This statistic shows the 20 countries* with the highest infant mortality rate in 2024. An estimated 101.3 infants per 1,000 live births died in the first year of life in Afghanistan in 2024. Infant and child mortality Infant mortality usually refers to the death of children younger than one year. Child mortality, which is often used synonymously with infant mortality, is the death of children younger than five. Among the main causes are pneumonia, diarrhea – which causes dehydration – and infections in newborns, with malnutrition also posing a severe problem. As can be seen above, most countries with a high infant mortality rate are developing countries or emerging countries, most of which are located in Africa. Good health care and hygiene are crucial in reducing child mortality; among the countries with the lowest infant mortality rate are exclusively developed countries, whose inhabitants usually have access to clean water and comprehensive health care. Access to vaccinations, antibiotics and a balanced nutrition also help reducing child mortality in these regions. In some countries, infants are killed if they turn out to be of a certain gender. India, for example, is known as a country where a lot of girls are aborted or killed right after birth, as they are considered to be too expensive for poorer families, who traditionally have to pay a costly dowry on the girl’s wedding day. Interestingly, the global mortality rate among boys is higher than that for girls, which could be due to the fact that more male infants are actually born than female ones. Other theories include a stronger immune system in girls, or more premature births among boys.

As of 2022, the countries with the highest death rates worldwide were Ukraine, Bulgaria, and Moldova. In these countries, there were 17 to 21 deaths per 1,000 people. The country with the lowest death rate is Qatar, where there is just one death per 1,000 people. Leading causes of death The leading causes of death worldwide are by far, ischaemic heart disease and stroke, accounting for a combined 27 percent of all deaths in 2019. In that year, there were 8.89 million deaths worldwide from ischaemic heart disease and 6.19 million from stroke. Interestingly, a worldwide survey from that year found that people greatly underestimate the proportion of deaths caused by cardiovascular disease, but overestimate the proportion of deaths caused by suicide, interpersonal violence, and substance use disorders. Death in the United States In 2022, there were around 3.27 million deaths in the United States. The leading causes of death in the United States are currently heart disease and cancer, accounting for a combined 40 percent of all deaths in 2022. Lung and bronchus cancer is the deadliest form of cancer worldwide, as well as in the United States. In the U.S. this form of cancer is predicted to cause around 65,790 deaths among men alone in the year 2024. Prostate cancer is the second-deadliest cancer for men in the U.S. while breast cancer is the second deadliest for women. In 2022, the fourth leading cause of death in the United States was COVID-19. Deaths due to COVID-19 resulted in a significant rise in the total number of deaths in the U.S. in 2020 and 2021 compared to 2019.

The leading causes of death in Massachusetts are cancer, heart disease, unintentional injury, stroke, and chronic lower respiratory disease. These mortality rates tend to be higher for people of color; and Black residents have a higher premature mortality rate overall and Asian residents have a higher rate of mortality due to stroke.

Death rate has been age-adjusted by the 2000 U.S. standard populaton. All-cause mortality is an important measure of community health. All-cause mortality is heavily driven by the social determinants of health, with significant inequities observed by race and ethnicity and socioeconomic status. Black residents have consistently experienced the highest all-cause mortality rate compared to other racial and ethnic groups. During the COVID-19 pandemic, Latino residents also experienced a sharp increase in their all-cause mortality rate compared to White residents, demonstrating a reversal in the previously observed mortality advantage, in which Latino individuals historically had higher life expectancy and lower mortality than White individuals despite having lower socioeconomic status on average. The disproportionately high all-cause mortality rates observed among Black and Latino residents, especially since the onset of the COVID-19 pandemic, are due to differences in social and economic conditions and opportunities that unfairly place these groups at higher risk of developing and dying from a wide range of health conditions, including COVID-19.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This publication of the SHMI relates to discharges in the reporting period September 2023 - August 2024. The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. The SHMI covers patients admitted to hospitals in England who died either while in hospital or within 30 days of being discharged. To help users of the data understand the SHMI, trusts have been categorised into bandings indicating whether a trust's SHMI is 'higher than expected', 'as expected' or 'lower than expected'. For any given number of expected deaths, a range of observed deaths is considered to be 'as expected'. If the observed number of deaths falls outside of this range, the trust in question is considered to have a higher or lower SHMI than expected. The expected number of deaths is a statistical construct and is not a count of patients. The difference between the number of observed deaths and the number of expected deaths cannot be interpreted as the number of avoidable deaths or excess deaths for the trust. The SHMI is not a measure of quality of care. A higher than expected number of deaths should not immediately be interpreted as indicating poor performance and instead should be viewed as a 'smoke alarm' which requires further investigation. Similarly, an 'as expected' or 'lower than expected' SHMI should not immediately be interpreted as indicating satisfactory or good performance. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided, as well as a breakdown of the data by diagnosis group. Further background information and supporting documents, including information on how to interpret the SHMI, are available on the SHMI homepage (see Related Links).

In the United States in 2021, the death rate was highest among those aged 85 and over, with about 17,190.5 men and 14,914.5 women per 100,000 of the population passing away. For all ages, the death rate was at 1,118.2 per 100,000 of the population for males, and 970.8 per 100,000 of the population for women. The death rate Death rates generally are counted as the number of deaths per 1,000 or 100,000 of the population and include both deaths of natural and unnatural causes. The death rate in the United States had pretty much held steady since 1990 until it started to increase over the last decade, with the highest death rates recorded in recent years. While the birth rate in the United States has been decreasing, it is still currently higher than the death rate. Causes of death There are a myriad number of causes of death in the United States, but the most recent data shows the top three leading causes of death to be heart disease, cancers, and accidents. Heart disease was also the leading cause of death worldwide.

Cameroon CM: Mortality Rate: Infant: per 1000 Live Births data was reported at 47.000 Ratio in 2022. This records a decrease from the previous number of 48.300 Ratio for 2021. Cameroon CM: Mortality Rate: Infant: per 1000 Live Births data is updated yearly, averaging 89.300 Ratio from Dec 1960 (Median) to 2022, with 63 observations. The data reached an all-time high of 165.600 Ratio in 1960 and a record low of 47.000 Ratio in 2022. Cameroon CM: Mortality Rate: Infant: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Social: Health Statistics. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Weighted average;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Chad TD: Mortality Rate: Infant: per 1000 Live Births data was reported at 64.100 Ratio in 2022. This records a decrease from the previous number of 65.700 Ratio for 2021. Chad TD: Mortality Rate: Infant: per 1000 Live Births data is updated yearly, averaging 103.800 Ratio from Dec 1972 (Median) to 2022, with 51 observations. The data reached an all-time high of 129.300 Ratio in 1972 and a record low of 64.100 Ratio in 2022. Chad TD: Mortality Rate: Infant: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chad – Table TD.World Bank.WDI: Social: Health Statistics. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.;Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.;Weighted average;Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys. Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

BackgroundWhile the high prevalence of preterm births and its impact on infant mortality in the US have been widely acknowledged, recent data suggest that even full-term births in the US face substantially higher mortality risks compared to European countries with low infant mortality rates. In this paper, we use the most recent birth records in the US to more closely analyze the primary causes underlying mortality rates among full-term births.Methods and findingsLinked birth and death records for the period 2010–2012 were used to identify the state- and cause-specific burden of infant mortality among full-term infants (born at 37–42 weeks of gestation). Multivariable logistic models were used to assess the extent to which state-level differences in full-term infant mortality (FTIM) were attributable to observed differences in maternal and birth characteristics. Random effects models were used to assess the relative contribution of state-level variation to FTIM. Hypothetical mortality outcomes were computed under the assumption that all states could achieve the survival rates of the best-performing states. A total of 10,175,481 infants born full-term in the US between January 1, 2010, and December 31, 2012, were analyzed. FTIM rate (FTIMR) was 2.2 per 1,000 live births overall, and ranged between 1.29 (Connecticut, 95% CI 1.08, 1.53) and 3.77 (Mississippi, 95% CI 3.39, 4.19) at the state level. Zero states reached the rates reported in the 6 low-mortality European countries analyzed (FTIMR < 1.25), and 13 states had FTIMR > 2.75. Sudden unexpected death in infancy (SUDI) accounted for 43% of FTIM; congenital malformations and perinatal conditions accounted for 31% and 11.3% of FTIM, respectively. The largest mortality differentials between states with good and states with poor FTIMR were found for SUDI, with particularly large risk differentials for deaths due to sudden infant death syndrome (SIDS) (odds ratio [OR] 2.52, 95% CI 1.86, 3.42) and suffocation (OR 4.40, 95% CI 3.71, 5.21). Even though these mortality differences were partially explained by state-level differences in maternal education, race, and maternal health, substantial state-level variation in infant mortality remained in fully adjusted models (SIDS OR 1.45, suffocation OR 2.92). The extent to which these state differentials are due to differential antenatal care standards as well as differential access to health services could not be determined due to data limitations. Overall, our estimates suggest that infant mortality could be reduced by 4,003 deaths (95% CI 2,284, 5,587) annually if all states were to achieve the mortality levels of the best-performing state in each cause-of-death category. Key limitations of the analysis are that information on termination rates at the state level was not available, and that causes of deaths may have been coded differentially across states.ConclusionsMore than 7,000 full-term infants die in the US each year. The results presented in this paper suggest that a substantial share of these deaths may be preventable. Potential improvements seem particularly large for SUDI, where very low rates have been achieved in a few states while average mortality rates remain high in most other areas. Given the high mortality burden due to SIDS and suffocation, policy efforts to promote compliance with recommended sleeping arrangements could be an effective first step in this direction.

Norway NO: Mortality Rate: Under-5: per 1000 Live Births data was reported at 2.600 Ratio in 2017. This stayed constant from the previous number of 2.600 Ratio for 2016. Norway NO: Mortality Rate: Under-5: per 1000 Live Births data is updated yearly, averaging 9.550 Ratio from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 22.600 Ratio in 1960 and a record low of 2.600 Ratio in 2017. Norway NO: Mortality Rate: Under-5: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank: Health Statistics. Under-five mortality rate is the probability per 1,000 that a newborn baby will die before reaching age five, if subject to age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

Trinidad and Tobago TT: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 20.300 Ratio in 2016. This records a decrease from the previous number of 21.000 Ratio for 2015. Trinidad and Tobago TT: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 24.500 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 32.700 Ratio in 1990 and a record low of 20.300 Ratio in 2016. Trinidad and Tobago TT: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Trinidad and Tobago – Table TT.World Bank: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted Average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 5 procedures performed (Abdominal Aortic Aneurysm Repair, Unruptured/Open, Abdominal Aortic Aneurysm Repair, Unruptured/Endovascular, Carotid Endarterectomy, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2022 IMIs were generated using AHRQ Version 2023, while previous years' IMIs were generated with older versions of AHRQ software (2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

ObjectiveEven though patients with sepsis and DIC have a higher mortality rate compared to those without DIC, screening for DIC is not currently part of sepsis management protocols. This may be due to a lack of literature on the frequency of DIC occurrence in sepsis patients, as well as the absence of evidence on the optimal DIC criteria to use for identifying DIC and predicting mortality among the five criteria available. To address this gap, this study investigates the predictive value of five different criteria for diagnosing DIC and its relationship to patient outcomes in our population of sepsis patients.MethodsIn the Medicine department of Aga Khan University Hospital, a retrospective observational study was conducted, enrolling all adult patients with International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) coding of sepsis and clinical suspicion of DIC between January 2018 and December 2020. To diagnose DIC, five different criteria were utilized, namely the International Society of Thrombosis and Hemostasis (ISTH), the Korean Society on Thrombosis and Hemostasis (KSTH), the Japanese Association for Acute Medicine (JAAM), the revised-JAAM (RJAAM), and the Japanese Ministry of Health and Welfare (JMHW). The study analyzed the sensitivity, specificity, negative predictive value, positive predictive value, and accuracy of these five criteria, as well as the overall prediction of mortality.ResultsOf 222 septic patients included in this study with clinical suspicion of DIC, 94.6% of patient had DIC according to KSTH criteria, followed by JAAM (69.4%), ISTH (64.0%), JMHW (53.2%) and lastly R-JAAM (48.6%). KSTH had sensitivity of 95.4% in diagnosing DIC and predicting mortality with a positive predictive value of 70% but specificity of 7.3% only. JAAM had sensitivity of 75.9%, positive predictive value of 75.9% with a specificity of 45.5%. ISTH had sensitivity of 69.4%, positive predictive value 75.3% and specificity of 48.5%.ConclusionDIC can impose a significant burden on septic patients and its presence can lead to higher mortality rates. Early detection through screening for DIC in septic patients can potentially reduce mortality. However, it is necessary to identify the most appropriate diagnostic criteria for each population, as various criteria have demonstrated different performance in different populations. Establishing a gold standard for each population can aid in accurate diagnosis of DIC.

Maternal mortality ratio is defined as the number of female deaths due to obstetric causes (ICD-10 codes: A34, O00-O95, O98-O99) while pregnant or within 42 days of termination of pregnancy. The maternal mortality ratio indicates the likelihood of a pregnant person dying of obstetric causes. It is calculated by dividing the number of deaths among birthing people attributable to obstetric causes in a calendar year by the number of live births registered for the same period and is presented as a rate per 100,000 live births. The number of live births used in the denominator approximates the population of pregnant and birthing people who are at risk. Data are not presented for geographies with number of maternal deaths less than 11.Compared to other high-income countries, women in the US are more likely to die from childbirth or problems related to pregnancy. In addition, there are persistent disparities by race and ethnicity, with Black pregnant persons experiencing a much higher rate of maternal mortality compared to White pregnant persons. Improving the quality of medical care for pregnant individuals before, during, and after pregnancy can help reduce maternal deaths.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

Background and objectiveSince 2023, COVID-19 induced by SARS-CoV-2 XBB variants have been a global epidemic. The XBB variant-induced epidemic was largest in the Okinawa Prefecture among areas in Japan, and healthcare institutions have been burdened by increased COVID-19 hospitalizations. This study aimed to evaluate the clinical features of XBB variant-induced COVID-19 and risk factors for severe COVID-19.MethodsThis retrospective study included adult patients hospitalized for COVID-19 between May and July 2023 at four tertiary medical institutions in Okinawa, Japan. Patients with bacterial infection-related complications were excluded. According to oxygen supplementation and intensive care unit admission, patients were divided into two groups, mild and severe. Patient backgrounds, symptoms, and outcomes were compared between both groups, and the risk factors for severe COVID-19 were analyzed using a multivariate logistic regression model.ResultsIn total of 367 patients included, the median age was 75 years, with 18.5% classified into the severe group. The all-cause mortality rate was 4.9%. Patients in the severe group were more older, had more underlying diseases, and had a higher mortality rate (13.2%) than those in the mild group (3.0%). Multivariate logistic regression analysis showed that diabetes mellitus was an independent risk factor for severe COVID-19 (95% confidence interval [CI], 1.002–3.772), whereas bivalent omicron booster vaccination was an independent factor for less severe COVID-19 (95% CI, 0.203–0.862).ConclusionThis study implies that assessing risk factors in older adults is particularly important in the era of omicron variants.

In both 2021 and 2022, the children of Black mothers in the United States had the highest infant mortality rate, at almost 11 deaths per 1,000 live births. This statistic shows the infant mortality rate in the United States in 2021 and 2022 by the race and ethnicity of the mother.

BackgroundInequalities between men and women in morbidity and mortality show a contrast, which has been called gender paradox. Most studies evaluating this paradox were conducted in high-income countries and, until now, few investigations have been performed in Brazil. This study aims to estimate the magnitude of inequalities between adult men and women in several dimensions: demographic and socioeconomic, health behaviors, morbidity, use of health services and mortality.MethodsThe data were obtained from population-based household survey carried out in Campinas (Campinas Health Survey 2008/09) corresponding to 957 people, and data from the Mortality Information System (MIS) between 2009 and 2011. Prevalences and prevalence ratios were analyzed in order to verify the differences between men and women regarding socioeconomic and demographic variables, health behaviors, morbidities and consultations in the last two weeks. Mortality rates and the ratio between coefficients considering the underlying causes of death were calculated.ResultsWomen had a greater disadvantage in socioeconomic indicators, chronic diseases diagnosed by a health professional and referred health problems as well as make more use of health services, while men presented higher frequency of most unhealthy behaviors and excessive mortality for all causes investigated.ConclusionsThe findings contribute to the discussion of gender paradox and demonstrate the need to employ health actions that consider the differences between men and women in the various health dimensions analyzed. The premature male mortality from preventable causes was outstanding, making clear the need for more effective prevention and health promotion directed to this segment of the population.

Guatemala GT: Mortality Rate: Infant: per 1000 Live Births data was reported at 23.100 Ratio in 2017. This records a decrease from the previous number of 23.900 Ratio for 2016. Guatemala GT: Mortality Rate: Infant: per 1000 Live Births data is updated yearly, averaging 63.850 Ratio from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 146.700 Ratio in 1960 and a record low of 23.100 Ratio in 2017. Guatemala GT: Mortality Rate: Infant: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank: Health Statistics. Infant mortality rate is the number of infants dying before reaching one year of age, per 1,000 live births in a given year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

ABSTRACT Background : The Covid-19 pandemic associated with the SARS-CoV-2 has caused very high death tolls in many countries, while it has had less prevalence in other countries of Africa and Asia. Climate and geographic conditions, as well as other epidemiologic and demographic conditions, were a matter of debate on whether or not they could have an effect on the prevalence of Covid-19. Objective : In the present work, we sought a possible relevance of the geographic location of a given country on its Covid-19 prevalence. On the other hand, we sought a possible relation between the history of epidemiologic and demographic conditions of the populations and the prevalence of Covid-19 across four continents (America, Europe, Africa, and Asia). We also searched for a possible impact of pre-pandemic alcohol consumption in each country on the two year death tolls across the four continents. Methods : We have sought the death toll caused by Covid-19 in 39 countries and obtained the registered deaths from specialized web pages. For every country in the study, we have analysed the correlation of the Covid-19 death numbers with its geographic latitude, and its associated climate conditions, such as the mean annual temperature, the average annual sunshine hours, and the average annual UV index. We also analyzed the correlation of the Covid-19 death numbers with epidemiologic conditions such as cancer score and Alzheimer score, and with demographic parameters such as birth rate, mortality rate, fertility rate, and the percentage of people aged 65 and above. In regard to consumption habits, we searched for a possible relation between alcohol intake levels per capita and the Covid-19 death numbers in each country. Correlation factors and determination factors, as well as analyses by simple linear regression and polynomial regression, were calculated or obtained by Microsoft Exell software (2016). Results : In the present study, higher numbers of deaths related to Covid-19 pandemic were registered in many countries in Europe and America compared to other countries in Africa and Asia. The analysis by polynomial regression generated an inverted bell-shaped curve and a significant correlation between the Covid-19 death numbers and the geographic latitude of each country in our study. Higher death numbers were registered in the higher geographic latitudes of both hemispheres, while lower scores of deaths were registered in countries located around the equator line. In a bell shaped curve, the latitude levels were negatively correlated to the average annual levels (last 10 years) of temperatures, sunshine hours, and UV index of each country, with the highest scores of each climate parameter being registered around the equator line, while lower levels of temperature, sunshine hours, and UV index were registered in higher latitude countries. In addition, the linear regression analysis showed that the Covid-19 death numbers registered in the 39 countries of our study were negatively correlated with the three climate factors of our study, with the temperature as the main negatively correlated factor with Covid-19 deaths. On the other hand, cancer and Alzheimer's disease scores, as well as advanced age and alcohol intake, were positively correlated to Covid-19 deaths, and inverted bell-shaped curves were obtained when expressing the above parameters against a country’s latitude. Instead, the (birth rate/mortality rate) ratio and fertility rate were negatively correlated to Covid-19 deaths, and their values gave bell-shaped curves when expressed against a country’s latitude. Conclusion : The results of the present study prove that the climate parameters and history of epidemiologic and demographic conditions as well as nutrition habits are very correlated with Covid-19 prevalence. The results of the present study prove that low levels of temperature, sunshine hours, and UV index, as well as negative epidemiologic and demographic conditions and high scores of alcohol intake may worsen Covid-19 prevalence in many countries of the northern hemisphere, and this phenomenon could explain their high Covid-19 death tolls. Keywords : Covid-19, Coronavirus, SARS-CoV-2, climate, temperature, sunshine hours, UV index, cancer, Alzheimer disease, alcohol.

Chart and table of the U.S. infant mortality rate from 1950 to 2025. United Nations projections are also included through the year 2100.