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.
Materials and Methods: The 500 individuals of both males and females aged 40 years and older with missing posterior teeth and not rehabilitated with any prosthesis were gone through a clinical history, intraoral examination, and anthropometric measurement to get information regarding age, sex, socioeconomic status, missing posterior teeth, and body mass index (BMI). Subjects were divided into five groups according to BMI (underweight > 18.5 kg/m2, normal weight 18.5-23 kg/m2, overweight 23-25 kg/m2, obese without surgery 25-32.5 kg/m2, obese with surgery < 32.5 kg/m2). Multivariate logistic regression was used to adjust data according to age, sex, number of missing posterior teeth, and socioeconomic status.
Results: People with a higher number of tooth loss were more obese. Females with high tooth loss were found to be more obese than male. Low socioeconomic group obese female had significantly higher tooth loss than any other group. No significant relation between age and obesity was found with regard to tooth loss.
Conclusion: The BMI and tooth loss are interrelated. Management of obesity and tooth loss can help to maintain the overall health status.