Tag: public health blog

Qatar, Macau, Singapore, Kuwait…

Posted on by Prav

What do all the countries above have in common?

They have the highest GNI per capita in the world.

Let’s take a step back though, the first thing we need to understand is economic growth.

Economic growth is when we see an increase in the goods/products/services produced over a period of time. It is good to note that it is inflation adjusted.

So GDP or Gross Domestic Product is how we measure it. Basically it tells us the output or production of a country, regardless of who has made the product. This tells us how healthy the economy is but does not really clue us in into the financial well being or wealth of the people or the country itself.

GNP is Gross National Product which is the same as Gross National Income. Regardless of which term you choose, the concept is the same. Both measure the production/income of a country and all it’s people, regardless of whether it is within their borders. Think of GNI as GDP + salaries and other incomes of the country’s residents earned abroad.

For example Amazon is a US based and owned company, although it is operating in the UK/Malaysia/*insert country of choice* it contributes to the GNI/GNP of the United States while contributing to the GDP of the UK.

But how does that help us understand the wealth/well-being of a country? Surely Amazon is not a reflection alone of the USA being a wealthy country?

Hence we have GNI/GNP per capita which is essentially the average income of a country. We take the entire income by people from/in the country and divide it by the total population. This allows us to draw comparisons between different countries regardless of population size and tell us what their income is which is used by the World Bank to classify countries into low, middle and high income countries.

However, this does not reflect the economic situation of a country as it is the average and as we are all too familiar with, wealth is not distributed equally across the board. Therefore it says nothing about the standards of living for the entire population involved. For this reason, it cannot be used on its own.

So how do we overcome this and get a better idea of what is going on in each country? More in the next post.

What I wished we covered in Medical School

Posted on by Prav

In writing this title, I thought to myself – if I was being completely honest there are a whole host of ‘real-life’ topics I wished we covered in medical school. Yes of course I could have looked it up myself etc but you can only look up what you know, have heard of or what is interest driven. Macroeconomics as a topic I believe is essential and pertinent in everything especially health – which is why push come to shove it would be the one thing I wish we covered additionally in medical school. (No we did not have other elective modules in my university).

So what is Macroeconomics anyway?

Like public health, it is all about taking a step back from the individual and looking at the overall picture. We are looking at how an economy performs, its structure, how it behaves and functions as well as the decision making involved.

Giving us an overall idea of the relationship of economic growth with things like employment rates and inflation. It also helps us understand the cause and effect of short term booms in economy as well as determine what we can do to ensure long term economic growth and increase in overall national income.

Over the next few posts, I will delve into a bit about GDP, GNI, inflation and other topics before going into it’s role in health.

Prevalence

Posted on by Prav

In the last post we talked briefly about both Incidence and Prevalence.

Quick recap

Prevalence is the number of incidents/cases/chocolate that are already in the population/box of chocolates at that point in time.

Occasionally it is called Point Prevalence. This is to let us know that the prevalence is being measured over a single point if time, versus a period of time (weeks – months)

Period Prevalence – Prevalence measured over weeks/months

Given that prevalence represents a part of a whole group, it can never be more than 1 when expressed as an equation.

Prevalence is basically a useful tool in giving us an initial idea of the extent of a disease/outcome. However if does not tell us how many new cases take place, how often this particular outcome happens, how long the disease/outcome lasts and what the risk factors are.

Given that it is election time that will be my example.

So Blythe Valley typically was a Labour safe seat. And the labour party held a majority based on the last election (point of time). However, this does not take into consideration the new voters who voted Conservative or the people who changed their mind and voted Conservative in this election.

Life is like a box of chocolates – Prevalence vs Incidence

Posted on by Prav

When talking about frequency we consider it in two ways, namely prevalence and incidence.

Prevalence – Is the number of known cases at a given point in time
Incidence – Is the number of new cases in a population over a specified time

Why is this significant? I like to think of it as a box of chocolates.

Out of this box of chocolates that I am using for our Christmas party, my favourite is the purple one – the awesome hazelnut and caramel filled milk chocolate. So my Prevalence in this instance would be all the purple chocolates I have at that particular point of time in said box of chocolates. I do not take into account the amount of chocolates that might be eaten during the Christmas party, gotten rid of because the wrappers were ruined nor do I consider the fact that perhaps some other kind souls would bring me more chocolate to contribute to the sweet bowl. All it tells me is the amount of purple chocolates present out of the 48 chocolates currently available.

My Incidence however, is the hope that some kind soul will bring more chocolates to said party and the potential new purple chocolates that might be added to the sweet bowl. I do not consider the amount of chocolates that have been eaten, those with ripped packaging that have to be put aside or removed cause no one likes them (here’s looking at you coconut cream). I just look at the potential amount of new purple chocolates that might be present over the total number of chocolates in hand.

However there is a lot more to incidence and prevalence and we will discuss it in the next few posts.

Defining a population

Posted on by Prav

Measuring disease and health outcomes, Part 2 – Population

As mentioned in part 1 understanding frequency alone can be misleading – which is why we need to know our population.

The fact is that health outcomes can differ for a variety of reasons. From different countries, regions, socio-economic backgrounds and even time. Therefore to study an outcome we first have to clearly identify who we want to include – this is our Target population.

Our target population is basically the individuals we want to identify or whose health we want to improve. For example being concerned about the population of Hull City. However if we were to look at the all 200,000 people in Hull City – we would probably be overwhelmed as realistically it would be too difficult to gather and quantify all that data.

To make things easier we identify a Study Population. And this would be a smaller subset of individuals with a particular disease/health outcome we would like to study. For example the study population would be the number of individuals over the age of 30 who are diabetic. If this sample is too large – lets say 50,000 individuals, then an alternative method of collecting information is needed. Which is why we use a Study Sample, which is a smaller number of the Study Population, selected at random to represent the 50,000 individuals.

Target Population = Individuals in Hull City
Study Population = Diabetics > 30 years of age living in Hull ( eg = 50,000 identified)
Study Sample = 2000 individuals chosen at random from the 50,000, to represent the diabetics over 30 years of age.

Why do we need to define our populations?

So we know who are the population at risk. These are individuals who have the risk factor for the condition being studied regardless of whether they are currently healthy or ill. This can be clear cut or vague depending on what is being studied.

For example, if the population at risk for developing lung cancer is smokers, they are clearly identifiable cause we can assess smoking histories. But in some instances such as rare diseases we cannot always identify who is at risk for a variety of reasons. In these instances we use the whole population as an estimate.

So once we identify the population, we can figure the frequency of an incident taking place. Ie if there are 500 cases of lung cancer out of a study population of 4000 smokers(population at risk), then we know the frequency is 1/8.

But let’s say we only have 2 researchers and 4000 is too large a group to manage, this is when we use a study sample. So 1000 smokers are identified at random to represent the 4000. Out of the 1000 smokers, we find 120 cases with lung cancer. The frequency in this instance would still be 1/8 or 120 per 1000 cases. Which when compared with the study population would still give you 500 cases of lung cancer out of the total population at risk of 4000 smokers.

In the next post, we will talk about how we can express frequency – incidence vs prevalence.