BHA 240 Trident University Global Health Risks, Mortality and Diseases Excel Worksheet

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User Generated

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gcunz0993

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Health Medical

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BHA 240

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Trident University

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BHA

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Introduction to Epidemiology

Part I is case assignment

Part II is SLP assignment, included with excel file.

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8/29/2020 Case - BHA240 Introduction to Research Utilization (2020JUL27FT-1) Listen Module 3 - Case INTRODUCTION OF EPIDEMIOLOGY Assignment Overview In recent years, it has become increasingly important to highlight the role of epidemiology within the framework of decision making regarding management and delivery of health services. As a healthcare administrator/manager, you may be tasked with reviewing epidemiology data. It is vital not only to be able to gather data, but also to explain it to your colleagues and organization. Case Assignment Go to the CDC’s National Center on Health Statistics Health, United States health statistics report at the following web site: https://www.cdc.gov/nchs/hus/index.htm Complete the following: 1. Select data from the provided subject listing. 2. Select data on a population subgroup. In a 2- to 3-page report, outline and explicitly explain the data gathered. Based on the data that you have selected, identify 2-3 patterns or trends within your data. Provide a potential explanation or argument for the observed trends, incorporating the reviewed literature to support your argument. Include recommendations for future research. Provide an explanation of how this information, or epidemiology data similar to this, would be of a benefit to a healthcare administrator/manager. https://tlc.trident.edu/d2l/le/content/149674/viewContent/3651568/View 1/2 8/29/2020 Case - BHA240 Introduction to Research Utilization (2020JUL27FT-1) Assignment Expectations 1. Conduct additional research to gather sufficient information to justify/support your report. 2. Limit your response to a maximum of 3 pages (title and reference page is not included in page number count). 3. Support your report with peer-reviewed articles, with at least 2-3 references. Use the following link for additional information on how to recognize peer-reviewed journals: http://www.angelo.edu/services/library/handouts/peerrev.php. 4. You may use the following source to assist in your formatting your assignment: https://owl.english.purdue.edu/owl/resource/560/01/. 5. For additional information on reliability of sources review the following source: https://nccih.nih.gov/health/webresources. 6. This assignment will be graded based on the content in the rubric. Privacy Policy | Contact https://tlc.trident.edu/d2l/le/content/149674/viewContent/3651568/View 2/2 8/29/2020 SLP - BHA240 Introduction to Research Utilization (2020JUL27FT-1) Listen Module 3 - SLP INTRODUCTION OF EPIDEMIOLOGY Visually displaying data is an important part of research. Graphs and charts are often used to visually show how data differs from one another. For this assignment, you will create a data dashboard containing epidemiology data. A data dashboard is a management tool that visually tracks, analyzes, and displays metrics and key data through the use of graphs and diagrams. See the sample of a dashboard below. For some visual examples of dashboards, follow/click the following link: https://www.pinterest.com/pin/134122895137302166/?lp=true For this assignment, use the following link to download the Microsoft (MS) Excel file of World Health Statistics Health Status – Mortality: http://www.who.int/healthinfo/statistics/whostat2005_mortality.xls?ua=1 The data presented shows the mortality rates by gender and country. Select two countries to compare with the United States and create the following in MS Excel: 1. A bar graph comparison of the United States and your two selected countries using the Life Expectancy at Birth by gender (columns F & G). 2. A pie graph comparison using the Healthy life expectancy by gender (columns H & I). 3. A scatter graph comparison using the Probability of dying per 1000 population between 15 and 60 years by gender (columns J & K). 4. A 2-D graph comparison using the Probability of dying per 1000 live births by years 2003 and 2000 (columns L & M). https://tlc.trident.edu/d2l/le/content/149674/viewContent/3651571/View 1/2 8/29/2020 SLP - BHA240 Introduction to Research Utilization (2020JUL27FT-1) Once you have completed the four graphs, provide a brief explanation (4-6 sentences) of what each graph is depicting. The explanation should not only identify the X- and Y-axis, but also provide a detailed explanation of the data. For example: “Observed in the Life Expectancy at Birth by gender bar graph, the United States has a significant higher life expectancy for both males and females when compared to Angola and Ghana. This can be attributed to insignificant access to healthcare services in unindustrialized countries, as noted by Robinson (2018).” For full credit, submit an MS Word file (containing your explanations) and an MS Excel file (containing the dashboard). Note: The best way to complete this assignment is to “hide/sort” the columns and rows that will not be used in the graphical comparison. SLP Assignment Expectations 1. Conduct additional research to gather sufficient information to support the explanation. 2. You may use the following source to assist in formatting your assignment: https://owl.english.purdue.edu/owl/resource/560/01/. 3. For additional information on reliability of sources, review the following source: https://nccih.nih.gov/health/webresources. 4. This assignment will be graded based on the content in the rubric. Privacy Policy | Contact https://tlc.trident.edu/d2l/le/content/149674/viewContent/3651571/View 2/2 Figures computed by WHO to improve comparability; they are not necessarily the official statistics of Member States, which may use alternative rigorous methods. Life expectancy Healthy life expectancy (HALE) Probability of dying per 1000 population WHO at birtha at birthb between 15 and 60 yearsa (years) (years) (adult mortality rate) Country region Males 2003 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 Afghanistan Albania Algeria Andorra Angola Antigua and Barbuda Argentina Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bhutan Bolivia Bosnia and Herzegovina Botswana Brazil Brunei Darussalam Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Central African Republic Chad Chile China Colombia Comoros Congo Cook Islands Costa Rica Côte d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic People's Republic of Korea Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Fiji Finland France Gabon Gambia Georgia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau Guyana Haiti EMR EUR AFR EUR AFR AMR AMR EUR WPR EUR EUR AMR EMR SEAR AMR EUR EUR AMR AFR SEAR AMR EUR AFR AMR WPR EUR AFR AFR WPR AFR AMR AFR AFR AFR AMR WPR AMR AFR AFR WPR AMR AFR EUR AMR EUR EUR SEAR AFR EUR EMR AMR AMR AMR EMR AMR AFR AFR EUR AFR WPR EUR EUR AFR AFR EUR EUR AFR EUR AMR AMR AFR AFR AMR AMR 41 69 69 78 38 70 71 65 78 76 62 69 73 63 71 63 75 65 52 61 63 69 37 66 75 69 44 40 50 47 78 67 42 44 74 70 68 62 53 68 75 42 71 75 76 72 65 42 75 53 71 65 68 65 67 50 58 65 49 66 75 76 55 56 67 76 57 76 66 64 51 45 61 52 Females 2003 42 75 72 84 42 75 78 72 83 82 68 75 75 63 78 75 82 71 54 64 67 76 36 73 79 76 46 45 57 48 82 73 43 47 80 73 77 66 55 74 80 49 78 79 81 79 68 47 80 56 76 72 74 69 73 52 61 77 51 71 82 84 60 59 75 82 60 81 69 69 53 48 64 54 Males 2002 Females 2002 35 60 60 70 32 60 63 59 71 69 56 61 64 55 63 57 69 58 43 53 54 62 36 57 65 63 35 33 46 41 70 59 37 40 65 63 58 54 45 61 65 38 64 67 67 66 58 35 69 43 62 57 60 58 57 45 49 59 41 57 69 69 50 49 62 70 49 69 58 55 44 40 53 44 Males 2003 36 63 62 75 35 64 68 63 74 74 59 66 64 53 68 65 73 62 45 53 55 66 35 62 66 67 36 37 50 42 74 63 38 42 70 65 66 55 47 63 69 41 69 70 69 71 60 39 71 43 66 62 64 60 62 46 51 69 42 61 74 75 53 51 67 74 50 73 60 60 46 42 57 44 Females 2003 510 167 155 107 584 193 176 240 89 115 220 257 117 251 189 370 125 257 393 261 247 190 850 240 114 216 533 654 441 503 93 213 641 513 133 164 231 254 434 166 129 558 173 137 99 166 231 578 121 376 210 250 212 242 248 464 359 319 450 275 134 132 397 332 195 115 352 118 258 289 403 479 290 450 Probability of dying per 1000 live births under 5 yearsa before 28 daysa (under–5 mortality rate) (neonatal mortality rate) Both sexes 2003 448 92 125 41 488 122 90 108 51 59 120 146 81 258 106 130 66 153 332 202 180 89 839 129 86 91 462 525 285 461 57 129 590 444 66 103 97 182 381 112 76 450 70 87 47 74 168 452 73 311 118 147 127 157 138 404 301 114 386 173 57 59 323 262 76 59 295 48 220 165 342 405 255 385 Both sexes 2000 257 21 41 5 260 12 17 33 6 6 91 14 9 69 13 10 5 39 154 85 66 17 112 35 6 15 207 190 140 166 6 35 180 200 9 37 21 73 108 21 10 193 7 7 6 5 55 205 5 138 12 35 27 39 36 146 85 8 169 20 4 5 91 123 45 5 95 6 23 47 160 204 69 119 Maternal mortality ratioa (per 100 000 live births) 2000 60 12 20 4 54 8 10 17 3 3 36 10 11 36 8 5 3 18 38 38 27 11 40 15 4 8 36 41 40 40 4 10 48 45 6 21 14 29 32 12 7 65 5 4 4 2 22 47 4 38 7 19 16 21 16 40 25 6 51 9 2 3 31 46 25 3 27 4 13 19 48 48 25 34 1 900 55 140 ... 1 700 ... 70 55 6 5 94 60 33 380 95 36 10 140 850 420 420 31 100 260 37 32 1 000 1 000 450 730 5 150 1 100 1 100 30 56 130 480 510 ... 25 690 10 33 47 9 67 990 7 730 ... 150 130 84 150 880 630 38 850 75 5 17 420 540 32 9 540 10 ... 240 740 1 100 170 680 Figures computed by WHO to improve comparability; they are not necessarily the official statistics of Member States, which may use alternative rigorous methods. Life expectancy Healthy life expectancy (HALE) Probability of dying per 1000 population WHO at birtha at birthb between 15 and 60 yearsa (years) (years) (adult mortality rate) Country region 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 Honduras Hungary Iceland India Indonesia Iran (Islamic Republic of) Iraq Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Kiribati Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libyan Arab Jamahiriya Lithuania Luxembourg Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Mauritania Mauritius Mexico Micronesia (Federated States of) Monaco Mongolia Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands New Zealand Nicaragua Niger Nigeria Niue Norway Oman Pakistan Palau Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Qatar Republic of Korea Republic of Moldova Romania Russian Federation Rwanda Saint Kitts and Nevis Saint Lucia Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia AMR EUR EUR SEAR SEAR EMR EMR EUR EUR EUR AMR WPR EMR EUR AFR WPR EMR EUR WPR EUR EMR AFR AFR EMR EUR EUR AFR AFR WPR SEAR AFR EUR WPR AFR AFR AMR WPR EUR WPR EMR AFR SEAR AFR WPR SEAR EUR WPR AMR AFR AFR WPR EUR EMR EMR WPR AMR WPR AMR AMR WPR EUR EUR EMR WPR EUR EUR EUR AFR AMR AMR AMR WPR EUR AFR EMR Males Females 2003 2003 65 69 68 77 78 82 60 63 65 68 67 72 50 61 76 81 78 82 78 84 71 74 78 85 69 73 56 67 50 49 62 67 76 79 59 68 58 60 66 76 68 72 35 40 40 43 71 76 66 78 76 82 55 59 41 42 70 75 66 64 44 46 76 81 60 63 48 53 69 76 72 77 68 71 78 85 62 69 69 73 44 46 56 63 50 53 58 65 60 61 76 81 77 82 68 73 42 41 45 46 68 74 77 82 71 77 62 62 66 70 73 78 59 62 69 75 68 73 65 71 71 79 74 81 75 74 73 80 63 71 68 75 58 72 43 46 69 72 69 75 68 72 67 70 78 84 58 60 68 74 Males 2002 Females 2002 56 62 72 53 57 56 49 68 71 71 64 72 60 53 44 52 67 52 47 58 59 30 34 62 59 69 47 35 62 59 38 70 54 43 60 63 57 71 53 60 36 50 43 53 53 70 70 60 36 41 59 70 63 54 59 64 51 60 60 57 63 67 67 65 57 61 53 36 60 61 60 59 71 54 60 Males 2003 61 68 74 54 59 59 52 72 72 75 66 78 62 59 45 56 67 58 47 68 62 33 37 65 68 74 50 35 65 57 38 72 56 46 65 68 58 75 58 61 38 54 44 58 51 73 72 63 35 42 62 74 65 52 61 68 52 64 62 62 69 72 64 71 62 65 64 40 63 64 62 60 76 55 63 Females 2003 248 257 81 283 241 201 466 100 92 93 165 96 189 419 495 304 73 339 335 306 199 912 590 172 302 115 337 652 195 165 486 84 333 408 218 166 206 110 310 159 621 337 619 448 290 93 98 209 508 511 189 96 163 225 226 146 309 171 193 271 202 150 93 155 303 239 480 541 200 224 233 235 73 295 196 Probability of dying per 1000 live births under 5 yearsa before 28 daysa (under–5 mortality rate) (neonatal mortality rate) Both sexes 2003 181 111 53 213 204 125 205 60 51 47 123 45 120 187 521 191 53 160 303 120 138 781 484 101 106 63 260 615 108 146 427 49 280 312 115 95 172 47 179 103 543 222 529 303 284 66 65 138 477 470 133 58 91 199 205 84 246 119 133 149 81 63 76 61 152 107 182 455 145 131 192 203 32 244 119 Both sexes 2000 41 9 3 87 41 39 125 6 6 5 20 4 28 73 123 66 12 68 91 13 31 84 235 16 9 4 126 178 7 72 220 6 61 184 17 28 23 4 68 39 158 106 65 30 82 6 6 38 262 198 33 4 12 103 28 24 93 29 34 36 8 6 13 5 32 20 16 203 22 14 22 24 4 118 27 Maternal mortality ratioa (per 100 000 live births) 2000 18 6 2 43 18 22 63 4 4 3 10 2 17 32 29 27 6 31 35 7 20 28 66 11 5 4 33 40 5 37 55 5 26 70 12 15 12 3 26 21 48 40 25 14 40 4 4 18 43 53 13 3 6 57 14 11 32 16 16 15 6 3 5 3 16 9 9 45 12 10 11 13 2 38 12 110 11 0 540 230 76 250 4 13 5 87 10 41 210 1 000 ... 12 110 650 61 150 550 760 97 19 28 550 1 800 41 110 1 200 ... ... 1 000 24 83 ... ... 110 220 1 000 360 300 ... 740 16 7 230 1 600 800 ... 10 87 500 ... 160 300 170 410 200 10 8 7 20 36 58 65 1 400 ... ... ... ... ... ... 23 Figures computed by WHO to improve comparability; they are not necessarily the official statistics of Member States, which may use alternative rigorous methods. Life expectancy Healthy life expectancy (HALE) Probability of dying per 1000 population WHO at birtha at birthb between 15 and 60 yearsa (years) (years) (adult mortality rate) Country region 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 Senegal AFR Serbia and Montenegro EUR Seychelles AFR Sierra Leone AFR Singapore WPR Slovakia EUR Slovenia EUR Solomon Islands WPR Somalia EMR South Africa AFR Spain EUR Sri Lanka SEAR Sudan EMR Suriname AMR Swaziland AFR Sweden EUR Switzerland EUR Syrian Arab Republic EMR Tajikistan EUR Thailand SEAR The former Yugoslav Republic of Macedonia EUR Timor-Leste SEAR Togo AFR Tonga WPR Trinidad and Tobago AMR Tunisia EMR Turkey EUR Turkmenistan EUR Tuvalu WPR Uganda AFR Ukraine EUR United Arab Emirates EMR United Kingdom EUR United Republic of Tanzania AFR United States of America AMR Uruguay AMR Uzbekistan EUR Vanuatu WPR Venezuela AMR Viet Nam WPR Yemen EMR Zambia AFR Zimbabwe AFR Males Females 2003 2003 54 57 70 75 67 77 37 39 78 82 70 78 73 81 69 73 43 45 48 50 76 83 68 75 57 62 63 69 33 36 78 83 78 83 69 74 59 63 67 73 69 75 55 61 50 54 71 71 67 73 70 74 68 73 56 65 61 62 47 50 62 73 72 75 76 81 44 46 75 80 71 80 63 69 67 69 71 77 68 74 57 61 39 39 37 36 Males 2002 Females 2002 Males 2003 Females 2003 Probability of dying per 1000 live births under 5 yearsa before 28 daysa (under–5 mortality rate) (neonatal mortality rate) Both sexes 2003 Both sexes 2000 Maternal mortality ratioa (per 100 000 live births) 2000 47 63 57 27 69 63 67 55 36 43 70 59 47 57 33 72 71 60 53 58 62 48 44 62 60 61 61 52 53 42 55 64 69 40 67 63 58 59 62 60 48 35 34 49 65 65 30 71 69 72 57 38 45 75 64 50 61 35 75 75 63 56 62 65 52 46 62 64 64 63 57 53 44 64 64 72 41 71 69 61 59 67 63 51 35 33 350 186 235 597 87 204 165 196 518 642 116 235 348 306 894 79 90 188 225 267 202 324 448 155 249 167 176 352 313 533 384 168 103 587 139 180 226 214 181 205 298 719 830 280 99 92 517 51 77 69 145 431 579 46 120 248 180 790 50 50 126 169 153 86 228 377 188 155 113 111 171 274 459 142 121 64 550 82 87 142 173 97 129 227 685 819 137 14 15 283 3 8 5 22 225 66 5 15 93 39 153 4 5 18 118 26 12 125 140 19 20 24 39 102 51 140 20 8 6 165 8 15 69 38 21 23 113 182 126 31 9 9 56 1 5 4 12 49 21 3 11 29 18 38 2 3 9 38 13 9 40 40 10 13 14 22 35 22 32 9 5 4 43 5 7 27 19 12 15 37 40 33 690 9 ... 2 000 15 10 17 130 1 100 230 5 92 590 110 370 8 7 160 100 44 13 ... 570 ... 110 120 70 31 ... 880 38 54 11 1 500 14 20 24 ... 78 130 570 750 1 100 40 62 54 62 53 63 42 67 55 68 54 66 522 179 275 234 257 164 466 102 212 100 187 100 171 25 78 23 92 36 43 12 38 11 40 19 910 140 460 39 460 80 Region African Region Region of the Americas South-East Asia Region European Region Eastern Mediterranean Region Western Pacific Region AFR AMR SEAR EUR EMR WPR 46 71 61 68 61 70 48 77 64 77 64 74 … Data not available or not applicable. The World Health Report 2005: make every mother and child count. Geneva, World Health Organization, 2005. (http://www.who.int/whr/2005/en/index.html) The World Health Report 2004: changing history. Geneva, World Health Organization, 2004. (http://www.who.int/whr/2004/en/report04_en.pdf) a b
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Epidemiology on Heart Disease, Cancer and Stroke Among American Adults

Student’s Name
Institutional Affiliation
Course number and name
Instructor’s name
Date

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Epidemiology on Heart Disease, Cancer and Stroke Among American Adults
There is a need to embrace and apply clinical epidemiology in establishing patient health
because the analytical designs and trials used in epidemiologic research allow smooth progress in
management, treatment, and defining the quality of health care delivery. Epidemiology gives a
descriptive approach to health situations with a keen evaluation of epidemic outbreaks to provide
suitable solutions and interventions. It also allows for integral epidemic management both at
individual and communal levels, thus making it possible to identify risk factors to diseases
(Díaz-Vélez, Soto-Cáceres, Peña-Sánchez, Segura, & Galán-Rodas, 2013). The evidence-based
health data from epidemiology maintains the cause-effect logic and allows for the addition of
health determinants such as culture and lifestyle. This information is a critical, inefficient
decision-making process on health matters both at the hospital and state levels. This paper will
endeavor to explicitly explain the prevalence of cancer among American adults, its trend, and the
importance of epidemiology.
The data gathered is about cancer, heart disease, and stroke among Americans in the
United States from 1997-2017. For most of the best part of the two decades, heart disease has
been the leading epidemic in the United States trailed by cancer and stroke. The disease
dominance among the population increases with age, with younger adults suffering fewer risks.
Adults aged 65 years and above are at higher chances of suffering from the three diseases
attributed to immobility and less physical activities, which leads to the accumulation of fats in
their bodies due to the inability to burn calories. More males suffer from the three diseases as
compared to females. First, this can be attributed to the lifestyle whereby men consume alcohol
and smoke at a higher rate than women, making it responsible for more men being at risk of
contracting cancer and heart diseases than women (Peate, 2011). Secondly, the male psyche

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whereby men tend to keep psychological problems to themselves and rarely open up on health
matters, but women quickly open up to health professionals throughout their lifecycle.
Economically the diseases are more dominant among the rich than the poor, which can be related
to financial stability to afford hazardous lifestyles such as fast foods, soft beverages, and alcohol.
According to race, Indian Americans are most affected, followed by African Americans (Deen,
Adams, Fretts, Jolly, Navas‐Acien, Devereux, & Howard, 2017). The prevalence of the diseases
among the two races can be attributed to poverty since most of them reside in rural areas with
limited access to healthy retail foods.
Based on the observed trends, heart disease was the leading killer disease followed by
cancer, although cancer overturned the direction from 2000 and became the top killer. Heart
disease and stroke are on the declining trend, while cancer is rising sharply. If the trend remains
constant, cancer will, by no doubt, be the leading killer disease beyond 2020. The disease
prevalence also increases with age, which means older people are at higher risk of suffering from
cancer, heart disease, and stroke than young adults. The aging population causes this trend
because of the large cohort born around World War II when there were fewer family planning
methods is entering the 65-75 year age bracket (Weir, Anderson, King, Soman, Thompson,
Hong, & Leadbetter, 2016).
Recommendations to Future Researchers
To facilitate accuracy in future research and studies, this paper recommends using more
extensive cohorts in health care research. Although the cohorts seem to be sophisticated and
expensive, they provide a basis for obtaining detailed knowledge on a wide variety of diseases
beyond cancer, heart disease, and stroke. To reduce scientific dead-ends in future researches and
maximize cohort values, it is also essential to embed experimental methods such as randomized

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trials in the cohorts by relying on economies of scale to test hypotheses in the observations.
Future researchers should emphasize themes such as data sharing, evaluation of new
technologies, approaches, and new methods of measuring susceptibility, exposures, and
outcomes (Khoury, & Wei, 2015). Improvement in data collection methods at the macro-level
and personal data is also crucial to advancing future epidemiological research. Coordination and
Collaboration among agencies are also recommended to facilitate the sharing of data and
resources such as genome sequencing of participants to help easy epidemiologic studies across
all disease spectra and population ages.
Importance of Epidemiological Information to the Healthcare
Such information is of good importance to health care because it helps public health
practitioners in disease surveillance and investigating epidemic outbreaks. The data is also
critical in identifying risk factors of human and animal zoonotic infections and direct
investigations and research in implementing control measures to contain the diseases (DíazVélez et al., 2013). Such information is also useful in describing the population health status in
proportions of age by showing the disease's distribution among different age groups, gender, and
trends over time. By studying the information carefully, medics gain insights such as the
relationship between the disease and the cause plus the age bracket at risk enabling adequate
preventive measures such as isolation.
Epidemiology is crucial in the study of disease trends, effects, and dominance among
individuals. Therefore, it is essential to embrace epidemiology in healthcare research to
effectively establish disease risk factors and come up with significant inventions to curb
epidemics. Cancer is expected to be the next killer epidemic in America, and therefore adequate
awareness needs to be created.

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References
Deen, J. F., Adams, A. K., Fretts, A., Jolly, S., Navas‐Acien, A., Devereux, R. B., & Howard, B.
V. (2017). Cardiovascular disease in American Indian and Alaska Native youth: unique
risk factors and areas of scholarly need. Journal of the American Heart
Association, 6(10), e007576.
Khoury, M. J., & Wei, G. (2015). The future of epidemiology in the age of precision medicine:
cancer, cardiovascular disease, and beyond. Cancer Epidemiology Matters Blog.
Peate, I. (2011). Men and cancer: the gender dimension. British Journal of Nursing, 20(6), 340343.
Díaz-Vélez, C., Soto-Cáceres, V., Peña-Sánchez, R. E., Segura, M. A. A., & Galán-Rodas, E.
(2013). Clinical epidemiology and its relevance for public health in developing countries.
In Current Topics in Public Health. IntechOpen.
Weir, H. K., Anderson, R. N., King, S. M. C., Soman, A., Thompson, T. D., Hong, Y., ... &
Leadbetter, S. (2016). Peer-reviewed: heart disease and cancer deaths—trends and
projections in the United States, 1969–2020. Preventing chronic disease, 13.

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1

Epidemiology on Heart Disease, Cancer and Stroke Among American Adults

Student’s Name
Institutional Affiliation
Course number and name
Instructor’s name
Date

2
Epidemiology on Heart Disease, Cancer and Stroke Among American Adults
There is a need to embrace and apply clin...