101 Health Research

101 Health Research Empowering researchers. Strengthening health systems. Protocols | Biostatistics | Data Science
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Assistance in study design and methodology, statistical planning and analysis, health research training workshops, and clinical and public health research

What is uACR—and why is it changing how we detect kidney disease? 🩺🩸Many people assume that if their urine dipstick is n...
30/07/2026

What is uACR—and why is it changing how we detect kidney disease? 🩺🩸

Many people assume that if their urine dipstick is negative for protein, their kidneys are healthy.
Not always.

One of the earliest signs of chronic kidney disease (CKD) is albuminuria, small amounts of albumin leaking into the urine. At this stage, routine dipstick urinalysis may still appear normal.

This is where the urine albumin-to-creatinine ratio (uACR) comes in.
Unlike routine dipsticks, uACR specifically measures albumin relative to urine creatinine, providing a standardized estimate that is less affected by how concentrated or dilute the urine is.

Current international guidelines (KDIGO, ADA, and others) recommend uACR for CKD screening, particularly among people with:
✔ Diabetes
✔ Hypertension
✔ Cardiovascular disease
✔ Obesity
✔ Older age
✔ Family history of kidney disease

uACR Categories
🟢 A1: 300 mg/g (severely increased albuminuria)

Remember:
Kidney damage often starts before kidney function declines.
Early detection means earlier intervention, and potentially slowing or preventing CKD progression.

Evaluating a New Device? Don't Fall into the Correlation Trap! 🔬🩺Suppose you're evaluating a new handheld spirometer aga...
29/07/2026

Evaluating a New Device? Don't Fall into the Correlation Trap! 🔬🩺

Suppose you're evaluating a new handheld spirometer against the standard laboratory spirometer for measuring FEV₁ in patients with asthma.

Which statistical method should you use? 🤔

Many researchers immediately calculate a Pearson correlation coefficient.

But correlation answers a completely different question.

💡 The Key Difference:
Correlation asks: Do patients with higher values on one method also tend to have higher values on the other?

It measures association, not interchangeability.

A high correlation can exist even if one device consistently reads 0.3 L higher than the other!

If your goal is to determine whether two methods produce similar measurements that can be used interchangeably, you need to assess agreement.

📊 The Solution: Agreement Analysis
One of the most widely used methods is the Bland–Altman plot, which evaluates the differences between paired measurements and helps identify systematic bias and the limits within which most differences lie.

In Short:
📈 Correlation tells you whether measurements move together.
🤝 Agreement tells you whether they give similar values.

Choosing the wrong analysis can lead to the wrong conclusion! ❌

Before analyzing two measurements, always ask yourself:
Am I asking whether they are associated, or whether they agree? 💡✨

Viral hepatitis is preventable, diagnosable, and treatable—yet many people are still missed. 🎗️🩺Not because the science ...
28/07/2026

Viral hepatitis is preventable, diagnosable, and treatable—yet many people are still missed. 🎗️🩺

Not because the science is lacking, but because access, awareness, and stigma shape who gets tested, treated, and followed up. 💔

Hepatitis is often silent for years. Many people feel completely well until liver disease is advanced. Others delay care because of fear, misinformation, or judgment. 🛑

💡 Ending hepatitis requires more than vaccines and medicines.

It requires:
✅ Routine screening 🩸
✅ Strong, accessible care systems 🏛️
✅ Respectful language 🗣️
✅ Stigma-free care ❤️

This carousel looks at hepatitis from public health, clinical, vaccine, and liver health perspectives, centered on people, not labels. 🤝✨

👉 Swipe through to learn more! 📲



[This is for informational purposes only and not medical advice. Please consult a qualified healthcare professional for personal medical concerns.]

When Should You Use Repeated Measures Analysis? 📊⏱️One of the most common statistical mistakes in clinical research is t...
27/07/2026

When Should You Use Repeated Measures Analysis? 📊⏱️

One of the most common statistical mistakes in clinical research is treating repeated measurements as if they came from different people. ❌

Imagine you're evaluating patients after knee replacement surgery. Pain scores are measured:
• Before surgery 🩺
• 1 month 🗓️
• 3 months 🗓️
• 6 months 🗓️

Can you simply perform multiple paired t-tests? Not recommended. 🚫

🤔 Why?
Because these measurements come from the same patient. They are correlated, not independent!

Performing multiple pairwise tests also inflates your risk of false-positive findings (Type I error). 📈⚠️

Instead, use statistical methods specifically designed for repeated measures.

💡 Which test should you use? (By outcome type):

🔹 Nominal:
• Cochran's Q test (3 or more time points)
• McNemar test (2 time points)

🔹 Ordinal:
• Friedman test

🔹 Continuous (approximately normal):
• Repeated measures ANOVA

📋 Interpreting the Results:

The null hypothesis assumes the outcome does not change over time.

If the overall test is statistically significant, follow-up post hoc comparisons can identify which specific time points differ, just make sure to apply an appropriate adjustment for multiple testing! 🎯

Before choosing a statistical test, always ask yourself:
Are these observations from different participants, or repeated measurements from the same participant?

That single question can completely change your entire analysis. 💡✨

Not all diabetes develops slowly.Type 1 diabetes (T1DM) is an autoimmune disease where the body's immune system destroys...
26/07/2026

Not all diabetes develops slowly.

Type 1 diabetes (T1DM) is an autoimmune disease where the body's immune system destroys the insulin-producing cells of the pancreas. Without insulin, blood glucose rises rapidly and can become life-threatening within days or weeks. 🚨

Unlike type 2 diabetes, type 1 diabetes cannot be prevented through lifestyle changes. It often presents abruptly, particularly in children and adolescents, though it can occur at any age.

⏳ Early recognition matters.

Watch for these classic warning signs:
💧 Excessive thirst
🚽 Frequent urination (including new bedwetting)
🍽️ Increased hunger
⚖️ Unexplained weight loss
😴 Extreme fatigue
👁️ Blurred vision

🚨 RED FLAG WARNING:

If these symptoms are accompanied by:
⚠️ Vomiting
⚠️ Abdominal pain
⚠️ Deep or rapid breathing
⚠️ Decreased alertness

Seek emergency medical care immediately. 🚑
These may be signs of Diabetic Ketoacidosis (DKA), a life-threatening medical emergency.

This National Diabetes Awareness Week, let's remember that diabetes is not just one disease. Recognizing the early signs of Type 1 diabetes can save lives. 🎗️✨



[This is for informational purposes only and not medical advice. Please consult a qualified healthcare professional for personal medical concerns.]

In sports, adding more players to the field doesn’t automatically improve performance. ⚽️🏈At some point, coordination br...
25/07/2026

In sports, adding more players to the field doesn’t automatically improve performance. ⚽️🏈

At some point, coordination breaks down—even if everyone on the team is talented.

Health research teams work the exact same way. 🔬

Yes, team size depends on the scope of the project. Large, multi-site studies naturally need larger teams. But research across science, medicine, and organizations consistently shows one thing:

👉 More people is NOT necessarily better.

💡 The 5–7 Rule:
Many high-performing teams cluster around 5–7 core members. This sweet spot is:
✅ Large enough to bring diverse expertise 🧠
✅ Small enough to communicate, decide, and move efficiently ⚡️

⚠️ What happens when teams get too big?
Beyond that threshold, teams often suffer from:
❌ Diffusion of responsibility
❌ Slower decision-making
❌ Unclear ownership

Network science (including work by Barabási), health services research (including Trisha Greenhalgh), and organizational studies all point to the same lesson:

Impact comes from how teams work together, not how big they are. 🌐✨

🏆 The Takeaway:
Just like in sports, great teams are designed, not crowded.

What is Correlation? 🤔📈One of the most common questions in clinical research is:Are these two variables related?Correlat...
24/07/2026

What is Correlation? 🤔📈

One of the most common questions in clinical research is:
Are these two variables related?

Correlation helps answer that question by measuring the strength and direction of the relationship between two variables. 🔍

💡 Which test should you use?

📈 Pearson Correlation
Used for continuous variables with an approximately linear relationship.

📊 Spearman Correlation
Used for ordinal variables, when the relationship is monotonic (but not necessarily linear), or when Pearson's assumptions are not met.

✔️ 4 Things to check BEFORE interpreting a correlation coefficient:

1️⃣ Is the relationship linear or monotonic?
2️⃣ Have I looked at the scatter plot? 📉
3️⃣ Are there influential outliers?
4️⃣ Am I only interpreting the relationship within the observed range of my data?

⚠️ 2 Golden Rules to Remember:

🔴 Correlation does NOT imply causation.
🔴 A strong correlation does NOT necessarily mean two methods agree. (More on agreement in a future post! 😉)

👉 Swipe through to learn when to use Pearson vs. Spearman correlation, their key assumptions, interpretation, and common pitfalls! 📲✨

Imagine you conducted the perfect statistical analysis.Your p-values are correct. 🎯Your confidence intervals are correct...
23/07/2026

Imagine you conducted the perfect statistical analysis.

Your p-values are correct. 🎯
Your confidence intervals are correct. 📊
Your regression model is correct. 📈
..But your study still reaches the wrong conclusion.

How? 🤔
Because statistics cannot fix a biased study.

Bias is a systematic error in the design, conduct, measurement, or analysis of a study that causes the estimated effect to differ from the truth.

Unlike random error, bias is not reduced simply by increasing your sample size. 🛑

💡 One useful way to think about bias is to ask:
Does it move my results away from the truth?

• Sometimes bias makes an association appear stronger than it truly is (away from the null).
• Sometimes it makes an association appear weaker or disappear altogether (toward the null).

Understanding the likely direction of bias helps researchers interpret study findings much more critically.

📌 3 Common Types of Bias:

🔹 Selection Bias
Participants included in the study are systematically different from those who are not.
(Common in cohort and case-control studies)

🔹 Information (Measurement) Bias
Variables are measured inaccurately or inconsistently.
(Can occur in almost any study design)

🔹 Recall Bias
A subtype of information bias where participants remember past exposures differently.
(Particularly important in retrospective case-control studies)

⚙️ The Takeaway:
The best time to address bias is before data collection begins. Good study design is always more powerful than sophisticated statistical analysis. 💡✨

During a catastrophic climate event, generic relief solutions fall short if they ignore human dignity. What happens when...
22/07/2026

During a catastrophic climate event, generic relief solutions fall short if they ignore human dignity. What happens when emergency food packs are culturally unaccepted? Or when therapeutic food is rejected by children due to taste?

The Caraga Region (Region XIII) and BARMM champion a deeply inclusive health agenda built for island isolation, indigenous realities, and populations healing through armed conflicts.

Did you know that healthcare delivery itself leaves a significant environmental footprint? True resilience means examini...
21/07/2026

Did you know that healthcare delivery itself leaves a significant environmental footprint? True resilience means examining both the impact of climate hazards on disease and the impact of clinics on our ecosystems.

Region XI (Davao) and Region XII (SOCCSKSARGEN) pair macro environmental tracking with behavioral medicine—tackling everything from hospital waste tracing to standardized cost-efficiency metrics and multi-cultural vaccine hesitancy.

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