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On February 12, 2026, 10:51:25 AM UTC, Gravatar IDP Admin:
  • Added the following fields to resource Incidence and Rate of Suicides in Suicides in India

      indicators with value similar_resources with value mapping_status with value additional_info with value lgd_mapping with value


  • Removed the following fields from resource Incidence and Rate of Suicides in Suicides in India

      tags similar_datasets village_no


  • Changed value of field idp_ready to True in resource Incidence and Rate of Suicides in Suicides in India


  • Changed value of field data_insights of resource Incidence and Rate of Suicides to The Incidence and Rate of Suicides dataset provides essential insights into the scale, trends, and demographic distribution of suicides across India. By analyzing both the absolute number of suicide cases and the rate per lakh population, researchers can distinguish between regions experiencing high suicide counts due to population size and those facing disproportionately high suicide rates relative to their population.This distinction is critical for identifying high-burden states and emerging risk zones, enabling policymakers to prioritize mental health interventions where they are needed most. Temporal analysis reveals how suicide incidence and rates have evolved over the years, reflecting the impact of social change, economic shifts, or national crises on mental health outcomes. Combined with demographic and socioeconomic data, incidence and rate of suicide dataset can help uncover correlations between suicide trends and factors such as unemployment, education, urbanization, and income inequality. (previously The Incidence and Rate of Suicides dataset provides essential insights into the scale, trends, and demographic distribution of suicides across India. By analyzing both the absolute number of suicide cases and the rate per lakh population, researchers can distinguish between regions experiencing high suicide counts due to population size and those facing disproportionately high suicide rates relative to their population. This distinction is critical for identifying high-burden states and emerging risk zones, enabling policymakers to prioritize mental health interventions where they are needed most. Temporal analysis reveals how suicide incidence and rates have evolved over the years, reflecting the impact of social change, economic shifts, or national crises on mental health outcomes. Combined with demographic and socioeconomic data, incidence and rate of suicide dataset can help uncover correlations between suicide trends and factors such as unemployment, education, urbanization, and income inequality.) in Suicides in India