QHO430 Data Analysis Tools and Application Assignment Sample

1. Introduction - QHO430 Data Analysis Tools and Application Assignment

Trend Analysis Of Suicide Rates In Different Regions Over Time

The rate of suicides has continued to rise in all the various parts of the world and this has ranked the issue as a serious global threat. These changes cannot be purely attributed to the social, economical, mental health and demographic factors the topic warrants further analysis. Studying such patterns may be helpful for designing suitable prevention strategies and legislations concerning the issue of suicide. The purpose of this project is to identify the current trend of suicidal case occurrences over the period in different regions. This will assist determine key patterns that have characterized rates as well as factors leading to this variation in rates in the various geographic regions. In particular, the following broad goal is set: To achieve the overall aim, statistical tools and data analysis tools including correlation analysis, descriptive statistics must be employed. Suggesting correlations and trend analysis, the aim of this work is disclosing the possibilities to establish the foundation for future policies or preventative measures for addressing this problem.

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1.1 Group Project Aims

This is so because studying the rates of suicides cannot be explained by a single factor since many aspects contribute to the occurrence of such terrible events. In this case, as a member of this group assignment, the purpose is to identify and; discuss factors associated with suicide patterns in various areas and years. All the members of the group have chosen different aspects of the given topic.

• Member 1 has analyzed suicide trends in relation to socioeconomic factors such as unemployment and poverty.
• Member 2 has focused on examining demographic changes and their correlation with suicide rates.
• Member 3 has addressed public health policies and mental health service availability in relation to regional suicide rates.
• Member 4 has analyzed the influence of social media and digital platforms on mental health and suicide trends.
• Member 5 has investigated the psychological and cultural factors contributing to suicide rates across regions.

1.2 Individual Analysis Objectives

More precisely, the individual analysis carried out in this report examining regional suicide rates will examine the tendencies observed over the years, and the links with economic and social aspects (Cai et al. 2022). The purpose is to observe the values and characteristics of history data and forecast possible futures by mathematical processes. Key objectives for this analysis include:

• Identifying trends in suicide rates over time across various regions.
• Performing correlation analysis to determine if suicide rates in different regions exhibit similar patterns.
• Examining the relationship between socioeconomic variables such as unemployment rates and suicide trends, focusing on potential triggers or risk factors.

Therefore, it is the intent of the present study to perform an examination of suicide rate characteristics based on geographical region and time, in order to identify the patterns that become useful in policy-making and in designing preventive measures.

2. Methodology

Data Collection

The data for the analysis of suicide rate was collected from government websites and other public domain which contains the suicide statistics of different areas spanning several years. These sources include World Health Organization, National Bureau of Statistics, and Demographic data from more than one country in a region (Pirkis et al. 2021). Namely, statistics of annual suicide rates by region was extracted from the official websites of local health departments and crosschecked with governmental reports so that the information used was up-to-date and reliable (Radeloff et al. 2021). To cater for this analysis, emphasis was shifted on regions which have fairly detailed records of suicide rates dating a decade back. Some of the variables include the annual rate for suicide, region identifier, and occasionally a demographic breakdown including the ages, gender, and the level of income of the person. The data was extracted in a structured form and the use of such format is helpful when grouping them in the view of observing change over some time period or within some region. The criteria for selecting each dataset was as follows: To help maintain comparability in reporting methods across datasets The datasets were selected to provide a solid platform for statistical analysis and projection of trends.

Data Preparation

Before engaging in quantitative analysis, some procedures were followed to make sure that the data collected were clean.

Data Understanding: Some of the variables in the dataset collected include: Number of suicides, the year, and the region. The suicide rate of each region was documented and categorized according to the regions in the given years from 2001 to 2023.

Data Cleaning: While analyzing the data for suicide rates, some gaps were observed in the suicide data for some of the years in the data set. Other outlier observations which include missing data points were managed through forward filling as data gap was filled to maintain data flow for additional analysis (Pirkis et al. 2022). Possible outliers were selected by graphical analysis and numerical techniques, and such values that influenced skewed distribution were marked for further study.

Data Pre-processing: For this analysis, only the suicide rate columns were considered for both genders and age groups. According to the type of data, it was divided by year, and the regions were examined separately and comparatively. The dataset was managed in Excel format and there was no need to merge data from other sources as all data was stored in one file.

Data Analysis

To get to the objectives of the analysis, several analytical tools and methods were employed. Due to these reasons, the following methods were chosen as they fitted the trend analysis and forecasting needs (Karaye, 2022). Exploratory statistics such as descriptive analysis was applied in an attempt to identify the general trend of suicide rates in the various regions.

Figure 1: Regional comparison suicide rate

(Source: Self-created in MS Excel)

For each region the mean, median, standard deviation was found which gave an idea of suicide rate distributive in the regions where the rate was higher or lower than the normal average. For instance, the mean of suicide rate in North East region was 267 with standard deviation of 33.84. Likewise, the semi-annual suicide rate also revealed that the East Midlands had a median rate of 376 which is comparatively less than those registered at the center of some other zones.

Figure 2: Suicide rate from each region 2001 to 2023

(Source: Self-created in MS Excel)

In this case, correlation analysis was adopted with the aim of identifying data coherence which presents the tendency of suicide rates in different areas. With the formula entered `=CORREL(range1, range2)`, then the level of the correlation between regions was established. For instance, North West region was reported to have a correlation coefficient of 0.899 this indicates that suicide rates in this region have been rising through the years, which supports the notion that there is an upward trend in the rates that can be investigated for possible causes. Trend analysis was done by comparing the proportion change of suicide rate from 2001 to 2023 in each region (Goto et al. 2022). This made it possible to carry out simple assessment of whether rates were growing, declining or in fact stagnating. Those with a rising trend were then examined more closely for possible sources of the increase. For instance, the condition in the North West region has not changed after it recorded a 17.90% increase from 2001 to 2023, the highest of all regions and is expected to increase even further (Glenn et al. 2020). The South West on the other hand only presented a 0.33% a relatively stable trend over the same period. These analytic methods were beneficial in terms of observing and identifying trends and changes in the suicide rate across different areas of England and Wales, which created the foundation for further research of factors that may be responsible for such trends and ways of reversing them.

Figure 3: North East Region Linear regression

(Source: Self-created in MS Excel)

Results

The data indicated a huge difference in the suicide trends in the different regions under study. For instance, suicide rates in the North East region have been continuously increasing year in year out, showing an ever-increasing trend. Both the correlation coefficient and the percent change over time depicted this trend, implying that the suicides had been on the increase all through the course of the study (Yoshioka et al. 2022). The West Midlands was the most varied region, with some years having noticeable increases followed by drops. In comparison to the North East, this region's coefficient was smaller due to the level of fluctuations in suicide rates. While this is not a total overall shift from start to finish, there are some ups and downs throughout the journey. Some areas manifested a moderate to high positive correlation within this study, indicating that similar social or economic trends are causing the rise in suicide rates in those locations (Fu et al. 2023). For example, there was a highly positive correlation between the North West and the South East regions, indicating that their trends were quite similar over time. Although the magnitude of those rises was different, both regions did see a significant rise in the suicide rate. Other regions had very weak or negative relationships, so they didn't have the same trend as the others. For instance, in the case of the East of England, there was a very weak positive relationship between the suicide rate and that of the other regions (Yu et al. 2020). While on average, suicides had tended to increase between the inception and the end of the study period, the association is weak, suggesting that this was not a continuous increase. The correlations are intriguing, since even though the average trend has been for an increase from inception to end of the study period, there is a negative correlation for London, suggesting a possible diminishing tendency with time. Such a negative relationship would imply that, rather than other cities, year-to-year changes in London tended to covary negatively with one another fairly frequently, possibly for reasons of certain urban characteristics or programs.

Figure 4: Suicide Percentage change over time

(Source: Self-created in MS Excel)

The three regions, Yorkshire, The Humber, and The East Midlands, showed generally positive correlations in that general trends tended not to be consistently followed but with major exceptions. Both locations witnessed increases in suicides over the period of study but their patterns deviated from each other and from the rest of the regions. The variations in patterns across different places illustrate just how complex factors are that influence the suicide rate. However, some sites show similar trends that might indicate similar underlying causes, while others reflect some important divergence that suggests the significance of regional environment and maybe a difference in effectiveness of suicide prevention strategies as well. This profile is intended to bring out the necessity to treat suicide trends both at the national and regional levels with regard to specific trends and factors that may have had a role in every location.

3. Project Management

The project scheduled all the stages of the study so that it would complete each stage of the study within the set schedule. With regard to the analytical data steps like data collection, cleaning, analysis, and reporting was planned using the Gantt chart. Important benchmarks have been established at the conclusion of each week to ensure steady progress over the duration of the project. Data collection involves obtaining the suicide rates for Wales and England for the period between 2001 and 2023. Everything collected during the process should be verified for accuracy and identically between multiple locations during this step. Thus, during the cleaning of data, issues such as missing values, outliers as well as issues of format inconsistency have been dealt with in order to make the dataset ready for analysis (Martinez et al. 2020). It is a group project that requires meetings of all the participants for project updates and for sharing the outcomes. To ensure all team members had the most updated versions of the data and analysis, the team used Microsoft Teams to share working documents. The members reported that the online skills discussion boards enhanced the communication processes and offered a platform where members could seek clarification or advice from others as well as engage in discussions on statistical techniques when interpreting result. At the analysis phase in using the cleaned data set, descriptive statistics, correlation analysis and trend analysis were conducted (Casant and Helbich, 2022). As the team members worked through various facets of the analysis, they would often check in to try to ensure consistency of approach and interpretation. The three reports were brought together to make a clear story that highlighted attention on key trends in suicide rates and geographical variation at the end of the reporting phase.

Learning Reflection

When I was considering the whole educational process, I came to a realization that it is very important to understand as much subtlety connected with the data analysis in order to carry out the project. Smaller tasks resolved through individual and group research were overcoming the initial problems: filling in missing data and choosing the right statistical approaches for conducting trend analysis. The group found that stakeholders understood the complex trends better only if presented with visuals like line graphs and heat maps. Now that all is said and done, maybe more time could have been spent in the data cleaning procedure to make for a smoother execution of the analysis stage. More advanced forecasting methods also would have led to stronger projections of the future suicide rates. They will help later for planning of similar ventures.

Timeline

Figure 5: Gantt chart

(Source: Self-created in Project)

4. Conclusion

This analysis of suicides by geographic region within Wales and England was enlightening about patterns and possible directions for future research. Patterns in different regions could be discovered by using statistical techniques such as correlation analysis and descriptive analysis. According to the results, suicide rates are increasing in some areas, for instance, North East and North West, whereas they become stable in other areas, such as South East. Clearly, the findings foreground the need for region-specific interventions and policies in suicide prevention. Both the North East and North West have very high association coefficients and steeply rising trends, clearly indicating that they definitely require more focused and intense preventative efforts. On the other hand, the steady nature of the trajectory of the South East suggests that the measures already in place are working pretty well and should continue to be in place or minimally modified. The study also found significant regional disparities in trends. For example, the negative correlation specific to London means that risk factors that influence suicide rates in the city may be different from those that prevail in other regions, so a stratified approach may be necessary. The subtle, region-specific solution required is further supported by the low correlation in the East of England and higher variability in the West Midlands. Although there is no particular prediction or exponential smoothing in the analysis done, the trends seen can be used as a basis for further resource allocation and planning.

Reference List

Journals

• Cai, Z., Chen, M., Ye, P. and Yip, P.S., 2022. Socio-economic determinants of suicide rates in transforming China: a spatial-temporal analysis from 1990 to 2015.The Lancet Regional Health–Western Pacific,19.
• Pirkis, J., John, A., Shin, S., DelPozo-Banos, M., Arya, V., Analuisa-Aguilar, P., Appleby, L., Arensman, E., Bantjes, J., Baran, A. and Bertolote, J.M., 2021. Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries.The Lancet Psychiatry,8(7), pp.579-588.
• Radeloff, D., Papsdorf, R., Uhlig, K., Vasilache, A., Putnam, K. and von Klitzing, K., 2021. Trends in suicide rates during the COVID-19 pandemic restrictions in a major German city.Epidemiology and psychiatric sciences,30, p.e16.
• Pirkis, J., Gunnell, D., Shin, S., Del Pozo-Banos, M., Arya, V., Aguilar, P.A., Appleby, L., Arafat, S.Y., Arensman, E., Ayuso-Mateos, J.L. and Balhara, Y.P.S., 2022. Suicide numbers during the first 9-15 months of the COVID-19 pandemic compared with pre-existing trends: An interrupted time series analysis in 33 countries.EClinicalMedicine,51.
• Karaye, I.M., 2022. Differential trends in US suicide rates, 1999–2020: Emerging racial and ethnic disparities.Preventive medicine,159, p.107064.
• Goto, R., Okubo, Y. and Skokauskas, N., 2022. Reasons and trends in youth's suicide rates during the COVID-19 pandemic.The Lancet Regional Health–Western Pacific,27.
• Glenn, C.R., Kleiman, E.M., Kellerman, J., Pollak, O., Cha, C.B., Esposito, E.C., Porter, A.C., Wyman, P.A. and Boatman, A.E., 2020. Annual research review: A meta‐analytic review of worldwide suicide rates in adolescents.Journal of child psychology and psychiatry,61(3), pp.294-308.
• Yoshioka, E., Hanley, S.J., Sato, Y. and Saijo, Y., 2022. Impact of the COVID-19 pandemic on suicide rates in Japan through December 2021: An interrupted time series analysis.The Lancet Regional Health–Western Pacific,24.
• Fu, X.L., Qian, Y., Jin, X.H., Yu, H.R., Wu, H., Du, L., Chen, H.L. and Shi, Y.Q., 2023. Suicide rates among people with serious mental illness: a systematic review and meta-analysis.Psychological medicine,53(2), pp.351-361.
• Yu, J., Yang, D., Kim, Y., Hashizume, M., Gasparrini, A., Armstrong, B., Honda, Y., Tobias, A., Sera, F., Vicedo-Cabrera, A.M. and Kim, H., 2020. Seasonality of suicide: a multi-country multi-community observational study.Epidemiology and psychiatric sciences,29, p.e163.
• Martinez-Ales, G., Hernandez-Calle, D., Khauli, N. and Keyes, K.M., 2020. Why are suicide rates increasing in the United States? Towards a multilevel reimagination of suicide prevention.Behavioral neurobiology of suicide and self harm, pp.1-23.
• Casant, J. and Helbich, M., 2022. Inequalities of suicide mortality across urban and rural areas: A literature review.International journal of environmental research and public health,19(5), p.2669.

Author Bio

Owen Foster   8 years | Masters

I am Own Foster and I am a professional programmer residing in London. I have tons of professional experience as a coder and I have been providing students with programming assignments for 9 years now. I can assure you that complete attention to drafting high-quality work will be there. I work day and night so I can be there to solve any of your queries at any time.