Low Birth Weight Babies and Term Paper

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Since 61% of the total had complete medical records, an estimate of that number would be important. The reader would also benefit by a split-out of the mothers less than 18 and the asthma rates of their babies, in order to establish if low birth weights were more common in such populations, and other confounding variables were different.

How was the sample selection done and what was the final sample size on which the data was analyzed? was there a control group?

The final sample size appeared to be 2,032 or 1,845, depending on the variables analyzed. That is because the patients in the 1,845 group had complete medical data available on all study variables, while the 2,032 included all those with some medical data. This means that there were 187 subjects for whom not all data was available. A subtraction of multiple-birth events brings us to 1,803 in the sample.

There was no control group per se. The objective of the study was to have enough patients to be able to run multiple correlate exercises and eliminate variables which are confounding. In this type of study, therefore, a classic 'control group' is not required.

What is a confounding variable, What confounding variable and biases were taken in consideration by the researcher in this study?

Confounding variables are those which may be correlated with the sample population, but bring little or no new information. An example might be socioeconomic level (i.e. how much income is earned in the household) with marital status, which is a confounding variable because it has been established that single mothers have lower incomes than married mothers. An additional confounding variable is vacancy rates in the neighborhood; if there are high vacancy rates, it is likely that there are more people with confounded low socioeconomic status as compared to the rest of the population.

The researchers included several variables, but the degree of confoundedness was intermingled.

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In the discussion of results, the authors indicated that low birth weight is associated with increased rates of asthma, and that predictors of asthma also included "census tracts with higher vacancy rates, fewer people per nonvacant household, and higher poverty rates." It would have been helpful to run a test of correlation between these three variables, in order to establish the degree of independence between them. Perhaps a better way to correlate low birth weight, asthma and socioeconomic status is to develop a socioeconomic status index, much as the authors referred to in the UK example, which includes these three confounding variables.

What were the results (give actual values and their interpretation and conclusion from the study.

The actual results indicated that there is a strong correlation between low birth weight and asthma (p

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