Search Results (82)

Background: The 2022 Pakistan floods devastated healthcare access for pregnant women in already impoverished areas in Sindh province. This study examines how Cash for Health assistance (CH) of USD 112 alleviated financial burdens and improved maternal health outcomes and resilience, bridging a critical literature gap on cash effectiveness in humanitarian crises. Methodology: This study used a mixed-methods approach to assess the CH assistance intervention for families of pregnant/lactating women in flood-affected rural Sindh, Pakistan. A pre-post quantitative analysis of baseline (May–June 2024) and endline (August–November 2024) survey data in ~100 villages (Jamshoro/Sehwan) examined changes in healthcare access, expenditure, and preferences using t-tests, proportion tests, and multivariable regression. Concurrently, five qualitative case studies from key informant interviews provided thematic content analysis, triangulating findings on economic, health, and social impacts. Results: Respondents predominantly had low literacy rates and were from households of daily wage laborers in vulnerable, flood-affected areas. While income and education remained low, instances of forgone care due to financial barriers increased (68% to 97%, p < 0.001). CH significantly improved healthcare access (58% to 98%, p < 0.001). Access to regular physicians (20% to 69%) and private facilities (10% to 41%) notably expanded. Healthcare expenditure significantly increased from USD 9.3 to USD 25, with a shift in spending preference towards medication, consultations, and diagnostics. CH also significantly improved food security (21% to 97%), meal frequency, and overall household stability, including reducing domestic violence. Qualitative data emphasized pre-existing vulnerabilities and CH’s role in addressing health, nutrition, and psychosocial needs. Conclusions: CH significantly improved healthcare access and reduced financial burdens for vulnerable pregnant women post-disaster. However, a sustainable impact requires integrated “cash plus” models, combining financial aid with stronger health systems, psychosocial support, and literacy for long-term resilience. Full article

Climate variability and extreme weather events, particularly flooding, pose growing threats to agricultural productivity worldwide, including in Pakistan. Traditional crop damage assessments during flood events have relied on field surveys, which are often time-intensive and spatially limited. Recent advancements in remote sensing technologies now allow for rapid and large-scale estimation of flood-induced agricultural damage. This study assesses agricultural damage from two recent extreme flood events in Pakistan, integrating crop condition and flood intensity metrics. We present remote sensing-based case studies that employ an interdisciplinary approach, using Moderate Resolution Imaging Spectroradiometer (MODIS), Sentinel-1, and Sentinel-2 imagery along with crop data. Our results show that flood timing, crop stage, and inundation duration were the most influential factors in determining crop loss. We determined that Northern Sindh province and areas along the Indus River and its tributaries are highly vulnerable to flooding, resulting in extensive damage to infrastructure, crops, and loss of lives during flood events in 2010 and 2022, followed by Punjab, Balochistan, and Khyber Pakhtunkhwa. Remote sensing-derived damage estimates were closely aligned with post-event ground reports, validating the approach. Full article

Background: The current polio epidemiology in Pakistan poses a unique challenge for global eradication, with polio transmission dynamics influenced by regional variations in immunity and disparities in immunization coverage. This study assesses the immunity level for all three poliovirus types among children aged 6–11 months in polio high-risk regions of Pakistan. Methods: Four consecutive rounds of cross-sectional serological surveys were conducted in polio high-risk areas of Pakistan between November 2016 and October 2023. Twelve high-risk areas were covered in the first three rounds of the survey, while 44 high-risk areas were covered in the fourth round. 25 clusters from each geographical stratum were selected utilizing probability proportional to size. Results: Across the four rounds of the survey, 32,907 children aged 6–11 months from 2084 clusters and 32,371 households were covered. Comparative analysis across the survey rounds showed that seroprevalence of poliovirus type 1 was high in provinces (>95%), albeit consistently lower in Balochistan (going down to 89.7% in Round 4). Type 2 seroprevalence was significantly lower and more heterogeneous, from 34.6% in Sindh to 83.4% in Punjab, with sharp declines by round 4, particularly in Balochistan (40.4%). Type 3 seroprevalence was overall high (>94% in Punjab, Sindh, and KPK) but dropped in the last round, while Balochistan exhibited continually lower immunity (81.1%). Conclusions: The findings reflect the variations in population immunity to poliovirus in the country, with notable fluctuations over the years. The gaps in type 2 immunity over time and consistently lowest in Balochistan highlight the need for continued monitoring of immunity levels and adaptable vaccination strategies. Full article

To counter the high unmet need for family planning in urban areas of Sindh province, Pakistan, Greenstar Social Marketing began implementation of The Challenge Initiative (TCI) in collaboration with the government departments of Population Welfare and Health in eight urban districts of Sindh province. This study aimed to assess the effectiveness of TCI’s Community Health Volunteers (CHVs) on public sector service provision of family planning services in eight urban districts of Sindh province, Pakistan. The Contraceptive Logistics Management Information System (cLMIS) and District Health Information System 2 (DHIS2) were used to obtain monthly contraceptive data from June 2022 to December 2024. CHVs began implementation at different time points in each district, starting from January 2023 to October 2023, when CHVs became operational in all eight districts. Descriptive statistics and two-sample t-tests were used for data analysis. CHVs significantly improved family planning service provision, particularly for short- and long-acting methods at the facility level, with greater change observed in Department of Health facilities. This study provides preliminary evidence of the effectiveness of CHVs in increasing public sector service provision of contraceptives, particularly for Department of Health facilities. CHVs bridge the gap between the community and the facility, particularly in areas uncovered by the government’s existing mobilization staff. Full article

Background: This study aimed to analyze the patterns and underlying reasons associated with population movement across Sindh, Pakistan. Methods: Cross-sectional surveys were conducted in response to the detection of WPV1 in various districts in Sindh province, where genetic linkages with poliovirus isolates in Karachi had been identified. The surveys targeted union councils (UCs) contributing sewage to the environmental sample collection sites where WPV1 was detected. Results: In the Karachi division a total of 1392 participants were interviewed, and outside Karachi 1471 participants were included. A significantly higher proportion of female participants were interviewed in Karachi (n = 72, 55.0%) compared to other divisions of Sindh (n = 794, 45.0%) (p < 0.001). Linguistic distribution varied significantly between regions, with Pashto speakers predominating in Karachi (n = 336, 86.4%), and Sindhi in other divisions (n = 501, 79.4%) (p < 0.001). OPV coverage exceeded 90% across all districts, and over 85% of children received RI vaccines. Travel patterns also differed significantly; participants from Karachi (n = 686, 44.2%) were less likely to report travel compared to other divisions (n = 865, 55.8%), who frequently traveled for family events, business, or employment (p < 0.001). Conclusions: It is critical to redefine high-risk populations annually based on updated mobility data, social survey analyses, and virus detection via surveillance to better identify and reach unvaccinated children in the Pakistan polio program. In addition, strategically placed PTPs along both formal and informal travel corridors based on an updated risk framework will enhance vaccination, thereby reducing the risk of virus spread. Full article

As critical infrastructure for flood control and disaster mitigation, the completeness of a dam spatial database directly impacts regional emergency disaster response. However, existing dam data in some developing countries suffer from severe gaps and outdated information, particularly concerning small- and medium-sized dams, hindering rapid response during disasters. There is an urgent need to improve the physical dam database and implement dynamic monitoring. Yet, current remote sensing identification methods face limitations, including a lack of diverse dam samples, limited analysis of geographical factors, and low efficiency in full-image processing, making it difficult to efficiently enhance dam databases. To address these issues, this study proposes a dam extraction framework integrating comprehensive geographical factor analysis with deep learning detection, validated in Sindh Province, Pakistan. Firstly, multiple geographical factors were fused using the Random Forest algorithm to generate a dam existence probability map. High-probability candidate areas were delineated using dynamic threshold segmentation (precision: 0.90, recall: 0.76, AUC: 0.86). Subsequently, OpenStreetMap (OSM) water body data excluded non-dam potential areas, further narrowing the candidate areas. Finally, a dam image dataset was constructed to train a dam identification model based on YOLOv11, achieving an mAP50 of 0.85. This trained model was then applied to high-resolution remote sensing imagery of the candidate areas for precise identification. Ultimately, 16 previously unrecorded small and medium-sized dams were identified in Sindh Province, enhancing its dam location database. Experiments demonstrate that this method, through the synergistic optimization of geographical constraints and deep learning, significantly improves the efficiency and reliability of dam identification. It provides high-precision data support for dam disaster emergency response and water resource management, exhibiting strong practical utility and regional scalability. Full article

Meteorological variables play a significant role in the transmission of viruses such as influenza and the coronavirus pandemic (COVID-19). Previous studies have identified the relationship between changes in meteorological variables, humidity, rainfall, and temperature, and the infection rate of COVID-19 at the national level in Pakistan. However, the current study applied the logistic regression analysis technique to determine such a relationship on a more detailed scale, that is, subnational levels in addition to the national level in Pakistan, using a long-term analysis of two years of COVID-19 data. At the subnational level, the logistic regression analysis technique was applied, with infection rate as the predictive variable. The results showed an increase in the infection rate of COVID-19 with increasing humidity levels. In contrast, an increase in temperature has slowed the spread of COVID-19 cases at both the national and subnational levels. The minimum temperature was statistically significant (p < 0.001) for provinces, KPK and Sindh. Also, two federal territories, AJK and Islamabad, showed statistically significant p-values. At the national level, both maximum temperature and humidity showed such values that is, p < 0.001. We believe that this is the first study conducted in Pakistan to explore the direct and indirect relationship between variables such as temperature (min and max), humidity, and rainfall as predictive parameters for COVID-19 infection rates at a detailed level. The pattern observed in this study can help us predict the future spread of COVID-19, subject to climatic parameters in Pakistan at both the national and subnational levels. Full article

Background/Objective: Early-onset diabetes (EOD), diagnosed at ≤35 years, is a growing public health crisis in low- and middle-income countries, including Pakistan. Identifying modifiable and non-modifiable risk factors is critical for developing effective prevention strategies. This study aimed to investigate the risk factors associated with EOD in Sindh, Pakistan, focusing on genetic, lifestyle, and metabolic determinants. Methods: A multicenter cross-sectional study was conducted across diabetic clinics in Sindh, with primary data collection at Baqai Institute of Diabetology and Endocrinology (Karachi, Pakistan) and secondary sites in Hyderabad, Larkana, and Sukkur. Following institutional ethical approval and informed consent, we enrolled 754 individuals (type 1 and type 2 diabetes, age at diagnosis: 15–35 years). Data on anthropometric, clinical, biochemical, and lifestyle parameters were collected via structured questionnaires. Statistical analyses included Pearson’s Chi Square tests and multivariate logistic regression in determining associations. Results: Logistic regression revealed key predictors of early-onset diabetes (EOD). A two-generation diabetes family history showed a strong association (aOR:1.86, 1.12–3.43). Significant lifestyle risks included physical inactivity (OR:1.40, 1.03–1.90), frequent sugary beverage intake (OR:1.93, 1.89–1.98), and abnormal sleep duration (<6 h: OR:1.58, 1.04–2.40; >8 h: OR:1.86, 1.21–2.85). Hypertension was a major metabolic predictor (elevated BP: OR:1.79, 1.28–1.54; Stage I: OR:1.81, 1.34–1.77). Cardiovascular disease and uncontrolled fasting glucose lost significance after adjustment, indicating confounding effects. Conclusions: This study highlights familial predisposition, sedentary behavior, poor diet, sleep disturbances, and hypertension as key contributors to EOD in young Pakistani adults. Early screening and targeted lifestyle interventions are urgently needed to mitigate this escalating epidemic. Full article

Degenerative and developmental eye disorders, including inherited retinal dystrophies (IRDs), anophthalmia, and congenital cataracts arise from genetic mutations, causing progressive vision loss or congenital structural abnormalities. IRDs include a group of rare, genetically, and clinically heterogeneous retinal diseases. It is caused by variations in at least 324 genes, affecting numerous retinal regions. In addition to IRDs, other developmental eye disorders such as anophthalmia and congenital cataracts also have a strong genetic basis. Autosomal recessive IRDs, anophthalmia, and congenital cataracts are common in consanguineous populations. In many endogamous populations, including those in Pakistan, a significant proportion of IRD and anophthalmia cases remain genetically undiagnosed. The present study investigated the variations in IRDs, anophthalmia, and congenital cataracts genes in 50 affected families. These unrelated consanguineous families were recruited from the different provinces of Pakistan including Punjab, Khyber Pakhtoon Khwa, Sindh, Gilgit Baltistan, and Azad Kashmir. Whole exome sequencing (WES) was conducted for the proband of each family. An in-house customized pipeline examined the data, and bioinformatics analysis predicted the pathogenic effects of identified variants. The relevant identified DNA variants of selected families were assessed in parents and healthy siblings via Sanger sequencing. WES identified 12 novel variants across 10 known IRD-associated genes. The four most frequently implicated genes were CRB1 (14.3%), GUCY2D (9.5%), AIPL1 (9.5%), and CERKL (7.1%) that together accounted for 40.4% of all molecularly diagnosed cases. Additionally, 25 reported variants in 19 known IRDs, anophthalmia, and congenital cataracts-associated genes were found. Among the identified variants, p. Trp278X, a stop–gain mutation in the AIPL1 (NM_014336) gene, was the most common causative variant detected. The most frequently observed phenotype was retinitis pigmentosa (46.5%) followed by Leber congenital amaurosis (18.6%). Furthermore, 98% of pedigrees (49 out of 50) were affected by autosomal recessive IRDs, anophthalmia and congenital cataracts. The discovery of 12 novel likely pathogenic variants in 10 IRD genes, 25 reported variants in 19 known IRDs, anophthalmia and congenital cataracts genes, atypical phenotypes, and inter and intra-familial variability underscores the genetic and phenotypic heterogeneity of developmental and degenerative eye disorders in the Pakistani population and further expands the mutational spectrum of genes associated with these ocular disorders. Full article

Introduction: Pakistan is confronted with the formidable challenge of high population growth, which is compounded by cultural norms that prioritize male offspring, leading to adverse implications for family planning efforts and demographic trends. Despite efforts to promote contraception, including a national family planning program, Pakistan continues to struggle with low and stagnant contraceptive prevalence rates among married women. The influence of gender composition on modern contraceptive uptake remains underexplored, necessitating research to elucidate its impact on reproductive behavior. Materials and methods: This study used the dataset of a facility-based cross-sectional survey conducted in six districts of the Sindh and Punjab provinces in Pakistan. A subset of 495 married women of reproductive age seeking health services from March to June 2019 was used for this study. Logistic regression analysis was employed to examine the association between the gender composition of children and modern contraceptive uptake, adjusting for covariates such as province, the age of the women, and the type of health facility. Results: The analysis revealed a significant association between the gender composition of children and modern contraceptive uptake among married women. As the number of daughters increased without sons, the likelihood of contraceptive uptake remained low (adjusted odds ratio [AOR]: 0.12; 95% CI: 0.04–0.34; p < 0.000), while having at least one son substantially increased the odds of contraceptive use (AOR: 19.91; 95% CI: 8.00–49.50; p < 0.000). Notably, the gender composition of having one daughter with two sons had the highest level of contraceptive uptake, potentially because of family composition preferences. Discussion: The findings highlight the pervasive influence of gender composition on reproductive decision-making in Pakistan, with a clear preference for sons driving modern contraceptive behavior. These results underscore the need for targeted interventions to address gender norms and biases while promoting equitable access to family planning services. Engaging men in family planning initiatives is crucial for challenging traditional gender norms and fostering informed decision-making regarding contraception. Conclusions: Gender preference influences modern contraceptive uptake among women in Pakistan, with the strong preference for sons driving reproductive behavior. Addressing gender norms and biases while promoting informed, self-determined choice is essential for enhancing modern contraceptive uptake and achieving sustainable population growth. Targeted interventions, including male engagement strategies, are needed to challenge societal gender norms and empower individuals to make autonomous decisions regarding family planning. Full article

Wind energy has emerged as a viable alternative to fossil fuels due to its clean and cost-effective nature. Pakistan, facing growing energy demands and the imperative to reduce carbon emissions, has invested significantly in wind power to supply electric power in rural and urban communities, particularly in the Thatta district of Sindh Province of Pakistan. However, the sustainability of wind energy generation is contingent upon consistent and sufficient wind resources. This study examines the wind potential of Thatta district from 2004 to 2023 to assess its suitability for large-scale wind power development. To evaluate the wind potential of Thatta district, seasonal wind speed and direction data were collected and analyzed. Wind shear at different heights was determined using the power law, and wind potential maps were generated using GIS interpolation techniques. Betz’s law was employed to assess wind turbine power density. Box–Jenkins ARIMA and SARIMA models were applied to predict future wind patterns. This study revealed that Thatta district experienced sufficient wind speeds during the study period, with averages of 9.7 m/s, 7.6 m/s, 7.4 m/s, and 4.8 m/s for summer, autumn, spring, and winter, respectively. However, a concerning trend of decreasing wind speeds has been observed since 2009. The most significant reductions occurred in summer, coinciding with Pakistan’s peak electricity demand. While Thatta district has historically demonstrated potential for wind energy, the declining wind speeds pose a challenge to the sustainability of wind power projects. Further research is necessary to identify the causes of this trend and to explore mitigation strategies. Full article

Global precipitation products (GPPs) are vital in weather forecasting, efficient water management, and monitoring floods and droughts. However, the precision of these datasets varies considerably across different climatic regions and topographic conditions. Therefore, the accuracy assessment of the precipitation dataset is crucial at the local scale before its application. The current study initially compared the performance of recently modified and upgraded precipitation datasets, including Climate Research Unit Time-Series (CRU TS v4.08), fifth-generation ERA5-Land (ERA-5), Integrated Multi-satellite Retrievals for GPM (IMERG) final run (IMERG v07B), and Multi-Source Weighted-Ensemble Precipitation (MSWEP v2.8), against ground observations on the provincial basis across Pakistan from 2003 to 2020. Later, the study area was categorized into four regions based on the elevation to observe the impact of elevation gradients on GPPs’ skills. The monthly and seasonal precipitation estimations of each product were validated against in situ observations using statistical matrices, including the correlation coefficient (CC), root mean square error (RMSE), percent of bias (PBias), and Kling–Gupta efficiency (KGE). The results reveal that IMERG7 consistently outperformed across all the provinces, with the highest CC and lowest RMSE values. Meanwhile, the KGE (0.69) and PBias (−0.65%) elucidated, comparatively, the best performance of MSWEP2.8 in Sindh province. Additionally, all the datasets demonstrated their best agreement with the reference data toward the southern part (0–500 m elevation) of Pakistan, while their performance notably declined in the northern high-elevation glaciated mountain regions (above 3000 m elevation), with considerable overestimations. The superior performance of IMERG7 in all the elevation-based regions was also revealed in the current study. According to the monthly and seasonal scale evaluation, all the precipitation products except ERA-5 showed good precipitation estimation ability on a monthly scale, followed by the winter season, pre-monsoon season, and monsoon season, while during the post-monsoon season, all the datasets showed weak agreement with the observed data. Overall, IMERG7 exhibited comparatively superior performance, followed by MSWEP2.8 at a monthly scale, winter season, and pre-monsoon season, while MSWEP2.8 outperformed during the monsoon season. CRU TS showed a moderate association with the ground observations, whereas ERA-5 performed poorly across all the time scales. In the current scenario, this study recommends IMERG7 and MSWEP2.8 for hydrological and climate studies in this region. Additionally, this study emphasizes the need for further research and experiments to minimize bias in high-elevation regions at different time scales to make GPPs more reliable for future studies. Full article

Background/Objectives: Full immunization coverage in Pakistan remains suboptimal at 66%. An in-depth assessment is needed to understand the long-term trends in immunization and identify the extent of defaulters and associated risk factors of them being left uncovered by the immunization system. Methods: We conducted a 5-year analysis using the Government’s Provincial Electronic Immunization Registry data for the 2018–2023 birth cohorts in Sindh province. We analyzed 8,792,392 child-level immunization records from 1 January 2018 to 31 May 2024 to examine trends in immunization coverage, timeliness, defaulter rates, and associated risk factors; Results: Our findings indicate gradual improvements in immunization coverage, with full immunization rates increasing by 23.2% (from 47.5% to 70.7%) from 2018 to 2022. While timeliness declined from 2018 to 2021, it recovered in 2022 and 2023. Over the 5-year study period, >90% of children defaulted on vaccinations, with 34.8% fully covered and 9.1% uncovered. Children from urban areas (OR = 1.54; 95% CI = 1.52, 1.56; p-value < 0.001) and those enrolled through fixed immunization sites (OR = 2.11; 95% CI = 2.08, 2.15; p-value < 0.001) and mobile immunization vans (OR = 1.13; 95% CI = 1.13, 1.77; p-value = 0.003) were at higher risk of being uncovered defaulters. Conclusions: This study demonstrates improvements in immunization coverage in Sindh while highlighting the challenge of low timeliness and high default rates. Our findings provide insights to strengthen immunization access and timeliness, particularly in high-default areas, and can guide policies in similar low-income settings for more equitable and comprehensive immunization coverage. Full article

Precise estimation of net radiation (Rn) is fundamental to understanding surface energy balance and is critical for accurately determining crop water requirements, especially using remote sensing and geospatial techniques. The core objective of this study is to evaluate multi-satellite-based net radiations on major cropped areas of the Punjab and Sindh provinces of Pakistan. In this study, overlapping scenes from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, and Sentinel 2 were used from 2016 to 2020 along with three temperature products MOD11A1, Landsat 8 (brightness temperature), and ERA5. The multi-satellite-based net radiation estimations on overlapping days were compared with the Global Land Data Assimilation System (GLDAS) dataset. The models based on Landsat 8 and Sentinel 2 data exhibited good performance, with a Nash–Sutcliffe Efficiency (NSE) of 68.9%, a mean error (ME) of 13.918 W/m², and a bias of 50.669 W/m². The results indicated that Landsat 8 and Sentinel 2 data produced reliable estimations of net radiation, while MODIS data tended to overestimate due to its higher spatial resolution and broader coverage area. Landsat 8-based estimations are good compared to others, as it has good correlation coefficient and lower RMSE values. The study concludes that Landsat 8 provides the most reliable estimates of net radiation for determining crop water requirements, outperforming other datasets in accuracy. The findings underscore the importance of using high-resolution multi-satellite data for precise agricultural water management, recommending its use in future studies and water resource planning in Pakistan. Full article

Land use and land cover changes (LULCCs) are vital indicators for assessing the dynamic relationship between humans and nature, particularly in diverse and evolving landscapes. This study employs remote sensing (RS) data and machine learning algorithms (MLAs) to investigate LULCC dynamics within the Indus River Delta region of Sindh, Pakistan. The focus is on tracking the trajectories of land use changes within mangrove forests and associated ecosystem services over twenty years. Our findings reveal a modest improvement in mangrove forest cover in specific areas, with an increase from 0.28% to 0.4%, alongside a slight expansion of wetland areas from 2.95% to 3.19%. However, significant increases in cropland, increasing from 22.76% to 28.14%, and built-up areas, increasing from 0.71% to 1.66%, pose risks such as altered sedimentation and runoff patterns as well as habitat degradation. Additionally, decreases in barren land from 57.10% to 52.7% and a reduction in rangeland from 16.16% to 13.92% indicate intensified land use conversion and logging activities. This study highlights the vulnerability of mangrove ecosystems in the Indus Delta to agricultural expansion, urbanization, resource exploitation, and land mismanagement. Recommendations include harmonizing developmental ambitions with ecological conservation, prioritizing integrated coastal area management, reinforcing mangrove protection measures, and implementing sustainable land use planning practices. These actions are essential for ensuring the long-term sustainability of the region’s ecosystems and human communities. Full article