ABSTRACT
Background: PLHIV often suffer from depression and anxiety during their quest to adjust to the diagno-sis and face the difficulties of living with a chronic illness, of which permanent cure is yet to be discov-ered. Positive people network linkages should be established by each ART centre for its respective locali-ty. The objective of the study is to assess depression among HIV positive beneficiaries registered with Network of Surat People Living with HIV (NSP+) in Surat. Methods: A cross sectional study was conducted among 30 PLHIV on ART from Network of Surat People Living with HIV (NSP+). Beck Depression Inventory was applied to assess depression. Results: The mean BDI score among male and female participants was 25.6 (± 11.39) and 23 (± 12) re-spectively, 9 male participants were having moderate depression, 3 as severe, and 2 as extreme depres-sion. Among females 4 participants were found to having mild mood disturbance while 4 were classified as having moderate depression, whereas only 1 participant was classified as having extreme depression (score of 53). Pearson’s correlation between CD4 count and BDI score was found to be -0.35. Conclusion: Though majority of the participants are in WHO clinical stage 1, yet all the participants had borderline to extreme depression. Males had lower CD4 count, higher proportion of OI and higher mean score of BDI. Lower the CD4 count higher the severity of depression.
ABSTRACT
Background: In each geographic region, risk of new cases of COVID19 are driven by internal factors such as agent, host, and environment characteristics, as well as external factors, such as population mo-bility and cross border transmission of disease. COVID19 control measures are best implemented when local governments and health teams are well aware of these internal and external risks. These risks are dynamic in nature and hence need to be reviewed at regular intervals. The study conducted to develop a composite spatiotemporal Hazard Index comprising of three factors – presence of susceptible popula-tion, population density and presence of active cases with corresponding growth rates, to rank areas within an administrative boundary by their fortnightly risk of active COVID19 cases. Methods: Using Principal Component Analysis, the weights of each of these factors were determined and applied to transformed values of factors in the districts of Gujarat state for months of January to July 2021. Hazard Index thus obtained was used to rank the districts. Results: Spearman correlation between the Hazard Index and number of active cases 15 days later was moderate and significant (p<0.01) throughout the study period. Conclusion: Hazard Index can predict Districts at highest risk of active cases in the given time period. These districts with high Hazard Index would require different control measures, depending on the fac-tor that resulted in higher index value.