Susetyowati

A simple dietary assessment tool to monitor food intake of hospitalized adult patients

Background/objectives Monitoring food intake of patients during hospitalization using simple methods and minimal training is an ongoing problem in hospitals. Therefore, there is a need to develop and validate a simple, easy to use, and quick tool that enables staff to estimate dietary intake. Thus, this study aimed to develop and validate the Pictorial Dietary Assessment Tool (PDAT). Subjects and methods A total of 37 health care staff members consisting of dietitians, nurses, and serving assistants estimated 130 breakfast and lunch meals consumed by 67 patients using PDAT. PDAT was developed based on the hospital menu that consists of staple food (rice or porridge), animal source protein (chicken, meat, eggs, and fish), and non-animal source protein (tau fu and tempeh), with a total of six pictorials of food at each meal time. Weighed food intake was used as a gold standard to validate PDAT. Agreement between methods was analyzed using correlations, paired t-test, Bland–Altman plots, kappa statistics, and McNemar’s test. Sensitivity, specificity, and area under the curve of receiver operating characteristic were calculated to identify whether patients who had an inadequate food intake were categorized as at risk by the PDAT, based on the food weighing method. Agreement between different backgrounds of health care staff was calculated by intraclass correlation coefficient and analysis of variance test. Results There was a significant correlation between the weighing food method and PDAT for energy (r=0.919, P<0.05), protein (r=0.843, P<0.05), carbohydrate (r=0.912, P<0.05), and fat (r=0.952; P<0.05). Nutrient intakes as assessed using PDAT and food weighing were rather similar (295±163 vs 292±158 kcal for energy; 13.9±7.8 vs 14.1±8.0 g for protein; 46.1±21.4 vs 46.7±22.3 g for carbohydrate; 7.4±3.1 vs 7.4±3.1 g for fat; P>0.05). The PDAT and food weighing method showed a satisfactory agreement beyond chance (k) (0.81 for staple food and animal source protein; 0.735 for non-animal source protein). Intraclass correlation coefficient ranged between 0.91 and 0.96 among respondents. There were no differences in energy, protein, carbohydrate, and fat intake estimated among health care staff (P=0.967; P=0.951; P=0.888; P=0.847, respectively). Conclusion In conclusion, PDAT provides a valid estimation of macronutrient consumption among hospitalized adult patients.

Designing and collecting data for a longitudinal study : the Sleman Health and Demographic Surveillance System (HDSS).

Background: This paper describes the methodological considerations of developing an urban Health and Demographic Surveillance System (HDSS), in the Sleman District of Yogyakarta, Indonesia. Methods: 1) The Sleman District was selected because it is mostly an urban area. 2) The minimum sample size was calculated to measure infant mortality as the key variable and resulted in a sample of 4942 households. A two-stage cluster sampling procedure with probability proportionate to size was applied; first, 216 Censuses Blocks (CBs) were selected, and second, 25 households in each CB were selected. 3) A baseline survey was started in 2015, and collected data on demographic and economic characteristics and verbal autopsy (VA); the 2nd cycle collected updated demographic data, VA, type of morbidity (communicable and non-communicable diseases, disability and injury) and health access. 4) The data were collected at a home visit through a Computer-Assisted Personal Interview (CAPI) on a tablet device, and the data were transferred to the server through the Internet. 5) The quality control consisted of spot-checks of 5% of interviews to control for adherence to the protocol, re-checks to ensure the validity of the interview, and computer-based data cleaning. 6) A utilization system was designed for policy-makers (government) and researchers. Results: In total, 5147 households participated in the baseline assessment in 2015, and 4996 households participated in the second cycle in 2016 (97.0% response rate). Conclusions: Development of an urban HDSS is possible and is beneficial in providing data complementary to the existing demographic and health information system at local, national and global levels.

Nutrition screening tools as predictor of malnutrition for hemodialysis patients in Dr. Sardjito Hospital in Yogyakarta, Indonesia

The risk of malnutrition in maintenance hemodialysis (MHD) patients must be monitored routinely through nutrition screening so that morbidity and mortality can be decreased. Comparing the validity of the simple nutrition screening tool (SNST) and nutritional risk screening 2002 (NRS 2002) as valid and reliable nutrition screening tools in predicting malnutrition. The data were collected from March to April 2015 in the Hemodialysis Unit of Dr. Sardjito Hospital, Indonesia as an observational study. A cross-sectional design study was used to screen 105 MHD patients using the SNST and NRS 2002, and then, the nutritional status of all individuals was assessed used the following subjective parameters: subjective global assessment (SGA) and dialysis malnutrition score (DMS). The objective parameters were the following: Body mass index (BMI), mid-upper-arm circumference (MUAC), handgrip strength (HGS), and a three-day food record. Chi-squared test, t-test, and receiving operating characteristic curve were used for the statistical analysis. In predicting malnutrition, the validity of the SNST is better than the NRS 2002 in MHD patients against either SGA (Se 94.3% vs. 82.9%; Sp 60% vs. 58.6%; and area under curve (AUC) 0.847 vs. 0.749) or DMS (Se 90.0% vs. 81.6%; Sp 74.0% vs. 62.8%; and AUC 0.833 vs. 0.746), while the NRS 2002 is better than the SNST based on BMI, MUAC, HGS, and energy intake (P <0.001). In predicting malnutrition, SNST is better than NRS 2002 based on the subjective assessments (SGA and DMS), and NRS 2002 is better than SNST based on the objective assessments (BMI, MUAC, and HGS).

Evaluation of Pictorial Dietary Assessment Tool for Hospitalized Patients with Diabetes: Cost, Accuracy, and User Satisfaction Analysis

Although nutritional screening and dietary monitoring in clinical settings are important, studies on related user satisfaction and cost benefit are still lacking. This study aimed to: (1) elucidate the cost of implementing a newly developed dietary monitoring tool, the Pictorial Dietary Assessment Tool (PDAT); and (2) investigate the accuracy of estimation and satisfaction of healthcare staff after the use of the PDAT. A cross-over intervention study was conducted among 132 hospitalized patients with diabetes. Cost and time for the implementation of PDAT in comparison to modified Comstock was estimated using the activity-based costing approach. Accuracy was expressed as the percentages of energy and protein obtained by both methods, which were within 15% and 30%, respectively, of those obtained by the food weighing. Satisfaction of healthcare staff was measured using a standardized questionnaire. Time to complete the food intake recording of patients using PDAT (2.31 ± 0.70 min) was shorter than when modified Comstock (3.53 ± 1.27 min) was used (p < 0.001). Overall cost per patient was slightly higher for PDAT (United States Dollar 0.27 ± 0.02) than for modified Comstock (USD 0.26 ± 0.04 (p < 0.05)). The accuracy of energy intake estimated by modified Comstock was 10% lower than that of PDAT. There was poorer accuracy of protein intake estimated by modified Comstock (<40%) compared to that estimated by the PDAT (>71%) (p < 0.05). Mean user satisfaction of healthcare staff was significantly higher for PDAT than that for modified Comstock (p < 0.05). PDAT requires a shorter time to be completed and was rated better than modified Comstock.