Traditionally, health interventions adopted by large institutions follow a vertical model where resources are allocated to address specific diseases, such as HIV and tuberculosis. However, over the last decade, a more horizontal approach of integrated health interventions designed to improve broader population health has proven, through data collection, to be a more effective way to improve health outcomes in under-resourced communities. At an event hosted by the Harvard Global Health Institute last Thursday (September 18th), Associate Professor at Harvard Medical School, Matthew Bonds, explained how his research and fieldwork through Pivot, a nonprofit organization he co-founded, uses this integrated approach to achieve scalable success in Madagascar.
“At the beginning of the Millennium Development era in the 2000s-2010s, we started to see more sophisticated models showing that almost all of the common illnesses and causes of mortality were easily preventable if we improved the general health of the population, instead of focusing on treating specific diseases,” said Bonds. “The predictions were that if we implement programs that promote population health, and if we reach the goals around service coverage, we can reduce mortality by 85-90 percent.”
The challenging story of impact
However, the evidence available at the time diverged from the predictions of these models. While there have been a lot of success stories, showing success has not always been easy. Since most of these health interventions are implemented in some of the most under-resourced communities, the infrastructure to collect reliable patient health information is often unavailable. There is also the problem of finding valid baseline metrics with which to compare the results of the interventions.
When there is a lack of evidence that the horizontal model of integrated programs produces measurable results in mortality rates, not only are we unable to know of the real impact these programs have, or if any impact was made at all, but it also makes it difficult to secure funding to continue the work.
What the data doesn’t tell you
On top of the difficulty of getting good data, there is also the issue of accounting for external factors and the legacy of inequities. Even with appropriate health interventions, sometimes there is not a significant reduction in mortality rate because other upstream factors that would cause premature deaths are in play, such as poverty, disease, and environmental drivers.
Carole Mitnick, professor in global health and social medicine at Harvard Medical School and a speaker at the event, agreed that simply counting the cases of disease averted or the mortality rate does not show the full picture of what is happening in the community.
“If you’re doing an intervention on tuberculosis (TB) treatment, the question you’re trying to answer is how many more people were cured by that TB treatment,” Mitnick explained. “But these numbers are only part of the picture. Because if I just look at the cures of tuberculosis, I totally lose sight of the chronic conditions that have been caused by TB and other environmental and occupational risks that may result in premature death, even though we’ve managed to cure more people with TB successfully.”