Ameloblastic carcinoma: a clinicopathologic analysis of cases seen in a Nigerian Teaching Hospital and review of literature

Introduction: ameloblastic carcinoma is a rare malignant odontogenic neoplasm that exhibits histological features of ameloblastoma in combination with cytological atypia. It may arise de novo or secondarily through malignant de-differentiation of pre-existing ameloblastoma or odontogenic cyst. Secondary ameloblastic carcinomas often results from repeated surgical intervention, which is a mainstay of odontogenic tumor management in resource limited settings. To date, relatively few cases of ameloblastic carcinomas have been reported and many cases have been misdiagnosed as ameloblastoma. This is due to its wide range of clinicopathological feature which range from indolent to aggressive. It may present as an aggressive ulcerated mass or as a simple cystic lesion; hence, it often challenging to delineate from its benign counterpart, ameloblastoma. Methods: this study reviewed the clinicopathological data on 157 cases of odontogenic tumors diagnosed over a 10 years period from the pathology archive of the Oral Pathology Unit of Obafemi Awolowo University Teaching Hospital Complex (OAUTHC), Ile-Ife, Nigeria. Results: of all these cases, we identified that 64.9% were Ameloblastomas, while 8.3% were ameloblastic carcinomas. Primary subtypes of ameloblastic carcinoma constituted 23.08%, while 69.23% of the cases were of the secondary subtype. We also found that the secondary subtype of ameloblastic carcinomas showed a higher mean duration value of 7.7 years. Most lesions were found in posterior mandible and presented with ulceration, perforation and ill-defined borders radiographically. Conclusion: this study is among the few that have documented higher frequency of secondary ameloblastic carcinoma in the scientific literature

A geographical analysis of ethnic distribution of jaw ameloblastoma in Nigerians

Introduction: Ameloblastoma is the most common odontogenic tumour in Nigeria. A definite geographic variation has been observed in the frequency of odontogenic tumors from different parts of the world. However, there is no study on the regional variations in Nigeria. Hence, this study was designed to document the ethnic and geographical distribution of jaw ameloblastoma in Nigeria.Methods: Archival data on ameloblastoma from 10 health facilities were obtained. Global Moran’s I detected geographic clustering in its distribution while Local Getis Ord indicated the location of ameloblastoma clusters. Chi-square tested associations between variables at 0.05 level of significance.Results: A total of 1,246 ameloblastoma cases were recorded in Nigeria. Besides substantial state variations, a South-North gradient was noticed in its distribution. Significant positive spatial autocorrelation was observed in the three major groups while ameloblas- toma hotspots were found in the SouthWestern and Northwestern Nigeria. The Igbos had a higher prevalence of ameloblastoma outside their home region than within.Conclusion: The study hypothesized that the geographical distribution of ameloblastoma in Nigeria is the result of all or one of the following:  the country’s tropical climate, migration patterns and health seeking behavior. Hopefully, these claims should lead to further enquiry on the underlying causes.Keywords: Ameloblastoma, ethnicity, spatial analysis, Nigeria

A geographical analysis of ethnic distribution of jaw ameloblastoma in Nigerians

Introduction: Ameloblastoma is the most common odontogenic tumour in Nigeria. A definite geographic variation has been observed in the frequency of odontogenic tumors from different parts of the world. However, there is no study on the regional variations in Nigeria. Hence, this study was designed to document the ethnic and geographical distribution of jaw ameloblastoma in Nigeria. Methods: Archival data on ameloblastoma from 10 health facilities were obtained. Global Moran\u2019s I detected geographic clustering in its distribution while Local Getis Ord indicated the location of ameloblastoma clusters. Chi-square tested associations between variables at 0.05 level of significance. Results: A total of 1,246 ameloblastoma cases were recorded in Nigeria. Besides substantial state variations, a South-North gradient was noticed in its distribution. Significant positive spatial autocorrelation was observed in the three major groups while ameloblastoma hotspots were found in the SouthWestern and Northwestern Nigeria. The Igbos had a higher prevalence of ameloblastoma outside their home region than within. Conclusion: The study hypothesized that the geographical distribution of ameloblastoma in Nigeria is the result of all or one of the following: the country\u2019s tropical climate, migration patterns and health seeking behavior. Hopefully, these claims should lead to further enquiry on the underlying causes. DOI: https://dx.doi.org/10.4314/ahs.v19i1.44 Cite as: Adisa AO, Osayomi T, Effiom OA, Kolude B, Lawal AO, Soyele OO, et al. A geographical analysis of ethnic distribution of jaw ameloblastoma in Nigerians. Afri Health Sci. 2019;19(1). 1677-1686. https://dx.doi.org/10.4314/ ahs. v19i1.4

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

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 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

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

Reconstruction of mandibular defects using nonvascularized autogenous bone graft in Nigerians

Objectives: The aim of this study is to evaluate the success rate and complications of mandibular reconstruction with nonvascularized bone graft in Ile-Ife, Nigeria. Patients and Methods: A total of 25 patients who underwent reconstruction of mandibular discontinuity defects between January 2003 and February 2012, at the Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife constituted the study sample. Relevant information was retrieved from the patients′ records. This information include patients′ demographics (age and sex) as well as the type of mandibular defect, cause of the defect, type of mandibular resection done, source of the bone graft used, and the method of graft immobilization. Morbidity associated with the graft procedures were assessed by retrieving information on graft failures, length of hospital stay following surgery, rehabilitation device used and associated graft donor and recipient site complications. Result: There were 12 males and 13 females with a male:female ratio was 1:1.1. The age of the patients ranged from 13 to 73 years with a mean age for males 32.7 ± standard deviation (SD) 12.9 and for females 35.0 ± SD 17.1. Jaw defect was caused by resection for tumours and other jaw pathologies in 92% of cases. Complete symphyseal involvement defect was the most common defect recorded 11 (44%). Reconstruction with nonvascularized rib graft accounted for 68% of cases while iliac crest graft was used in 32% of the patients. Successful take of the grafts was recorded in 22 patients while three cases failed. Wound dehiscence (two patients) and postoperative wound infection (eight patients) were the most common complications recorded. Conclusion: The use of nonvascularized graft is still relevant in the reconstruction of large mandibular defects caused by surgical ablation of benign conditions in Nigerians. Precise surgical planning and execution, extended antibiotic therapy, and meticulous postoperative care contributed to the good outcome

Biological profile of ameloblastoma and its location in the jaw in 1246 Nigerians

OBJECTIVES: Ameloblastoma is a benign, slow-growing, locally invasive epithelial tumor of odontogenic origin, with unlimited growth capacity and a strong tendency to recur. This multicentric study analyzed ameloblastoma diagnosed in Nigeria among different ethnic groups. STUDY DESIGN: This retrospective study included ameloblastoma cases diagnosed from 1964 to 2017 at 10 hospitals or medical centers in Nigeria. Age, sex, tribe, and location of the ameloblastoma in the jaw were analyzed. Associations between variables were tested by using χ2 and Fisher's exact test. RESULTS: A total of 1246 ameloblastoma cases were recorded (mean patient age 32.51 ± 14.54 years; range 4-86 years; male-to-female ratio 1.2:1). Approximately 60% of ameloblastoma cases occurred in young adults (age range 18-40 years). Ninety-eight lesions were located in the maxilla and 1103 in the mandible; the posterior mandible was the most common site (31.3% on the right and 26.5% on the left, respectively), followed by the anterior (26.0%) mandible. No significant differences were noted in the distribution of ameloblastoma within the tribes with respect to age (P = .92) and sex (P = .71). CONCLUSIONS: The mandible is a common site of ameloblastoma in patients in Nigeria, and in most cases, it occurs in young adults. Early presentation, diagnosis, and treatment are important to reduce postoperative disfigurement and morbidity.status: publishe

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

International audienc

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

BackgroundRegular, 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.MethodsThe 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.FindingsThe 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.InterpretationLong-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