THE USE OF ARM CIRCUMFERENCE FOR SIMPLIFIED SCREENING FOR MALNUTRITION BY MINIMALLY TRAINED HEALTH WORKERS

Because of its simplicity and low cost, arm circumference (AC) is being used increasingly in screening for protein energy malnutrition among pre-school children in many parts of the developing world, especially where minimally trained health workers are employed. The objectives of this study were as follows: (1) To determine the relationship of the AC measure with weight for age and weight for height in the detection of malnutrition among pre-school children in a Guatemalan Indian village. (2) To determine the performance of minimally trained promoters under field conditions in measuring AC, weight and height. (3) To describe the practical aspects of taking AC measures versus weight, age and height.^ The study was conducted in San Pablo La Laguna, one of four villages situated on the shores of Lake Atitlan, Guatemala, in which a program of simplified medical care was implemented by the Institute for Nutrition for Central America and Panama (INCAP). Weight, height, AC and age data were collected for 144 chronically malnourished children. The measurements obtained by the trained investigator under the controlled conditions of the health post were correlated against one another and AC was found to have a correlation with weight for age of 0.7127 and with weight for height of 0.7911, both well within the 0.65 to 0.80 range reported in the literature. False positive and false negative analysis showed that AC was more sensitive when compared with weight for height than with weight for age. This was fortunate since, especially in areas with widespread chronic malnutrition, weight for height detects those acute cases in immediate danger of complicating illness or death. Moreover, most of the cases identified as malnourished by AC, but not by weight for height (false positives), were either young or very stunted which made their selection by AC better than weight for height. The large number of cases detected by weight for age, but not by AC (false negative rate--40%) were, however, mostly beyond the critical age period and had normal weight for heights.^ The performance of AC, weight for height and weight for age under field conditions in the hands of minimally trained health workers was also analyzed by correlating these measurements against the same criterion measurements taken under ideally controlled conditions of the health post. AC had the highest correlation with itself indicating that it deteriorated the least in the move to the field. Moreover, there was a high correlation between AC in the field and criterion weight for height (0.7509); this correlation was almost as high as that for field weight for height versus the same measure in the health post (0.7588). The implication is that field errors are so great for the compounded weight for height variable that, in the field, AC is about as good a predictor of the ideal weight for height measure.^ Minimally trained health workers made more errors than the investigator as exemplified by their lower intra-observer correlation coefficients. They consistently measured larger than the investigator for all measures. Also there was a great deal of variability between these minimally trained workers indicating that careful training and followup is necessary for the success of the AC measure.^ AC has many practical advantages compared to the other anthropometric tools. It does not require age data, which are often unreliable in these settings, and does not require sophisticated subtraction and two dimensional table-handling skills that weight for age and weight for height require. The measure is also more easily applied with less disturbance to the child and the community. The AC tape is cheap and not easily damaged or jarred out of calibration while being transported in rugged settings, as is often the case with weight scales. Moreover, it can be kept in a health worker's pocket at all times for continual use in a widespread range of settings. ^

THE RELATIONSHIP BETWEEN SERUM CHOLESTEROL AND CANCER INCIDENCE IN THE POPULATION OF THE HYPERTENSION DETECTION AND FOLLOW-UP PROGRAM

The relationship between serum cholesterol and cancer incidence was investigated in the population of the Hypertension Detection and Follow-up Program (HDFP). The HDFP was a multi-center trial designed to test the effectiveness of a stepped program of medication in reducing mortality associated with hypertension. Over 10,000 participants, ages 30-69, were followed with clinic and home visits for a minimum of five years. Cancer incidence was ascertained from existing study documents, which included hospitalization records, autopsy reports and death certificates. During the five years of follow-up, 286 new cancer cases were documented. The distribution of sites and total number of cases were similar to those predicted using rates from the Third National Cancer Survey. A non-fasting baseline serum cholesterol level was available for most participants. Age, sex, and race specific five-year cancer incidence rates were computed for each cholesterol quartile. Rates were also computed by smoking status, education status, and percent ideal weight quartiles. In addition, these and other factors were investigated with the use of the multiple logistic model.^ For all cancers combined, a significant inverse relationship existed between baseline serum cholesterol levels and cancer incidence. Previously documented associations between smoking, education and cancer were also demonstrated but did not account for the relationship between serum cholesterol and cancer. The relationship was more evident in males than females but this was felt to represent the different distribution of occurrence of specific cancer sites in the two sexes. The inverse relationship existed for all specific sites investigated (except breast) although a level of statistical significance was reached only for prostate carcinoma. Analyses after exclusion of cases diagnosed during the first two years of follow-up still yielded an inverse relationship. Life table analysis indicated that competing risks during the period of follow-up did not account for the existence of an inverse relationship. It is concluded that a weak inverse relationship does exist between serum cholesterol for many but not all cancer sites. This relationship is not due to confounding by other known cancer risk factors, competing risks or persons entering the study with undiagnosed cancer. Not enough information is available at the present time to determine whether this relationship is causal and further research is suggested. ^

PREDICTIVE VARIABLES FOR WEIGHT CHANGE 2 YEARS FOLLOWING PARTICIPATION IN A WEIGHT LOSS INTERVENTION PROGRAM

A historical prospective study was designed to assess the man weight status of subjects who participated in a behavioral weight reduction program in 1983 and to determine whether there was an association between the dependent variable weight change and any of 31 independent variables after a 2 year follow-up period. Data was obtained by abstracting the subjects records and from a follow-up questionnaire administered 2 years following program participation. Five hundred nine subjects (386 females and 123 males) of 1460 subjects who participated in the program, completed and returned the questionnaire. Results showed that mean weight was significantly different (p < 0.001) between the measurement at baseline and after a 2 year follow-up period. The mean weight loss of the group was 5.8 pounds, 10.7 pounds for males and 4.2 pounds for females after a 2 year follow-up period. A total of 63.9% of the group, 69.9% of males and 61.9% of females were still below their initial weight after the 2 year follow-up period. Sixteen of the 31 variables assessed utilizing bivariate analyses were found to be significantly (p (LESSTHEQ) 0.05) associated with weight change after a 2 year follow-up period. These variables were then entered into a multivariate linear regression model. A total of 37.9% of the variance of the dependent variable, weight change, was accounted for by all 16 variables. Eight of these variables were found to be significantly (p (LESSTHEQ) 0.05) predictive of weight change in the stepwise multivariate process accounting for 37.1% of the variance. These variables included: Two baseline variables (percent over ideal body weight at enrollment and occupation) and six follow-up variables (feeling in control of eating habits, percent of body weight lost during treatment, frequency of weight measurement, physical activity, eating in response to emotions, and number of pounds of weight gain needed to resume a diet). It was concluded that a greater amount of emphasis should be placed on the six follow-up variables by clinicians involved in the treatment of obesity, and by the subjects themselves to enhance their chances of success at long-term weight loss. ^