The impact of loneliness on depression, mental health, and physical well-being

Oluwasegun Akinyemi; Waliah Abdulrazaq; Mojisola Fasokun; Fadeke Ogunyankin; Seun Ikugbayigbe; Uzoamaka Nwosu; Miriam Michael; Kakra Hughes; Temitope Ogundare

doi:10.1371/journal.pone.0319311

. 2025 Jul 9;20(7):e0319311. doi: 10.1371/journal.pone.0319311

The impact of loneliness on depression, mental health, and physical well-being

Oluwasegun Akinyemi ^1,^*, Waliah Abdulrazaq ², Mojisola Fasokun ³, Fadeke Ogunyankin ⁴, Seun Ikugbayigbe ⁵, Uzoamaka Nwosu ², Miriam Michael ⁶, Kakra Hughes ⁷, Temitope Ogundare ⁸

Editor: Renata Komalasari⁹

¹The Clive O Callender Outcomes Research Center, Howard University College of Medicine, Washington, District of Columbia, United States of America

²Department of Psychiatry and Behavioral Sciences, Howard University College of Medicine, Washington, District of Columbia, USA

³Department of Epidemiology, University of Alabama at Birmingham, Birmingham, Alabama, United States of America

⁴Department of Research Data Science and Analytics, Cook Children’s Health Care System: Cook Children’s Medical Center, Fort Worth, Texas, United States of America

⁵Department of Biological Sciences, Eastern Illinois University, Charleston, Illinois, United States of America

⁶Department of Internal Medicine, Howard University College of Medicine, Washington, District of Columbia, United States of America

⁷Department of Surgery, Howard University College of Medicine, Washington, District of Columbia, United States of America

⁸Department of Psychiatry, Boston University School of Medicine, Boston, Massachusetts, United States of America

⁹Andalusia Nursing Academy, INDONESIA

^✉

* E-mail: austineakinyemi@gmail.com

Competing Interests: The authors have declared that no competing interests exist.

Roles

Oluwasegun Akinyemi: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Waliah Abdulrazaq: Conceptualization, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Mojisola Fasokun: Conceptualization, Data curation, Investigation, Project administration, Resources, Validation, Visualization, Writing – original draft, Writing – review & editing

Fadeke Ogunyankin: Conceptualization, Data curation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Seun Ikugbayigbe: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Uzoamaka Nwosu: Conceptualization, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Miriam Michael: Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Kakra Hughes: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing

Temitope Ogundare: Conceptualization, Data curation, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing

Renata Komalasari: Editor

PMCID: PMC12240311 PMID: 40632698

Abstract

Background

Loneliness is a growing public health concern, with substantial implications for mental and physical health. Despite increasing attention, it remains an underrecognized determinant of health outcomes in population-based research.

Objective

This study evaluates the association between loneliness and key health outcomes, including depression diagnosis, poor mental health days, and poor physical health days, using a nationally representative sample.

Methods

We analyzed Behavioral Risk Factor Surveillance System (BRFSS) data from 2016 to 2023. Loneliness was measured with the question, “How often do you feel lonely?” and categorized into five levels: Always, Usually, Sometimes, Rarely, and Never. We estimated average treatment effects (ATE) using inverse probability weighting (IPW), adjusting for sociodemographic characteristics and incorporating BRFSS sampling weights and fixed effects for state, and year.

Results

The study included 47,318 individuals, predominantly White (73.3%), female (62.1%), and aged 18–64 years (72.1%). Over 80% of participants reported some degree of loneliness. Compared to those who reported “Never” being lonely, individuals who reported being “Always” lonely had a significantly higher predicted probability of depression (50.2% vs. 9.7%, ATE = +40.5 percentage points, p < 0.001), 10.9 more poor mental health days, and 5.0 more poor physical health days per month (all p < 0.001). Disparities were evident across sex, race/ethnicity, and age. Women consistently reported more poor mental health days than men across most loneliness levels. Black individuals reporting loneliness had significantly lower probabilities of depression and fewer poor mental health days than White peers. Older adults (>64) experienced more poor physical health days than younger adults across all loneliness categories.

Conclusion

Loneliness is a strong and independent predictor of depression and poor health outcomes. Public health interventions aimed at addressing loneliness—especially among high-risk subgroups—are critical to improving mental and physical well-being at the population level.

Introduction

Loneliness has emerged as a significant public health concern in the United States, with profound implications for mental and physical health [1–3]. Defined as the subjective feeling of being socially disconnected or lacking meaningful interpersonal relationships [1], loneliness affects approximately 36% of Americans, rising to 61% among young adults and 51% among mothers with young children [4]. Women report loneliness at higher rates than men, and the phenomenon cuts across demographic and socioeconomic lines [5,6]. Critically, loneliness has been linked to a 26% increase in premature mortality, a risk comparable to smoking and obesity, underscoring its urgency as a public health priority [1,4].

The health consequences of loneliness are wide-ranging and well-documented [1,7]. Previous studies have established strong associations between loneliness and depression, identifying a bidirectional relationship where loneliness both contributes to and is exacerbated by depressive symptoms [8–10]. Lonely individuals are estimated to have a 15–30% higher risk of developing depressive disorders [11,12]. On average, lonely individuals report two to three more poor mental health days per month [10,13]. Physical health is similarly affected; loneliness contributes to one to two additional poor physical health days monthly, often linked to inflammation, immune dysfunction, and heightened risk of chronic diseases such as hypertension and cardiovascular disorders [10,14,15].

Despite growing recognition of loneliness as a serious health threat, previous studies have limitations that restrict our understanding of its full impact [3]. Much of the existing literature has relied on cross-sectional or correlational designs, limiting causal inference [3,16]. In addition, many studies use localized or non-representative samples, making it difficult to generalize findings across diverse populations in the U.S [16,18]. Moreover, the complex interplay between loneliness and its health outcomes, particularly across different demographic subgroups, has not been adequately explored [17,18]. Key social determinants of health such as race/ethnicity, income, education, and access to care are often overlooked, leaving gaps in our understanding of how loneliness disproportionately affects certain populations [3,16].

This study addresses these limitations by leveraging nationally representative data from the Behavioral Risk Factor Surveillance System (BRFSS) and applying rigorous causal inference methods, including inverse probability weighting (IPW). By estimating the average treatment effect of loneliness on depression, mental health days, and physical health days, we aim to provide a clearer picture of loneliness as an independent and modifiable risk factor. Our analysis not only quantifies the burden of loneliness on health outcomes but also evaluates variation across key demographic characteristics, including sex, age, and race/ethnicity.

In doing so, this research advances current knowledge in three critical ways: (1) by moving beyond association to estimate causal effects; (2) by using a large, nationally representative sample that enhances generalizability; and (3) by investigating how loneliness interacts with social determinants of health to influence depression and self-rated health outcomes. These contributions have important implications for designing equitable public health interventions aimed at reducing loneliness and its associated health burdens. We hypothesize that higher levels of loneliness will be significantly associated with increased likelihood of depression and greater frequency of poor mental and physical health days, even after adjusting for sociodemographic and health-related covariates.

Methodology

This study utilized data from the Behavioral Risk Factor Surveillance System (BRFSS) collected between January 2016 and December 2023 [19]. The BRFSS is a nationally representative, cross-sectional survey conducted annually by the Centers for Disease Control and Prevention (CDC) [20]. It employs a complex multistage sampling design incorporating stratification, clustering, and unequal probabilities of selection. The survey is administered via landline and cellular telephone interviews across all 50 U.S. states, the District of Columbia, and U.S. territories. Survey weights are applied to adjust for non-response, sampling design, and demographic post-stratification, ensuring generalizability to the U.S. non-institutionalized adult population.

Study population

The target population for this study included adults aged 18 years and older residing in the United States who participated in the BRFSS survey between 2016 and 2023. Respondents were included in the analytic sample if they had complete responses to the key exposure variable (loneliness), all three outcome variables (depression diagnosis, mental health days, and physical health days), and all relevant covariates. To preserve analytic integrity and ensure comparability, individuals were excluded if they responded, “Don’t know/Not sure,” “Refused,” or provided missing responses to any variable of interest. After applying these inclusion and exclusion criteria, the final analytic sample consisted of 47,318 respondents.

Explanatory variable

The primary explanatory variable in this study was self-reported loneliness. Loneliness was measured using the question: “How often do you feel lonely?” with five response categories: “Always,” “Usually,” “Sometimes,” “Rarely,” and “Never.” This variable captures the subjective experience of social isolation and was treated as an ordinal categorical variable, reflecting increasing severity of loneliness. The gradation in response categories allowed for the investigation of potential dose-response relationships between levels of loneliness and adverse health outcomes.

Outcome variables

We examined three primary health outcomes that reflect mental and physical well-being. Depression was assessed through the BRFSS item: “(Ever told) (you had) a depressive disorder (including depression, major depression, dysthymia, or minor depression)?” Responses to this item were coded as binary (yes or no), indicating the presence or absence of a clinical depression diagnosis by a healthcare professional. Mental and physical health status were evaluated using the number of days in the past 30 during which respondents reported that their mental or physical health was “not good.” These were treated as continuous variables, capturing both frequency and severity of poor health days. This multidimensional approach to outcome assessment enables a comprehensive understanding of the health burden associated with loneliness.

Covariates and coding

Covariates were selected based on known associations with loneliness and health outcomes. Age was included as a continuous variable. Gender was coded as male or female. Race/ethnicity was grouped into four categories: non-Hispanic White, non-Hispanic Black, Hispanic, and Other. Education level was categorized as less than high school, high school graduate, some college, or college graduate. Marital status was coded as married, divorced or separated, never married, or widowed. Employment status included employed, unemployed, retired, or unable to work. Insurance type included private, Medicare, Medicaid, self-pay, and other. Metropolitan status distinguished between metro and non-metro areas, and urbanicity was coded as urban or rural. Language spoken at home was categorized as English, Spanish, or Other. State and year of survey were included as fixed effects. All categorical covariates were dummy coded for inclusion in modeling.

Statistical analysis

We employed inverse probability weighting to estimate the average treatment effects of loneliness on depression, poor mental health days, and poor physical health days. This approach approximates a randomized trial by weighting individuals based on the inverse of their probability of exposure, thereby balancing observed covariates across loneliness categories and reducing confounding bias in the estimation of causal effects.

Step 1: Propensity Score Estimation

Propensity scores were estimated using a multinomial logistic regression with loneliness categories as the outcome and all covariates as predictors:

mlogit loneliness age i.sex i.race i.education i.marital_status i.employment i.insurance i.metro i.urban, baseoutcome(Never)
predict ps1 ps2 ps3 ps4 ps5, pr

Step 2: Compute Inverse Probability Weights

Inverse probability weights were calculated based on the predicted probability of each respondent’s observed loneliness category:

gen ipw = .
replace ipw = 1/ps1 if loneliness == “Always”
replace ipw = 1/ps2 if loneliness == “Usually”
replace ipw = 1/ps3 if loneliness == “Sometimes”
replace ipw = 1/ps4 if loneliness == “Rarely”
replace ipw = 1/ps5 if loneliness == “Never”

To limit the influence of extreme weights, values were trimmed at the 1st and 99th percentiles:

summarize ipw, detail
scalar p1 = r(p1)
scalar p99 = r(p99)
replace ipw = p1 if ipw < p1
replace ipw = p99 if ipw > p99

Step 3: Incorporate Survey Weights and Inverse Probability Weights

To ensure national representativeness, the BRFSS sampling weight (_llcpwt) was multiplied by the IPW to generate the final analysis weight (pweight):

gen pweight = _llcpwt * ipw

Models were estimated using survey-weighted regression with robust standard errors clustered by primary sampling unit:

svyset _psu [pw = pweight], strata(_ststr) singleunit(centered)
svy: regress mental_health_days i.loneliness
svy: regress physical_health_days i.loneliness
svy: logit depression i.loneliness
Marginal effects were then calculated for each level of loneliness:
margins Lonely, post

Subgroup Analysis: Interaction and Marginal Effects.

To evaluate whether sex, age group, and race/ethnicity moderated the relationship between loneliness and outcomes (depression, poor mental health days, and poor physical health days), interaction terms were included in each model:

Depression Model with Interaction:
- svy: logit Depression i.Lonely##Female
- Marginal effects were computed for subgroups:
- margins Female, at(Lonely = (0 1 2 3 4)))) pwcompare(effects) post
- margins Age_group, at(Lonely = (0 1 2 3 4)))) pwcompare(effects) post
- margins Race & Ethnicity, at(Lonely = (0 1 2 3 4)))) pwcompare(effects) post

These were repeated for mental health days and physical health days.

Interpretation of marginal effects and predicted probabilities.

Marginal effects represent the adjusted differences in outcomes between loneliness categories and the reference group (“Never lonely”), holding all other covariates constant. These estimates reflect the predicted change in the outcome associated with each level of loneliness relative to the reference group, after accounting for confounding variables.

For example, a marginal effect of 20.0 for poor mental health days among individuals who report “Always” feeling lonely indicates that, on average, these individuals experience 20 more poor mental health days per month compared to those who report never feeling lonely, after adjusting for sociodemographic and structural factors. Similarly, in the logistic regression models for depression, marginal effects were translated into predicted probabilities of a depression diagnosis for each loneliness level and subgroup (e.g., sex, age group, race/ethnicity).

Subgroup-specific marginal effects were calculated using interaction terms (e.g., i.loneliness##Female) to assess whether the effect of loneliness on each outcome varied by demographic factors. These predicted probabilities enabled the identification of differential vulnerability to loneliness across key subpopulations.

Software and reproducibility

All statistical analyses were performed using Stata/SE version 18.0 (StataCorp LLC, College Station, TX). Propensity scores were estimated using multinomial logistic regression, and inverse probability weights (IPW) were manually computed to simulate treatment assignment across loneliness categories. These weights were combined with BRFSS-provided sampling weights to generate final analysis weights that accounted for both treatment selection bias and complex survey design.

Survey-adjusted regression models were fitted using the svy suite of commands with robust standard errors clustered by primary sampling unit. Post-estimation marginal effects and predicted probabilities were calculated using the margins command to quantify outcome differences across loneliness categories and to examine subgroup-specific effects by sex, age group, and race/ethnicity.

All analytic procedures were fully documented, and the complete annotated Stata code is available upon request to support transparency, reproducibility, and peer verification.

Sensitivity analysis

Sensitivity analyses were conducted to assess robustness by modifying the loneliness definition, excluding groups with ambiguous employment or health conditions, and adjusting model specifications. Subgroup analyses by age, gender, and language were also explored.

Ethics and data access

All analyses were based on publicly available, de-identified data. Therefore, IRB approval was not required. This study adhered to ethical guidelines as outlined in the Declaration of Helsinki.

Results

Demographic and clinical characteristics by loneliness level

Table 1 summarizes the demographic and clinical characteristics of 47,318 U.S. adults stratified by self-reported loneliness levels. Statistically significant differences were observed across all variables (all p < 0.001). Age: The youngest age group (18–44 years) comprised 37.1% of the total sample but was most represented in the “Usually” lonely group (45.5%). Conversely, older adults (>64 years) accounted for 28.0% of the total population and were more likely to report “Never” feeling lonely (33.8%) compared to other loneliness categories.

Table 1. Demographic and clinical profile of adults across loneliness levels in a national survey.

Variable	Total Population (N = 47,318)	Never (n = 8,353)	Always (n = 2,929)	Usually (n = 3,945)	Sometimes (n = 17,947)	Rarely (n = 14,144)	chi2	p-value
Age (Yr.)							561.52	<0.001
18-44	17,556 (37.1%)	2,362 (24.3%)	976 (33.3%)	1,794 (45.5%)	6,776 (37.8%)	5,648 (39.9%)
45-64	16,536 (35.0%)	3,171 (38.0%)	1,232 (42.1%)	1,287 (32.6%)	6,036 (33.6%)	4,810 (34.0%)
>64	13,226 (28.0%)	2,820 (33.8%)	721 (24.6%)	864 (21.9%)	5,135 (28.6%)	3,686 (26.1%)
Female	29,383 (62.1%)	4,895 (58.6%)	1,597 (54.5%)	2,257 (57.2%)	11,573 (64.5%)	9,061 (64.1%)	221.41	<0.001
Race & Ethnicity							379.03	<0.001
White	34,079 (73.3%)	5,915 (72.3%)	1,836 (64.4%)	2,750 (71.3%)	12,712 (72.0%)	10,866 (78.0%)
Black	3,750 (8.1%)	765 (9.4%)	299 (10.5%)	269 (7.0%)	1,592 (9.0%)	825 (5.9%)
Hispanic	4,713 (10.1%)	860 (10.5%)	420 (14.7%)	483 (12.5%)	1,796 (10.2%)	1,154 (8.3%)
Other	3,938 (8.5%)	643 (7.9%)	295 (10.4%)	357 (9.3%)	1,553 (8.8%)	1,090 (7.8%)
Marital Status							3.30E + 03	<0.001
Married	20,054 (42.6%)	4,802 (57.9%)	544 (18.7%)	964 (24.6%)	6,396 (35.9%)	7,348 (52.2%)
Divorced	11,015 (23.4%)	1,195 (14.4%)	873 (30.0%)	1,337 (34.2%)	4,771 (26.8%)	2,839 (20.2%)
Widowed	7,348 (15.6%)	1,100 (13.3%)	733 (25.2%)	774 (19.8%)	2,994 (16.8%)	1,747 (12.4%)
Separated	4,521 (9.6%)	624 (7.5%)	462 (15.9%)	432 (11.0%)	2,084 (11.7%)	919 (6.5%)
Never Married	1,302 (2.8%)	168 (2.0%)	163 (5.6%)	166 (4.2%)	557 (3.1%)	248 (1.8%)
Member of an Unmarried Couple	2,786 (5.9%)	410 (4.9%)	132 (4.5%)	242 (6.2%)	1,028 (5.8%)	974 (6.9%)
Employed (ref. Unemployed)	22,405 (47.7%)	3,946 (47.7%)	857 (29.4%)	1,658 (42.4%)	8,124 (45.6%)	7,820 (55.6%)	814.3	<0.001
Education Level							1.60E + 03	<0.001
High School or Less	14,754 (31.3%)	2,797 (33.6%)	1,489 (51.1%)	1,449 (36.9%)	5,837 (32.6%)	3,182 (22.5%)
College	14,092 (29.9%)	2,435 (29.3%)	894 (30.7%)	1,341 (34.1%)	5,385 (30.1%)	4,037 (28.6%)
Advanced	18,344 (38.9%)	3,090 (37.1%)	531 (18.2%)	1,142 (29.0%)	6,682 (37.3%)	6,899 (48.9%)
Spanish Speaking (ref. English)	1,770 (3.7%)	364 (4.4%)	209 (7.1%)	234 (5.9%)	653 (3.6%)	310 (2.2%)	249.94	<0.001
Metro (ref.non-metro)	33,551 (72.1%)	5,785 (70.4%)	1,935 (68.6%)	2,814 (73.5%)	12,798 (72.5%)	10,219 (72.9%)	38.05	<0.001

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Sex: Overall, 62.1% of respondents were female. Women were overrepresented in the “Sometimes” (64.5%) and “Rarely” (64.1%) categories, compared to 58.6% in the “Never” group and 54.5% in the “Always” lonely group.

Race and Ethnicity: White individuals made up the majority of the population (73.3%) and were particularly concentrated in the “Rarely” lonely group (78.0%). In contrast, Black respondents represented 8.1% overall but comprised 10.5% of the “Always” lonely group. Hispanics accounted for 10.1% of the total sample and were more represented in the “Always” (14.7%) and “Usually” (12.5%) lonely categories compared to “Never” (10.5%).

Marital Status: Marital status strongly differentiated loneliness levels. Among those who reported “Never” being lonely, 57.9% were married. This proportion declined substantially in the “Always” lonely group (18.7%) and “Usually” (24.6%). Divorced (30.0%), widowed (25.2%), and separated (15.9%) individuals were disproportionately represented in the “Always” lonely category.

Employment: Employment was associated with lower loneliness. Of the total sample, 47.7% were employed. However, only 29.4% of the “Always” lonely group were employed, compared to 55.6% in the “Rarely” lonely group.

Education: Educational attainment also followed a gradient across loneliness categories. Over half (51.1%) of individuals in the “Always” lonely group had a high school education or less, compared to just 22.5% in the “Rarely” lonely group. Conversely, 48.9% of those who reported “Rarely” being lonely had advanced education, compared to only 18.2% in the “Always” group.

Language: Spanish speakers were more prevalent among those reporting higher loneliness, constituting 7.1% of the “Always” lonely group compared to 2.2% of the “Rarely” group.

Metropolitan Residence: Overall, 72.1% of respondents resided in metro areas. The distribution was relatively even across loneliness categories, ranging from 68.6% in the “Always” group to 73.5% in the “Usually” group.

These findings highlight significant social and demographic gradients in loneliness, with younger age, lower education, unemployment, and minority race/ethnicity associated with higher loneliness levels.

Loneliness and depression

Adjusted analysis showed a strong dose-response relationship between loneliness and the likelihood of depression (Table 2). The adjusted predicted probability of depression increased from 9.7% among those who “Never” felt lonely to 16.3% (“Rarely”), 30.6% (“Sometimes”), 47.7% (“Usually”), and peaked at 50.2% for those who “Always” felt lonely (p < 0.001). This clear dose-response relationship highlights that as feelings of loneliness intensify, the risk of depression rises substantially, even after adjusting for demographic and socioeconomic factors (Table 2).

Table 2. Adjusted predicted probability of depression by loneliness category.

Depression	Margin	Std. Err.	t	95% CI		P > t
Lonely
Never	0.097	0.002	39.51	0.092	0.101	<0.001
Always	0.502	0.017	29.55	0.469	0.536	<0.001
Usually	0.477	0.013	37.35	0.452	0.502	<0.001
Sometimes	0.306	0.005	62.77	0.297	0.316	<0.001
Rarely	0.163	0.003	57.36	0.157	0.169	<0.001

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Table 2 presents the adjusted predicted probabilities of depression by loneliness category, using inverse probability weighting. Individuals reporting “Always,” “Usually,” “Sometimes,” or “Rarely” feeling lonely were compared to those reporting “Never.” Models adjusted for age, race/ethnicity, sex, marital status, employment status, education, language, metro status, and included state, year, and month fixed effects. All results are statistically significant at p < 0.001, with higher loneliness levels associated with greater likelihood of depression.

Sex differences in the association between loneliness and depression

Women consistently exhibited a higher likelihood of depression compared to men across all levels of loneliness (Table 3). The largest sex difference was observed among those who “Always” felt lonely, where women had a 12.9 percentage point higher predicted probability of depression than men (p < 0.001). Even among those reporting “Never” feeling lonely, women showed a 6.0 percentage point higher risk. These findings highlight significant sex-based disparities in the mental health impact of loneliness, independent of demographic and socioeconomic factors.

Table 3. Sex differences in the association between loneliness and depression (Marginal Effects Model).

	Sex	Margin	Std. Err.	t	95% CI		P > t
Lonely	Depression
Never	Female vs. Male	0.060	0.005	13.170	0.051	0.069	<0.001
Always	Female vs. Male	0.129	0.032	4.000	0.066	0.192	<0.001
Usually	Female vs. Male	0.110	0.024	4.550	0.063	0.157	<0.001
Sometimes	Female vs. Male	0.122	0.010	11.830	0.102	0.142	<0.001
Rarely	Female vs. Male	0.086	0.005	15.960	0.076	0.097	<0.001

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Table 3 displays the marginal effects comparing females to males in the association between loneliness and depression, assessing sex as a moderator. Estimates reflect the difference in predicted probability of depression across loneliness categories between women and men. Models were adjusted for age, race/ethnicity, marital status, employment, education, language, metro status, and included state, year, and month fixed effects. All differences were statistically significant at p < 0.001.

Racial and ethnic differences in the association between loneliness and depression

Across all loneliness categories, Black and Hispanic individuals had significantly lower predicted probabilities of depression compared to White individuals (Supplementary S1 Table). The largest disparities were observed among those who “Always” felt lonely, where Black individuals had a 25.6 percentage point lower risk and Hispanic individuals a 14.3 percentage point lower risk relative to Whites (p < 0.05 for all comparisons). These consistent negative margins suggest that, despite higher loneliness, minority groups reported lower levels of depression across all categories.

Age differences in the association between loneliness and depression

Older adults generally exhibited lower predicted probabilities of depression compared to younger adults across most loneliness categories (Supplementary S2 Table). Notably, individuals aged >64 years had a significantly reduced risk of depression, with the largest difference observed among those who were “Sometimes” lonely (–12.7 percentage points, p < 0.001). Middle-aged adults (45–64 years) showed minimal differences, with few reaching statistical significance. These findings suggest a potential protective effect of older age against depression in the context of loneliness.

Loneliness and poor mental health days

Higher levels of loneliness were strongly associated with an increased number of poor mental health days (Table 4). Individuals who reported “Always” feeling lonely experienced an average of 20 poor mental health days per month, compared to 9.4 days among those who “Never” felt lonely (p < 0.001). A clear gradient was observed, with the number of poor mental health days rising consistently as loneliness intensified, even after adjusting for demographic and socioeconomic factors.

Table 4. Adjusted number of poor mental health days by loneliness category.

	Margin	Std. Err.	t	95% CI		p-value
Lonely	Mental Health (Days)
Never	9.36	0.18	51.97	9.01	9.72	<0.001
Always	19.95	0.38	52.74	19.20	20.69	<0.001
Usually	16.45	0.31	52.69	15.84	17.06	<0.001
Sometimes	11.39	0.12	91.17	11.14	11.63	<0.001
Rarely	8.00	0.11	72.22	7.79	8.22	<0.001

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Table 4 presents the adjusted average number of poor mental health days reported across loneliness categories, estimated using inverse probability weighting. Predicted values were obtained using marginal effects models and account for differences in demographic and socioeconomic factors. Models were adjusted for age, race/ethnicity, sex, marital status, employment, education, language, and metro status, with fixed effects for state, year, and month. Higher loneliness levels were associated with significantly more poor mental health days (p < 0.001).

Sex differences in poor mental health days across loneliness levels

Women reported significantly more poor mental health days than men among those who felt “Sometimes” (+0.84 days, p = 0.002) and “Rarely” lonely (+0.65 days, p = 0.003) (Table 5). No significant differences were observed in other loneliness categories. These findings suggest that sex-based disparities in mental health are most evident at moderate levels of loneliness.

Table 5. Sex differences in the association between loneliness and number of poor mental health days (Marginal Effects Model).

	Sex	Margin	Std. Err.	t	95% CI		P > t
Lonely	Menth
Never	Female vs. Male	−0.65	0.353	−1.83	−1.34	0.05	0.067
Always	Female vs. Male	0.93	0.708	1.31	−0.46	2.31	0.191
Usually	Female vs. Male	0.84	0.599	1.41	−0.33	2.02	0.16
Sometimes	Female vs. Male	0.84	0.275	3.07	0.31	1.38	0.002
Rarely	Female vs. Male	0.65	0.216	3.00	0.23	1.07	0.003

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Table 5 displays the marginal effects comparing females to males in the association between loneliness and the number of poor mental health days, assessing sex as a moderator. Estimates represent the difference in predicted number of poor mental health days across loneliness categories between women and men. Models were adjusted for age, race/ethnicity, marital status, employment, education, language, and metro status, and included fixed effects for state, year, and month. Statistically significant differences are noted for those reporting “Sometimes” and “Rarely” feeling lonely (p < 0.01).

Racial and ethnic differences in poor mental health days

Detailed analysis revealed few significant racial or ethnic differences in the number of poor mental health days across loneliness categories (Supplementary S3 Table). The only notable finding was among individuals who reported “Always” feeling lonely, where Black individuals experienced 3.75 fewer poor mental health days per month compared to White individuals (p = 0.005). No significant differences were observed between Hispanic and White individuals across any loneliness category.

Age differences in poor mental health days

Age-related differences in poor mental health days were limited across loneliness categories (Supplementary S4 Table). However, older adults (>64 years) who reported feeling “Sometimes” lonely experienced 2.15 fewer poor mental health days compared to younger adults aged 18–44 (p < 0.001). Additionally, older adults in the “Rarely” lonely group reported 1.03 fewer days (p = 0.009). Middle-aged adults (45–64 years) showed minimal and inconsistent differences across categories.

Loneliness and poor physical health days

Higher levels of loneliness were associated with an increased number of poor physical health days per month (Table 6). Individuals who reported “Always” feeling lonely experienced an average of 15.8 poor physical health days, compared to 11.2 days among those who “Never” felt lonely (p < 0.001). A clear gradient was observed, with poorer physical health outcomes corresponding to greater feelings of loneliness, even after adjusting for demographic and socioeconomic factors.

Table 6. Adjusted number of poor physical health days by loneliness category.

	Margin	Std. Err.	t	95% CI		p-value
Lonely	Physical Health (Days)
Never	11.22	0.26	42.89	10.71	11.73	<0.001
Always	15.83	0.48	32.94	14.89	16.77	<0.001
Usually	13.61	0.39	34.97	12.85	14.38	<0.001
Sometimes	11.36	0.19	59.85	10.98	11.73	<0.001
Rarely	9.76	0.19	50.63	9.39	10.14	<0.001

Open in a new tab

Table 6 presents the adjusted average number of poor physical health days reported across loneliness categories, estimated using inverse probability weighting. Marginal effects were derived from models adjusted for age, race/ethnicity, sex, marital status, employment status, education level, language, and metro status, with fixed effects for state, year, and month. Individuals reporting higher levels of loneliness experienced significantly more days of poor physical health (p < 0.001).

Sex differences in poor physical health days across loneliness levels

There were no significant differences in the number of poor physical health days between women and men across any loneliness category (Table 7). While minor variations were observed, none reached statistical significance (p > 0.05), suggesting that the impact of loneliness on physical health was similar for both sexes after adjusting for demographic and socioeconomic factors.

Table 7. Sex differences in the association between loneliness and number of poor physical health days.

	Sex	Margin	Std. Err.	t	95% CI		P > t
Lonely	Physical Health (Days)
Never	Female vs. Male	−0.79	0.518	−1.51	−1.8	0.23	0.13
Always	Female vs. Male	0.18	0.89	0.21	−1.56	1.92	0.837
Usually	Female vs. Male	0.62	0.745	0.83	−0.84	2.08	0.407
Sometimes	Female vs. Male	0.29	0.424	0.68	−0.54	1.12	0.495
Rarely	Female vs. Male	−0.25	0.394	−0.64	−1.02	0.52	0.523

Open in a new tab

Table 7 presents the marginal effects comparing females to males in the association between loneliness and the number of poor physical health days, assessing sex as a moderator. Estimates reflect differences in predicted number of poor physical health days across loneliness categories between women and men. Models were adjusted for age, race/ethnicity, marital status, employment, education, language, and metro status, and included state, year, and month fixed effects. No statistically significant differences were observed between females and males across any loneliness category (p > 0.05).

Racial and ethnic differences in poor physical health days

Racial and ethnic differences in poor physical health days varied across loneliness categories (Supplementary S5 Table). Among individuals who were “Always” lonely, Black individuals reported 3.6 fewer poor physical health days per month compared to White individuals (p = 0.014). Additionally, Hispanic individuals in the “Sometimes” lonely group experienced 1.4 more poor physical health days than their White counterparts (p = 0.041). No other significant differences were observed across loneliness categories.

Age differences in poor physical health days

Both middle-aged (45–64 years) and older adults (>64 years) consistently reported significantly more poor physical health days compared to younger adults (18–44 years) across all loneliness categories (Supplementary S6 Table). The largest difference was observed among older adults who were “Usually” lonely, reporting 5.7 additional poor physical health days per month (p < 0.001). These findings indicate a strong association between increasing age and poorer physical health, regardless of loneliness severity.

Discussion

This study offers compelling and novel evidence linking loneliness to significant adverse mental and physical health outcomes in a nationally representative sample of U.S. adults. By employing inverse probability weighting (IPW) alongside the BRFSS complex survey design, we move beyond previous correlational studies to provide a rigorous, causal interpretation of loneliness as a social determinant of health. Our methodology enables us to generate generalizable estimates that underscore loneliness not just as a symptom of broader psychosocial dysfunction, but as a potential driver of disease burden in the population.

The association between loneliness and depression was both striking and consistent with biological plausibility and prior empirical work. Individuals who reported “Always” feeling lonely had a dramatically elevated risk of depression, 50.2 percentage points higher than those who reported “Never” feeling lonely, whose baseline risk stood at just 9.7%. This strong, graded association resonates with existing neurobiological research showing that loneliness dysregulates stress-response systems such as the hypothalamic-pituitary-adrenal (HPA) axis and affects key neurotransmitters including serotonin and dopamine [21–23]. These neurochemical changes, in conjunction with the psychological toll of perceived social disconnection, likely amplify the risk for depressive symptoms [24–26]. While prior studies have identified this link [8,9,27], our findings are among the few studies to demonstrate its magnitude using causal inference techniques applied to national data, making a strong case for targeting loneliness in depression prevention efforts.

Importantly, our subgroup analyses provide evidence that this relationship between loneliness and depression is moderated by sex, race/ethnicity, and age. Females exhibited a higher marginal effect of depression across all loneliness categories compared to males, suggesting increased vulnerability among women. Although loneliness increased the risk of depression for all racial/ethnic groups, Black and Hispanic individuals reported comparatively lower odds of depression across all levels of loneliness, pointing to potential cultural or social buffering mechanisms [28–30]. Furthermore, adults aged 45 and above exhibited slightly higher depression rates than younger individuals, especially in lower loneliness categories, suggesting that age-related factors may enhance susceptibility to loneliness-related depression [31,32].

Beyond clinical depression, the mental health toll of loneliness was evident in day-to-day experiences. Participants who reported feeling “Always” lonely experienced a substantial burden of psychological distress, reporting an average of 20 poor mental health days per month, compared to 9.4 days among those who reported never feeling lonely. This more than twofold increase underscores the profound impact of persistent loneliness on daily emotional well-being. This dose-response relationship suggests that loneliness does not merely co-occur with mental health challenges but may exacerbate chronic psychological distress and emotional instability [33,34]. Subgroup analysis showed that females generally reported more poor mental health days than males, though statistical significance varied by loneliness category. Also, only among those who always felt lonely did White individuals report significantly higher mental health days compared to other racial/ethnic groups. Individuals aged 45 and older consistently experienced more poor mental health days across most loneliness categories as a meaningful moderator [35].

The impact of loneliness extended beyond the mind, manifesting in physical well-being [1,36]. Our analysis showed that loneliness was associated with five additional poor physical health days per month among those who “Always” felt lonely. This adds to growing evidence that social isolation not only compromises mental health but accelerates physical decline [36,37]. Subgroup findings revealed that while sex differences in poor physical health days were not statistically significant, Whites who reported always feeling lonely experienced more poor physical health days than other groups, whereas the reverse was true for those who only sometimes felt lonely. In addition, individuals aged 45 and above consistently reported more poor physical health days across all loneliness categories, underscoring the cumulative impact of loneliness with advancing age.

In terms of its contribution to literature, this study is both confirmatory and forward-driving. It confirms long-standing associations identified in prior work, but it does so using a design that accounts for confounding and complex sampling, enabling more robust generalizations. By incorporating subgroup analyses, we provide more nuanced insight into how demographic and social identity factors interact with loneliness to influence health. Moreover, by simultaneously evaluating mental and physical health outcomes, we offer a multidimensional perspective on loneliness, positioning it as a key target for integrated health interventions. This work serves as a bridge between theory and actionable insight.

Responding to concerns regarding self-reported diagnoses, particularly for depression, it is important to note that BRFSS asks whether a healthcare professional has ever diagnosed the respondent. While this still relies on self-report, the phrasing reduces the likelihood of self-diagnosis and enhances the clinical validity of the measure. Nevertheless, future research incorporating clinician-administered assessments or electronic health record data could further validate these outcomes.

Policy implications

The findings of this study carry urgent policy and practice implications. Given the pervasiveness and potency of loneliness, it should be prioritized alongside traditional risk factors in public health planning. Routine loneliness screening in clinical settings could help identify high-risk individuals. Social prescribing initiatives, where healthcare providers refer patients to community-based activities, should be expanded to include virtual platforms, especially in rural and underserved populations. Community-level investments that promote social infrastructure, such as libraries, recreation centers, and intergenerational programming, may yield long-term health dividends. Workplace policies that support social connection and inclusion are also essential in mitigating the downstream effects of social disconnection. Moreover, targeted interventions for older adults and strategies that address racial/ethnic disparities in the experience and impact of loneliness should be considered.

Limitations

Several limitations must be acknowledged. While IPW reduces bias from measured confounders, residual confounding by unmeasured factors such as personality traits or life events remains possible. The reliance on self-reported measures introduces potential recall and social desirability bias. Additionally, the cross-sectional design limits temporal interpretation; we cannot definitively state that loneliness preceded the health outcomes observed. The loneliness measures itself, though ordinal, may not capture the full depth or nuance of the emotional experience. Lastly, telephone-based survey methods may underrepresent the most socially isolated individuals, potentially underestimating the true burden of loneliness in the population.

Conclusion

Our findings underscore loneliness as a pressing public health issue with wide-reaching effects on mental and physical health. The use of a causal analytic framework and nationally representative data advances the field, making a strong case for upstream interventions. Subgroup analyses further reveal that the effects of loneliness are not uniform but instead moderated by demographic factors such as sex, age, and race/ethnicity. Addressing loneliness may not only improve individual well-being but also reduce societal healthcare burdens. Future studies should explore the long-term effectiveness of loneliness-reduction strategies and the contextual factors that moderate their impact.

Supporting information

S1 Table. Racial and ethnic differences in the association between loneliness and depression (Marginal Effects Model).

(DOCX)

pone.0319311.s001.docx^{(16.6KB, docx)}

S2 Table. Age differences in the association between loneliness and depression (Marginal Effects Model).

(DOCX)

pone.0319311.s002.docx^{(16.7KB, docx)}

S3 Table. Racial and ethnic differences in the association between loneliness and number of poor mental health days (Marginal Effects Model).

(DOCX)

pone.0319311.s003.docx^{(16.6KB, docx)}

S4 Table. Age differences in the association between loneliness and number of poor mental health days.

(DOCX)

pone.0319311.s004.docx^{(16.7KB, docx)}

S5 Table. Racial and ethnic differences in the association between loneliness and number of poor physical health days.

(DOCX)

pone.0319311.s005.docx^{(16.6KB, docx)}

S6 Table. Age differences in the association between loneliness and number of poor physical health days.

(DOCX)

pone.0319311.s006.docx^{(16.8KB, docx)}

S1 File. STROBE checklist.

(DOC)

pone.0319311.s007.doc^{(86KB, doc)}

Acknowledgments

The authors thank all participants and personnel who supported this project. Special thanks to the Clive O. Callender Outcomes Research Center at the Howard University College of Medicine for their invaluable support.

Data Availability

All relevant data are within the paper and its Supporting information files.

Funding Statement

This project was supported (in part) by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number 2U54MD007597. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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PLoS One. 2025 Jul 9;20(7):e0319311. doi: 10.1371/journal.pone.0319311.r001

Author response to Decision Letter 0

30 Jan 2025

PLoS One. doi: 10.1371/journal.pone.0319311.r002

Decision Letter 0

Renata Komalasari

May 08 2025

PONE-D-25-04971The Impact of Loneliness on Depression, Mental Health Days, and Physical HealthPLOS ONE

Dear Dr. Akinyemi,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

==============================

ACADEMIC EDITOR:

This study investigates the associations of loneliness with depression, mental health, and physical health, accounting for confounders and state and year fixed effects. Data analysis used Inverse Probability Weighting (IPW) to estimate the Average Treatment Effect (ATE) of loneliness on depression, mental health days, and physical health days. Findings suggest loneliness as a strong predictor of depression and poor mental and physical health. While this method was used to minimize selection bias, please include some rationales underlying the use of matched and unmatched groups in the study methods.

Per the RECORD checklist, please provide information on the data cleaning methods used in the study.

==============================

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Reviewer #1: Thank you for the opportunity to review this manuscript. This study leverages data from the Behavioral Risk Factor Surveillance System (BRFSS) (2016–2023) to examine the relationship between loneliness and mental and physical health outcomes. The findings indicate that loneliness is significantly associated with an increased likelihood of depression diagnosis (+39.3 percentage points), as well as a greater number of poor mental (+10.9 days) and physical health days (+5.0 days). These results emphasize the urgent need for interventions addressing social isolation to improve population health.

I appreciate the thorough approach taken by the authors and offer the following comments for consideration:

The introduction effectively outlines the importance of studying loneliness and clearly defines the key health outcomes of interest. Please expand on the limitations of previous studies and how this study advances current knowledge. What gaps in the literature does this work specifically address? You note that social determinants may influence the relationship between loneliness and health outcomes, yet they are treated solely as covariates rather than moderators. Although subgroup analyses for race and ethnicity are included, a more granular exploration of how specific socioeconomic and structural factors contribute to disparities could provide additional insights.

Regarding the methods, please describe the inclusion and exclusion criteria for BRFSS participants. Additionally, clarify how the survey design ensures national representativeness—while this may be well-known to BRFSS users, including it will enhance accessibility for broader audiences. Further detail on how covariates were coded (e.g., categorical vs. continuous) would strengthen transparency in the methodology section.

The statistical methods appear rigorous and appropriate, particularly the use of robust standard errors and the treatment of outcomes as ordinal variables. However, additional details are needed to enhance clarity and reproducibility. Please provide stepwise equations for IPW estimation, including the propensity score model, how inverse probabilities were computed, and the final weighting approach. How were sampling weights from BRFSS incorporated into the IPW procedure? Were they applied multiplicatively? Did you implement strategies to address extreme propensity scores? Many studies cap weights at reasonable thresholds—please clarify your approach. Additional details on how the ATE was computed and what the reported values represent in the context of loneliness and health outcomes would be helpful. Would you be open to providing the statistical code (e.g., in a GitHub repository or as supplementary material)? This would significantly enhance transparency and reproducibility. Please specify the statistical software and functions/packages used for the analysis.

A useful reference providing sufficient statistical detail on similar methods is:

Morrish, N., Mujica-Mota, R., & Medina-Lara, A. (2022). Understanding the effect of loneliness on unemployment: propensity score matching. BMC Public Health, 22(1), 740. https://doi.org/10.1186/s12889-022-13107-x

The discussion effectively contextualizes the findings, but the contribution relative to prior studies could be made more explicit. Currently, the results seem to parallel previous research rather than extending it. Please clarify how your study improves upon prior work—for example, through a more robust statistical approach, use of nationally representative data, or exploration of new potential mechanisms linking loneliness to health. Strengthening the discussion on mechanisms and policy implications would further enhance its contribution.

Reviewer #2: Thank you for the opportunity to review this study.

The study's topic is important to address a common mental health risk, but there are some observations:

how was self-diagnosis avoided regarding depression. was there a mechanism used to ensure that data collected regarding diagnosis is accurate? relying on self-reported assessment for mental health makes it hard to take collected data as reliable.

**********

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Reviewer #1: Yes: Tyler Bell

Reviewer #2: No

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PLoS One. 2025 Jul 9;20(7):e0319311. doi: 10.1371/journal.pone.0319311.r003

Author response to Decision Letter 1

24 Apr 2025

Response to Academic Editor

Thank you for your thoughtful feedback. We appreciate the opportunity to clarify key methodological aspects of our study.

Academic Editor:

Comment :

This study investigates the associations of loneliness with depression, mental health, and physical health, accounting for confounders and state and year fixed effects. Data analysis used Inverse Probability Weighting (IPW) to estimate the Average Treatment Effect (ATE) of loneliness on depression, mental health days, and physical health days. Findings suggest loneliness as a strong predictor of depression and poor mental and physical health. While this method was used to minimize selection bias, please include some rationales underlying the use of matched and unmatched groups in the study methods.

Per the RECORD checklist, please provide information on the data cleaning methods used in the study.

Rationale for Use of Matched and Unmatched Groups in the Study Methods

In this study, we applied Inverse Probability Weighting (IPW) rather than traditional propensity score matching. The rationale for using IPW lies in its ability to retain the full analytic sample (unmatched approach) while minimizing selection bias due to observed confounders. Unlike matching methods, which exclude unmatched individuals and potentially reduce generalizability, IPW allows for adjustment across all levels of loneliness by weighting individuals inversely proportional to their probability of being in a given loneliness category.

This approach was particularly suited to our objective of estimating the Average Treatment Effect (ATE) across a multi-level exposure (five categories of loneliness) within a nationally representative dataset. The use of IPW, combined with BRFSS sampling weights and state/year fixed effects, enabled us to balance covariates across groups without sacrificing sample size or representativeness.

We have clarified this rationale in the Statistical Analysis section of the manuscript to explicitly distinguish why a weighting strategy was preferred over matching techniques.

Data Cleaning Methods (RECORD Checklist Compliance)

In adherence to the RECORD guidelines, we have expanded the Methodology section to detail our data cleaning procedures:

We conducted initial data integrity checks to identify incomplete or inconsistent responses.

Participants were excluded if they provided responses such as "Don’t know," "Refused," or had missing data for key variables, including the exposure (loneliness), outcomes (depression diagnosis, poor mental health days, poor physical health days), and covariates.

We verified coding consistency across survey years (2016–2023) to ensure harmonization of variable definitions.

Extreme values for continuous variables (e.g., health days) were assessed, and implausible entries outside the BRFSS-defined ranges were removed.

For the IPW procedure, we trimmed extreme weights at the 1st and 99th percentiles to mitigate undue influence from outliers.

All cleaning steps were performed using Stata, with code documentation available upon request to ensure transparency and reproducibility.

We have updated the manuscript to explicitly describe these steps under a new subsection titled Data Cleaning and Preparation within the Methodology section.

We trust that these clarifications address your concerns. We remain committed to ensuring methodological transparency and adherence to best practices.

Response to Reviewers

We are grateful for the thoughtful and constructive feedback provided by both reviewers. We have carefully considered each comment and revised the manuscript accordingly to strengthen clarity, methodological transparency, and overall contribution. Below we provide a point-by-point response detailing how each comment has been addressed.

Reviewer #1

Comment: “The introduction effectively outlines the importance of studying loneliness and clearly defines the key health outcomes of interest. Please expand on the limitations of previous studies and how this study advances current knowledge. What gaps in the literature does this work specifically address?”

Response:

We appreciate this suggestion and have revised the introduction to explicitly highlight the limitations of previous research, including over-reliance on correlational designs, use of non-representative samples, and insufficient attention to the interplay between loneliness and social determinants of health. We now clarify how our study addresses these gaps through the use of a large, nationally representative dataset (BRFSS), application of inverse probability weighting to approximate causal inference, and exploration of demographic variations. These revisions can be found in the revised Introduction section.

Comment: “You note that social determinants may influence the relationship between loneliness and health outcomes, yet they are treated solely as covariates rather than moderators. Although subgroup analyses for race and ethnicity are included, a more granular exploration of how specific socioeconomic and structural factors contribute to disparities could provide additional insights.”

Response:

We agree with the reviewer that moderator analysis offers further insight into disparities. While our initial analyses treated social determinants as covariates, we have added subgroup analyses stratified by sex, race/ethnicity, and age groups.

Comment: “Regarding the methods, please describe the inclusion and exclusion criteria for BRFSS participants.”

Response:

We have revised the Methodology section to clearly define the inclusion and exclusion criteria. Participants were included if they were aged 18 or older and had complete responses to the loneliness measure, all three outcome variables, and all covariates. Participants who responded “Don’t know,” “Refused,” or had missing data were excluded.

Comment: “Clarify how the survey design ensures national representativeness.”

Response:

We have added a paragraph explaining the BRFSS's complex multistage sampling design and the application of sampling weights to ensure state and national representativeness. These revisions are now detailed in the Methodology section.

Comment: “Further detail on how covariates were coded (e.g., categorical vs. continuous) would strengthen transparency.”

Response:

We have expanded the Covariates and Coding subsection to clearly indicate how each variable was treated in the analysis (e.g., age as continuous, education as categorical with dummy coding, etc.).

Comment: “Please provide stepwise equations for IPW estimation, including the propensity score model, how inverse probabilities were computed, and the final weighting approach.”

Response:

We have added a detailed, step-by-step explanation of the IPW process in the Statistical Analysis section, including the multinomial logistic regression used to generate propensity scores, computation of inverse probabilities, trimming of extreme weights at the 1st and 99th percentiles, and the final multiplication with BRFSS sampling weights.

Comment: “How were sampling weights from BRFSS incorporated into the IPW procedure? Were they applied multiplicatively? Did you implement strategies to address extreme propensity scores?”

Response:

Yes, the BRFSS sampling weight (pweight = _llcpwt) was applied multiplicatively with the IPW weights to create the final analysis weight. We have now clarified this process in the methodology and included code examples. We also describe our trimming of extreme weights at the 1st and 99th percentiles to reduce their influence.

Comment: “Additional details on how the ATE was computed and what the reported values represent in the context of loneliness and health outcomes would be helpful.”

Response:

We expanded the Interpretation of ATE Estimates subsection to explain how the ATE represents the average differences in outcomes between loneliness levels, adjusted for confounders. We provide clear examples (e.g., “10.8 more poor mental health days for ‘Always lonely’ vs. ‘Never lonely’ individuals”).

Comment: “Would you be open to providing the statistical code?”

Response:

Yes. We have stated in the revised manuscript that the full annotated Stata code is available upon request and will also be posted on a public GitHub repository upon acceptance for publication. This is now noted in the Software and Reproducibility section.

Comment: “The discussion effectively contextualizes the findings, but the contribution relative to prior studies could be made more explicit... Strengthening the discussion on mechanisms and policy implications would further enhance its contribution.”

Response:

We revised the Discussion to clearly articulate how our study builds on prior work—emphasizing the use of causal methods, nationally representative data, and the joint analysis of mental and physical health outcomes. We also elaborated on the biological and psychosocial mechanisms linking loneliness to health outcomes and expanded the section on policy and clinical implications to include practical interventions (e.g., social prescribing, digital tools, community investments).

Reviewer #2

Comment: “How was self-diagnosis avoided regarding depression? Was there a mechanism used to ensure that data collected regarding diagnosis is accurate?”

Response:

We appreciate this important question. The BRFSS depression item specifically asks whether a healthcare professional has ever told the respondent they had depression. This helps mitigate concerns about self-diagnosis and aligns the variable more closely with clinical depression. We have clarified this in the Discussion section under “Addressing Self-Reported Limitations.”

Once again, we thank both reviewers for their insightful comments, which have significantly improved the clarity and impact of the manuscript. We believe these revisions address all concerns and enhance the scientific rigor, transparency, and contribution of our work.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0319311.s008.docx^{(17.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0319311.r004

Decision Letter 1

Renata Komalasari

The Impact of Loneliness on Depression, Mental Health, and Physical Well-being

PONE-D-25-04971R1

Dear Dr. Akinyemi,

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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

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Reviewers' comments:

Reviewer's Responses to Questions

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Reviewer #1: All comments have been addressed

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

Reviewer #1: Yes

**********

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

Reviewer #1: Yes

**********

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

Reviewer #1: Yes

**********

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

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors have addressed all of my concerns. I thank them for their responsiveness, especially providing the statistical code.

**********

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Reviewer #1: Yes: Tyler Reed Bell

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PLoS One. doi: 10.1371/journal.pone.0319311.r005

Acceptance letter

Renata Komalasari

PONE-D-25-04971R1

PLOS ONE

Dear Dr. Akinyemi,

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. Racial and ethnic differences in the association between loneliness and depression (Marginal Effects Model).

(DOCX)

pone.0319311.s001.docx^{(16.6KB, docx)}

S2 Table. Age differences in the association between loneliness and depression (Marginal Effects Model).

(DOCX)

pone.0319311.s002.docx^{(16.7KB, docx)}

S3 Table. Racial and ethnic differences in the association between loneliness and number of poor mental health days (Marginal Effects Model).

(DOCX)

pone.0319311.s003.docx^{(16.6KB, docx)}

S4 Table. Age differences in the association between loneliness and number of poor mental health days.

(DOCX)

pone.0319311.s004.docx^{(16.7KB, docx)}

S5 Table. Racial and ethnic differences in the association between loneliness and number of poor physical health days.

(DOCX)

pone.0319311.s005.docx^{(16.6KB, docx)}

S6 Table. Age differences in the association between loneliness and number of poor physical health days.

(DOCX)

pone.0319311.s006.docx^{(16.8KB, docx)}

S1 File. STROBE checklist.

(DOC)

pone.0319311.s007.doc^{(86KB, doc)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0319311.s008.docx^{(17.9KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting information files.

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PERMALINK

The impact of loneliness on depression, mental health, and physical well-being

Oluwasegun Akinyemi

Waliah Abdulrazaq

Mojisola Fasokun

Fadeke Ogunyankin

Seun Ikugbayigbe

Uzoamaka Nwosu

Miriam Michael

Kakra Hughes

Temitope Ogundare

Roles

Abstract

Background

Objective

Methods

Results

Conclusion

Introduction

Methodology

Study population

Explanatory variable

Outcome variables

Covariates and coding

Statistical analysis

Subgroup Analysis: Interaction and Marginal Effects.

Interpretation of marginal effects and predicted probabilities.

Software and reproducibility

Sensitivity analysis

Ethics and data access

Results

Demographic and clinical characteristics by loneliness level

Table 1. Demographic and clinical profile of adults across loneliness levels in a national survey.

Loneliness and depression

Table 2. Adjusted predicted probability of depression by loneliness category.

Sex differences in the association between loneliness and depression

Table 3. Sex differences in the association between loneliness and depression (Marginal Effects Model).

Racial and ethnic differences in the association between loneliness and depression

Age differences in the association between loneliness and depression

Loneliness and poor mental health days

Table 4. Adjusted number of poor mental health days by loneliness category.

Sex differences in poor mental health days across loneliness levels

Table 5. Sex differences in the association between loneliness and number of poor mental health days (Marginal Effects Model).

Racial and ethnic differences in poor mental health days

Age differences in poor mental health days

Loneliness and poor physical health days

Table 6. Adjusted number of poor physical health days by loneliness category.

Sex differences in poor physical health days across loneliness levels

Table 7. Sex differences in the association between loneliness and number of poor physical health days.

Racial and ethnic differences in poor physical health days

Age differences in poor physical health days

Discussion

Policy implications

Limitations

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Decision Letter 0

Renata Komalasari

Roles

Author response to Decision Letter 1

Decision Letter 1

Renata Komalasari

Roles

Acceptance letter

Renata Komalasari

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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