Specialty choice among dental students in Ibadan, Nigeria

Background. The unequal distribution of workforce across dental specialties in Nigeria poses a significant problem in the delivery of specialists’ oral healthcare to the Nigerian population.Objectives. To determine dental specialties preferences among dental students at the University of Ibadan, Nigeria, and to explore the factors that influence their choices.Methods. We obtained ethical approval to conduct this study. Only the dental students who rotated through all the dental specialties were selected to participate in this questionnaire-based study. Data were analysed using SPSS version 16 (SPSS Inc., USA).Results. The majority of dental students at the University of Ibadan preferred the oral and maxillofacial surgery (OMS) specialty above all other dental specialties, while prosthetic dentistry was least preferred. Of all the factors to take into consideration when choosing a dental specialty, personal interest was the only factor considered by nearly all respondents. Only male respondents considered prestige as an influencing factor in their choice of a specialty. Lifestyle and job description were factors considered by a higher proportion of the male respondents (10/13) than females (5/14). The mean age of the 27 respondents who participated in this study was 22.6 years, 52% of whom were females.Conclusion. OMS was the most preferred specialty among our respondents (n=8). Nearly all dental students chose residency training in the specialty that most appealed to them. The interest of dental students towards the least appealing dental specialties needs to be developed to solve the problem ofskewed distribution of the dental workforce in Nigeria. Our findings suggest that this may be accomplished by changing dental students’ perceptions of certain specialties, building on male students’ interests in job security and private practice potential, and the female students’ interests in family-friendly specialties and increasing flexibility in dental residency programmes. 

Prevalence and predictors of cigarette smoking and alcohol use among secondary school students in Nigeria

BACKGROUND: Cigarette and alcohol use are the most common causes of noncommunicable diseases. Studies related to cigarette and alcohol use among Nigerian adolescents have shown increases in the habits and require urgent intervention. Nationally representative data is needed to develop effective national policies and interventions, but this is lacking. Hence, this study aimed to provide nationally representative empiric information about cigarette and alcohol use prevalence and predictors among Nigerian secondary school students. METHODS: This study included 2,530 Nigerian students in Nigeria from five of the six geopolitical zones in Nigeria. A self-administered questionnaire was used to obtain information about the participants’ sociodemographic and school-based characteristics, cigarette and alcohol use status, and harm perception of tobacco and alcohol use. Data were analysed with SPSS version 25 at p<0.05. RESULTS: Participants' mean age (±SD) was 16.34 (±2.0) years. The prevalences (95%CI) for ever-cigarette and current-cigarette smoking were 11.1% (95%CI:9.9-12.4) and 8.4% (95%CI:7.3-9.5), respectively. While 21.0% (95%CI:19.4-22.7) and 15.6% (14.2-17.1) were the prevalences for lifetime and current alcohol use, respectively. The predictors of current cigarette smoking were studying in northern-Nigeria (aOR:1.94;95%CI:1.10-3.44), attending private-schools (aOR:1.56;95%CI:1.03-2.38), boarding-student (aOR:1.75;95% CI:1.15-2.69), male-gender (aOR:3.03; 95%CI:1.80-5.10), current alcohol use (aOR:12.50;95%CI:8.70-18.18), having no (aOR:2.59;95%CI:1.58-4.26) or low tobacco harm perception (aOR:2.04;95%CI:1.18-3.53). The predictors of current alcohol use were male (aOR:1.32; 95%CI:1.01-1.72) and current cigarette smoking (aOR:12.5;95%CI:8.77-17.86). CONCLUSION: The prevalences of cigarette and alcohol use were high among Nigerian secondary school students, and both habits were strongly associated. Their predictors were school-related factors, sociocultural characteristics, and tobacco harm perception

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