Xia Jing, Temiloluwa Sokoya, Yuchun Zhou, Sebastian Diaz, Timothy Law, Lina Himawan, Francisca Lekey, Lu Shi, Ronald W Gimbel
Submitted to: Journal of Medical Internet Research on: March 18, 2022
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Table of Contents
Health Indicators as a Measure of Individual Health Status: Public Perspectives
Xia Jing1 MD, PhD; Temiloluwa Sokoya2 MPH; Yuchun Zhou3 PhD; Sebastian Diaz4 PhD, JD; Timothy Law3 DO; Lina Himawan3 MSc; Francisca Lekey3 MPH; Lu Shi5 PhD; Ronald W Gimbel5 PhD
1Jackson State University Jackson US
2Ohio University Athens US
3College of Medicine, Northeast Ohio Medical University Rootstown US
4Department of Public Health Sciences Clemson University Clemson US
Corresponding Author:
Xia Jing MD, PhD
Abstract
Background: Disease status, such as cancer stage, has been used in routine clinical practice to determine more accurate treatment plans. Health-related indicators, such as mortality, morbidity, and life expectancy for the population group, also have been used. Few studies, however, focus on more comprehensive and objective measures of individual health status.
Objective: We examined the perspectives of the general public on 29 health indicators to provide evidence for further prioritizing the indicators, which were obtained from the literature review. Health status is different from disease status, which can refer to different stages of cancer.
Methods: Design: This study uses a cross-sectional design.
Setting: An online survey was administered through Ohio University, ResearchMatch, and Clemson University.
Participants: Participants included the general public who are 18 years or older. A total of 1153 valid responses were included in the analysis.
Primary outcomes measures: Participants rated the importance of the 29 health indicators. The data were aggregated, cleaned, and analyzed in three ways: (1) to determine the agreement among the three samples on the importance of each indicator (IV = the three samples, DV = individual survey responses); (2) to examine the mean differences between the retained indicators with agreement across the three samples (IV = the identified indicators, DV = individual survey responses); and (3) to rank the groups of indicators after grouping the indicators with no mean differences (IV = the groups of indicators, DV = individual survey responses).
Results: The descriptive statistics indicate that the top-five rated indicators are drug or substance abuse, smoking or tobacco use, alcohol abuse, major depression, diet and nutrition. The importance of 13 of the 29 health indicators was agreed upon among the three samples. The 13 indicators were categorized into seven groups. Groups 1-3 were rated as significantly higher than Groups 4-7.
Conclusions: This study provides a baseline for prioritizing further the 29 health indicators, which can be used by electronic health records or personal health record system developers. Currently, self-rated health status is used predominantly. Our study provides a foundation to track and measure preventive services more accurately and to develop an individual health status index.
(JMIR Preprints 18/03/2022:38099)
DOI: https://doi.org/10.2196/preprints.38099
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Original Manuscript
Health Indicators as a Measure of Individual Health Status: Public Perspectives
Temiloluwa Sokoya, MPH1; Yuchun Zhou, PhD2; Sebastian Diaz, PhD, JD3; Timothy Law, DO4; Lina Himawan, MS5; Francisca Lekey, MPH6; Lu Shi, PhD7; Ronald W. Gimbel, PhD7; Xia Jing, MD, PhD*7
1Temiloluwa Sokoya, Jackson State University, Jackson, Mississippi
2Yuchun Zhou, College of Education, Ohio University, Athens, Ohio
3Sebastian Diaz, College of Medicine, Northeast Ohio Medical University, Rootstown, Ohio
4Timothy Law, Ohio Musculoskeletal and Neurologic Institute, Ohio University, Athens, Ohio
5Lina Himawan, Department of Psychology, College of Art and Sciences, Ohio University, Athens, Ohio.
6Francisca Lekey, College of Health Sciences and Professions, Ohio University, Athens, Ohio
7Lu Shi, Ronald W. Gimbel, and Xia Jing, Department of Public Health Sciences, College of Behavioral, Social, and Health Sciences, Clemson University, Clemson, South Carolina
*Corresponding Author Xia Jing, MD, PhD Edwards Hall 511
Department of Public Health Sciences
College of Behavioral, Social, and Health Sciences Clemson University
Clemson, SC 29634 Email: xjing@clemson.edu Tel: 864-656-3347
Abstract
Background: Disease status—such as cancer stage—has been used in routine clinical practice to determine more accurate treatment plans. Health-related indicators, such as mortality, morbidity, and population group life expectancy, have also been used. However, few studies specifically focus on the comprehensive and objective measures of individual health status.
Objective: The perspectives of the public toward 29 health indicators obtained from a literature review were analyzed to provide evidence for further prioritization of the indicators. The difference between health status and disease status should be considered.
Methods: This study used a cross-sectional design. Online surveys were administered through Ohio
University, ResearchMatch, and Clemson University, resulting in three samples. Participants included individuals aged ≥ 18 years. Participants rated the importance of the 29 health indicators. The rating results were aggregated, cleaned, and analyzed as follows: (1) to determine the agreement among the three samples regarding the importance of each indicator (IV = the three samples, DV = individual survey responses); (2) to examine the mean differences between the retained indicators with agreement across the three samples (IV = the identified indicators, DV = individual survey responses); and (3) to rank the groups of indicators into various levels after grouping the indicators with no mean differences (IV = the groups of indicators, DV = individual survey responses).
Results: A total of 1,153 valid responses were analyzed. Descriptive statistics revealed that the top- five rated indicators were drug or substance abuse, smoking or tobacco use, alcohol abuse, major depression, and diet and nutrition. Among the 29 health indicators, the three samples agreed upon the importance of 13. Inferential statistical analysis indicated that some of the 13 indicators held equal importance. Therefore, the 13 indicators were categorized by rank into seven levels as follows: Level 1: blood sugar level and immunization/vaccination; Level 2: LDL cholesterol; Level 3: HDL cholesterol, blood triglycerides, cancer screening detection, and total cholesterol; Level 4: health literacy rate; Level 5: personal care needs and air quality index > 100; Level 6: self-rated health status and HIV testing; and Level 7: the supply of dentists. Levels 1–3 were rated significantly higher than Levels 4–7. Among the 13 health indicators, blood sugar level and immunization/vaccination status were the most important, and the supply of dentists was the least important.
Conclusions: This study provides a baseline for prioritizing 29 health indicators, which can be used by electronic health records or personal health record system designers or developers to determine what can be included in the systems to capture an individual’s health status. Currently, self-rated health status is used predominantly. Additionally, this study provides a foundation for tracking and measuring preventive healthcare services more accurately and developing an individual health status
index.
Keywords:
Health status measurement; Individual health indicators; Public perspectives; Surveys and questionnaires
Strengths and limitations of this study
- The work establishes the foundation to measure individual health status more comprehensively and objectively
- The work reflects perspectives from three communities with a relatively large sample size
- The work provides the foundation to prioritize the 29 health indicators
- With real-world longitudinal data, the public perspective data on individual health status measurement could be verified and validated further
Introduction
Disease status, such as cancer stages, has frequently been used in routine clinical practice to determine more accurate treatment plans. Health-related indicators, such as mortality, morbidity, and life expectancy for a given population group, have also been used. Few studies, however, have focused on more comprehensive and objective measures of an individual’s health status. Self-rated health status has previously been identified as a reliable indicator of an individual’s overall health status [1, 2], but this is subjective, and the rating criteria are unclear. Although there is research on health indicators used for the measurement of care quality [3], as well as social and behavioral measures in electronic health record (EHR) systems [4, 5]; more comprehensive and objective health indicators of an individual’s health status are lacking. These must be developed and used to measure health status more accurately, objectively, and consistently, as well as to evaluate the outcomes of preventive medicine services [1, 6]. As the healthcare paradigm shifts from treatment to prevention [7, 8], the accurate, objective, and convenient measurement of preventive services and their long- term outcomes become an urgent and growing need.
Individual health status refers to a person’s overall physical, mental, and social well-being, as well as freedom from illness or injury. In contrast, individual disease status refers to a person’s
physical or mental symptoms with or without diagnosis [9]. Accurate individual health status measures can guide customized preventive medicine services and lifestyle suggestions and be applied to population health programs by aggregating an individual’s health data into meaningful groups. Chronic diseases are increasingly costly to both patients and society, and most can be prevented or delayed via preventive medicine services. These services need to be provided in a routine and consistent manner [7, 8], thus maximizing the potential to control healthcare costs.
The Institute of Medicine (IOM) reviewed social and behavioral domain measures, as seen in EHR systems [4, 5]. They identified 17 domains, and these measurements were used as a foundation for the Office of the National Coordinator for Health Information Technology’s (ONC) EHR Meaningful Use reporting requirements [4, 5]. In 2015, the Centers for Disease Control and Prevention’s National Center for Health Statistics released 15 selected health indicators based on the National Health Interview Survey [10]. Other research [1, 2, 6, 11] also considered health indicators, although none focused on comprehensive, objective measures of an individual’s health status.
Although preventive medicine has been recognized for its critical role in health care, such services are not provided consistently to the majority of the American population [12]. Because chronic diseases represent a large portion of healthcare expenditures, it is critical to prevent or delay the onset of chronic diseases via preventive services [13]. The tracking of health indicators has been reported to help policymakers note changes needed in coverage and to influence policy decisions [14]. Such tracking also enables governments to better allocate health resources [14]. Nevertheless, accurate measurements of preventive services are inadequate or lacking.
We conducted a literature review of existing health indicators [15-22]. We consolidated the described health indicators and determined that 29 health indicators could be utilized to measure an individual’s health. We then examined four commercial EHR systems used in rural, primary care, and ambulatory settings to explore the availability and presentation of these indicators as a pilot
study. None of these systems were found to capture all of the indicators [9], but each system provided data on some available health indicators, and all four systems had preventive medicine panels. The pilot study indicated that no established group of health indicators existed for individuals, nor were indicators incorporated or used consistently and routinely across different EHR systems. Therefore, there is at least a need to provide more evidence for these health indicators. This includes what should be included among the individual health indicators used by EHR systems and whether these indicators can be prioritized based on their importance. Additionally, we recognize that these health indicators have much broader potential use beyond incorporating them into EHR [9].
The current study aimed to examine public perspectives on the importance of 29 health indicators to inform their relative perceived priority. This would, for example, allow the separation of the health indicators into core and secondary sets, which could be incorporated into EHR or similar systems [23]. Such health indicators could capture an individual’s health status, thus informing and enabling preventive services to make them more accurate, consistent, and convenient without overburdening providers’ data collection workload. These public perspectives could also provide a foundation for developing an individual health index which could be used to stratify healthy populations into subgroups based on the corresponding study requirements. There is no established list or ranking of health indicators according to importance, nor is there a matural methodology to develop such a list. Therefore, we attempted to use public perspective surveys as a starting point in this study. We plan to validate the results quantitatively through longitudinal health record analysis in a future study. The assumption is that an individual’s perception of the importance of each health indicator may be associated with their conscious or unconscious behaviors, which would ultimately affect health outcomes. This paper focuses on the public perspectives, the approach, the results, and the analysis of the results.
Methods
General study design
The overall design of this project is illustrated in Figure 1 to provide context for this manuscript. The work reported here focuses on public perspectives, the methods utilized, and the results. The first three steps in Figure 1 have been completed and published [9, 23]. The section (about quantitative validation) on the far-right side, shown in Figure 1, illustrates directions we plan to explore in future studies.
Figure 1. The overall design of the project; public perspectives are the focus of this manuscript; the three sections connected via green arrows were completed, and the far-right section is for future work
Data collection
An online survey (Appendix A) was administered through Ohio University (Summer 2017), ResearchMatch [24] (Summer 2018), and Clemson University (Summer 2020), providing three samples. The Institutional Review Boards of Ohio University (17-X-142) and Clemson University (IRB2019-441) approved the study. The inclusion criterion for participation in the survey was 18 years of age or older. The Participants were allowed to share the survey’s URL link, and all respondents acknowledged informed consent.
The survey included seven demographic questions and rating items related to the importance of the 29 health indicators. Definitions of these health indicators were provided within the survey (Appendix B). In the survey, the 29 health indicators were separated into five subscales [15, 19, 21]:
- Health Risks and Behaviors, with eight indicators
- alcohol abuse, body mass index (BMI), diet and nutrition, drug or substance abuse, family history of cancer, physical inactivity, smoking or tobacco use, sun protection
- Healthcare, with three indicators
- immunization/vaccination, insurance coverage, personal care needs
- Healthcare Provider Supply, with three indicators
- cancer screening detection, hypertension screening, HIV testing
- Blood Tests in Physical Exams, with five indicators
- blood sugar level, blood triglycerides, HDL cholesterol, LDL cholesterol, total cholesterol
- Other Health Indicators, with ten indicators
- self-rated health status, high school diploma, air quality index > 100, supply of dentists, engagement in life, health literacy rate, major depression, having a sense of purpose in one’s life, race and ethnicity, and being unemployed
After removing invalid data, the final sample yielded 362 responses from Ohio University, 694 from ResearchMatch, and 97 from Clemson University (Appendix C). Items were rated on a scale of 0–10 (ie, 0 refers to not at all important and 10 refers to extremely important) in the survey used by Ohio University and Clemson University, whereas items in the ResearchMatch sample were measured using a scale of 0–100 (ie, 0 refers to not at all important and 100 refers to extremely important). Therefore, as part of the data cleaning process, the data from ResearchMatch were converted to a scale of 0–10 (Appendix D contains the codebook). In the Ohio University survey, there were five health indicators: blood sugar, blood triglycerides, HDL, LDL, and total cholesterol, for which a scale of 0–11, instead of 0–10, was used. Due to this error, data for these five indicators were removed from the Ohio University dataset. As a result, the total sample size of these five
indicators was 791, whereas the total sample size of the other indicators was 1153 (Table 1).
The internal reliability of the survey instruments were calculated for the overall set and the three institutional subsets using Cronbach’s alpha [25].
Data analytic strategies
Data analyses included rating the 29 health indicators based on their perceived importance. After aggregating data from the three samples with descriptive statistics, a three-step analysis was conducted. The first step of the analysis involved determining whether the three samples had a unanimous agreement on the importance of each indicator. A one-way ANOVA with a post hoc test was conducted in SPSS v.27 (IBM) for each indicator to examine any group mean difference (IV = the three samples, DV = individual survey responses). A Levene’s test was used to test the homogeneity of variance for each indicator before running an ANOVA. The indicators with no group mean differences across samples were retained for the following analysis step.
The second step of the analysis examined the mean differences between the retained indicators via a one-way ANOVA (IV = the identified indicators, DV = individual survey responses). Any indicators with no significant mean differences were grouped together because they could not be ranked.
The third step of the analysis ranked the groups of indicators into various levels after grouping the indicators with no mean differences. A one-way ANOVA with a post hoc test was conducted to examine the mean differences between the levels of indicators (IV = the levels of indicators, DV = individual survey responses). Any significant mean difference between any two levels of indicators indicated the ranking order of the two levels.
Results
The overall layout of the findings
The primary purpose of this study was to identify the public perspectives on the importance of the 29 selected health indicators, whether they agreed with each other, and if so, what were the
importance rankings of the health indicators that agreed. Figure 2 summarizes the analytic strategies and the main results of each step. The following paragraphs elaborate on the detailed results for each step.
Figure 2. The primary analytic strategies and overall results of each step
Results of descriptive statistics
Descriptive statistics for the 29 health indicators are reported in Table 1. The descriptive statistics for the demographic information for all respondents are reported in Appendix E. Descriptive analyses show that drug and substance abuse, smoking and tobacco use, alcohol abuse, major depression, and diet and nutrition were found to be the five most important health indicators, as rated by the study participants. Additionally, race and ethnicity, possession of a high school diploma, engagement in life, unemployment status, and sun protection were the five least important health indicators. Self- rated health status, the most used health indicator to assess an individual’s health status, was ranked in the 20th position.
Results of inferential statistics
A Levene’s test was conducted to test the homogeneity of variance for each indicator before running an ANOVA. This resulted in nine health indicators with homogenous variance: blood sugar level, HDL cholesterol, LDL cholesterol, total cholesterol, immunization/vaccination, insurance coverage, cancer screening detection, air quality index > 100, and self-rated health status (Appendix F). Twenty health indicators were found to have heterogeneous variance. These included the following indicators: blood triglycerides, alcohol abuse, BMI, diet and nutrition, drug or substance abuse,
family history of cancer, physical inactivity, smoking and tobacco use, sun protection, personal care needs, hypertension screening, HIV testing, high school diploma as a health indicator, supply of dentists, engagement in life, health literacy rate, major depression, having a sense of purpose in one’s life, race and ethnicity, and unemployment (Appendix G).
For the nine indicators with homogeneous variance, a one-way ANOVA was used. A one-way ANOVA Welch’s test was used for the 20 indicators with heterogeneous variance. As a result, 13 indicators were found to have no statistically significant mean differences among the three samples. This indicates that survey participants generally agreed on the relative level of importance of these indicators (Table 2). The 13 indicators were blood sugar level, blood triglycerides, HDL cholesterol, LDL cholesterol, total cholesterol, personal care needs, HIV testing, self-rated health status, supply of dentists, health literacy rate, immunization/vaccination, cancer screening detection, and air quality index >100. The means and standard deviations of their ratings are presented in Table 2. These 13 indicators were retained for the second step of the analysis. Significant mean differences were found among the other 16 indicators, which indicates that survey participants disagreed on their level of importance (Appendix H contains the post hoc results).
In the second step of the analysis, a one-way ANOVA was run for the 13 retained indicators (IV = 13 indicators; DV = individual survey responses). The indicators with no mean differences were grouped into the same level (Table 3) because they were rated as equally important and could not be ranked within a level. As a result, seven levels were formed (Table 3). Level 1 to Level 7 rankings were organized based on the mean importance of the health indicators from high to low within and between levels. Level 1 included blood sugar level and immunization/vaccination; Level 2 included LDL cholesterol; Level 3 included HDL cholesterol, blood triglycerides, cancer screening detection, and total cholesterol; Level 4 included health literacy rate; Level 5 included personal care needs and air quality index > 100; Level 6 included self-rated health status, and HIV testing; Level 7 included the supply of dentists.
In the third step of the analysis, one-way ANOVA was utilized to rank the seven levels of indicators (IV = 7 levels; DV = individual survey responses). There were seven indicators in Level 1, Level 2, and Level 3 (blood sugar level, immunization/vaccination, LDL cholesterol, HDL cholesterol, blood triglycerides, cancer screening detection, and total cholesterol). These indicators were found to be significantly more important to the survey participants than the six indicators ranked in Level 4, Level 5, Level 6, and Level 7 (ie, health literacy rate, personal care needs, air quality index > 100, self-rated health status, HIV testing, and supply of dentists; Table 4).
Among the more important indicators, the two indicators in Level 1 (blood sugar level and immunization/vaccination) were rated as significantly more important than the four indicators in Level 3 (HDL cholesterol, blood triglycerides, cancer screening detection, and total cholesterol). Therefore, based on the surveys and our analysis results, blood sugar level, and immunization/vaccination were the most important health indicators among these 13 health indicators, and the perspectives of the participants were agreed upon across all three samples.
Furthermore, among the less-important indicators, the indicator assigned to Level 4 (health literacy rate) was found to be significantly more important than the two indicators in Level 6 (self- rated health status and HIV testing), and the indicator assigned to Level 7 (supply of dentists). Additionally, the two indicators assigned to Level 5 (air quality index > 100 and personal care needs) were found to be significantly more important than the indicator assigned to Level 7 (supply of dentists). Therefore, the surveys and analysis results showed that the supply of dentists was the least important health indicator among the 13 health indicators with agreeable perspectives across the three samples. Additionally, the inferential statistical test results among levels provide more confidence in ranking the seven levels from the most important (ie, blood sugar level, immunization/ vaccination) to the least important (ie, supply of dentists). The statistical significance test results among the levels provide evidence for prioritizing 13 health indicators.
Survey instruments reliability
The survey instrument’s items (n = 29) showed good levels of internal reliability (Cronbach’s alpha = 0.912), as did each of the three subsets related to institutions where the survey was administered (see Table 5). Instruments with Cronbach’s alpha values equal to or higher than 0.7 or
0.75 are generally considered to be reliable [25].
Table 1. Descriptive statistics for all 29 health indicators
Health Indicators ug or substance abuse | ResearchMatch | Ohio University | Clemson University | Total | ||||||||
Mean | SD | n | Mean | SD | n | Mean | SD | n | Mean | SD | n | |
| 8.75 | 1.5 | 694 | 8.13 | 1.96 | 362 | 8.36 | 1.87 | 97 | 8.53 | 1.71 | 1153 | |
| oking, tobacco use | 8.8 | 1.52 | 694 | 8.02 | 2.06 | 362 | 8.18 | 1.84 | 97 | 8.5 | 1.77 | 1153 |
| cohol abuse | 8.34 | 1.71 | 694 | 7.56 | 2.03 | 362 | 8.06 | 1.64 | 97 | 8.07 | 1.84 | 1153 |
| ajor depression | 8.1 | 1.6 | 685 | 7.79 | 1.94 | 362 | 8.03 | 1.57 | 97 | 7.99 | 1.72 | 1144 |
| et and nutrition | 8.01 | 1.58 | 694 | 7.8 | 1.93 | 362 | 8.36 | 1.65 | 97 | 7.97 | 1.71 | 1153 |
| ood sugar level | 7.76 | 1.63 | 694 | 7.59 | 1.75 | 97 | 7.74 | 1.65 | 791 | |||
| ysical inactivity | 7.9 | 1.68 | 694 | 7.41 | 2.13 | 362 | 7.68 | 1.77 | 97 | 7.73 | 1.85 | 1153 |
| munization/vaccination | 7.49 | 2.12 | 694 | 7.67 | 2.3 | 362 | 7.72 | 2.4 | 97 | 7.57 | 2.2 | 1153 |
| pertension screening | 7.59 | 1.91 | 694 | 7.17 | 2.29 | 362 | 7.42 | 2.03 | 97 | 7.45 | 2.05 | 1153 |
| L cholesterol | 7.43 | 1.85 | 694 | – | – | – | 7.56 | 1.91 | 97 | 7.45 | 1.86 | 791 |
| ood triglycerides | 7.32 | 1.78 | 694 | – | – | – | 7.34 | 1.95 | 97 | 7.32 | 1.80 | 791 |
| DL cholesterol | 7.31 | 1.83 | 694 | – | – | – | 7.43 | 1.91 | 97 | 7.32 | 1.84 | 791 |
| ving a sense of purpose one’s life | 7.59 | 1.94 | 685 | 6.67 | 2.53 | 362 | 7.88 | 1.93 | 97 | 7.32 | 2.19 | 1144 |
| ncer screening tection | 7.22 | 2.06 | 694 | 7.26 | 2.3 | 362 | 7.49 | 2.09 | 97 | 7.25 | 2.14 | 1153 |
| tal cholesterol | 7.2 | 2.02 | 694 | – | – | – | 7.6 | 1.85 | 97 | 7.25 | 2.00 | 791 |
| alth literacy rate | 6.99 | 2.02 | 685 | 7.06 | 2.26 | 362 | 7.34 | 2.01 | 97 | 7.04 | 2.10 | 1144 |
| rsonal care needs | 6.82 | 2.08 | 694 | 7.01 | 2.3 | 362 | 7.21 | 2.1 | 97 | 6.91 | 2.16 | 1153 |
| r quality index > 100 | 6.74 | 1.92 | 685 | 6.76 | 2.13 | 362 | 6.89 | 1.93 | 97 | 6.76 | 1.99 | 1144 |
| mily history of cancer | 6.98 | 2.06 | 694 | 6.37 | 2.24 | 362 | 6.25 | 1.98 | 97 | 6.73 | 2.13 | 1153 |
| lf-rated health status | 6.63 | 2.2 | 694 | 6.62 | 2.15 | 362 | 6.92 | 1.89 | 97 | 6.65 | 2.16 | 1153 |
| V testing | 6.62 | 2.36 | 694 | 6.62 | 2.64 | 362 | 6.84 | 2.37 | 97 | 6.64 | 2.45 | 1153 |
| urance coverage | 6.4 | 2.88 | 694 | 6.79 | 2.91 | 362 | 7.26 | 2.51 | 97 | 6.6 | 2.87 | 1153 |
| MI | 6.86 | 2.28 | 694 | 5.8 | 2.54 | 362 | 6.64 | 2.45 | 97 | 6.51 | 2.42 | 1153 |
| pply of dentists | 6.53 | 2.02 | 685 | 6.34 | 2.26 | 362 | 6.04 | 1.99 | 97 | 6.43 | 2.10 | 1144 |
| n protection | 6.63 | 2 | 694 | 5.73 | 2.3 | 362 | 5.54 | 2.18 | 97 | 6.25 | 2.16 | 1153 |
| employed individual | 6.07 | 2.34 | 685 | 5.52 | 2.68 | 362 | 6.2 | 2.69 | 97 | 5.91 | 2.49 | 1144 |
| gagement in life | 6.38 | 2.18 | 685 | 4.82 | 2.87 | 362 | 6.4 | 2.33 | 97 | 5.89 | 2.54 | 1144 |
| gh school diploma as a alth indicator | 5.02 | 2.57 | 694 | 6.04 | 3.07 | 362 | 5.56 | 2.75 | 97 | 5.38 | 2.79 | 1153 |
| ce and ethnicity | 5.28 | 2.53 | 685 | 4.32 | 2.76 | 362 | 5.02 | 2.85 | 97 | 4.96 | 2.67 | 1144 |
BMI = Body mass index; SD = Stadnard deviation; -, no value.
Table 2. The 13 indicators with non-significant mean differences across the three samples
| Health indicator | n | Mean | SD | Source | ANOVA/t-test Sig. |
Blood sugar level | 694 | 7.756 | 1.6303 | ResearchMatch | 0.345 |
| 97 | 7.588 | 1.7485 | Clemson | ||
| 791 | 7.736 | 1.645 | Total | ||
Blood triglycerides | 694 | 7.318 | 1.7786 | ResearchMatch | 0.914 |
| 97 | 7.34 | 1.952 | Clemson | ||
| 791 | 7.32 | 1.7995 | Total | ||
HDL cholesterol | 694 | 7.307 | 1.8264 | ResearchMatch | 0.527 |
| 97 | 7.433 | 1.9143 | Clemson | ||
| 791 | 7.322 | 1.8366 | Total | ||
LDL cholesterol | 694 | 7.43 | 1.8489 | ResearchMatch | 0.531 |
| 97 | 7.557 | 1.9147 | Clemson | ||
| 791 | 7.446 | 1.8563 | Total | ||
Total cholesterol | 694 | 7.203 | 2.0177 | ResearchMatch | 0.069 |
| 97 | 7.598 | 1.8465 | Clemson | ||
| 791 | 7.252 | 2.0006 | Total | ||
Personal care needs | 694 | 6.816 | 2.0786 | ResearchMatch | 0.140 |
| 362 | 7.011 | 2.3026 | OU | ||
| 97 | 7.206 | 2.1013 | Clemson | ||
| 1153 | 6.91 | 2.1551 | Total | ||
HIV testing | 694 | 6.616 | 2.361 | ResearchMatch | 0.690 |
| 362 | 6.619 | 2.6439 | OU | ||
| 97 | 6.835 | 2.3747 | Clemson | ||
| 1153 | 6.635 | 2.453 | Total | ||
Self-rated health status | 694 | 6.63 | 2.2032 | ResearchMatch | 0.445 |
| 362 | 6.619 | 2.1504 | OU | ||
| 97 | 6.918 | 1.8912 | Clemson |
| 1153 | 6.651 | 2.1619 | Total | ||
Supply of dentists | 685 | 6.525 | 2.0215 | ResearchMatch | 0.068 |
| 362 | 6.34 | 2.2572 | OU | ||
| 97 | 6.041 | 1.9944 | Clemson | ||
| 1144 | 6.425 | 2.0999 | Total | ||
Health literacy rate | 685 | 6.986 | 2.0199 | ResearchMatch | 0.260 |
| 362 | 7.061 | 2.2617 | OU | ||
| 97 | 7.34 | 2.0098 | Clemson | ||
| 1144 | 7.039 | 2.0991 | Total | ||
Immunization/ vaccination | 694 | 7.494 | 2.1184 | ResearchMatch | 0.372 |
| 362 | 7.666 | 2.3041 | OU | ||
| 97 | 7.722 | 2.3968 | Clemson | ||
| 1153 | 7.567 | 2.2023 | Total | ||
Cancer screening detection | 694 | 7.217 | 2.0625 | ResearchMatch | 0.515 |
| 362 | 7.257 | 2.3045 | OU | ||
| 97 | 7.485 | 2.0922 | Clemson | ||
| 1153 | 7.252 | 2.1432 | Total | ||
Air quality index > 100 | 685 | 6.736 | 1.9232 | ResearchMatch | 0.784 |
| 362 | 6.76 | 2.125 | OU | ||
| 97 | 6.887 | 1.9304 | Clemson | ||
| 1144 | 6.756 | 1.9885 | Total |
IV = The three samples; DV = Individual survey data; SD = Standard deviation.
Table 3. The seven levels of health indicators with no significant mean differences within levels
Level | Indicator # | Individual Survey Data [DV] | ||||
| Indicators with no group mean differences [IV] | n | Mean | SD | ANOVAresults within groups | ||
1 | 1 | Blood sugar | 791 | 7.74 | 1.65 | 0.053 |
2 | Immunization/ vaccination | 1153 | 7.57 | 2.20 | ||
| 2 | 3 | LDL cholesterol | 791 | 7.45 | 1.86 | 0 |
3 | 4 | HDL cholesterol | 791 | 7.32 | 1.84 | 0.773 |
| 5 | Blood triglycerides | 791 | 7.32 | 1.80 | ||
6 | Cancer screening detection | 1153 | 7.25 | 2.14 | ||
| 7 | Total cholesterol | 791 | 7.25 | 2.00 | ||
| 4 | 8 | Health literacy rate | 1144 | 7.04 | 2.10 | 0 |
| 9 | Personal care needs | 1153 | 6.91 | 2.16 | 0.075 | |
5 | 10 | Air quality index > 100 | 1144 | 6.76 | 1.99 | |
6 | 11 | Self-rated health status | 1153 | 6.65 | 2.16 | 0.873 |
| 12 | HIV testing | 1153 | 6.64 | 2.45 | ||
| 7 | 13 | Supply of dentists | 1144 | 6.43 | 2.10 | 0 |
| Total | 14305 | 7.08 | 2.101 |
IV = The levels of indicators; DV = Individual survey responses; SD = Standard deviation.
Table 4. ANOVA post hoc test results for the seven levels of indicators
Health Indicator | Health indicator | Sig. |
| Level 1 | Level 2 | 0.582 |
| Level 1 | Level 3 | <0.001 |
| Level 1 | Level 6 | <0.001 |
| Level 1 | Level 7 | <0.001 |
| Level 2 | Level 4 | 0.006 |
| Level 2 | Level 3 | 0.678 |
| Level 2 | Level 5 | <0.001 |
| Level 2 | Level 7 | <0.001 |
| Level 3 | Level 4 | 0.063 |
| Level 3 | Level 5 | <0.001 |
| Level 4 | Level 5 | 0.274 |
| Level 4 | Level 1 | <0.001 |
| Level 4 | Level 6 | <0.001 |
| Level 5 | Level 6 | 0.136 |
| Level 5 | Level 1 | <0.001 |
| Level 5 | Level 7 | <0.001 |
| Level 6 | Level 7 | 0.207 |
| Level 6 | Level 2 | <0.001 |
| Level 6 | Level 3 | <0.001 |
| Level 7 | Level 2 | <0.001 |
| Level 7 | Level 3 | <0.001 |
| Level 7 | Level 4 | <0.001 |
IV = The levels of indicators, DV = Individual survey responses.
Table 5. Cronbach’s alpha for the survey instrument (entire survey and subscales)
| Survey components | Data analyzed | Cronbach’s alpha |
Entire survey (all items) | All three samples | 0.912 |
| ResearchMatch | 0.922 | |
| Ohio University | 0.893 | |
| Clemson University | 0.925 | |
Survey subscales | Health risks and behaviors indicators | 0.795 |
| Healthcare | 0.613 | |
| Healthcare provider supply | 0.831 | |
| Blood tests in physical exams | 0.934 | |
| Other health indicators | 0.823 |
Discussion
Interpretation of the results
Among all three samples, the ranking of the importance of the 13 (13/29, 44.8%) health indicators showed agreement (Table 3). However, these health indicators were not necessarily more important than the other 16 health indicators; instead, participants were observed to have more consistently perceived importance among these 13 health indicators. When we compared the 13 health indicators (Table 3) and their corresponding ranks in Table 1, we noticed that the 13 health indicators were placed between the 6th and 24th rankings in Table 1. This indicated more agreement among participants regarding the middle-ranked health indicators than the higher- or lower-ranked ones. The perspectives were more heterogeneous for those higher- or lower-ranked health indicators. Noticeably, the currently wide-used standard individual health indicator, the self-rated health status, was ranked 20th based on the results of the descriptive statistics. These results indicate a need for new and improved health indicators.
Among these 13 health indicators found in the seven levels, all levels were not significantly different from their immediate next level (Table 4), ie, there were no significant differences between Levels 1 and 2 (ie, between n and n + 1). There were, however, significant differences between Level 1 and Levels 3 to 7 (ie, between n and any level > n + 1). These results pertain to the further prioritization of health indicators.
Given descriptive statistics and inferential test results, our findings among the 13 health indicators can reasonably be generalized to some extent to a broader population beyond our survey respondents. We do not claim the complete generalizability of our results mainly because our respondents were not perfectly representative of the composition of the American population. However, we believe that the 13 health indicators and their importance rankings within and among levels can provide substantial and useful evidence when such indicators need to be prioritized.
Cronbach’s alpha is one of the more cited statistics for informing internal consistency for the items of an instrument. If Cronbach’s alpha is greater than 0.7, the instrument is reliable [25]. The Cronbach’s alpha for the entire survey among the three samples was between 0.893 and 0.925. This indicates that we developed a reliable survey instrument. When examining the subscales, only the healthcare category, which included vaccination/immunization, insurance coverage, and personal care need, was below 0.7. The items in this category are among the most discussed topics in health care in the United States. Understandably, the reliability is lower since the respondents have relatively less consistent perspectives for these items.
Significance and comparison with related research
This study provides a more comprehensive understanding of the indicators affecting an individual’s health status, particularly as compared to self-rated health status, the most commonly used health status measurement [2]. Although there are advantages associated with using a single health indicator during clinical encounters, we believe that the multidimensional measurement of an individual’s health status may be more objective and can provide additional insights into the individual’s health status, particularly if we are concerned with improving and maximizing the preventive healthcare services offered. Obtaining these public perspectives is the first step toward a more accurate and effective measurement of individual health status.
This work can be potentially used in two ways: 1) more comprehensive and objective
measurement of an individual’s health status, and 2) development of a health index for an individual. Additionally, these results can be used to prioritize various health indicators, eg, to distinguish between core and secondary indicators. They can also be referenced by designers and developers for EHR systems, personal health records (PHR) systems, or other data capture and analysis applications to determine what health indicators to include in the systems. Furthermore, these results can contribute to developing a health index, which can be used to stratify healthy research participants to make them more comparable. This would be analogous to the Charlson Comorbidity Index [26] or propensity scores [27], which are broadly used in clinical epidemiology data analytics, both of which, however, are disease-oriented. Although the health indicators reported here are not in a formula format, this will be a focus for future research. These results set the foundation for further weighting, prioritizing, and validating the health indicators via additional data resources.
Additionally, these measurements can track overall health status, measure the outcomes of preventive services, or aggregate data to examine community health. Although having more data points provides increased accuracy and specificity for health indicators embedded within an EHR or PHR, it is important to consider clinician burnout [28] in using technology. Therefore, it is necessary to be mindful of the impacts that creating more data capture requirements or expectations of clinical users may have. In this regard, prioritizing health indicators is a necessary step.
Over the years, other systems have been developed to assess various health risks and associations. The Johns Hopkins Adjusted Clinical Group (ACG) system, developed and maintained by Johns Hopkins University for over 30 years, is a global tool used in population health analytics [29]. This system is focused on chronic conditions and comorbidities, and its goal is therefore fundamentally different from ours, which is to measure individual health (not disease) status more accurately. Another system, the Committee on Quality Measures for the Healthy People Leading Health Indicators [3], focuses more on quality measures with an aim to align the measurements within a framework of assessment, improvement, and accountability. The focus, however, is on
monitoring and reporting at the population level, not necessarily individual health [3].
There are other health-related surveys broadly used worldwide. For example, the SF-36, developed by the RAND Corporation [30], measures life quality and health outcomes. Similarly, compared with the related but smaller SF-12 [31], our health indicators provide a more comprehensive measurement beyond physical and mental health. The PHQ-9 [32] is a validated tool to measure depression severity. However, we were looking for more objective indicators to measure an individual’s physical and mental health status in our work.
Our health indicators have good but not all-inclusive coverage. The Committee on the Recommended Social and Behavioral Domains and Measures for Electronic Health Records of IOM identified measures across the individual and neighborhood levels that involve sociodemographic, psychological, and behavioral data [4, 5]. Among the 17 domains identified by the committee [4, 5], ten were included in our 29 health indicators. Healthy People 2030 [33] proposed 22 leading health indicators for different age groups, of which 16 are included in our health indicators.
Limitations of the current study
The main limitation of this study is that it is only the first step in determining the importance of these health indicators, and, notably, the results, which are based on public perspectives, are subjective. Further validation of these results via additional objective measures (such as healthcare expenditure by disease category [34] and the burden of illness estimates for specific disease categories [35]) is needed to support these findings. Currently, for each health indicator, the sample size of valid responses ranges from 791 to 1153. We recognize that larger sample sizes may generate more conclusive and generalizable results. Therefore, our results about the 13 health indicators, even though they are inferential statistics, should be treated as preliminary baseline results; future research may be needed to validate these findings in other settings.
Another limitation concerns the survey respondents. Females comprised the majority of
survey respondents, at 72.1%, 77.7%, and 69% from Ohio University, ResearchMatch, and Clemson University, respectively. We noticed a similar phenomenon in other studies conducted via ResearchMatch. While we are pleased with the relatively large sample size, responses may reflect the perspectives of well-educated females more than those of other groups. For example, survey respondents with an educational level of college and beyond represent 54.6%, 82.2%, and 74% of Ohio University, ResearchMatch, and Clemson University respondents.
In addition to the distribution imbalance in gender and educational background among our respondents, we also noticed that race/ethnicity groups (Appendix E) are not perfectly representative of the composition of the American population. The breakdown by racial groups among respondents to our surveys was: White American, 87.3%; African American, 3.3%; Hispanic and Latino American, 2.2%; Asian American, 1.6%; Native American, 0.4%; two or more races, 2%. We recognize that our dataset’s gender and ethnicity imbalances are limitations of our current convenience sampling method. In the future, a stratified random sampling based on census-based population demographical data might provide more representative results and be a better option. This is a critical point that should be considered when using the results from this study.
Future research
We foresee several potential directions in which to continue this project. Our primary goal for future research is to validate the results obtained from the three completed surveys. This can be accomplished in several ways. Because we wish to measure individual health status accurately over time, the use of longitudinal data would be ideal. One data source is a citizen science project initiated by the National Institutes of Health, the All of US [36] research program. Another source is the UK Biobank initiated in the United Kingdom [37], but the most ideal source would be well-documented longitudinal data of a group of individuals that include not only their EHR data but also other data that correlate with our health indicators. Such ideal data sources would allow for examining the
corresponding health indicators and validation of the importance of health indicators via EHR records and additional health-related data. In this way, public perspectives will be considered along with more concrete quantitative evidence to ensure more confidence in prioritizing health indicators and using them for various purposes.
Additionally, to mitigate the effect of the current imbalances seen in respondents by gender, race/ethnicity, and other factors, we could explore the possibility of stratified random sampling to proactively select more representative participants. The respondent pool can be more proportionally representative of the composition of the American population. As a potential future project, we may also explore the possible correlations between the demographic variables and the rating results.
Conclusion
Well-designed health indicators are critical tools needed to accurately measure individual health status. They enable the determination of effective preventive services and verify their outcomes. Obtaining the public’s perspective on specific health indicators is the first step toward prioritizing them for analytical and clinical use. The current study found that the top five rated health indicators were drug and substance abuse, smoking and tobacco use, alcohol abuse, major depression, and diet and nutrition. Our respondents, however, had heterogeneous views on the top-and bottom-rated health indicators. The middle 13 health indicators were rated more homogeneously among all the respondents. These 13 health indicators were separated into seven levels based on their perceived importance, providing further evidence used to prioritize these health indicators. Levels 1 to 7 were organized based on the mean importance of health indicators from high to low within and between each level. Level 1 included blood sugar level and immunization/vaccination; Level 2 included LDL cholesterol; Level 3 included HDL cholesterol, blood triglycerides, cancer screening detection, and total cholesterol; Level 4 included health literacy rate; Level 5 included personal care needs and air quality index > 100; Level 6 included self-rated health status, and HIV testing; and Level 7 had the
supply of dentists. The results of this study can provide evidence to EHR or PHR system designers and developers, which they can then use to select health indicators to incorporate into their systems.
Acknowledgements
We want to thank all the respondents who answered the surveys. Without their input, this study would not have been possible.
Funding
This work is partially supported by the Clemson University, College of Behavioral, Social, and Health Sciences, Department of Public Health Sciences, which provided start-up funding for Xia Jing.
Conflict of Interest
We have no conflicts of interest to declare.
Data statement
This manuscript’s datasets and statistical analysis codes are available by request from the corresponding author.
Ethics statement
This study was approved by the Institutional Review Boards of Ohio University (17-X-142) and Clemson University (IRB2019-441).
Author contributions:
Conceptualization and design: Xia Jing, Temiloluwa Sokoya, Francisca Lekey, Sebastian Diaz Acquisition, analysis, or interpretation of data: Xia Jing, Temiloluwa Sokoya, Yuchun Zhou, Sebastian Diaz, Timothy Law, Lina Himawan, Lu Shi, Ronald W. Gimbel
Drafting of the manuscript: Temiloluwa Sokoya, Yuchun Zhou, Xia Jing
Critical revision of the manuscript: Xia Jing, Temiloluwa Sokoya, Yuchun Zhou, Sebastian Diaz, Timothy Law, Lina Himawan, Lu Shi, Ronald W. Gimbel
Statistical analysis: Temiloluwa Sokoya, Yuchun Zhou, Sebastian Diaz, Lina Himawan, Xia Jing Obtaining funding: Xia Jing
Supervision: Xia Jing, Ronald W. Gimbel
Abbreviations
ACG: Adjusted Clinical Group BMI: body mass index
DV: dependent variable EHR: electronic health record
HDL: high-density lipoprotein
HIV: human immunodeficiency virus IOM: Institute of Medicine
IRB: Institutional Review Board IV: independent variable
LDL: low-density lipoprotein
ONC: Office of the National Coordinator for Health Information Technology OU: Ohio University
PHQ-9: Patient Health Questionnaire-9 PHR: personal health record
SD: standard deviation
SF-36: 36-item Short Form Survey UK: United Kingdom
References
- C, W., Health indicators: a review of reports currently in use. . 2008.
- Medicine, I.o., Vital Signs: Core Metrics for Health and Health Care Progress, ed. D. Blumenthal, E. Malphrus, and J.M. McGinnis. 2015, Washington, DC: The National Academies Press. 308.
- IOM (Institute of Medicine), Toward quality measures for population health and the leading health indicators. 2012, Washington, DC: : The National Academies Press.
- Institute of Medicine, Capturing Social and Behavioral Domains and Measures in Electronic Health Records: Phase 2. 2014, Washington, DC: The National Academies Press. 374.
- Institute of Medicine, Capturing Social and Behavioral Domains in Electronic Health Records: Phase 1. 2014, Washington, DC: The National Academies Press. 136.
- CDC National Center for Health Statistics. National Health Interview Survey. 2021 [cited 2021 Aug 29]; Available from: https://www.cdc.gov/nchs/nhis/index.htm.
- Hensrud, D.D., Clinical preventive medicine in primary care: background and practice: 1. Rationale and current preventive practices. Mayo Clin Proc, 2000. 75(2): p. 165-72.
- Hensrud, D.D., Clinical preventive medicine in primary care: background and practice: 2. Delivering primary preventive services. Mayo Clin Proc, 2000. 75(3): p. 255-64.
- Jing, X., et al., Health indicators within EHR systems in primary care seṄngs: Availability and presentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016. 10038 LNCS.
- Clarke, T.C., et al., Early Release of Selected Estimates Based on Data From the National Health Interview Survey, 2015. 2016, CDC National Center for Health Statistics.
- Medicine, I.o., State of the USA Health Indicators: Letter Report. 2009, Washington, DC: The National Academies Press. 98.
- Gallup-Healthways Well-Being Index, State of global well-being: results of the Gallup- Healthways global well-being index (2014). 2014.
- Broussard, D.L., et al., Core state preconception health indicators: a voluntary, multi-state selection process. Matern Child Health J, 2011. 15(2): p. 158-68.
- Hunter, B.M., et al., National health policy-makers’ views on the clarity and utility of Countdown to 2015 country profiles and reports: findings from two exploratory qualitative studies. Health Res Policy Syst, 2014. 12: p. 40.
- Wold, C. Health Indicators: a review of reports currently in use (2008). 2009 [cited 2017; Available from: http://www.cherylwold.com/images/Wold_Indicators_July08.pdf.
- Healthy People 2020, H., Healthy People 2020 Leading Health Indicators: Progress Update. 2020.
- National Academies of Sciences, E., Medicine, Leading Health Indicators 2030: Advancing Health, Equity, and Well-Being. 2020, Washington, DC: The National Academies Press. 172.
- Medicine, I.o., State of the USA Health Indicators: Letter Report. 2009, Washington, DC: The National Academies Press.
- Medicine, I.o., Vital Signs: Core Metrics for Health and Health Care Progress. 2015, Washington, DC: The National Academies Press.
- National Center for Health Statistics, C. National Health Interview Survey. 2022 [cited 2022 April 12]; Available from: https://www.cdc.gov/nchs/nhis/.
- CMS. Physician Quality Reporting System (PQRS) Measure List. 2016; Available from: https:// www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/PQRS/ MeasuresCodes.
- Index, G.-H.W.-B., State of global well-being: results of the Gallup-Healthways global well- being index (2014). 2015.
- Jing, X., et al., Health indicators to measure individual health status: public perspectives- preliminary results, in AMIA Summit 2019. 2019: San Francisco. p. 970-971.
- Vanderbilt University Medical Center. ResearchMatch.org. [cited 2021 Sept 19]; Available from: https://www.researchmatch.org/.
- Christmann, A. and S. Van Aelst, Robust estimation of Cronbach’s alpha. Journal of multivariate analysis, 2006. 97(7): p. 1660-1674.
- Charlson, M.E., et al., A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis, 1987. 40(5): p. 373-83.
- Austin, P.C., An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivariate Behav Res, 2011. 46(3): p. 399-424.
- McPeek-Hinz, E., et al., Clinician Burnout Associated With Sex, Clinician Type, Work Culture, and Use of Electronic Health Records. JAMA Network Open, 2021. 4(4): p. e215686-e215686.
- The John Hopkins University. John Hopkins ACG System. [cited 2021 Sept 19]; Available from: https://www.hopkinsacg.org/.
- Ware, J.E., Jr. and C.D. Sherbourne, The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care, 1992. 30(6): p. 473-83.
- Corporation, R. 12-Item Short Form Survey (SF-12). [cited 2021 Sept 28]; Available from: https://www.rand.org/health-care/surveys_tools/mos/12-item-short-form.html.
- Kroenke, K., R.L. Spitzer, and J.B. Williams, The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med, 2001. 16(9): p. 606-13.
- HHS Office of Disease Prevention and Health Promotion. Helthy People 2030-Leading Health Indicators. [cited 2021 Aug 30]; Available from: https://health.gov/healthypeople/objectives-and-data/leading-health-indicators.
- Ozieh, M.N., et al., Trends in healthcare expenditure in United States adults with chronic kidney disease: 2002-2011. BMC Health Serv Res, 2017. 17(1): p. 368.
- Cannon, A., et al., Burden of Illness in Type 2 Diabetes Mellitus. J Manag Care Spec Pharm, 2018. 24(9-a Suppl): p. S5-s13.
- The “All of Us” Research Program. New England Journal of Medicine, 2019. 381(7): p. 668- 676.
- About UK Biobank.
Appendices
Appendix A. The online survey instrument
Appendix B. Definitions of the health indicators
Appendix C. Records from three datasets: before and after data cleaning
Appendix D. Codebook
Appendix E. Demographic descriptive statistics for all respondents Appendix F. Total of nine indicators with homogenous variance Appendix G. Total of 20 indicators with heterogeneous variance
Appendix H. Total of 16 indicators with significant mean differences post hoc results for the three samples
Supplementary Files
Figures
The overall design of the project; public perspectives are the focus of this manuscript; the three sections connected via green arrows were completed, and the far-right section is for future work.
The primary analytic strategies and overall results of each step.
Multimedia Appendixes
Survey instrument: Health indicators to measure an individual’s health status: a public perspective survey. URL: http://asset.jmir.pub/assets/35249e99c63e61cdc34253dc4d4a2f1d.pdf
Health indicators-definitions used in the survey.
URL: http://asset.jmir.pub/assets/04c9982eb7b5c0b2d585473e17415c4c.pdf
Records from three datasets: before and after data cleaning.
URL: http://asset.jmir.pub/assets/f99fb82846b0f9bd7dfd2d071940b92d.pdf
Codebook.
URL: http://asset.jmir.pub/assets/8ff20738a2db71fe01bc3c05d2d20f89.pdf
Demographic descriptive statistics for all respondents.
URL: http://asset.jmir.pub/assets/53fa6bfedb75d0fd67997621051ad69a.pdf
Total of nine indicators with homogenous variance.
URL: http://asset.jmir.pub/assets/0a744f57e794f7fd52efd3ad40890c89.pdf
Total of 20 indicators with heterogeneous variance.
URL: http://asset.jmir.pub/assets/386814ecd2396f2ecdf4107a85b53d55.pdf
Total of 16 indicators with significant mean differences post hoc results for the three samples. URL: http://asset.jmir.pub/assets/d7da0aeee2ee4e5de1c203914e795558.pdf