Online Custom «Statistical Data of Congenital Heart Disease» Essay Sample

Statistical Data of Congenital Heart Disease

Why is it Important to Investigate the Statistical Data of Congenital Heart Disease and its Relevance to a Better Quality of Life?

Executive summary                           

The invention of invasive procedures into pediatric cardiology has challenged the natural history of many congenital heart diseases. Individuals, born with congenital heart lesion tend to live longer and better today than they did some fifty years ago. The major achievement of modern medicine is preventive strategy – i.e. the prevention of a certain disorder is often superior to treating a disease in health benefit both for individuals and the whole society. Prevention of congenital heart disease is challenging. In this paper, three theoretical models are discussed of how to apply statistical tools in the study of congenital heart disease. All these explain why it is important to investigate the statistical data on congenital heart disease.

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Etiology refers to the cause of a certain phenomenon. Etiology describes the reason that makes a process occur and thus is an important attribute to the scientific approach. The etiology of congenital heart disease is still generally unknown. Some congenital heart defects develop in genetic abnormalities, correlating with other inborn disorders. A disease called complete atrioventricular canal is common in chromosomal abnormality of the 23rd pair of chromosomes (Down’s syndrome). Other congenital heart diseases may also have some genetic predispositions. However, this is not typical for all inborn heart diseases. Only 15% of children with a congenital heart defect have a genetic abnormality (CDC, 2013). The defect called transposition of the great arteries (i.e. aorta normally starts from the left ventricle but, in this case, arises from the right ventricle) never coincides with chromosomal abnormalities suggesting a baby who successfully passes cardiac surgery has a favorable mental prognosis (Freedom et al., 2004). Should medicine discover the exact etiological factor for congenital heart disease, improvement in health care is expected to be enormous.

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Literature review

Survival in newborns with congenital heart defects had been extraordinarily poor in the past. Helen Taussig, one of the founders of pediatric cardiology who contributed to pediatric cardiac surgery as well, in preface to her book in 1947 wrote: “the majority of cyanotic infants do not survive for more than a year and a half” (Taussig, 1947). Not so long ago, namely up to the 1960th authorities in cardiology were actually involved in describing congenital heart defects instead of intervening (Freedom et al, 2004). The historical retrospective is contradiction to what is seen today (CDC, 2013). In present times, congenital heart defects are still among the leading causes of disability or even death in the newborn period, albeit substantial improvement in health care technologies makes possible for these infants to live longer, better and to survive into adulthood. The Centers for Disease Control and Prevention (2013) claimed:

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It is estimated that nearly one million adults in the United States are living with a congenital heart defect. It is important for children and adults living with a congenital heart defect to see a specialized health care provider regularly throughout their lives. (Facts about congenital heart defects)

On one hand, medical advances have already saved a million of lives in the US only. All these individuals are sons for their mothers, daughters for their fathers, sisters for their siblings, friends for their neighbors, loving for their couples, or even parents to their children for everyone to love and to be loved. On the other hand, the costs that the society needs to pay for these benefits may be challenging.

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It is unclear whether the incidence of congenital heart defects had been different from what is described now, but assuming that scholars in the past attempted to describe the post mortem findings as long as 2,000 years ago (Freedom et al., 2004), may suggest the unknown etiological factors did exist and continue until today. What has changed for sure is the health care.

Purpose of the Study

In this study, an attempt to show how statistics can be applied in social madical aspects – the prevention and monitoring of congenital heart disease aiming at improvement long-term health care delivery.


The following theoretical models show why it is important to investigate the incidence of congenital heart defects.

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Model 1. Supposing, a new medical approach has been developed to prevent the occurrence of congenital heart defects. Statistics is the only reliable tool to detect its efficacy. The reported incidence in a given population over a known period is to be compared to the incidence of congenital heart defects after the target intervention applied. The difference is to be tested:

H0:  µ0 = µ1  Null hypothesis (intervention does not decrease the incidence of congenital heart defects, i.e. no effect of the intervention)

H1: Null hypothesis rejected (intervention does influence the incidence of congenital heart defects)

The independent test sampling via the null hypothesis significance testing may detect the contribution of the new medical approach.

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Model 2. Alcohol drinking in women, especially if associated with pregnancy, is a notorious etiological factor of birth defects (CDC, 2013). To check the potential negative impact of alcohol intake, a comparison test could be performed. Thus, different social groups with varying alcohol consumption levels are to be collected (none – some religious communities or personal features, low and high – from different social environments) and compared by the independent test.

H0:  µ0 = µ1  Null hypothesis (no difference between the groups)

H1: Null hypothesis rejected (some significant difference calculated).

The steps for the statistical calculations are given in the spreadsheet.

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Table 1. Hypothesis testing steps.

Step #




Set up the null and alternative hypotheses

Before the testing samples must be checked for adequacy to meet the goal of the study


Decide the significance level

Usually α < .05 (>95% of significance)


Calculate the z-test statistic

z-test is a figure that shows how the different the two samples are at a given statistical level


Decide whether to reject/fail to reject H0

Refers to statistical tools only


Draw an appropriate conclusion

A more general conclusion on the phenomena

Model 3 (with mathematical explanation, dataset taken form CDC website). It is well-known that Down’s syndrome is typically associated with congenital heart defects (Freedom et al., 2004). Thus, if appreciated the incidence of Down’s syndrome, the statistics of heart lesions can be ruled out. In this model, we assume the risk to deliver a baby with Down’s syndrome is linked to the season of fertilization. If a fetus is conceived in autumn, the risk of being infected by seasonal infections is surely different from the risk of contamination during summer. “Autumn” babies are born in June-August period, while “summer” babies are born in March-May. We hypothesize that the incidence to bear a baby with Down’s syndrome is different for those who fertilize in summer from those in autumn. If this is true, family planning could theoretically diminish the risk of advert outcome.

The CDC (2013) data on keywords ‘Down syndrome’ and ‘March, April, May’ vs ‘June, July, August’ in the ‘Birth Characteristics’ chart Box results in two figures. It is shown that there were 420 babies born in the ‘March, April, May’ period with Down’s syndrome over the period of 2007-2010 (of total 991,914 for this period of time), while during the months of ‘June, July, August’ there were 522 such newborns delivered (of total 1,029,461 deliveries for this period of time). We test for the difference.

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H0:  µ0 = µ1  Null hypothesis (no difference between the groups)

H1: µ0 ≠ µ1  Null hypothesis rejected (some significant difference).

The significance level is to be 95% (p<.05)

P1 = 420 / 991,914 = 0.000423

P2 = 522 / 1,029,461 = 0.0005

The 95% confidence interval for the difference in the incidence is the following:

P1 – P2  ± 1.96 √ (P1 * (1 – P1) / n1 + (P2 * (1 – P2) / n2 = 0.0423 – 0.05 ± √ 0.00000004 + 0.000000046 = 0.0077% ± 0.00029

A rather small difference (less than 1%) with 95% chance of probability.

 Next, we test this difference for hypothesis.

The exact calculation is performed by the statistics calculator EpiTools epidemiological calculators (2013).











Figure 1. The confidence interval is shown, which is a very small number. The test is insignificant (EpiTools epidemiological calculators, 2013)

 The calculated z-score of this difference is 1.5, which is poor and thus the null hypothesis is not rejected. On the basis of this test we assume that no matter when (at what season) the baby is fertilized, the chance of having a baby with a congenital disorder  is independent of the season. Health care assistants may feel free to advise planning a child regardless of the season. This result reflects how difficult it is to find what makes a congenital disorder occur and how uneasy it is to make progress in preventing congenital heart diseases.

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In conclusion, statistical methods provide an effective tool to study the incidence of congenital heart defects is detail.

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