Representative Cluster Research Articles

Research on energy sharing ability and adaptability of building Complex: A case study with smart community in Japan

Energy sharing within building complexes depends greatly on the complex load adaptability and policy support. However, most building complex microgrid research concentrates on configuration and operation optimization, rather than energy contributions from different users. The objective of this study is to analyze energy sharing ability and load adaptability for a building complex comprising multiple building types, under transaction liberalization restrictions in different energy markets. The specific research for this study is a smart community in Kitakyushu, Japan, with 49 buildings of different types. The complex's microgrid system in this study comprises photovoltaic, battery, and vehicle to grid services. We obtained six representative typical clusters by reducing the 49 buildings’ dimensionality using principal components and clustering, then Monte Carlo simulation to predict permissible discharge capacities for vehicle to grid (V2G) services. We propose three demand-side liberalization scenarios: self-use, photovoltaic feed-in tariffs (FITs), and free trade; optimizing system configuration under different scenarios using a genetic algorithm. Results confirm that shopping malls achieve best economic benefits with self-use scenario; whereas office building adaptability is more significant for the other scenarios. These results will contribute to user selection and system design for building complex microgrid systems.

Characterization of Clostridioides difficile Strains from an Outbreak Using MALDI-TOF Mass Spectrometry.

The epidemiology of Clostridioides difficile infection (CDI) has changed over the last two decades, due to the emergence of C. difficile strains with clinical relevance and responsible for nosocomial outbreaks with severe outcomes. This study reports an outbreak occurred in a Long-term Care Unit from February to March 2022 and tracked by using a Matrix-Assisted Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) typing approach (T-MALDI); subsequently, a characterization of the toxigenic and antimicrobial susceptibility profiles of the C. difficile isolates was performed. A total of 143 faecal samples belonging to 112 patients was evaluated and C. difficile DNA was detected in 51 samples (46 patients). Twenty-nine C. difficile isolates were obtained, and three different clusters were revealed by T-MALDI. The most representative cluster accounted 22 strains and was considered to be epidemic, in agreement with PCR-Ribotyping. Such epidemic strains were susceptible to vancomycin (MIC ≤ 0.5 mg/mL) and metronidazole (MIC ≤ 1 mg/mL), but not to moxifloxacin (MIC > 32 mg/mL). Moreover, they produced only the Toxin A and, additionally, the binary toxin. To our knowledge, this is the first reported outbreak referable to a tcdA+/tcdB-/cdt+ genotypic profile. In light of these results, T-MALDI is a valid and rapid approach for discovering and tracking outbreaks.

Silver Ion-Induced Formation of Unprecedented Thorium Nonamer Clusters via Lacuna-Construction Strategy

Silver Ion-Induced Formation of Unprecedented Thorium Nonamer Clusters via Lacuna-Construction Strategy

An evidence accumulation based block diagonal cluster model for intent recognition from EEG

Most of the probabilistic mixture models perform clustering by observing the eigenvectors of the data sample and these models rely on the layout of features. Clustering ensemble based on similarity matrices avoids complex processing of samples by only accessing basic clusters. However, while there are many literatures on the probability mixture model for clustering, there is almost no study focusing on applying the similarity matrix to the probability mixture model. Therefore, a new clustering method called block clustering structure of evidence accumulation matrix (BEAM) is proposed in this study by combining the clustering ensemble and the probability mixture model. Specifically, evidence accumulation (EA) is developed to obtain a similarity matrix of samples. The interpretability of the similarity matrix can be improved due to sample-based similarity measures, and then the diagonal block model is designed to identify representative block cluster structures from the similarity matrix. The proposed method has been evaluated on the BCI Competition IV Data set 1 and the block-diagonal structure of the similarity matrix is discovered, which ensures high similarity within the same cluster and large separation between the clusters. In addition, the Davies-Bouldin index (DBI) and adjusted rand index (ARI) are used to evaluate BEAM performance. The results show that the proposed method is superior to the state-of-the-art approaches.

Differences in levels of E. coli contamination of point of use drinking water in Bangladesh.

This study aimed to quantify the inequalities and identify the associated factors of the UN sustainable development goal (SDG) targets in relation to safe drinking water. The concentration of the gut bacterium Escherichia coli in drinking water at the point of use (POU) and other information were extracted from the latest wave of the nationally representative Bangladesh Multiple Indicator Cluster Survey (MICS 2019). Bivariate and multivariable multinomial logistic regression models were used to identify potential predictors of contamination, whereas, classification trees were used to determine specific combinations of background characteristics with significantly higher rates of contamination. A higher risk of contamination from drinking water was observed for households categorized as middle or low wealth who collected water from sources with higher concentrations of E. coli. Treatment of drinking water significantly reduced the risk of higher levels of contamination, whereas owning a pet was significantly associated with recontamination. Regional differences in the concentrations of E. coli present in drinking water were also observed. Interventions in relation to water sources should emphasize reducing the level of E. coli contamination. Our results may help in developing effective policies for reducing diarrheal diseases by reducing water contamination risks.

Temporal Clustering of the Causes of Death for Mortality Modelling

Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates better approaches in tracking mortality changes, for instance, using the causes of deaths features. This paper aims to determine temporal homogeneous clusters using unsupervised learning, a clustering approach to group causes of death based on (dis)similarity measures to set representative clusters in detection and monitoring death trends. The causes of death dataset were derived from the World Health Organization, Global Health Estimates for males and females, from 2000 to 2019, for Kenya. A hierarchical agglomerative clustering technique was implemented with modified Dynamic Time Warping distance criteria. Between 6 and 14 clusters were optimally achieved for both males and females. Using visualisations, principal clusters were detected. Over time, the causes of death trends of these clusters have demonstrated a correlated association with mortality and longevity rates, rationalizing why insurance and pension offices may include this approach as a preliminary step to undertake mortality and longevity modelling.

Does the composition in PtGe clusters play any role in fighting CO poisoning?

The high catalytic activity of Pt is accompanied by a high affinity for CO, making it extremely susceptible to poisoning. Such CO poisoning limits the use of proton exchange membrane fuel cells. In this work, using global minima search techniques and exhaustive electronic structure characterization, the dopant concentration is pinpointed as a crucial factor to improve the CO tolerance of Pt catalysts. By investigating the PtGe nanoclusters of different sizes and compositions, we found that, for those clusters with roughly the same amount of Pt and Ge, the binding to CO is weakened significantly. The uniqueness of the PtGe equimolar clusters is traced down to the electronic effects. The strong covalency and electrostatic stabilization arising from the advantageous Pt-Ge mixing make the equimolar clusters highly resistant toward CO poisoning and therefore more durable. Importantly, the novel catalysts not only are more resistant to deactivation but also remain catalytically active toward hydrogen oxidation. Representative clusters are additionally deposited on graphene with a pentagon-octagon-pentagon (5-8-5) reconstructed divacancy. The remarkable results of free-standing clusters hold true for surface mounted clusters, in which the interaction with CO is dramatically weakened for those compounds with a Pt:Ge ratio of 1:1. Our results demonstrate that Ge can be a promising alloying agent to mitigate the deactivation of Pt and that the dopant concentration is a critical factor in the design of advanced catalysts.

Foods Served in Child Care Programs Participating in the Child and Adult Care Food Program and Alignment with Program Meal Patterns

ObjectiveAssess Child and Adult Care Food Program (CACFP) program compliance with meal component requirements for children aged 3-5 years by program type, and describe foods and beverages most commonly served. DesignCross-sectional analysis of 1-week menu surveys during winter/spring 2017. SettingUS CACFP-participating child care programs. ParticipantsNationally representative multistage cluster sample of 664 programs: 222 child care centers, 247 Head Start programs, 195 family child care homes. Main Outcome Measure(s)Percentage of meals including required components; frequently served foods and beverages. AnalysisMean percentages; 2-tailed t tests; alpha = 0.05 significance level. ResultsMost breakfasts (97%), lunches (88%), and afternoon snacks (97%) included all required CACFP meal components. Most breakfasts included fruits (96%), but not vegetables; 16% included a meat/meat alternate. Most lunches (81%) included both fruits and vegetables. Afternoon snacks were mostly grains/breads (80%) and fruits (57%). Most frequently served foods included 1% unflavored milk and fresh fruits such as apples and bananas. Most menus limited juice, offered low-sugar cereal, and did not include flavored milk; very few menus included noncreditable foods with added sugar. Conclusions and ImplicationsMost CACFP meals provided required components, but there is room for improvement, particularly for increasing vegetables served and limiting foods high in added sugar and fat.

Socioeconomic and demographic correlates of child nutritional status in Nepal: an investigation of heterogeneous effects using quantile regression

BackgroundChild undernutrition continues to be a major public health problem in many countries, including Nepal. The repercussions of undernutrition are not only limited to the affected children and families but also transcend to the national and global economy. Earlier studies from Nepal have predominantly used either ordinary least squares (OLS) regression or binary regression to analyse the socioeconomic and demographic correlates of the nutritional outcome. In this study, quantile regression was used to understand a complete and more precise estimate of the effects of the covariates on the child nutritional status.MethodsThis study was based on the most recent nationally representative Nepal Multiple Indicator Cluster Survey (MICS) 2019. Height-for-age z scores (HAZ) were used as an indicator for assessing the nutritional status of under-five children. Quantile regression was used to examine the heterogeneous association of covariates with conditional HAZ distribution across the different quantiles (0.10, 0.30, 0.50, 0.85). As a comparison, the effects of covariates at conditional mean of HAZ using OLS regression was also analysed. The graphs were plotted to visualize the changes in the coefficients for each regressor across the entire conditional HAZ distribution.ResultsAge of children, sex of children, province and wealth had a consistent and statistically significant association with HAZ in both OLS and quantile regression. Improved toilet facility was positively correlated with HAZ at the lower tails (tenth and thirtieth percentiles). Ethnicity (Janajati and Newer) was positively correlated with HAZ at the lower tail (thirtieth percentile) and mean (OLS regression). Maternal education was a significant predictor of improved height-for-age across conditional quantiles, except at the tenth percentile. Maternal age, number of under-five children in household, number of household members, and improved source of drinking water showed heterogeneous effects across different quantiles of conditional HAZ distribution.ConclusionUse of quantile regression approach showed that the effect of different factors differed across the conditional distribution of HAZ. Policymakers should consider the heterogeneous effect of different factors on HAZ so that the targeted intervention could be implemented to maximize the nutritional benefits to children.

Micro-Coevolution of Genetics Rather Than Diet With Enterotype in Pigs.

Based on the characteristic of low diarrhea in native Chinese breeds, we introduce the enterotype model for piglets, which is a new perspective to decipher the colonization and the transition of the gut microbiota among various pig breeds. After eliminating environmental influences represented by diet, the microbiota, mainly shaped by host genetics, is focused. Three representative enterotype clusters were identified, which were represented by Bacteroides, Streptococcus, and Lactobacillus. Chinese native breeds were distributed in enterotype 1 (E1) and E3, which collectively drove the diversification and functionality of the microbial community of various Chinese pig breeds. Next, the Lactobacillus reuteri (L. reuteri), which is the representative strain of E3, was specifically isolated in all three enterotypes. The excellent stress-resistance of L. reuteri-E3 not only highlighted the stronger disease resistance of Chinese breeds but also had a great potential to intervene in weaned piglet diseases. Enterotype classification based on host genetics is much more deterministic and predictable, clarifying the driver of the host-microbiome dynamics and constructing the picture of the micro-coevolution of human host genetics with the gut microbiome.

Discovery and Characterization of Rubrinodin Provide Clues into the Evolution of Lasso Peptides.

Lasso peptides are unique natural products that comprise a class of ribosomally synthesized and post-translationally modified peptides. Their defining three-dimensional structure is a lariat knot, in which the C-terminal tail is threaded through a macrolactam ring formed between the N-terminal amino group and an Asp or Glu side chain (i.e., an isopeptide bond). Recent genome mining strategies have revealed various types of lasso peptide biosynthetic gene clusters and have thus redefined the known chemical space of lasso peptides. To date, over 20 different types of these gene clusters have been discovered, including several different clades from Proteobacteria. Despite the diverse architectures of these gene clusters, which may or may not encode various tailoring enzymes, most currently known lasso peptides are synthesized by two discrete clades defined by the presence of an ATP-binding cassette transporter or its absence and (sometimes) concurrent appearance of an isopeptidase, raising questions about their evolutionary history. Herein, we discovered and characterized the lasso peptide rubrinodin, which is assembled by a gene cluster encoding both an ATP-binding cassette transporter and an isopeptidase. Our bioinformatics analyses of this and other representative cluster types provided new clues into the evolutionary history of lasso peptides. Furthermore, our structural and biochemical investigations of rubrinodin permitted the conversion of this thermolabile lasso peptide into a more thermostable scaffold.

Extracting Land Use Change Patterns of Rural Town Settlements with Sequence Alignment Method

Understanding land use change patterns of rural town settlements (RTSs) is crucial for rural and small-town planning; however, few studies have explored pattern mining approaches to RTS trajectory analysis. In this study, we adopted a novel method by building sequence alignment method (SAM) to detect representative trajectory clusters of land use change of 1158 RTSs in seven waves from 1980 to 2015 in Guangdong, China. The results suggest that there are 10 clusters of RTSs with varying trajectories of land use change, implying their differences in the development processes and underlying socioeconomic, demographical, and institutional factors. A spatial distribution map of RTSs shows that stable cultivated ecological and stable ecologically dominant RTSs are distributed in the northern, eastern, and western parts of Guangdong, whereas stable rural construction and stable mixed construction RTSs are mostly located around the provincial boundary. Notably, 73% of the RTSs that have undergone changes in land use types are located in the Pearl River Delta (PRD), including urbanized and agricultural upgraded RTSs. The analysis presented here summarizes the driving forces of the spatial evolution of RTSs, including the location, landforms, industries, and policy factors. This study provides dynamic policy implications to understand longitudinal and sequential spatial restructuring and regional coordinated development in the fast-growing PRD area.

Historically understanding the spatial distributions of particle surface area concentrations over China estimated using a non-parametric machine learning method

A non-parametric ensemble model was proposed to estimate the long-term (2015–2019) particle surface area concentrations (SA) over China for the first time on basis of a vilification dataset of measured particle number size distribution. This ensemble model showed excellent cross-validation R2 value (CV R2 = 0.83) as well as a relatively low root-mean-square error (RMSE = 195.0 μm2/cm3). No matter in which year, considerable spatial heterogeneity of SA was found over China with higher SA in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Middle Lower Reaches of Yangtze River (MLYR). From 2015 to 2019, SA significantly decreased in representative city clusters. The reduction rates were 140.1 μm2·cm−3·a−1 in BTH, 110.7 μm2·cm−3·a−1 in Pearl River Delta (PRD), 105.2 μm2·cm−3·a−1 in YRD, and 92.4 μm2·cm−3·a−1 in Sichuan Basin (SCB), respectively. Even though such quick reduction, high SA (ranged from ~800 μm2/cm3 to ~1750 μm2/cm3) during the heavy pollution period (PM2.5 > 75 μg/m3) still existed in the above-mentioned city clusters and may provide rich reaction vessels for multiphase chemistry. A dichotomy of enhanced annual 4th maximum daily 8-h average O3 concentrations (4MDA8 O3) and decreased SA during summertime was found in Shanghai, a representative city of YRD. In Chengdu (SCB), increased 4MDA8 O3 concentration was associated with a synchronous increase of SA from 2017 to 2019. Differently, 4MDA8 O3 concentrations enhanced in Beijing (BTH) and Guangzhou (PRD), while not significant for SA before 2018. This work will greatly deepen our understanding of the historical variation and spatial distributions of SA over China.

Prevalence and long-term prognostic implications of prolonged QRS duration in left ventricular hypertrophy: a population-based observational cohort study

ObjectivesECG left ventricular hypertrophy (ECG-LVH) has been associated with left ventricular dysfunction and adverse prognosis, but little is known about the prevalence and prognostic significance of different levels of QRS...

Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data

Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a method that is used to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We further performed gene set enrichment analysis to evaluate the proposed method’s performance. As such, our results show that modified locally linear embedding combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different datasets.

Pharmacoinformatic study of inhibitory potentials of selected flavonoids against papain-like protease and 3-chymotrypsin-like protease of SARS-CoV-2

BackgroundInhibition of papain-like protease (PLpro) and 3-chymotrypsin-like protease (3CLpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is projected to terminate its replication. Hence, these proteases represent viable therapeutic targets.MethodsSixty-one flavonoids with reported activities against other RNA viruses were selected and docked in PLpro and 3CLpro. Flavonoids with better binding energies compared to reference inhibitors (lopinavir and ritonavir) in their interaction with PLpro and 3CLpro were selected for drug-likeness and ADMET analysis. The best representative flavonoid for each protease from the ADMET filtering analysis was subjected to molecular dynamics simulations (MDS) and clustering analysis of the trajectory files.ResultsLicorice, ugonin M, procyanidin, silymarin, and gallocatechin gallate had better binding energies (-11.8, -10.1, -9.8, -9.7 and -9.6 kcal/mol respectively) with PLpro compared to lopinavir and ritonavir (-9.1 and -8.5 kcal/mol respectively). Also, isonymphaeol B, baicalin, abyssinone II, tomentin A, and apigetrin had better binding energies (-8.7, -8.3, -8.2, -8.1, and -8.1 kcal/mol respectively) with 3CLpro compared to lopinavir and ritonavir (-7.3 and -7.1 kcal/mol respectively). These flavonoids interacted with the proteases via hydrogen and non-hydrogen bonding. Of these flavonoids, silymarin and isonymphaeol B demonstrated most favourable combination of attributes in terms of binding energies, compliance with Lipinski rule for drug-likeness and favourable pharmacokinetics in silico. These two flavonoids exhibited appreciable degree of structural stability, maintaining strong interaction with residues in the different representative clusters selected during the MDS run.ConclusionSilymarin and isonymphaeol B are proposed for further studies as compounds with potential activities against SARS-CoV-2.

Measuring Group Separability in Geometrical Space for Evaluation of Pattern Recognition and Dimension Reduction Algorithms

Evaluating group separability is fundamental to pattern recognition. A plethora of dimension reduction (DR) algorithms has been developed to reveal the emergence of geometrical patterns in a low-dimensional space, where high-dimensional sample similarities are approximated by geometrical distances. However, statistical measures to evaluate the group separability attained by DR representations are missing. Traditional cluster validity indices (CVIs) might be applied in this context, but they present multiple limitations because they are not specifically tailored for DR. Here, we introduce a new rationale called projection separability (PS), which provides a methodology expressly designed to assess the group separability of data samples in a DR geometrical space. Using this rationale, we implemented a new class of indices named projection separability indices (PSIs) based on four statistical measures: Mann-Whitney U-test p-value, Area Under the ROC-Curve, Area Under the Precision-Recall Curve, and Matthews Correlation Coefficient. The PSIs were compared to six representative cluster validity indices and one geometrical separability index using seven nonlinear datasets and six different DR algorithms. The results provide evidence that the implemented statistical-based measures designed on the basis of the PS rationale are more accurate than the other indices and can be adopted not only for evaluating and comparing group separability of DR results but also for fine-tuning DR algorithms’ hyperparameters. Finally, we introduce a second methodological innovation termed trustworthiness, a statistical evaluation that accounts for separability uncertainty and associates to the measure of each index a p-value that expresses the significance level in comparison to a null model.

Electrodes for Paracetamol Sensing Modified with Bismuth Oxide and Oxynitrate Heterostructures: An Experimental and Computational Study

In this work, novel platforms for paracetamol sensing were developed by the deposition of Bi2O3, Bi5O7NO3 and their heterostructures onto screen-printed carbon-paste electrodes. An easy and scalable solid state synthesis route was employed, and by setting the calcination temperatures at 500 °C and 525 °C we induced the formation of heterostructures of Bi2O3 and Bi5O7NO3. Cyclic voltammetry measurements highlighted that the heterostructure produced at 500 °C provided a significant enhancement in performance compared to the monophases of Bi2O and Bi5O7NO3, respectively. That heterostructure showed a mean peak-to-peak separation Ep of 411 mV and a sensitivity increment of up to 70% compared to bare electrodes. A computational study was also performed in order to evaluate the geometrical and kinetic parameters of representative clusters of bismuth oxide and subnitrate when they interact with paracetamol.

Analysis of Hydrogeochemical Characteristics of Tunnel Groundwater Based on Multivariate Statistical Technology

Following tunnel excavation, which is influenced by hydraulic fracturing and geological structure, a series of hydrochemical reactions occur in the karst aquifer, which has a significant impact on groundwater hydrology and the earth process. Based on five sets of 38 samples collected in the Tongzi Tunnel in 2020 and 2021, the main geochemical processes and water quality conditions of the karst aquifer system during tunnel construction were revealed by multivariate statistical analysis and graphical methods. The results showed that water-rock action is the main mechanism controlling groundwater chemistry in the study area; HCO3-, Ca2+, and Mg2+ are associated with the widely distributed carbonate rocks in the study area. SO42- is derived from gypsum and sulfate rocks and special strata, which are another important source of Ca2+. Sodium-containing silicates and reverse cation exchange as the causal mechanisms of Na+ whereas F- is derived from fluorite. According to the mineral saturation index calculations, the dissolution and precipitation of minerals such as alum, gypsum, calcite, dolomite, and salt rock have an important influence on the main chemical components in water. The 38 samples were subjected to cluster analysis, and the results could be classified into seven categories. The representative clusters 1, 3, and 5 were selected for principal component analysis. Clusters 1 and 5 of groundwater represent weathering, dissolution, and ion exchange of carbonate and sulfate rocks and are closely related to the lithologic limestone, limestone intercalated with carbonaceous mudstone, carbonaceous mudstone, and coal-measure strata in the aquifer. Cluster 3 is dominated by upper surface river water and characterizes the geochemistry in natural water bodies dominated by the dissolution of carbonate, sulfate, and salt rocks. Finally, groundwater quality is mostly found in Class IV, with NO3- and F- being the main contaminants in the water.

Predictors of wealth-related inequality in institutional delivery: a decomposition analysis using Nepal multiple Indicator cluster survey (MICS) 2019

BackgroundInequality in maternal healthcare use is a major concern for low-and middle-income countries (LMICs). Maternal health indicators at the national level have markedly improved in the last couple of decades in Nepal. However, the progress is not uniform across different population sub-groups. This study aims to identify the determinants of institutional delivery, measure wealth-related inequality, and examine the key components that explain the inequality.MethodsMost recent nationally representative Multiple Indicator Cluster Survey (MICS) 2019 was used to extract data about married women (15-49 years) with a live birth within two years preceding the survey. Logistic regression models were employed to assess the association of independent variables with the institutional delivery. The concentration curve (CC) and concentration index (CIX) were used to analyze the inequality in institutional delivery. Wealth index scores were used as a socio-economic variable to rank households. Decomposition was performed to identify the determinants that explain socio-economic inequality.ResultsThe socio-economic status of households to which women belong was a significant predictor of institutional delivery, along with age, parity, four or more ANC visits, education status of women, area of residence, sex of household head, religious belief, and province. The concentration curve was below the line of equality and the relative concentration index (CIX) was 0.097 (p < 0.001), meaning the institutional delivery was disproportionately higher among women from wealthy groups. The decomposition analysis showed the following variables as the most significant contributor to the inequality: wealth status of women (53.20%), education of women (17.02%), residence (8.64%) and ANC visit (6.84%).ConclusionsTo reduce the existing socio-economic inequality in institutional delivery, health policies and strategies should focus more on poorest and poor quintiles of the population. The strategies should also focus on raising the education level of women especially from the rural and relatively backward province (Province 2). Increasing antenatal care (ANC) coverage through outreach campaigns is likely to increase facility-based delivery and decrease inequality. Monitoring of healthcare indicators at different sub-population levels (for example wealth, residence, province) is key to ensure equitable improvement in health status and achieve universal health coverage (UHC).