In 1919, there were almost 13 deaths from measles per 100,000 population in the United States. However, this rate had dropped to zero by the year 2021. In early 2025, an outbreak of measles in Texas resulted in the death of a child. This was the first measles death in the United States since 2015. Measles is a highly contagious disease, that is especially dangerous for children. However, vaccines have significantly decreased the rate of cases and deaths in the United States.

In the year 2000, there were around 3,397 deaths due to measles in Europe. This number dropped to just 222 in 2023. This statistic shows the estimated number of deaths due to measles worldwide in 2000 and 2023, by region.

From 2000 to 2023, measles mortality in Africa reduced by 79 percent. Over this period, every region saw a decrease in measles mortality. This statistic shows the percentage change in measles mortality worldwide from 2000 to 2023, by region.

There were 285 new cases of measles in the U.S. in 2024. Measles, also known as rubeola, is an infectious disease that is highly contagious and affects mostly children. Common symptoms of measles include fever, runny nose, sore throat, cough, and a rash. Although death rates from measles have decreased around the world, it is still responsible for around 81,000 deaths worldwide per year. Measles vaccination The main reason for the decrease in measles cases and deaths is due to high vaccination rates. The widely used MMR vaccine protects against measles, mumps, and rubella and is safe and effective. In 2023, around 91 percent of adolescents in the U.S. aged 13 to 17 years had received an MMR vaccination. However, in recent years there has been a rise in measles cases in many parts of the world due to vaccine hesitancy. Vaccine hesitancy Vaccine hesitancy refers to a refusal or reluctance to have children vaccinated, despite the overwhelming evidence that vaccines are safe and effective. This hesitancy comes from a misunderstanding of the ingredients in vaccines and how they work, a mistrust of doctors and pharmaceutical companies, and belief in the unfounded associations of vaccines with other diseases and disorders.

In 1970, there were 22.79 new cases of measles per 100,000 population in the United States. However, this rate dropped to .08 in the year 2024. This statistic shows the number of new cases of measles per 100,000 population in the United States from 1919 to 2024.

*Observations: 980; Countries: 44; Observations per country (min: 5; avg: 22.3; max: 43); R-squared (within: 0.62; between: 0.33; overall: 0.46); F: 129.84; p:

In 2024, there were a total of 285 cases of measles in the United States, with 120 of these cases among children aged under five years. From January 1 to July 1, 2025, there were 1,267 cases of measles. There were also three reported deaths from the disease during this time, the first deaths since 2015. Measles is a highly contagious disease that can be especially dangerous for young children. Vaccines against measles resulted in a significant decrease in cases in the United States over the last few decades; however, increasing vaccine hesitancy and skepticism has been blamed for recent outbreaks.

There were 285 new cases of measles in the U.S. in 2024. Measles, also known as rubeola, is an infectious disease that is highly contagious and affects mostly children. Common symptoms of measles include fever, runny nose, sore throat, cough, and a rash. Although death rates from measles have decreased around the world, it is still responsible for around 81,000 deaths worldwide per year. Measles vaccination The main reason for the decrease in measles cases and deaths is due to high vaccination rates. The widely used MMR vaccine protects against measles, mumps, and rubella and is safe and effective. In 2023, around 91 percent of adolescents in the U.S. aged 13 to 17 years had received an MMR vaccination. However, in recent years there has been a rise in measles cases in many parts of the world due to vaccine hesitancy. Vaccine hesitancy Vaccine hesitancy refers to a refusal or reluctance to have children vaccinated, despite the overwhelming evidence that vaccines are safe and effective. This hesitancy comes from a misunderstanding of the ingredients in vaccines and how they work, a mistrust of doctors and pharmaceutical companies, and belief in the unfounded associations of vaccines with other diseases and disorders.

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

The data shows the percentage of Infant deaths due to Measles to the total reported Infant deaths

Attack rates, case fatality ratios and measles-specific mortality rates, by age and IDP status.

The association between armed conflict and maternal and child mortality (adjusted beta coefficients, 95% confidence intervals).

Description of data used in the study.

In 2021, there were ** reported deaths caused by measles in the Philippines. This was a decrease compared to the previous year, which reached a number of ** deaths across the country.

IntroductionThough still high, the infant mortality rate in Guinea-Bissau has declined. We aimed to identify risk factors including vaccination coverage, for infant mortality in the rural population of Guinea-Bissau and assess whether these risk factors changed from 1992–3 to 2002–3.MethodsThe Bandim Health Project (BHP) continuously surveys children in rural Guinea-Bissau. We investigated the association between maternal and infant factors (especially DTP and measles coverage) and infant mortality. Hazard ratios (HR) were calculated using Cox regression. We tested for interactions with sex, age groups (defined by current vaccination schedule) and cohort to assess whether the risk factors were the same for boys and girls, in different age groups in 1992–3 and in 2002–3.ResultsThe infant mortality rate declined from 148/1000 person years (PYRS) in 1992–3 to 124/1000 PYRS in 2002–3 (HR = 0.88;95%CI:0.77–0.99); this decline was significant for girls (0.77;0.64–0.94) but not for boys (0.97;0.82–1.15) (p = 0.10 for interaction). Risk factors did not differ significantly by cohort in either distribution or effect. Mortality decline was most marked among girls aged 9–11 months (0.56;0.37–0.83). There was no significant mortality decline for girls 1.5–8 months of age (0.93;0.68–1.28) (p = 0.05 for interaction). DTP and measles coverage increased from 1992–3 to 2002–3.ConclusionsRisk factors did not change with the decline in mortality. Due to beneficial non-specific effects for girls, the increased coverage of measles vaccination may have contributed to the disproportional decline in mortality by sex and age group.

The research on life expectancy in countries takes the spotlight in the notebook's machine learning model. Substantial data analysis and predictive algorithms are used to uncover the reasons causing differences in longevity among countries. With the aid of strong statistical tools, valuable insights into the complex link between healthcare, socioeconomic factors, and life expectancy are sought |Description|Column| |:------:|:--------:| |Country under study|Country| |year|Year| |Status of the country's development|Status| |Population of country|Population| |Percentage of people finally one year old who were immunized against hepatitis B|Hepatitis B| |The number of reported measles cases per 1000 people|Measles| |Percentage of 1-year-olds immunized against polio|Polio| |Percentage of people finally one year old who were immunized against diphtheria|Diphtheria| |The number of deaths caused by AIDS of the last 4-year-olds who were born alive per 1000 people|HIV/AIDS| |The number of infant deaths per 1000 people|infant deaths| |he number of deaths of people under 5 years old per 1000 people|under-five deaths| |The ratio of government medical-health expenses to total government expenses in percentage|Total expenditure| |Gross domestic product|GDP| |The average body mass index of the entire population of the country|BMI| |Prevalence of thinness among people 19 years old in percentage|thinness 1-19 years| |Liters of alcohol consumption among people over 15 years old|Alcohol| |The number of years that people study|Schooling| |Country life expectancy|Life expectancy [target variable]|

Labels: "Status" (Development Status: Developing or Developed)

Features: 1. Country: The name of the country. 2. Year: The year of data recording. 3. Life Expectancy: The average number of years a newborn, person at different age ranges, or the entire population is expected to live 4. Adult Mortality: Probability of dying between 15 and 60 years per 1000 population. 5. Infant Deaths: Number of infant deaths per 1000 live births. 6. Alcohol: Alcohol consumption per capita (in liters of pure alcohol). 7. Percentage Expenditure: Expenditure on health as a percentage of total government spending or GDP. 8. Hepatitis B: Hepatitis B immunization coverage among 1-year-olds (percentage). 9. Measles: Measles immunization coverage among 1-year-olds (percentage).

The effect of health expenditure on health outcomes.

There was a protracted measles outbreak in Kismayo, Somalia between 2020–2021. The outbreak persisted despite availability of measles containing vaccine (MCV) through Expanded Program on Immunization (EPI) services and reactive vaccination campaigns. We sought to estimate measles burden and MCV coverage during the outbreak while further identifying barriers and facilitators to care and vaccinations. We adopted a cross-sectional, sequential mixed-method approach with a retrospective household survey followed by key informant interviews (KIIs) and focus group discussions (FGDs). We used proxy-reported interview data from a household survey with a two-year recall period to estimate attack rates (ARs), case fatality ratios (CFRs), measles-specific mortality and MCV coverage. We performed thematic analysis on qualitative data from 12 KIIs and 8 FGDs. We surveyed 1,050 households representing 6,664 individuals and estimated an urban population of 405,181 (95%CI: 389,335–422,331). We identified 338 measles cases (AR: 5.1% [95%CI: 4.6-5.6]) and 11 measles deaths (CFR: 3.3% [95%CI: 1.4-5.2]). During the outbreak, we interpolated that 20,664 (95%CI: 17,909–21,651) measles cases and 682 (95%CI: 251–1230) deaths occurred across Kismayo. At start of recall, 49.5% (95%CI: 46.5-52.6) aged 6–59 months had one-or-more doses of MCV and this increased to 69.6% (95%CI: 66.9-72.2) by end of recall. Thematic analysis produced qualitative insights on barriers to accessing medical care, barriers to routine vaccination through EPI, barriers to vaccination through mass campaigns and facilitating factors for care and vaccination. We show an unacceptably high burden of measles due to limited access to medical care and low MCV coverage despite a widespread willingness to be vaccinated. To mitigate the problem of protracted outbreaks, we suggest adopting a consistent, community-centered approach to risk communication and community engagement, reducing non-healthcare costs associated with accessing care, ensuring daily availability of EPI vaccinations in all public facilities and overhauling the ways in which mass vaccination campaigns are implemented.

File Description: "Life Expectancy Data.csv" This dataset contains 2,938 entries and 22 columns, covering life expectancy and related health indicators for multiple nations from 2000 to 2015. It includes country-wise data and other economic, social, and health metrics. Column Description: 1. Country – Name of the country. 2. Year – Data year (ranging from 2000 to 2015). 3. Status – Economic classification (Developing/Developed). 4. Life expectancy – Average lifespan in years. 5. Adult Mortality – Probability of death between ages 15-60 per 1,000 individuals. 6. Infant Deaths – Number of infant deaths per 1,000 live births. 7. Alcohol – Per capita alcohol consumption. 8. Percentage Expenditure – Government health expenditure as a percentage of GDP. 9. Hepatitis B – Immunization coverage percentage. 10. Measles – Number of reported measles cases. 11. BMI – Average Body Mass Index. 12. Under-Five Deaths – Mortality rate for children under five. 13. Polio & Diphtheria – Immunization rates. 14. HIV/AIDS – Deaths due to HIV/AIDS per 1,000 individuals. 15. GDP – Gross Domestic Product per capita. 16. Population – Total population of the country. 17. Thinness (1-19 years, 5-9 years) – Percentage of underweight children. 18. Income Composition of Resources– Human development index proxy. 19. Schooling– Average number of years of schooling. Missing Data: Some columns (like Hepatitis B, GDP, Population, Total Expenditure) contain missing values. Further File Information: Total Countries: 193 Years Covered: 2000–2015 Total Entries: 2,938 Missing Data Overview: Some columns have missing values, notably: Hepatitis B (553 missing) GDP (448 missing) Population (652 missing) Total expenditure (226 missing) Income Composition of Resources (167 missing) Schooling (163 missing) Summary Statistics: Life Expectancy:

Range: 36.3 to 89 years Mean: 69.2 years Adult Mortality:

Mean: 165 per 1,000 Max: 723 per 1,000 GDP per Capita:

Mean: $7,483 Max: $119,172 Population:

Mean: ~12.75 million Max: 1.29 billion Education:

Schooling Average: 12 years Max: 20.7 years

Futuristic Scope of this data: For comparative analysis of the 2000–2015 life expectancy dataset with new datasets on the same parametres , you can perform several statistical tests and analytical methods based on different research questions. Below are some key tests and approaches:

1. Trend Analysis (Time-Series) Objective: Identify trends in life expectancy and related indicators over time. Methods: Moving Averages: Smooth fluctuations to detect trends. Linear/Polynomial Regression: Check whether life expectancy follows an increasing or decreasing trend. Time-Series Decomposition: Separate data into trend, seasonality, and residuals.
2. Descriptive Statistics & Comparative Summary Objective: Compare summary statistics between years or groups. Tests/Methods: Mean, Median, Standard Deviation: Compare distributions of life expectancy, GDP, or schooling. Boxplots & Histograms: Show variations over different years or between developing vs. developed countries. Coefficient of Variation (CV): Compare variability in life expectancy across regions.
3. Correlation & Regression Analysis Objective: Examine relationships between variables. Methods: Pearson/Spearman Correlation: Check relationships between life expectancy and GDP, health expenditure, etc. Multiple Linear Regression: Predict life expectancy based on GDP, immunization, and schooling. Multicollinearity (VIF Test): Ensure independent variables are not highly correlated.
4. Hypothesis Testing (Comparative Analysis) Test Objective When to Use? t-Test (Independent Samples) Compare life expectancy between developed & developing nations Two groups (e.g., 2000 vs. 2015, or developed vs. developing) Paired t-Test Compare life expectancy in the same country over two time periods Before/after comparison (e.g., 2000 vs. 2015 for the same country) ANOVA (One-Way) Compare life expectancy across multiple groups More than two groups (e.g., continents or income groups) Chi-Square Test Test if categorical distributions (e.g., immunization coverage) differ over time Categorical variables (e.g., immunization rates vs. year)
5. Clustering & Classification (Machine Learning) Objective: Group countries based on life expectancy patterns. Methods: K-Means Clustering: Identify groups with similar life expectancy trends. Hierarchical Clustering: Create a country similarity tree. Decision Trees/Random Forest: Classify countries based on development status using life expectancy factors.
6. Forecasting Future Trends Objective: Predict life expectancy in future years using historical data. Methods: ARIMA (AutoRegressive Integrated Moving Average): Time-series forecasting. Exponential Smoothing: Forecast gradual trends. Machine Learning (LSTM, XGBoost): Predict based on multiple indicators.
7. Comparative Regional Analysis O...