When good leadership meets smart technology for malaria prevention

By Parysa Oskouipour on October 30, 2019 in Health Data Systems, Malaria, News

On a sweltering summer day in Siavonga, a lakeside jewel of Zambia’s Southern Province, I sat down with Mr. Bisael Phiri, the District Surveillance Officer for Siavonga District, to get his perspective on the malaria situation in his region and at large in Zambia. A breeze lazily floating through an open window and a small desktop fan provided the only reprieve from the oppressive heat as we got down to brass tacks about the progress made in the last few seasons of malaria interventions and how Reveal has impacted that progress.

Mr. Phiri has been working in public health for several years, and is a big picture thinker when it comes to tackling malaria in Zambia. His motivations to work in this field are straightforward, “I know the kind of impact that good health can have on people’s lives. I wanted to do this work to help change the environment so it doesn’t constitute a danger to the public.” His work is based in one of the select districts of Southern Province that utilizes Reveal for their indoor residual spraying (IRS), mass drug administration (MDA), and reactive IRS malaria interventions through a PATH MACEPA and Akros-implemented program. Reveal is a powerful, open-source platform that aids in household-level intervention management and data authenticity by using spatial intelligence and smart planning tools. Mr. Phiri stresses that learning how to use this technology is self-explanatory and that the dashboards, which are tailored to his administrative level, greatly assist his day-to-day responsibilities. In his own words, “the dashboard is important to view on a daily basis because it identifies the status of various teams and shows us where we need to go and where the problem areas are. When we have this information, we can change our strategy based on how well our teams are performing, and change targets midway if need be. For instance, if I didn’t receive updated data from a certain district, I would not know that there’s an issue there. Now because I have these data, I would be able to make a quick plan for how best to move forward with that district.”

Beyond program planning, Mr. Phiri’s work is strengthened by the hard proof the platform provides that work is being done where it is supposed to be done.

Reveal takes aim at malaria parasite through mass drug administration campaigns in Southern Province, Zambia

By Parysa Oskouipour on September 18, 2019 in Health Data Systems, Malaria, News

Deep in Zambia’s Southern Province, in a town a three-hour drive away from the district’s largest city and economic hub (Siavonga), lies Manchamvwa Health Facility. This clinic serves as the focal point for the health needs of hundreds of people who live in the surrounding villages, and as such, is often overwhelmed with the many health needs of its patients. Malaria season in particular tends to put a great strain on the facility, with peak periods in previous years seeing anywhere from 100 to 200 cases per month.

Over the last couple of years, the Government of the Republic of Zambia (GRZ), with the assistance of Akros and PATH’s Bill and Melinda Gates Foundation-funded Malaria Control and Elimination Partnership in Africa (MACEPA), have been working with the National Malaria Elimination Program district staff to overcome these numbers and improve the health of the local community by using geospatial technology to optimize indoor residual spraying (IRS) campaigns. Recently, the two organizations teamed up again to be the first to ever use Reveal’s spatial intelligence approach to maximize reach and ensure accountability in a mass drug administration (MDA) campaign that distributed antimalarials to the doorstep of each community member in three districts of Southern Province.

The recent history of malaria in Southern Province is one of resounding progress thus far. Due to its proximity to Lake Kariba’s glistening, still water, it is unfortunately a heavily malaria-burdened region by nature. But malaria in this region is highly seasonal, linked to the annual arrival of rainfall from December to April, leaving ample overgrowth and standing water—prime mosquito-breeding real estate. This seasonality provides an attractive window through which most interventions have taken aim. The result has been an impressive decrease in prevalence of malaria parasitaemia among children less than five years of age, from 15.5% in 2006, to 5.5% in 2010, and 0.0% in 2018.^1,2 Trends like these make Southern Province appealing as a prime candidate for malaria elimination. However, despite overall improvement in the province’s malaria burden at large, districts directly adjacent to the lake are still at higher risk, as malaria cases have shown to be persistently high in some health facilities despite ongoing interventions.

To propel Southern Province closer to elimination, in 2014 MACEPA supported the national program with a malaria MDA research study in the Southern Province districts lining Lake Kariba, an area with an estimated population of 300,000 people. The rapid malaria reduction in the study area resulted in Zambia adding MDA to its arsenal of interventions in 2017. The country’s experience of malaria MDA—two rounds with one month in between doses­—has shown it to be an effective intervention in areas with a strong foundation of vector control, case management, and surveillance. Recognizing that MDA campaigns are most effective when every household and individual in the targeted region are reached, MACEPA engaged Akros for its technical expertise in introducing Reveal as a novel approach to maximize the impact of MDA for malaria control and elimination in this area.

Operationalizing spatial intelligence means saving lives

By Anna Winters on March 21, 2019 in Health Data Systems, Malaria, News

So, I too built a lot of maps and models during graduate school. I mapped the risk of West Nile virus (WNV) in Colorado, USA. At the time, WNV had, somewhat shockingly, erupted in that region of the US. I also modeled human plague in Uganda—effectively developing maps to precisely depict areas at high and low risk of plague transmission. “Target interventions where it’s red” was the more-or-less summary, where red equaled high-risk areas. Point made. Thesis closed. Safe on the shelf.

But here I sit on the other end of the world, far away from hallowed academic halls that are often lined with dead dissertations and theses like mine. Here in southern Africa, disease transmission is much more tangible. Before, I read about death rates due to malaria and HIV from my school in Colorado, US. Here, I witness the impact of those death rates every day when I drop my kids off at school—new graves being dug closer and closer to the road. I am involved with a local school that is inundated with orphans and vulnerable children—even from one of the more affluent regions of Zambia. In this environment, high morbidity and mortality rates are incessant. Help is highly dependent on securing increasingly limited resources. Navigating the challenging logistics of getting those resources to the right people at the right time and in the right place are often broken. However, despite the acute need to target limited resources, mapping approaches like the one I developed in school are rarely seen nor used to inform interventions.

It is time for the public health community—both globally and locally—to do business differently. It’s time to more appropriately lean on the idea of spatial intelligence through epidemiological and map-based approaches to inform the practice of intervention planning and delivery. Academic, math-based modeling can lend a good understanding of where and how we should focus our limited resources to save the most lives. “Why aren’t these approaches being actively used?” you might ask. Part of the challenge is a lack of tools and finite planning approaches to translate maps and models into operational, boots on the ground, public health programming decisions. Questions like, “Where are all the houses located?,” “Which houses exactly should receive the intervention based upon the model output?,” and “Has the intervention effectively reached everywhere it was targeted?” are challenging to assess, particularly in regions like here in southern Africa, where so many areas consist of rural villages with no addresses.