Nearly 2800 US local public health agencies that set policies and program priorities for their jurisdiction are challenged to find ways to monitor and measure improved health at a substate level.1 Community health assessment (CHA) and community health improvement planning (CHIP) are recommended and mandated best-practice approaches frequently leveraged by local agencies when developing public health strategy.2 At least 17 states mandate that public health agencies conduct a CHA.3 In 2016, 78% and 67% of local health agencies reported participating in a CHA and CHIP process, respectively.1 Similarly, the Patient Protection and Affordable Care Act of 2010 requires nonprofit hospitals to engage in community health needs assessment to maintain tax-exempt status.4 For all CHA/CHIP processes, data review is critical.2 , 5–8 As such, local public health and health care organizations seek relevant community health data to assess health and inform decision making about the types of programs, services, and policies they should develop. Regularly collected quantitative data describing the most important public health issues, such as childhood obesity, are especially needed to support this work.9
To find population health data, local public health agencies frequently turn to secondary survey sources (eg, National Health and Nutrition Examination Survey, Behavioral Risk Factor Surveillance System, and Youth Risk Behavior Surveillance System). While these data sources provide national- or state-level health estimates, these surveys are not able to produce accurate estimates for smaller geographic areas (eg, county or subcounty). In rural locations, small survey sample sizes decrease precision and stability of estimates and also pose a risk of identifiability. Because of these survey limitations, it is necessary to explore other data sources for CHA and planning in rural areas. Electronic health records (EHRs) may provide an alternative for describing community health.
Electronic health records digitally store patient health information collected during a health care visit. While recent legislation has encouraged wider EHR use among health care organizations, the application of these resources for public health surveillance and community health planning has just started.10–12 Research exploring how EHR data may be applied for public health practice is needed, with an emphasis on the collaborative benefits of sharing EHR data between public health and health care organizations.13–16 However, the challenges of using EHR data for CHA include governance, technical, legal, capacity, and regulatory issues.13 , 17–20 Some states, large cities, and large health organizations have already incorporated EHR data in population health assessment.21–24 However, rural communities have not typically benefited from use of these data for CHA and planning.
To address these questions, we compared childhood/youth obesity prevalence estimates and data quality of EHR data and routine survey data in 2 rural counties in Colorado. We explored the idea that alternative data sources, such as data obtained through routine health care and captured within EHRs, could be used to complement self-reported survey data for CHA and planning.
Colorado has a decentralized public health system with 54 local public health agencies. As a result of Colorado's 2008 Public Health Act, SB 08-194, all local public health agencies were required to engage in health assessment and improvement planning between 2008 and 2013 and then every 5 years in the future.25 Among CHAs conducted during 2008-2013, 77% (37 of 48) of public health agencies or partnerships in Colorado prioritized obesity reduction and prevention.26 Of the 47 Colorado counties designated as rural, 87% (n = 41) determined that obesity was an important health issue for action (Figure 1). We collaborated with 2 counties that represented distinct geographical regions of Colorado: La Plata and Prowers Counties. At the time of study, La Plata County, in southwestern Colorado, had a total population of 52 457 (population density = 29 people/square mile); Prowers County, in southeastern Colorado, had total population of 12 004 (population density = 8 people/square mile).27
Electronic health record data
Legal data-sharing agreements allowed for the extraction of EHR data used in this analysis. The agreements defined the data users, permitted uses, and conditions of use of the data. In La Plata County, EHRs from Pediatric Partners of the Southwest were used to create 1 of the analytic data sets. At the time of study, Pediatric Partners of the Southwest was the largest provider of general pediatric care in La Plata County. The practice employed 9 doctors, had approximately 25 000 patient contacts per year, provided care for all children regardless of insurance status, and had been using EHR since 2005. In Prowers County, EHR data were obtained from the Colorado BMI Monitoring System, which is a collaboration between multiple health care organizations and the state health department.28 In this system, body mass index (BMI) and demographic data are shared in a privacy-protecting manner across multiple data partners. Prowers County EHR data were obtained from High Plains Community Health Centers. High Plains Community Health Centers is a federally qualified health center, the largest provider of health care in the county, and has 9 medical providers. High Plains Community Health Centers has been using an EHR since 2007, and in 2013, it had 35 000 patient visits.
Data on patients' sex, age, weight, height, race, ethnicity, insurance type (ie, public, private, none, or other), and state and county of residence were extracted from both EHR systems. Age, sex, height, and weight were used to calculate a BMI percentile for each child. We assigned BMI percentiles on the basis of the Centers for Disease Control and Prevention (CDC) 2000 Growth Charts and calculated BMI using the CDC's SAS Program for the 2000 Growth Charts.29 , 30 Body mass index values were categorized as not overweight (<85th percentile) or overweight/obese (≥85th percentile). Biologically implausible values (BIV) for height, weight, and BMI were excluded from the analysis per the CDC's method to determine BIVs.30 All data were managed and analyzed using SAS version 9.4 and R version 3.2.5. The Colorado BMI Monitoring System was reviewed and approved by Kaiser Colorado's Institutional Review Board and the University of Colorado's Multiple Institutional Review Board. Under a separate protocol, use of the data from La Plata County was reviewed and approved by University of Colorado's Multiple Institutional Review Board.
For comparison with EHR data, we obtained overweight/obesity prevalence estimates from 2 public health surveys. The first survey, the Colorado Child Health Survey (CHS), is a statewide survey to monitor children's (1-14 years of age) health in Colorado. Developed and administered by the Colorado Department of Public Health and Environment, the CHS sample was selected from the Behavioral Risk Factor Surveillance System respondents who reported living with a child 14 years of age or younger in the household.31 After the adult respondent agreed to answer questions about the child's health, the primary caregiver was called to complete the survey regarding the child. To determine BMI, the primary caregivers were asked to report the child's height, weight, and age. Statistical weighting procedures were used to adjust survey data to estimate the prevalence of overweight/obesity among children 2 to 14 years of age and used as a comparison to EHR estimates.
The second survey, the Healthy Kids Colorado Survey (HKCS), is conducted in Colorado as part of a national youth survey effort developed by the CDC, the Youth Risk Behavior Survey.32 The HKCS was used to describe obesity/overweight prevalence among children 15 to 19 years of age. This survey was administered in a school setting to youth in public middle and high schools and results were weighted by demographic characteristics. Self-reported height and weight were used to calculate BMI percentiles.
Comparing population, EHR, and survey data
To describe population coverage, we divided estimated population counts by EHR counts. County-level sex, age, race, and ethnicity distributions were based on State Demography Office Projections, Vintage 2015.27 American Community Survey 1-Year Public Use Files were used to estimate county-level population insurance status.33
Point estimates and exact 95% confidence intervals (CIs) were calculated and compared for overweight/obesity prevalence in each county on the basis of EHR data and each survey (CHS and HKCS). To compare data sources for CHA and planning, we also described data quality indicators and potential data uses. To define data quality, we relied on several recent reviews in developing a list of quality indicators. In summarizing these reviews, the quality of data can be understood by its representativeness, timeliness, accessibility, validity, accuracy, reliability, precision, comparability, and understandability.34–38 Using these indicators of quality, we evaluated listed features and descriptive elements of data that each lends to a data source's overall quality. In addition, we operationalized these quality indicators to suggest the potential uses of these data sources in the context of a CHA and improvement planning process.
La Plata County
During the study period, 7661 unique patients with EHR data were available for analysis. Patients who did not reside in La Plata County (n = 2549) were excluded, leaving 5112 patients. Of those, patients outside the age range of 2 to 19 years were excluded, resulting in 4394 records. Records were deleted that had missing height (n = 192), missing weight (n = 5), or BIV (n = 232) for a final sample of 3965 children/youth for analysis. In the final EHR data set, the mean age was 9 years; 48% were female, and 36% had public health insurance (Table 1).
The EHR records included approximately 35% (3965) of the total county population of children/youth 2 to 19 years of age (Table 1). The EHR coverage was highest for the youngest children, 2 to 4 years of age (56% coverage). Coverage was lowest in the oldest age group, 15 to 19 years of age (23%). Compared with the county's overall child/youth population, the EHR had similar distributions for sex (48% female) and children on public insurance (36% EHR vs 33% population). The EHR overrepresented younger children 2 to 9 years of age compared with population estimates (57%-42%, respectively). Racial distributions between EHR and total population were difficult to compare due to the proportion of missing race/ethnicity in the EHR (19%).
Survey data obtained for comparison included CHS and HKCS. To ensure a sufficient sample size for CHS estimates (>50 responses), 4 years of survey sampling (2010-2013) were combined. The CHS survey included responses from 70 parents during this time. In 2013, La Plata County school districts participated in the HKCS. A total of 1303 students (75% response rate) completed the self-administered, anonymous questionnaire (Table 2).
In Prowers County, 10 632 unique patients with EHR data were initially obtained. We excluded patients who did not reside in Prowers County (n = 3764) and those outside the age range of 2 to 19 years (n = 4601), leaving 2267 records. Although no records were missing height and weight data, patients with BIV were excluded (n = 48), leaving 2219 records in the analytical data set. The EHR records covered about 70% of the total population of children/youth 2 to 19 years of age (Table 1). The EHR coverage was highest for the older children (15-19 years of age), with the EHR data covering about 84% of children in this age group. The racial and ethnic categories differed from the population estimates; the EHR showed a larger proportion of Hispanic/Latino (53%) than the population (44%).
Survey data in southeast Colorado were not available at the county level. Therefore, we used a regional comparison, also derived through the CHS, to estimate of the prevalence of overweight/obesity in this area. Health Statistic Regions are aggregations of Colorado counties developed by the Colorado Department of Public Health and Environment to allow for regional health data estimates. Region 6 contains 8 rural Colorado counties, including Prowers County (Figure 1). Prowers County includes about 18% of the total population in Health Statistic Region 6. The county is demographically similar to the region (Table 1) and has a similar percentage of children below the federal poverty line compared with the region (30% vs 32%, respectively). To ensure a sufficient sample size, 3 years (2010-2012) of regional CHS data were combined. The survey samples for the CHS and HKCS survey in Health Statistic Region 6 were 72 and 763 respondents, respectively (Table 2).
Comparison of prevalence
Figure 2 shows the point estimates and CIs for overweight/obesity prevalence derived from EHR and surveys for La Plata and Prowers Counties. In La Plata, EHR data indicated that 17.3% (95% CI, 16.0%-18.6%) of children 2 to 14 years of age were overweight/obese. This was not statistically different than the prevalence estimated from the CHS (19.4%), though the CIs for CHS were wider (95% CI, 9.1%-29.6%). Among youth 15 to 19 years of age, analysis of the EHR showed that 15.4% (95% CI, 12.5%-18.3%) of children were overweight or obese while the HKCS estimated a prevalence of 17.0% (95% CI, 14.9%-19.0%).
In Prowers County, estimates based on EHR data indicated that 37.7% (95% CI, 35.3%-40.2%) of children 2 to 14 years of age were overweight or obese. This was not statistically different than the prevalence estimated from the CHS of 31.8% (95% CI, 18.9%-44.6%). However, among youth 15 to 19 years of age, analysis of the EHR indicated that 41.0% (95% CI, 37.2%-44.8%) of children were overweight or obese. The regional HKCS estimated a comparatively lower and statistically different prevalence, 30.9% (95% CI, 27.7%-34.2%).
Comparison of data quality indicators
To explore the differences in quality between the data sources, we assessed accessibility, comparability, precision, timeliness, representativeness, reliability, and validity of each data source (Table 3). The survey data had several advantages with regard to quality indicators. The survey data were easier for the local public health agencies to access. Colorado Department of Public Health and Environment administers, maintains, reports, and archives the survey data; provides data access through a public portal; and can provide custom reports upon request. Furthermore, as surveys are administered throughout the state, survey data were more suited for comparisons across geography. The ability for local health departments to compare data against other geographical areas is considered to be an important part of the CHA process (eg, Public Health Accreditation Board Standard 1.3.1 A).2 Another strength of the survey was the intent of the sampling design and weighting scheme to create estimates that are generalizable and represent the population. One major disadvantage of the survey data was the additional time it took for the survey to be conducted, reported, and aggregated across years.
In comparison, EHR data were collected on an ongoing basis by health care providers, which (1) allowed for objective measurements of health indicators (eg, height and weight) and (2) created opportunities for both intervention evaluation and longitudinal analysis. The larger sample size available through the EHR resulted in greater precision for overweight/obesity prevalence estimates, especially among children 2 to 14 years of age. The EHR's larger sample size facilitated subgroup analyses by patient demographics such as race and ethnicity (see Figures, Supplemental Digital Content 1, available at http://links.lww.com/JPHMP/A322, which demonstrate the ability to visualize BMI distributions among subgroups). In addition, the EHR allowed for geographic analysis that did not require aggregation of several counties for sufficient data. However, a major weakness of the EHR data is that it included only those who accessed care, which could be nonrepresentative of the overall county population.
This project explored an alternative to health survey data for an important public health issue, childhood obesity. Nationally, the percentage of children who are obese tripled from 5.2% in 1971-1974 to 16.9% in 2011-2012.39 In Colorado, about 1 in 4 children 5 to 14 years of age and about 20% of high school–aged youth are estimated to be overweight or obese.40 , 41 In addition, about half of youth in Colorado do not meet the recommendations for physical activity.41 As a result, many local public health systems in Colorado are currently working to address obesity. High-quality, local data describing childhood obesity can enable evaluation efforts and support identification of effective programs as local organizations work to decease this condition.42 New technologies and trends in data collection have opened access to less traditional data sources that may complement or replace data collected by surveys for local CHA and CHIP. We found that EHRs may be used as a data source for public health in rural communities. Especially in urban areas, EHRs are becoming a more common source of data to describe childhood obesity.21 , 22 , 43–45 Others have also shown that EHRs have the potential to improve surveillance in small population groups mainly due to the increased sample size in EHR systems compared with other sources for surveillance.22 , 46 , 47 Our results reinforce the possibility of using EHRs as a source of data to describe health in rural populations that are traditionally difficult to measure.
We described the quality indicators of survey data and EHR data to assist practitioners in understanding the similarities, differences, and potential uses of these sources in CHA/CHIP processes. Appreciating that “data quality is the capability of data to be used effectively, economically and rapidly to inform and evaluate decisions,”48 (p138) we focus on the descriptions and decisions each of these data sources would be suited toward supporting.
Overall, easier-to-access survey data have been used by local health agencies to create a broad picture of community health in rural locations and compare to other geographical regions. These qualities make survey data a good source for CHA and for supporting the selection of health priority areas. Following the identification of obesity as a community health priority, EHR data may be preferable to survey data for detailed planning and evaluation. For example, EHR data create more detailed descriptions of health burden by permitting more refined analyses stratified by person, place, and time. For program, policy, or service evaluation, pre- and postdata are necessary to assess effectiveness. While the surveys provide repeated cross-sectional estimates, they do not contain repeated responses from the same individuals across sampling years. If patients have multiple encounters with the health care system, EHR data can be used for longitudinal monitoring, creating cohorts for tracking changes over time.44 However, it is important to recognize that the sharing, extraction, and use of EHR require significant resources. Public health agencies in rural areas may lack the informatics workforce that is needed to use EHR.49 Therefore, we recommend that resource-limited agencies invest in data sharing with EHR from health providers only if it will directly support the community's priority areas for action.
There are several limitations to this project. Experience in states other than Colorado may be different as public health surveys vary in their conduct and availability. Typically, Behavioral Risk Factor Surveillance System and some version of Youth Risk Behavior Survey are available in most communities, but with limited total surveys completed, their utility for CHA/CHIP may be hampered. However, as health care systems are adopting and implementing EHRs as part of the meaningful use incentive program, these records can be resources for height and weight measures for nearly all patients seen.50
In addition, our project focused on a health outcome that is feasible to measure with EHR data since height and weight are routinely collected during health care visits. It is unlikely that EHR systems could completely replace surveys for surveillance, since public health surveys are designed to capture many important health exposures, behaviors, and outcomes (eg, food insecurity, consumption patterns) not readily accessible through clinical encounters.
Finally, we operationalized a rubric for data quality. Data quality is a multifaceted construct and our approach may not have completely addressed the complexities in evaluating data quality across multiple sources. Local public health agencies may benefit from developing a working definition of data quality to guide decision making about routine and alternative sources of data for CHA/CHIP purposes.
Implications for Policy & Practice
- Many rural areas lack existing, routinely collected public health survey data essential for assessment and planning.
- Electronic health records may be an alternative community data source to measure health indicators, such as childhood obesity.
- As rural areas are typically served by a smaller number of health care providers, EHR systems may capture a larger proportion of the population compared with urban settings.
- Furthermore, EHR data may be timelier, more granular, and contain more objective measures of height and weight than survey data.
- However, EHR data may be less generalizable than survey data, as population-based survey methods typically incorporate sampling designs and statistical weighting techniques to better infer representative community health measures.
- While challenging to extract, share, and analyze, EHR data in rural regions can support local CHA by providing baseline measures and metrics for health planning and evaluation.
Existing public health survey data systems describe disease prevalence at the state and national levels, with limited capacity to estimate prevalence at a local level. In areas with relatively smaller populations (eg, rural), public health surveys can rarely be used to describe differences among subpopulations, such as age groups or racial/ethnic minorities. This project compared data quality between EHR and surveys in 2 rural settings in Colorado. Our findings indicate that there is an opportunity to use EHR data to describe childhood and youth obesity/overweight at the local level. The use of EHR data may complement public health surveys in CHA and CHIP processes, especially in rural areas.
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