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Now that we have identified some of the barriers to engagement with HIV care globally, we can consider strategies to overcome them. One critical approach is “Citizen Science.” Although this term is broadly used, here we are talking about an initiative of the International Treatment Preparedness Coalition (ITPC) that really moves from data extraction to data democracy.
Citizen Science shifts between these models by combining community-led interventions with operations or implementation science, along with life mapping, which refers to tracking people’s lived experiences. By combining these approaches, we can achieve advocacy.
Citizen Science acknowledges that people are experts in their own lives and pivots away from the notion that the only legitimate knowledge creation method is academic research and analysis.
The name “Citizen Science” signals the democratization of data collection and analysis, thereby empowering participants and respecting their expertise.
CLM is a process by which communities take the lead to monitor an issue that matters to their community. This is not an outside donor telling a group to do work based on indicators that are defined externally. Instead, it is about the community pinpointing the top priorities for that particular community.
The community creates the indicators to track those priorities, collects data, analyzes results, and then shares the insights from those data with a larger group of stakeholders, all with the purpose of eventually effecting change for the better. In this way, communities may work alongside policymakers to cocreate solutions—not just pointing fingers over problems, but looking at what the data show and identifying what they think they can do to make change.
This graphic details the process more in-depth. When problems uncovered through CLM are not resolved, communities can then escalate to advocacy. There is no requirement for HCPs to endorse or validate the data—in fact, HCPs are just one part of the process, along with communities and other key stakeholders.
All the parts of the model are connected: education, evidence, engagement, and advocacy. You may start with education because you cannot develop indicators or track something if you do not know about it. You need to learn the science behind the disease, but also the standards of care. What is the optimal care that should be offered, starting at the normative guidance level from the WHO, but also considering national HIV strategic plans? And then, what has actually been implemented? For example, you should know what the WHO and national guidance say about how many times you should be getting HIV-1 RNA tests and how you track them.
Then the process shifts into evidence, including the documentation of community experiences with health services and outcomes. This evidence can help reveal trends and problems in the community that move into mutually reinforcing engagement and advocacy, discussing the findings with stakeholders in a community consultative group, and identifying targeted solutions.
In the engagement step, the community and stakeholders come together and sit down with the data. An academic institution is usually engaged to conduct audits to verify the data, so we know it is high quality. Then we can say, “This is what we have seen” and “Here are insights that we have gathered from the data.”
Those discussions usually are very heated and exciting, with different viewpoints. One group may say this is a one-off incident. Another may argue that no, it is not a one-off incident but in fact is a trend that reveals the need for a larger change. There may be a lot of debate, but it is a healthy environment where the intention is always to improve the situation for the recipients of care. And depending on the outcome or the specific issue, it may be escalated to further targeted action, or it can be resolved there through iterative changes.
One example shows the success that we have had with CLM in Sierra Leone. In this case, a group called the Network of HIV Positives in Sierra Leone (NETHIPS) turned the lack of key population-specific community treatment observatory (CTO) data into a government commitment to develop a differentiated service delivery policy—a big advocacy win.
The national network in Sierra Leone did not have data disaggregated by key population, which made it impossible to know whether services were differentiating and reaching these key communities. NETHIPS brought that fact to the attention of the community consultative group and, in May 2019, it secured a signed commitment from the government to develop a differentiated service delivery policy, which had never been done in Sierra Leone.
More recently, in September 2020, NETHIP identified a lack of data on antiretroviral therapy (ART) treatment failure for HIV during the COVID-19 pandemic, because current service registers do not capture this indicator. They were able to convince the National AIDS Control Program to commit to collecting treatment failure information with new service registers, turning “no data” into a new national indicator in Sierra Leone.
NETHIPS Program Manager Martin Ellie said the beauty of projects like this is that they identify how people fall through the cracks in existing systems. They can now bring the issue to the community consultative group and advocate to the National AIDS Control Program to accelerate the production of new service registers that include treatment failure.
This is the community taking actionable steps to change things for their citizens.
CTOs can also broadly monitor availability, accessibility, acceptability, affordability, and appropriateness of care and services. As a model, these measures can be applied to various services, not only HIV care. For any disease or focus area, these are some of the things you would want to consider: Are required services available, and how can people actually access them? Are the services acceptable, high quality, and delivered without stigma or discrimination? What are the costs, both out of pocket and at the government level? Are there issues with intellectual property, such as those we have seen with vaccine access? Finally, are the services appropriate for people’s age and gender and tailored to the needs of vulnerable populations?
This slide provides some of our data from the regional CTO in West Africa. This is a 3-year project in 11 countries. Here I summarize many of the data points that were collected, including the numbers of healthcare facilities, quantitative reports, interviews and focus groups, HIV and HIV-1 RNA tests performed, and key populations and young people reached. We looked at more than 100,000 people receiving ART, which is a representative sample size for that region of the world.
This slide shows the key results. The top row shows stock-outs in the monitored facilities: the frequency of recorded ART, the frequency of recorded HIV-1 RNA lab supply, and the length of ART stock-outs. All of these measures decreased in the monitored facilities across the study periods, which is what we would hope to see.
Shown in the bottom row, the measures we would hope to see increase due to CLM did: the quality of care rating as seen by recipients of care themselves, the number of HIV-1 RNA tests performed, and the rate of viral suppression. These all increased, giving good indications of success.
This same global fund project in West Africa also looked specifically at the reasons for not accessing ART in vulnerable populations. The intent of this project was to go beyond numbers and get deeper into the insights and nuances of why people are not being treated, why people are not coming back, and why services are not appropriate.
Young people aged 15-24 years cited confidentiality and privacy as the major issues for not accessing ART, and men who have sex with men (MSM), sex workers, and people injecting drugs cited the fear of stigma and discrimination. Communities made efforts to urge the Economic Community of West African States to enforce the Dakar Declaration, which holds countries accountable for collecting data on key populations and investing in stigma reduction programs.
Empowering communities to end tuberculosis (TB) is another area where CLM can be effective. This framework is done with the Stop TB Partnership with OneImpact and ITPC. Quantitative data showed that 70% of OneImpact users report experiencing stigma and discrimination, but a more detailed, qualitative exploration revealed that the most common reason for TB stigma is a lack of information among family members. With this additional nuance, now we know that targeted interventions for family members may be more useful than broad dissemination of information. The data are now more actionable.
It is critical to remember that stigma remains a pervasive issue within the HIV world. In 2017-2018, ITPC conducted a HIV treatment access survey in 14 low- and middle-income countries from 7 regions of the world.
Stigma was the main issue that arose. Members of key populations were especially vulnerable to stigma from HCPs. For example, MSM and sex workers reported denial of health services significantly more than the general population—approximately twice as often. Almost 60% of the respondents showed internalized stigma, such as self-blame for their HIV status, decisions not to have sex, and decisions not to have children at all.
Here are some examples of what some recipients of care said in the survey. An MSM from Uganda said, “They should offer [key population]–friendly services and not threaten to report us to the police.”
A person who is transgender in Zimbabwe talked about HCPs asking “funny questions” and laughing at them—a very difficult situation.
A man living with HIV in Ukraine said that he did not want to tell people at work that he needed to go to the hospital or AIDS center because he was afraid that his bosses would think that he was too sick to perform his job.
In Burkina Faso, a man living with HIV described hiding his medicine. He would wait for his wife to leave the room, and when she saw him taking the medicine, he said he had food poisoning. This shows that disclosure is still an issue.
Finally, a female sex worker in Kenya said that she could not access the prevention of mother-to-child transmission services during her pregnancy because she could not produce a husband. If you are a sex worker, you cannot access those services. She had to go to the next county to access those services.
We have a lot of work to do, and the community is a critical aspect of determining and taking the next steps. We must know what we need to change, as well as where and how we need to intervene before 2030. It is impossible to do this without seeing data from both the supply side—HCPs and governments—and the demand side from users and recipients of care. Traditional monitoring and evaluation systems do not adequately integrate community data as part of the data story to appropriately design and target interventions. This means that CLM is a critical tool in our arsenal.
ITPC offers many resources on CLM. These include peer-reviewed journal articles, short videos, fact sheets, and other materials.
All of these resources are available for more in-depth reading on the ideas and examples I have discussed.
To summarize, we cannot be colorblind in our approach to patient care. It is an unfortunate fact that the type of healthcare that one receives is predetermined before their clinic appointment. Knowing that racism is a driver of social determinants of health, a holistic and structural approach is essential to improve health outcomes for vulnerable populations.
CLM is a critical intervention led by affected communities that contributes to improving health outcomes. Community data sources should complement traditional academic data to ensure that we are seeing the whole data story and can calibrate interventions in HIV care accordingly.