Spatial analysis has been vital in mapping the spread of diseases and assisting in policy making. Targeting diarrhea transmission hotspots is one of the potential strategies for reducing diarrhea cases. This study aimed to examine the spatial-temporal variations and to identify the modifiable determinants of diarrhea while controlling for the spatial dependence in the data. An ecological study on diarrhea data from DLHS-3 and NFHS- 4 in India. Moran’s I and LISA were used to detect the spatial clustering of diarrhea cases and to test for clustering in the data. Spatial regression was used to identify the modifiable factors associated with the prevalence of diarrhea. The study comprised of the prevalence of diarrhea among the children below the age of five years (U-5 s) across different states in India. The determinants of diarrhea were obtained using spatial lag models. The software used were GeoDa 1.6.6 and QGIS 2.0. The presence of spatial autocorrelation in DLHS-3 and NFHS-4 (Moron’s I = 0.577 and 0.369 respectively) enforces the usage of geographical properties while modeling the diarrhea data. The geographic clustering of high-prevalence districts was observed in the state of UP consistently. The spatial pattern of the percentage of children with diarrhea was persistently associated with the household with a sanitation facility (%) (p = 0.023 and p = 0.011). Compared to the diarrhea cases in the period 2007–2008, no much reduction was observed in the period 2015–2016. The prevalence of diarrhea and percentage of household with sanitation were ranging between 0.1–33.8% and 1.3–96.1% in the period 2007–2008 and 0.6–29.1% and 10.4–92.0% in the period 2015–2016 respectively. The least and highest prevalence of diarrhea being consistently from Assam and UP respectively. Despite improvements in controlling spread of diarrheal disease, the burden remains high. Focus on widespread diarrheal disease control strategy by addressing the social determinants of health like basic sanitation is crucial to reduce the burden of diarrhea among U-5 s in India. The identification of hotspots will aid in the planning of control strategies for goal setting in the targeted regions.

An optical fiber based immunosensing platform formed by two identical chirped long period gratings (CLPGs) for sensitive and label-free detection of Escherichia coli (E. coli) is presented. The proposed immunosensor having two CLPGs with an inter-grating space (IGS) works like a Mach-Zehnder interferometer. The important feature of this sensor is that by changing the refractive index (RI) induced phase at the IGS region, a corresponding shift in resonance wavelength can be realized without affecting the actual grating region. Additionally, the confining bandwidth of the interference fringes promotes greater resolution in the resonance wavelength analysis of the transmission spectrum. The main aim of this study was the specific and rapid detection of the E. coli bacteria in phosphate buffer saline (detection range: 10 cfu/ml to 60 cfu/ml) by using the bio-functionalized IGS region as a sensing probe of the dual CLPG structure. The observed detection limit was 7 cfu/ml. For specificity analysis, Salmonella typhimurium and Staphylococcus aureus were tested and no significant shift in resonance wavelength was observed. In addition, the proposed immunosensor has the ability to selectively detect E. coli in real samples including lake water. The high specificity, good sensitivity to ambient RI, and robustness of the developed sensing platform will open a new avenue in optical immunosensing technology, and cascaded long CLPG sensors can contribute significantly to the detection of pathogenic bacteria in water and food samples.

Exploring the barriers and facilitators of cervical cancer screening (CCS) is essential to reduce the incidence and mortality, particularly in low and middle-income countries. The present study investigates the direct, indirect, and total effects of the barriers and facilitators on CCS in India through the generalized structural equation modeling using data from women files of the fourth round of the National Family Health Survey (NFHS-4). Generalized structural equation models were used to quantify the hypothetical pathway via fitting a series of regression equations. Age, body mass index, religion, years of schooling, awareness of sexually transmitted infection, contraception use, lifetime number of sex partners, number of children, and wealth index were shown to have significant direct effects on the CCS. Older women had 1.16 times the odds of getting screened for cervical cancer as compared to their younger counterpart. The odds of CCS among the women in richest wealth quintile is 2.50 times compared to the poorest. Those who are aware of STIs have 1.39 times the odds of getting screened for cervical cancer. Wealth index, years of schooling, and religion have a substantial indirect and total impact on the CCS. The findings will aid in policy formulations for enhancing the CCS in India.