Social harm involves incidents resulting in physical, financial, and emotional hardships such as crime, drug overdoses and abuses, traffic accidents, and suicides. These incidents require various law-enforcement and emergencyresponding agencies to coordinate together for mitigating their impact on society. In this paper, we discuss the enhancements made to Community Data Analytic for Social Harm Prevention (CDASH) - a system that we have created for analyzing historical social harm events. CDASH predicts `hot-spots’ and displays them graphically to law-enforcement officials. The enhanced system, called Trusted-CDASH (T-CDASH), superimposes a trust estimation framework on top of CDASH. We discuss the importance and necessity of associating a degree of trust with each social harm incident reported to T-CDASH. We also describe different trust models that can be incorporated for assigning trust while examining their impact on prediction accuracy of future social harm events. To validate the trust models, we run simulations on historical social harm data of Indianapolis metro area, illustrating the behavior of each trust model and exploring their significance.