). However, notable health gains were seen among other leading communicable, maternal, neonatal, and nutritional (CMNN) diseases. Globally between 2010 and 2021, the age-standardised DALY rates for HIV/AIDS decreased by 47·8% (43·3-51·7) and for diarrhoeal diseases decreased by 47·0% (39·9-52·9). Non-communicable diseases contributed 1·73 billion (95% UI 1·54-1·94) DALYs in 2021, with a decrease in age-standardised DALY rates since 2010 of 6·4% (95% UI 3·5-9·5). Between 2010 and 2021, among the 25 leading Level 3 causes, age-standardised DALY rates increased most substantially for anxiety disorders (16·7% [14·0-19·8]), depressive disorders (16·4% [11·9-21·3]), and diabetes (14·0% [10·0-17·4]). Age-standardised DALY rates due to injuries decreased globally by 24·0% (20·7-27·2) between 2010 and 2021, although improvements were not uniform across locations, ages, and sexes. Globally, HALE at birth improved slightly, from 61·3 years (58·6-63·6) in 2010 to 62·2 years (59·4-64·7) in 2021. However, despite this overall increase, HALE decreased by 2·2% (1·6-2·9) between 2019 and 2021. INTERPRETATION Putting the COVID-19 pandemic in the context of a mutually exclusive and collectively exhaustive list of causes of health loss is crucial to understanding its impact and ensuring that health funding and policy address needs at both local and global levels through cost-effective and evidence-based interventions. A global epidemiological transition remains underway. Our findings suggest that prioritising non-communicable disease prevention and treatment policies, as well as strengthening health systems, continues to be crucially important. The progress on reducing the burden of CMNN diseases must not stall; although global trends are improving, the burden of CMNN diseases remains unacceptably high. Evidence-based interventions will help save the lives of young children and mothers and improve the overall health and economic conditions of societies across the world. Governments and multilateral organisations should prioritise pandemic preparedness planning alongside efforts to reduce the burden of diseases and injuries that will strain resources in the coming decades. FUNDING Bill & Melinda Gates Foundation.
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Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00367-2. [PMID: 38582094 DOI: 10.1016/s0140-6736(24)00367-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/15/2024] [Accepted: 02/22/2024] [Indexed: 04/08/2024]
Abstract
BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation.
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Global, regional, and national burden of disorders affecting the nervous system, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet Neurol 2024; 23:344-381. [PMID: 38493795 PMCID: PMC10949203 DOI: 10.1016/s1474-4422(24)00038-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 01/23/2024] [Accepted: 01/26/2024] [Indexed: 03/19/2024]
Abstract
BACKGROUND Disorders affecting the nervous system are diverse and include neurodevelopmental disorders, late-life neurodegeneration, and newly emergent conditions, such as cognitive impairment following COVID-19. Previous publications from the Global Burden of Disease, Injuries, and Risk Factor Study estimated the burden of 15 neurological conditions in 2015 and 2016, but these analyses did not include neurodevelopmental disorders, as defined by the International Classification of Diseases (ICD)-11, or a subset of cases of congenital, neonatal, and infectious conditions that cause neurological damage. Here, we estimate nervous system health loss caused by 37 unique conditions and their associated risk factors globally, regionally, and nationally from 1990 to 2021. METHODS We estimated mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs), with corresponding 95% uncertainty intervals (UIs), by age and sex in 204 countries and territories, from 1990 to 2021. We included morbidity and deaths due to neurological conditions, for which health loss is directly due to damage to the CNS or peripheral nervous system. We also isolated neurological health loss from conditions for which nervous system morbidity is a consequence, but not the primary feature, including a subset of congenital conditions (ie, chromosomal anomalies and congenital birth defects), neonatal conditions (ie, jaundice, preterm birth, and sepsis), infectious diseases (ie, COVID-19, cystic echinococcosis, malaria, syphilis, and Zika virus disease), and diabetic neuropathy. By conducting a sequela-level analysis of the health outcomes for these conditions, only cases where nervous system damage occurred were included, and YLDs were recalculated to isolate the non-fatal burden directly attributable to nervous system health loss. A comorbidity correction was used to calculate total prevalence of all conditions that affect the nervous system combined. FINDINGS Globally, the 37 conditions affecting the nervous system were collectively ranked as the leading group cause of DALYs in 2021 (443 million, 95% UI 378-521), affecting 3·40 billion (3·20-3·62) individuals (43·1%, 40·5-45·9 of the global population); global DALY counts attributed to these conditions increased by 18·2% (8·7-26·7) between 1990 and 2021. Age-standardised rates of deaths per 100 000 people attributed to these conditions decreased from 1990 to 2021 by 33·6% (27·6-38·8), and age-standardised rates of DALYs attributed to these conditions decreased by 27·0% (21·5-32·4). Age-standardised prevalence was almost stable, with a change of 1·5% (0·7-2·4). The ten conditions with the highest age-standardised DALYs in 2021 were stroke, neonatal encephalopathy, migraine, Alzheimer's disease and other dementias, diabetic neuropathy, meningitis, epilepsy, neurological complications due to preterm birth, autism spectrum disorder, and nervous system cancer. INTERPRETATION As the leading cause of overall disease burden in the world, with increasing global DALY counts, effective prevention, treatment, and rehabilitation strategies for disorders affecting the nervous system are needed. FUNDING Bill & Melinda Gates Foundation.
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Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021. Lancet 2024:S0140-6736(24)00476-8. [PMID: 38484753 DOI: 10.1016/s0140-6736(24)00476-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Revised: 12/08/2023] [Accepted: 03/06/2024] [Indexed: 04/13/2024]
Abstract
BACKGROUND Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period. METHODS 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING Bill & Melinda Gates Foundation.
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5
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Dzianach PA, Rumisha SF, Lubinda J, Saddler A, van den Berg M, Gelaw YA, Harris JR, Browne AJ, Sanna F, Rozier JA, Galatas B, Anderson LF, Vargas-Ruiz CA, Cameron E, Gething PW, Weiss DJ. Evaluating COVID-19-Related Disruptions to Effective Malaria Case Management in 2020-2021 and Its Potential Effects on Malaria Burden in Sub-Saharan Africa. Trop Med Infect Dis 2023; 8:216. [PMID: 37104342 PMCID: PMC10143572 DOI: 10.3390/tropicalmed8040216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/09/2023] Open
Abstract
The COVID-19 pandemic has led to far-reaching disruptions to health systems, including preventative and curative services for malaria. The aim of this study was to estimate the magnitude of disruptions in malaria case management in sub-Saharan Africa and their impact on malaria burden during the COVID-19 pandemic. We used survey data collected by the World Health Organization, in which individual country stakeholders reported on the extent of disruptions to malaria diagnosis and treatment. The relative disruption values were then applied to estimates of antimalarial treatment rates and used as inputs to an established spatiotemporal Bayesian geostatistical framework to generate annual malaria burden estimates with case management disruptions. This enabled an estimation of the additional malaria burden attributable to pandemic-related impacts on treatment rates in 2020 and 2021. Our analysis found that disruptions in access to antimalarial treatment in sub-Saharan Africa likely resulted in approximately 5.9 (4.4-7.2 95% CI) million more malaria cases and 76 (20-132) thousand additional deaths in the 2020-2021 period within the study region, equivalent to approximately 1.2% (0.3-2.1 95% CI) greater clinical incidence of malaria and 8.1% (2.1-14.1 95% CI) greater malaria mortality than expected in the absence of the disruptions to malaria case management. The available evidence suggests that access to antimalarials was disrupted to a significant degree and should be considered an area of focus to avoid further escalations in malaria morbidity and mortality. The results from this analysis were used to estimate cases and deaths in the World Malaria Report 2022 during the pandemic years.
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Affiliation(s)
- Paulina A. Dzianach
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Susan F. Rumisha
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Jailos Lubinda
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Adam Saddler
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | | | - Yalemzewod A. Gelaw
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Joseph R. Harris
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Annie J. Browne
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Francesca Sanna
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Jennifer A. Rozier
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
| | - Beatriz Galatas
- Strategic Information for Response, Global Malaria Programme, World Health Organization, 1211 Geneva, Switzerland
| | - Laura F. Anderson
- Strategic Information for Response, Global Malaria Programme, World Health Organization, 1211 Geneva, Switzerland
| | - Camilo A. Vargas-Ruiz
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
| | - Ewan Cameron
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
| | - Peter W. Gething
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
| | - Daniel J. Weiss
- Child Health Analytics, Telethon Kids Institute, Nedlands, WA 6009, Australia
- Faculty of Health Sciences, Curtin University, Perth, WA 6102, Australia
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Annan E, Bukhari MH, Treviño J, Abad ZSH, Lubinda J, da Silva EA, Haque U. The ecological determinants of severe dengue: A Bayesian inferential model. ECOL INFORM 2023. [DOI: 10.1016/j.ecoinf.2023.101986] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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7
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Hast M, Mharakurwa S, Shields TM, Lubinda J, Searle K, Gwanzura L, Munyati S, Moss WJ. Characterizing human movement patterns using GPS data loggers in an area of persistent malaria in Zimbabwe along the Mozambique border. BMC Infect Dis 2022; 22:942. [PMID: 36522643 PMCID: PMC9756631 DOI: 10.1186/s12879-022-07903-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 11/29/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Human mobility is a driver for the reemergence or resurgence of malaria and has been identified as a source of cross-border transmission. However, movement patterns are difficult to measure in rural areas where malaria risk is high. In countries with malaria elimination goals, it is essential to determine the role of mobility on malaria transmission to implement appropriate interventions. METHODS A study was conducted in Mutasa District, Zimbabwe, to investigate human movement patterns in an area of persistent transmission along the Mozambique border. Over 1 year, a convenience sample of 20 participants/month was recruited from active malaria surveillance cohorts to carry an IgotU® GT-600 global positioning system (GPS) data logger during all daily activities. Consenting participants were tested for malaria at data logger distribution using rapid antigen diagnostic tests and completed a survey questionnaire. GPS data were analyzed using a trajectory analysis tool, and participant movement patterns were characterized throughout the study area and across the border into Mozambique using movement intensity maps, activity space plots, and statistical analyses. RESULTS From June 2016-May 2017, 184 participants provided movement tracks encompassing > 350,000 data points and nearly 8000 person-days. Malaria prevalence at logger distribution was 3.7%. Participants traveled a median of 2.8 km/day and spent a median of 4.6 h/day away from home. Movement was widespread within and outside the study area, with participants traveling up to 500 km from their homes. Indices of mobility were higher in the dry season than the rainy season (median km traveled/day = 3.5 vs. 2.2, P = 0.03), among male compared to female participants (median km traveled/day = 3.8 vs. 2.0, P = 0.0008), and among adults compared to adolescents (median total km traveled = 104.6 vs. 59.5, P = 0.05). Half of participants traveled outside the study area, and 30% traveled into Mozambique, including 15 who stayed in Mozambique overnight. CONCLUSIONS Study participants in Mutasa District, Zimbabwe, were highly mobile throughout the year. Many participants traveled long distances from home, including overnight trips into Mozambique, with clear implications for malaria control. Interventions targeted at mobile populations and cross-border transmission may be effective in preventing malaria introductions in this region.
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Affiliation(s)
- Marisa Hast
- grid.21107.350000 0001 2171 9311Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Sungano Mharakurwa
- grid.418347.d0000 0004 8265 7435Biomedical Research and Training Institute, Harare, Zimbabwe ,grid.442719.d0000 0000 8930 0245Africa University, Old Mutare, Mutare, Zimbabwe
| | - Timothy M. Shields
- grid.21107.350000 0001 2171 9311Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
| | - Jailos Lubinda
- grid.414659.b0000 0000 8828 1230Telethon Kids Institute, Malaria Atlas Project, Nedlands, WA Australia
| | - Kelly Searle
- grid.17635.360000000419368657School of Public Health, University of Minnesota, Minneapolis, MN USA
| | - Lovemore Gwanzura
- grid.418347.d0000 0004 8265 7435Biomedical Research and Training Institute, Harare, Zimbabwe
| | - Shungu Munyati
- grid.418347.d0000 0004 8265 7435Biomedical Research and Training Institute, Harare, Zimbabwe
| | - William J. Moss
- grid.21107.350000 0001 2171 9311Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA
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8
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Lubinda J, Bi Y, Haque U, Lubinda M, Hamainza B, Moore AJ. Spatio-temporal monitoring of health facility-level malaria trends in Zambia and adaptive scaling for operational intervention. Commun Med 2022; 2:79. [PMID: 35789566 PMCID: PMC9249860 DOI: 10.1038/s43856-022-00144-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Accepted: 06/15/2022] [Indexed: 12/02/2022] Open
Abstract
Background The spatial and temporal variability inherent in malaria transmission within countries implies that targeted interventions for malaria control in high-burden settings and subnational elimination are a practical necessity. Identifying the spatio-temporal incidence, risk, and trends at different administrative geographies within malaria-endemic countries and monitoring them in near real-time as change occurs is crucial for developing and introducing cost-effective, subnational control and elimination intervention strategies. Methods This study developed intelligent data analytics incorporating Bayesian trend and spatio-temporal Integrated Laplace Approximation models to analyse high-burden over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. Results The results show that at least 5.4 million people live in catchment areas with increasing trends of malaria, covering over 47% of all health facilities, while 5.7 million people live in areas with a declining trend (95% CI), covering 27% of health facilities. A two-scale spatio-temporal trend comparison identified significant differences between health facilities and higher-level districts, and the pattern observed in the southeastern region of Zambia provides the first evidence of the impact of recently implemented localised interventions. Conclusions The results support our recommendation for an adaptive scaling approach when implementing national malaria monitoring, control and elimination strategies and a particular need for stratified subnational approaches targeting high-burden regions with increasing disease trends. Strong clusters along borders with highly endemic countries in the north and south of Zambia underscore the need for coordinated cross-border malaria initiatives and strategies. Malaria is an infectious disease that is widespread in many African countries. Malaria transmission within a country can vary between regions, so tailored interventions for malaria control and elimination targeted to different regions are necessary. To achieve this, it is important to measure and monitor the frequency of malaria infections, its risk, and trends at different geographic administrative scales. This study analysed over 32 million reported malaria cases from 1743 health facilities in Zambia between 2009 and 2015. The results showed an increasing national trend in malaria risk and malaria infection frequency and identified differences between health facility and district trends. These findings support a flexible approach when implementing and expanding national malaria monitoring, control and elimination strategies, especially in areas bordering countries where malaria is widespread, cross-border movement is common, and cross-border initiatives could be beneficial. Lubinda et al. analyse over 32 million health-facility reported malaria cases in Zambia (2009–15) to examine spatially-structured temporal trends. They observe overall increasing trends in risk and rates and highlight the potential benefits of using an adaptive scaling approach in national malaria strategies, and a need for cross-border initiatives.
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9
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Schaber KL, Kobayashi T, Hast M, Searle KM, Shields TM, Hamapumbu H, Lubinda J, Thuma PE, Lupiya J, Chaponda M, Munyati S, Gwanzura L, Mharakurwa S, Moss WJ, Wesolowski A. What Heterogeneities in Individual-level Mobility Are Lost During Aggregation? Leveraging GPS Logger Data to Understand Fine-scale and Aggregated Patterns of Mobility. Am J Trop Med Hyg 2022; 107:1145-1153. [PMID: 36252797 PMCID: PMC9709031 DOI: 10.4269/ajtmh.22-0202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 08/18/2022] [Indexed: 01/25/2023] Open
Abstract
Human movement drives spatial transmission patterns of infectious diseases. Population-level mobility patterns are often quantified using aggregated data sets, such as census migration surveys or mobile phone data. These data are often unable to quantify individual-level travel patterns and lack the information needed to discern how mobility varies by demographic groups. Individual-level datasets can capture additional, more precise, aspects of mobility that may impact disease risk or transmission patterns and determine how mobility differs across cohorts; however, these data are rare, particularly in locations such as sub-Saharan Africa. Using detailed GPS logger data collected from three sites in southern Africa, we explore metrics of mobility such as percent time spent outside home, number of locations visited, distance of locations, and time spent at locations to determine whether they vary by demographic, geographic, or temporal factors. We further create a composite mobility score to identify how well aggregated summary measures would capture the full extent of mobility patterns. Although sites had significant differences in all mobility metrics, no site had the highest mobility for every metric, a distinction that was not captured by the composite mobility score. Further, the effects of sex, age, and season on mobility were all dependent on site. No factor significantly influenced the number of trips to locations, a common way to aggregate datasets. When collecting and analyzing human mobility data, it is difficult to account for all the nuances; however, these analyses can help determine which metrics are most helpful and what underlying differences may be present.
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Affiliation(s)
- Kathryn L. Schaber
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Tamaki Kobayashi
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Marisa Hast
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Kelly M. Searle
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, Minnesota
| | - Timothy M. Shields
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Jailos Lubinda
- Telethon Kids Institute, Malaria Atlas Project, Nedlands, Australia
| | - Philip E. Thuma
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - James Lupiya
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - Mike Chaponda
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - Shungu Munyati
- Biomedical Research and Training Institute, Harare, Zimbabwe
| | - Lovemore Gwanzura
- Biomedical Research and Training Institute, Harare, Zimbabwe
- College of Health Sciences, University of Zimbabwe, Harare, Zimbabwe
| | - Sungano Mharakurwa
- Biomedical Research and Training Institute, Harare, Zimbabwe
- College of Health, Agriculture and Natural Sciences, Africa University, Mutare, Zimbabwe
| | - William J. Moss
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of Molecular Microbiology and Immunology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
- Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Amy Wesolowski
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
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10
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Dong B, Khan L, Smith M, Trevino J, Zhao B, Hamer GL, Lopez-Lemus UA, Molina AA, Lubinda J, Nguyen USDT, Haque U. Spatio-temporal dynamics of three diseases caused by Aedes-borne arboviruses in Mexico. Commun Med (Lond) 2022; 2:134. [PMID: 36317054 PMCID: PMC9616936 DOI: 10.1038/s43856-022-00192-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 09/20/2022] [Indexed: 11/07/2022] Open
Abstract
Background The intensity of transmission of Aedes-borne viruses is heterogeneous, and multiple factors can contribute to variation at small spatial scales. Illuminating drivers of heterogeneity in prevalence over time and space would provide information for public health authorities. The objective of this study is to detect the spatiotemporal clusters and determine the risk factors of three major Aedes-borne diseases, Chikungunya virus (CHIKV), Dengue virus (DENV), and Zika virus (ZIKV) clusters in Mexico. Methods We present an integrated analysis of Aedes-borne diseases (ABDs), the local climate, and the socio-demographic profiles of 2469 municipalities in Mexico. We used SaTScan to detect spatial clusters and utilize the Pearson correlation coefficient, Randomized Dependence Coefficient, and SHapley Additive exPlanations to analyze the influence of socio-demographic and climatic factors on the prevalence of ABDs. We also compare six machine learning techniques, including XGBoost, decision tree, Support Vector Machine with Radial Basis Function kernel, K nearest neighbors, random forest, and neural network to predict risk factors of ABDs clusters. Results DENV is the most prevalent of the three diseases throughout Mexico, with nearly 60.6% of the municipalities reported having DENV cases. For some spatiotemporal clusters, the influence of socio-economic attributes is larger than the influence of climate attributes for predicting the prevalence of ABDs. XGBoost performs the best in terms of precision-measure for ABDs prevalence. Conclusions Both socio-demographic and climatic factors influence ABDs transmission in different regions of Mexico. Future studies should build predictive models supporting early warning systems to anticipate the time and location of ABDs outbreaks and determine the stand-alone influence of individual risk factors and establish causal mechanisms.
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Affiliation(s)
- Bo Dong
- Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080 USA
| | - Latifur Khan
- Department of Computer Science, University of Texas at Dallas, Richardson, TX 75080 USA
| | - Madison Smith
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX USA
| | - Jesus Trevino
- Department of Urban Affiars at the School of Architecture, Universidad Autónoma de Nuevo León, 66455 San Nicolás de los Garza, Nuevo Léon Mexico
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Gabriel L Hamer
- Department of Entomology, Texas A&M University, College Station, TX USA
| | - Uriel A Lopez-Lemus
- Department of Health Sciences, Center for Biodefense and Global Infectious Diseases, Colima, 28078 Mexico
| | - Aracely Angulo Molina
- Department of Chemical and Biological Sciences, University of Sonora, Hermosillo 83000 Sonora, Mexico
| | - Jailos Lubinda
- Telethon Kids Institute, Malaria Atlas Project, Nedlands, WA Australia
| | - Uyen-Sa D T Nguyen
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX USA
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX USA
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Haque U, Naeem A, Wang S, Espinoza J, Holovanova I, Gutor T, Bazyka D, Galindo R, Sharma S, Kaidashev IP, Chumachenko D, Linnikov S, Annan E, Lubinda J, Korol N, Bazyka K, Zhyvotovska L, Zimenkovsky A, Nguyen USDT. The human toll and humanitarian crisis of the Russia-Ukraine war: the first 162 days. BMJ Glob Health 2022; 7:bmjgh-2022-009550. [PMID: 36167408 PMCID: PMC9511605 DOI: 10.1136/bmjgh-2022-009550] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 08/29/2022] [Indexed: 11/03/2022] Open
Abstract
BACKGROUND We examined the human toll and subsequent humanitarian crisis resulting from the Russian invasion of Ukraine, which began on 24 February 2022. METHOD We extracted and analysed data resulting from Russian military attacks on Ukrainians between 24 February and 4 August 2022. The data tracked direct deaths and injuries, damage to healthcare infrastructure and the impact on health, the destruction of residences, infrastructure, communication systems, and utility services - all of which disrupted the lives of Ukrainians. RESULTS As of 4 August 2022, 5552 civilians were killed outright and 8513 injured in Ukraine as a result of Russian attacks. Local officials estimate as many as 24 328 people were also killed in mass atrocities, with Mariupol being the largest (n=22 000) such example. Aside from wide swaths of homes, schools, roads, and bridges destroyed, hospitals and health facilities from 21 cities across Ukraine came under attack. The disruption to water, gas, electricity, and internet services also extended to affect supplies of medications and other supplies owing to destroyed facilities or production that ceased due to the war. The data also show that Ukraine saw an increase in cases of HIV/AIDS, tuberculosis, and Coronavirus (COVID-19). CONCLUSIONS The 2022 Russia-Ukraine War not only resulted in deaths and injuries but also impacted the lives and safety of Ukrainians through destruction of healthcare facilities and disrupted delivery of healthcare and supplies. The war is an ongoing humanitarian crisis given the continuing destruction of infrastructure and services that directly impact the well-being of human lives. The devastation, trauma and human cost of war will impact generations of Ukrainians to come.
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Affiliation(s)
- Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Amna Naeem
- Department of Statistics, Quaid-i-Azam University, Islamabad, Pakistan
| | - Shanshan Wang
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Juan Espinoza
- Children's Hospital Los Angeles, Los Angeles, CA, USA
| | | | - Taras Gutor
- Danylo Halytsky Lviv National Medical University, Lviv, Ukraine
| | - Dimitry Bazyka
- National Research Centre for Radiation Medicine, Kyiv, Ukraine
| | - Rebeca Galindo
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA
| | - Sadikshya Sharma
- School of Health Professions, University of Southern Mississippi, Hattiesburg, MA, USA
| | | | - Dmytro Chumachenko
- Department of mathematical modeling and artificial intelligence, National Aerospace University, Kharkiv, Ukraine
| | - Svyatoslav Linnikov
- Department of health promotion, Odesa Regional Center for Public Health, Odesa, Ukraine
| | - Esther Annan
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA
| | | | - Natalya Korol
- National Research Centre for Radiation Medicine, Kyiv, Ukraine
| | | | - Liliia Zhyvotovska
- Department of Psychiatry, Narcology and Medical Psychology, Poltava State Medical University, Poltava, Ukraine
| | | | - Uyen-Sa D T Nguyen
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA
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12
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Lubinda J, Haque U, Bi Y, Shad MY, Keellings D, Hamainza B, Moore AJ. Climate change and the dynamics of age-related malaria incidence in Southern Africa. Environ Res 2021; 197:111017. [PMID: 33766570 DOI: 10.1016/j.envres.2021.111017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 02/27/2021] [Accepted: 03/11/2021] [Indexed: 06/12/2023]
Abstract
In the last decade, many malaria-endemic countries, like Zambia, have achieved significant reductions in malaria incidence among children <5 years old but face ongoing challenges in achieving similar progress against malaria in older age groups. In parts of Zambia, changing climatic and environmental factors are among those suspectedly behind high malaria incidence. Changes and variations in these factors potentially interfere with intervention program effectiveness and alter the distribution and incidence patterns of malaria differentially between young children and the rest of the population. We used parametric and non-parametric statistics to model the effects of climatic and socio-demographic variables on age-specific malaria incidence vis-à-vis control interventions. Linear regressions, mixed models, and Mann-Kendall tests were implemented to explore trends, changes in trends, and regress malaria incidence against environmental and intervention variables. Our study shows that while climate parameters affect the whole population, their impacts are felt most by people aged ≥5 years. Climate variables influenced malaria substantially more than mosquito nets and indoor residual spraying interventions. We establish that climate parameters negatively impact malaria control efforts by exacerbating the transmission conditions via more conducive temperature and rainfall environments, which are augmented by cultural and socioeconomic exposure mechanisms. We argue that an intensified communications and education intervention strategy for behavioural change specifically targeted at ≥5 aged population where incidence rates are increasing, is urgently required and call for further malaria stratification among the ≥5 age groups in the routine collection, analysis and reporting of malaria mortality and incidence data.
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Affiliation(s)
- Jailos Lubinda
- School of Geography and Environmental Sciences, Ulster University, Coleraine, UK; School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom; School of Nursing, Faculty of Life & Health Sciences, Jordanstown, Newtownabbey, United Kingdom.
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Centre, Fort Worth, TX, 76107, USA; Department of Geography, University of Florida, Gainesville, FL, USA; Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA
| | - Yaxin Bi
- School of Computing, Ulster University, Jordanstown, Newtownabbey, UK
| | | | - David Keellings
- Department of Geography, University of Alabama, Tuscaloosa, AL, USA
| | - Busiku Hamainza
- Ministry of Health, National Malaria Elimination Center, Lusaka, Zambia
| | - Adrian J Moore
- School of Geography and Environmental Sciences, Ulster University, Coleraine, UK
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13
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Lubinda J, Bi Y, Hamainza B, Haque U, Moore AJ. Modelling of malaria risk, rates, and trends: A spatiotemporal approach for identifying and targeting sub-national areas of high and low burden. PLoS Comput Biol 2021; 17:e1008669. [PMID: 33647029 PMCID: PMC7951982 DOI: 10.1371/journal.pcbi.1008669] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 03/11/2021] [Accepted: 01/04/2021] [Indexed: 01/16/2023] Open
Abstract
While mortality from malaria continues to decline globally, incidence rates in many countries are rising. Within countries, spatial and temporal patterns of malaria vary across communities due to many different physical and social environmental factors. To identify those areas most suitable for malaria elimination or targeted control interventions, we used Bayesian models to estimate the spatiotemporal variation of malaria risk, rates, and trends to determine areas of high or low malaria burden compared to their geographical neighbours. We present a methodology using Bayesian hierarchical models with a Markov Chain Monte Carlo (MCMC) based inference to fit a generalised linear mixed model with a conditional autoregressive structure. We modelled clusters of similar spatiotemporal trends in malaria risk, using trend functions with constrained shapes and visualised high and low burden districts using a multi-criterion index derived by combining spatiotemporal risk, rates and trends of districts in Zambia. Our results indicate that over 3 million people in Zambia live in high-burden districts with either high mortality burden or high incidence burden coupled with an increasing trend over 16 years (2000 to 2015) for all age, under-five and over-five cohorts. Approximately 1.6 million people live in high-incidence burden areas alone. Using our method, we have developed a platform that can enable malaria programs in countries like Zambia to target those high-burden areas with intensive control measures while at the same time pursue malaria elimination efforts in all other areas. Our method enhances conventional approaches and measures to identify those districts which had higher rates and increasing trends and risk. This study provides a method and a means that can help policy makers evaluate intervention impact over time and adopt appropriate geographically targeted strategies that address the issues of both high-burden areas, through intensive control approaches, and low-burden areas, via specific elimination programs.
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Affiliation(s)
- Jailos Lubinda
- School of Geography and Environmental Sciences, Ulster University, Coleraine, United Kingdom
- School of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, United Kingdom
| | - Yaxin Bi
- School of Computing, Ulster University, Newtownabbey, United Kingdom
| | - Busiku Hamainza
- Ministry of Health, National Malaria Elimination Centre, Lusaka, Zambia
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Centre, Fort Worth, Texas, United States of America
- Department of Geography, University of Florida, Gainesville, Florida, United States of America
- Emerging Pathogens Institute, University of Florida, Gainesville, Florida, United States of America
| | - Adrian J. Moore
- School of Geography and Environmental Sciences, Ulster University, Coleraine, United Kingdom
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Khan IM, Haque U, Zhang W, Zafar S, Wang Y, He J, Sun H, Lubinda J, Rahman MS. COVID-19 in China: Risk Factors and R 0 Revisited. Acta Trop 2021; 213:105731. [PMID: 33164890 PMCID: PMC7581355 DOI: 10.1016/j.actatropica.2020.105731] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2020] [Revised: 09/25/2020] [Accepted: 10/14/2020] [Indexed: 01/06/2023]
Abstract
The COVID-19 epidemic spread rapidly through China and subsequently proliferated globally leading to a pandemic situation around the globe. Human-to-human transmission, as well as asymptomatic transmission of the infection, have been confirmed. As of April 03, 2020, public health crisis in China due to COVID-19 was potentially under control. We compiled a daily dataset of case counts, mortality, recovery, temperature, population density, and demographic information for each prefecture during the period of January 11 to April 07, 2020. Understanding the characteristics of spatial clustering of the COVID-19 epidemic and R0 is critical in effectively preventing and controlling the ongoing global pandemic. Considering this, the prefectures were grouped based on several relevant features using unsupervised machine learning techniques. Subsequently, we performed a computational analysis utilizing the reported cases in China to estimate the revised R0 among different regions. Finally, our overall research indicates that the impact of temperature and demographic factors on virus transmission may be characterized using a stochastic transmission model. Such predictions will help in prevention planning in an ongoing global pandemic, prioritizing segments of a given community/region for action and providing a visual aid in designing prevention strategies for a specific geographic region. Furthermore, revised estimation and our methodology will aid in improving the human health consequences of COVID-19 elsewhere.
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Affiliation(s)
- Irtesam Mahmud Khan
- Department of Computer Science & Engineering, Bangladesh University of Engineering & Technology, West Palasi, Dhaka 1205, Bangladesh.
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA.
| | - Wenyi Zhang
- Center for Disease Surveillance and Research, Center for Disease Control and Prevention of PLA, Beijing, People's Republic of China.
| | | | - Yong Wang
- Center for Disease Surveillance and Research, Center for Disease Control and Prevention of PLA, Beijing, People's Republic of China.
| | - Junyu He
- Ocean College, Zhejiang University, Zhoushan, People's Republic of China; Ocean Academy, Zhejiang University, Zhoushan, People's Republic of China.
| | - Hailong Sun
- Center for Disease Surveillance and Research, Center for Disease Control and Prevention of PLA, Beijing, People's Republic of China.
| | - Jailos Lubinda
- School of Geography and Environmental Sciences, Ulster University, Coleraine, UK.
| | - M Sohel Rahman
- Department of Computer Science & Engineering, Bangladesh University of Engineering & Technology, West Palasi, Dhaka 1205, Bangladesh.
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15
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Mihreteab S, Lubinda J, Zhao B, Rodriguez-Morales AJ, Karamehic-Muratovic A, Goitom A, Shad MY, Haque U. Retrospective data analyses of social and environmental determinants of malaria control for elimination prospects in Eritrea. Parasit Vectors 2020; 13:126. [PMID: 32164770 PMCID: PMC7068948 DOI: 10.1186/s13071-020-3974-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 02/17/2020] [Indexed: 11/17/2022] Open
Abstract
Background The present study focuses on both long- and short-term malaria transmission in Eritrea and investigates the risk factors. Annual aggregates of information on malaria cases, deaths, diagnostics and control interventions from 2001 to 2008 and monthly reported data from 2009 to 2017 were obtained from the National Malaria Control Programme. We used a generalized linear regression model to examine the associations among total malaria cases, death, insecticide-treated net coverage, indoor residual spraying and climatic parameters. Results Reduction in malaria mortality is demonstrated by the milestone margins of over 97% by the end of 2017. Malaria incidence likewise declined during the period (from 33 to 5 per 1000 population), representing a reduction of about 86% (R2 = 0.3) slightly less than the decline in mortality. The distribution of insecticide treated nets generally declined between 2001 and 2014 (R2 = 0.16) before increasing from 2015 to 2017, while the number of people protected by indoor residual spraying slightly increased (R2 = 0.27). Higher rainfall was significantly associated with an increased number of malaria cases. The covariates rainfall and temperature are a better pair than IRS and LLIN to predict incidences. On the other hand, IRS and LLIN is a more significant pair to predict mortality cases. Conclusions While Eritrea has made significant progress towards malaria elimination, this progress should be maintained and further improved. Distribution, coverage and utilization of malaria control and elimination tools should be optimized and sustained to safeguard the gains made. Additionally, consistent annual performance evaluation of malaria indicators would ensure a continuous learning process from gains/threats of epidemics and resurgence in regions already earmarked for elimination.
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Affiliation(s)
- Selam Mihreteab
- National Malaria Control Programme, Ministry of Health, Asmara, Eritrea.
| | - Jailos Lubinda
- School of Geography and Environmental Sciences, Ulster University, Coleraine, UK
| | - Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alfonso J Rodriguez-Morales
- Public Health and Infection Research Group, Faculty of Health Sciences, Universidad Tecnológica de Pereira, Pereira, Risaralda, Colombia.,Medical School, Faculty of Health Sciences, UniFranz, Cochabamba, Bolivia.,Grupo de Investigación Biomedicina, Faculty of Medicine, Fundación Universitaria Autónoma de las Américas, Pereira, Risaralda, Colombia
| | | | - Aman Goitom
- National Malaria Control Programme, Ministry of Health, Asmara, Eritrea
| | | | - Ubydul Haque
- Department of Geography, University of Florida, Gainesville, FL, USA.,Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA.,Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX, USA
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Hast M, Searle KM, Chaponda M, Lupiya J, Lubinda J, Sikalima J, Kobayashi T, Shields T, Mulenga M, Lessler J, Moss WJ. The use of GPS data loggers to describe the impact of spatio-temporal movement patterns on malaria control in a high-transmission area of northern Zambia. Int J Health Geogr 2019; 18:19. [PMID: 31426819 PMCID: PMC6701131 DOI: 10.1186/s12942-019-0183-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2019] [Accepted: 08/10/2019] [Indexed: 12/01/2022] Open
Abstract
Background Human movement is a driver of malaria transmission and has implications for sustainable malaria control. However, little research has been done on the impact of fine-scale movement on malaria transmission and control in high-transmission settings. As interest in targeted malaria control increases, evaluations are needed to determine the appropriateness of these strategies in the context of human mobility across a variety of transmission settings. Methods A human mobility study was conducted in Nchelenge District, a high-transmission setting in northern Zambia. Over 1 year, 84 participants were recruited from active malaria surveillance cohorts to wear a global positioning system data logger for 1 month during all daily activity. Participants completed a survey questionnaire and underwent malaria testing and treatment at the time of logger distribution and at collection 1 month later. Incident malaria infections were identified using polymerase chain reaction. Participant movement was characterized throughout the study area and across areas targeted for an indoor residual spraying (IRS) intervention. Participant movement patterns were compared using movement intensity maps, activity space plots, and statistical analyses. Malaria risk was characterized across participants using spatial risk maps and time spent away from home during peak vector biting hours. Results Movement data were collected from 82 participants, and 63 completed a second study visit. Participants exhibited diverse mobility patterns across the study area, including movement into and out of areas targeted for IRS, potentially mitigating the impact of IRS on parasite prevalence. Movement patterns did not differ significantly by season or age, but male participants traveled longer distances and spent more time away from home. Monthly malaria incidence was 22%, and malaria risk was characterized as high across participants. Participants with incident parasitemia traveled a shorter distance and spent more time away from home during peak biting hours; however, these relationships were not statistically significant, and malaria risk score did not differ by incident parasitemia. Conclusions Individual movement patterns in Nchelenge District, Zambia have implications for malaria control, particularly the effectiveness of targeted IRS strategies. Large and fine-scale population mobility patterns should be considered when planning intervention strategies across transmission settings. Electronic supplementary material The online version of this article (10.1186/s12942-019-0183-y) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Marisa Hast
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | - Kelly M Searle
- University of Minnesota, School of Public Health, Minneapolis, MN, USA
| | - Mike Chaponda
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - James Lupiya
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - Jailos Lubinda
- Macha Research Trust, Choma District, Choma, Zambia.,Ulster University, Coleraine, Northern Ireland, UK
| | - Jay Sikalima
- The Tropical Diseases Research Centre, Ndola, Zambia
| | - Tamaki Kobayashi
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Timothy Shields
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | - Justin Lessler
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - William J Moss
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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17
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Lubinda J, Treviño C JA, Walsh MR, Moore AJ, Hanafi-Bojd AA, Akgun S, Zhao B, Barro AS, Begum MM, Jamal H, Angulo-Molina A, Haque U. Environmental suitability for Aedes aegypti and Aedes albopictus and the spatial distribution of major arboviral infections in Mexico. Parasite Epidemiol Control 2019; 6:e00116. [PMID: 31528740 PMCID: PMC6742751 DOI: 10.1016/j.parepi.2019.e00116] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 07/25/2019] [Accepted: 08/01/2019] [Indexed: 11/30/2022] Open
Abstract
Background This paper discusses a comparative geographic distribution of Aedes aegypti and Aedes albopictus mosquitoes in Mexico, using environmental suitability modeling and reported cases of arboviral infections. Methods Using presence-only records, we modeled mosquito niches to show how much they influenced the distribution of Ae. aegypti and Ae. albopictus based on mosquito records collected at the municipality level. Mosquito surveillance data were used to create models regarding the predicted suitability of Ae. albopictus and Ae. aegypti mosquitos in Mexico. Results Ae. albopictus had relatively a better predictive performance (area under the curve, AUC = 0.87) to selected bioclimatic variables compared to Ae. aegypti (AUC = 0.81). Ae. aegypti were more suitable for areas with minimum temperature of coldest month (Bio6, permutation importance 28.7%) −6 °C to 21.5 °C, cumulative winter growing degree days (GDD) between 40 and 500, and precipitation of wettest month (Bio13) >8.4 mm. Minimum temperature range of the coldest month (Bio6) was −6.6 °C to 20.5 °C, and average precipitation of the wettest month (Bio13) 8.9 mm ~ 600 mm were more suitable for the existence of Ae. albopictus. However, arboviral infections maps prepared from the 2012–2016 surveillance data showed cases were reported far beyond predicted municipalities. Conclusions This study identified the urgent necessity to start surveillance in 925 additional municipalities that reported arbovirus infections but did not report Aedes mosquito.
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Affiliation(s)
- Jailos Lubinda
- School of Geography and Environmental Sciences, Ulster University, Coleraine, United Kingdom
| | - Jesús A Treviño C
- Universidad Autónoma de Nuevo León, San Nicolás de los Garza, Nuevo Léon, Mexico
| | - Mallory Rose Walsh
- Department of Public Health and Prevention Sciences, Baldwin Wallace University, Berea, OH 44017, USA
| | - Adrian J Moore
- School of Geography and Environmental Sciences, Ulster University, Coleraine, United Kingdom
| | - Ahmad Ali Hanafi-Bojd
- Department of Medical Entomology and Vector Control, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Seval Akgun
- Baskent University School of Medicine, Public Health Department, Baskent University, Turkey
| | - Bingxin Zhao
- Department of Biostatistics, University of North Carolina at Chapel Hill, NC, USA
| | - Alassane S Barro
- African Group Organized for Research and Actions in Health, Burkina Faso
| | - Mst Marium Begum
- Department of Pharmacy, East West University, Dhaka 1212, Bangladesh
| | - Hera Jamal
- Department of Biology, University of Miami, Florida, USA
| | - Aracely Angulo-Molina
- Department of Chemical & Biological Sciences/DIFUS, University of Sonora (UNISON), Luis Encinas and Rosales S/N, Col. Centro, C.P. 83000, Hermosillo, Sonora, Mexico
| | - Ubydul Haque
- Department of Biostatistics and Epidemiology, University of North Texas Health Science Center, Fort Worth, TX 76107, USA
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18
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Deutsch-Feldman M, Hamapumbu H, Lubinda J, Musonda M, Katowa B, Searle KM, Kobayashi T, Shields TM, Stevenson JC, Thuma PE, Moss WJ, For The Southern Africa International Centers Of Excellence For Malaria Research. Efficiency of a Malaria Reactive Test-and-Treat Program in Southern Zambia: A Prospective, Observational Study. Am J Trop Med Hyg 2018; 98:1382-1388. [PMID: 29557330 DOI: 10.4269/ajtmh.17-0865] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
To improve malaria surveillance and achieve elimination, the Zambian National Malaria Elimination Program implemented a reactive test-and-treat program in Southern Province in 2013 in which individuals with rapid diagnostic test (RDT)-confirmed malaria are followed-up at their home within 1 week of diagnosis. Individuals present at the index case household and those residing within 140 m of the index case are tested with an RDT and treated with artemether-lumefantrine if positive. This study evaluated the efficiency of this reactive test-and-treat strategy by characterizing infected individuals missed by the RDT and the current screening radius. The radius was expanded to 250 m, and a quantitative polymerase chain reaction (qPCR) test was performed on dried blood spot specimens. From January 2015 through March 2016, 145 index cases were identified at health centers and health posts. A total of 3,333 individuals residing in 525 households were screened. Excluding index cases, the parasite prevalence was 1.1% by RDT (33 positives of 3,016 participants) and 2.4% by qPCR (73 positives of 3,016 participants). Of the qPCR-positive cases, 62% of 73 individuals tested negative by RDT. Approximately half of the infected individuals resided within the index case household (58% of RDT-positive individuals and 48% of qPCR-positive individuals). The low sensitivity of the RDT and the high proportion of secondary cases within the index case household decreased the efficiency of this reactive test-and-treat strategy. Reactive focal drug administration in index case households would be a more efficient approach to treating infected individuals associated with a symptomatic case.
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Affiliation(s)
- Molly Deutsch-Feldman
- Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina
| | | | | | | | - Ben Katowa
- Macha Research Trust, Choma District, Zambia
| | - Kelly M Searle
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Tamaki Kobayashi
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Timothy M Shields
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Jennifer C Stevenson
- Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.,Macha Research Trust, Choma District, Zambia
| | - Philip E Thuma
- Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.,Macha Research Trust, Choma District, Zambia
| | - William J Moss
- Department of Molecular Microbiology and Immunology, Johns Hopkins Malaria Research Institute, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland.,Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
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19
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Searle KM, Lubinda J, Hamapumbu H, Shields TM, Curriero FC, Smith DL, Thuma PE, Moss WJ. Characterizing and quantifying human movement patterns using GPS data loggers in an area approaching malaria elimination in rural southern Zambia. R Soc Open Sci 2017; 4:170046. [PMID: 28573009 PMCID: PMC5451810 DOI: 10.1098/rsos.170046] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2017] [Accepted: 04/04/2017] [Indexed: 05/23/2023]
Abstract
In areas approaching malaria elimination, human mobility patterns are important in determining the proportion of malaria cases that are imported or the result of low-level, endemic transmission. A convenience sample of participants enrolled in a longitudinal cohort study in the catchment area of Macha Hospital in Choma District, Southern Province, Zambia, was selected to carry a GPS data logger for one month from October 2013 to August 2014. Density maps and activity space plots were created to evaluate seasonal movement patterns. Time spent outside the household compound during anopheline biting times, and time spent in malaria high- and low-risk areas, were calculated. There was evidence of seasonal movement patterns, with increased long-distance movement during the dry season. A median of 10.6% (interquartile range (IQR): 5.8-23.8) of time was spent away from the household, which decreased during anopheline biting times to 5.6% (IQR: 1.7-14.9). The per cent of time spent in malaria high-risk areas for participants residing in high-risk areas ranged from 83.2% to 100%, but ranged from only 0.0% to 36.7% for participants residing in low-risk areas. Interventions targeted at the household may be more effective because of restricted movement during the rainy season, with limited movement between high- and low-risk areas.
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Affiliation(s)
- Kelly M. Searle
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Author for correspondence: Kelly M. Searle e-mail:
| | | | | | - Timothy M. Shields
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Frank C. Curriero
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - David L. Smith
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, WA, USA
| | | | - William J. Moss
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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Searle KM, Hamapumbu H, Lubinda J, Shields TM, Pinchoff J, Kobayashi T, Stevenson JC, Bridges DJ, Larsen DA, Thuma PE, Moss WJ. Evaluation of the operational challenges in implementing reactive screen-and-treat and implications of reactive case detection strategies for malaria elimination in a region of low transmission in southern Zambia. Malar J 2016; 15:412. [PMID: 27527347 PMCID: PMC4986207 DOI: 10.1186/s12936-016-1460-x] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 07/29/2016] [Indexed: 11/16/2022] Open
Abstract
Background As malaria transmission declines in many regions of sub-Saharan Africa, interventions to identify the asymptomatic reservoir are being deployed with the goals of improving surveillance and interrupting transmission. Reactive case detection strategies, in which individuals with clinical malaria are followed up at their home and household residents and neighbours are screened and treated for malaria, are increasingly used as part of malaria elimination programmes. Methods A reactive screen-and-treat programme was implemented by the National Malaria Control Centre in Southern Province, Zambia, in which individuals residing within 140 m of an index case were screened with a malaria rapid diagnostic test (RDT) and treated if positive. The operational challenges during the early stages of implementing this reactive screen-and-treat programme in the catchment area of Macha Hospital in Southern Province, Zambia were assessed using rural health centre records, ground truth evaluation of community health worker performance, and data from serial cross-sectional surveys. The proportion of individuals infected with Plasmodium falciparum who were identified and treated was estimated by simulating reactive screen-and-treat and focal drug administration cascades. Results Within the 1st year of implementation, community health workers followed up 32 % of eligible index cases. When index cases were followed up, 66 % of residents were at home in the index households and 58 % in neighbouring households. Forty-one neighbouring households of 26 index households were screened, but only 13 (32 %) were within the 140-m screening radius. The parasite prevalence by RDT was 22 % in index households and 5 % in neighbouring households. In a simulation model with complete follow-up, 22 % of the total infected population would be detected with reactive screen-and-treat but 57 % with reactive focal drug administration. Conclusions With limited resources, coverage and diagnostic tools, reactive screen-and-treat will likely not be sufficient to achieve malaria elimination in this setting. However, high coverage with reactive focal drug administration could be efficient at decreasing the reservoir of infection and should be considered as an alternative strategy. Electronic supplementary material The online version of this article (doi:10.1186/s12936-016-1460-x) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Kelly M Searle
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. .,Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
| | | | | | - Timothy M Shields
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.,Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Jessie Pinchoff
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.,Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Tamaki Kobayashi
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.,Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Jennifer C Stevenson
- Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.,Macha Research Trust, Choma District, Zambia
| | | | - David A Larsen
- Akros, Cresta Golfview Grounds, Great East Road, Lusaka, Zambia.,Department of Public Health, Food Studies and Nutrition, Syracuse University, Syracuse, NY, USA
| | - Philip E Thuma
- Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.,Macha Research Trust, Choma District, Zambia
| | - William J Moss
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.,Johns Hopkins Malaria Research Institute, Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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21
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Shields T, Pinchoff J, Lubinda J, Hamapumbu H, Searle K, Kobayashi T, Thuma PE, Moss WJ, Curriero FC. Spatial and temporal changes in household structure locations using high-resolution satellite imagery for population assessment: an analysis in southern Zambia, 2006-2011. Geospat Health 2016; 11:410. [PMID: 27245798 PMCID: PMC4890610 DOI: 10.4081/gh.2016.410] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Revised: 01/07/2016] [Accepted: 01/10/2016] [Indexed: 06/05/2023]
Abstract
Satellite imagery is increasingly available at high spatial resolution and can be used for various purposes in public health research and programme implementation. Comparing a census generated from two satellite images of the same region in rural southern Zambia obtained four and a half years apart identified patterns of household locations and change over time. The length of time that a satellite image-based census is accurate determines its utility. Households were enumerated manually from satellite images obtained in 2006 and 2011 of the same area. Spatial statistics were used to describe clustering, cluster detection, and spatial variation in the location of households. A total of 3821 household locations were enumerated in 2006 and 4256 in 2011, a net change of 435 houses (11.4% increase). Comparison of the images indicated that 971 (25.4%) structures were added and 536 (14.0%) removed. Further analysis suggested similar household clustering in the two images and no substantial difference in concentration of households across the study area. Cluster detection analysis identified a small area where significantly more household structures were removed than expected; however, the amount of change was of limited practical significance. These findings suggest that random sampling of households for study participation would not induce geographic bias if based on a 4.5-year-old image in this region. Application of spatial statistical methods provides insights into the population distribution changes between two time periods and can be helpful in assessing the accuracy of satellite imagery.
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Affiliation(s)
- Timothy Shields
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD.
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Kamuliwo M, Kirk KE, Chanda E, Elbadry MA, Lubinda J, Weppelmann TA, Mukonka VM, Zhang W, Mushinge G, Mwanza-Ingwe M, Haque U. Spatial patterns and determinants of malaria infection during pregnancy in Zambia. Trans R Soc Trop Med Hyg 2015; 109:514-21. [PMID: 26160256 DOI: 10.1093/trstmh/trv049] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2015] [Accepted: 05/18/2015] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Malaria in pregnancy (MiP) is a major concern in Zambia. Here we aim to determine the burden and risk factors of MiP. METHODS Monthly reported district-level malaria cases among pregnant women (count data) from January 2009 to December 2014 were obtained from the Zambian District Health Information System. Negative binomial regression model was used to investigate the associations between vector control tools, coverage of health care facilities, transportation networks and population density. Data on MiP treatment were obtained from the 2012 Zambian Malaria Indicator Survey. Yearly clusters of MiP were investigated using spatial statistics in ArcGIS v 10.1. RESULTS The results indicated that MiP decreased in Zambia between 2010 and 2013. MiP was observed throughout the year, but showed a strong seasonal pattern. Persistent hotspots of MiP were reported in the southeast and northeast regions of Zambia, with districts that had better access to rail road and presence of water bodies associated with decreased prevalence of MiP. Better indoor residual spraying and long-lasting insecticide-treated nets coverage was demonstrated to be protective against MiP. CONCLUSIONS Mapping the distribution of MiP to track the future requirements for scaling up essential disease-prevention efforts in stable hotspots can help the Zambian National Malaria Control Center to further develop strategies to reduce malaria prevalence in this vulnerable sub-population.
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Affiliation(s)
- Mulakwa Kamuliwo
- Ministry of Health, National Malaria Control Centre, Lusaka, Zambia
| | | | - Emmanuel Chanda
- Vector Control Specialist/Consultant, 11 Granite Street, Plot 33421/917 Kamwa South, Lusaka, Zambia
| | - Maha A Elbadry
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | | | - Thomas A Weppelmann
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA Department of Environmental and Global Health, College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA
| | - Victor M Mukonka
- Department of Public Health, Copperbelt University, School of Medicine, Ndola, Zambia
| | - Wenyi Zhang
- Institute of Disease Control and Prevention, Center for Disease Control, Beijing, People's Republic of China
| | | | | | - Ubydul Haque
- Emerging Pathogens Institute, University of Florida, Gainesville, FL, USA Department of Geography, University of Florida, Gainesville, FL, USA
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Mukonka VM, Chanda E, Kamuliwo M, Elbadry MA, Wamulume PK, Mwanza-Ingwe M, Lubinda J, Laytner LA, Zhang W, Mushinge G, Haque U. Diagnostic approaches to malaria in Zambia, 2009-2014. Geospat Health 2015; 10:330. [PMID: 26054519 DOI: 10.4081/gh.2015.330] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2015] [Revised: 04/04/2015] [Accepted: 04/09/2015] [Indexed: 06/04/2023]
Abstract
Malaria is an important health burden in Zambia with proper diagnosis remaining as one of the biggest challenges. The need for reliable diagnostics is being addressed through the introduction of rapid diagnostic tests (RDTs). However, without sufficient laboratory amenities in many parts of the country, diagnosis often still relies on non-specific, clinical symptoms. In this study, geographical information systems were used to both visualize and analyze the spatial distribution and the risk factors related to the diagnosis of malaria. The monthly reported, district-level number of malaria cases from January 2009 to December 2014 were collected from the National Malaria Control Center (NMCC). Spatial statistics were used to reveal cluster tendencies that were subsequently linked to possible risk factors, using a non-spatial regression model. Significant, spatio-temporal clusters of malaria were spotted while the introduction of RDTs made the number of clinically diagnosed malaria cases decrease by 33% from 2009 to 2014. The limited access to road network(s) was found to be associated with higher levels of malaria, which can be traced by the expansion of health promotion interventions by the NMCC, indicating enhanced diagnostic capability. The capacity of health facilities has been strengthened with the increased availability of proper diagnostic tools and through retraining of community health workers. To further enhance spatial decision support systems, a multifaceted approach is required to ensure mobilization and availability of human, infrastructural and technological resources. Surveillance based on standardized geospatial or other analytical methods should be used by program managers to design, target, monitor and assess the spatio-temporal dynamics of malaria diagnostic resources country-wide.
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