Could Personal Network Analytics Help Create More Effective Influenza Immunization Campaigns?

Public Health campaigns focus mainly on how older adults are at greater risk of influenza and its complications. They principally cite health and healthcare-related consequences, such as pneumonia, hospitalization, and death. Young, otherwise healthy adults can indeed become ill and die from influenza complications. Still, seniors bear the brunt of hospitalizations and deaths, so it’s no wonder promotional campaigns are directed at them.

Because the virus spreads from person to person, those in close contact, like couples or families sharing a household, are at risk of transmitting it to one another. There are also risks when community vaccination rates are low and complicating flu season all the more; the rapid antigenic evolution of the virus means flu vaccines have lower efficacy than other vaccines.

That is why current influenza immunization rates are troublesome. Only 45% of children under seventeen, 43% of adults, and 47% of seniors are immunized. In some states, rates are as low as 33%.

Why Such Low Immunization Rates?

Is access the problem? Probably not, at least not in most places. Flu shots are ubiquitous and available at pharmacies, grocery stores, workplaces, and clinics.

Is cost the problem? Probably not. Flu shots are usually free and sometimes come with coupons for savings on other store items.

Is vaccine hesitancy the problem? Maybe. Though vaccine acceptance has declined, this does not explain why immunization rates are this low.

Is lack of health information the problem? Probably not. Every public health message cites the health and hospitalization risks. Ad nauseam.

Is the lack of motivational information the problem? Bingo. Motivating someone to act requires messages relevant to them and their circumstances.

Is it Time for a New Generation of Vaccine Promotional Messages?

I have worked in the public health and vaccine promotion arenas for decades and know the promotional campaigns we have waged to engage clinicians and the population we serve. They are accurate, and I thought they were adequate.

As I mapped personal networks, I began to see how that accurate information was not adequate because the messages failed to “connect” with the realities of a person’s life and what matters to them.

Does hospitalization and death matter? Today, I would argue it does not. Here is why: using our research data on the average adult's first-degree connections and the number of influenza deaths each year, I estimate that only 3.5% of all adult Americans know anyone who has died from the flu. That means a flu-related death is a remote possibility for most people. Therefore, it is not as persuasive as we believe it to be.

On the other hand, given the number of influenza cases each year, most people have first-hand experience of how disruptive it can be. It happened to me when I was ten and learned to cook a Thanksgiving turkey because my mom could not lift her head off the pillow.

Might showing people how flu disrupts life convince more of them to get flu shots? Might Personal Network Analytics help? I believe it could be because it is a comprehensive way to discover, describe, and quantify disruptions for children vs adults vs seniors. For men vs women. For single vs married people. For gig vs salaried workers. Might those analytics yield new messages for flu vaccine campaigns about the consequences that matter and motivate people to get a flu shot?

Meet Jane and John

Jane and John (not their real names) are a healthy, recently retired couple. They have been married for 40 years and, unlike 40% of Americans who live an average of 700 miles away from family, they have three married children and nine grandchildren living nearby.

All couples divide tasks, and they are no exception. Jane manages their household, social, and family obligations. She tracks when regular medical or dental visits are due and, when needed, schedules the appointments. She also provides after-school care for their grandchildren. John manages the care of their yard, major household appliances, and cars.

Jane’s responsibilities have grown in recent months because:

• Jane and John moved her elderly mother into their home after she suffered a disabling accident. Jane cares for her mother in an upstairs bedroom with staff from a home care agency. Agency help can be unreliable, so sometimes Jane has no help.
• John’s brother and sister-in-law were diagnosed with cancer. Since they live in a distant town, they often stay at Jane and John’s home when they need care at the major medical center in Jane and John’s city, usually for a week each time.

In total, Jane and John connect with 239 people. That number includes people they often see, like family, and those they see only episodically, like their tax accountant. It also includes 41 people (+2 pets) Jane connects with to care for her mother.  That number does not tell the whole story, and therein lies the opportunity to test new ways market research can quantify and describe the actual burden of this or other infectious disease conditions. 

New insights emerge after examining how Jane and John divided their workload. In the mindmap, we’ve color-coded the connections to show who is responsible for managing the connection:

• The 183 connections color-coded red are those managed solely or principally by Jane, including the 41 (+2) pets to care for her mother.
• The 32 connections color-coded blue are those managed solely or principally by John.
• The 24 connections in color-coded grey are those managed jointly.

Jane’s connection workload is quantitatively greater than John’s. It is also qualitatively different because:

• Jane’s involves meeting others’ needs multiple times each day. John’s are occasional and episodic.
• Jane’s involves satisfying needs (e.g., meals, medications, and personal care) that cannot be postponed. John’s (e.g., car/truck maintenance) is rarely urgent and can often be delayed without consequences.

What’s the Influenza Disruption Story that Matters to Them?

Because Jane connects with more people—in her home (e.g., home healthcare agency staff) and the community—she is at greater risk of encountering the virus. To help her avoid it, Jane gets an annual flu shot. Though his physician recommends it, John refuses. Yet, consider the added costs and disruptive consequences:

• If John becomes ill, another person will need Jane’s care.
• If Jane becomes ill while caring for John, she will be unable to care for her mother, the household, and after school for grandchildren for at least a week. Nor will either Jane or John be able to attend school or sporting events to see their grandchildren perform.
• Jane’s responsibilities will fall on her adult children, disrupting their households, families, and jobs and risking influenza spreading to others. If their grandchildren become ill, they will miss school and sports during the tournaments held in the winter months.
• Jane’s mother will be at heightened risk of contracting influenza and suffering its most serious health consequences.
• John’s brother and sister-in-law will need to find alternative lodging if they have cancer treatments during that time.
• Caregiving agencies may refuse to allow staff to assist with Jane’s mother’s care for the duration of the household’s quarantine.

Would understanding consequences such as these convince John to get a flu shot? Would stories like this one, tailored to the daily life realities of others, convince them?

I have anecdotal data to suggest it might, but that is not good enough. Insights from market research will get us closer to knowing for sure.

It certainly is worth trying! Our current campaigns are suboptimal, and influenza is costly. It sucks $11 billion out of the US economy annually, of which $8 billion are Indirect costs like Jane, John and their family will incur if influenza moves in with them. We need to find better messages to make that matter.