The coordinated approach to child health in underserved population (CATCH UP) curriculum: A quantitative evaluation of an innovative preschool obesity prevention program

Obesity has been on the rise in the United States over the last 30 years for all populations, including preschoolers. The purpose of the project was to develop an observation tool to measure physical activity levels in preschool children and use the tool in a pilot test of the CATCH UP curriculum at two Head Start Centers in Houston. Pretest and posttest interobserver agreements were all above 0.60 for physical activity level and physical activity type. Preschoolers spent the majority of their time in light physical activity (75.33% pretest, 87.77% posttest), and spent little time in moderate to vigorous physical activity (MVPA) (24.67% pretest, 12.23% posttest). Percent time spent in MVPA decreased significantly pretest to posttest from (F=5.738, p=0.043). While the pilot testing of the CATCH UP curriculum did not show an increase in MVPA, the SOFIT-P tool did show promising results as being a new method for collecting physical activity level data for preschoolers. Once the new tool has undergone more reliability and validity testing, it could allow for a more convenient method of collecting physical activity levels for preschoolers. ^

Air Force web-based preventive health assessement alcohol screening effectiveness using the Alcohol Use Disorders Identification Tool (AUDIT)

Alcohol consumption has a long-standing tradition in the United States Air Force (USAF). From squadron bars to officers and enlisted clubs, alcohol has been used in social settings to increase morale and also as a way to help decrease the stress of military operations. Surveys have demonstrated that the USAF has more than double the percentage of heavy drinkers than the US population. More than one-third of the Air Force reports binge drinking in the last month while only six percent of the nation reports the same consumption pattern.^ However, alcohol has a significant harmful health effect if consumed in excess. As part of an overall prevention and treatment program aimed at curbing the harmful effects of alcohol consumption, the USAF uses the Alcohol Use Disorder Identification Test (AUDIT) to screen for high-risk alcohol consumption patterns before alcohol disorder and disability occur. All Air Force active-duty members are required to complete a yearly Preventive Health Assessment questionnaire. Various health topics are included in this questionnaire including nutrition, exercise, tobacco use, family history, mental health and alcohol use. While this questionnaire has been available in a web-based format for several years, mandatory use was not implemented until 2009.^ Although the AUDIT was selected due to its effectiveness in assessing high-risk alcohol consumption in other populations, its effectiveness in the Air Force population had not been studied previously. In order to assess the sensitivity, specificity, and positive predictive value of this screening tool, the Air Force Web-based Preventive Health Assessment alcohol screening results were compared to whether any alcohol-related diagnosis was made from January 1, 2009 to March 31, 2010.^ While the AUDIT has previously been shown to have a high sensitivity and specificity, the Air Force screening values were 27.9% and 93.0% respectively. Positive predictive value was only 4.9%. With the screening statistics found, less than one-third of those having an alcohol disorder will be found with this screening tool and only 1 out of 20 Airmen who require further evaluation actually have an alcohol-related diagnosis.^

The development of poultry farms risk assessment tool for avian influenza in Imo State, Nigeria

Background: Nigeria was one of the 13 countries where avian influenza outbreak in poultry farms was reported during the 2006 avian influenza pandemic threat and was also the first country in Africa to report the presence of H5N1influenza among its poultry population. There are multiple hypotheses on how the avian influenza outbreak of 2006 was introduced to Nigeria, but the consensus is that once introduced, poultry farms and their workers were responsible for 70% of the spread of avian influenza virus to other poultry farms and the population. ^ The spread of avian influenza has been attributed to lack of compliance by poultry farms and their workers with poultry farm biosecurity measures. When poultry farms fail to adhere to biosecurity measures and there is an outbreak of infectious diseases like in 2006, epidemiological investigations usually assess poultry farm biosecurity—often with the aid of a questionnaire. Despite the importance of questionnaires in determining farm compliance with biosecurity measures, there have been few efforts to determine the validity of questionnaires designed to assess poultry farms risk factors. Hence, this study developed and validated a tool (questionnaire) that can be used for poultry farm risk stratification in Imo State, Nigeria. ^ Methods: Risk domains were generated using literature and recommendations from agricultural organizations and the Nigeria government for poultry farms. The risk domains were then used to develop a questionnaire. Both the risk domain and questionnaire were verified and modified by a group of five experts with a research interest in Nigeria's poultry industry and/or avian influenza prevention. Once a consensus was reached by the experts, the questionnaire was distributed to 30 selected poultry farms in Imo State, Nigeria that participated in this study. Survey responses were received for all the 30 poultry farms that were selected. The same poultry farms were visited one week after they completed the questionnaires for on-site observation. Agreement among survey and observation results were analyzed using a kappa test and rated as poor, fair, moderate, substantial, or nearly perfect; and internal consistency of the survey was also computed. ^ Result: Out of the 43 items on the questionnaire, 32 items were validated by this study. The agreement between the survey result and onsite observation was analyzed using kappa test and ranged from poor to nearly perfect. Most poultry farms had their best agreements in the contact section of the survey. The least agreement was noted in the farm management section of the survey. Thirty-two questions on the survey had a coefficient alpha > 0.70, which is a robust internal consistency for the survey. ^ Conclusion: This study developed 14 risk domains for poultry farms in Nigeria and validated 32 items from the original questionnaire that contained 43 items. The validated items can be used to determine the risk of introduction and spread of avian influenza virus in poultry farms in Imo State, Nigeria. After further validations in other states, regions and poultry farm sectors in Nigeria; this risk assessment tool can then be used to determine the risk profile of poultry farms across Nigeria.^