Doing good with data

Pakistan has done much better than its neighbours, and has done a lot better than many would have predicted


Muhammad Hamid Zaman October 05, 2021
The writer is a Howard Hughes Medical Institute professor of Biomedical Engineering, International Health and Medicine at Boston University. He tweets @mhzaman

In the last 18 months, the ministry of health, NCOC, provincial ministries and local health units have been busy. That is probably an understatement. They have not been perfect, but we must give credit where it is due.

Pakistan has done much better than its neighbours, and has done a lot better than many would have predicted. It is not to say that the death of tens of thousands of people, all over Pakistan, is an insignificant number. That statistic masks the pain and the loss of millions of people in those families. It is to simply acknowledge the effort of many people — across multiple political parties — coming together (sometimes) to blunt the effect of a terrible disease.

A consequence of the Herculean effort to diagnose, treat and vaccinate is that it has generated an enormous amount of data about disease dynamics and the public health system at large. While this data has been used to impose smart lockdowns and create awareness campaigns, there is a lot more that can be done to understand our national challenges with health access.

The government should allow — with proper and strict ethical protocols in place — for that data to be used by researchers to understand our own health system and for creating a more equitable society. There are so many important questions that we do not know about our own public health system. This data can start to piece together a better picture. First, we already know that globally communities that are socioeconomically marginalised, whether they are in New York, London or Rio, have done worse with Covid-19 than the communities that are more affluent.

This is likely true in Pakistan as well, and some early studies have already indicated that. A lot more, however, needs to be done. The reasons for correlation between incidence (and mortality) and socioeconomic inequity are often complex, ranging from healthcare access to malnutrition and underlying conditions. Data such as this can allow us to understand our own society better. It can also allow us to understand the trends between urban, peri-urban and rural areas and help us identify clusters where high incidence, poor outcomes and inequality correlate, as well as areas where they do not.

There are, similarly, other questions about vaccine uptake and resistance that are often viewed from simplistic lenses. We should also investigate whether population density or environmental degradation correlate with infectivity and mortality in Karachi or Lahore as they do in smaller towns. There is a long list of important questions about our own society, and its relationship with health access, that we can begin to investigate.

That investigation, however, needs to rely on three premises. First, ethical use of the data that stands on highest ground of integrity, anonymity and care. Second, engagement beyond a single discipline. While there are studies that have been carried out by economists, or public health experts, or more recently data scientist, we need all of them (and other disciplines) to collaborate. Some questions, for example, will require a historical perspective, to understand whether the areas that have done worse have suffered repeated outbreaks of other diseases.

Other questions will require anthropologists and demographers to connect statistics with lived realities. Most importantly, we need to engage local universities and institutions, and not just rely on those in large domestic cities or foreign centres of learning, to analyse the data. This does not mean that there should be no collaboration.

It simply means that local institutions and local researchers, representing communities large and small, must be part of the research and analysis teams. They will learn, gain important skills and contribute richly to the effort. We should remember that not everything is captured in data, and the nuance and lived realities are absolutely critical to make real sense of the data and use it for the good of all people.

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