Quantifying the interconnectedness between poverty, health access, and rabies mortality.

The global 2030 goal set by the World Organization for Animal Health (WOAH), the World Health Organization (WHO), and the Food and Agriculture Organization (FAO), to eliminate dog-mediated human rabies deaths, has undeniably been a catalyst for many countries to re-assess existing dog rabies control programmes. Additionally, the 2030 agenda for Sustainable Development includes a blueprint for global targets which will benefit both people and secure the health of the planet. Rabies is acknowledged as a disease of poverty, but the connections between economic development and rabies control and elimination are poorly quantified yet, critical evidence for planning and prioritisation. We have developed multiple generalised linear models, to model the relationship between health care access, poverty, and death rate as a result of rabies, with separate indicators that can be used at country-level; total Gross Domestic Product (GDP), and current health expenditure as a percentage of the total gross domestic product (% GDP) as an indicator of economic growth; and a metric of poverty assessing the extent and intensity of deprivation experienced at the individual level (Multidimensional Poverty Index, MPI). Notably there was no detectable relationship between GDP or current health expenditure (% GDP) and death rate from rabies. However, MPI showed statistically significant relationships with per capita rabies deaths and the probability of receiving lifesaving post exposure prophylaxis. We highlight that those most at risk of not being treated, and dying due to rabies, live in communities experiencing health care inequalities, readily measured through poverty indicators. These data demonstrate that economic growth alone, may not be enough to meet the 2030 goal. Indeed, other strategies such as targeting vulnerable populations and responsible pet ownership are also needed in addition to economic investment.

The Correlation Between Gross Domestic Product Per Capita and the Success Rate of Assisted Reproductive Technologies Worldwide.

OBJECTIVE: Although there has been increased utilization of assisted reproductive technologies (ART) in the world, there is no conclusive definition about the relationship between the success rate of ART and national wealth. METHODS: In this study, using the data from the International Committee for Monitoring Assisted Reproductive Technologies (ICMART), we sought to determine whether there is a correlation between the success rate of ART (represented by pregnancy and delivery rates) and national wealth represented by the gross domestic product (GDP) per capita. Moreover, to further understand the effect of GDP per capita on ART effectiveness, we analyzed the association between ART success rate and GDP per capita in 50 US states. RESULTS: Our data showed that the number of ART treatment cycles increased as the GDP per capita increased. However, we found a negative correlation between ART success rates and GDP per capita in ICMART countries, although no correlation was seen in the US states. Using rough estimation, we derived that the success rate of ART was not related to GDP per capita in the ICMART countries with a GDP per capita greater than USD 13,000. CONCLUSIONS: In conclusion, for the first time, we showed that when the GDP per capita of an economic territory reaches (or exceeds) USD 13,000, ART pregnancy and delivery rates were not associated with GDP per capita, and ART success rates remained stable.

Número de cirurgiões-dentistas e PIB per capitanos municípios paraibanos com cursos de graduação em Odontologia / Number of dentists and GDP per capita in the municipalities of the state of Paraíba with undergraduate courses in Dentistry

O objetivo deste estudo foi relacionar a distribuição dos cursos de Odontologia, número de cirurgiões-dentistas(CD)ePIBper capita em municípios paraibanos com cursos de graduação em Odontologia. Trata-se de um estudo transversal descritivo com utilização de dados coletados diretamente dossites do Conselho Federal de Odontologia, do Instituto Brasileiro de Geografia e Estatísticae do Cadastro Nacional de Cursose Instituições de Ensino Superior. Todos os dados utilizados nesta pesquisa são de acesso público. AParaíba possui 16 cursos de graduação em Odontologia, distribuídos em 6 munícipios: Araruna (1; 6,25%), Cabedelo (1; 6,25%), Cajazeiras (2; 12,50%), Campina Grande (6; 37,50%), João Pessoa (4; 25,00%) e Patos (2; 12,50%).Os municípios com maiores valores de PIB per capita, como João Pessoa (R$24.319,82) e Campina Grande (R$21.077,30), apresentaram também maior número de CD, com uma correlação positiva entre os valores do PIB per capitade cada município e a quantidade de cursos e profissionais. Apesar disso, Cabedelo, município paraibano com apenas um curso de Odontologia, sendo este na rede privada, e 126 CDs em atividade, tem o maior PIB per capita do estado (R$ 42.556,16). O PIB per capita parece ser responsável pela distribuição de CDs nos municípios com cursos de graduação em Odontologia, no entanto, fatores como qualidade de vida, mercado de trabalho e saúde, tendem a exercerforte influência na distribuição destes profissionais (AU).

This study aimed at relating the distribution of Dentistry courses, number of dental surgeons (DS) and GDP per capitain municipalities of the state of Paraíbawith undergraduate courses in Dentistry. This is a descriptive cross-sectional study using secondary data from the Brazilian Federal Council of Dentistry (CFO), the Brazilian Institute of Geography and Statistics (IBGE) and the National Registry of HigherEducation Courses and Institutions. All data used in this study is publicly accessible. The state has 16 undergraduate courses in Dentistry, distributed in 6 municipalities: Araruna (1; 6.25%), Cabedelo (1; 6.25%), Cajazeiras (2; 12.50%), Campina Grande (6; 37.50%), João Pessoa (4; 25.00%) and Ducks (2; 12.50%). The municipalities with the highest GDP per capita, such as João Pessoa (R$ 24,319.82) and Campina Grande (R$ 21,077.30), also had a higher number of DS, with a positive correlation between the values of GDPper capita of each municipality and the number of courses and professionals. Besides, Cabedelo, a municipality in Paraíbawith only one Dentistry course, which is in the private network, and 126 active DS, has the highest GDPper capita in the state (R$ 42,556.16) (table 2). According to the results presented in this study, GDP per capita seems to be responsible for the distribution of SD in municipalities with undergraduate courses in Dentistry. However, factors such as quality of life, labor market and health tend to strongly influence the distribution of these professionals (AU).

Favorable colorectal cancer mortality-to-incidence ratios in countries with high expenditures on health and development index: A study based on GLOBOCAN database.

BACKGROUND: Global variation in the incidence and outcomes of colorectal cancer (CRC) is associated with many factors, among which screening policies and early treatment play substantial roles. However, screening programs and intense treatment are expensive and require good health care systems. For CRC, no clear association has yet been established between clinical outcomes and health care disparities. METHOD: We used the mortality-to-incidence ratio (MIR) of CRC as a measure of clinical outcomes for comparison with the Human Development Index (HDI), current health expenditure (CHE), and current health expenditure as a percentage of gross domestic product (CHE/GDP) using linear regression analyses. We included 171 countries based on data from the GLOBOCAN 2018 database. RESULTS: We found that the regions with the lowest MIRs for CRC are Oceania and North America. A significant correlation was observed between incidence, mortality and HDI, CHE, and CHE/GDP among the countries enrolled. Furthermore, lower MIRs of CRC significantly correlated with higher HDI, CHE, and CHE/GDP (P < 0.001, P < 0.001, and P < 0.001, respectively). CONCLUSION: : CRC MIRs tend to be most favorable in countries with high health care expenditures and a high HDI.

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e. the Hubei province in China, the epicentre of the coronavirus disease-2019 (COVID-19) pandemic], we collected data on atmospheric pollutants (PM2.5, PM10 and CO2) and economic growth (GDP), along with daily series on COVID-19 indicators (cases, resuscitations and deaths). Then, we adopted an innovative Machine Learning approach, applying a new image Neural Networks model to investigate the causal relationships among economic, atmospheric and COVID-19 indicators. Empirical findings emphasise that any change in economic activity is found to substantially affect the dynamic levels of PM2.5, PM10 and CO2 which, in turn, generates significant variations in the spread of the COVID-19 epidemic and its associated lethality. As a robustness check, the conduction of an optimisation algorithm further corroborates previous results.

A simple method to describe the COVID-19 trajectory and dynamics in any country based on Johnson cumulative density function fitting.

A simple method is utilised to study and compare COVID-19 infection dynamics between countries based on curve fitting to publicly shared data of confirmed COVID-19 infections. The method was tested using data from 80 countries from 6 continents. We found that Johnson cumulative density functions (CDFs) were extremely well fitted to the data (R2 > 0.99) and that Johnson CDFs were much better fitted to the tails of the data than either the commonly used normal or lognormal CDFs. Fitted Johnson CDFs can be used to obtain basic parameters of the infection wave, such as the percentage of the population infected during an infection wave, the days of the start, peak and end of the infection wave, and the duration of the wave's increase and decrease. These parameters can be easily interpreted biologically and used both for describing infection wave dynamics and in further statistical analysis. The usefulness of the parameters obtained was analysed with respect to the relation between the gross domestic product (GDP) per capita, the population density, the percentage of the population infected during an infection wave, the starting day and the duration of the infection wave in the 80 countries. We found that all the above parameters were significantly associated with GDP per capita, but only the percentage of the population infected was significantly associated with population density. If used with caution, this method has a limited ability to predict the future trajectory and parameters of an ongoing infection wave.

Singapore's health-care system: key features, challenges, and shifts.

Since Singapore became an independent nation in 1965, the development of its health-care system has been underpinned by an emphasis on personal responsibility for health, and active government intervention to ensure access and affordability through targeted subsidies and to reduce unnecessary costs. Singapore is achieving good health outcomes, with a total health expenditure of 4·47% of gross domestic product in 2016. However, the health-care system is contending with increased stress, as reflected in so-called pain points that have led to public concern, including shortages in acute hospital beds and intermediate and long-term care (ILTC) services, and high out-of-pocket payments. The main drivers of these challenges are the rising prevalence of non-communicable diseases and rapid population ageing, limitations in the delivery and organisation of primary care and ILTC, and financial incentives that might inadvertently impede care integration. To address these challenges, Singapore's Ministry of Health implemented a comprehensive set of reforms in 2012 under its Healthcare 2020 Masterplan. These reforms substantially increased the capacity of public hospital beds and ILTC services in the community, expanded subsidies for primary care and long-term care, and introduced a series of financing health-care reforms to strengthen financial protection and coverage. However, it became clear that these measures alone would not address the underlying drivers of system stress in the long term. Instead, the system requires, and is making, much more fundamental changes to its approach. In 2016, the Ministry of Health encapsulated the required shifts in terms of the so-called Three Beyonds-namely, beyond health care to health, beyond hospital to community, and beyond quality to value.

Analyzing the 2019 Chilean social outbreak: Modelling Latin American economies.

In this work, we propose a quantitative model for the 2019 Chilean protests. We utilize public data for the consumer price index, the gross domestic product, and the employee and per capita income distributions as inputs for a nonlinear diffusion-reaction equation, the solutions to which provide an in-depth analysis of the population dynamics. Specifically, the per capita income distribution stands out as a solution to the extended Fisher-Kolmogorov equation. According to our results, the concavity of employee income distribution is a decisive input parameter and, in contrast to the distributions typically observed for Chile and other countries in Latin America, should ideally be non-negative. Based on the results of our model, we advocate for the implementation of social policies designed to stimulate social mobility by broadening the distribution of higher salaries.

Research on the evolution and driving forces of the manufacturing industry during the "13th five-year plan" period in Jiangsu province of China based on natural language processing.

The development of China's manufacturing industry has received global attention. However, research on the distribution pattern, changes, and driving forces of the manufacturing industry has been limited by the accessibility of data. This study proposes a method for classifying based on natural language processing. A case study was conducted employing this method, hotspot detection and driving force analysis, wherein the driving forces industrial development during the "13th Five-Year plan" period in Jiangsu province were determined. The main conclusions of the empirical case study are as follows. 1) Through the acquisition of Amap's point-of-interest (POI, a special point location that commonly used in modern automotive navigation systems.) data, an industry type classification algorithm based on the natural language processing of POI names is proposed, with Jiangsu Province serving as an example. The empirical test shows that the accuracy was 95%, and the kappa coefficient was 0.872. 2) The seven types of manufacturing industries including the pulp and paper (PP) industry, metallurgical chemical (MC) industry, pharmaceutical manufacturing (PM) industry, machinery and electronics (ME) industry, wood furniture (WF) industry, textile clothing (TC) industry, and agricultural and food product processing (AF) industry are drawn through a 1 km× 1km projection grid. The evolution map of the spatial pattern and the density field hotspots are also drawn. 3) After analyzing the driving forces of the changes in the number of manufacturing industries mentioned above, we found that manufacturing base, distance from town, population, GDP per capita, distance from the railway station were the significant driving factors of changes in the manufacturing industries mentioned above. The results of this research can help guide the development of manufacturing industries, maximize the advantages of regional factors and conditions, and provide insight into how the spatial layout of the manufacturing industry could be optimized.

Robust determinants of income distribution across and within countries.

Multicollinearity widely exists in empirical studies, which leads to imprecise estimation and even endogeneity when omitted variables are correlated with any regressors. We apply an innovative strategy, different from the usual tools (instrumental variable, ridge regression, and least absolute shrinkage and selection operator), to estimate the robust determinants of income distribution. We transform panel data into (quasi-) cross-sectional data by removing country and time effects from the data so that all variables become zero mean and orthogonal to the country dummies and time variable, and multicollinearity becomes very low or even disappears with the quasi-cross sectional data in any specifications regardless of country dummies and time variable being included or not. Our contribution is threefold. First, we build a general method to address the multicollinearity issue in panel data, which is to isolate the common contents of correlated variables and ensures robust estimates in different specifications (dynamic or static specifications) and estimators (within- or between-effects estimators). Second, we find no evidence for the Kuznets hypothesis within and across countries; investment is economically and statistically the most robust determinant of income inequality; meanwhile, labor income share shows robustly and consistently positive effects on income inequality, which challenges the related literature. Last, simulations with our estimates show that the total marginal effects of development (regarding GDP, capital stock and investment) on income inequality are very likely to be positive within and between countries except that the impacts on middle-60% and top-quintile income shares are not so likely to increase income inequality across countries.

Impact of Antidepressant Prescriptions on Suicidal Behavior in Times of Severe Financial Strain.

ABSTRACT: This descriptive study observes the relationship between antidepressant prescriptions and the suicide rate in Italy in the 2000s to the mid-2010s, which includes a period of severe economic crisis. The observation period was from 2000 to 2015. Suicide and unemployment rates disaggregated by age and sex were collected from the Italian Institute of Statistics. Statistical analyses were performed using correlations between suicide rates and the defined daily dose, with reference to selective serotonin reuptake inhibitors (SSRIs), tricyclic antidepressants (TCAs), and other types of antidepressants. Fixed-effects panel regressions were also run. Increases in SSRIs prescriptions were associated with decreases in suicide rates among both men and women. However, when the analyses were adjusted for the rate of growth of the unemployment rate and for gross domestic product, the associations were weaker. The potential protective factor of SSRIs with respect to suicidal behavior may be reduced by severe recessions, especially when unemployment increases.

Methodology for modelling the new COVID-19 pandemic spread and implementation to European countries.

After the outbreak of the new COVID-19 disease, the mitigation stage has been reached in most of the countries in the world. During this stage, a more accurate data analysis of the daily reported cases and other parameters became possible for the European countries and has been performed in this work. Based on a proposed parametrization model appropriate for implementation to an epidemic in a large population, we focused on the disease spread and we studied the obtained curves, as well as, investigating probable correlations between the country's characteristics and the parameters of the parametrization. We have also developed a methodology for coupling our model to the SIR-based models determining the basic and the effective reproductive number referring to the parameter space. The obtained results and conclusions could be useful in the case of a recurrence of this insidious disease in the future.

Checklist for Early Recognition and Treatment of Acute Illness and Injury: An Exploratory Multicenter International Quality-Improvement Study in the ICUs With Variable Resources.

OBJECTIVES: To determine whether the "Checklist for Early Recognition and Treatment of Acute Illness and Injury" decision support tool during ICU admission and rounding is associated with improvements in nonadherence to evidence-based daily care processes and outcomes in variably resourced ICUs. DESIGN, SETTINGS, PATIENTS: This before-after study was performed in 34 ICUs (15 countries) from 2013 to 2017. Data were collected for 3 months before and 6 months after Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation. INTERVENTIONS: Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation using remote simulation training. MEASUREMENTS AND MAIN RESULTS: The coprimary outcomes, modified from the original protocol before data analysis, were nonadherence to 10 basic care processes and ICU and hospital length of stay. There were 1,447 patients in the preimplementation phase and 2,809 patients in the postimplementation phase. After adjusting for center effect, Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation was associated with reduced nonadherence to care processes (adjusted incidence rate ratio [95% CI]): deep vein thrombosis prophylaxis (0.74 [0.68-0.81), peptic ulcer prophylaxis (0.46 [0.38-0.57]), spontaneous breathing trial (0.81 [0.76-0.86]), family conferences (0.86 [0.81-0.92]), and daily assessment for the need of central venous catheters (0.85 [0.81-0.90]), urinary catheters (0.84 [0.80-0.88]), antimicrobials (0.66 [0.62-0.71]), and sedation (0.62 [0.57-0.67]). Analyses adjusted for baseline characteristics showed associations of Checklist for Early Recognition and Treatment of Acute Illness and Injury implementation with decreased ICU length of stay (adjusted ratio of geometric means [95% CI]) 0.86 [0.80-0.92]), hospital length of stay (0.92 [0.85-0.97]), and hospital mortality (adjusted odds ratio [95% CI], 0.81 (0.69-0.95). CONCLUSIONS: A quality-improvement intervention with remote simulation training to implement a decision support tool was associated with decreased nonadherence to daily care processes, shorter length of stay, and decreased mortality.

Gender differences in trends of bladder cancer mortality-to-incidence ratios according to health expenditure in 55 countries.

The association between bladder cancer mortality-to-incidence ratios (MIRs) and healthcare disparities has gender differences. However, no evidence supports gender as an issue in the association between changes in the MIR and health expenditures on bladder cancer. Changes in the MIR were defined as the difference in data from the years 2012 and 2018, which was named δMIR. Current health expenditures (CHE) and the human development index (HDI) were obtained from the World Health Organization and the Human Development Report Office. The association between variables was analyzed by Spearman's rank correlation coefficient. In total, 55 countries were analyzed according to data quality and the exclusion of missing data. Globally, the MIR changed according to the HDI level in both genders. Among the 55 countries studied, a high HDI and CHE were significantly associated with a favorable age-standardized rate-based MIR (ASR-based MIR) in both genders and the subgroups according to gender (for both genders, MIR vs. HDI: ρ = -0.720, p < 0.001; MIR vs. CHE per capita: ρ = -0.760, p < 0.001; MIR vs. CHE as a percentage of gross domestic product (CHE/GDP): ρ = -0.663, p < 0.001). Importantly, in females only, the CHE/GDP but neither the HDI score nor the CHE per capita was significantly associated with a favorable ASR-based δMIR (ASR-based δMIR vs. CHE/GDP: ρ = 0.414, p = 0.002). In the gender subgroups, the association between the HDI and the CHE was statistically significant for females and less significant for males. In conclusion, favorable bladder ASR-based MIRs were associated with a high CHE; however, improvement of the ASR-based δMIR data was more correlated with the CHE in females. Further investigation of the gender differences via a cohort survey with detailed information of clinical-pathological characteristics, treatment strategies, and outcomes might clarify these issues and improve therapeutic and/or screening strategies for bladder cancer.

Utilizing Google Trends to Assess Worldwide Interest in Irritable Bowel Syndrome and Commonly Associated Treatments.

BACKGROUND: Examining the prevalence of irritable bowel syndrome (IBS) across regions has been challenging given significant methodological heterogeneity. AIMS: We aimed to perform a uniform assessment of the global burden of IBS using data from Google Trends, a novel, online tool. METHODS: Google Trends measures popularity of a search term in a given week compared to popularity of all search terms in that week, calculated as relative search volume (RSV). We compiled data on the popularity of IBS and its treatments across 173 countries between 2014 and 2018. We compared Google Trends popularity for IBS with prior epidemiological prevalence data, while controlling for gross domestic product (GDP) per capita and physician density. RESULTS: Of the 173 countries with Google Trends data, 137 countries also had data for GDP per capita and physician density. Worldwide popularity of IBS as a search topic increased from 79 to 89 (13% increase by RSV) over the 5-year period between 2014 and 2018. Country-specific change in IBS RSV ranged from - 35% (Nigeria) to + 64% (Pakistan). There was poor correlation between the Google Trends data and prior epidemiological data (0.08, Pearson correlation, p = 0.64). Popularity of the low-FODMAP diet increased the most among 8 common therapies (RSV 41 to 89, 117% increase). CONCLUSIONS: Google Trends is a novel tool that can complement traditional epidemiological methods in gastrointestinal disease. Future research is needed to assess its utility and accuracy as a measure of disease burden across different gastrointestinal diseases.

Effects of Pandemics-Related Uncertainty on Household Consumption: Evidence From the Cross-Country Data.

The COVID-19 pandemic has affected various macroeconomic indicators. Given this backdrop, this research investigates the effects of the pandemics-related uncertainty on household consumption. For this purpose, we construct a simple theoretical model to study the effects of the pandemics-related uncertainty on household consumption. To estimate the theoretical model, we consider the panel dataset of 138 countries for the period from 1996 to 2017. We also use the Pandemic Uncertainty Index to measure the pandemics-related uncertainty. The theoretical model and the empirical findings from the Feasible Generalized Least Squares (FGLS) estimations indicate that the gross fixed capital formation, government consumption, balance of trade, and the Pandemic Uncertainty Index negatively affect household consumption. The results are also valid in the panel dataset of 42 high-income economies and the remaining 96 emerging economies.

COVID-19 case-fatality rate and demographic and socioeconomic influencers: worldwide spatial regression analysis based on country-level data.

OBJECTIVE: To investigate the influence of demographic and socioeconomic factors on the COVID-19 case-fatality rate (CFR) globally. DESIGN: Publicly available register-based ecological study. SETTING: Two hundred and nine countries/territories in the world. PARTICIPANTS: Aggregated data including 10 445 656 confirmed COVID-19 cases. PRIMARY AND SECONDARY OUTCOME MEASURES: COVID-19 CFR and crude cause-specific death rate were calculated using country-level data from the Our World in Data website. RESULTS: The average of country/territory-specific COVID-19 CFR is about 2%-3% worldwide and higher than previously reported at 0.7%-1.3%. A doubling in size of a population is associated with a 0.48% (95% CI 0.25% to 0.70%) increase in COVID-19 CFR, and a doubling in the proportion of female smokers is associated with a 0.55% (95% CI 0.09% to 1.02%) increase in COVID-19 CFR. The open testing policies are associated with a 2.23% (95% CI 0.21% to 4.25%) decrease in CFR. The strictness of anti-COVID-19 measures was not statistically significantly associated with CFR overall, but the higher Stringency Index was associated with higher CFR in higher-income countries with active testing policies (regression coefficient beta=0.14, 95% CI 0.01 to 0.27). Inverse associations were found between cardiovascular disease death rate and diabetes prevalence and CFR. CONCLUSION: The association between population size and COVID-19 CFR may imply the healthcare strain and lower treatment efficiency in countries with large populations. The observed association between smoking in women and COVID-19 CFR might be due to the finding that the proportion of female smokers reflected broadly the income level of a country. When testing is warranted and healthcare resources are sufficient, strict quarantine and/or lockdown measures might result in excess deaths in underprivileged populations. Spatial dependence and temporal trends in the data should be taken into account in global joint strategy and/or policy making against the COVID-19 pandemic.

Population risk factors for COVID-19 deaths in Nigeria at sub-national level.

INTRODUCTION: Nigeria is the most populous country in the African continent. The aim of this study was to analyze risk factors for COVID-19 prevalence and deaths in all 6 geopolitical regions and 37 States in Nigeria. METHODS: we analyzed the data retrieved from various sources, including Nigeria CDC, Nigeria National Bureau of Statistics, Unicef-Nigeria multiple indicator cluster survey and the Institute of Health Metrics and Evaluation, University of Washington. We examined 4 clinical risk factors (prevalence of TB, HIV, smoking and BCG vaccination coverage) and 5 sociodemographic factors (age ≥65, population density, literacy rate, unemployment and GDP per capita). Multivariate modeling was conducted using generalized linear model. RESULTS: our analysis showed that the incidence of confirmed COVID-19 cases differed widely across the 37 States, from 0.09 per 100,000 in Kogi to 83.7 in Lagos. However, more than 70% of confirmed cases were concentrated in just 7 States: Lagos, Abuja, Oyo, Kano, Edo, Rivers and Delta. Case mortality rate (CMR) also varied considerably, with Lagos, Abuja and Edo having CMR above 9 per million population. On bivariate analysis, higher CMR correlated positively with GDP (r=0.53) and to a lesser extent with TB (r=0.36) and population density (r=0.38). On multivariate analysis, which is more definitive, States with higher HIV prevalence and BCG coverage had lower CMR, while high GDP States had a greater CMR. CONCLUSION: this study indicates that COVID-19 has disproportionately affected certain States in Nigeria. Population susceptibility factors include higher economic development but not literacy or unemployment. Death rates were mildly lower in States with higher HIV prevalence and BCG vaccination coverage.