THE BACON CHOW STUDY: THE RELATIONSHIP BETWEEN INFECTIOUS ILLNESS AND GROWTH OF INFANTS

Multiple dietary deficiencies and high rates of infectious illness are major health problems leading to malnutrition and limitation of growth of children in developing countries. Longitudinal studies which provide information on illness incidence and growth velocity are needed in order to untangle the complex interrelationship between nutrition, illness and growth. From 1967 to 1973, researchers led by Dr. Bacon Chow of the Johns Hopkins University School of Hygiene undertook a quasi-experimental prospective study in Suilin Township, Taiwan to determine the effects of a nutritional supplement to the diets of pregnant and lactating women on the growth, development and resistance to disease of their offspring. This dissertation presents results from the analysis of infant morbidity and postnatal growth.^ Maternal nutritional supplementation has no apparent effect on the postnatal growth or morbidity of infants. Significant sex differences exist in growth response to illness and in illness susceptibility. Male infants have more diarrhea and upper respiratory illness. Respiratory illness is positively associated with growth rate in weight in the first semester of life. Diarrhea is significantly negatively associated with growth in length in the second semester. Small-for-date infants are more susceptible to illness in general and have a different pattern of growth response than large-for-date infants.^ Principal components analysis of illness data is shown to be an effective technique for making more precise use of ambiguous morbidity data. Multiple regression with component scores is an accurate method for estimating variance in growth rate predicted by indepenent illness variables. A model is advanced in which initial postnatal growth rate determines subsequent susceptibility to nutritional stress and infection. Initial growth rate is a function of prenatal nutrition, but is not significantly affected by maternal supplementation during gestation or lactation. Critical evaluation is made of nutritional supplementation programs which do not afford disease control.^

Development and preliminary validation of the cancer family impact scale for colorectal cancer

This study aimed to develop and validate The Cancer Family Impact Scale (CFIS), an instrument for use in studies investigating relationships among family factors and colorectal cancer (CRC) screening when family history is a risk factor. We used existing data to develop the measure from 1,285 participants (637 families) across the United States who were in the Johns Hopkins Colon Cancer Genetic Testing study. Participants were 94% white with an average age of 50.1 years, and 60% were women. None had a personal CRC history, and eighty percent had 1 FDR with CRC and 20% had more than one FDR with CRC. The study had three aims: (1) to identify the latent factors underlying the CFIS via exploratory factor analysis (EFA); (2) to confirm the findings of the EFA via confirmatory factor analysis (CFA); and (3) to assess the reliability of the scale via Cronbach's alpha. Exploratory analyses were performed on a split half of the sample, and the final model was confirmed on the other half. The EFA suggested the CFIS was an 18-item measure with 5 latent constructs: (1) NEGATIVE: negative effects of cancer on the family; (2) POSITIVE: positive effects of cancer on the family; (3) COMMUNICATE: how families communicate about cancer; (4) FLOW: how information about cancer is conveyed in families; and (5) NORM: how individuals react to family norms about cancer. CFA on the holdout sample showed the CFIS to have a reasonably good fit (Chi-square = 389.977, df = 122, RMSEA= 0.058 (.052-.065), CFI=.902, TLI=.877, GF1=.939). The overall reliability of the scale was α=0.65. The reliability of the subscales was: (1) NEGATIVE α = 0.682; (2) POSITIVE α = 0.686; (3) COMMUNICATE α = 0.723; (4) FLOW α = 0.467; and (5) NORM α = 0.732. ^ We concluded the CFIS to be a good measure with most fit levels over 0.90. The CFIS could be used to compare theoretically driven hypotheses about the pathways through which family factors could influence health behavior among unaffected individuals at risk due to family history, and also aid in the development and evaluation of cancer prevention interventions including a family component. ^

Determinants of CD4 cell count decline after treatment interruption

Patients who had started HAART (Highly Active Anti-Retroviral Treatment) under previous aggressive DHHS guidelines (1997) underwent a life-long continuous HAART that was associated with many short term as well as long term complications. Many interventions attempted to reduce those complications including intermittent treatment also called pulse therapy. Many studies were done to study the determinants of rate of fall in CD4 count after interruption as this data would help guide treatment interruptions. The data set used here was a part of a cohort study taking place at the Johns Hopkins AIDS service since January 1984, in which the data were collected both prospectively and retrospectively. The patients in this data set consisted of 47 patients receiving via pulse therapy with the aim of reducing the long-term complications. ^ The aim of this project was to study the impact of virologic and immunologic factors on the rate of CD4 loss after treatment interruption. The exposure variables under investigation included CD4 cell count and viral load at treatment initiation. The rates of change of CD4 cell count after treatment interruption was estimated from observed data using advanced longitudinal data analysis methods (i.e., linear mixed model). Using random effects accounted for repeated measures of CD4 per person after treatment interruption. The regression coefficient estimates from the model was then used to produce subject specific rates of CD4 change accounting for group trends in change. The exposure variables of interest were age, race, and gender, CD4 cell counts and HIV RNA levels at HAART initiation. ^ The rate of fall of CD4 count did not depend on CD4 cell count or viral load at initiation of treatment. Thus these factors may not be used to determine who can have a chance of successful treatment interruption. CD4 and viral load were again studied by t-tests and ANOVA test after grouping based on medians and quartiles to see any difference in means of rate of CD4 fall after interruption. There was no significant difference between the groups suggesting that there was no association between rate of fall of CD4 after treatment interruption and above mentioned exposure variables. ^