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. 2023 Jun 2;18(6):e0286691. doi: 10.1371/journal.pone.0286691

Is anyone truly healthy? Trends in health risk factors prevalence and changes in their associations with all-cause mortality

Winnie W Yu 1, Rubin Pooni 1, Chris I Ardern 1, Jennifer L Kuk 1,*
Editor: Vikramaditya Samala Venkata2
PMCID: PMC10237477  PMID: 37267338

Abstract

Objective

The purpose of the study was to determine trends in the prevalence of individual health risk factors across time and to examine if their associations with mortality have changed over time.

Methods

Data from the National Health and Nutrition Examination Surveys (NHANES III– 1988–1994 and NHANES 1999–2014; age ≥20 years) was used to examine differences in the odds ratio (OR) of 5-year mortality risk associated with various common health risk factors over the two survey periods using weighted logistic regression analysis adjusting for age, sex, obesity category and white ethnicity (n = 28,279).

Results

Over 97% of individuals had at least one of the 19 risk factors examined with no difference in the prevalence over time (P>0.34). The prevalence of lifestyle, social/mental and physical risk factors (2.2 to 19.1%) increased over time (P<0.0002), while the prevalence of having physiological risk factors decreased by ~6.5% (P<0.0001). Having any lifestyle or social/mental risk factor was significantly associated with a higher 5-year OR for mortality risk in 1999–2014, than 1988–94. In particular, having low education or use of mental health medication were not associated with mortality risk in 1988–94 (P>0.1), but were significantly associated with a higher 5-year OR for mortality in 1999–2014 (P<0.0001). Conversely, physiological risk factors were more weakly related with mortality risk in 1988–1994, than 1999–2014. Having any physical risk factor, and poor self-rated health were similarly related with 5-year mortality risk at both timepoints.

Conclusion

Health risk factors have both increased and decreased in prevalence over time, along with changes in the association between many of the risk factors and mortality risk. Taken together, these changes complicate interpretation of temporal trends and warrant cautious interpretation of population health patterns based on surveillance data.

Introduction

Surveillance of known risk factors has been a common aim for public health and research. Cardiovascular disease (CVD) and cancer have consistently been the leading causes of mortality in the U.S. [1], though the prevalence of CVD mortality has reduced over time [2]. It is well known that lifestyle and social factors are associated with CVD and cancer risk [3]. However, little attention to date has been paid to the potential changing association between these risk factors and morbidity and mortality over time. Indeed, changes in lifestyle behaviours in addition to the built environment, health care or social programs can all influence many dimensions of chronic health risk [4]. For example, there have been decreases in the prevalence of CVD risk over time which may be reflective of the improvements in medications and treatments for CVD [5]. These improvements in health care may have made lifestyle behaviours, such as physical activity and diet, less beneficial over time. Conversely, over a similar time period, the prevalence of new cancer cases has increased [3], while cancer mortality rates have declined [6]. Over time, there have also been improvements in the stigma of mental health and addiction, and public health promotion efforts to decrease smoking [7] and alcohol abuse in youth [8]. Decreases in stigma and/or later adoption of drug use may result in better care and lower mortality risk.

Thus, the objective of this study was to examine the changes in the prevalence of various health risk factors and their association with mortality risk over time.

Methods

Study population

The current study is a cross-sectional comparison of two nationally representative samples from 1988–1994 versus 1999–2014 with 5-year mortality follow-up. Participant data was obtained from the publicly available National Health and Nutrition Examination Surveys (NHANES) III (n = 33,994) and continuous between 1999 and 2014 (n = 82,901), so that 5-year mortality follow-up would be available. All survey participants gave written informed consent. NHANES III is a nationally representative cross-sectional survey that was conducted between 1988 and 1994 by the National Center for Health Statistics of the Centers for Disease Control and Prevention, in persons aged 2 months or older. The Continuous NHANES are a series of nationally representative cross-sectional surveys that are released biannually starting in 1999. As this is an analysis of publicly available data, the current study did not require ethics approval from our institutional review board.

Both surveys are collected using a stratified, multistage, probability cluster design of the non-institutionalized U.S. population. Data was collected at home interviews and health examinations at Mobile Examination Centers (MEC). Details regarding study design, protocols, laboratory and clinical measurements and analytical guidelines have been previously published elsewhere [911].

Participants were included in the analytical sample if they were 20 years or older (n = 62,575). Participants were further excluded if they were pregnant or had missing body mass index (BMI) leaving 48,003 participants. Participants with missing data for lifestyle factors (physical activity; high fat intake; smoking status; alcohol intake), physiological health risk factors (high blood pressure; hyperglycemia; dyslipidemia; CVD; cancer; lung problems), social/mental factors (lack of health insurance; low education; food insecurity; low income; use of mental health medications), physical factors (pain medications; arthritis; limitations of activities of daily living (>60 yr of age only); obesity) and general health status were excluded from the analytical dataset, leaving 28,306 participants.

To make the follow-up more comparable between NHANES III and continuous, five-year mortality status was determined using public access National Center for Health Statistics (NCHS) Mortality Linkage Files with follow-up to December 31 at years 2006, 2011, 2015 and 2019. Follow-ups were truncated at 5 years. Deaths that occurred after a follow-up of greater than 5 years were recoded as censored events. Individuals with less than 5-year follow-up were excluded from the analytical dataset, leaving 28,279 participants.

Defining health risk factors

Health risk factors were chosen because they are common health risk factors that were consistently available across all survey years. Risk factors are classified as the absence (0) or presence (1) of any of the risk factors within these 4 categories:

Lifestyle–no physical activity; high fat diet; current smoking; alcohol consumption

Physiological–high blood pressure; hyperglycemia; dyslipidemia; CVD; cancer; lung problems

Social/Mental–lack of health insurance; low education; food insecurity; low income; use of mental health medication

Physical–arthritis; use of pain medication; obesity; limitations of activities of daily living (> 60 only).

General self-rated health reported as: excellent, very good, good, fair and poor, was re-categorized as poor or not poor self-rated health.

Lifestyle risk factors

Physical activity was self-reported as the amount of moderate/vigorous leisure time physical activity over the past month. Those reporting none were categorized as ‘No Physical Activity’. High fat diet was classified as a fat consumption of greater than 35% of total calories from the diet records. Current smoking status was assessed by self-report. Excessive alcohol consumption was classified as consuming greater than an average of 2 drinks on the days they consume alcohol for men and greater than 1 drink per day for women over the past 12 months [12].

Physiological risk factors

Blood pressure, fasting glucose, triglycerides, cholesterol, LDL and HDL were assessed at the mobile exam center and analyzed using standard methods [13]. High blood pressure was classified as measured blood pressure > = 140/90 mmHg [14], self-report diagnosed hypertension or use of hypertensive medication. High lipid was classified as measured fasting cholesterol > = 5.2 mM [15], triglycerides > = 2.0 mM [15], self-report diagnosed high cholesterol or use of lipid medication. High fasting glucose was classified as measured blood glucose > = 6.1 mM [16], self-report diagnosed diabetes or use of diabetic medication. Self-reported CVD was classified as ever having myocardial infarction, coronary or congenital heart disease, or stroke, or reported use of CVD medications. History of any cancer was assessed by self-report. Lung disease was self-reported history of emphysema or chronic bronchitis.

Social/Mental risk factors

Participants were asked about their access to health insurance (none versus any), highest educational attainment (less than high school versus high school or more), income (Poverty Income Ratio (PIR) > = 1.3) [17] and food insecurity (any food insecurity versus none). Use of mental health medications was considered as self-reported use of anxiety, antipsychotic or depression medications.

Physical risk factors

Limitations in activities of daily living (ADL) were assessed in adults over 60 years of age [18] and were classified as difficulties with any of the following: walking for a quarter mile; walking up ten steps; stooping, crouching or kneeling; lifting or carrying; house chores; preparing meals; managing money; walking between rooms on the same floor; standing up from an armless chair; getting in and out of bed; using a fork, knife or drinking from a cup; or dressing themselves. This list was used as these ADLs were asked in all surveys.

Individuals were asked to self-report arthritis and use of pain medication. Obesity was classified as having a body mass index (BMI) over 30 kg/m2 [19]. Measured height and weight were assessed using standard protocols [20].

Statistical analysis

Prevalence for each health risk factor and category were determined for NHANES III and averaged over four-year periods for NHANES continuous to improve the stability of the estimated prevalence, particularly for risk factors that were more rare. Linear regression was used to determine differences in prevalence over time within the samples combined. Logistic regression models were used to estimate the 5-year odds ratios (OR) with 95% confidence intervals for all-cause mortality with adjustment for age, sex (male versus female), BMI category (underweight, normal weight, overweight and obesity) and white ethnicity separately within the NHANES III and Continuous cycles. Estimates stratified by survey (i.e., NHANES III and continuous separately) were weighted to be nationally representative. For analyses comparing the two surveys (NHANES III and continuous differences), it was not possible to weight the analysis to be nationally representative. Statistical analyses were performed using the SAS statistical software (version 9.4; SAS Institute, Cary, NC) with significance defined at p<0.05.

Results

Characteristics of the NHANES III and continuous surveys 1999–2014 are presented in Table 1. The weighted prevalence of all 19 risk factors were categorized into 4 categories: Lifestyle, Physical, Physiological, and Mental/Social. Changes in prevalence over time was noted in 18 of the 19 health risk factors in men and/or women (P<0.05, Table 1). Over 97% of individuals had at least one of the 19 risk factors at all time points with no difference in the prevalence of having any risk factor over time (P>0.34, M: 98.1 to 98.8%; F: 98.1 to 97.5%). Self-reported ‘poor’ health subtly decreased in prevalence over the study period in both men and women (P<0.02, M: 14.3 to 13.9%; F: 16.3 to 14.2%), with no sex difference.

Table 1. Participant characteristics between 1988–94 to 2011–14 in men and women.

Men 1988–94 99–02 03–06 07–10 11–14 p Trend over Time
Age 43.8 (0.5) 42.5 (0.4) 43.7 (0.6) 44.6 (0.4) 49.4 (0.6) 0.07
BMI 26.5 (0.1) 27.4 (0.1) 28.2 (0.2) 28.6 (0.2) 28.7 (0.2) <0.0001
Health Factor (#) 2.4 (0.1) 2.4 (0.1) 2.5 (0.1) 2.6 (0.1) 2.6 (0.1) <0.0001
Health Risk factor (%)
Lifestyle 81.3 (1.1) 81.7 (1.3) 81.1 (0.8) 84.0 (1.0) 83.6 (1.0) <0.0001
Physiological 85.2 (1.3) 75.4 (1.3) 74.0 (1.2) 75.8 (0.9) 78.6 (1.4) <0.0001
Social/Mental 38.1 (1.8) 45.1 (1.9) 45.3 (1.6) 46.7 (1.5) 48.7 (2.1) 0.0002
Physical 33.3 (1.2) 38.8 (1.1) 47.4 (1.6) 49.4 (1.5) 52.5 (1.6) <0.0001
No Risk Factors 1.9 (0.4) 2.7 (0.6) 2.4 (0.4) 2.1 (0.4) 1.2 (0.3) 0.82
Women 1988–94 99–02 03–06 07–10 11–14 p Trend over Time
Age 44.8 (0.6) 43.4 (0.4) 44.8 (0.5) 45.2 (0.6) 50.3 (0.6) 0.03
BMI 26.4 (0.2) 27.6 (0.2) 27.9 (0.2) 28.1 (0.1) 29.0 (0.2) <0.0001
Health Factor (#) 2.5 (0.1) 2.5 (0.1) 2.6 (0.1) 2.6 (0.1) 2.8 (0.1) <0.0001
Health Risk Factor (%)
Lifestyle 77.8 (1.1) 84.4 (1.1) 83.8 (1.2) 87.5 (1.0) 86.2 (1.3) <0.0001
Physiological 85.7 (1.1) 76.2 (1.1) 73.3 (1.4) 74.3 (0.9) 79.3 (1.3) <0.0001
Social/Mental 38.3 (1.7) 45.0 (1.8) 47.6 (1.9) 50.8 (1.2) 51.6 (2.4) <0.0001
Physical 43.9 (1.5) 48.6 (1.2) 53.7 (1.1) 51.7 (1.2) 59.0 (1.6) <0.0001
No Risk Factors 1.9 (0.4) 2.5 (0.5) 1.8 (0.3) 1.4 (0.3) 2.5 (0.4) 0.34

Values presented are weighted means (SE). Differences by survey are tested using unweighted analyses (P<0.05).

Figs 1 and 2 show the differences in the prevalence of the various risk factor categories and individual risk factors. The prevalence of lifestyle risk factors (M: 81.3 to 83.6%; F: 77.8 to 86.2%), social/mental risk factors (M: 38.1 to 48.7%; F: 38.3 to 51.6%) and physical factors (M: 33.3 to 52.5%; F: 43.9 to 59.0%) increased between 1988–1994 to 2011–2014 (P<0.0002), while the prevalence of having physiological risk factors decreased (M: 85.2 to 78.6%; F: 85.7 to 79.3%, P<0.0001) over the same time frame. Overall, having lifestyle and physical factors were more common in women than men (P<0.03), while there were no sex differences for social/mental, physiological or any health risk factors (P>0.05).

Fig 1. Changes in the prevalence of having any health risk factors, poor self-rated health, lifestyle risk factors and physiological risk factors between 1988–1994 (NHANES III) and 2011–14 (NHANES continuous).

Fig 1

Each prevalence datapoint is weighted to be nationally representative. NH3 = NHANES III (1988–1994). BP = Blood Pressure; CVD = Cardiovascular Disease.* Significant trend over time were examined with unweighted analyses (P<0.05). † Sex main effect (P<0.05).

Fig 2. Changes in the prevalence of having any Social/Mental and physical risk factors between 1988–1994 (NHANES III) and 2011–14 (NHANES continuous).

Fig 2

Each prevalence datapoint is weighted to be nationally representative. NH3 = NHANES III (1988–1994). ADL = Activities of Daily Living. * Significant trend over time were examined with unweighted analyses (P<0.05). † Sex main effect (P<0.05).

Within lifestyle factors, lack of physical activity and excessive alcohol consumption increased in prevalence (P<0.0001), while smoking decreased in prevalence in men and women (P<0.02) and the prevalence of consuming of a high fat diet decreased from 49 to 44% in only men (P<0.0001, Fig 1).

Within physiological risk factors, having high blood pressure or glucose or cancer increased in prevalence, while having high lipids decreased in prevalence (Fig 1). The presence of CVD tended to decrease between NHANES III (1998–1994) and NHANES continuous 1999–2000 and then increased, but was generally lower in 2011–2014 than NHANES III in women, while lung disease did not change in prevalence over time (P>0.05).

For social/mental risk factors, there was an increase in the prevalence of individuals without health insurance, with food insecurity and taking mental health medications (Fig 2, P<0.0001), while there was a decrease in the prevalence of those with less than a high school education (P<0.0001). There was a modest but significant decline in the prevalence of low income in females (P<0.0001), but no significant change in men over time (P = 0.3).

For physical risk factors (Fig 2), use of pain medications, arthritis and obesity all significantly increased in prevalence over time in both men and women, while there was a decrease in ADL problems in adults over 60 years of age (P<0.05).

Over the 5-year follow-up there were 1319 deaths. The 5-year odds ratios for all-cause mortality are presented in Table 2. There were no deaths in those without any of the 19 risk factors. As compared to NHANES III (1988–1994), having any risk factors in NHANES continuous was associated with 30% lower odds of 5-year mortality (OR = 0.71, 0.6–0.8; P<0.0001).

Table 2. Odds ratios for 5-year mortality risk in 1988–94 and 1999–2014.

NH3 1988–94 5 yr OR (95% CI) P NHC 1999–2014 5 yr OR (95% CI) P
Lifestyle Factors 1.41 (1.0–2.0) 0.07 2.63 (2.0–3.5) < .0001
    No Exercise 1.92 (1.4–2.6) 0.0001 2.53 (2.1–3.1) < .0001
    High Fat Diet 1.18 (0.9–1.5) 0.22 0.93 (0.8–1.1) 0.44
    Smoking 1.69 (1.2–2.5) 0.01 2.41 (1.9–3.1) < .0001
    Excessive Alcohol 0.97 (0.6–1.5) 0.87 1.39 (1.1–1.8) 0.01
Social/Mental Factors 1.66 (1.3–2.2) 0.001 2.20 (1.8–2.7) < .0001
    No Health Insurance 2.02 (0.8–5.2) 0.14 1.85 (1.3–2.7) 0.001
    Low Education 1.27 (0.9–1.7) 0.13 1.99 (1.5–2.6) < .0001
    Food Insecure 2.44 (1.3–4.4) 0.004 2.27 (1.8–2.9) < .0001
    Low Income 2.03 (1.4–3.0) 0.0004 2.55 (2.1–3.1) < .0001
    Mental Health Med 0.87 (0.3–2.4) 0.78 1.65 (1.3–2.1) < .0001
Physiological Factors 3.52 (1.6–7.6) 0.002 1.65 (1.1–2.5) 0.02
    Cancer 1.40 (1.0–2.0) 0.07 1.49 (1.2–1.8) 0.0003
    CVD 2.12 (1.6–2.8) < .0001 1.74 (1.4–2.2) < .0001
    Lung Disease 2.33 (1.6–3.5) < .0001 2.07 (1.6–2.7) < .0001
    High BP 1.96 (1.4–2.7) 0.0001 1.69 (1.3–2.1) < .0001
    High Glucose 2.03 (1.4–2.9) 0.0003 1.64 (1.3–2.0) < .0001
    High Lipid 1.43 (1.0–2.1) 0.07 0.97 (0.8–1.2) 0.77
Physical Factors 1.93 (1.4–2.7) 0.001 1.88 (1.4–2.5) < .0001
    Arthritis 1.14 (0.8–1.7) 0.50 1.28 (1.0–1.6) 0.02
    Pain Medication 0.82 (0.5–1.4) 0.46 1.19 (0.9–1.5) 0.16
    ADL Problem 2.00 (1.4–2.9) 0.0003 2.13 (1.6–2.8) < .0001
    ADL Problem (>60 yr) 2.3 (2.5–3.5) 0.0002 2.47 (1.9–3.3) < .0001
    Obesity 1.05 (0.8–1.4) 0.77 1.15 (1.0–1.4) 0.10
Poor Health 2.97 (2.1–4.2) < .0001 3.28 (2.6–4.1) < .0001

ADL = Activities of Daily Living; BP = Blood Pressure; CVD = Cardiovascular Disease

Models were adjusted for age, sex, BMI category and white ethnicity, and weighted within each survey separately.

Having any lifestyle risk factor was significantly associated with increased 5-year OR for mortality risk in 1999–2014, but not 1988–94 (Table 2). Specifically, the 5-year OR for mortality risk were all higher for lack of exercise, smoking and excessive alcohol consumption in 1999–2014 than 1988–94. Consumption of a high fat diet was not associated with 5-year mortality risk at either time point.

Social and mental factors were also associated with increased mortality risk at both timepoints (P≤0.0001, Table 2), with a subtly higher OR in the later survey years. In particular, having low education or use of mental health medication were not associated with mortality risk in 1988–94 (P>0.1), but were significantly associated with a higher 5-year OR for mortality in 1999–2014 (P<0.0001).

Having any physiological risk factor was strongly associated with increased mortality risk in 1988–1994, but was more weakly related in 1999–2014 (P<0.02; Table 2). Most of the physiological risk factors were similarly related with mortality risk at both timepoints, though they generally tended to have a lower 5-year OR for mortality at the later timepoint.

Having any physical risk factor, the individual physical risk factors and poor self-rated health were similarly related with 5-year mortality risk at both timepoints (Table 2).

Discussion

The prevalence of individuals who are free of all of the 19 examined risk factors was less than 3%, at all time points. The prevalence of the various risk factors varied in the absolute prevalence and changes over time. The prevalence of poor self-rated health was generally ~15% across the study period. The 5-year mortality risk associated with these risk factors also varied over time. This demonstrates that there may not only be changes in the prevalence of risk factors, but also differences in how certain risk factors relate with mortality risk over time.

Public health has had a long focus on health promotion that has targeted lifestyle behaviours, such as physical activity, diet, smoking and excessive alcohol use [3], with variable success over time. Tobacco control has been one of the more successful programs [7] and the current study observed that the prevalence of smoking decreased from 32 to 22% in men and 27 to 18% in women. On the other hand, the prevalence of physical inactivity and excessive alcohol consumption has increased by 15% and 10%, respectively, since NHANES III (1988–94), while the prevalence of low fat diets were relatively consistent over the study period (~45%). The popularity of low fat diets first began in the 1960s when the link between high fat diets and CVD was first discovered [21]. Of late, the wisdom of prescribing low fat diets for health and particularly for obesity management has been called into question [22]. In this study, consuming a high fat diet was the only examined lifestyle factor that was not significantly associated with mortality risk at either time point. This may be due, in part, to differences in the association between high saturated versus high unsaturated fat with CVD [23]. Further, improvements in the treatment of CVD have likely reduced the negative impact of high fat diets [24, 25]. In fact, the association between cardiovascular risk factors (i.e., high blood pressure, high glucose, high lipid) and prevalent CVD with 5-year mortality risk was reduced over time. This is fortunate given the increased prevalence of high blood pressure and high glucose. Despite the higher rate of some CVD risk factors, there is a decreasing rate of premature heart disease mortality [25]. Nevertheless, heart disease is still the leading cause of death in the United States [26], and thus, understanding changes in the way risk factors relate with mortality risk over time warrants further investigation.

Many studies in the literature report that men consume more servings of alcohol than women and are more likely to have excessive consumption [27]. However, the dietary guidelines for Americans [12] suggest that women should consume less servings of alcohol when they drink due to their smaller size and slower alcohol clearance [28]. Thus, in our study that used sex-specific cut-offs for alcohol, we observed that excessive alcohol was more prevalent in women, and has increased over time. Excessive alcohol consumption has been cited to be associated with increased mortality risk, CVD, cancer and injuries [29]. In the current study, excessive alcohol consumption was associated with higher mortality risk in 1999–2014, but not in 1988–1994. This is problematic when you consider the increased mortality risk in conjunction with the increased prevalence. Reasons for the changes in the pattern of alcohol consumption or the association with mortality risk is unclear, but may reflect differences in the types of alcohol consumed over time or perhaps differences in the reasons why individuals consume alcohol. Though not examined in this study, this may reflect greater incidence of negative health impacts of binge drinking or alcohol addiction [30, 31]. Alcohol is also a common coping mechanism for stress [32], and thus, excessive alcohol intake may be an indicator of other issues that contribute to poor health, particularly in later years. In fact, some more recent guidelines suggest even lower limits of no more than 2 servings per week [33], and that for cancer, there may be no safe limits for alcohol consumption [34, 35]. More work is needed to clarify the optimal amounts of alcohol consumption and health and to determine whether there are changes over time.

Use of mental health medications was also only significantly associated with 5-year mortality risk at the second survey, indicating a greater mortality risk associated with the use of mental health medications over time. This may be, in part, due to the overall low prevalence (<2%), and thus, low number of deaths in those taking mental health medications in 1988–1994 versus 1999–2014. However, individuals taking mental health medications are also more likely to have low education [36] and smoking [37], both factors which were also more strongly associated with mortality risk over time. These changes may reflect changes in access to care or perhaps decreases in stigma around mental health issues [38] and the importance of seeking treatment [39]. However, these changes in mortality risk may also suggest that the severity and negative effects of mental health issues may be more detrimental over time. Despite improvements in stigma, there is still significant stigma within health care that can lead to delays in the diagnosis and treatment of non-mental health conditions in patients with mental health disease [39]. A recent systematic review suggests that there are suboptimal adoption of the clinical guidelines [40]. Thus, it appears that improvements in care and treatment for mental health conditions may be needed.

The most dramatic difference in mortality risk was the decreased strength of association between having any physiological risk factor and mortality risk. Each of the cardiometabolic risk factors and lung disease each had modestly lower ORs for 5-year mortality risk in 1999–2014 than 1988–1994. These observations mirror other studies that report a decreasing prevalence of CVD mortality in the U.S. [41]. Improvements in CVD prevention and treatment are likely contributors to this improvement. There are concerns that declines in CVD may be derailed by the increasing prevalence of diabetes in the U.S. [41]. However, we observed that the association between diabetes and 5-year mortality risk is also decreasing in strength. Thus, the increased prevalence of risk factors in the population, may or may not translate into greater population mortality burden.

Strengths and limitations

Strengths and limitations of the current study warrant mention. NHANES is designed to be representative of the U.S. population, and thus, is ideal for examining changes in the prevalence of health risk factors over time. One of the primary assumptions of mortality analyses using cox proportional hazards is that the relative risk is constant over time [42]. However, our study demonstrates that this assumption may not hold true for all health risk factors. One limitation is that the reference groups for our analyses differed between time points. Thus, the risk ratios are a reflection of changes in the mortality risk for those with and without the condition examined. Nevertheless, these analyses are still reflections of the associations of the risk factors within each timepoint examined, with some analyses changing in their statistical significance. Further studies should examine how the clustering of these risk factors act together in changing mortality risk over time, and perhaps sub-populations wherein these observations may differ.

Conclusions

This study demonstrates that developments in health care, social programs and the built environment may have altered how traditional risk factors relate to health and mortality over time. Thus, attention to not only the changing prevalence of traditional risk factors, but also examination of how these risk factors change in their association with morbidity and mortality is needed.

Data Availability

The data is publicly available online. https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.

Funding Statement

The author(s) received no specific funding for this work.

References

Decision Letter 0

Vikramaditya Samala Venkata

2 May 2023

PONE-D-23-10979Is anyone truly healthy? Trends in health risk factors prevalence and changes in their associations with all-cause mortalityPLOS ONE

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 Excellent study with good practical implications. a)In methods section of abstract: Can you elaborate further on what specific methods were used? b)In methods section of the main article: Can you specify what kind of study this, currently you just mentioned that SPSS software was used, that we used ODDS ratio. May be a line indication what kind of observational study this is?  c)At the end of the article, can we have separate brief conclusion section: include a take away point in the end d)Also may be a separate brief strength and limitation section? Please submit your revised manuscript by Jun 16 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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4. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Good study, recommend edits as noted below

In abstract: In methods section, please describe more about what methods were used. Its too vague

In main article: In methods section, would it be possible to describe clearly what type of study this is. What kind of observational study this is? Cohort study?

End of discussion, can we have a separate conclusion section and a separate strengths/limitation section?

After above revision. Manuscript will be ready for publication

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have provided an analysis and reviewed data on the prevalence of various health risk factors over a period of nearly three decades in order to identify patterns in population health. By analyzing trends in the prevalence of these risk factors, they likely sought to determine how the health of the population has changed over time.

The examples of health risk factors that the authors have analyzed include smoking, alcohol consumption, physical inactivity, poor nutrition, and obesity amongst other social, physiological risk factors.

By examining changes in the prevalence of these risk factors over time, the authors have been able to identify trends and patterns that could inform public health policy and interventions aimed at improving population health.

Overall, it appears that the authors have conducted a comprehensive analysis of health risk factors over a long period of time in order to gain insights into population health patterns. The findings of this analysis could have important implications for efforts to promote healthier lifestyles and reduce the burden of morbidity in the population.

Reviewer #2: Interesting topic. Well presented. Its surprising to see the average BMI below 30 over time. I suspect as people have decresead the use of tobacco, they have caught up on alcohol consumption. The advances in medicine likely explain the overall decreasing Mortality risk of the various rsik factors.

Reviewer #3: Firstly, it is commendable that the paper tackles pertinent public health matters and offers solutions to significant questions. Such an approach implies that the paper has practical implications and can contribute to the ongoing efforts to enhance public health.

Secondly, the paper's exploration of how risk factors, previously believed to impact health, have evolved over time is also noteworthy. It underscores the need for continually updating our comprehension of these risk factors that can affect morbidity and mortality, and adjusting our methodologies accordingly.

Lastly, the paper's identification of emerging risk factors such as mental health, social factors like insurance status, income level, literacy level, and substance use, which significantly impact overall morbidity and mortality at both the individual and population level, is valuable as it enables us to be proactive in addressing emerging public health concerns.

Overall, the paper seems to be informative and relevant to the public health field, providing insights that can help improve health outcomes.

**********

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: Yes: Sakteesh V. Gurunathan

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

PLoS One. 2023 Jun 2;18(6):e0286691. doi: 10.1371/journal.pone.0286691.r002

Author response to Decision Letter 0


4 May 2023

Dear Editor Venkata,

We thank you and the reviewers for the opportunity to revise and improve our manuscript. Please see our point by point responses to your and the reviewer comments below.

Thank you for your consideration,

Jen Kuk

Editor Comments

Comment - a) In methods section of abstract: Can you elaborate further on what specific methods were used?

Response – We have elaborated the methods to as follows:

“Data from the National Health and Nutrition Examination Surveys (NHANES III – 1988-1994 and NHANES 1999-2014; age ≥20 years) was used to examine differences in the odds ratio (OR) of 5-year mortality risk associated with various common health risk factors over the two survey periods using weighted logistic regression analysis adjusting for age, sex, obesity category and white ethnicity (n=28,279). “

Comment b) In methods section of the main article: Can you specify what kind of study this, currently you just mentioned that SPSS software was used, that we used ODDS ratio. May be a line indication what kind of observational study this is?

Response – At the start of the methods we added this sentence:

“The current study is a cross-sectional comparison of two nationally representative samples from 1988-1994 versus 1999-2014 with 5-year mortality follow-up.”

Comment c) At the end of the article, can we have separate brief conclusion section: include a take away point in the end

Response – We have added a title heading for our “Conclusions” section. This was our original conclusion paragraph.

“This study demonstrates that developments in health care, social programs and the built environment may have altered how traditional risk factors relate to health and mortality over time. Thus, attention to not only the changing prevalence of traditional risk factors, but also examination of how these risk factors change in their association with morbidity and mortality is needed. “

- Apologies, but I am unclear if the second point is not sufficient for a “take away point”? I re-read the submission guidelines and a few recent publications and could not find reference to a formal ‘take away point’?

Comment d) Also may be a separate brief strength and limitation section?

Response – We have added a title heading for the “Strengths and Limitations” section

Journal Requirements

Comment 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

Response – We have revised the formatting of the main manuscript and author affiliations as stating in the documents.

Comment 2. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

Response – We have removed our reference to unreported results and have cited published literature stating the same point instead (page 16-7).

Comment 3. Please include your full ethics statement in the ‘Methods’ section of your manuscript file. In your statement, please include the full name of the IRB or ethics committee who approved or waived your study, as well as whether or not you obtained informed written or verbal consent. If consent was waived for your study, please include this information in your statement as well.

Response – Thank you. This is now included at the end of paragraph 1 on page 4.

Comment 4. Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Response – I have reviewed the reference list and did not find any retractions for our citations.

Additional Editor Comments:

Good study, recommend edits as noted below

Comment - In abstract: In methods section, please describe more about what methods were used. Its too vague

Response – We have elaborated the methods to as follows:

“Data from the National Health and Nutrition Examination Surveys (NHANES III – 1988-1994 and NHANES 1999-2014; age ≥20 years) was used to examine differences in the odds ratio (OR) of 5-year mortality risk associated with various common health risk factors over the two survey periods using weighted logistic regression analysis adjusting for age, sex, obesity category and white ethnicity (n=28,279). “

Comment - In main article: In methods section, would it be possible to describe clearly what type of study this is. What kind of observational study this is? Cohort study?

Response – At the start of the methods we added this sentence:

“The current study is a cross-sectional comparison of two nationally representative samples from 1988-1994 versus 1999-2014 with 5-year mortality follow-up.”

Comment - End of discussion, can we have a separate conclusion section and a separate strengths/limitation section?

Response – we have added heading to clearly note these sections.

Comment - After above revision. Manuscript will be ready for publication

Response – Thank you. We hope that our revisions have adequate addressed your concerns and we thank you for your contribution in this process.

5. Review Comments to the Author

Reviewer #1: The authors have provided an analysis and reviewed data on the prevalence of various health risk factors over a period of nearly three decades in order to identify patterns in population health. By analyzing trends in the prevalence of these risk factors, they likely sought to determine how the health of the population has changed over time.

The examples of health risk factors that the authors have analyzed include smoking, alcohol consumption, physical inactivity, poor nutrition, and obesity amongst other social, physiological risk factors.

By examining changes in the prevalence of these risk factors over time, the authors have been able to identify trends and patterns that could inform public health policy and interventions aimed at improving population health.

Overall, it appears that the authors have conducted a comprehensive analysis of health risk factors over a long period of time in order to gain insights into population health patterns. The findings of this analysis could have important implications for efforts to promote healthier lifestyles and reduce the burden of morbidity in the population.

Response – We thank the reviewer for their comments and for taking the time to review our manuscript.

Reviewer #2: Interesting topic. Well presented. Its surprising to see the average BMI below 30 over time. I suspect as people have decresead the use of tobacco, they have caught up on alcohol consumption. The advances in medicine likely explain the overall decreasing Mortality risk of the various rsik factors.

Response – Thank you. We completely agree with you.

Reviewer #3: Firstly, it is commendable that the paper tackles pertinent public health matters and offers solutions to significant questions. Such an approach implies that the paper has practical implications and can contribute to the ongoing efforts to enhance public health.

Secondly, the paper's exploration of how risk factors, previously believed to impact health, have evolved over time is also noteworthy. It underscores the need for continually updating our comprehension of these risk factors that can affect morbidity and mortality, and adjusting our methodologies accordingly.

Lastly, the paper's identification of emerging risk factors such as mental health, social factors like insurance status, income level, literacy level, and substance use, which significantly impact overall morbidity and mortality at both the individual and population level, is valuable as it enables us to be proactive in addressing emerging public health concerns.

Overall, the paper seems to be informative and relevant to the public health field, providing insights that can help improve health outcomes.

Response – Thank you. We appreciate the reviewer’s kind words and for reviewing our manuscript.

Attachment

Submitted filename: Response to Editor and Reviewers.docx

Decision Letter 1

Vikramaditya Samala Venkata

22 May 2023

Is anyone truly healthy? Trends in health risk factors prevalence and changes in their associations with all-cause mortality

PONE-D-23-10979R1

Dear Dr. Kuk,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Vikramaditya Samala Venkata

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for making all the required changes. Including explaining the methods, type of study and adding conclusions and limitations. Your article will surely help the medical community.

Reviewers' comments:

Acceptance letter

Vikramaditya Samala Venkata

24 May 2023

PONE-D-23-10979R1

Is anyone truly healthy? Trends in health risk factors prevalence and changes in their associations with all-cause mortality.

Dear Dr. Kuk:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Vikramaditya Samala Venkata

Academic Editor

PLOS ONE


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