Good Quality Data Research Articles

Development of Storm Surge Inundation Model and Database for Enhanced Climate Services in Bangladesh

Bangladesh's vulnerability to storm surge and success in reducing their impacts on the coastal communities are well-known. However, global warming is expected to increase both the frequency and the intensity of tropical cyclones. Also, the country still lacks in good quality data and information that can offer enhanced services to effectively design coastal defense systems and adapt to climate changes. This study developed a storm surge inundation model and database using the available tropical cyclones' database and simulated inundation for the past 31 cyclones in the Bay of Bengal, near the coastal regions of Bangladesh. The model uses a suite of Delft3D hydrodynamic and Delft Dashboard cyclone models. The model was calibrated and validated for two major cyclones, namely Sidr and Aila, using the available data for different bed roughness, track sources, and wind drag coefficient conditions. The results suggested that spatially varying bed roughness and wind varying drag conditions provide a more reliable prediction of inundation over the coast. The results also show that historical track data from Indian Meteorological Department optimize the model performances. The inundation database indicates that depending on the intensity and location, tropical cyclones can cause the maximum inundation of 1–5 m in the coastal lands of Bangladesh. More than 70% of the storms lead to the maximum inundation of more than 2 m, and about 25 and 7% of the storms can cause the maximum inundation of more than 3 and 4 m, respectively. The study also discusses the potential applications and shares insights on the implications of the inundation database that can offer improved information for enhanced climate services in Bangladesh. The database can be advanced with information about sectoral loss and damage as well as citizen science that will not only provide necessary information to design coastal protection structures and emergency measurements but also contribute to build long-term climate adaptation plans in the data-scarce region.

Robot-Assisted Minimally Invasive Surgery—Surgical Robotics in the Data Age

Telesurgical robotics, as a technical solution for robot-assisted minimally invasive surgery (RAMIS), has become the first domain within medicosurgical robotics that achieved a true global clinical adoption. Its relative success (still at a low single-digit percentile total market penetration) roots in the particular human-in-the-loop control, in which the trained surgeon is always kept responsible for the clinical outcome achieved by the robot-actuated invasive tools. Nowadays, this paradigm is challenged by the need for improved surgical performance, traceability, and safety reaching beyond the human capabilities. Partially due to the technical complexity and the financial burden, the adoption of telesurgical robotics has not reached its full potential, by far. Apart from the absolutely market-dominating da Vinci surgical system, there are already 60+ emerging RAMIS robot types, out of which 15 have already achieved some form of regulatory clearance. This article aims to connect the technological advancement with the principles of commercialization, particularly looking at engineering components that are under development and have the potential to bring significant advantages to the clinical practice. Current RAMIS robots often do not exceed the functionalities deriving from their mechatronics, due to the lack of data-driven assistance and smart human–machine collaboration. Computer assistance is gradually gaining more significance within emerging RAMIS systems. Enhanced manipulation capabilities, refined sensors, advanced vision, task-level automation, smart safety features, and data integration mark together the inception of a new era in telesurgical robotics, infiltrated by machine learning (ML) and artificial intelligence (AI) solutions. Observing other domains, it is definite that a key requirement of a robust AI is the good quality data, derived from proper data acquisition and sharing to allow building solutions in real time based on ML. Emerging RAMIS technologies are reviewed both in a historical and a future perspective.

Seismic Noise Analysis in InaTEWS Earthquake Station Network (Case Study: Flores Earthquake 7.4, 14 December 2021)

Meteorological, Climatology and Geophysics Agency (BMKG) is the agency responsible for providing information on earthquake parameters in Indonesia. BMKG has installed 411 seismometers network spread across Indonesia, this network is known as the Indonesia Tsunami Early Warning System (InaTEWS) network. The dense distribution of the seismometer is expected to be able to provide accurate earthquake parameter results. However, the accuracy of earthquake parameters does not only depend on many stations but also on the quality of the data recorded by the seismometer. In this study, an analysis of 15 data quality was carried out at the nearest station that recorded the Flores earthquake on December 14, 2021. Analysis of data quality using the power spectral density (PSD) and probability density functions (PDFs) methods, where good data quality can be seen in the spectrum of the seismograph recordings that are between the Peterson’s new high noise model (NHNM) and new low noise model (NLNM).

A Novel Polarimetric Channel Imbalance Phase Estimation Method Based on the Rotated Double-Bounce Backscatters in Urban Areas

Polarization calibration without artificial calibrators has been one of the focuses of research and discussion for PolSAR communities. However, there is limited research on the treatment of dual-polarization systems and the calibration methods for getting rid of distributed targets. In this paper, we contribute to proposing a new and convenient method for estimating the polarimetric channel imbalance phase at the transmitter and receiver, which can be used for both quad-pol and dual-pol SAR systems. We found a brand-new reference object in the urban area scene, namely the effective dihedrals. A statistical calculation method was proposed correspondingly, which obtained an effective estimation of the channel imbalance phases. The theoretical explanation of the proposed method was consistent with the statistical phenomena presented in the experiments. The technique was illustrated and verified through C-band SAR images, including GaoFen-3 (GF-3) data and Sentinel-1 data. The technique was also validated and successfully applied in airborne SAR data of P, L, S, C, and X bands. The estimation error could be within 7° when crosstalk items were less than −30 dB. The method realizes a fast and low-cost dual-polarization phase imbalance estimation and provides a new technical approach to supplement the traditional tropical-rainforest-based quad-pol system calibration. The method can be conveniently applied to the monitoring of polarization distortion parameters, ensuring good polarization SAR data quality.

Combined Radar Quality Index for Quantitative Precipitation Estimation of Heavy Rainfall Events

For quantitative precipitation estimation (QPE) based on polarimetric radar (PR) and rain gauges (RGs), the quality of the radar data is crucial for estimation accuracy. This paper proposes a combined radar quality index (CRQI) to represent the quality of the radar data used for QPE and an algorithm that uses CRQI to improve the QPE performance. Nine heavy rainfall events that occurred in Guangdong Province, China, were used to evaluate the QPE performance in five contrast tests. The QPE performance was evaluated in terms of the overall statistics, spatial distribution, near real-time statistics, and microphysics. CRQI was used to identify good-quality data pairs (i.e., PR-based QPE and RG observation) for correcting estimators (i.e., relationships between the rainfall rate and the PR parameters) in real-time. The PR-based QPE performance was improved because estimators were corrected according to variations in the drop size distribution, especially for data corresponding to 1.1 mm < average Dm < 1.4 mm, and 4 < average log10Nw < 4.5. Some underestimations caused by the beam broadening effect, excessive beam height, and partial beam blockages, which could not be mitigated by traditional algorithms, were significantly mitigated by the proposed algorithm using CRQI. The proposed algorithm reduced the root mean square error by 17.5% for all heavy rainfall events, which included three precipitation types: convective precipitation (very heavy rainfall), squall line (huge raindrops), and stratocumulus precipitation (small but dense raindrops). Although the best QPE performance was observed for stratocumulus precipitation, the biggest improvement in performance with the proposed algorithm was observed for the squall line.

Tailoring Mobile Data Collection for Intervention Research in a Challenging Context: Development and Implementation in the Malakit Study.

BackgroundAn interventional study named Malakit was implemented between April 2018 and March 2020 to address malaria in gold mining areas in French Guiana, in collaboration with Suriname and Brazil. This innovative intervention relied on the distribution of kits for self-diagnosis and self-treatment to gold miners after training by health mediators, referred to in the project as facilitators.ObjectiveThis paper aims to describe the process by which the information system was designed, developed, and implemented to achieve the monitoring and evaluation of the Malakit intervention.MethodsThe intervention was implemented in challenging conditions at five cross-border distribution sites, which imposed strong logistical constraints for the design of the information system: isolation in the Amazon rainforest, tropical climate, and lack of reliable electricity supply and internet connection. Additional constraints originated from the interaction of the multicultural players involved in the study. The Malakit information system was developed as a patchwork of existing open-source software, commercial services, and tools developed in-house. Facilitators collected data from participants using Android tablets with ODK (Open Data Kit) Collect. A custom R package and a dashboard web app were developed to retrieve, decrypt, aggregate, monitor, and clean data according to feedback from facilitators and supervision visits on the field.ResultsBetween April 2018 and March 2020, nine facilitators generated a total of 4863 form records, corresponding to an average of 202 records per month. Facilitators’ feedback was essential for adapting and improving mobile data collection and monitoring. Few technical issues were reported. The median duration of data capture was 5 (IQR 3-7) minutes, suggesting that electronic data capture was not taking more time from participants, and it decreased over the course of the study as facilitators become more experienced. The quality of data collected by facilitators was satisfactory, with only 3.03% (147/4849) of form records requiring correction.ConclusionsThe development of the information system for the Malakit project was a source of innovation that mirrored the inventiveness of the intervention itself. Our experience confirms that even in a challenging environment, it is possible to produce good-quality data and evaluate a complex health intervention by carefully adapting tools to field constraints and health mediators’ experience.Trial RegistrationClinicalTrials.gov NCT03695770; https://clinicaltrials.gov/ct2/show/NCT03695770

Data quality and associated factors in the health management information system at health centers in Shashogo district, Hadiya zone, southern Ethiopia, 2021

BackgroundPoor quality routine data contributes to poor decision-making, inefficient resource allocation, loss of confidence in the health system, and may threaten the validity of impact evaluations. For several reasons in most developing countries, the routine health information systems in those countries are described as ineffective. Hence, the aim of this study is to determine the quality of data and associated factors in the routine health management information system in health centers of Shashogo district, Hadiya Zone.MethodsA facility-based cross-sectional study was conducted from June 1, 2021, to July 1, 2021, and 300 participants were involved in the study through simple random sampling. The data was collected with a self-administered questionnaire by trained data collectors. After checking its completeness, the data was entered into EPI data version 3.1 and exported to SPSS version 25 for statistical analysis. Finally, variables with p < 0.05 during multivariable analysis were considered significant variables.ResultA total of 300(100%) participant were included in the interview and HMIS data quality was 83% in Shashogo district health centers. The data quality in terms of accuracy, completeness, and timeliness was 79%, 86%, and 84%, respectively. Conducting supportive supervision [AOR 3.5 (1.4, 8.9)], checking accuracy [AOR 1.3 (1.5, 3.5)], filling registrations [AOR 2.7 (1.44, 7.7)], and confidence level [AOR 1.9 (1.55, 3.35)] were all rated positively found to be factors associated with data quality.ConclusionThe overall level of data quality in Shashogo district health centers was found to be below the national expectation level. All dimensions of data quality in the district were below 90% in data accuracy, content completeness, and timeliness of data. Conducting supportive supervision, checking accuracy, filling registrations and confidence level were found to be factors associated with data quality. Hence, all stakeholders should give all necessary support to improve data quality in routine health information systems to truly attain the goal of providing good quality data for the decision-making process by considering the identified factors.

Comparison of RATA, CATA, sorting and Napping® as rapid alternatives to sensory profiling in a food industry environment

Today, there is a need to accelerate new product development to deliver faster innovations which are expected by consumers. This requires identifying rapid descriptive methodologies requiring less training than conventional sensory profiling (SP) still providing good quality data allowing to identify food sensory drivers of consumer liking. A set of nine samples covering a large sensory space has been designed systematically combining at three different ratio three commercial almond, rice, and oat-based milks. Ten expert panellists familiar with sensory profiling and naïve with the plant-based milk category performed sorting, Napping®, CATA, RATA tasks and then SP. Overall liking of each sample was rated by 80 plant-based milk consumers. Among the four alternative methodologies, RATA provided both an overall product configuration and description the closest to SP (RV = 0.93) and allowed to identify the sensory drivers of plant-based milk acceptance similarly to SP. CATA is relevant to build a product landscape and to highlight product clusters with major sensory differences whereas Sorting and Napping® are alternatives to CATA, as product mappings were close, when no previous glossary or knowledge exits on product attributes.

Injury and Illness Incidence in 2017 Super Rugby Tournament: A Surveillance Study on a Single South African Team.

BackgroundProfessional rugby presents significant injury and illness risks to players, which need to be regularly assessed to monitor the effects of interventions and competition rules changes.Hypothesis/PurposeThe purpose of this study was to determine the incidence and nature of time-loss injuries and illness during the pre-season and competition period of the 2017 Super Rugby tournament in a single South African team.Study DesignDescriptive Epidemiology StudyMethodsForty-five adult players were recruited from one 2017 Super Rugby South African team, with 39 included in the final data set. Daily injury and illness data were routinely collected during the season by support staff over a 28-week period (January to July 2017), based on standardized injury and illness definitions. Retrospective analyses of the data were performed.ResultsThe incidence of match injuries (241.0 per 1000 player hours) was significantly higher than training injuries (3.3 per 1000 player hours). Twenty one percent of all injuries occurred during the tackle; 37.5% of all injuries were of a “moderate” severity. The proportion of players who sustained a time-loss injury was 76.9% (n=30). The overall incidence of illness was 1.8 per 1000 player days. Acute respiratory tract infection (28.6%) was the most common diagnosis, and the majority of illnesses (64.3%) did not result in time-loss.ConclusionThis study presented a longer study period than previous research by including the pre-season training, but represented only one single team. The incidence of match injuries was significantly higher than previously reported in Super Rugby tournaments, whereas illness rates were significantly lower. Support staff in professional rugby need to be trained on the standardized Orchard System of Classifications to ensure good quality data that can be compared to other teams within the same or other sporting codes.Level of evidenceLevel 3

Pediatric transplantation: An international perspective

The international practice of transplant in the pediatric population is heterogenous. Global trends in pediatric transplant activity are increasing, with diffusion of transplant activities into developing and emerging economies. There have been impacts of the COVID-19 pandemic which have in the earlier part of the pandemic caused a decrease in the number of transplants. While deceased donor programs are well established in advanced economies, emerging and developing countries rely heavily on live donor programs. Prioritization of organs for children exists in different forms throughout the world. Pediatric transplantation as a sub-specialty is young but growing around the world with a need to train surgeons and physicians in this discipline. Outreach efforts with multi-national and multi-institutional partnerships have enabled resource poor countries to establish new transplant programs for children. Further international collaboration, good quality data collection and audit, prospective research and ongoing mentorship and education are needed to further improve outcomes of all children receiving solid organ transplants.

Management of Malignant Pleural Effusion: Where Are We Now?

Pleural malignancies are among the most common causes of pleural disease and form the basis of our daily pleural practice. There has been significant research and increase in both diagnostic and therapeutic management of malignant pleural diseases in the last decade. Good-quality data have led to a paradigm shift in the management options of pleural malignancies, and indwelling pleural catheter is now recommended and widely used as first-line intervention. Several trials compared different treatment modalities for pleural malignancies and continue to emphasize the need to reduce hospital length of stay and unnecessary pleural intervention, and the importance of patient choice in clinical decision making. This practical review aims to summarize the current knowledge for the management of pleural malignancies, and the understanding of the steps that we still have to climb to optimize management and reduce morbidity.

Tocopherol more bioavailable than tocopheryl-acetate as a source of vitamin E for broilers.

Vitamin E is typically supplied in the form of tocopheryl-acetate (T-Ac) since tocopherol (T) has stability issues. Tocopheryl-acetate, however, must be hydrolyzed in the intestines before it can be absorbed, a step that is purportedly rate-limiting for its bioavailability. The objective of this study was to compare the efficiency of absorption of T-Ac and T in broilers. In addition, two test procedures were evaluated in which animals received the test substances for either 2 or 4 days only. Animals were adapted to diets without supplemental vitamin E (feedstuffs contributed 14±1 ppm natural vitamin E (RRR-tocopherol)) till the age of 25 d (individual housing) or 28 d (group housing). Subsequently, they were fed T-Ac at 80, 53, 36, 24, or 16 ppm or T at 80, 40, 20, 10, or 5 ppm for a period of 4 d (4-di) or 2 d (2-dg), after which serum and liver were collected for analysis of vitamin E. Measured feed vitamin E levels were used for the data analysis; the recovery of T-Ac was 85%, and that of T was 39%. Both test procedures (2 or 4 days) yielded good quality data. Based on linear regression analysis, the relative efficiency with which T-Ac raised tissue levels as compared to T was 0.24 (2-dg) to 0.37 (4-di), with liver and serum yielding similar results. Analysis using more complex dose response models imply that the hydrolysis of T-Ac was strongly dose-dependent and that it could be saturated at doses above approximately 50 ppm in animals only briefly fed T-Ac; for T there was no evidence of saturation. These data imply that T, provided that stable forms can be developed, has the potential to be much more efficient at providing vitamin E to the animal, and on top, can yield much higher tissue levels, than T-Ac.

Severe pulmonary hypertension associated with COPD: long-term results of a prospective French multicentre cohort

<b>This prospective study dealing with 99 incident patients followed over several years, without any patients lost to follow-up, confirms, with good-quality data, that the prognosis associated with severe PH-COPD is very poor</b>https://bit.ly/3DHjYIm

Perception and Aspiration on Primary Healthcare Service Quality in Puskesmas in Pati Regency Government, Central Java Province, Indonesia

The research of the quality of primary public health services in the Government of Kabupaten Pati is measured using the quality of medical services that are being held by all of its local public health centers, Puskesmas. The number of respondents in this research is 640 respondents. The research is being done in 64 different places that are distributed equally so that it can gather good quality data. The result of this research shows that the quality of primary public health services in the Government of Kabupaten Pati is perceived as good by its citizens. The improvement on the quality of medical services of Puskesmas can be done by improving its facility, human resource, and its medicine quality.

Current status of medical device malfunction reporting: using end user experience to identify current problems.

IntroductionThe current under-reporting of medical device malfunctions, difficulties with the current system and absence of continuous good-quality data has removed the possibility for constant data interrogation and trend recognition to identify evolving issues. This research used end user experiences aiming to understand causes for the lack of data and knowledge on device performance and associated patient risks. This approach was used to identify existing barriers and methods for improvement.MethodsThis is a qualitative study involving semistructured interviews and surveys with clinicians (15 interviews, 39 surveys) and manufacturers (13 interview participants, 5 surveys). Multiple sources of recruiting were used. Data collected were thematically analysed. Interview results were used to design the surveys. Standards for Reporting Qualitative Research was used.ResultsMedical device use is based on personal experience rather than evidence which is scarce. Multiple barriers to reporting were identified alongside patient safety and system related aspects. Furthermore, the acceptable level of error was variable as were effects on working practice. Many workarounds have been developed to overcome problems and have become normalised in daily work. These factors were found to have a limiting impact on improvements and learning. Greater system transparency, feedback on submitted reports, a more efficient system of reporting and better communication with manufacturers were reported as some of the required improvements.ConclusionsThis study has identified numerous complex issues affecting reporting of medical device performance and their subsequent effect on patient safety and clinical staff. The focus on incidents has created many limitations to learning and development. The rich experience of end users should be appropriately used to identify system weaknesses and seek improvement methods. Better communication methods should be developed between healthcare and MedTech (Medical Technologies) industry.

Tiny hole inspection of aircraft engine nacelle in 3D point cloud via robust statistical fitting

In the aircraft manufacturing industry, drilling hole inspection is a vital task for the noise reduction ability and the aircraft structure stability. Due to the complexity of the inner surface of the engine nacelle, inspecting drilling holes of a small size and a large number is fairly challenging. In this paper, we propose a framework for automatic hole inspection on composite flat parts. The raw data of traditional 3D laser scanning usually contain considerable noise and outliers, which has a great influence on tiny hole inspection. First, to perform measurement efficiently and to get raw data of good quality, we design a measurement platform to perform automatic measurement on a preset path. Instead of applying common boundary detection methods, we design a method to detect boundary points by evaluating the boundary possibility of each point, which is more likely to detect the points on a real hole boundary than noise. Based on the characteristics of laser scanning data, we then combine an effective algebraic circle fitting method with an iterative fitting strategy and develop a robust circle fitting method. The improved circle fitting method is capable of estimating a circle closest to the ground truth. Experiments demonstrate that our fitting method performs better than other common fitting methods for comparison under both synthetic and real scanning data.

Quality of routine health facility data used for newborn indicators in low- and middle-income countries: A systematic review

BackgroundHigh-quality data are fundamental for effective monitoring of newborn morbidity and mortality, particularly in high burden low- and middle-income countries (LMIC).MethodsWe conducted a systematic review on the quality of routine health facility data used for newborn indicators in LMIC, including measures employed. Five databases were searched from inception to February 2021 for relevant observational studies (excluding case-control studies, case series, and case reports) and baseline or control group data from interventional studies, with no language limits. An adapted version (19-point scale) of the Critical Appraisal Tool to assess the Quality of Cross-Sectional Studies (AXIS) was used to assess methodological quality, and results were synthesized using descriptive analysis.ResultsFrom the 19 572 records retrieved, 34 studies in 16 LMIC countries were included. Methodological quality was high (>14/19) in 32 studies and moderate (10-14/19) in two. Studies were mostly from African (n = 30, 88.2%) and South-East Asian (n = 24, 70.6%) World Health Organization (WHO) regions, with very few from Eastern Mediterranean (n = 2, 5.9%) and Western Pacific (n = 1, 2.9%) ones. We found that only data elements used to calculate neonatal indicators had been assessed, not the indicators themselves. 41 data elements were assessed, most frequently birth outcome. 20 measures of data quality were used, most along three dimensions: 1) completeness and timeliness, 2) internal consistency, and 3) external consistency. Data completeness was very heterogeneous across 26 studies, ranging from 0%-100% in routine facility registers, 0%-100% in patient case notes, and 20%-68% in aggregate reports. One study reported on the timeliness of aggregate reports. Internal consistency ranged from 0% to 96.2% in four studies. External consistency (21 studies) varied widely in measurement and findings, with specificity (6.4%-100%), sensitivity (23.6%-97.6%), and percent agreement (24.6%-99.4%) most frequently reported.ConclusionsThis systematic review highlights a gap in the published literature on the quality of routine LMIC health facility data for newborn indicators. Robust evidence is crucial in driving data quality initiatives at national and international levels. The findings of this review indicate that good quality data collection is achievable even in high-burden LMIC settings, but more efforts are needed to ensure uniformly high data quality for neonatal indicators.

Digital Transformation Strategies Enabled by Internet of Things and Big Data Analytics: The Use-Case of Telecommunication Companies in Greece

Both Internet of Things (IoT) and Big Data Analytics (BDA) are innovations that already caused a significant disruption having a major impact on organizations. To reduce the attrition of new technology implementation, it is critical to examine the advantages of BDA and the determinants that have a detrimental or positive impact on users’ attitudes toward information systems. This article aims to evaluate the intention to use and the perceived benefits of BDA systems and IoT in the telecommunication industry. The research is based on the Technology Acceptance Model (TAM). Data were collected by 172 users and analyzed using Multivariate Regression Analysis. From our findings, we may draw some important lessons about how to increase the adoption of new technology and conventional practices while also considering a variety of diverse aspects. Users will probably use both systems if they think they will be valuable and easy to use. Regarding BDA, the good quality of data helps users see the system’s benefits, while regarding IoT, the high quality of the services is the most important thing.

Farmland change and its implications in the Three River Region of Tibet during recent 20 years.

Farmland is a key resource for safeguarding the regional food security and social stability, particularly in Tibet where the farmland is very limited due to its high altitude. With quick economic development during recent decades, farmland changes are great in China, and thus have been extensively studied. These studies generally focused on eastern regions, and seldom for Tibet due to the lack of good quality and available data. To this end, taking the Three River Region (TRR) as the case area, this study obtained 1 m spatial resolution farmland data for 2000 and 2018 by visual interpretation of the Google Earth high resolution satellite images, and then analyzed the farmland change, its driving factors and impact on grain production between 2000 and 2018. The results showed that farmland in the TRR decreased by 8.85% from 219.29 k ha in 2000 to 199.89 k ha in 2018, averagely reduced by 0.51% per year, mainly driven by the economic development, agricultural progress, urbanization, and population growth. The farmland losses largely occurred in urban areas and their surrounding counties due to urban land occupation, and caused the grain production reduced by 9.38%. To control the quick farmland losses and to ensure the regional food security of Tibet, it should strengthen the supervision on non-agricultural occupation of farmland and increase agricultural investment to improve the land productivity in the TRR.

Spatial dependency in child malnutrition across 640 districts in India: need for context-specific planning and interventions.

Child malnutrition remains a matter of concern in India as the current levels are high and the decline is slow. National Family Health Survey (NFHS-4, 2015-16) data, for the first time, provides credible, good quality data at district level on social, household and health characteristics. Techniques of spatial analysis on data in respect of 640 districts were used to identify spatial characteristics of the nutrition levels for children in the 0-60-month age group. Further, the principal component analysis (PCA) was used to identify 7 important correlates of the malnutrition out of 21 relevant components provided in the NFHS-4. The paper further uses three techniques, ordinary least squares (OLS), spatial lag model (SLM) and spatial error model (SEM) to assess the strength of correlation between the malnutrition levels and the shortlisted correlates. The use of SLM and SEM shows improvement in the strength of the association (high R-square) compared to OLS. Women's height and Iodized salt in stunting, child anaemia in wasting, women's height and child anaemia in underweight were found to be significant factors (P<0.01) along with spatial autoregressive constant. Such analysis, in combination with PCA, has shown to be more effective in prioritizing the programme interventions for tackling child malnutrition.