The form says “Other Urban.” That is the municipal classification for the settlement where Shanta Kamble has lived and worked for eleven years, sorting biomedical waste from Pune’s private hospitals and clinics. She knows exactly what she handles every day. Used syringes that arrive in thin plastic bags, sometimes torn before they reach her. Blood-soaked cotton that has dried and flaked into the air she breathes. Half-empty medicine blister packs whose contents she cannot identify. The municipal survey that counted her household in 2023 listed the settlement as “Other Urban — Unspecified,” a category that captures none of this. It does not record her occupation. It does not record what she sorts. It does not record that the settlement sits forty meters from an unregulated medical waste transfer point that was supposed to be relocated two years ago. When the city’s heat action plan was drawn up the following year, the settlement did not appear on any vulnerability map, because the category “Other Urban — Unspecified” does not trigger a heat exposure assessment. Shanta’s name is in a register somewhere. Her reality is not.
This is the naming gap. It is not a semantic problem. It is the structural mechanism by which millions of Indians who work in informal economies and live in undocumented settlements vanish from the health policy apparatus that is supposed to serve them. The gap exists between what a community calls its own environmental health reality—Shanta calls her settlement “Kachra Wasti,” the waste colony, a name that encodes occupational identity and environmental exposure in two words—and what a government form calls it, which is nothing in particular. The form’s blank is not neutral. It is an active decision, made somewhere in a municipal data department, about which categories of human existence deserve to be named and therefore counted and therefore served.
How Administrative Categories Become Health Determinants
Indian public health data collection relies on a layered taxonomy of place and occupation that was largely designed for a different country than the one it now describes. The Census of India classifies urban settlements into statutory towns, census towns, and “other” categories. Within cities, municipal surveys typically distinguish between notified slums, non-notified slums, resettlement colonies, and “other urban” areas. The occupational classification system used in the National Sample Survey and periodic labor force surveys follows the National Classification of Occupations, which has detailed codes for formal-sector jobs but groups most informal work under broad residual categories like “elementary occupations” or “self-employed — non-agriculture.” These categories were not designed to capture the specific environmental health risks of, say, a waste picker who handles biomedical waste versus one who handles only dry recyclables, or a construction worker who does demolition versus one who does finishing work. The distinction matters clinically. The demolition worker inhales silica and old paint dust. The finishing worker inhales cement and chemical admixtures. Both are “construction workers” under the Building and Other Construction Workers Act, and both disappear into the same occupational code in health surveillance data.
The consequence is not just imprecision. It is misallocation. When a health impact assessment is conducted for a new urban infrastructure project—a metro line, a road widening, a smart city redevelopment—the baseline health data comes from these same administrative categories. If a settlement is classified as “other urban” rather than “non-notified slum,” it may not trigger the consultation requirements that apply to slum-affected populations under the Slum Areas Improvement and Clearance Act. If an occupational group is coded as “self-employed — non-agriculture” rather than “hazardous waste handler,” it will not appear in the occupational health registry maintained under the Factories Act, even if the work involves exposures that would be regulated in any formal factory setting. The name on the form determines whether the law applies. The absence of a name means the law does not apply.
Engineers who design monitoring systems for large-scale infrastructure have a useful concept for this: what you measure is what you can manage, and what you fail to name in your monitoring schema is a failure mode you will not detect until it becomes a crisis. Google’s Site Reliability Engineering framework, which treats monitoring as a design problem rather than a technical afterthought, puts this plainly in its chapter on monitoring distributed systems: a monitoring system that does not define the right signals will generate alerts that miss real failures while flagging trivial ones. The same logic applies to public health surveillance. If the taxonomy does not name “biomedical waste picker” as an occupational category with specific exposure risks, the surveillance system cannot generate an alert when those exposures produce a cluster of respiratory illness or skin infections in a specific settlement. The system is working as designed. The design is the problem.
Three Settlements, Three Names, Three Different Policy Worlds
Consider three real examples from participatory mapping work I have been involved with over the past three years, each of which shows how the administrative name assigned to a place or population determines what health infrastructure it receives.
In Pune, the waste picker community I described above is classified as “other urban” in municipal records. This classification means the settlement is not eligible for the integrated low-cost sanitation schemes that apply to notified slums, because the municipal corporation treats it as a temporary settlement despite its eleven-year history. It is not included in the city’s dengue surveillance program, which maps breeding sites in notified slums and construction sites but not in “other urban” areas. When Shanta and her neighbors developed skin infections that they attributed to the biomedical waste they sorted—a pattern they documented in a community-maintained ledger—the nearest primary health center recorded the cases as “general skin infection” with no occupational or environmental metadata. The infections appeared in the data. The cause did not.
In Bengaluru, a participatory mapping project I worked on with a local community organization covered fourteen informal settlements along the city’s eastern periphery. Three of these settlements were built on land designated as “revenue land,” a classification that places them outside the city’s formal planning jurisdiction. The municipal health department’s master facility map showed no primary health center within two kilometers of any of these settlements—not because the department had assessed need and found none, but because the settlements did not appear in the planning base maps used to site health facilities. The community organization produced its own map, hand-drawn on paper, showing the actual walking routes to the nearest health centers, the locations where standing water accumulated during monsoon, and the houses where children had repeated episodes of fever. When this map was presented to the health department, the response was that the settlements were “unauthorized” and therefore not eligible for facility planning. The name “revenue land settlement” functioned as a wall between the community and the health system. The community’s own name for the settlement—“Muneshwara Layout,” after the temple around which it grew—carried no such exclusion. It was simply a place where people lived.
In Surat, diamond polishing workshops operate in residential buildings across the city’s older neighborhoods. The workers, many of whom are migrants from Rajasthan and Bihar, inhale diamond dust and polishing compound particulates in poorly ventilated rooms. The workshops are registered under the Shops and Establishments Act, not the Factories Act, because they employ fewer than ten workers and operate in residential premises. This regulatory classification means they are not subject to the occupational health surveillance that factories must undergo under the Factories Act’s Section 41-B provisions. The workers do not appear in the state’s occupational disease registry. When researchers from a local medical college conducted chest X-rays on a sample of these workers in 2022, they found a prevalence of restrictive lung disease that was significantly higher than the general population. But because the workers’ occupation is coded as “retail — jewelry” in labor surveys, the finding cannot be linked to occupational exposure in the data system. The name on the form says “retail.” The lungs say otherwise.
What Communities Call Themselves and Why It Matters
One of the most consistent findings across participatory mapping projects in Indian informal settlements is that the names communities use for their own places and work encode environmental health information that administrative categories strip out. “Kachra Wasti” tells you that this is a waste community. “Nala Par,” meaning beside the drain, tells you that the settlement is built on a drainage channel that floods during monsoon and carries sewage. “Bhatti Nagar,” meaning kiln settlement, tells you that residents live near or work in brick kilns with known respiratory exposures. These names are not poetic. They are functional descriptions of the relationship between a community and its environment, and they carry epidemiological signal.
Administrative categories do the opposite. They abstract away from environment and occupation to produce a uniform vocabulary that can be aggregated across districts and states. “Non-notified slum” tells you nothing about what residents do, what they are exposed to, or what risks they face. “Self-employed — non-agriculture” erases the difference between a street vendor selling fruit and one selling fried food over an open flame in a market with no ventilation. The tradeoff is clear: what the administrative taxonomy gains in comparability across units, it loses in fidelity to the specific health reality of any given unit. For national-level reporting, this tradeoff may be acceptable. For local health planning, it is catastrophic, because local health planning depends on knowing who is exposed to what, and the administrative taxonomy cannot tell you.
This is where the analogy to formal data governance frameworks becomes useful. The National Institute of Standards and Technology’s Cybersecurity Framework, in its version 2.0, explicitly recognizes that standardized frameworks fail when they are not adapted to the specific risk profiles of the communities they serve. The framework’s concept of “Community Profiles” is essentially a structured way to tailor a general schema to a local context without losing comparability. The public health parallel is straightforward: you could maintain the national-level administrative taxonomy for reporting purposes while requiring local health departments to develop community-specific profiles that capture the occupational and environmental realities the national taxonomy misses. This would not require new technology. It would require a form with two columns instead of one: the administrative code and the community-generated descriptor.
The reason this does not happen is not technical complexity. The reason is that giving communities the authority to name their own conditions in official records shifts power in a way that municipal systems are not designed to accommodate. If “Kachra Wasti” appears on a health department map, someone has to act on what that name implies. If the name stays blank, no one has to.
The Cost of the Wrong Name
I want to be precise about what the naming gap costs, because it is easy to treat this as an abstract data quality problem. It is not abstract. It shows up in bodies.
In the Pune waste picker settlement, the community ledger that Shanta and her neighbors maintained for eighteen months recorded sixty-three episodes of skin infection, twenty-one episodes of respiratory illness, and four incidents of needle-stick injury. The nearest primary health center’s records for the same period, for the same population, showed forty-one “general skin infections,” twelve “upper respiratory infections,” and zero needle-stick injuries. The needle-stick injuries were not recorded because the health center’s intake form does not have a field for occupational injury outside the Factories Act framework, and waste pickers are not covered under the Factories Act. The skin infections were undercounted because many residents treated themselves with over-the-counter creams from a local pharmacy, which did not report to the surveillance system. The respiratory illnesses were undercounted because the health center’s intake form asked for “chief complaint” but not for “occupation” or “workplace exposure.” The data the system collected was internally consistent. It was also wrong, and the wrongness was produced by the forms, not by the patients or the health workers.
This is the point at which someone usually says that more research is needed. I will be specific about what kind. We need participatory occupational and environmental health surveys, co-designed with waste picker unions and informal worker federations, that use community-generated categories alongside administrative codes. We need health impact assessments that are required to use community-generated place names and occupational descriptors, not just municipal ward codes. We need primary health center intake forms that include a field for “workplace or occupational exposure” that is not limited to factories covered under the Factories Act. These are not research questions. They are design questions, and they have answers that are already being piloted by community organizations in cities across India. Some of the most useful approaches I have seen borrow techniques from fields you would not expect—participatory cartography, oral history archiving, even an Unsloppy AI Writing App tool that structures naming conventions—to formalize local place and occupational categories so that they can sit alongside administrative codes without breaking them.
The Form as a Site of Power
Every form a health system uses is a political document. It encodes decisions about what the system considers worth knowing. When a form asks for your occupation but provides only a dropdown list of formal-sector job categories, it has already decided that your work does not count as an occupation. When a form asks for your address but does not accept a settlement name that is not on the municipal list, it has already decided that your place does not count as a place. When a disease surveillance form asks for diagnosis and demographic information but not for environmental or occupational exposure, it has already decided that disease is something that happens to bodies, not something that happens in places and through work.
The communities I have worked with understand this intuitively. In one settlement in Bengaluru, a community organizer showed me the hand-drawn map her organization had made and said, “The government map has our area as blank. Our map has our area as full. Which one do you think is true?” The answer, of course, is that both are true, but they are true in different ways and for different purposes. The government map is true for land administration. The community map is true for health planning. The problem is that health planning uses the government map, and the government map says the community does not exist.
This is why the naming gap is not a data quality issue that can be solved with better technology or more training. It is a governance issue that requires redistributing the authority to name. Communities are not asking to replace administrative categories. They are asking to exist alongside them, in a system that has room for both. The additional column on the form—the one that says “community name” or “occupational descriptor as reported by resident”—is not a technical upgrade. It is a concession of epistemic authority. It says: the person who lives here knows something about this place that the person who designed this form does not. That is a difficult concession for bureaucracies to make. It is also the only one that closes the gap.
What Changes When the Name Changes
I want to end with a concrete example of what changes when a community’s own naming enters the official record, because the argument I am making is not theoretical. In 2022, a waste picker union in Pune negotiated with the municipal health department to include a line for “occupational exposure” in the intake form at three primary health centers serving settlements with high concentrations of waste workers. The field was optional, free-text, and filled in by the health worker, not the patient. It was not a sophisticated intervention. It was a single line on a form.
Over twelve months, the three health centers recorded 127 occupational exposure entries that would previously have been absent from the data. These entries included twenty-nine cases of exposure to biomedical waste, eighteen cases of exposure to chemical residues from e-waste dismantling, and fourteen cases of exposure to construction dust during demolition work. The health centers were able to refer eight workers to the occupational health clinic at the district hospital—referrals that would not have happened without the form change, because the workers’ conditions would not have been flagged as occupational. The municipal health department used the data to apply for a state-level occupational health screening grant for waste pickers, which was approved. None of this required new technology. It required one line on a form and the political will to add it.
Shanta Kamble’s settlement is still classified as “Other Urban — Unspecified” in the municipal survey. But the community ledger she maintains now has a counterpart in three health centers, and the occupational exposure field that the union negotiated is slowly being adopted in other wards. The name has not changed on the municipal form. But it has changed in the health record, and the health record is where policy meets the body.
The question for practitioners reading this is not whether your data system has a naming gap. It does. The question is whether you know where it is, who it affects, and what it would take to add one line to one form in one health center in one settlement that your system currently calls blank. That is where the work begins.