Public Health days

World Braille Day	4 January
World Leprosy Day	Last Sunday in January
World Cancer Day	4 February
International Day of Zero Tolerance for Female Genital Mutilation	6 February
National Deworming Day	10 February
International Epilepsy Day	second Monday of February
International Day of Women and Girls in Science	11 February
World Unani Day	11 February
Sexual Reproductive Health Awareness Day	12 February
International Childhood Cancer Day	15 February
World Day of Social Justice	20 February
Rare Disease Day	28/29 February
International Condom Day	13 February
Zero Discrimination Day	01Mar
World Birth Defect Day	03Mar
World Hearing Day	03Mar
World Obesity Day	04Mar
No Smoking Day	Second Wednesday of March
World Glaucoma Day	12Mar
World Glaucoma Week	March 713 March 1218
International Women’s Day	08Mar
World kidney day	14Mar Second Thursday of March
Salt awareness week	11
Measles Immunization Day	March 16
World Kidney Day	Second Thursday in March
International Day of Happiness	20Mar
World Oral Health Day	20Mar
International Day for the Elimination of Racial Discrimination	21Mar
World Down Syndrome Day	21Mar
World Water Day	22Mar
World Tuberculosis Day	24Mar
International Day of Solidarity with Detained and Missing Staff Members	25Mar
World Autism Awareness Day	02Apr
International Day of Sport for Development and Peace	06Apr
World Health Day	07Apr
World health worker week	4
World Chagas Disease Day	14Apr
World haemophilia day	17Apr
World Liver Day	April 19
World Creativity and Innovation Day	21Apr
Earth Day	April 22
World Meningitis Day	24Apr
World Malaria Day	25Apr
World Immunization Week	The last week of April
World Day for Safety and Health at Work	28Apr
World Asthma Day	First Tuesday in the month of May
World Hand Hygiene Day	05May
International Day of the Midwife	05May
UN Global Road Safety Week	6 May
World Red Cross Day	8 May
World Thalassaemia Day	8 May
Mother's Day	Second Sunday of May
International Nurses Day	12May
International Day of Families	15May
National Dengue Day	May 16
International Day of action for women’s health	18May
World Family Doctor Day	19May
International Day against Homophobia, Transphobia and Biphobia	17May
World Hypertension Day	17May
International Day to End Obstetric Fistula	23May
World Multiple Sclerosis Day	May 25 (Last Wednesday of May)
Menstrual Hygiene Day	28May
International Day of Action for Women’s Health / International Women’s Health Day	28 May
World No Tobacco Day	31 May
World Environment Day	June 5
World Brain Tumour Day	June 8
World Blood Donor Day	14Jun
World Elder Abuse Awareness Day	15Jun
Autistic Pride Day	June 18
International Day for the Elimination of Sexual Violence in Conflict	19Jun
International Day of Yoga	21Jun
International Day Against Drug Abuse and Illicit Trafficking	26Jun
National Doctors Day	July 1
World Population Day	11Jul
World Brain Day	22Jul
World Drowning Prevention Day	25 July
World Hepatitis Day	28Jul
ORS Day	July 29
World Breastfeeding Week	1 to 7 August
World’s Indigenous People Day	09Aug
International Youth Day	12Aug
World Humanitarian Day	19Aug
World Mosquito Day	August 20
African Traditional Medicine Day	31Aug
National Eye Donation Fortnight	25th August  8th September
National Nutrition week	September 1 to 7
Spinal Cord Injury Day	September 5
World Physical Therapy Day	08Sep
World Suicide Prevention Day	10Sep
World Sepsis Day	13Sep
World Marrow Donor Day	September 16
World Patient Safety Day	September 17
World Alzheimer’s Day	21Sep
World Pharmacists Day	25Sep
World Lung Day	25Sep
World Rabies Day	28Sep
World Heart Day	29Sep
World Day of Deaf	Last Sunday of September
World Contraception Day	26Sep
Breast Cancer Awareness Month	October
International Day for the Elderly	1 October
World Vegetarian Day	October 1
International Day of Non-Violence	2 October
National Anti-Drug Addiction Day	October 2
World Sight Day	October 9
World Mental Health Day	10Oct
International Day of the Girl Child	11Oct
World Cerebral Palsy Day	First Wednesday of October
International Day for Disaster Reduction	13 October
World Thrombosis Day	13 October
Global Handwashing Day	October 15
World Sight Day	Second Thursday of October
World Hospice and Palliative Care Day	The second Saturday of October
World Arthritis Day	October 12
International Day of Rural Women	15 October
World Food Day	16 October
World Trauma Day	October 17
World Statistics Day	20Oct
World Osteoporosis Day	20 October
World Iodine Deficiency Day	October 21
United Nations Day	24 October
World Polio Day	24 October
World Obesity Day	October 26
World Psoriasis Day	29 October
World Stroke Day	29 October
World Thrift Day	October 30
World Cities Day	31 October
One Health Day	3 Nov
World Immunisation Day	November 10
World Pneumonia Day	12 November
World Antibiotic Awareness Week	18-24 Nov
World Diabetes Day	14 November
International Day for Tolerance	16 November
National Epilepsy Day	November 17
World COPD Day	19 November
World Toilet Day	19 November
World Day of Remembrance for Road Traffic Victims	The third Sunday of November
World Day of Research for Health	18 November
New Born Care Week	November 15-21
Universal Children’s Day	20 Nov
International Day for the Elimination of Violence against Women	25 Nov
World AIDS Day	1 December
National Pollution Prevention Day	December 2
International Day of Persons with Disabilities	3 December
International Volunteer Day for Economic and Social Development	5 December
FCHV Day	05Dec
World Patient Safety Day	9 December
International Anti-Corruption Day	9 December
Human Rights Day	10 December
International Universal Health Coverage Day	12 December
International Human Solidarity Day	20 Dec

Statistics in common language

Statistics can be thought in simple terms as a branch  of science dealing with the various methods through which we make sense out of the data. 

Data is the any information regarding anything in day to day life. It can be list of phone numbers of all the people in a particular area. It can be list of names and gender of teachers in a school. 

Pearson Chi square and Likelihood chi square

Pearson chi-square test

The Pearson chi-square statistic (χ2) involves the squared difference between the observed and the expected frequencies.

Likelihood-ratio chi-square test

The likelihood-ratio chi-square statistic (G2) is based on the ratio of the observed to the expected frequencies.

Chord Diagram

Description

This type of diagram visualises the inter-relationships between entities. The connections between entities are used to display that they share something in common. This makes Chord Diagrams ideal for comparing the similarities within a dataset or between different groups of data.

Nodes are arranged along a circle, with the relationships between points connected to each other either through the use of arcs or Bézier curves. Values are assigned to each connection, which is represented proportionally by the size of each arc. Colour can be used to group the data into different categories, which aids in making comparisons and distinguishing groups.

Over-cluttering becomes an issue with Chord Diagrams when there are too many connections displayed.

Anatomy

Standardised effect size measurements

If you’re in a field that uses Analysis of Variance, you have surely heard that p-values alone don’t indicate the size of an effect. You also need to give some sort of effect size measure.

Why? Because with a big enough sample size, any difference in means, no matter how small, can be statistically significant. P-values are designed to tell you if your result is a fluke, not if it’s big.

Truly the simplest and most straightforward effect size measure is the difference between two means. And you’re probably already reporting that. But the limitation of this measure as an effect size is not inaccuracy. It’s just hard to evaluate.

If you’re familiar with an area of research and the variables used in that area, you should know if a 3-point difference is big or small, although your readers may not. And if you’re evaluating a new type of variable, it can be hard to tell.

Standardized effect sizes are designed for easier evaluation. They remove the units of measurement, so you don’t have to be familiar with the scaling of the variables.

Cohen’s d is a good example of a standardized effect size measurement. It’s equivalent in many ways to a standardized regression coefficient (labeled beta in some software). Both are standardized measures-they divide the size of the effect by the relevant standard deviations. So instead of being in terms of the original units of X and Y, both Cohen’s d and standardized regression coefficients are in terms of standard deviations.

There are some nice properties of standardized effect size measures. The foremost is you can compare them across variables. And in many situations, seeing differences in terms of number of standard deviations is very helpful.

But they’re most useful if you can also recognize their limitations. Unlike correlation coefficients, both Cohen’s d and beta can be greater than one. So while you can compare them to each other, you can’t just look at one and tell right away what is big or small. You’re just looking at the effect of the independent variable in terms of standard deviations.

This is especially important to note for Cohen’s d, because in his original book, he specified certain d values as indicating small, medium, and large effects in behavioral research. While the statistic itself is a good one, you should take these size recommendations with a grain of salt (or maybe a very large bowl of salt). What is a large or small effect is highly dependent on your specific field of study, and even a small effect can be theoretically meaningful.

Another set of effect size measures for categorical independent variables have a more intuitive interpretation, and are easier to evaluate. They include Eta Squared, Partial Eta Squared, and Omega Squared. Like the R Squared statistic, they all have the intuitive interpretation of the proportion of the variance accounted for.

Eta Squared is calculated the same way as R Squared, and has the most equivalent interpretation: out of the total variation in Y, the proportion that can be attributed to a specific X.

Eta Squared, however, is used specifically in ANOVA models. Each categorical effect in the model has its own Eta Squared, so you get a specific, intuitive measure of the effect of that variable.

Eta Squared has two drawbacks, however. One is that as you add more variables to the model, the proportion explained by any one variable will automatically decrease. This makes it hard to compare the effect of a single variable in different studies.

Partial Eta Squared solves this problem, but has a less intuitive interpretation. There, the denominator is not the total variation in Y, but the unexplained variation in Y plus the variation explained just by that X. So any variation explained by other Xs is removed from the denominator. This allows a researcher to compare the effect of the same variable in two different studies, which contain different covariates or other factors.

In a one-way ANOVA, Eta Squared and Partial Eta Squared will be equal, but this isn’t true in models with more than one independent variable.

The drawback for Eta Squared is that it is a biased measure of population variance explained (although it is accurate for the sample). It always overestimates it.

This bias gets very small as sample size increases, but for small samples an unbiased effect size measure is Omega Squared. Omega Squared has the same basic interpretation, but uses unbiased measures of the variance components. Because it is an unbiased estimate of population variances, Omega Squared is always smaller than Eta Squared.

Odds Ratio Confidence Interval

Following formula is used to calculate the odds ratio (O.R.) and its confidence interval (C.I.). 

OR = a*d / b*c, where:

• a is the number of times both A and B are present,
• b is the number of times A is present, but B is absent,
• c is the number of times A is absent, but B is present, and
• d is the number of times both A and B are negative.

To calculate the confidence interval, we use the log odds ratio, log(OR) = log(a*d/b*c), and calculate its standard error:

se(log(OR)) = √1/a + 1/b + 1/c +1/d

The confidence interval, C.I., is calculated as:

CI = exp(log(OR) ± Zα/2­*√1/a + 1/b + 1/c + 1/d),

where Zα/2 is the critical value of the Normal distribution at α/2 (e.g. for a confidence level of 95%, α is 0.05 and the critical value is 1.96).

Note: The logarithms included in the formulae above are natural logarithms, i.e., log base e, sometimes denoted ln().