{
  "id": 3158533,
  "title": "Census 2027 will still fail to count India’s disabled citizens",
  "url": "https://urgent.news/2026/08/25/census-2027-will-still-fail-to-count-indias-disabled-citizens",
  "topic": "world",
  "section": "World",
  "published": "2026-08-25T01:15:47.000Z",
  "source": {
    "name": "The Indian Express",
    "slug": "the-indian-express",
    "url": "https://indianexpress.com/article/opinion/columns/census-2027-will-still-fail-to-count-indias-disabled-citizens-10847879/"
  },
  "original_language": "en",
  "account": "The upcoming Census 2027 in India aims to enumerate all citizens, including those with disabilities, but faces a significant issue: it may fail to accurately count disabled individuals. The enumeration process began on August 17 in several northern regions, with door-to-door surveys starting September 1 and concluding in February 2027. Of the 40 questions on the census form, the 13th concerns disability status. This question assesses whether the respondent is a person with a disability, and if so, selects up to three types from a list of nine. However, the disability categories on the census form have been criticized for inadequacy and exclusion.\n\nThe recent revision of the categories includes blood disorder, which was not present in 2011 but was previously classified as a separate condition under the Rights of Persons with Disabilities Act, 2016. This addition aims to recognize 21 distinct conditions, including dwarfism, muscular dystrophy, autism spectrum disorder, and sickle cell disease. However, the list still omits several conditions, causing underrepresentation and misclassification of disabled individuals. Moreover, the withdrawal of multiple disabilities and other categories has further narrowed the scope of the data collected.\n\nCritics argue that the classification system itself is flawed, with the nine options being broad heads of the Act, except for acid attack, which is considered under locomotor disability. This narrow categorization might lead to duplicate counting or the misrepresentation of multiple disabilities. The customary reply to this issue is the Unique Disability ID database, which aims to fill the gaps left by the census. However, the UDID only covers approximately half of the disabled population and requires access to certification and transportation, which is often lacking in remote areas of India.\n\nThe census enumeration is taking place in the least accessible districts, where assessment infrastructure is minimal, and formal diagnoses are rare. Enumerators lack the necessary training and clinical knowledge to accurately identify disabilities, particularly in conditions such as intellectual disability and mental illness. These districts also have limited access to specialized healthcare facilities and support services.\n\nThe consequences of undercounting disabled individuals are significant. Disaggregated prevalence data is crucial for budgetary allocation of welfare schemes and resources at both the national and state levels. Without accurate data, it becomes challenging to evaluate policies and programs effectively. For instance, conditions like thalassemia, haemophilia, and sickle cell disease, which require specific treatments and resources, may not receive the necessary attention and funding due to their absence in the census data. Additionally, specific learning disabilities, which are absent from the census categories, will be overlooked in the allocation of educational resources.\n\nThe underrepresentation of disabled individuals in the census also impacts the assessment of carrier burden for inherited conditions, which is essential for prevention efforts. Early intervention and specialized support services are often site-specific, requiring accurate data to determine the most effective locations for implementation. Furthermore, the misidentification of individuals based on misclassified disabilities can lead to inappropriate service provision, further marginalizing already vulnerable communities.",
  "summary": null,
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}