The Duration of Breastfeeding and the Risk of Asthma - Essay Example

Assignment Part I Study Two is selected Hypothesis H0: There is relationship between the duration of breastfeeding and the risk of asthma laterin life in Finland children H1: There is relationship between the duration of breastfeeding and the risk of asthma later in life in Finland children The null hypothesis assumes none effect of the duration of breastfeeding and the risk of asthma later in life in Finland children 2 A cohort study is the best for the investigation of the question in study. This is so because, the cohort study concerns itself with the amount of exposure in subjects with asthma and those without asthma (control group). It is advantageous in that it allows for the study of diseases with long durations between exposure and manifestation in addition to being cheaper than many research study designs. Further, this study design can be used to study other causes of the disease and not only the conditions of exposure. Finally, cohort study is exhaustive as the participants will have to be followed over time. 3 A sample will be drawn from a population of breastfeeding mothers. The breastfeeding mothers will form the population group and will be divided into non-overlapping groups and then simple random samples will be picked from each group and the samples will be the experimental groups. One sample will not be allowed to breastfeed their children and will form the control group. Different groups will be required to breastfeed their children for a certain period of time different from the other group while other groups will be allowed to feed their children with formulas of intact cows milk or soy protein for the same periods of time. Different measurements will be undertaken before the onset of the study. Clinical tests will be carried out at different times and the readings of the experimental groups and those of the control group will be compared. During the study, censoring of participants will be allowed in case of death or willingness to terminate participation. Compared results will be the summarized to determine whether the hypothesis is true or not. 4 The dependent variable in this study will be the incidence rate of getting asthma while the independent variables will be, age, duration of breastfeeding, gender, kind of feeding (breastfeeding or formulas of intact cows milk or soy protein). However, the dependent variables may be many depending on the kind of measurement intended. This can be measured depending on the type of variable if it will be continuous (age, duration of breastfeeding etc.), categorical (type of feed), ordinal (incidence rate) or nominal (gender). 5 A sample will be drawn from a population of breastfeeding mothers which will be the sampling frame. The breastfeeding mothers will form the population group and will be divided into non-overlapping groups (strata) and then simple random samples will be picked from each group (stratum) and the samples will be the experimental groups. One sample will not be allowed to breastfeed their children and will form the control group. Different groups will be required to breastfeed their children for a certain period of time different from the other group while other groups will be allowed to feed their children with formulas of intact cows milk or soy protein for the same periods of time. This samples data will be in form of time points. 6 The generalizability of sample results depends on the way sampling was done and whether the sample collected is un-biased. In this case, stratified sampling will be applied since the population data is not homogenous but heterogeneous and thus the sample will be un-biased. Further, gender representation in a sample is very important as it also affects results if not properly balanced since females have certain inclinations which males done have. However, history may affect the effectiveness of the results as asthma may be present due to certain historical aspects not related to the study conditions. Further, the study design needs follow-up over time which may not be possible to follow to the later thus compromising on the results. Censoring due to deaths and willingness to terminate participation affects the precision of the sample and hence the results. Assignment 1 Part 2 Study 1 1 Hypothesis 1 H0: Demographic characteristics of cancer patients are the same as those for the control group H1: Demographic characteristics of cancer patients are different from those of the control group Hypothesis 2 H0: Years of smoking, number of cigarettes smoked and alcohol consumption in cancer patients is the same as those of the group H1: Years of smoking, number of cigarettes smoked and alcohol consumption in cancer patients is different from those of the group 2 Variable Type Case Nominal Q1sex Nominal Q2age Continuous agediagnosis Continuous Q6enviro Nominal Q7live Nominal Q8xray Nominal Q8radio Nominal Q8poll Nominal Q1furth Nominal ses Ordinal Years.sm Ordinal amount Continuous alcohol Nominal 3 CASE OR CONTROL SEX Living environment Living condition WORK WITH X-RAY RADIOACTIVE SUBSTANCES AT WORK WORKED IN AIR POLLUTION FURTHER EDUCATION Socio-economic status smoking in years CONSUME ALCOHOL Valid 323 323 320 322 322 322 322 322 322 323 322 Missing 0 0 3 1 1 1 1 1 1 0 1 4 Descriptive Statistics N Minimum Maximum Mean Std. Deviation AGE 116 30 54 43.92 5.819 AGE AT DIAGNOSIS 116 20 45 38.78 5.731 No of Cigarettes Smoked PER DAY 116 0 60 12.08 13.621 The mean age of the cancer patients is 43.92 with a standard deviation of 5.819 and most of them were diagnosed with cancer at 38.78 years of age. Frequencies Frequency Percent Gender FEMALE 51 44.0 MALE 65 56.0 Total 116 100.0 Living Environment CITY 51 44.0 SUBURBS 46 39.7 RURAL 17 14.7 Total 114 98.3 Living Conditions ALONE 20 17.2 WITH PARTNER 84 72.4 WITH PARENTS 3 2.6 OTHER 8 6.9 Total 115 99.1 Work with X-ray NO 108 93.1 YES 6 5.2 DONT KNOW 1 .9 Total 115 99.1 Radio-active substance at work NO 107 92.2 YES 1 .9 DONT KNOW 7 6.0 Total 115 99.1 Worked in air pollution NO 81 69.8 YES 15 12.9 DONT KNOW 19 16.4 Total 115 99.1 Further Education NO 59 50.9 YES 57 49.1 Total 116 100.0 SES I&II 47 40.5 III 45 38.8 IV&V 13 11.2 non-class 11 9.5 Total 116 100.0 Smoking years less than 1 33 28.4 1-10 15 12.9 11-20 17 14.7 21+ 51 44.0 Total 116 100.0 Consume alcohol NO 16 13.8 YES 100 86.2 Total 116 100.0 Majority of the cancer patients; are males, they live in the city with a partner and most of them have not worked with X-rays, radio-active substances and areas with air pollution. Most of them have no further education and are in I & II socio-economic status and have more than 21 years of smoking. Majority consume alcohol. 5 Figure 1 No of Cigarettes Smoked PER DAY Mann-Whitney U 10489.500 Wilcoxon W 31399.500 Z -1.748 Asymp. Sig. (2-tailed) .081 Grouping Variable: CASE OR CONTROL Figure 1 shows that cancer patients smoke more cigarettes per day than normal people. However, the mean difference is not significant at 5% level of significance [U = 10,489.5, p = .081] Figure 2 Figure 3 Normal people have more smoking years and consume alcohol more compared to cancer patients. From the above results, we conclude that demographic characteristics of cancer patients are different from those of the control group and that years of smoking, number of cigarettes smoked and alcohol consumption in cancer patients is different from those of the group. Part 2 Research Questions Are there differences between three birth weight groups in maternal lead level? Is child’s birth weight associated with maternal age of the mother (at first child birth)? 1 The following variables will be used in answering the research questions Weight group; Blood lead level; Birth weight; and Maternal age. 2 Variable Type Birth weight Continuous Weight group Ordinal Maternal age Continuous Mother Blood lead level Continuous 3 Hypothesis 1 H0: Maternal lead levels are the same for the three weight groups H1: Maternal lead levels are different for the three weight groups Hypothesis 2 H0: There is association between child’s birth weight associated and maternal age of the mother H1: There is no association between child’s birth weight associated and maternal age of the mother 4 ANOVA is used to show the mean difference in blood lead levels between the three groups while multiple comparisons using LSD method shows the difference in the groups. Correlations are used to show associations between the variables. ANOVA Sum of Squares Df Mean Square F Sig. Between Groups 42.492 2 21.246 4.350 .014 Within Groups 1206.255 247 4.884 Total 1248.747 249 Multiple Comparisons (I) full term baby – birth weight by group (J) full term baby – birth weight by group Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Low birth weight Lower end of normal birth weight .59525 .47894 .215 -.3481 1.5386 Normal birth weight -.38953 .42320 .358 -1.2231 .4440 Lower end of normal birth weight Low birth weight -.59525 .47894 .215 -1.5386 .3481 Normal birth weight -.98477(*) .33541 .004 -1.6454 -.3241 Normal birth weight Low birth weight .38953 .42320 .358 -.4440 1.2231 Lower end of normal birth weight .98477(*) .33541 .004 .3241 1.6454 * The mean difference is significant at the .05 level. Correlations full term birth weight in grams maternal age of mother full term birth weight in grams Pearson Correlation 1 .180(**) Sig. (2-tailed) . .004 N 250 250 maternal age of mother Pearson Correlation .180(**) 1 Sig. (2-tailed) .004 . N 250 250 ** Correlation is significant at the 0.01 level (2-tailed). 5 Assumptions; The sample is un-biased; The population if homogenous; and There is no history bias in the data. 6 From the above results, maternal lead levels are different for the three weight groups [F (2,247) = 4.35, p = 0.014] and that mean difference in maternal lead levels for normal birth weight and lower end of normal birth weight are significantly different at 5% level of significance [MD = -.98477, p = 0.004]. Further, maternal age and child birth weight have a weak positive relationship which is significant at 1% level of significance [r = 0.180, p = 0.004]. Read More