Population health data helps us account for local conditions, instead of generalizing or making assumptions about what is needed, and prioritize strategies for community improvement. We often look at a recent “snapshot” of community data, or data from a single point in time; however, additional insights can be gleaned with multiple years of data. Examining population health measures over time—so-called “time series data”—paints a clearer picture of community health and its upstream factors by providing temporal context and demonstrating how conditions may be shifting. This added context illuminates where we might focus efforts and helps changemakers prioritize investments.
Trend-spotting
Time series data can shed light on important trends. For example, opioid overdose death rates in a community may be in line with state and national benchmarks; however, upon further investigation with time series data, we find that opioid overdose death rates have steadily increased over the years.
From the data, we see that there is a growing health concern upon which we may want to focus our efforts to prevent problem opioid use and overdose. Exploring multiple years of data and observing changes over time tells us whether a community is building, maintaining, or losing momentum with regard to specific population health indicators and categories.
Public health surveillance
Consistently examining time-series data is a type of public health surveillance, a core component of the field of public health defined as: the ongoing, systematic collection, analysis, and interpretation of health-related data essential to planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination of these data to those responsible for prevention and control. Data collected and made available through public health surveillance can be used for health action by public health personnel, government leaders, and the public to guide public health policy and programs. Among other uses, public health surveillance does the following which require time-series data:
Detect epidemics, health problems, changes in health behaviors
Monitor changes in infectious and environmental agents
Assess effectiveness of programs and control measures
Reliable and valid tracking data
Our ability to track trends over time is limited by the data at our disposal. It’s important to use reliable time-series data from trusted sources that have consistent measurement approaches year over year. When data collection methods change, time series data may not be reliable because the indicator is not measured in the same way across years. When data measurement methods are not consistent, it is not valid to make comparisons across years. Multiple sources may measure the same condition or outcome–for example the American Community Survey and Bureau of Labor Statistics both measure unemployment, but employ significantly different methods. It is not valid to make comparisons across years when using different sources.
Finding context in the trends
It’s important to keep in mind that time-series data can obscure or distort important community dynamics. Thus, it is wise to dig deeper into the data and ground-truth. One example of this phenomenon is illustrated by looking at unemployment rates. Coming out of a recession, we may observe that unemployment rates in some places rebound more quickly than expected—a positive change! However, this may have less to do with increased job opportunities and more to do with decreased labor force participation. Labor force participation decreases when people exit the workforce due to retirement, disability or other reasons. Persons out of the labor force are not counted in unemployment statistics.
In another example, we may observe median household income increasing year-to-year. On its face, this looks like rising prosperity for residents, but we may be failing to account for changes in residential mobility. Lower income households may be getting priced out of the community and relocating where housing costs are cheaper, resulting in fewer lower income households and a higher median household income. It must also be stated that while we may observe population-level improvements across multiple years, not all groups within a population may experience improvements. In fact, disparities between demographic groups could be increasing.
IP3 Assess is our web-based data solution to community assessment, which aggregates secondary data from myriad sources and provides users an apples-to-apples comparison of data against state and national benchmarks. Wherever possible, the platform shows a trend line for a given indicator to showcase time series data and provide users with the added context.
Overall, it’s helpful to monitor data over time to keep tabs on how relevant population health indicators are changing. Below is our top list of commonly-used, reliable secondary data sources (check them out!):
As you monitor time series data over time, there are a number of key takeaways to keep in mind:
When sourcing and using secondary data, rely on sources that are updated and published on a regular basis instead of one-off surveys or studies.
Read through the methods documentation and evaluate if subject definitions and/or measurement approaches may have changed—some sources like County Health Rankings will tell you if data can be tracked across years.
If your goal is to understand the impact of an intervention, be sure to read the methods carefully to determine whether the data are based on actual surveys or whether it was modeled.
Consider context—what is behind the trends? Hold community conversations and conduct interviews with key stakeholders and community members to learn more.
Please reach out to schedule a call with our team if you're interested in leveraging IP3 Assess to track data over time.