Urban Health in India


Rapid urbanization in India and across the Global South is raising new social, economic, public health, environmental, and health systems challenges. The urban population of the world has grown from 29% in 1950 to 52% in 2014. By 2050 67% of the world’s population is projected to be living in urban areas. This is true of India as well. More than 60% of India’s population is projected to be urban by 2050. While urban populations fare better in all health and development indicators as compared to rural populations, this masks the differentials within a city. Increasingly, available data has been analysed to assess the health and development status of the urban poor. This has revealed vast disparity, between the urban rich and poor. Invariably, the urban poor are seen to be doing worse than even the rural populations on health indicators.

Greater attention to urban populations has also led to insights that require a focus on the interplay of health and wellbeing within the urban ecosystem. Urban infrastructure – including systems that provide housing, transportation, food safety, communication, etc. – are important drivers of population health and wellbeing. Cities are at the forefront of tackling critical health and development issues, as morbidity and mortality are influenced by these systems. Health risks such as exposure to traffic, air pollution, noise, unhealthy food sources, poor sanitation, and limited access to clean water are critical considerations in urban development and urban governance.


The dynamic of movement of people into urban environments creates new challenges for municipalities and policy makers in responding to the health of these populations. To address this, municipal leaders need to have a holistic view of how the health, infrastructure and basic services are intertwined. Given this is uniquely important that decision makers from the community all the way up have available to them a snapshot a landscape view of the various factors that influence the health of the population i.e., transportation, education, disease patterns, housing, water supply, access to health care, WASH. An in-depth understanding of interplay of all these factors in a particular geography is crucial to making sound public health decision making by communities, service providers and policy makers at the municipal level.
Moreover, the impact of the COVID-19 pandemic has highlighted issues related to labor migration, health infrastructure for urban poor forced to migrate back to rural areas, access to care, and related inequalities. The scale and scope of the COVID-19 pandemic have reiterated the need for access to and analysis of timely accurate health data to support the implementation of control measures and to guide health interventions and evidence-based practices, paralleled with evidence about social determinants that significantly impact health.
Urban health problems are different for each city as the city dynamics varies, hence the solution will vary. One size fit all will not work for cities even within a state due to differences in changing demographics, pace of development, geographic and geopolitical characteristics. The Government of India has invested in the National Urban Health Mission with guidelines for implementation. While there may flexibility at the state and city level to customize, it is hard to do so due to lack of easily accessible data at the city level. While there is a huge discourse about data use and evidence-based decision making, it has not become a reality as data can be scary and overwhelming. At any point of time there will be more than a few sources of data available to analyze a particular problem and may be interpreted differently to yield opposing conclusions or at best intuitive guesses.

Scientific use of data requires for the data to be put in a context and analyzed to solve problems. Most importantly the data should be presented in manner that it is understood and helps people take best decisions. However, the reality is that perfect data does not exist and most often nor is there enough time and the resources to collect perfect data to solve problems. The best way then seems to be to use the best possible data source, analyze it scientifically to give the best pointers to solutions. The process is iterative and is enriched by a constant cycle of data analysis, review by program managers and decision makers who help refine the questions to further use the data for more refined analysis. The cycle continues.
Data science infrastructure and capacity needs to be proximally focused in the areas where the data emerge, to produce solutions that enable strong evidence-based practices to guide health interventions. Collecting, sharing, analyzing, and interpreting health-related data are essential to the planning, implementation, and evaluation of public health practice as well as enabling advanced knowledge for heath emergency preparedness. Many data resources are not reusable if they have not been sufficiently documented and contextualized, or appropriately collected and licensed for use, particularly in cases where secondary analyses are planned that were not originally anticipated when the data were originally generated.

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