Data collection and analysis are critical parts of public health initiatives and often the means by which organizations measure success. But not all data is created equal. In fact, most of our personal data — the clothes we wear, the text messages we send, the shampoo we use — does not directly provide actionable insights about our health.
IN THIS BLOG:
Why is collecting the right health data important for public health initiatives?
Collecting the right data is crucial because not all available data points provide actionable insights. Data scientists must identify timely, actionable, and relevant information that aligns with community health needs, informs interventions or policy changes, and supports accurate trend analysis. Without focusing on the right data, it becomes difficult to guide public health actions effectively.
How does AFMC's data sciences team make complex data easier to understand?
AFMC's data scientists use clear language, data visualizations like infographics and maps, and targeted key takeaways to make health data easier for diverse audiences to understand. They also employ GIS mapping to add a spatial dimension to their analysis, allowing stakeholders to visualize health disparities and trends geographically.
What challenges do data scientists face when working with public health data?
Some major challenges include the variability and constant change in data, inconsistent data quality, gaps in datasets, and shifting public health conditions. If data takes too long to collect and report, it can become outdated and less useful. AFMC addresses these issues by prioritizing reliable sources, validating data thoroughly, supplementing traditional datasets with community-collected data, and maintaining flexible analytic frameworks.
How does timing impact the usefulness of health data?
Timing is critical because data can quickly become outdated. Even high-quality data loses its value if it is not delivered promptly. Like a fresh meal that becomes unappetizing if served too late, delayed data may no longer accurately reflect real conditions or support effective decision-making.
How has AFMC’s data analysis work influenced health programs and policies?
AFMC’s data analyses have had a direct impact by supporting initiatives such as expanding naloxone distribution for opioid overdose hotspots and informing youth tobacco prevention policies. Their evaluations have also helped shape the peer recovery support services framework in New Hampshire and have been instrumental in securing grant funding and supporting coalition efforts.
Collecting the right data is a lot like fishing. Once you find the prized catch in a sea of guppies, you can’t eat it immediately. You have to store the fish, clean it, descale it, prepare it, and cook it how your guests like it. And only then will your guests truly enjoy what you prepared for them. Data works similarly: the right data points must be analyzed, parsed, cleaned, and prepared before others will understand it (and know what to do with it).
Data analytics is the process of examining data sets to identify trends and draw conclusions to inform decisions. At AFMC, our data scientists use health data to inform decision-makers nationwide. Their analysis involves determining which data points are essential for public health initiatives.
“We prioritize data points that are timely, actionable, and aligned with identified community health needs,” says Sydney Lewis, MPH, interim manager of public health programs at AFMC. “Criteria include relevance to key health outcomes, alignment with strategic goals, the ability to be disaggregated by geography and demographics, and the potential to inform interventions or policy changes.”
The team also looks at how available, reliable, and consistent the data is over time. This helps ensure the data can support accurate trend analysis. Since data can change, the team focuses on identifying information that will stay consistent and be easy to compare in the future — so the trends reflect real patterns, not just random shifts or missing data.
Presentation is a Gateway to Better Understanding
There’s an overwhelming amount of data available from just one person at any given time. If I tried to print out all my health data, I’d probably run out of ink and end up with thousands of pages, most of which wouldn’t be useful to me. Data scientists face the same problem. Not every data point matters. That’s why they use tools to filter through the noise and find the most useful, actionable information for the people who need it.
AFMC’s data sciences team uses GIS mapping to help understand health trends in a visual, more digestible manner. “GIS mapping adds a spatial dimension to traditional data analysis, allowing us to visualize disparities geographically,” Sydney says. This approach highlights patterns, such as access gaps or environmental factors, that might be missed in tables or charts alone. GIS mapping reveals relationships between data points that analysts may not consider. “By layering multiple datasets, we can identify underlying drivers of health disparities, target intervention more precisely, and tell a clearer, more compelling story to policymakers and the public.”
Making Data Actionable
Reports provide a detailed display of trends and critical information that can inform decisions about where to focus change efforts. However, a poorly written report produces poor results, so it’s critical to create reports that present data in a variety of different ways.
“We design our reports with clear language, data visualizations, and targeted key takeaways to support understanding across diverse audiences,” Sydney explains. “Infographics, maps, and executive summaries highlight critical findings without overwhelming readers.”
Involving other stakeholders in the reporting process can also help manage expectations. Communicating with stakeholders about preferred formats and content focus will ensure that the final products are useful and usable for community planning and decision-making.
Accurate Data Influences Health Policy and Programs
When the right people get the right data at the right time, it can positively impact health outcomes and initiatives. For AFMC, data collection has led to targeted projects around opioid use and peer recovery. “Our analyses have supported initiatives such as expanding naloxone distribution by identifying overdose hotspots and informing tobacco prevention policies by highlighting youth risk factors at the regional level,” Sydney says.
AFMC also evaluates state-funded peer recovery support services (PRSS) for the state of New Hampshire, which has played a key role in shaping the state’s PRSS framework, helping to guide service delivery models and future program development. “In many cases, our findings have also helped secure grant funding and support coalition efforts aimed at closing critical gaps in care and prevention,” Sydney adds.
Data Collection and Analysis Challenges
Data is constantly changing, and timing is everything. If it takes too long to collect, manage, and report the data, it might already be outdated by the time it's used. Think of it like ordering food at a restaurant. The ingredients might be fresh and the recipe perfect, but if the dish takes too long to arrive, it gets cold — and you might not even want it anymore. The same goes for data: even high-quality data loses value if it's not delivered in time to be useful or accurate.
Other challenges may include inconsistent data quality, gaps in available data sets, and rapidly changing public health conditions. Overcoming these challenges requires a focused, proactive strategy that uses technology to streamline the collection, analysis, and reporting processes.
“We address these [challenges] by prioritizing reliable, evidence-based sources, applying thorough data validation processes, supplementing traditional datasets with community-collected data when appropriate, and maintaining flexible analytic frameworks that adapt to emerging trends or needs,” Sydney says.
As public health trends and contract requirements shift, AFMC stays flexible and open to innovation. “We’re expanding our use of real-time data collection tools, interactive platforms like ArcGIS, and community-driven data sources,” Sydney explains. “We’re also investing in staff training to stay updated on new analysis techniques and building partnerships that make data sharing easier and more effective.”
Final Thoughts
Data is undoubtedly important in healthcare. Yet not every data element provides value to decision-makers. Data scientists take the head-spinning amount of data points and pare them down into relevant, useful, and usable information that drives public health initiatives. Data analysis is an often-underappreciated art that many of us take for granted. Those working behind the scenes know the complexity and challenge of navigating a vast pool of numbers and trends. Until they translate those numbers into compelling information that others can understand, the data may not provide us with the info we need to know. And that’s key to making positive change.
