Search results

The purpose of this study is to investigate the status of perceived need, seeking behavior and utilization of health services in the elderly population of Sanandaj (west of Iran).

This is a cross-sectional study conducted on 800 elderly people in Sanandaj. Subjects were selected using multistage sampling and data were collected using self-report questionnaires. A multivariate logistic model with odds ratios (ORs) was used to determine the relationship of independent variables with seeking perceived need. Also, the concentration index was used to measure the inequality in using health services.

The perceived need for outpatient (during the last 30 days) and inpatient health-care services (during the past 12 months) was 69.7% and 29.7%, respectively. Among them, the unmet need for outpatient and inpatient health-care services was 46.6% and 17%, respectively. Having health insurance (adjusted OR 12.08; 95% confidence interval [CI] 1.04–140.11), middle economic status (adjusted OR 5.18; 95% CI 1.30–20.51) and being in an age group of 65–70 years (adjusted OR 7.60; CI 1.42–40.61) increased the chance of seeking inpatient care. Also, being in an age group of 60–65 years (adjusted OR 0.41; 95% CI 0.18–0.95) reduced the chance of seeking outpatient care. There was also a pro-rich inequality in using outpatient health services.

The elderly population suffers from unmet health-care needs, especially in outpatient services. The most important reason for not seeking outpatient and inpatient services was financial barriers and self-medication, respectively. So, designing targeted policies and interventions to address barriers in the conversion of need to demand in the elderly population is essential.

This research is aimed at preparing landslide susceptibility using spatial analysis and soft computing machine learning techniques based on convolutional neural networks (CNNs), artificial neural networks (ANNs) and logistic regression (LR) models.

Using the Geographical Information System (GIS), a spatial database including topographic, hydrologic, geological and landuse data is created for the study area. The data are randomly divided between a training set (70%), a validation (10%) and a test set (20%).

The validation findings demonstrate that the CNN model (has an 89% success rate and an 84% prediction rate). The ANN model (with an 84% success rate and an 81% prediction rate) predicts landslides better than the LR model (with a success rate of 82% and a prediction rate of 79%). In comparison, the CNN proves to be more accurate than the logistic regression and is utilized for final susceptibility.

Land cover data and geological data are limited in largescale, making it challenging to develop accurate and comprehensive susceptibility maps.

It helps to identify areas with a higher likelihood of experiencing landslides. This information is crucial for assessing the risk posed to human lives, infrastructure and properties in these areas. It allows authorities and stakeholders to prioritize risk management efforts and allocate resources more effectively.

The social implications of a landslide susceptibility map are profound, as it provides vital information for disaster preparedness, risk mitigation and landuse planning. Communities can utilize these maps to identify vulnerable areas, implement zoning regulations and develop evacuation plans, ultimately safeguarding lives and property. Additionally, access to such information promotes public awareness and education about landslide risks, fostering a proactive approach to disaster management. However, reliance solely on these maps may also create a false sense of security, necessitating continuous updates and integration with other risk assessment measures to ensure effective disaster resilience strategies are in place.

Landslide susceptibility mapping provides a proactive approach to identifying areas at higher risk of landslides before any significant events occur. Researchers continually explore new data sources, modeling techniques and validation approaches, leading to a better understanding of landslide dynamics and susceptibility factors.