Trends in Obesity Among Adults in Mississippi, 2017–2023

Abstract

The aim of this study was to analyze trends in rates of obesity amongst Mississippi adults between 2017 and 2023 based on five health determinants: gender, education, age, race, and socioeconomic status. We calculated standard errors using Microsoft Excel and performed trend analysis using Joinpoint Regression. Obesity prevalence among men showed a statistically significant increasing trend from 2017 to 2023, with an Annual Percent Change of 0.86%. Among adults with a college-level education, obesity prevalence significantly increased by 2.33% per year. For most age groups, obesity prevalence did not show statistically significant trends from 2017 to 2023, indicating relatively stable rates over time. There was a significant annual increase of 0.65% from 2017 to 2023 for African Americans. From 2022 to 2023, there was a statistically significant decrease in obesity prevalence for Hispanics. There was an annual increase of 0.70%, which was not statistically significant, for Whites. For the combined ≥USD 50,000 income group, obesity prevalence increased significantly between 2017 and 2019. Obesity remains a critical public health issue with widespread health consequences. Future research should explore the long-term impact of these trends and assess the effectiveness of ongoing interventions to guide more precise strategies for obesity prevention and management.

1. Introduction

Obesity is a common, severe, and costly chronic disease that has emerged as one of the most significant public health challenges of the 21st century, impacting individuals and societies worldwide [1]. Characterized by excessive fat accumulation, obesity is often associated with a range of chronic conditions, including heart disease, type 2 diabetes, and certain cancers, as indicated in detail later. Obesity has profound and far-reaching effects on physical health, leading to a significant burden on individuals and healthcare systems. Obesity is influenced by factors such as sleep quality and insufficiency and genetic, environmental, and psychosocial factors [2].

Obesity causes an increased risk of cardiovascular diseases, including heart disease, stroke, and hypertension. Excess fat can lead to the buildup of fatty deposits in arteries, raising blood pressure and affecting blood flow. Also, obesity is the primary risk factor for developing type 2 diabetes. Excess fat in the body causes insulin resistance, making it difficult for the body to regulate blood sugar levels effectively. Over time, this can lead to the onset of diabetes [3]. Obesity can cause or worsen respiratory problems, such as sleep apnea, where the airway becomes obstructed during sleep. The respiratory issues related to obesity can result in fragmented sleep and low oxygen levels, contributing to fatigue and cardiovascular strain [4]. Carrying excess weight places added stress on joints, especially those in the knees, hips, and lower back. This increased pressure can lead to osteoarthritis, a degenerative joint disease that causes pain, stiffness, and limited mobility.

Obesity is also a recognized risk factor for several types of cancer, including breast, colon, endometrial, and liver cancer. This association involves hormonal imbalances, chronic inflammation, and insulin resistance, which are more prevalent in obese individuals. Obesity is also linked to various mental health challenges, including depression, anxiety, and low self-esteem [5]. The stigma and discrimination associated with obesity can lead to social isolation and exacerbate mental health problems.

The combined effects of these health risks contribute to a shorter life expectancy. Studies have shown that individuals with severe obesity can lose years of life compared to those with a healthy weight. Social determinants of health (SDoHs), for example, economic stability, education access and quality, healthcare quality and access, and neighborhoods and environments, are important contributors to obesity [6]. In conclusion, the impact of obesity on health is widespread and severe, affecting nearly every organ system in the body. Addressing obesity is critical in improving individual health and alleviating the significant economic and social burden that it imposes on society [7].

The prevalence of obesity has dramatically increased in recent decades, driven by various factors such as poor diet, sedentary lifestyles, genetics, and environmental influences [8]. Mississippi currently has an obesity prevalence of 40.1% and ranks in the top three states for obesity in the United States. Obesity in Mississippi is compounded by the state’s high rate of poverty, education, socio-economic factors such as gender, race, and income, and inadequate access to healthy resources [1]. This paper aims to explore the multifaceted nature of obesity, examining its causes, health consequences, and potential strategies for prevention and treatment. Understanding obesity requires a multidisciplinary approach integrating biological, psychological, and social perspectives to address its complex nature and mitigate its impact on global health.

2. Materials and Methods

In this study, we investigated trends in obesity prevalence among adults in Mississippi from 2017 to 2023 across five determinants: race, gender, age group, household income, and highest level of education attained.

Table 1. Demographic and determinant characteristics of obesity rates (2017–2023).

Demographic Determinant	Category	2017	2018	2019	2020	2021	2022	2023
Gender	Male	35.7	36.1	36.7	36.4	36.7	37	38
	Male (SE)	0.277
	Female	38.8	42.7	44.9	42.8	41.4	41.9	42
	Female (SE)	0.692
Age Group (Years)	1824	24.2	29.5	27.8	27.5	26.4	26.3	26.2
	18–24 (SE)	0.623
	25–34	39.9	38.5	37.2	38.9	40	43.3	39.6
	25–34 (SE)	0.714
	35–44	41.1	45.4	46.9	44.2	38.8	41.9	47.8
	35–44 (SE)	1.236
	45–54	41.4	46.3	50.1	49	51.6	48.3	47.3
	45–54 (SE)	1.242
	55–64	42.9	44.2	45.9	43.9	47.9	45.5	49.4
	55–64 (SE)	0.872
	65+	33.1	32.8	36.4	33.6	31.5	32.7	32.2
	65+ (SE)	0.592
Education Attainment	Less than High School	42.8	38.4	43.1	39.1	39.6	36	48.6
	Less than High School (SE)	1.564
	High School Graduate or GED	37.6	41.1	42.5	41.4	38.1	38.7	37.8
	High School Graduate or GED (SE)	0.76
	Post High School	37.8	40.8	41.2	42.2	40.4	41.8	40.6
	Post High School (SE)	0.54
	College Graduate or Higher	31.5	35.9	36.1	33.2	38.1	39.3	36.4
	College Graduate or Higher (SE)	1.015
Household Income	<USD15000	44.9	46.1	42.8	44.8	39.7	40.5	46.8
	<USD 15,000 (SE)	1.036
	USD 15,000–USD 24,999	40.4	44	43.1	42.1	45.3	41.2	49.8
	USD 15,000–USD 24,999 (SE)	1.194
	USD 25,000–USD 34,999	37.7	37.9	48.2	46.4	40.6	40.2	48.8
	USD 25,000–USD 34,999 (SE)	1.824
	USD 35,000–USD 49,999	36.5	39.7	43.9	38.4	42.7	47.2	40.7
	USD 35,000–USD 49,999 (SE)	1.362
	USD 50,000–USD 99,999					40.7	38.1	37
	USD 50,000–USD 99,999 (SE)					1.097
	USD 100,000–USD 199,999					35.2	35.5	40.1
	USD 100,000–USD 199,999 (SE)					1.586
	USD 200,000+					34	33.8	38.4
	USD $200,000+ (SE)					1.501
	USD 50,000+ (Combined)	32.1	38	38.1	38	40	39.7	37.1
	USD 50,000+ (Combined) (SE)	0.991
Race/Ethnicity	White	33.1	36.2	37.7	34.5	34.7	35.5	37.1
	White (SE)	0.603
	Black	45.7	45.7	46.3	48.1	46.9	46.8	47.6
	Black (SE)	0.344
	Hispanic						45.4	30.6
	Hispanic (SE)						7.4
	American Indian/Alaskan Native	42.9
	American Indian/Alaskan Native (SE)
	Multiracial non-Hispanic	29.6
	Multiracial non-Hispanic (SE)

SE: Standard error; GED: Graduate equivalency degree.

presents the data for these indicators. We obtained the annual crude prevalence of obesity from the CDC’s Behavioral Risk Factor Surveillance System (BRFSS) Prevalence & Trends Data [9]. We then calculated standard errors using Microsoft Excel and performed trend analysis using Joinpoint Regression (version 5.3.0.0, National Cancer Institute (Bethesda, MD, USA) [10].

For clarity and consistency, all p-values are reported to three decimal places. For p-values less than 0.001, we report ‘p < 0.001’.

Joinpoint Regression identifies potential “joinpoints” (inflection points) in segmented linear regression and estimates the Annual Percent Change (APC). For each trend, we set the year as the independent variable and obesity prevalence as the dependent variable and used a 95% confidence interval (CI), with statistical significance set at p < 0.05. Joinpoints indicate whether each segment of the trend is stationary, increasing, or decreasing (

Table 2. Regression analysis of trends in obesity prevalence by demographic determinants (2017–2023).

Demographic Determinant	Segments	2017	2018	2019	2020	2021	2022	2023
		APC (95% C)    P
Gender	Male	0.856 (0.233–1.516)    0.007 *
	Female	6.097 (1.413–12.447)    0.016 *			−1.801 (−6.674–−0.222)    0.033 *
Age Group (Years)	18–24	−0.512 (−3.788–2.899)    0.757
	25–34	1.081 (−1.361–3.838)    0.350
	35–44	0.517 (−4.760–6.187)    0.808
	45–54	10.338 (5.079–14.431)    <0.001 *			−1.326 (−4.435–0.285)    0.097
	55–64	1.934 (0.324–3.717)    0.021 *
	65+	−0.876 (−3.468–1.813)    0.466
Education Attainment	Less than High School	1.280 (−8.068–11.65)    0.649
	High School Graduate or GED	5.596 (1.735–9.095)    <0.001 *			−3.236 (−5.464–−2.003)    <0.001 *
	Post High School	4.571 (−1.278–11.913)    0.173			−0.582 (−7.240–3.332)    0.412
	College Graduate or Higher	2.325 (0.303–4.558)     0.022 *
Household Income	<USD15,000	−3.118 (−14.871–10.584)    0.226					5.853 (−8.219–18.869)    0.254
	USD15,000–USD24,999	2.229 (−1.3124–6.202)    0.203
	USD25,000–USD34,999	2.472 (−3.975–9.772)    0.364
	USD35,000–USD49,999	2.364 (−4.182–10.273)    0.385
	USD50,000–USD99,999					−4.708 (−6.215–−3.208)    <0.001 *
	USD100,000–USD199,999					6.977 (2.270–12.441)    <0.001 *
	USD200,000+					6.537 (1.061–12.934)    <0.001 *
	USD50,000+ (Combined)	8.617 (2.656–14.746)    0.001 *			−0.538 (−4.982–1.728)    0.460
Race/Ethnicity	White	0.703 (−2.495–4.13)    0.548
	Black	0.649 (0.114–1.191)     0.020 *

APC: Annual Percent Change; CI: Confidence Interval; P: p-value. Significant trends (p > 0.05) are italicized and indicated by asterisk.

).

The BRFSS data are collected annually through state-based telephone surveys of adults aged 18 years or older, and because they are publicly available and deidentified, no Research Ethics Committee approval was necessary.

3. Results

3.1. Data Availability Notes

BRFSS does not report prevalence estimates when the unweighted sample size for the denominator is <50 or if the relative standard error (RSE) is >0.3, or if the state did not collect data for a given year. Consequently, some underweight findings were not reported in multiple years. These reporting criteria should be considered when interpreting the trend analysis.

3.2. Trend Analysis

3.2.1. Gender Trends

Obesity prevalence among males showed a statistically significant increasing trend from 2017 to 2023, with an Annual Percent Change (APC) of 0.86% (95% CI: 0.23 to 1.52, p = 0.007). This suggests a slow but steady annual increase in obesity among adult males over the study period. For females, the trend was more complex. Obesity prevalence initially increased significantly from 2017 to 2019 (APC = 6.10%, 95% CI: 1.41 to 12.45, p = 0.016), indicating a rapid rise in the early part of the study period. However, this was followed by a significant decrease from 2019 to 2023 (APC = −1.80%, 95% CI: −6.67 to −0.22, p = 0.033), suggesting a potential reversal of the increasing trend in later years. These findings highlight a divergence in obesity trends between genders over time (Figure 1).

3.2.2. Education-Level Trends

Among adults with a college-level education, obesity prevalence significantly increased by 2.33% per year (95% CI: 0.30 to 4.56; p = 0.021). For those with a high school education, there was a significant rise from 2017 to 2019 (APC = 5.60%; 95% CI: 1.73 to 9.10; p < 0.00001), followed by a notable decline from 2019 to 2023 (APC = −3.24%; 95% CI: −5.46 to −2.00; p < 0.00001). In contrast, there were no statistically significant trends (p > 0.05) in individuals with education below high school level and those with education beyond high school (Figure 2).

3.2.3. Age Group Trends

For most age groups (18–24, 25–34, 35–44, and 65+), obesity prevalence did not show statistically significant trends from 2017 to 2023 (p-values > 0.05), indicating relatively stable rates over time.

However, among adults aged 45–54, obesity prevalence increased by 10.34% per year from 2017 to 2019 (95% CI: 5.08 to 14.43; p < 0.000001); representing a rapid increase in obesity in middle age, then showed a non-significant decrease thereafter (APC = −1.33%; p = 0.097). For adults aged 55–64, a significant annual increase of 1.93% (95% CI: 0.32 to 3.72; p = 0.021) was observed from 2017 to 2023 (Figure 3).

3.2.4. Race Trends

Data availability varied across racial and ethnic groups. Data for American Indian/Alaskan Native and Multiracial non-Hispanic populations were only consistently reported through 2017, precluding trend analysis beyond that year. Hispanic ethnicity data reporting began in 2022, limiting trend analysis to a single year-to-year change from 2022 to 2023.

Black: Significant annual increase of 0.65% from 2017 to 2023 (95% CI: 0.11 to 1.19; p = 0.020);

Hispanic: From 2022 to 2023, there was a statistically significant decrease in obesity prevalence (p < 0.001);

White: Annual increase of 0.70%, but not statistically significant (p = 0.548) (Figure 4).

3.2.5. Household Income Trends

The analysis of household income trends is complicated by changes in income category reporting over the study period. Income categories below USD 50,000 annually (<USD 15,000, USD 15,000–USD 24,999, USD 25,000–USD 34,999, and USD 35,000–USD 49,999) were consistently reported from 2017 to 2023. However, income categories above USD 50,000 were reported differently. From 2017 to 2020, income above USD 50,000 was reported as a single combined category. From 2021 to 2023, this was further broken down into USD 50,000–USD 99,999, USD 100,000–USD 199,999, and USD 200,000 and above. Therefore, trend analysis for higher-income groups is primarily focused on the period from 2021 to 2023, with some analysis possible for the combined >USD 50,000 group from 2017 to 2020. We calculated the collective obesity rate for the combined category of >USD 50,000 from 2021 to 2023 to include all the study period in the analysis.

For the combined ≥USD 50,000 income group, obesity prevalence increased significantly between 2017 and 2019 (APC = 8.62%, p = 0.001) but showed no significant trend thereafter. Examining the more granular income categories from 2021 to 2023, we observed divergent trends. Among adults earning USD 50,000–USD 99,999, obesity prevalence showed a significant decline (APC = −6.21%, p < 0.000001). Conversely, adults in higher income brackets (USD 100,000–USD 199,999 and USD 200,000+) experienced significant increases in obesity prevalence (APC = 6.38% and 5.85%, respectively, p < 0.000001). No statistically significant trends were observed in the income brackets below USD 50,000 annually (<USD 15,000, USD 15,000–USD 24,999, USD 25,000–USD 34,999, and USD 35,000–USD 49,999), suggesting relatively stable obesity rates in these lower-income groups during the study period (Figure 5).

4. Discussion

4.1. Gender

This study showed that while obesity continues to rise among certain demographic groups—particularly men, middle-aged adults, and higher-income individuals—there are also promising signs of decline among women, high school-educated individuals, and Hispanic populations. Current policies inadequately address overweight and obesity [11]. Throughout the COVID-19 pandemic, factors such as physical inactivity, sedentary behaviors, and poor dietary choices emerged as leading contributors to obesity. Moreover, unhealthy eating habits, heightened behavioral stress, depression, anxiety, and low mood, as well as age, gender, and belonging to ethnic minority groups, were also recognized as significant risk factors for obesity during this period [11]. Before the COVID-19 pandemic, many of the risk factors for obesity were already well established, such as physical inactivity, sedentary lifestyles, unhealthy eating habits, and behavioral stress. These factors were consistent contributors to obesity across various populations [11]. Without significant reforms, projected trends will have severe consequences at both individual and population levels, with the associated disease burden and economic costs continuing to rise [11]. Healthcare availability, lifestyle habits (including nutrition and exercise), and societal expectations have been documented as affecting obesity rates [12]. Diet and exercise may affect men and women differently due to social supports, cultural norms, or gender-specific health interventions [13]. Mental health, stress, and body image attitudes may also affect obesity patterns by gender [14,15]. Based on our study results, social emphasis on female body image may lead to more major behavioral changes, such as diets and weight loss. Male obesity continues to rise, requiring long-term prevention and control efforts. Focused efforts to improve men’s nutrition, exercise, and mental health may slow this tendency [16]. The post-2019 obesity decline indicated here may indicate that some therapies or social changes worked.

4.2. Education

Our results indicate that obesity therapies can be improved if customized according to educational attainment. For individuals with a high school education, sustained public health initiatives emphasizing nutrition, physical exercise, and awareness may prove successful [17,18]. The reduction in obesity rates within this cohort post-2019 indicates that certain interventions or habits have started to be effective, yet these must be maintained. Even though highly educated people typically have access to better health information, public health campaigns targeted at lifestyle behaviors, such as food and physical activity, may also be necessary for college-educated populations [19]. Educational achievement is frequently associated with social status [20]. An existing study found that highly educated individuals were able to adapt by engaging in healthier behaviors, such as maintaining regular physical activity at home, following balanced diets, and utilizing online fitness programs or virtual health consultations. Their awareness of the risks associated with obesity and COVID-19 likely motivated them to prioritize their health [13]. Public health interventions targeting the overarching social determinants of health—such as wealth disparity, food accessibility, and work prospects—can mitigate obesity across all educational strata [21].

4.3. Age

The significant increase in obesity among individuals aged 45–54 warrants essential public health measures aimed at middle-aged adults [22]. Highlighting weight management measures, advocating for consistent physical activity, and providing nutritional counseling may mitigate this escalating issue [23]. Specific prevention strategies are required to address obesity in the 55–64 age demographic, emphasizing mobility, chronic disease management, and healthy aging to avert additional rises in obesity prevalence [23,24]. Although obesity rates remain largely constant among younger and older people, ongoing surveillance and interventions emphasizing healthy aging, nutritional education, and the promotion of physical activity across all age demographics can assist in reducing the risk of obesity-related health complications [25]. The consistent incidence of obesity in both younger and older people indicates distinct underlying reasons and underscores the necessity for age-specific interventions to combat obesity throughout a person’s lifetime [26]. Metabolic and bariatric surgeries are most commonly performed on adults aged 18 to 65 years, with the majority of patients typically falling between their 30s and 50s [27]. This age group often seeks these procedures due to long-term struggles with obesity and related health conditions, such as type 2 diabetes or hypertension [27]. However, in recent years, there has been a growing recognition of the benefits of these surgeries for adolescents with severe obesity, particularly when other treatments have proven ineffective [27].

4.4. Race Trends

Between 2017 and 2023, the rise in obesity rates among the Black population was statistically significant. This suggests that the increase in obesity may be attributed to the unique difficulties or disparities that Black people experience [26,28]. Factors may encompass restricted access to nutritious foods, elevated rates of food insecurity, increased stress levels stemming from prejudice or socioeconomic pressures, and perhaps diminished access to healthcare services for obesity prevention or treatment [29]. While age, gender, and belonging to ethnic minority groups were recognized as influencing obesity risk before the pandemic, the pandemic highlighted and exacerbated these disparities. For example, ethnic minority groups faced heightened challenges due to systemic inequities, which were further magnified during the pandemic [30].

The prevalence of obesity among the Hispanic population considerably declined from 2022 to 2023. The significant reduction in obesity within this group indicates that variables or interventions, including health education, lifestyle modifications, or community health initiatives, may have exerted a beneficial influence [30]. It may also indicate behavior modifications, such as a heightened awareness of nutritious food or improved access to opportunities for physical activity [30].

The small rise in obesity among the White population indicates that, although a trend toward elevated obesity rates may exist, it is not significant enough to make conclusive judgments. This could result from various influences, including moderate reductions in detrimental habits or improvements in environmental influences.

4.5. Household Income

The positive trend in obesity rates among people earning over USD 100,000 suggests that having a higher income status does not necessarily equate to having better health outcomes. Affluent populations often enjoy more sedentary lifestyles, greater access to calorie-dense foods, and higher stress, which has been found to contribute to obesity [31]. The decline in obesity in the mid-level income group, also referred to as “middle-class” (USD 50,000–USD 99,999), suggests that health interventions and lifestyle changes are more effective for this group. It might also reflect improved access to preventive healthcare or nutrition education that promotes healthier choices [31]. The stable obesity rates in low-income categories may reflect the difficulty of overcoming socioeconomic barriers to healthier living, such as limited access to nutritious food and healthcare, despite the ongoing national emphasis on obesity-related health issues [32]. It also underscores the importance of focusing on environmental and structural factors that affect low-income individuals’ ability to make healthier lifestyle choices.

4.6. Limitations

While the study is very methodologically sound, there is one limitation. By using Joinpoint regression trend analysis, the analysis may oversimplify trends. This may result in trends being perceived as less accurate. There is also bias associated with the study design. The bias is a result of the data collection process. The data were collected using convenience sampling through telephone interviews, which could result in selection bias that could impact the data/analysis for the study.

5. Conclusions

Obesity remains a critical public health challenge with widespread health, social, and economic consequences. Our analysis of obesity trends in Mississippi from 2017 to 2023 highlights the multifaceted nature of this issue, influenced by gender, education, age, race, and socioeconomic status. The findings suggest that while obesity continues to rise among certain demographic groups—particularly males, middle-aged adults, and higher-income individuals—there are also promising signs of decline among females, high school-educated individuals, and Hispanic populations. These trends underscore the importance of targeted public health interventions that account for social determinants of health, lifestyle factors, and access to healthcare.

Efforts to address obesity should include gender-specific strategies, age-appropriate interventions, and culturally sensitive approaches that address racial disparities. Additionally, the findings emphasize the need for continued investment in nutrition education, physical activity promotion, and structural changes that enable healthier lifestyle choices across all income levels. By implementing evidence-based policies and community-driven initiatives, it is possible to curb the obesity epidemic and improve overall public health outcomes. Future research should explore the long-term impact of these trends and assess the effectiveness of ongoing interventions to guide more precise strategies for obesity prevention and management.