Access to specialized health care services among older Canadians.

Background: Canada is experiencing rapid population aging, which has a wide range of implications, including an increased need for health care services. However, very few studies have examined use of specialized health care services (e.g., visits to medical specialists, non-emergency tests, and surgeries) among older Canadians. Data and methods: Data from the Canadian Health Survey on Seniors - 2019/2020 were used to examine specialized health care service use among older Canadians. Latent class analysis was calibrated using a nationally representative sample of 39,047 Canadians aged 65 years or older to identify distinct patterns of need factors related to health care service use. Multivariable logistic regression, stratified by gender, was used to examine the association of predisposing characteristics, enabling resources, and need factors with specialized health care service use. Results: In 2019/2020, an estimated 2.6 million older Canadians (43.4%) visited medical specialists, 1.4 million (23.2%) got non-emergency tests, and 0.6 million (10.4%) had non-emergency surgeries. Among those, 15.6% reported experiencing difficulties accessing services. Women were less likely than men to have visited medical specialists and have received non-emergency tests. Lower education was consistently associated with lower odds of specialized health care service use. Individuals in the multimorbidity, high stress-multimorbidity-disability, and poor physical and mental health classes were more likely than those in the comparatively healthy class to use specialized health care services and to experience difficulties accessing them. Interpretation: Findings of this study highlight gender differences and the importance of considering multidimensional need factors - ranging from physical health to mental health to psychosocial factors - in examining use of specialized health care services.

Change in cyclone disaster vulnerability and response in coastal Bangladesh.

The number of deaths owing to tropical cyclones in Bangladesh has significantly reduced. Category 4 Cyclone Gorky in 1991 and Sidr in 2007 caused 147,000 and 4,500 deaths respectively, whereas Category 1 Cyclone Mora in 2017 resulted in six. Face-to-face interviews with 362 residents, participant observation, and focus-group discussions answer a research question about how change in coastal areas has contributed to this outcome. The study considered institutional approaches of disaster risk management through legal frameworks, administrative arrangements, cyclone preparedness activities, cyclone detection and early warning dissemination, construction of shelter centres, strengthening of various types of coastal embankments, paved roads, and pre-cyclone evacuation. The findings indicate significant improvement in house structures and design, income levels and diversification, education, awareness, individual capacity, poverty reduction, and lowering dependency on agriculture-based earning. Furthermore, the availability of mobile telephones, radio, television, and social media platforms enhanced social connectivity and greater gender equality and empowerment helped to facilitate disaster preparedness, evacuation, and response.

Immigrant status and loneliness among older Canadians.

Background: Prevalence of loneliness among Canadians has become an important concern because of its wider consequences on health and well-being. However, there are limited Canadian studies about loneliness disaggregated by gender and across various subgroups of older Canadians, particularly immigrant subgroups. Data and methods: Data from the Canadian Health Survey on Seniors (CHSS) - 2019/2020 were used to estimate the prevalence of loneliness among older Canadians in a nationally representative sample of 38,941 Canadians aged 65 and older. The association between immigrant status and loneliness was assessed using multivariable logistic regression adjusted for demographic, socioeconomic and health characteristics. Analyses were conducted for men and women combined and separately. Results: In 2019/2020, an estimated 1.1 million older Canadians (19.2%) experienced loneliness, with women having significantly higher likelihood of being lonely than men. Among men, both European and non-European immigrants were more likely to experience loneliness than the Canadian-born population. Among women, the likelihood of loneliness was higher among European immigrants than among those born in Canada. For both men and women, immigrants who migrated as adults (from ages 18 to 44) and long-term immigrants (20 years or more in Canada since immigration) were at higher risk of loneliness than the Canadian-born population. The likelihood of being lonely was higher among people living with multimorbidity or experiencing barriers to social participation. Interpretation: The findings underscore the importance of considering immigrant subgroups and gender in examining loneliness among older Canadians and when developing policies and programs to address loneliness.

Provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians.

BACKGROUND: Despite spending 11.0% of the total gross domestic product, the quality of healthcare services in Canada has received mixed reviews. We first separately examined provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians. Second, we evaluated provincial variations in the impact of not having a regular medical doctor on having unmet healthcare needs among Canadians. METHODS: We applied logistic regressions using data from the 2014 and 2017-2018 Canadian Community Health Surveys (CCHS). The total sample size for this study was 120,345 individuals aged 12 years and older: 61,240 from the 2014 CCHS and 59,105 from the 2017-2018 CCHS. RESULTS: We found significant provincial variations in not having a regular medical doctor and having unmet healthcare needs among Canadians. People in Quebec and the Territories were more likely not to have a regular medical doctor than their peers in Alberta. People in Quebec and the Territories were also more likely to have unmet healthcare needs than their counterparts in Alberta. Not having a regular medical doctor impacted whether Canadians reported having unmet healthcare needs to varying degrees across provinces. CONCLUSION: Findings from this study may contribute to designing province-specific policy interventions and inform efforts that seek to address barriers to having a regular medical doctor and reducing unmet healthcare needs among Canadians.

Study of molecular transport through a single nanopore in the membrane of a giant unilamellar vesicle using COMSOL simulation.

The antimicrobial peptide (AMP) magainin 2 induces nanopores in the lipid membranes of giant unilamellar vesicles (GUVs), as observed by the leakage of water-soluble fluorescent probes from the inside to the outside of GUVs through the pores. However, molecular transport through a single nanopore has not been investigated in detail yet and is studied in the present work by simulation. A single pore was designed in the membrane of a GUV using computer-aided design software. Molecular transport, from the outside to the inside of GUV through the nanopore, of various fluorescent probes such as calcein, Texas-Red Dextran 3000 (TRD-3k), TRD-10k and TRD-40k was then simulated. The effect of variation in GUV size (diameter) was also investigated. A single exponential growth function was fitted to the time course of the fluorescence intensity inside the GUV and the corresponding rate constant of molecular transport was calculated, which decreases with an increase in the size of fluorescent probe and also with an increase in the size of GUV. The rate constant found by simulation agrees reasonably well with reported experimental results for inside-to-outside probe leakage. Based on Fick's law of diffusion an analytical treatment is developed for the rate constant of molecular transport that supports the simulation results. These investigations contribute to a better understanding of the mechanism of pore formation using various membrane-active agents in the lipid membranes of vesicles and the biomembranes of cells.

Synthesis and Evaluation of Manganese(II)-Based Ethylenediaminetetraacetic Acid-Ethoxybenzyl Conjugate as a Highly Stable Hepatobiliary Magnetic Resonance Imaging Contrast Agent.

In this study, we designed and synthesized a highly stable manganese (Mn2+)-based hepatobiliary complex by tethering an ethoxybenzyl (EOB) moiety with an ethylenediaminetetraacetic acid (EDTA) coordination cage as an alternative to the well-established hepatobiliary gadolinium (Gd3+) chelates and evaluated its usage as a T1 hepatobiliary magnetic resonance imaging (MRI) contrast agent (CA). This new complex exhibits higher r1 relaxivity (2.3 mM-1 s-1) than clinically approved Mn2+-based hepatobiliary complex Mn-DPDP (1.6 mM-1 s-1) at 1.5 T. Mn-EDTA-EOB shows much higher kinetic inertness than that of clinically approved Gd3+-based hepatobiliary MRI CAs, such as Gd-DTPA-EOB and Gd-BOPTA. In addition, in vivo biodistribution and MRI enhancement patterns of this new Mn2+ chelate are comparable to those of Gd3+-based hepatobiliary MRI CAs. The diagnostic efficacy of the new complex was demonstrated by its enhanced tumor detection sensitivity in a liver cancer model using in vivo MRI.

REGIONAL VARIATIONS IN CHILD MARRIAGE IN BANGLADESH.

This study aimed to investigate the regional variations in the prevalence of child marriage in Bangladesh with a view to providing recommendations for division-specific policy interventions. Data from the 2011 Bangladesh Demographic and Health Survey were analysed using multivariate logistic regression. Substantial regional variations in child marriage were found in Bangladesh. Rangpur and Khulna had more than four times higher odds of child marriage than Sylhet (4.57 and 4.11 times, respectively). Barisal and Rajshahi had more than three times higher odds of child marriage than Sylhet (3.70 and 3.48 times, respectively). Chittagong and Dhaka had about two times odds of child marriage than Sylhet (1.98 and 2.67 times, respectively), even after controlling for selected socio-demographic, economic and cultural characteristics. Respondent's education, employment status, husband's education and wealth index were inversely associated with the prevalence of child marriage. The policy implications of these findings are discussed in the context of Bangladesh.

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta.

Determining the degree of high groundwater arsenic (As) and fluoride (F-) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F- concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F- contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Analyzing the impact of remittance, FDI and inflation rate on GDP: A comparative study of Bangladesh, Pakistan and Sri-Lanka using VAR and BEKK-GARCH approach.

This study examines the impact, conditional correlation and volatility spillover effect of remittances, foreign direct investment and inflation rate on GDP in Bangladesh, Pakistan, and Sri-Lanka, three Asian nations that are particularly vulnerable. While numerous studies have examined the relationship between remittances, FDI and IR on GDP but this paper approaches advanced econometric techniques to capture spillover effect and the dynamic interactions between the variables. For estimation purposes the study employs different econometric techniques such as Augmented Dickey-Fuller (ADF) test, VAR model, Granger causality tests, Impulse Response function, Variance Decomposition and BEKK-GARCH model. Bangladesh and Sri Lanka's REM, FDI and IR have no significant effects on GDP according to the VAR model. BEKK-GARCH demonstrates that three countries have both unidirectional and bidirectional transmissions of volatility, with the exception of Pakistan's REM and Bangladesh's FDI. We find that VAR model may not be adequate in capturing the complex dynamics between variables, which can be better captured by BEKK-GARCH model. Our comparison research shows how these variables affect GDP differently and similarly in each of the three nations, giving policymakers information they can use to create customized policies that encourage economic growth.

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh.

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study's results to inform evidence-based choices on water management and planning at the national level.

The 25-hydroxyvitamin D level in Kidney Transplant Recipients and Its Association With Renal Function and Other Cardio-Metabolic Risk Markers.

BACKGROUND: Vitamin D is the main hormone that plays a critical role in controlling mineral homeostasis. Transplant recipients frequently have altered levels of 25-hydroxyvitamin D (25[OH] D) and 1, 25-hydroxyvitamin (1, 25[OH] D). OBJECTIVES: To explore the status of vitamin D level in renal allograft recipients and its association with renal function and cardio-metabolic risk markers. METHODS: One hundred two adult kidney transplant recipients (KTRs) were included. Clinical history and information about transplantation and immunosuppression were recorded. Blood and urine samples were collected for relevant laboratory tests, including chronic kidney disease (CKD)-mineral and bone disorder markers (Ca, PO4, and 25[OH] D). RESULTS: The mean age was 35 ± 8 years, with a male/female ratio of 89/11%. The transplant duration was 34 ± 26 (4.5-112) months. All donors involved were living-related: fathers comprised 44%, wives 16%, sisters 13%, mothers 11%, and the others 16% (ie, brothers, sons, daughters, uncles, aunts). The immunosuppression regimen included prednisolone and tacrolimus in all, with mycophenolate mofetil in 96%. The estimated glomerular filtration rate showed CKD distribution from stage 1 to 5 in 6%, 29%, 44%, 15%, and 6%, respectively. Vitamin D level was lower in 22% (<20 ng/mL), insufficient (20-30 ng/mL) in 48%, and adequate (>30 ng/mL) in 30%. We compared different clinical and laboratory variables in 3 different vitamin D groups but found no difference in cardio-renal risk factors (P = Not Significant). Similarly, no correlation was seen between vitamin D levels and other clinical and metabolic factors. CONCLUSION: According to conventional cutoffs, the vitamin D (25[OH] D) level is inadequate in 70% of renal allograft recipients. The hormone level has no apparent association with renal function and major cardio-metabolic risk factors.

Economic viability of releasing Bt cotton in Bangladesh: An early insight.

Insect resistant genetically modified Bt cotton (containing a gene of Bacillus thuringiensis) has substantial potentiality of mounting cotton productivity. This study unveils an early insight on the economic viability of Bt cotton in Bangladesh. A total of 248 traditional cotton farmers and 8 Bt cotton experimental fields were surveyed in April 2022 for achieving the objectives. The data were analysed using descriptive statistics. Findings showed that the cost of Bt cotton production was slightly higher than that of conventional cotton. However, Bt cotton yielded a productivity increase of 0.81 t/ha. The cultivation of Bt cotton resulted in a higher net return (USD 2436/ha) compared to conventional cotton (USD 1624/ha). The results further indicated that the use of insecticides and pesticides in Bt cotton was significantly lower compared to traditional cotton, thereby contributing to the preservation of the natural environment. Overall, cultivation of Bt cotton is economically viable and may generate environmental benefits. Steps are warranted to disseminate and expand its cultivation.

Source identification and potential health risks from elevated groundwater nitrate contamination in Sundarbans coastal aquifers, India.

In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl-, the value of which is 0.71. Other factors like-As, F-, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.

Are hotspots and frequencies of heat waves changing over time? Exploring causes of heat waves in a tropical country.

Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991-2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools.

Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding.

The search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns "ensemble embeddings" of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.

Non-state initiatives on enhancing counter-trafficking of Rohingya influx in Cox's Bazar of Bangladesh.

Human trafficking is the third most lucrative form of trafficking in the world (following drugs and counterfeit goods). Multiple outbreaks of unrest between October 2016 and August 2017 in the Rakhine State of Myanmar triggered ~745,000 influxes of Rohingyas crossing into Bangladesh through the border boundaries at Teknaf and Ukhiya sub-districts of Cox's Bazar. In this regard, the media confirmed that over a thousand Rohingya people, particularly women and girls, were victims of human trafficking. This research aims to explore the underlying causes of human trafficking (HT) during emergency responses and seeks to understand how the knowledge and capacity of the refugee, local administration, and law enforcement agencies in Bangladesh can be improved in promoting counter-trafficking (CT) and safe migration processes. In order to achieve the objectives, this study reviews acts, rules, policies, and action plans of the Government of Bangladesh on the HT, CT, and safe migration processes. Then, a case study has been applied to present the ongoing CT and safe migration programs of an NGO called Young Power in Social Action (YPSA), which received funding and technical support from the International Organization of Migration (IOM) for this purpose. This study also evaluates the effectiveness of the program through conducting key informant interviews (KIIs) and focus group discussions (FGDs) with the beneficiary and non-beneficiary participants including refugees, law-enforcing agencies (LEAs), and NGOs in Teknaf and Ukhyia. Thus, this study identifies program-level strengths and weaknesses in relation to the CT and safe migration process and provides key directions on how they can be improved. It concludes that non-state actors have a significant role in preventing HT and promoting CT and safe migration for Rohingyas in Bangladesh.

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques.

Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980-2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann-Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027-2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

Assessing demographic and economic vulnerabilities to sea level rise in Bangladesh via a nighttime light-based cellular automata model.

The Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6) forecasts a sea level rise (SLR) of up to 2 m by 2100, which poses significant risks to regional geomorphology. As a country with a rapidly developing economy and substantial population, Bangladesh confronts unique challenges due to its extensive floodplains and 720 km-long Bay of Bengal coastline. This study uses nighttime light data to investigate the demographic repercussions and potential disruptions to economic clusters arising from land inundation attributable to SLR in the Bay of Bengal. By using geographical information system (GIS)-based bathtub modeling, this research scrutinizes potential risk zones under three selected shared socioeconomic pathway (SSP) scenarios. The analysis anticipates that between 0.8 and 2.8 thousand km2 of land may be inundated according to the present elevation profile, affecting 0.5-2.8 million people in Bangladesh by 2150. Moreover, artificial neural network (ANN)-based cellular automata modeling is used to determine economic clusters at risk from SLR impacts. These findings emphasize the urgency for land planners to incorporate modeling and sea inundation projections to tackle the inherent uncertainty in SLR estimations and devise effective coastal flooding mitigation strategies. This study provides valuable insights for policy development and long-term planning in coastal regions, especially for areas with a limited availability of relevant data.

Correction: Climate change in Bangladesh: Temperature and rainfall climatology of Bangladesh for 1949-2013 and its implication on rice yield.

[This corrects the article DOI: 10.1371/journal.pone.0292668.].

Climate change in Bangladesh: Temperature and rainfall climatology of Bangladesh for 1949-2013 and its implication on rice yield.

Bangladesh has been ranked as one of the world's top countries affected by climate change, particularly in terms of agricultural crop sector. The purpose of this study is to identify spatial and temporal changes and trends in long-term climate at local and national scales, as well as their implications for rice yield. In this study, Modified Mann-Kendall and Sen's slope tests were used to detect significant trends and the magnitude of changes in temperature and rainfall. The temperature and rainfall data observed and recorded at 35 meteorological stations in Bangladesh over 65-years in the time span between the years 1949 and 2013 have been used to detect these changes and trends of variation. The results show that mean annual Tmean, Tmin, and Tmax have increased significantly by 0.13°C, 0.13°C, and 0.13°C/decade, respectively. The most significant increasing trend in seasonal temperatures for the respective Tmean, Tmin, and Tmax was 0.18°C per decade (post-monsoon), 0.18°C/decade (winter), and 0.23°C/decade (post-monsoon), respectively. Furthermore, the mean annual and pre-monsoon rainfall showed a significant increasing trend at a rate of 4.20 mm and 1.35 mm/year, respectively. This paper also evaluates climate variability impacts on three major rice crops, Aus, Aman, and Boro during 1970-2013. The results suggest that crop yield variability can be explained by climate variability during Aus, Aman, and Boro seasons by 33, 25, and 16%, respectively. Maximum temperature significantly affected the Aus and Aman crop yield, whereas rainfall significantly affected all rice crops' yield. This study sheds light on sustainable agriculture in the context of climate change, which all relevant authorities should investigate in order to examine climate-resilient, high-yield crop cultivation.