Prav – 365 days of public health

Qatar, Macau, Singapore, Kuwait…

Posted on 19th Dec 2019 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 18th Dec 2019 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.

1,2,3… Dodge!

Posted on 13th Dec 2019 by Prav

Incidence.

Is the number of new cases/events in a specific population at a specific time.

However it can be expressed as Incidence Risk, Incidence Odds, Incidence Rate.

If you have ever played dodgeball, you know that the object of the game is to DODGE the ball being thrown. So if we were to use incidence in this scenario it would be measuring the chances of dodging the ball.

Risk – looks at the amount of players left on the court after a specified amount of time. (after 10 minutes of game play)

Odds – looks at the amount of players left on the court vs the amount of players out of the game (cause they did not dodge the ball) at a specific time. (after 30 minutes of game play)

Rate – tells us how many people have dodged the ball during any point of the game and looks at the total amount of time a person has participated, allowing for people to be swapped in and out of the teams involved. (time here is the entire game)

Examples in medicine?

Newly diagnosed diabetics in Hospital Queen Elizabeth (HQE), Sabah in November 2017.

Risk – Amount of new individuals diagnosed with diabetes in HQE in November 2017 out of all patients seen during that time.

Odds – Amount of newly diagnosed diabetics in November 2017 vs all the other patient who were not diagnosed with Diabetes in HQE in November 2017.

Incidence Rate – Amount of newly diagnosed diabetics in HQE in 2017 (Number of cases per year)

Prevalence

Posted on 13th Dec 201913th Dec 2019 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 8th Dec 2019 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 8th Dec 20198th Dec 2019 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.

50,000 vs 200,000 – Which is greater?

Posted on 6th Dec 2019 by Prav

Measuring disease and health outcomes, Part 1 – Frequency

How do we measure how often a particular disease happens in a population? How do we identify which diseases are more far-reaching or have the worst outcomes? How do we decide at what level we should intervene?

By knowing the frequency.

Frequency guides us by telling us how often an event happens in a particular population over a specific time. It helps us understand how a disease affects a population or rather the health outcomes by measuring it.

To do this we have to clearly identify what we are looking for and this is called a case definition. This is because not all health outcomes or diseases are black and white, therefore having a clear cut-off point helps us measure how many people are affected by it. For example, if we want to see how many obese people are in a community our case definition would be to look for people with a BMI > 30. It can go beyond clinical issues such as looking at the number of people over the age of 60 who had fallen in the last month. In the latter, the case could occur once or even more than that.

Basically by getting a clear definition. We are able to identify the individuals in the population.

However, knowing the frequency of an event can be misleading as population size can affect our perception of it. For example, if I said there were 200,000 known diabetics in London and 50,000 in Hull City. You would think that Hull had fewer diabetics, right? Now, what if I told you that the population of London is 8 million people while Hull only has 200,000 people. Does that change your mind?

This is why we need to know the population and identify the population at risk.

We will talk more about that in Part 2.

Where is the YOU in healthcare?

Posted on 5th Dec 20195th Dec 2019 by Prav

When we think about the healthcare system, there is often one aspect of care we do not talk about – YOU!

The fact is that every individual plays a rather significant role and I don’t just mean in terms of a being a recipient of healthcare services, a patient or working as a trained healthcare professional. I mean everyday people providing an element of service.

This is known as lay care.

It basically consists of self care, caring for a family member, giving advice, sharing information, emotional support and even volunteering. In fact it accounts for about 80% of all care and is rooted in every form of formal healthcare.

From the time we were children from our parents looking after us when we are ill, asking for advice from family and friends when we have any health issues, seeking over the counter pain relief to making the decision to go into hospital – all these are forms of lay care. Another big aspect is from volunteers providing a listening ear and emotional support to carers looking after the elderly/disabled/sick children. It must be mentioned that in most contexts, it is unpaid care/work.

As an intern in the Geriatric ward in Sabah, I recall not always being able to discharge medically stable inpatients. Why? Because they were unable to care for themselves nor were their families equipped to do so. This led to prolonged hospital stay, additional costs and a whole set of issues for the carer such as potential loss of income, issues at work and additional stresses to the individual. Thus begging the question of what services may need to be made available to address these needs and at a higher level perhaps even policies.

Why is it so important for formal healthcare to understand the reach of lay care?

It’s important because it is supplementary to formal healthcare at all levels. It gives us insight into the understanding of illness in the community, when people will seek healthcare, who they are comfortable seeking it from, as well as how changing times and socio-economics is causing a shift in the ability of ordinary people to provide care. This information is crucial in designing our healthcare services, identifying areas we need to improve on and expanding services accordingly.

I would love to hear about your experiences in providing lay care or where it is being provided. Do you think this has changed over time or do you see it changing? What do you think could be done in those instances.

Red Pill or Blue Pill – Randomised Controlled Trials

Posted on 3rd Dec 20195th Dec 2019 by Prav

Remember that iconic scene in the matrix where Morpheus asks Neo to choose the red pill or the blue pill? That’s how I like to think of RCT’s.

Basically the researcher is “Morpheus” and the study subject is “Neo”, but instead of just 1 Neo choosing the red/blue pill we have a few. Each Neo is randomly assigned either the red or blue pill. Morpheus then sits back and watches what happens to the Neo’s and we see by the end of it who is more content – the Neo living life in blissful ignorance in the Matrix or the one steeped in reality.

Which is basically what RCT’s do. They assess how different interventions/treatments affect two different groups by following up participants. When done well, it provides the best evidence on the cause and effect relationship by studying it in real time. (temporal sequence)

There is an experimental group given the intervention/treatment (Neo leaving the Matrix) and the comparison group (controls) that gets a placebo or the current conventional treatment (Neo in the Matrix). They are then followed up to assess how effective the new intervention/treatment is in comparison to the current standard/placebo group. (Is Neo more content out of the Matrix or in?)

However unlike Neo in the Matrix, there is no choice involved and the individuals involved are randomly assigned.

Why? To avoid any selection bias by the testers choosing individuals who may have better outcomes and to ensure that both groups are as similar as possible therefore being able to distribute the confounding factors.

What are confounding factors?
It is basically any other variable that can affect your dependent variable.

For example, if we are doing a study on a sedentary lifestyle causing weight gain. The independent variable here is the sedentary lifestyle while the dependent variable is weight gain. And the confounding factors can be anything from stress, diet, food portions, genetics and metabolism – all of which can affect weight gain (dependent variable). The hope is that by randomising it, we will get an equal amount of these other confounding factors in every group therefore giving us a clearer picture on the intervention being studied.

Despite being the best method, RCT’s still has some short-comings. They tend to be expensive, time consuming and you could argue that if a person is in an RCT – they are probably more likely to be compliant therefore not really giving us a real world experience.