Prince Michael Amegbor

Assistant Professor of Global and Environmental Health

Professional overview

As a health geographer using big data and a multi-methods approach in his research on the environmental and social determinants of health, Dr. Prince Michael Amegbor is an assistant professor in the Department of Global and Environmental Health. He specializes in visualizing the geospatial distribution of risks and burdens on health that are associated with environmental exposures. He works to unravel how factors such as climate change, air pollution and other environmental exposures contribute to health inequalities, particularly in Sub-Saharan Africa and other geographic contexts (e.g., Denmark).

Prior to his appointment at GPH, Dr. Amegbor was a postdoctoral research fellow with the Big Data Centre for Environment and Health (BERTHA) and the Department of Environmental Science at Aarhus University (Denmark). He is also a guest researcher at Statistics Denmark and has worked as a co-task leader of two European Union Horizon 2020 Projects: REGREEN and ICARUS (Integrated Climate forcing and Air pollution Reduction in Urban Systems).

Dr. Amegbor has published dozens of articles in peer-reviewed scholarly journals including Scientific Report, Health & Place and Applied Geography. He earned his PhD in human geography from Queen’s University in Ontario, and holds an MPhil in development geography from the University of Oslo. He obtained his undergraduate degree in geography and resource development from the University of Ghana, Legon.

 

 

Below are links to the results from ICARUS – Favorite Location Study published in the Environment and Planning B: Urban Analytics and City Science journal:

• Results of Baseline Models for Copenhagen
• Results of Main Models for Copenhagen
• Results of Baseline Models for Roskilde
• Results of Main Models for Roskilde

Education

PhD, Department of Geography & Planning, Queen’s University, Kingston, Ontario

MPhil Developmental Geography, University of Oslo, Oslo, Norway

BA Geography & Resource Development, University of Ghana, Accra

Honors and awards

Principal's International Doctoral Award, Queen’s University (201520162017)

Quota Scheme Scholarship, Department of Sociology & Human Geography, University of Oslo (201220132014)

Areas of research and study

Aging and the Life Course

Alternative Medicine

Child Health

Complementary Medicine

Environmental Public Health Services

Immigrant Health

Public Health Policy

Social Determinants of Health

Socio-cultural Identities and Health Seeking Behaviors

Traditional Medicine

Urban Geography

Violence and Victimisation

Women's Health

Publications

Publications

Early-life air pollution and green space exposures as determinants of stunting among children under age five in Sub-Saharan Africa

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

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Publication year

2024

Journal title

The Lancet

Volume

403

Issue

10440

Page(s)

2100-2132

10.1016/S0140-6736(24)00367-2

Abstract

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.

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

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Publication year

2024

Journal title

The Lancet

Volume

403

Issue

10440

Page(s)

2133-2161

10.1016/S0140-6736(24)00757-8

Abstract

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.

Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990–2021: a systematic analysis from the Global Burden of Disease Study 2021

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Publication year

2024

Journal title

The Lancet Infectious Diseases

Volume

24

Issue

9

Page(s)

974-1002

10.1016/S1473-3099(24)00176-2

Abstract

Abstract

Background: Lower respiratory infections (LRIs) are a major global contributor to morbidity and mortality. In 2020–21, non-pharmaceutical interventions associated with the COVID-19 pandemic reduced not only the transmission of SARS-CoV-2, but also the transmission of other LRI pathogens. Tracking LRI incidence and mortality, as well as the pathogens responsible, can guide health-system responses and funding priorities to reduce future burden. We present estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 of the burden of non-COVID-19 LRIs and corresponding aetiologies from 1990 to 2021, inclusive of pandemic effects on the incidence and mortality of select respiratory viruses, globally, regionally, and for 204 countries and territories. Methods: We estimated mortality, incidence, and aetiology attribution for LRI, defined by the GBD as pneumonia or bronchiolitis, not inclusive of COVID-19. We analysed 26 259 site-years of mortality data using the Cause of Death Ensemble model to estimate LRI mortality rates. We analysed all available age-specific and sex-specific data sources, including published literature identified by a systematic review, as well as household surveys, hospital admissions, health insurance claims, and LRI mortality estimates, to generate internally consistent estimates of incidence and prevalence using DisMod-MR 2.1. For aetiology estimation, we analysed multiple causes of death, vital registration, hospital discharge, microbial laboratory, and literature data using a network analysis model to produce the proportion of LRI deaths and episodes attributable to the following pathogens: Acinetobacter baumannii, Chlamydia spp, Enterobacter spp, Escherichia coli, fungi, group B streptococcus, Haemophilus influenzae, influenza viruses, Klebsiella pneumoniae, Legionella spp, Mycoplasma spp, polymicrobial infections, Pseudomonas aeruginosa, respiratory syncytial virus (RSV), Staphylococcus aureus, Streptococcus pneumoniae, and other viruses (ie, the aggregate of all viruses studied except influenza and RSV), as well as a residual category of other bacterial pathogens. Findings: Globally, in 2021, we estimated 344 million (95% uncertainty interval [UI] 325–364) incident episodes of LRI, or 4350 episodes (4120–4610) per 100 000 population, and 2·18 million deaths (1·98–2·36), or 27·7 deaths (25·1–29·9) per 100 000. 502 000 deaths (406 000–611 000) were in children younger than 5 years, among which 254 000 deaths (197 000–320 000) occurred in countries with a low Socio-demographic Index. Of the 18 modelled pathogen categories in 2021, S pneumoniae was responsible for the highest proportions of LRI episodes and deaths, with an estimated 97·9 million (92·1–104·0) episodes and 505 000 deaths (454 000–555 000) globally. The pathogens responsible for the second and third highest episode counts globally were other viral aetiologies (46·4 million [43·6–49·3] episodes) and Mycoplasma spp (25·3 million [23·5–27·2]), while those responsible for the second and third highest death counts were S aureus (424 000 [380 000–459 000]) and K pneumoniae (176 000 [158 000–194 000]). From 1990 to 2019, the global all-age non-COVID-19 LRI mortality rate declined by 41·7% (35·9–46·9), from 56·5 deaths (51·3–61·9) to 32·9 deaths (29·9–35·4) per 100 000. From 2019 to 2021, during the COVID-19 pandemic and implementation of associated non-pharmaceutical interventions, we estimated a 16·0% (13·1–18·6) decline in the global all-age non-COVID-19 LRI mortality rate, largely accounted for by a 71·8% (63·8–78·9) decline in the number of influenza deaths and a 66·7% (56·6–75·3) decline in the number of RSV deaths. Interpretation: Substantial progress has been made in reducing LRI mortality, but the burden remains high, especially in low-income and middle-income countries. During the COVID-19 pandemic, with its associated non-pharmaceutical interventions, global incident LRI cases and mortality attributable to influenza and RSV declined substantially. Expanding access to health-care services and vaccines, including S pneumoniae, H influenzae type B, and novel RSV vaccines, along with new low-cost interventions against S aureus, could mitigate the LRI burden and prevent transmission of LRI-causing pathogens. Funding: Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care (UK).

Modelling the spatial risk pattern of dementia in Denmark using residential location data: A registry-based national cohort

Smoke exposure, hemoglobin levels and the prevalence of anemia: a cross-sectional study in urban informal settlement in Southern Ghana

Appiah-Dwomoh, C., Tettey, P., Akyeampong, E., Amegbor, P., Okello, G., Botwe, P. K., & Quansah, R. (n.d.).

Publication year

2024

Journal title

BMC public health

Volume

24

Issue

1

10.1186/s12889-024-18304-4

Abstract

Abstract

Background: In sub-Saharan African cities, more than half of the population lives in informal settlements. These settlements are close to smoky dumpsites, industrial plants, and polluted roads. Furthermore, polluting fuels remain their primary sources of energy for cooking and heating. Despite evidence linking smoke and its components to anaemia, none of these studies were conducted on populations living in urban informal settlements. This study investigated the risks of anemia/mean Haemoglobin (HB) levels in an informal settlement in Accra, Ghana. Exposure to smoke was examined across various sources, encompassing residences, neighborhoods, and workplaces. Methods: The study was a facility-based cross-sectional design among residents at Chorkor, an informal settlement in the Greater Accra region of Ghana. A questionnaire was administered at a community hospital during an interview to gather data on sources of smoke exposure in the household, in the neighbourhood, and in the workplace. A phlebotomist collected blood samples from the participants after the interview to assess their anaemia status. Results: The population (n = 320) had a high prevalence of anemia, with 49.1% of people fitting the WHO’s definition of anemia, while the average HB level was 12.6 ± 2.1 g/dL. Anemia was associated with the number of different types of waste burnt simultaneously [(1 or 2: prevalence ratio (PR): 95% confidence interval (CI), 1.14, 0.99–1.28: 3+: 1.16, 1.01–1.63, p-for-trend = 0.0082)], fuel stacking [(mixed stacking: 1.27, 1.07–1.20: dirty stacking:1.65, 1.19–2.25, p-for-trend = 0.0062)], and involvement in fish smoking (1.22, 0.99–1.06). However, the lower limit of the CIs for number of different forms of garbage burned simultaneously and engagement in fish smoking included unity. Reduced mean HB levels were associated with the number of different types of waste burnt simultaneously [(1 or 2: regression coefficient (β): 95% confidence interval (CI), -0.01, -0.97- -0.99: 3+: -0.14, -0.77- -0.05)], current smoker [(yes, almost daily: -1.40, -2.01- -0.79: yes, at least once a month: -1.14, -1.79- -0.48)], Second-Hand-Smoking (SHS) (yes, almost daily: -0.77, -1.30- -0.21), fuel stacking [(mixed stacking-0.93, -1.33–0.21: dirty stacking-1.04, -1.60- -0.48)], any smoke exposure indicator in the neighbourhood (-0.84, -1.43- -0.25), living close to a major road (-0.62, -1.09- -0.49), and fish smoking (-0.41,-0.93- -0.12). Conclusion: Although the cross-sectional design precludes causality, smoke exposure was associated with mean HB levels and anaemia among populations living in informal settlements.

Spatial modelling of psychosocial benefits of favourite places in Denmark: A tale of two cities

Assessing the association between overcrowding and human physiological stress response in different urban contexts: a case study in Salzburg, Austria

Global Burden of Cardiovascular Diseases and Risks, 1990-2022

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Publication year

2023

Journal title

Journal of the American College of Cardiology

Volume

82

Issue

25

Page(s)

2350-2473

10.1016/j.jacc.2023.11.007

Spatiotemporal analysis of the effect of global development indicators on child mortality

Assessing the association between urban features and human physiological stress response using wearable sensors in different urban contexts

Early-life environmental exposures and anaemia among children under age five in Sub-Saharan Africa: An insight from the Demographic & Health Surveys

Effect of individual, household and regional socioeconomic factors and PM2.5 on anaemia: A cross-sectional study of sub-Saharan African countries

Health and socioeconomic risk factors for overnight admission among older adults in Ghana

Individual and contextual predictors of overweight or obesity among women in Uganda: a spatio-temporal perspective

Assessing the current integration of multiple personalised wearable sensors for environment and health monitoring

Determinants of the type of health care sought for symptoms of Acute respiratory infection in children: analysis of Ghana demographic and health surveys

Examining Spatial Variability in the Association Between Male Partner Alcohol Misuse and Intimate Partner Violence Against Women in Ghana: A GWR Analysis

Social Frailty and Depression Among Older Adults in Ghana: Insights from the WHO SAGE Surveys

The effect of sociodemographic factors on the risk of poor mental health in Akron (Ohio): A Bayesian hierarchical spatial analysis

Yankey, O., Amegbor, P. M., & Lee, J. (n.d.).

Publication year

2021

Journal title

Spatial and Spatio-temporal Epidemiology

Volume

38

10.1016/j.sste.2021.100438

Abstract

Abstract

We examined the association of sociodemographic factors on mental health risk within the city of Akron (Ohio). A Spatial Bayesian Hierarchical model was used in this study. We found that the risk of poor mental health was positively associated with the proportion of people lacking sufficient sleep (RR = 0.42, 95% CI:0.22-0.62), the percentage of people below poverty (RR = 0.12, 95% CI: 0.09, 0.16), and the percentage of married couples (RR = 0.02, 95% CI: -0.05, 0.08). On the contrary, the percentage of female population (RR = -0.06, 95% CI: -0.13, 0.01), the percentage of the black population (RR = -0.05, 95% CI: -0.08, -0.02), and the college-educated population (RR = -0.03, 95% CI: -0.09, 0.04) was negatively associated with the risk of poor mental health. We also found that the sociodemographic variables have spatially varying effects across different neighborhoods. Future studies will examine the joint spatial effect of poor mental health risk and suicide ideation in the study area.

The effect of socioeconomic and environmental factors on obesity: A spatial regression analysis

Understanding unmet health-care need among older Ghanaians: A gendered analysis

Urban Health and Wellbeing

Variations in Emotional, Sexual, and Physical Intimate Partner Violence Among Women in Uganda: A Multilevel Analysis

Determinants of Overnight Stay in Health Centres and Length of Admission: A Study of Canadian Seniors