Epidemiological Statistics

📢 Research Alert: GBD 2023 : Burden of Diseases, Injuries, & Risk Factors, 1990–2023 🌍 🎉 Congratulations to the GBD 2023 Disease & Injury & Risk Factor Collaborators for their new study “Burden of 375 diseases & injuries, risk-attributable burden of 88 risk factors, & healthy life expectancy in 204 countries & territories, including 660 subnational locations, 1990–2023,” now published in The Lancet. 📚 This flagship paper provides a comprehensive audit of world health, combining updated estimates of disease and injury burden, risk-attributable DALYs, and healthy life expectancy (HALE). It draws on more than 310,000 data sources and advances in modelling, including the partial transition to DisMod-AT, offering unprecedented insight into long-term global health trends. 🔑 Key Insights: • Epidemiological transition: Total DALYs rose slightly from 2.64 billion in 2010 to 2.80 billion in 2023, but age-standardised DALY rates declined by 12.6%, underscoring steady improvements in population health despite demographic pressures. • NCD dominance: Non-communicable diseases accounted for 1.8 billion DALYs in 2023, led by ischaemic heart disease (193m), stroke (157m), and diabetes (90m). The steepest relative increases since 2010 were seen in anxiety disorders (+63%), depressive disorders (+26%), and diabetes (+15%). • Progress in CMNN diseases: DALYs due to communicable, maternal, neonatal, and nutritional (CMNN) conditions fell by 25.8% in age-standardised terms between 2010 and 2023. Declines were particularly striking for diarrhoeal diseases (–49%), HIV/AIDS (–43%), and tuberculosis (–42%). We must be vigilant. • Risk factor landscape: Nearly 46% of all DALYs in 2023 were attributable to 88 modifiable risk factors. Leading contributors were high systolic blood pressure, particulate matter pollution, high fasting plasma glucose, smoking, and low birthweight/short gestation. • Emerging challenges: Rising burdens linked to metabolic risks (notably high BMI and glucose) and mental health conditions highlight the growing urgency of addressing the NCD syndemic. 🌍 Impact: This analysis underscores the dual narrative of global health, remarkable (but potentially reversable) success in reducing communicable disease burden, alongside the relentless rise of NCDs and risk-attributable causes. With almost half of all DALYs preventable, the findings provide a powerful call for leaders to better target prevention, sustain health financing, and improve equitable health system strengthening. 👏 Funding: Supported by the Gates Foundation and Bloomberg Philanthropies. 🔗 Dive deeper into the study here: https://lnkd.in/ggg3vF6W. 🗓️ Published online 12 October 2025, this foundation paper provides one of the most complete assessments yet of the forces shaping global health and disease worldwide.

📊 Measures of Disease Frequency: The Foundation of Epidemiology and Public Health Understanding how diseases are distributed within populations is one of the cornerstones of evidence-based public health. Measures such as incidence, prevalence, attack rate, mortality rate, and case fatality rate provide essential insights for disease surveillance, outbreak investigations, health program evaluation, and policy development. To support students, educators, researchers, and public health professionals, I created this comprehensive infographic that summarizes the most important Measures of Disease Frequency in a clear, visual, and practical format. This infographic includes: ✅ Definitions and interpretations of key measures ✅ Formulas and epidemiologic indicators ✅ Appropriate applications and target populations ✅ Real-world public health examples ✅ Comparison table for quick reference ✅ Practical tips for accurate interpretation Whether you’re studying for exams, teaching epidemiology, conducting research, or working in population health, I hope this resource serves as a valuable learning and teaching aid. I welcome your feedback and would be delighted if you shared this resource with colleagues, students, and fellow public health professionals. #PublicHealth #Epidemiology #DiseaseFrequency #Biostatistics #PopulationHealth #GlobalHealth #HealthResearch #EvidenceBasedPractice #PublicHealthEducation #MedicalEducation #HealthData #DiseaseSurveillance #OutbreakInvestigation #GraduateEducation #ResearchMethods #DataScience #HealthPolicy #AcademicMedicine #ArizonaStateUniversity #PhDJourney #LinkedInLearning

Recent population health data suggests that residents in the northern region of Singapore, particularly towns such as Woodlands, Yishun, and Sembawang, have a higher prevalence of diabetes and hypertension compared with national averages. While the numbers are clear, the underlying causes are less certain. Several plausible factors may contribute: • Demographic structure, including an older population profile • Socioeconomic gradients that influence diet, stress, and health behaviours • Differences in physical activity and lifestyle patterns • Ethnic distribution and associated metabolic risk profiles • More active screening and detection through primary care networks However, these remain hypotheses. More rigorous research is needed to understand the drivers behind this geographic clustering of chronic disease. One promising approach is the use of artificial intelligence for population health monitoring. AI can integrate multiple data sources such as electronic medical records, screening programmes, pharmacy data, wearable devices, and socioeconomic indicators to detect emerging patterns of disease. With machine learning and geospatial analytics, health systems could identify high-risk neighbourhoods earlier, monitor behavioural risk factors such as physical activity, and predict which communities are most vulnerable to chronic disease. This would allow health systems to move beyond reactive care toward proactive population health management. Instead of waiting for complications to appear, we can anticipate risk, target prevention programmes, and evaluate whether interventions are working. Understanding why disease burden concentrates in specific communities is essential for designing effective public health strategies. Combining epidemiological research with AI-driven monitoring may help us better understand these patterns and ultimately improve the health of our population. #PopulationHealth #Diabetes #Hypertension #AIinHealthcare #PublicHealth #Singapore

In global health, one number can influence millions of lives. But only when we interpret it correctly. One of the most misunderstood yet most powerful concepts in epidemiology and biostatistics is the Odds Ratio (OR). From outbreak investigations and vaccine effectiveness studies to nutrition surveillance, maternal health, HIV programs, and humanitarian response evaluations, Odds Ratios help us answer a critical question: How strongly is an exposure associated with an outcome? The challenge is that many professionals memorize formulas without fully understanding the real-world interpretation behind them. But in public health, interpretation is everything. That is why I developed this visual guide on Odds Ratio simplifying: 1. What OR actually means 2. Step-by-step calculations 3. Interpretation of OR < 1, OR = 1, and OR > 1 4. Case-control study applications 5. Public health examples and scenarios 6. Common interpretation mistakes 7. Epidemiological importance in surveillance and decision-making In disease surveillance and program evaluation, statistics should never remain theoretical. They must translate into: Better targeting of vulnerable populations Smarter prevention strategies Stronger evidence-based policies Faster outbreak response More efficient resource allocation Data alone does not save lives. Correct interpretation of data does. As epidemiology increasingly intersects with AI, data science, humanitarian programming, and global development, statistical literacy is no longer optional it is a leadership competency. I would be interested to hear from professionals across: Public Health | Epidemiology | Monitoring & Evaluation | Data Science | Clinical Research | Humanitarian Response | Global Health Policy In your experience, what is the most commonly misunderstood statistical concept in public health practice? #Epidemiology #PublicHealth #GlobalHealth #Biostatistics #DataScience #MonitoringAndEvaluation #HealthSystems #DiseaseSurveillance #ResearchMethods #EvidenceBasedPolicy #HealthEquity #HumanitarianResponse #UNICEF #WHO #SDGs #GlobalDevelopment #Statistics #OddsRatio #FieldEpidemiology

Relative risk looks simple until people start interpreting it incorrectly. I see this often with students reading papers, writing theses, or preparing for public health exams. RR answers one clean question: → How does the risk of an outcome compare between two groups? If RR = 1: → The risk is the same in both groups If RR > 1: → The exposed group has higher risk If RR < 1: → The exposed group has lower risk The common mistakes: → Confusing risk with odds ↳ RR compares risk, not odds. → Saying RR = 5 means “5% higher risk” ↳ It means 5 times the risk. → Ignoring the confidence interval ↳ If the 95% CI crosses 1, be careful with the interpretation. → Using RR when the study design does not allow direct risk estimation ↳ In many case-control studies, odds ratio is usually the correct measure. A simple memory cue: RR is best when you can calculate risk in both groups. That is why it fits naturally with cohort studies. Before interpreting RR, ask: → What is the exposed group? → What is the unexposed group? → What outcome was measured? → Does the confidence interval include 1? Small errors in interpreting RR can change how people read an entire paper. Which measure should I simplify next: odds ratio, hazard ratio, risk difference, or attributable risk? #Epidemiology #PublicHealth #ResearchMethods #Biostatistics

One of the biggest lessons I've learned while transitioning into public health data analytics is that numbers don't tell the whole story. Early on, I focused on building dashboards and analyzing datasets. Over time, I realized that the real value comes from understanding what each metric actually represents. A chart is only as meaningful as the epidemiological indicator behind it. Here are a few indicators I believe every public health data analyst should understand: Incidence – Shows the number of new cases over a specific period and helps identify emerging outbreaks. Prevalence – Measures all existing cases, giving a picture of the overall disease burden. Mortality Rate – Reflects the number of deaths in a population and helps assess the impact of diseases. Case Fatality Rate (CFR) – Indicates how severe a disease is by measuring the proportion of diagnosed cases that result in death. Attack Rate – Often used during outbreaks to determine how quickly a disease spreads among those at risk. Relative Risk (Risk Ratio) – Compares disease risk between exposed and unexposed groups, helping identify potential risk factors. These aren't just definitions I memorized during my public health training, they're measures I now look for whenever I explore health data. They help me ask better questions, build more meaningful dashboards, and turn data into insights that can support better decisions. As public health data analysts, our role goes beyond reporting numbers. We help transform data into evidence that can influence policies, strengthen surveillance systems, and ultimately improve health outcomes. Which of these epidemiological indicators do you work with most often, and how has it shaped your analysis? I'd love to hear your experience. #PublicHealth #Epidemiology #HealthData #DataAnalytics #DiseaseSurveillance #HealthInformatics #PowerBI #DataVisualization #EvidenceBasedPractice

Biostatistics for Epidemiology and Public Health Using R The book Biostatistics for Epidemiology and Public Health Using R by Bertram K. C. Chan is a specialized and practical resource that integrates biostatistical methods with R programming to address challenges in public health and epidemiology. It provides a structured approach to study design, data analysis, and visualization, making it invaluable for researchers, students, and healthcare professionals.   📘 Why This Book Matters Public health decisions rely on robust statistical evidence. This book equips readers with the tools to design epidemiological studies, analyze health data, and apply statistical methods using R. By bridging biostatistics with programming, it ensures that research outcomes are both rigorous and reproducible.   📑 Key Content Covered Foundations: Introduction to biostatistics and R programming. Research Design: Principles of epidemiology and public health study design. Data Analysis with R: Practical applications for handling and analyzing health data. Graphics in R: Visualization techniques for clear communication of results. Probability & Statistics: Core concepts applied to biostatistics. Case–Control & Cohort Studies: Epidemiological methods explained with R. Randomized Trials & Survival Analysis: Advanced methods including logistic regression.   💡 Key Benefits Applied Focus: Real‑world examples tailored to public health contexts. Comprehensive Coverage: From basic probability to advanced survival analysis. Hands‑On Learning: Practical R code for immediate application. Policy Relevance: Supports evidence‑based decision‑making in healthcare.   👥 Who Should Read It Public Health Researchers & Epidemiologists: To apply biostatistics in study design and analysis. Healthcare Professionals: To interpret statistical evidence for clinical and policy decisions. Students of Biostatistics & Epidemiology: To build a strong foundation in applied methods. Data Analysts in Health: To strengthen R programming skills in medical contexts.   🌍 The Professional Edge This book is more than a statistics manual—it is a strategic toolkit for evidence‑based public health research. By mastering its methods, professionals can design credible studies, analyze complex health data, and contribute to advancing population health outcomes. 🔖 Hashtags #Biostatistics #Epidemiology #PublicHealth #RProgramming #HealthcareAnalytics #EvidenceBasedMedicine #ProfessionalDevelopment #DataDrivenHealth

"Shortly after Kennedy was nominated, questions swirled over how he might overhaul America’s immunization system. Two Stanford University researchers wondered how many people would suffer if vaccination rates dropped or shots became entirely unavailable for four of the most infamous diseases: polio, measles, rubella and diphtheria. Outbreaks often start when an American catches one of these illnesses abroad and returns home. So epidemiologists Mathew Kiang and Nathan Lo, who is also an infectious diseases doctor, built a model to simulate how the four contagions could spread from sick travelers based on each state’s vaccination rates. Since a sizable chunk of the population is currently vaccinated, some of the infections wouldn’t get a foothold right away. But over time, as more babies are born and not vaccinated, a larger share of the population would become susceptible. The professors ran thousands of simulations for each disease, producing a range of possible outcomes. From there, they figured out the average number of deaths and disabilities over a 25-year period. Their model shows that at current vaccination rates, the nation is already teetering on the brink of an explosion in measles cases — one that would be virtually wiped out with just a 5% increase in vaccination. But if current rates drop by half, all four diseases could return." https://lnkd.in/gCXPq6M6

In my years as a pharmacoepidemiologist, I've learned that the biggest barrier to credible Real-World Evidence isn't data access—it's statistical literacy. Too often, I see critical decisions made based on misunderstood metrics. A p-value < 0.05 is treated as proof of causality. Odds ratios are reported as risks for common outcomes. Confidence intervals are ignored in favor of point estimates. In observational research, where bias often dwarfs random error, these distinctions aren't just academic—they impact patient care and regulatory outcomes. I've just published a new article: From Precision to Estimation: Real-World Evidence beyond P-Values. Inside, I break down the fundamentals we need to get right: 🔹 Risk vs. Rate vs. Odds (and why confusing them matters) 🔹 How to truly interpret Confidence Intervals 🔹 Why context dictates the value of a P-Value 🔹 Choosing the right Effect Measure (RD, RR, OR, HR) My goal is to move us from chasing "significance" to embracing "estimation", robustness, and clinical plausibility. 👉 Read the full article below. I'd love to hear from my network: What statistical concept do you find most frequently misinterpreted in RWE studies? Let's discuss in the comments. 👇 #RealWorldEvidence #Pharmacoepidemiology #RWE #Biostatistics #EvidenceBasedMedicine #ClinicalResearch #DataScience #RegulatoryScience #HealthData #CausalInference