Editorial 1 : The COVID-19 death toll in India, getting it right
Context:
COVID-19 pandemic extracted a heavy mortality toll across the world during 2020 and 2021, and this has been a huge global public health concern. But we were not able to accurately assess COVID-related death toll.
Gaps in registration data :
- Given the challenges in direct causal attribution of deaths to COVID-19 infection, the international public health community emphasised the need to measure pandemic impact in terms of excess mortality, derived by comparing observed mortality during the pandemic with expected mortality based on pre pandemic trends. Ideally, excess mortality estimation requires robust population based mortality data from death registration systems.
- India faced a big challenge in estimating excess deaths directly due to COVID-19 due to the following issues:
- Death registration in India is still about 70%, which further varies widely across States and districts.
- Pandemic severity was particularly observed in India during the second wave in April-June 2021, when death statistics were not accurately captured.
- India’s death registration data also does not give weekly or monthly mortality data which is essential for excess death calculation.
Civil Registration System (CRS) and Sample Registration System (SRS) data:
- To assess the mortality toll, independent investigators compiled CRS mortality records from local offices of the government in 14 States and 9 cities across India from 2018 to 2021. Various scientific teams utilised these and other available mortality data from the SRS and household surveys to develop modelled excess mortality estimates for India.
- COVID-19 related death estimates varied widely from one to another study — the highest was 4.7 million excess deaths in India during 2020-21 using available local data as inputs for mortality models.
- There has been intense debate and controversy around the likely plausibility of various mortality estimates for India, which have focused on the statistical methods and data assumptions employed for estimation.
- However, most of these studies could not overcome some crucial biases in the input data for the modelling exercises, as well as in the assumptions applied to fill data gaps.
- Some studies are based on insufficient samples while others have information bias at various levels. It is very likely that the COVID-related excess deaths in India may have been overestimated.
Set up a task force
- The official CRS report for 2021 is scheduled for release shortly, and holds much promise for providing the best possible primary evidence. However, it may also lead to new debates if variations such as improvements in reporting, delayed registration, and remaining deficiencies in data completeness across States by sex and age are not considered and appropriately accounted for, while inferring from this data.
- Therefore, it is necessary that the government should convene a task force of national experts in this field to attend to this matter. This task force could be provided access to all the microdata from the CRS, SRS, and other relevant data sources as necessary.
- Once equipped with all the available and required information, the task force will be enabled to conduct a thorough analysis using standard statistical methods that utilise empirical data, to provide measures of excess mortality by sex and age at national, State and district levels.
District- level interventions
- Going forward, the imperative for accurate district level mortality measures is urgent, for evidence based health action to tackle the quadruple burden from maternal and child health conditions, infectious diseases, non-communicable diseases (NCDs) and injuries.
- Hence, while the outputs of such a detailed analysis would complete the debate on pandemic mortality in India, the analytical operations will establish capacity for subnational mortality measurement, and also inform interventions to strengthen local data quality.
- Concomitantly, there is also an urgent need to strengthen attribution of causes of death, either through medical certification for physician attended deaths, or through the use of retrospective interview methods for household events.
Conclusion:
- Taken together, the activities of the National Task Force for data analysis, along with proposed initiatives for data quality improvement, will vastly enhance the utility of the CRS for routine local and national mortality measurement in India.
Editorial 2 : The expansion of settlements into floodprone areas
Context:
- India’s urban areas have been flooding more and more often, destroying lives and livelihoods. Yet, according to a study led by the World Bank and published in Nature recently, flood risk in many cities is rising because they are expanding into flood prone areas.
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Findings of the paper:
According to the paper, since 1985, human settlements in flood prone areas have more than doubled. Experts say the findings spotlight the risk of unsustainable urbanisation in India. The study also found that middle income countries like India have more urban settlements in flood prone zones than low and high income countries.
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How is India at risk?
- India isn’t among the 20 countries whose settlements are most exposed to flood hazards, but it was the third highest contributor to global settlements, after China and the U.S., and also third — after China and Vietnam — among countries with new settlements expanding into flood prone areas, all from 1985 to 2015.
- India is at significant risk of flood related problems that could worsen in the coming years if the country wasn’t careful. At the heart of flood related hazards is where we build or expand our cities.
- For example, Bengaluru floods cost the city ₹225 crore. During the last century, the city’s population grew from around 1.6 lakh to more than a crore. To accommodate these people, the city expanded — but new localities overlooked the local topography.
Who are the most affected?
- The risks are disproportionately higher for those living in informal structures. Informal housing in cities is on land that is vacant and less desirable, so that they are not immediately driven off. So they often lie in low lying, flood prone areas.
- An important reason why urbanisation has expanded into flood prone areas is that we don’t have the governance processes to say that this kind of development is environmentally unsustainable.
- When environmental regulations are applied to new constructions, they are often applied only to big infrastructure projects and not to medium and small scale modifications of localities. This contradicts the notion that certain localities are more flood prone and that flooding and flood risk are locality level issues.
- People commonly violate existing government regulations. For example, there has been a rise in ecotourism resorts on forest land and the construction of large structures, including government buildings and even religious structures, on rivers’ floodplains.
What is to be done?
- As cities continue to expand, we can no longer avoid expanding into flood prone areas. Market forces tend to push expansion into flood prone areas. But recognising what these areas are and that we are actually expanding into them is the first step towards sustainable urban planning that addresses the risks.
- Some forms of adaptation are necessary and they need to differentiate between low income residents and unauthorised structures erected for the elite.
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Examples of local and sustainable housing models include riverside settlements that use stilt houses, like those used by the Mishing and the Miyah communities along the Brahmaputra.
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- Every city needs to do a proper scientific mapping of the flood prone areas. Urban governments need to make housing in such areas more flood resilient and protect low income housing.
Conclusion:
- Moving habitations from low lying, flood prone areas to higher grounds, regulating them, and building ecologically and economically sustainable houses for the poor will contribute towards sustainable development goal number 11, ie, sustainable cities and communities.