The Effects of Latitude, Population Density and Pollution On Asthma 1

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1 The Effects of Latitude, Population Density and Pollution On Asthma 1 The Effects of Latitude, Population Density and Pollution On Asthma in Children and Teenagers John Doe Mr. Martin Sir John A. Macdonald Secondary School Nov 27, 2016

2 The Effects of Latitude, Population Density and Pollution On Asthma 2 ABSTRACT The purpose of this study is to discover which factor of; latitude; population density and pollution, have the most influence in causing asthma in children and teenagers. I analyzed various trends between twenty-different countries, twenty chosen by a stratified random sample and six chosen by choice. I compared the asthma ever percentages of each of these countries with each of my three key variables to come to a final conclusion. In the end, I came to the conclusion that latitude has the largest influence on asthma rates in children and teenagers.

3 The Effects of Latitude, Population Density and Pollution On Asthma 3 INTRODUCTION A respiratory condition marked by spasms in the bronchi of the lungs, causing difficulty in breathing. This is known as chronic inflammatory disease of the airway or more commonly as asthma. Asthma is a condition that affects a large portion of the population and ranges from weak to severe. While conveyed in pop culture as a the stereotypical nerd Achilles heel causing them to lose their control in fits of wheezing whilst grasping for their inhalers, asthma is a much different problem than that. An asthma attack can be caused by a variety of factors. Some of these factors include; changing temperatures, physical exertion, stress and even laughter or crying. If an activity affects an asthmatic s normal breathing cycle, it can cause asthma. An asthma attack can range in severity from mild difficulty breathing to a severe lack of oxygen. During asthma attacks the affected person may feel like they are breathing through a straw or as if someone is sitting on their chest. What is actually happening is the muscles around the bronchial tubes are squeezing, causing the user to have great difficulty in breathing, as the bronchi are passages that allow air to enter and leave the lungs. Luckily, most of these effects can be avoided through the use of an inhaler. An inhaler contains medication that when inhaled will ease up inflammatory feelings in the lungs. Inhalers are quick and effective solution to the problem that is asthma. While it may seem like asthma is an extreme condition, most people outgrow their asthma at a young age. Overall, asthma is a condition that will affect many people in the world but knowing that it is so common brings up the question, what factors cause asthma? Throughout this study I will be attempting to determine which of the following three factors; latitude, population density and pollution, cause and impact asthma the most. To do this, I will be examining data from twenty different countries, selected by a stratified random sample,

4 The Effects of Latitude, Population Density and Pollution On Asthma 4 to determine which of the previous three factors impact asthma the most. From these three countries I will be examining two sets of values, one for ages six to seven and another for ages thirteen to fourteen. These sets include a country s current wheezing percentage, severe wheezing percentage amongst current wheezers and the percentage of the population that has ever had asthma. The key variable in this study along with the previously listed asthma statistics are each country s latitude, population density and pollution. By examining correlation between these variables, I will determine the factor that impacts asthma the most of the three. Using these various methods, I will determine which factor of, latitude, population density and pollution, are the most prevalent in causing asthma in children and young teens. Selection of Countries: As previously stated, there were twenty countries chosen through a stratified random sample. I initially split the sample size into groups, which were continents and then based on percentage of the population that each continent holds, assigned proportionate values to each continent. Asia has seven countries, Europe has six, Africa has three, North America and South America have two and Oceania has one. However, these numbers are the result of rounding to the nearest whole number if the nearest whole number was not zero and are not exactly proportionate to the population this is misrepresentation of data. Continents like Asia are underrepresented while continents like Oceania are overrepresented. I rounded in this manner to ensure that each stratum was represented my study. For example, the strata of Oceania should have two percent of the population or 0.4 countries leading to it not being represented if rounded to the nearest whole number. However, to counteract this rounding error, I added one to the sample size making the sample size twenty-one instead of twenty so there was less

5 The Effects of Latitude, Population Density and Pollution On Asthma 5 misrepresentation of data in my study. Additionally, I wanted certain major countries to be represented in this survey. These countries include USA, Germany, China, South Africa, Brazil and Australia. A major country to me was a country that had the largest global presence in its continent. These countries were separate from the stratified random sample except for South Africa. South Africa had to be chosen because not enough countries in Africa had the asthma data I needed to conduct my study. While I feel that major countries are necessary in this survey as they can represent the general trend of a continent on their own, their addition to the sample size could introduce some household bias as Oceania, North America and South America are over represented. Furthermore, countries were only randomly selected if they had all the data I needed from the global asthma reported conducted in If a country did not have this data, I selected another random number using random.org and then tested it using the same criteria. This led to sampling bias as the sample of Africa is too small. Africa only had three countries with data on any asthma topics and one of those three countries was a major country leading to Africa being represented in the same amount of North and South America. While this is a problem, I decided to move past it since I felt it would help my survey if stronger-developed and industrialized continents were represented more heavily than continents with more third-world countries. In the end, I ended up with twenty-six countries, twenty that were selected through a stratified random sample and six that were selected as major countries.

6 Pakistan Singapore South Korea Thailand Iran Vietnam Taiwan Croatia Lithuania Belgium Sweden Hungary Bulgaria Germany* Nigeria South Africa* Panama Costa Rica Uruguay Venezueala Brazil* New Zealand Australia* Current Wheezing % The Effects of Latitude, Population Density and Pollution On Asthma 6 Asthma Ever Percentages by Country: Using my twenty countries, I created a bar graph to compare their asthma ever percentages. This would give me a preliminary indication of which countries had the highest prevalence of asthma so that I could start to find correlations. My first figure was for ages six to seven % 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% Countries By Asthma Ever % (Age 6-7) Countries Figure 1: Data collected from Global Asthma Report (2009) The following countries Kenya, USA and China were not represented in this graph, as they did not have data for ages six to seven. This graph is the initial source of data that I would be comparing other variables to. Overall, in the ages six to seven, the mean is 10.07%, the median is 6.0%, there is no mode, the range is 32.60% and the interquartile range is 9.7%. The large interquartile range and regular range indicate a high degree of spread throughout this data. This shows that there are external factors affecting the data. My second graph is similar to the first but contains asthma information for ages thirteen to fourteen. Unlike the first graph, all twenty-six countries are represented here.

7 Pakistan Singapore South Korea Thailand Iran Vietnam Taiwan China* Nigeria South Africa* Kenya Uruguay Venezuela Brazil* Panama Costa Rica USA* Croatia Lithuania Belgium Sweden Hungary Bulgaria Germany* New Zealand Australia* Current Wheezing % The Effects of Latitude, Population Density and Pollution On Asthma % Asthma Ever % (Age 13-14) 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% Countries Figure 2: Data collected from Global Asthma Report (2009) It is important to note that the starred countries are the selected major countries. In the second graph of ages thirteen to fourteen, the mean is 16.86%, the median is 11.85%, there is no mode, the range is 46.30% and the interquartile range is 24.05% This graph has a lesser degree of spread than the first indicating that age may be a factor in asthma. Overall, between these two graphs it can be concluded that asthma is affected by external factors and one of those factors may be either; latitude, population density or pollution. Pollution and Asthma: First, I looked at the relationship between pollution and asthma. I did this by creating a scatter plot of values between asthma ever percentages and pollution index values. The pollution index is a value that is representative of how polluted the air is in a country. It takes into account various factors and assigns them different weights, the most heavily weighted are air pollution

8 Pollution Index Value The Effects of Latitude, Population Density and Pollution On Asthma 8 and water pollution but many different types of pollution are included into the calculation. This means that the pollution index is a continuous data as the various factors involved in the calculation are measured. The asthma ever percentages are continuous as well Asthma Ever % vs Pollution Index Value (Ages 6-7) y = x R² = % 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% Asthma Ever % Figure 3: Asthma ever percentages versus pollution index values, Data for pollution from Numbeo Pollution Index (2016), and for asthma collected from Global Asthma Report (2009) Surprisingly, pollution has a very weak effect on people having asthma. The r-value, correlation coefficient of this graph is This indicates an extremely weak negative correlation. There are also very few outliers in this graph, the issue is that the points are spread out with a large group of values at the beginning of the graph. This may show some household bias as groups with lower asthma rates are overrepresented but this bias occurred partially by random chance since twenty countries in this study were chosen by a stratified random sample. Next, I would compare the same variable with asthma ever percentages for ages thirteen to fourteen.

9 The Effects of Latitude, Population Density and Pollution On Asthma % 50.00% Asthma Ever % vs Pollution Index Value (Ages 13-14) y = x R² = % 30.00% 20.00% 10.00% 0.00% Figure 4: Asthma ever percentages versus pollution index values, Data for pollution from Numbeo Pollution Index (2016), and for asthma collected from Global Asthma Report (2009) The second r-value is 0.46, which once again indicates a weak negative correlation. An explanation for this correlation being negative is because pollution, while being a strong factor in triggering asthma attacks, is not a strong factor in directly causing asthma in children and teens. Pollution can often weaken the respiratory system of young children causing them to develop problems later in life. Since both sets of data concern younger ages, it can be assumed that they have not developed asthma due to pollution yet. Another explanation is that most of the time, countries with higher pollution are well-developed countries. This means that there are many medical centres and other areas of care for any children who are exposed to large degrees of pollution. Overall, pollution does not have a strong effect in causing asthma in younger children.

10 Population Density The Effects of Latitude, Population Density and Pollution On Asthma 10 Population Density and Asthma Ever %: My second factor was population density. Once again, I created a scatter plot to show a trend between population density and asthma ever percentages. Population density and asthma ever percentages are both continuous data so a scatter plot is once again the most appropriate way to compare the two. The first scatter plot is for population density and asthma ever percentages for ages six to seven Population Density versus Asthma Ever % (Age 6-7) y = x R² = % 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% Asthma Ever % Figure 5: Asthma ever percentages versus population density values, Data for population density from Worldometer (2016), and for asthma collected from Global Asthma Report (2009) From this graph it is once again clear that there is a very poor correlation between the population density of a country and that country s asthma ever percentage. The r-value is 0.14, which indicates an extremely weak correlation. There is one value that is an outlier and that is Singapore who s population density is 8,138 people / km 2.

11 Population Density (pop/km 2 ) The Effects of Latitude, Population Density and Pollution On Asthma Population Density versus Asthma Ever % (Age 6-7) Without Singapore y = x R² = % 5.00% 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% Asthma Ever % Figure 6: Asthma ever percentages versus population density values, Data for population density from Worldometer (2016), and for asthma collected from Global Asthma Report (2009) Surprisingly, removing the value of Singapore made the r-value even less. The r-value is now 0.12, which is 0.2 lower than its previous value. This indicates that regardless of Singapore, in the ages six to seven, population density has a very weak effect on those who have asthma.

12 Population Density (pop/km 2 ) The Effects of Latitude, Population Density and Pollution On Asthma Population Density vs Asthma Ever % (Age 13-14) y = x R² = % 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% Asthma Ever % Figure 7: Asthma ever percentages versus population density values, Data for population density from Worldometer (2016), and for asthma collected from Global Asthma Report (2009) This graph is similar to the ages six to seven graph. There is a large congregation of data values near the bottom left of this corner and the same outlier of Singapore. The r-value for this graph is 0.27, which shows that there is a stronger correlation between population density and asthma in thirteen to fourteen year olds than there is in six to seven year olds. Once again, I made another graph with the outlier removed to see what kind of trend the data may have.

13 Population Density The Effects of Latitude, Population Density and Pollution On Asthma Population Density versus Asthma Ever % (Age 13-14) Without Singapore y = -451x R² = % 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% Asthma Ever % Figure 8: Asthma ever percentages versus population density values, Data for population density from Worldometer (2016), and for asthma collected from Global Asthma Report (2009) Now, the r-value is This shows a weak negative correlation. This increase is because the data points are now more focused around the trend line whereas in the ages six to seven graph, they were spread out in the bottom but close to the trend line due to Singapore s extremely high value. Overall, it can be concluded that population density has an extremely weak negative effect on asthma percentages of children and teens. A reason for this may be because countries with high population densities often have many public resource buildings such as hospitals and other emergency care. These places might be able to deal with asthma in its early stages where it can be treated before it manifests into a serious problem.

14 Latitude (Degrees) The Effects of Latitude, Population Density and Pollution On Asthma 14 Latitude and Asthma Ever %: The final factor is latitude. Similar to previous factors, I constructed a scatter plot to see if there was a correlation between the two variables. Furthermore, latitude is continuous variable as well in theory because it is a measured angle. Unlike the other graphs, a logarithmic line of best seemed to fit the data but since I am comparing r-values, I used a linear regression line Latitude versus Asthma Ever % (Age 6-7) y = x R² = % % 10.00% 15.00% 20.00% 25.00% 30.00% 35.00% Asthma Ever % Figure 9: Data on latitudes collected from Google Developers (2012). Data for asthma collected from Global Asthma Report (2009) The x-axis in this graph represents the equator as latitude is measured in degrees north or south from the equator and that is shown here. Many countries in the five percent asthma ever area, have latitudes above the equator ranging from approximately two to sixty. The values with higher asthma ever percentages have latitudes that are closer to the equator. This is because countries around the equator have very humid conditions, which can trigger asthma attacks and induce respiratory problems in younger children. Furthermore, the r-value of this graph is 0.54, which is significantly larger than the r-value for any previous factor tested in this study. I believe

15 Latitude (Degrees) The Effects of Latitude, Population Density and Pollution On Asthma 15 that this is because latitude is a factor that causes asthma, as being close or far from the equator can affect the respiratory system of younger children, while population density and pollution trigger asthma attacks and agitate those who already have asthma. To see if this trend continues in older ages, I constructed a similar scatter plot but used values for ages thirteen to fourteen Latitude vs Asthma Ever % (Age 13-14) y = x R² = % % 20.00% 30.00% 40.00% 50.00% 60.00% Asthma Ever % Figure 10: Data on latitudes collected from Google Developers (2012). Data for asthma collected from Global Asthma Report (2009) Unlike the previous graph, the correlation between latitude and asthma is not as strong here. The r-value has dropped to 0.35 and the graph has a weak negative correlation. Similar to the previous graph, countries that are further from the equator have lower asthma percentages while countries closer to the equator have higher percentages. One reason for why this graph has a weaker correlation may be because the respiratory system of thirteen and fourteen year olds may have developed enough to the point where humid conditions won t seriously impact them. Overall, between the two graphs, this is the strongest correlation of the three factors. Conclusion:

16 The Effects of Latitude, Population Density and Pollution On Asthma 16 After analyzing all the data that I have collected and compared, it appears that latitude has the most prevalent effect on asthma rates in children and teenagers than population density or pollution. Specifically, latitude has the largest effect in causing asthma while it may be, though not proven, that population density or pollution have a larger effect in causing asthma attacks and triggering those with asthma already. Throughout the course of this study, I collected various asthma statistics as well as statistics for my three key variables. Initially, I was positive that pollution would have the largest impact but in the end, pollution the second weakest impacts on asthma. The r-value of pollution for ages six to seven was 0.39 while the r-value for ages thirteen to fourteen was For population density, r-value for ages six to seven was 0.14 while the r- value for ages thirteen to fourteen was However, after removing the outlier that was Singapore, the r-values became 0.12 and 0.35 for ages six to seven and thirteen to fourteen respectively. This change made the correlation for ages thirteen to fourteen slightly stronger but overall, it was still a weak negative correlation. Finally, for latitude the r-value for ages six to seven was 0.54 and the r-value for ages thirteen to fourteen was This was the first time in the study that an r-value was above 0.5 and indicates a possible correlation between the two sets of data values. Pollution had a close effect in that it had an r-value of 0.46 for ages thirteen to fourteen. However, overall the two r-values of 0.35 and 0.54 indicate a stronger correlation than the two r-values of 0.39 and It can be stated that if the population was only thirteen to fourteen year olds then pollution has the largest effect in causing asthma. Nonetheless, since this study concerns asthma rates in the ages six to seven and the ages thirteen to fourteen, the strongest factor of the three is latitude.

17 The Effects of Latitude, Population Density and Pollution On Asthma 17 References Google Developers. (2016). Retrieved 22 November 2016, from Population by Country -Worldometers. (2016). Retrieved 23 November 2016, from Pollution Index by Country 2016 Mid Year (2016). Retrieved 23 November 2016, from Global Asthma Report (2009). Retrieved 21 November 2016, from