Analysis of the Population of South Eastern Sydney - Assignment Example

?Q1. The population which I chose was the South Eastern Sydney LHD data for Population by age and sex, South Eastern Sydney LHD, Population Age (years) Males Females Persons 0-4 25,734 24,409 50,142 5-9 21,984 20,532 42,516 10-14 20,639 19,639 40,279 15-19 22,984 22,024 45,008 20-24 34,458 34,120 68,577 25-29 41,552 40,852 82,404 30-34 38,212 36,950 75,162 35-39 34,233 33,261 67,494 40-44 30,243 29,814 60,057 45-49 28,658 28,138 56,796 50-54 26,467 26,435 52,902 55-59 22,823 23,617 46,440 60-64 21,594 21,857 43,451 65-69 16,478 17,125 33,602 70-74 12,050 13,401 25,451 75-79 9,578 11,265 20,844 80-84 7,557 10,380 17,936 85+ 6,061 11,887 17,947 All ages 421,304 425,706 847,010 Comparative Presentation Of The Above Data with the Hypothetical Data South Eastern Sydney(SES) LHD, 2011 Age Group (In years) Population Deaths 0-24 246,522 315 25-64 484,706 2588 65+ 115,780 7600 Total 847,010 10503 HYPOTHETICAL POPULATION Age Group (In years) Population Deaths 0-24 803169 803 25-64 669132 2400 65+ 468075 7300 Total 1940376 10503 Crude death rate= number of deaths during time period____ X 100,000 total population at mid-point of time period Crude death rate= number of deaths during 2011 in SES____ X 100,000 total population of SES in 2011 Hence Crude Death Rate(per thousand) of South Eastern Sydney(SES) LHD, 2011 = 1240/100000 population * 1000 = 12.4 deaths/year Crude death rate= number of deaths in 2011 in Hypothethetical Area____ X 100,000 total population of Hypothetical Area in 2011 Hence Crude Death Rate(per thousand) of Hypothetical Area, 2011 = 541 /100000 population * 1000 = 5.41 deaths/year Age-specific Death Rate = __number of deaths among persons of a given age group__ X 100,000 population of persons in given age group at mid-point of time period Age-specific Death Rates (per thousand) South Eastern Sydney(SES) LHD, 2011 Age Group (In years) Population Deaths Age specific death Rates(per 1000) 0-24 246,522 315 1.27 deaths/year 25-64 484,706 2588 5.33 deaths/year 65+ 115,780 7600 65.64 deaths/year Total 847,010 10503 12.4 deaths/year HYPOTHETICAL POPULATION 2011 Age Group (In years) Population Deaths Age specific death Rates(per 1000) 0-24 803169 803 0.999 deaths/year 25-64 669132 2400 3.58 deaths/year 65+ 468075 7300 15.5 deaths/year Total 1940376 10503 5.41 deaths/year DISCUSSION FOR A STANDARDIZED CALCULATION Analyzing the data above it reveals that the hypothetical population has both lower crude death rates and all age adjusted death rates compared to the South Eastern Sydney (SES) LHD population in 2011. But we have seen that mortality rate as it has changed over time in. Even within the same country, mortality can change due to differences in environmental or cultural factors. Also, a population’s age and gender composition can cause variations in mortality rates. Truly speaking from a Statisticians viewpoint it becomes extremely necessary to analyze mortality or disease in two locations by not just collecting a death rate data but to find on the impact of various factors or variables impacting the population in the two areas or at two scales which might be spatial factors such as the local natural or built environment or cultural factors that influence a rate. In order to make such a comparison, one needs to adjust for differences in age composition. Since the highest age-specific mortality rates occur at the youngest and oldest age cohorts, populations with large child and elderly populations will have higher mortality rates. In order to eliminate the influence of age composition, one can compute a standardized death rate. A standardized death rate is a crude death rate that has been adjusted for differences in age composition between the region under study and a standard population. Standardization allows for comparisons when the population structures differ and is key in assessing the potential influence of environmental or cultural factors on death rates in a region. There are two ways of computing standardized death rates – direct and indirect standardization. The results will be a little bit different. The one you would use varies based on the data available to you. Direct Standardization (SDR1) calculates a weighted average of the region’s age-specific mortality rates where the weights represent the age-specific sizes of the standard population. Indirect Standardization (SDR2) uses age-specific mortality rates from the standard population to derive expected deaths in the region’s population. Direct Standardization: SDR1 = [Sum age groups (Mar Pas)]/Ps x 1000 Mar is the age-specific mortality rate for the region. Pas is the number of people in the age group in the standard population. Ps is the total standard population. To compute the direct standardized mortality rate: 1. For each age group, you need to multiply Mar by Pas. 2. Add them up. 3. Divide the sum by the total standard population. 4. Multiply by 1000, or give the rate in terms of "per thousand population." Indirect Standardization: SDR2 = Dr/[Sum age groups (Mas Par)] x CDRs Mas is the age-specific mortality rate for the standard population. Par is the number of people in the age group in the region’s population. Dr is the number of deaths in the region. CDRs is the crude death rate for the standard population. To compute the indirect standardized mortality rate: 1. For each age group, you need to multiply Mas by Par. 2. Add them up. 3. Divide the sum into the number of deaths in the region. 4. Multiply by the crude death rate. Choosing which formula to use will depend on what data you have access to. More typically, the data you have will be the components for indirect standardization. You are more likely to be able to find age-specific mortality rates for a standard population than for a specific region such as a state. This will be a more realistic approach to analyze “Data on Deaths”. Hence Standardised death rates (SDRs) enable the comparison of death rates between populations with different age structures by relating them to a standard population. The current standard population is all persons in the Australian population at 30 June 2001. SDRs are expressed per 1,000 or per 100,000 persons. For large populations with reliable age-specific death rates, the direct method of calculating SDRs is used. The SDR is the overall death rate that would have prevailed in the standard population if it had experienced at each age, the death rates of the population under study. For smaller populations where age-specific death rates are unknown or unreliable, the indirect method of calculation is used. The SDR is an adjustment to the crude death rate (number of deaths per 1,000 of the estimated resident population) to account for the variation between the actual number of deaths in the population under study and the number of deaths that would have occurred if the population under study had experienced the age-specific death rates of the standard population. Q2. The two main general factors that contribute to prevalence rates in the AHS area of NSW that I identified are : Cause of Death with ICD Code Rank Total Prevalence Ischaemic heart diseases (I20-I25) 1 21 708 Cerebrovascular diseases (I60-I69) 2 11 204 Ischemic Heart Disease, also known as Coronary Artery Disease, is a condition that affects the supply of blood to the heart. The blood vessels are narrowed or blocked due to the deposition of cholesterol on their walls. This reduces the supply of oxygen and nutrients to the heart muscles, which is essential for proper functioning of the heart. This may eventually result in a portion of the heart being suddenly deprived of its blood supply leading to the death of that area of heart tissue, resulting in a heart attack.As the heart is the pump that supplies oxygenated blood to the various organs, any defect in the heart immediately affects the supply of oxygen to the vital organs like the brain, kidneys, liver, etc. This leads to the death of tissue within these organs and their eventual failure. Ischemic Heart Disease is the most common cause of death in many countries around the world. Cerebrovascular disease refers to a group of conditions that affect the circulation of blood to the brain, causing limited or no blood flow to affected areas of the brain. A fall in blood pressure during sleep can then lead to a marked reduction in blood flow in the narrowed blood vessels causing ischemic stroke in the morning. Conversely, a sudden rise in blood pressure due to excitation during the daytime can cause tearing of the blood vessels resulting in intracranial hemorrhage. Cerebrovascular disease primarily affects people who are elderly or have a history of diabetes, smoking, or ischemic heart disease. Analyzing the data it becomes clear as the prevalence of IHD is high so the associated cerebrovascular disease ranks second in the list which is aggravated by conditions like smoking, hypertension and diabetes. Programme Planned To Decrease Prevalence of Both With a knowledge of the range of risk factors for coronary heart disease (CHD) by using tools such as the Framingham Risk Score the at risk group of 45-66 years to be identified. It is known that the risk of cardiovascular disease can be modified by a number of behavioural changes and by treatment and screening of undiagnosed hypertension and lipid disorders. However, many individuals are unaware of their level of risk and do not have access to information that might influence their health behavior so that is why a programme called “HEARTY WAY” to be implemented. All individuals in the relevant age group (45-64) will be sent an invitation to self-screen for eligibility to attend for an assessment of their risk of heart disease until all eligible patients in the GP practices had been invited. The invitation will be in the form of a "Hearty way" media campaign and a personal card containing a number or questions and enclosing a red 80 cm piece of string in the case of women, and enclosing a green 94 cm piece of string in the case of men. The invitation card, which will be intended to stimulate reflection on CHD risk factors, included the following: "Have you ever been told you have high blood pressure? Have you ever been told you have high cholesterol? Do you smoke now or have you smoked in the past 15 years? Has your mother, father, brother or sister ever had heart problems, before they were 60 years old? If you put the enclosed string around your waist, is it too short? You could benefit from a free health check to help prevent heart problems if you ticked any of the 'yes' boxes". The card will invite eligible individuals to phone and make an appointment for the assessment. A single reminder will be sent out two weeks after the first invitation. When individuals telephoned the free telephone number provided, a range of dates were offered over the subsequent four weeks for the assessment to be undertaken. Framingham scores will be calculated using a computer programme called CV-R Profile. This produced a printout, which was given to each individual seen. Advice will be provided for relevant risk factors identified using agreed guidelines. Where relevant, onward referral will also made to the individual's GP, a dietician, or an exercise referral scheme, or to local smoking cessation services, using a set of guidelines based on published sources. (BMC Cardiovascular Disorders 2008, 8:18 ) The overall impact of this programme on Clinical Budget Services will be cost effective as media advertisements and cost of some printing is required which can be made from Disease Prevention Fund.(DPF) Q3. Statistical Analysis Of Pregnancy Test(Sheskin DJ (2004) Handbook of parametric and nonparametric statistical procedures. 3rd ed. Boca Raton: Chapman & Hall /CRC) PregnancyTest Pregnancy n Not Pregnant n Total Positive True Positive a= False Positive c= a + c = 503 Negative False Negative b= True Negative d= b + d = 157 Total   a + b = 494   c + d = 166   Bottom of Form Results Of the above Contingency Table Sensitivity a a + b = 93.12 % 95% CI: 90.51 % to 95.19 % Specificity d c + d = 74.10 % 95% CI: 66.74 % to 80.57 % Positive Likelihood Ratio Sensitivity 100 - Specificity = 3.59 95% CI: 2.78 to 4.65 Negative Likelihood Ratio 100 - Sensitivity Specificity = 0.09 95% CI: 0.07 to 0.13 Pregnancy prevalence a + b a + b + c + d = 74.85 % 95% CI: 71.36 % to 78.12 % Positive Predictive Value a a + c = 91.45 % 95% CI: 88.66 % to 93.74 % Negative Predictive Value d b + d = 78.34 % 95% CI: 71.07 % to 84.51 % Definitions Analyzed Sensitivity: probability that a test result will be positive when Pregnancy is present (true positive rate). = a / (a+b) Specificity: probability that a test result will be negative when the Pregnancy is not present (true negative rate). = d / (c+d) Positive likelihood ratio: ratio between the probability of a positive test result given the presence of the Pregnancy and the probability of a positive test result given the absence of Pregnancy, i.e. = True positive rate / False positive rate = Sensitivity / (1-Specificity) Negative likelihood ratio: ratio between the probability of a negative test result given the presence of the Pregnancy and the probability of a negative test result given the absence of the Pregnancy, i.e. = False negative rate / True negative rate = (1-Sensitivity) / Specificity Positive predictive value: probability that the Pregnancy is present when the test is positive. = a / (a+c) Negative predictive value: probability that Pregnancy is not present when the test is negative. = d  / (b+d) Discussion Of this pregnancy test with reference to literature survey. Selecting the best pregnancy test is based a three key benchmarks: test sensitivity, the ease of use/interpretation, and price. In considering which test to buy, we also need to compare test formats and consider branded versus generic tests. Brand tests (found in drugstores) are typically quite expensive and come in midstream format. Unbranded tests (like those found at Early-Pregnancy-Tests.com) are very affordable, but equally reliable, FDA-approved, and typically more sensitive than drugstore tests (see our pregnancy test sensitivity chart below). Unbranded tests come in both midstream and test strip formats. Test strips are the most affordable option and are the same style of tests you will find at a doctor's office, clinic, or university. All of the tests discussed below are very accurate; indeed, they exhibit over 99% accuracy in clinical settings. Pregnancy tests are designed to detect the hormone "human chorionic gonadotropin", commonly abbreviated as hCG, which is produced by the placenta in the very early stages of pregnancy. hCG is first produced when the fertilized egg (or embryo) "implants" in the uterus. This takes place about six days or so after conception, or the union of sperm and egg. The fertilization takes place in the fallopian tube. Once the egg is fertilized, the embryo travels down the fallopian tube to the uterus. Once in the uterus, the embryo will implant in the lining of the uterine wall. At this point, the placenta develops and hCG is rapidly produced. This whole process between ovulation and implantation may take five to seven days or so.Following implantation of the embryo, the amount of hCG increases rapidly - on a daily basis nearly exponentially. The hCG hormone does travel through the woman's system and ultimately appears in urine samples which is detected by Pregnancy Tests:( a comparison given below) (J Am Phar Asso. 2003 ,45(5):608-15.Sensitivity of over-the-counter pregnancy tests) Sensitivity Chart Pregnancy Test Brand Sensitivity (or the hCG threshold at which a positive result is indicated). The lower the number, the higher the test sensitivity. Answer Early Result Pregnancy Test 25 mIU/hCG Clearblue early 25 mIU/hCG One Step Be Sure Pregnancy Test 25 mIU/hCG Fact Plus Pregnancy Test 40 mIU/hCG Clearblue Digital 50 mIU/hCG WalMart Brand 50 mIU/hCG First Response Early Result had an analytical sensitivity of 6.3 mIU/mL, which was estimated to detect greater than 95% of pregnancies on the day of missed period. The sensitivity of Clearblue Easy Earliest Results was 25 mIU/mL, which indicated detection of 80% of pregnancies. The sensitivity of the five other products was 100 mIU/mL or greater, indicating detection of 16% or less of pregnancies. Three of these last products included faulty devices. The product in question has a sensitivity(93.12%) and specificity (74.10%) which is nearly equal to the most popular Early Result test (95%). Though it is sensitive but Universal claims for OTC-PTs of more than 99% laboratory accuracy and use as early as the first day of missed period, is ambiguous and inappropriate for many products, according to these data. Recommendation of the Pregnancy Test with relevance to estimate the In Vitro Fertilization(IVF): As per the above analysis it can be recommended for an IVF programme but truly speaking this should be used as a screening test and confirmation of a successful IVF should be done by the Laboratory Blood and Urine Tests even after showing positive results with this New Pregnancy Test Q4. Calcuulation Of Relative risk (done by medcalc software: Sheskin DJ (2004) Handbook of parametric and nonparametric statistical procedures. 3rd ed. Boca Raton: Chapman & Hall ) Description of the test Allows to calculate a relative risk (or risk ratio). The relative risk is the ratio of the proportions of cases having a positive outcome in two groups included in a retrospective study. In a retrospective study cases are allocated to two groups and it is observed how many times the event of interest has occurred. The program calculates the relative risk and a 95% confidence interval (Altman 1991, Daly 1998, Sheskin 2004). The relative risk is the ratio of the proportions of cases having a positive outcome in the two groups. In the example, there was a positive outcome in 8835 cases and a negative outcome in 10800 cases in a group given treatment regimen A(cigarette smoke). In a second group with treatment regimen( control) B, 590 cases had a positive and 58315 cases had a negative outcome Top of Form Exposed group Number with positive outcome: (children affected exposed to cigarette smoke a= Number with negative outcome: (children not affected exposed to cigarette smoke b= Control group Number with positive outcome: Asthma in children not exposed to cigarette smoke c= Number with negative outcome: d=  Healthy children not exposed to cigarette smoke Results Relative risk 44.9237 95 % CI 41.3968 to 48.7512 z statistic 91.212 P < 0.0001 Bottom of Form The relative risk is the ratio of the proportions of cases having a positive outcome in two groups.Relative Risk = (a / (a+b)) / (c / (c+d)) Interpretation & Analysis The risk in the first group was 45% (8835/19635) and in the second group 1% (590/58905). The relative risk for a positive outcome was 44.95%) with a 95% confidence interval ranging from 41.39 to 48.75; the z-statistic is 91.21 and the associated P-value is 0.0001. The conclusion is that there is a 45-fold increased risk in children who are exposed to cigarette smoke to suffer from asthma than children who are not exposed to cigarette smoke and the result is statistically significant(P value=0.0001). This means that only 1 out of 10000 this result might have happened due to chance or by chance 1 out of 10000 will suffer from asthma but the rest 9999 times the test will reflect the same result and will justify this observation has not happened due to chance but the independent variable(cigarette smoke ) is being responsible for the dependant variable(asthma) in 9999 cases and thus provides the statistical power of the test. Q5. As a Sonographer in a healthcare organization I specialize in doing the ultrasonography. Ultrasonography or sonography is a medical procedure that makes use of high frequency sound waves called ultrasound to produce images of the internal organs of the body. Such visual images produced by sonography are used for medical diagnosis of the patient. Sonography can be used to examine various body parts such as female womb, breast, abdomen, heart, arteries, etc. A sonographer can specialize in doing diagnosis for a particular body part or organ. For example, there are specialized sonographers for cardiovascular, female reproductive organs, breasts (mammography), abdomen, etc. My role is of a Sonographer for female reproductive organs. My job duties include Going through the patient’s medical history before performing the procedure. Explaining the entire procedure to the patient and answer any queries. Preparing the imaging equipment for the diagnosis. Using transducers to send ultra sound waves to the patient’s body. Check the images on the computer to ensure good quality images are being created. Record and analyze the findings of the sonography and present it in a proper documented form to the doctors. Highlight any abnormality noticed. In this context of keeping and analyzing results I have my role in maintaining the records of prevalence of ovarian cancer in my territory with the analysis of Clinical Statistician in our health care unit. As the patient reveals all her medical, personal and professional history I can correlate the disease with causative factors and put it in a health record system and thus aiding in epidemiological analysis and creation of localized data bank. As far this situation I only come across those who are detected in late stages of ovarian cancer and I do not get a feedback as to get the age wise prevalence in the population. So I proposed a study design which was: Finding Out The Prevalence Of Ovarian Cancer Stagewise and Agewise in the South Eastern Sydney Population I with the help of local management of the Institute will plan a screening programme called “ LadyCare” to detect ovarian Cancer by Transvaginal Sonography . Method: The awareness will be done by media campaigns and those asymptomatic women aged>or=50 years and women aged>or=25 years who had a family history of ovarian cancer were eligible for participation in this trial. Aim: Ovarian cancer has the highest mortality rate of all gynecologic malignancies, and most women present with advanced-stage disease. The current investigation will be performed to screen the same at an early stage and quite easily. Proposed Outcome: TVS screening, when it is performed annually, is known to be associated with a decrease in disease stage at detection and with case-specific ovarian cancer mortality. This will also provide an age specific prevalence rate of the ovarian cancer. Read More