Province clustering based on the percentage of communicable disease using the BCBimax biclustering algorithm

Indonesia needs to lower its high infectious disease rate. This requires reliable data and following their temporal changes across provinces. We investigated the benefits of surveying the epidemiological situation with the imax biclustering algorithm using secondary data from a recent national scale survey of main infectious diseases from the National Basic Health Research (Riskesdas) covering 34 provinces in Indonesia. Hierarchical and k-means clustering can only handle one data source, but BCBimax biclustering can cluster rows and columns in a data matrix. Several experiments determined the best row and column threshold values, which is crucial for a useful result. The percentages of Indonesia's seven most common infectious diseases (ARI, pneumonia, diarrhoea, tuberculosis (TB), hepatitis, malaria, and filariasis) were ordered by province to form groups without considering proximity because clusters are usually far apart. ARI, pneumonia, and diarrhoea were divided into toddler and adult infections, making 10 target diseases instead of seven. The set of biclusters formed based on the presence and level of these diseases included 7 diseases with moderate to high disease levels, 5 diseases (formed by 2 clusters), 3 diseases, 2 diseases, and a final order that only included adult diarrhoea. In 6 of 8 clusters, diarrhea was the most prevalent infectious disease in Indonesia, making its eradication a priority. Direct person-to-person infections like ARI, pneumonia, TB, and diarrhoea were found in 4-6 of 8 clusters. These diseases are more common and spread faster than vector-borne diseases like malaria and filariasis, making them more important.

Supplementary Biscuits for the Recovery of Malnourished Children in Indonesia

The objective of this research was to study the nutritional status of children before receiving biscuits, the biscuit supplementation regularity, the adequacy of the biscuits received and consumed by the children according to recommendations, and the relationship between supplementary biscuits consumption and nutritional status. Using a cross-sectional study design, the study was conducted in 2017 involving 586 children in three regions (11 provinces) in Indonesia. A chi-square test was performed to see the relationship between supplementary biscuits consumption and nutritional status. The most common nutritional problems were stunting-wasting (34.1%), stunting (32.6%), and wasting (23.9%). The nutritional problems occurred most often in the eastern region (96.4%), followed by the central region (95.5%) and the western (87.1%). Within three months, 66–78% of respondents received supplementary biscuits regularly, but only 10–29% received the supplementary biscuits as recommended in the same period. Within the three months’ period, from respondents that received the supplementary biscuits as recommended, the region in which children consumed the biscuits according to the recommendations the most was the eastern region (45.2%), followed by the western (23.3%) and central regions (5.3%). Children who consumed biscuits as recommended in the three months’ period had a 2.9 times possibility of having a normal nutritional status (categorized based on the weight-for-age index) (p-value=0.049).

Spatial distribution and identifying biochemical factors affecting haemoglobin levels among women of reproductive age for each province in Indonesia: A geospatial analysis

Anaemia is still a public health problem in Indonesia. The iron supplement program, known as Tablet Tambah Darah (Blood Add Tablet) has not yet produced optimal results. This study aimed to identify the cause of anaemia and the factors that influence it. Biochemical indicator data are haemoglobin (Hb), C-reactive protein (CRP), ferritin and serum transferrin receptor (sTfR) from 9,463 women of reproduction age. Data from the Basic Health Research (Riskesdas) project of 2013 were used for the study. ANOVA as well as global and local regression approaches (classical regression and geo-weighted regression) were used to compare the mean Hb and CRP values between provinces and to determine the factors that influence Hb concentrations. The results showed that the distribution of anaemia in Indonesia is uneven and not always caused by iron deficiency. The lowest Hb mean coupled with the highest iron deficiency was found in Papua, where there are high rates of parasitic infections. In contrast, the highest mean Hb coupled with low iron deficiency, and also low infection rates, was found in North Sulawesi. The Hb concentrations were significantly associated by ferritin, CRP and sTfR and there were varying magnitudes between provinces. Although anaemia is mainly influenced by the iron concentration, CRP, ferritin and sTfR can also affect it through their association with inflammatory reactions. Identification of all causes of anaemia in each province needs to be done in the future, while blanket iron supplementation should be reviewed.

Estimating 24 Hour Sodium Urine from Spot Urine: A Correlation Model Among Aged 25-64 in Indonesia

INTRODUCTION: Excess salt intake is known to raise blood pressure and turn increase the risk of cardiovascular disease. Data and information on sodium consumption in Indonesia based is limited, while examination method using 24-hour urine examination is a complex method to apply in population based survey. Objective: to estimate value of spot urinary excretion against 24-hour urine values ​​and correction factors. METHOD: Validation was carried out through 24-hour urine and spot urine examination, in 423 individual aged 25-65 years in t Kebun Kelapa Village, Bogor City, Indonesia in 2017. Urine sodium examination method was carried out by laboratory examination inductively coupled plasma optical emission spectrometry (ICP-OES). Accuracy and precision are valued by paired test correlations and mean differences. The coefficient of determination (R Square) of is calculated for sodium intake estimation and correction factors. RESULTS: The morning value of sodium spot urine has better precision and a stronger correlation with the 24-hour sodium urine than the afternoon spot urine. The difference in average morning sodium urine with 24-hour urine sodium according to age and sex was not significantly different (t test 0.30 and p = 0.77), had a positive and moderate correlation (paired r = 0.50 and p = 0.00). The average difference between the estimated evening urine sodium spot was different (t test = 7.32 and p = 0.00), and the correlation was very weak (r = 0.25; p = 0.00). CONCLUSION: The urine content / sodium spot level was accurate to estimate urine sodium value / content 24 hours.