The final report is aimed at a senior decision-maker. Given the relatively short attention span of such people, I suggest you put your main message into your main document (preferably in graphic form) and keep any details and ancillary material in appendices.
I suggest the following structure:
- Background and motivation- You recycle your intro and justification here. Any argument needs to be supported by references (use your bibliography).
- Material for analysis- Brief (!) comments on what data you got and what is the data quality. Your data dictionary goes into the appendices <copy&paste from Assignment 1...>- it would be rather disruptive to have these details in the main document. Data quality issues should be commented (recycle your Assig1 material), but put any lengthy tables, statistics etc. in an appendix.
- Analytic approach- What you have done with the data (use your Analysis plan as a start)? Typically here would go a schematic diagram on what you did with the data (and most details would be listed in the appendix). NB: your actual analysis may differ from the plan stated in Assignment 1 - as you proceed with analysis your plan may change: make all relevant changes.
- Findings - Here go the relevant (from the business point of view) results(all other results will go into an appendix). To keep the message prominent, the graphic representation is preferred (NB: use captions explaining what each graph, table shows - with reference to a more detailed result in the appendix wherever appropriate).
- Interpretations- Here you express your opinions on what the results mean - you interpret the results.
- Recommendations- You write your suggestions, based on your results and their interpretations - what are the options for further steps for the decision-makers.
E.g.: in your analysis, you describe how you identify outliers (method); in your results, you show what you found; in the interpretation, you comment whether these are errors, extreme/rare values, or a mix of different subpopulations (or what?). If you think the outliers are important and the decision-makers need to do something about them - you write a recommendation (NB: not all findings lead to a recommendation).
E.g. if you remember the Disco process mining exercise: there are 198 cases when a patient bypassed triage (finding); these cases can be an error in coding, or this can be patients arriving by an ambulance and triage was already done by the paramedics (2 alternate interpretations of the finding - list both); if this is considered to be important enough, you can recommend following options: 1.
get the IDs of these patients and do an audit (go to archive, pull out the paper records and try to find reasons why the patient was not triaged); 2. get more data (from the full data dictionary you know that there is a piece of data stating the mode of arrival - seeing the patients in question coded as arrived by an ambulance will support that interpretation), 3. run a prospective study looking into recording triage... (The decision-makers then judge these options from the point of view of time, effort, expenses etc.)
The study of deaths and life expectancy are seen are crucial metrics that provide a base on which health statistics and reports can be made. These reports are used make recommendations to government agencies and social wellbeing stakeholders on appropriate actions that can be taken to improve the situation (Xu et al. 2018). Age-adjusted death rate is used as a parameter to evaluate the rate of death for a specific population e.g. the resident of Idaho.
This metric does however take into consideration difference in age composition and structures: thus differentiating it from a standard death rate measure of population mortality. Understanding the distribution of deaths within genders, age-groups, and races, allows for the formulation of policies and incentives that a tailored specifically to combat mortality in a particular group e.g. young black men in the state of California (Kalisch 2012).
Illness and diseases are normally studies to aid in the determination of population affliction and overall social welfare. A society that is overrun with illness will record lower income per capita and contribute an insignificant percentage towards the national GDP (Beyers 2017). As such, the betterment of such a community is both in the government's and societal members' best interests. The fields of pathology and epidemiology are highly funded by governments across the world to ensure that the general population is awarded the best possible health. The quality and accessibility of healthcare should be made universal for all citizens, regardless of income, area of residence, race or gender (PRB 2013).
There are two sets of data that are presented for analysis. The first set of data provides information on average life expectancy and age-adjusted death rate for individuals based on their sex and race. The data is for the time period between 1900 and 2015. Therefore there are five variables in this first data set; of which two are categorical and the others are numeric. The second set of data provides information on age-adjusted death rate and deaths for individuals based on the state of residency, ailment/diseases, and the case of the disease.
The data is made available for the time period between 1999 and 2015. In this data set there are six variables: of which three are categorical and the others are numeric. There are some outliners in the second set of data which is brought about by inclusion of totals within the individual data items. For instance, the total deaths associated with all causes in included in the data for individual causes; as such, when plotting a graph there will be a considerably high figure for all causes compared to other data items. Other outliners in the data are United States (states variable) and all causes (113 causes variable); this outliners should be removed from the data to prevent distortion of the results.
The main objective in the analytics segment is to generate visual representations of the data in the two sets in a manner that is informative and helpful to decision-makers. The data has been investigated with regard to different focus objective. For instance, the number of deaths observed across all 52 American States. The output of proper visualization tools will be done to ensure that the decision-maker's interest in the report is not diverted when he/she is reviewing the result interpretations and recommendations. Tableau 2018 was employed as the visualization tool of choice because of its ability to be easily manipulated and generate highly informative charts and graphs. Two graphical representation tools were used in the assessment bar graphs (stacked and non-stacked) and line graphs.
With regard to the first data set the following graphs where generated in Tableau 2018. Chart 1 represents the age-adjusted death rate with regard to race. Chart 2 illustrates the age-adjusted death rate between 1900 and 2015 with regard to race. Chart 3 provides a visual representation of age-adjusted deaths based on gender. Chart 4 showcases of average life expectancy based on gender. Chart 5 represents the average life expectancy based on race. And lastly, chart 6 illustrates the average life expectancy between 1900 and 2015 with regard to race.
With regard to the first data set the following graphs where generated in Tableau 2018.Chart 7 below illustrates the deaths in different states between 1999 and 2015. Chart 8 indicates number of deaths caused by varies diseases and illnesses. Chart 9 indicates that number of deaths that can be associated with a given cause e.g. heart failure. Chart 10 illustrates the distribution of deaths between 1999 and 2015.
Chart 11 showcases the distribution of age-adjusted death rates between 1999 and 2015. Chart 12 prescribes the age-adjusted death rates caused by varies diseases and illnesses. Chart 13 illustrates the age-adjusted death rates in different states between 1999 and 2015. And finally, chart 14 showcases the age-adjusted death rates linked with a given cause.
The from the results in data set one it is clear that black males have the lowest life expectancy and highest age-adjusted death rates; While, white females have the highest life expectancy and lowest age-adjusted death rates. Also, males have the lowest life expectancy and highest death rates compared to women regardless of race. Based on results from data set two, the greatest causes of deaths and age-adjusted death rates are cancer and heart diseases. And the greatest factors causing illness that result in elevated levels of deaths and age-adjusted death rates are diseases of the heart and malignant neoplasms. The States with highest deaths and age-adjusted death rates are California and Mississippi respectively.
Incentives need to be made that will help better the quality of life for males especially black males. In addition, research needs to be focused on finding treatment for diseases like cancer and heart related illnesses. Lastly, steps need to be taken to lower the deaths and age-adjusted death rates observed in California and Mississippi.
Beyers, M 2017, 'An Introduction to Measures of Mortality Assessing Overall Health, Cause of Death Rankings, Health-Adjusted Life Expectancy, and Socioeconomic Conditions in Alameda County', Government Report, Alameda County Public Health Department, Alameda County Health Care Services Agency, Alameda County Public Health Department, Oakland.
Kalisch, D 2012, 'Mortality and life expectancy of Indigenous Australians', Australian Institute of Health and Welfare, pp. 1-60.
PRB, S 2013, 'The Health and Life Expectancy of Older Blacks and Hispanics in the United States', Population Reference Bureau, vol I, no. 28, pp. 1-8.
Xu, J, Murphy, SL, Kochanek, KD, Bastian, B & Arias, E 2018, 'Deaths: Final Data for 2016', National Vital Statistics Reports, vol LXVI, no. 5, pp. 1-76.
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