METHOD: This study used micro-level household datasets from multiple indicator cluster surveys (MICS) to estimate the DMI. To find out how different the DMI scores were, the inequality ratio and slope were used. This study further utilized spatial autocorrelation tests to determine the magnitude and location of the spatial dependence of the clusters with high and low mortality rates. The Geographically Weighted Regression (GWR) model was also applied to examine the spatial impact of socioeconomic, environmental, health, and housing attributes on DMI.
RESULTS: The inequality ratio for DMI showed that the upper decile districts are 16 times more prone to mortalities than districts in the lower decile, and the districts of Baluchistan depicted extreme spatial heterogeneity in terms of DMI. The findings of the Local Indicator of Spatial Association (LISA) and Moran's test confirmed spatial homogeneity in all mortalities among the districts in Pakistan. The H-H clusters of maternal mortality and DMI were in Baluchistan, and the H-H clusters of child mortality were seen in Punjab. The results of GWR showed that the wealth index quintile has a significant spatial impact on DMI; however, improved sanitation, handwashing practices, and antenatal care adversely influenced DMI scores.
CONCLUSION: The findings reveal a significant disparity in DMI and spatial relationships among all mortalities in Pakistan's districts. Additionally, socioeconomic, environmental, health, and housing variables have an impact on DMI. Notably, spatial proximity among individuals who are at risk of death occurs in areas with elevated mortality rates. Policymakers may mitigate these mortalities by focusing on vulnerable zones and implementing measures such as raising public awareness, enhancing healthcare services, and improving access to clean drinking water and sanitation facilities.
Methods: A total of 116 post-menopausal female patients of orthopedic menopause clinic were recruited using a purposive sampling approach. Data on osteoporosis awareness and knowledge were collected using validated structured questionnaires Osteoporosis Prevention and Awareness Tool and Osteoporosis Attitude Knowledge Test. The chi-square test was used to determine the association between post-menopausal women's socio-demographic characteristics and their knowledge and attitude towards maintaining bone health.
Results: Participants' age ranged between 49 and 82 years (61.84, SD=7.87). The knowledge of osteoporosis varied significantly by age (p=0.014) and education (p=0.001) among the studied population. No significant diffrences were found for participants' attitude towards bone health.
Conclusion: This study showed that the age and education levels have significantly different knowledge of bone health.