The association between all-cause mortality and HIV acquisition risk groups in the United States, 2001–2014

Fatma M Shebl; Yiqi Qian; Julia H A Foote; Nattanicha Wattananimitgul; Krishna P Reddy; Anne M Neilan; Andrea L Ciaranello; Elena Losina; Kenneth A Freedberg; Emily P Hyle

doi:10.1371/journal.pone.0290113

. 2023 Aug 17;18(8):e0290113. doi: 10.1371/journal.pone.0290113

The association between all-cause mortality and HIV acquisition risk groups in the United States, 2001–2014

Fatma M Shebl ^1,^2,^*, Yiqi Qian ¹, Julia H A Foote ^1,², Nattanicha Wattananimitgul ¹, Krishna P Reddy ^1,^2,³, Anne M Neilan ^1,^2,^4,⁵, Andrea L Ciaranello ^1,^2,^5,⁶, Elena Losina ^6,^7,⁸, Kenneth A Freedberg ^1,^2,^5,^6,^9,¹⁰, Emily P Hyle ^1,^2,^5,⁶

Editor: Veincent Christian Pepito¹¹

¹Medical Practice Evaluation Center, Massachusetts General Hospital, Boston, Massachusetts, Unite States of America

²Harvard Medical School, Boston, Massachusetts, United States of America

³Division of Pulmonary and Critical Care Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America

⁴Division of General Academic Pediatrics, Massachusetts General Hospital, Boston, Massachusetts, United States of America

⁵Division of Infectious Diseases, Massachusetts General Hospital, Boston, Massachusetts, United States of America

⁶Harvard University Center for AIDS Research (CFAR), Cambridge, Massachusetts, United States of America

⁷Department of Orthopedic Surgery, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America

⁸Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, United States of America

⁹Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America

¹⁰Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, United States of America

¹¹Ateneo de Manila University Ateneo School of Medicine and Public Health, PHILIPPINES

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

^✉

* E-mail: fshebl@gmail.com

Roles

Fatma M Shebl: Conceptualization, Data curation, Formal analysis, Supervision, Writing – original draft, Writing – review & editing

Yiqi Qian: Data curation, Formal analysis, Writing – review & editing

Julia H A Foote: Writing – review & editing

Nattanicha Wattananimitgul: Writing – review & editing

Krishna P Reddy: Funding acquisition, Writing – review & editing

Anne M Neilan: Funding acquisition, Writing – review & editing

Andrea L Ciaranello: Funding acquisition, Writing – review & editing

Elena Losina: Writing – review & editing

Kenneth A Freedberg: Funding acquisition, Supervision, Writing – review & editing

Emily P Hyle: Conceptualization, Supervision, Writing – original draft, Writing – review & editing

Veincent Christian Pepito: Editor

PMCID: PMC10434931 PMID: 37590260

Abstract

Objective

To investigate associations between all-cause mortality and human immunodeficiency virus (HIV) acquisition risk groups among people without HIV in the United States.

Methods

We used data from 23,657 (NHANES) participants (2001–2014) and the Linked Mortality File to classify individuals without known HIV into HIV acquisition risk groups: people who ever injected drugs (ever-PWID); men who have sex with men (MSM); heterosexually active people at increased risk for HIV (HIH), using low income as a proxy for increased risk. We used Cox proportional hazards models to estimate adjusted and unadjusted all-cause mortality hazard ratios (HR) with 95% confidence intervals (CI).

Results

Compared with sex-specific heterosexually active people at average risk for HIV (HAH), the adjusted HR (95% CI) were: male ever-PWID 1.67 (1.14, 2.46), female ever-PWID 3.50 (2.04, 6.01), MSM 1.51 (1.00, 2.27), male HIH 1.68 (1.04, 2.06), female HIH 2.35 (1.87, 2.95), and male ever-PWID 1.67 (1.14, 2.46).

Conclusions

Most people at increased risk for HIV in the US experience higher all-cause mortality than people at average risk. Strategies addressing social determinants that increase HIV risk should be incorporated into HIV prevention and other health promotion programs.

Introduction

With antiretroviral therapy, HIV-related mortality has declined markedly, leading to improved life expectancy for people with HIV (PWH) [1, 2]. However, PWH often experience greater mortality compared with the general population due to an increased risk of non-HIV-related diseases and health conditions, such as cancer, liver disease, cardiovascular disease, hepatitis C virus (HCV), tobacco use, and substance use disorder [3–5]. Although some of the increased non-HIV-related mortality among PWH may be due directly to HIV infection and chronic inflammation [6], there is also likely to be a contribution from health conditions and social determinants of health that are not directly related to HIV status. To better understand the extent to which this excess mortality is due to HIV-related versus non-HIV-related causes, we focused on people without HIV who met the criteria for HIV acquisition risk groups as defined by the Centers for Disease Control and Prevention (CDC) [7].

The CDC-defined HIV acquisition risk groups that experience the highest burden of HIV in the United States include people who ever injected drugs (ever-PWID), men who have sex with men (MSM), and heterosexually active people at increased risk for HIV (HIH), which has been associated with low income [7–9]. Previously published studies have demonstrated a range of findings regarding excess mortality among people in HIV acquisition risk groups, depending on the definition and comparator groups [8, 10–13].

To quantify excess all-cause mortality among these key populations without HIV, we estimated mortality rates and compared age-adjusted, sex-stratified mortality with people at average risk for HIV acquisition in the United States to assess for associations between all-cause mortality and HIV acquisition risk groups.

Methods

Study population and data sources

We used publicly available data from the 2001 to 2014 National Health and Nutrition Examination Survey (NHANES) [14] and the Linked Mortality File (LMF) [15], which has been updated with National Death Index (NDI) mortality follow-up data through December 31, 2019. The LMF includes death dates for those who have died [15], and NHANES is a multi-stage study that summarizes the health status of noninstitutionalized civilians residing in the United States, including interviews and physical examinations [14].

To define the study population, we first identified the population eligible for the mortality analysis (people 18 years or older with linked mortality data), to whom the questionnaires that allow classification into one of the HIV acquisition risk groups were administered and had no missing data (N = 23,657). Using HIV antibody test information from NHANES laboratory data, we restricted the population to participants without an HIV diagnosis by excluding individuals with a reactive HIV antibody test result (N = 101). We also excluded participants who died without an underlying cause of death listed in the linked mortality files. Among participants with the cause of death listed as “other,” we excluded individuals without a documented, non-reactive HIV antibody test result (N = 152) because HIV-associated causes of death could be included in this “other” category. We also excluded women who had sex with women without a history of injection drug use (N = 1,008) because they are not defined as an HIV acquisition risk group by the CDC [7] and are not “heterosexually active at low risk for HIV acquisition.” After applying all the exclusion criteria, 22,396 adults met the study-defined inclusion criteria (S1 Fig in S4 Appendix).

Ethical approval

The research ethics review board at the National Center for Health Statistics (NCHS), approved the study protocol [14]. This project was reviewed by the institutional review board at Mass General Brigham and was deemed as “not human subjects research”.

Outcome

All-cause mortality

We ascertained mortality statuses, dates, and causes of death from the public-use National Death Index (NDI)-linked data of survey participants followed through December 31, 2019 [15]. Follow-up time included the time from the NHANES interview date until death (for those who died) or December 31, 2019 (for living participants) [15].

To estimate mortality by age-at-risk, we conducted Lexis expansion to stratify follow-up by intervals of one year, converting the one observation per person to one observation for each time interval (one year of age) per person so that each participant contributes to each age stratum until death or censoring [16].

Exposures

People who ever injected drugs (ever-PWID)

We categorized participants as “ever-PWID” if they self-reported ever using a needle to inject illegal or street drugs based on their answers to drug use questionnaires available for NHANES participants aged 20–59 years (S1 Appendix). All other participants were classified as “never-PWID” or unknown PWID status based on their self-response to the PWID questions.

Men who have sex with men (MSM)

We classified male participants as MSM if they reported that they were gay, bisexual, or had at least one sexual experience with a man during their lifetime; we categorized all other male participants as heterosexually active if they responded to the sexual behavior questions and did not meet the MSM criteria (S1 Appendix).

Heterosexually active people at increased risk for HIV (HIH)

Because the risk profiles of people’s sexual partners are often unknown, it is challenging to apply and operationalize the CDC definition of “high-risk heterosexual contact” (i.e., persons who have ever had heterosexual contact with a person known to have, or to be at high risk for, HIV infection) [7]. The National HIV Behavior Surveillance (NHBS) uses income as a proxy for being at increased risk for HIV acquisition because HIV prevalence is markedly higher among heterosexually active people with lower incomes compared with higher incomes in the United States. The NHBS, therefore, recruits heterosexually active male and female participants from census tracts areas in which at least 25% of residents live below the U.S. Census Bureau’s poverty threshold [17]. Among male and female participants categorized as heterosexually active, we used the poverty income ratio (PIR) as an indicator for SES [14, 18]. We defined PIR as the ratio of the midpoint of the observed family income category to the poverty guidelines from the Department of Health and Human Services in each calendar year to adjust for inflation across NHANES survey years [14, 18]. We categorized heterosexually active participants as HIH if their PIR was <1.0, which is the official federal poverty line [18]. The remainder of heterosexually active participants who had PIR ≥1.0 were classified as at average risk for HIV acquisition (HAH) [18].

HIV acquisition risk group categorization

Because some participants met the criteria for more than one HIV acquisition risk group (e.g., MSM who have ever used injection drugs), we used the CDC hierarchy of risk factor assignment to categorize participants into one of the five mutually exclusive HIV acquisition risk groups: 1) male ever-PWID (including MSM and male HIH who ever injected drugs), 2) female ever-PWID (including female HIH who ever injected drugs), 3) MSM (who have never injected drugs), 4) male HIH (who are not MSM and have never injected drugs), and 5) female HIH (who have never injected drugs). The remaining participants met the criteria for heterosexually active persons at average risk for HIV acquisition (HAH), stratified by self-reported gender as used in NHANES questionnaires. In sensitivity analysis, we categorized participants into multiple HIV acquisition risk groups (e.g., male HIH who inject drugs contributes to both male ever-PWID and male HIH).

Covariates of interest

To examine mortality rates among individuals in the HIV acquisition risk groups, we assessed an a priori list of potential effect modifiers and confounders based on the published literature, including age, race/ethnicity, education, health insurance, health status, at-risk alcohol use, tobacco use, body mass index, condom use, lifetime sexual partners, and history of sexually transmitted infections [11, 12, 19–22]. These effect modifiers and cofounders are further defined in S1 Appendix.

Statistical analysis

We used SAS 9.4 to perform the statistical analyses (Cary, NC, USA). We used design weights, strata, and primary sampling units in all analyses, as suggested by the National Center for Health Statistics (NCHS) [23]. To correct for possible estimation bias resulting from restricting the analysis to the eligible sample, we inflated the sampling weights [24]. We calculated unweighted counts, weighted percentages, and weighted and unweighted rates (the number of deaths divided by the follow-up time). For bivariate associations, we used the Rao-Scott F adjusted chi-square statistic to assess the relationship between covariates and the primary exposure (HIV acquisition risk groups) (i.e. to assess whether the distribution of the proportions of HIV acquisition risk groups is independent of the covariate) [25]. We assessed the relationship between covariates and the outcome of interest (all-cause mortality) using Cox proportional hazards regression models, accounting for the complex survey design [25]. Cox proportional hazards regression model is a semiparametric model; therefore, no assumption of the baseline hazard function is needed. Results of the bivariate associations were used as the first step in assessing the a priori list of potential confounders.

We also examined effect modification and confounding to identify covariates other than the HIV acquisition risk group to be included in the final, most parsimonious multivariable regression models. We stratified risk groups by gender for two reasons, 1) there was a statistically significant interaction between the four-level risk group and gender on all-cause mortality, 2) it is more appropriate to compare women to women and men to men to account for unmeasured confounders that are related to self-reported gender, and the difference in mortality rates between men and women. We then assessed whether “age at risk” is a potential effect modifier by including HIV acquisition risk group, age and the interaction term between age and HIV acquisition risk group in Cox proportional hazards model. Age would be considered as an effect modifier, if the p value of the interaction term was less than the alpha level of .05.

Subsequently, we assessed confounding due to covariates that were not effect modifiers. For each potential confounder, we calculated the difference between crude and adjusted regression coefficient betas, which we compared with the adjusted regression coefficient beta. We considered the covariate to be a potential confounder if the ratio of the difference in the betas to the adjusted beta was greater than 10%. Subsequently, only variables with an alpha level < .05 were retained in the final, most parsimonious multivariable regression models.

Additionally, we assessed the Cox proportional hazard assumption using two standard approaches: 1) to visually assess the parallelisms of the lines of the graph of the log of -log survival versus the log of survival time (log-negative log plot), and 2) to include and assess the significance of the time-dependent variable in the model. If the lines were parallel and the time-dependent variable was not significant, then we considered the proportional hazard assumption to not be violated.

Results

At the time of the survey interview, the mean age (standard error [SE]) of participants was 36.5 (0.22) years; 11,526 (52.2%) were male, and 10,870 (47.8%) were female. Participants self-identified as non-Hispanic White (10,004 [69.2%]), Black/African American (4,789 [11.0%]), Hispanic (5,814 [13.8%]), and Other (1,789 [6.0%]).

HIV acquisition risk groups

Of the 22,396 participants who met the study-defined inclusion criteria, 509 (2.3%) participants were ever-PWID (male, 340 [68.2%]; female, 169 [31.8%]); 565 (2.8%) participants were MSM; and 4,558 (13.5%) heterosexually active persons met the criteria for low income and were classified as HIH (male, 2,133 [47.0%]; female, 2,425 [53.0%]; S1 Fig in S4 Appendix). The remaining 16,764 (81.5%) participants met the criteria for being at average risk for HIV acquisition (male, 8,488 [51.0%]; female, 8,276 [49.0%]). The associations between demographic characteristics and HIV acquisition risk groups are presented in Fig 1, S2 Appendix and S1 Table in S3 Appendix.

Fig 1 — We display the weighted percentages of the four mutually exclusive groups, **PWID**, people who ever injected drugs (grey); **MSM**, men who have sex with men (green); **HIH**, heterosexually active people at increased risk for HIV (purple); **HAH**, heterosexually active people at average risk for HIV (blue), within each of the population characteristics. **BMI**, body mass index; **STI**, sexually transmitted infection.

Mortality

During the follow-up period, 22,396 participants contributed 249,326 person-years (PY), and 1,139 deaths were reported (S2 Table in S3 Appendix). The weighted all-cause mortality rate was 407.1 deaths/100,000 PY. The unweighted all-cause mortality rates and the associations of mortality with population characteristics are also provided in S2 Table (S3 Appendix).

Mortality association with HIV acquisition risk groups

Among participants categorized into the mutually exclusive HIV acquisition risk groups, the weighted all-cause mortality rate was highest among female ever-PWID (1,289.8 deaths/100,000 PY), followed by male ever-PWID (1,105.1 deaths/100,000 PY, Table 1). Mortality rates were similar among MSM (626.0 deaths/100,000 PY) and male HIH (631.7 deaths/100,000 PY), with lower mortality among female HIH (552.7 deaths/100,000 PY). When combining all people at high risk for HIV acquisition (i.e., male and female ever-PWID, MSM, and male and female HIH), the weighted all-cause mortality rate was 666.6 deaths/100,000 PY. Persons at average risk for HIV acquisition had significantly lower mortality rates (males: 443.5 deaths/100,000 PY; females: 256.2 deaths/100,000 PY; and males and females combined: 351.1 deaths/100,000 PY). We detected no significant interaction between age and HIV acquisition risk groups, F(6,104) = 1.88, p = .091 (S3 Table in S3 Appendix). S4 Table in S3 Appendix displays mortality rates when participants were categorized into multiple risk acquisition groups.

Table 1. All-cause mortality rates by mutually exclusive HIV acquisition risk groups*.

Covariate	Participants, N (%)^a	Deaths N	Length of follow-up years^b	Unweighted all-cause mortality rate per 100,000 PY	Weighted all-cause mortality rate per 100,000 PY^c^,^d	p-value ^e
HIV Acquisition Risk Groups						<0.0001
Male HAH	8,488 (41.6)	468	94,592	494.8	443.5
Female HAH	8,276 (39.9)	260	94,883	274.0	256.2
Male ever-PWID	340 (1.5)	50	3,610	1,385.0	1,105.1
Female ever-PWID	169 (0.7)	26	1,780	1,460.3	1,289.8
MSM	565 (2.8)	37	6,039	612.7	626.0
Male HIH	2,133 (6.3)	170	22,377	759.7	631.7
Female HIH	2,425 (7.1)	128	26,044	491.5	552.7
Increased HIV Acquisition Risk						<0.0001
All high-risk groups HAH	16,764 (81.5)	728	189,476	384.2	351.1
Combined ^f	5,632 (18.5)	411	59,851	686.7	666.6

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PY, person-year; MSM, men who have sex with men; PWID, people who ever injected drugs; HIH, heterosexually active people at increased risk for HIV; HAH, heterosexually active people at average risk for HIV.

^a Weighted percentage

^b Unweighted, rounded to one full year

^c Adjusted for the design weights, strata, and primary sampling units

^d The time of death or the time of exit from the study

^e P-value from Cox proportional hazards regression model

^f Included ever-PWID, MSM, HIH.

Multivariable regression models of the association between mortality and HIV acquisition risk groups

We did not detect a violation of the proportional hazard assumption. After adjusting for confounders including age, race/ethnicity, education, smoking status, at-risk alcohol consumption, and health status, the adjusted all-cause mortality hazard ratio (aHR, 95% CI) was significantly higher among female ever-PWID (aHR, 3.50 [95% CI 2.04, 6.01]), male ever-PWID (aHR, 1.67 [95% CI 1.14, 2.46]), MSM (aHR, 1.51 [95% CI 1.00, 2.27]), male HIH (aHR, 1.68 [95% CI 1.36, 2.06]), and female HIH (aHR, 2.35 [95% CI 1.87, 2.95]) compared with male and female HAH, respectively (Table 2).

Table 2. Unadjusted and adjusted association of mutually exclusive HIV acquisition risk group and all-cause mortality.

HIV Acquisition Risk Group	Unadjusted ^a^,^b	Adjusted ^a^,^b^,^c
HIV Acquisition Risk Group	All-cause mortality HR (95% CI)	All-cause mortality HR (95% CI)
HAH ^e	Ref.	Ref.
Male PWID	2.49 (1.72, 3.61)	1.67 (1.14, 2.46)
Female PWID	5.04 (3.03, 8.38)	3.50 (2.04, 6.01)
MSM	1.41 (0.94, 2.12)	1.51 (1.00, 2.27)
Male HIH ^d	1.42 (1.15, 1.76)	1.68 (1.36, 2.06)
Female HIH ^d	2.16 (1.75, 2.67)	2.35 (1.87, 2.95)

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HR, hazard ratio; CI, confidence interval; MSM, men who have sex with men; PWID, people who inject drugs; HIH, heterosexually active people at increased risk for HIV; HAH, heterosexually active people at average risk for HIV

^a Cox proportional hazards regression model.

^b Adjusted for the design weights, strata, cluster, and primary sampling units.

^c Adjusted for age, race/ethnicity, education, smoking status, at-risk alcohol consumption, and health status.

^d Low socioeconomic status is used as a proxy for heterosexually active people at increased risk for HIV.

^e Sex-specific reference categories.

The adjusted hazard ratio estimates did not change substantially when participants with unknown HIV acquisition risk categories were included in the Cox model (S5 Table in S3 Appendix).

Discussion

In a representative sample of the noninstitutionalized US adult population not diagnosed with HIV, we found that the age-adjusted, sex-stratified, all-cause mortality among people who self-reported CDC-defined risks for HIV acquisition was significantly higher compared with heterosexually active people at average risk for HIV. Although risk factors for mortality among adults with HIV have previously been well-described [5, 26, 27], few studies have examined the mortality rates for people without diagnosed HIV who can be categorized into one of the CDC-defined HIV acquisition risk groups [11–13, 28, 29].

We found the highest unadjusted crude all-cause mortality rate among people at risk for HIV among people who ever used injection drugs, at more than double the rate for heterosexually active people at average risk for HIV. Other studies have found similarly increased crude all-cause mortality rates among people who ever used injection drugs compared with the general population or with people who have never used injection drugs, due to ongoing substance use, infections other than HIV, drug overdose, sudden cardiac death, or serious mental illness [8, 30–33]. Our results also support previously reported gender differences in mortality among people who ever used injection drugs. Although crude mortality rates are often higher among men who use injection drugs than women [30–32], adjusted mortality relative risks are typically higher among women [8, 30–32] as our analysis also shows. The attenuation of the magnitude of difference in mortality between male injection drug users and heterosexually active males at average risk for HIV after adjusting for confounders indicates that the excess mortality could be attributed to the health risk behaviors such as smoking and alcohol consumption, as well as racial disparities.

The magnitude of increased mortality among people who ever used injection drugs has been greater in other analyses than that reported in our analysis; standardized mortality ratios (SMR) as high as 14.68 (95% CI: 13.01–16.35) [30] and 11.09 (95% CI: 6.68–18.39) have been previously reported [33]. The current analysis may have found less excess mortality because participants in NHANES may underrepresent people who are either actively using injection drugs or are at risk of being institutionalized, incarcerated, or experiencing marginal housing. Therefore, estimated excess mortality among NHANES participants is likely to be an underestimate compared with the entire population of people ever using injection drugs. Additionally, HCV-related mortality was greater in the years prior to effective curative treatment and may have been included in other published estimates. In some published analyses, PWID with HIV were also included in mortality estimates, which would further increase mortality [8, 30]. With rising rates of overdose in the setting of fentanyl contamination [34], excess mortality among people who use injection drugs is likely to increase further; systematic approaches to support access to treatment are essential to reduce mortality.

Our results also suggest that MSM without diagnosed HIV infection may experience excess mortality compared with heterosexually active persons at average risk for HIV acquisition, after adjustment for confounders. Previously published studies have shown no increase in all-cause mortality among MSM without HIV compared with heterosexually active males [22, 28], although other studies from NHANES showed a trend similar to our findings [11, 12]. We found an increased risk of all-cause mortality among self-identified MSM compared with heterosexually active men with incomes above the poverty line, as a proxy for average risk for HIV acquisition. Importantly, this analysis used heterosexually active men with incomes above the poverty line as the comparator group and excluded individuals who inject drugs, whereas other studies used all heterosexually active men, which could bias the results towards the null given the inclusion of heterosexually active men with low income or who inject drugs in the heterosexually active men comparator group [11, 12, 22, 28]. The observed excess all-cause mortality could be due to previously reported increased risk of suicide, tobacco use, at-risk alcohol use, non-injection substance use, mental health disorders, and other adverse social determinants of health among MSM [29, 35, 36]. Our findings highlight the importance of an improved understanding of risk factors for excess mortality among MSM independent of HIV status.

Age-adjusted, sex-stratified all-cause mortality rates were higher among heterosexually active adults at increased risk for HIV acquisition compared with heterosexually active adults at average risk, as defined by the poverty-to-income ratio. Our results are consistent with other studies regarding the magnitude of the association between low income or socioeconomic status and all-cause mortality. A multi-cohort, international study of approximately 1.7 million participants from seven high income countries reported a 30–40% excess mortality among people with low socioeconomic status compared with people with high socioeconomic status [37]. A recently published analysis underscored that increasing numbers of disadvantageous social determinants of health were associated with all-cause mortality in the United States with a significant linear trend; among people with six or more disadvantageous social determinants of health, all-cause mortality was increased more than 7-fold [38]. Our findings highlight that heterosexually active people at increased risk for HIV acquisition also experience increased mortality, which further highlights the need to address social determinants of health and improve health outcomes among people with low income who are at risk for HIV acquisition.

This study has several limitations. First, data regarding sexual behaviors and injection drug use were self-reported and only available at the initial NHANES interview. However, if participants’ risk behaviors changed during follow-up, the comparative effect on mortality would be further reduced. Second, although misclassification of HIV status is possible given that NHANES public-use data does not include details regarding HIV status among all participants, we excluded any possible HIV-related mortality and missing data that are likely to be missing at random. Although we adjusted for multiple confounders, including smoking, alcohol use, health status, and race/ethnicity, residual confounding may persist due to missing data, measurement error, unreported confounders, or incomplete self-report especially because NHANES does not include data regarding time-varying covariates [39]. Given small numbers of outcomes, we were not able to perform the analyses stratified by race. The use of change in estimate approach to identify confounders has been criticized [40]. However, since it is advised not to control for variables that are not confounders to avoid introducing "unnecessary adjustment" bias, [41] we used the change in estimate criterion as a complementary method to evaluate a list of a priori potential confounders. In identifying the list of a priori potential confounders, we followed the recommendations of VanderWeele 2019, [40] by selecting variables that could be causes of the exposure, outcome, or both, but excluding colliders, mediators and instrumental variables. Although the dataset is large, we observed a sparse data problem because the outcome is relatively rare, which precluded us from including all the potential confounders in the model [42]. Therefore, we elected not to adjust except for variables associated with both outcome and exposure that meaningfully changed the estimate as determined by the change in the estimate approach. Last, our results might not be generalizable to all people at increased risk of HIV acquisition given that people with active injection drug use, serious mental illness, or disadvantageous social determinants of health may be less likely to participate in NHANES.

This study highlights that people at increased risk for HIV acquisition in the US have greater all-cause mortality rates compared with people at average risk. Although the gap in life expectancy between people with HIV and people without HIV is decreasing with effective antiretroviral therapy [1, 2], more attention must be focused on improving health outcomes that are not directly related to HIV status but affect ever-PWID, MSM, and heterosexually active people with low income. Clinical and public health efforts should be focused on improving access to care and services that address risks and social determinants of health in people at increased risk for HIV.

Supporting information

S1 Appendix. Supplemental methods.

(DOCX)

Click here for additional data file.^{(31.5KB, docx)}

S2 Appendix. Supplemental results.

(DOCX)

Click here for additional data file.^{(22.7KB, docx)}

S3 Appendix. Supplemental tables.

(DOCX)

Click here for additional data file.^{(53.6KB, docx)}

S4 Appendix. Supplemental figures.

(DOCX)

Click here for additional data file.^{(46KB, docx)}

Acknowledgments

We thank Ms. Stephanie Horsfall, Mr. Kyu-Young Kevin Chi, Mr. Munashe Machoko and Ms. Dina Ashour for their assistance with manuscript preparation and Ms. Michaelle Destinoble for her administrative and organizational support.

Data Availability

All data from National Health and Nutrition Examination Survey and Linked Mortality files are available from the CDC database at www.cdc.gov/nchs/nhanes/index.htm and www.cdc.gov/nchs/data-linkage/mortality.htm.

Funding Statement

This work was supported by the National Institute of Allergy and Infectious Disease (R01AI042006 [KAF]) and R01AI093269 [KPR]), the Eunice Kennedy Shriver National Institute of Child Health and Human Development (K08HD094638 [AMN]), and the National Heart, Lung, and Blood Institute (K01HL123349 [EPH]) of the National Institutes of Health (https://www.nih.gov/), the National Institute on Aging (R01AG069575 [EPH]), and the Steve and Deborah Gorlin MGH Research Scholars Award (KAF), the MGH Jerome and Celia Reich Endowed Scholar Award (EPH), and the MGH Scholar Award in Population and Health Care Research (ALC) of the MGH Executive Committee on Research (https://ecor.mgh.harvard.edu/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

1.Antiretroviral Therapy Cohort Collaboration. Causes of death in HIV-1-infected patients treated with antiretroviral therapy, 1996–2006: collaborative analysis of 13 HIV cohort studies. Clin Infect Dis. 2010;50(10):1387–96. Epub 2010/04/13. doi: 10.1086/652283 [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Samji H, Cescon A, Hogg RS, Modur SP, Althoff KN, Buchacz K, et al. Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada. PLoS One. 2013;8(12):e81355. Epub 2013/12/25. doi: 10.1371/journal.pone.0081355 [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Croxford S, Kitching A, Desai S, Kall M, Edelstein M, Skingsley A, et al. Mortality and causes of death in people diagnosed with HIV in the era of highly active antiretroviral therapy compared with the general population: an analysis of a national observational cohort. Lancet Public Health. 2017;2(1):e35–e46. Epub 2017/12/19. doi: 10.1016/S2468-2667(16)30020-2 . [DOI] [PubMed] [Google Scholar]
4.Hutton HE, McCaul ME, Chander G, Jenckes MW, Nollen C, Sharp VL, et al. Alcohol use, anal sex, and other risky sexual behaviors among HIV-infected women and men. AIDS Behav. 2013;17(5):1694–704. Epub 2012/05/09. doi: 10.1007/s10461-012-0191-4 . [DOI] [PubMed] [Google Scholar]
5.Shirley DK, Kaner RJ, Glesby MJ. Effects of smoking on non-AIDS-related morbidity in HIV-infected patients. Clin Infect Dis. 2013;57(2):275–82. Epub 2013/04/11. doi: 10.1093/cid/cit207 [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Hart BB, Nordell AD, Okulicz JF, Palfreeman A, Horban A, Kedem E, et al. Inflammation-related morbidity and mortality among HIV-positive adults: How extensive is it? J Acquir Immune Defic Syndr. 2018;77(1):1–7. Epub 2017/10/11. doi: 10.1097/QAI.0000000000001554 [DOI] [PMC free article] [PubMed] [Google Scholar]
7.Centers for Disease Control and Prevention (CDC). HIV surveillance report, 2018 (updated); vol. 31 2020 [cited 2022 October 31]. http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html.
8.Walker ER, Pratt LA, Schoenborn CA, Druss BG. Excess mortality among people who report lifetime use of illegal drugs in the United States: A 20-year follow-up of a nationally representative survey. Drug Alcohol Depend. 2017;171:31–8. Epub 2016/12/25. doi: 10.1016/j.drugalcdep.2016.11.026 [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Centers for Disease Control and Prevention (CDC). HIV infection, risk, prevention, and testing behaviors among heterosexually active adults at increased risk for HIV infection—National HIV Behavioral Surveillance, 23 U.S. cities, 2019. HIV surveillance special report 26 2021 [cited 2022 October 31]. https://www.cdc.gov/hiv/pdf/library/reports/surveillance/cdc-hiv-surveillance-special-report-number-26.pdf.
10.Nandi A, Glymour MM, Subramanian SV. Association among socioeconomic status, health behaviors, and all-cause mortality in the United States. Epidemiology (Cambridge, Mass). 2014;25(2):170–7. doi: 10.1097/EDE.0000000000000038 [DOI] [PubMed] [Google Scholar]
11.Cochran SD, Mays VM. Sexual orientation and mortality among US men aged 17 to 59 years: results from the National Health and Nutrition Examination Survey III. Am J Public Health. 2011;101(6):1133–8. Epub 2011/04/16. doi: 10.2105/AJPH.2010.300013 [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Cochran SD, Björkenstam C, Mays VM. Sexual orientation and all-cause mortality among US adults aged 18 to 59 Years, 2001–2011. Am J Public Health 2016;106(5):918–20. Epub 2016/03/18. doi: 10.2105/AJPH.2016.303052 . [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Frisch M, Bronnum-Hansen H. Mortality among men and women in same-sex marriage: a national cohort study of 8333 Danes. Am J Public Health. 2009;99(1):133–7. Epub 2008/11/15. doi: 10.2105/AJPH.2008.133801 [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey sample design, survey questionnaire, examination protocol, laboratory protocol, survey data, and analytic guidelines [2001–2014] [cited 2022 October 31]. https://www.cdc.gov/nchs/nhanes/index.htm.
15.National Center for Health Statistics Division of Analysis and Epidemiology. Continuous NHANES Public-use Linked Mortality Files, 2019. Hyattsville, Maryland. https://www.cdc.gov/nchs/data-linkage/mortality-public.htm. [cited 2023 June 15].
16.Nitika, Mishra SS, Lohani P. Lexis expansion: a prerequisite for analyzing time changing variables in a cohort study. Nepal J Epidemiol. 2017;7(2):681–4. Epub 2017/11/29. doi: 10.3126/nje.v7i2.17974 [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Miles IJ, Le Binh C, Wejnert C, Oster A, DiNenno E, Paz-Bailey G. HIV infection among heterosexuals at increased risk United States, 2010. MMWR Morbidity and Mortality Weekly Report. 2013;62(10):183–8. [PMC free article] [PubMed] [Google Scholar]
18.United States Census Bureau. How the Census Bureau mesaures poverty [cited 2022 October 31]. https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html#:~:text=The%20total%20family%20income%20divided,Ratio%20of%20Income%20to%20Poverty.&text=The%20difference%20in%20dollars%20between,(for%20families%20above%20poverty.
19.Suresh S, Sabanayagam C, Shankar A. Socioeconomic status, self-rated health, and mortality in a multiethnic sample of US adults. J Epidemiol. 2011;21(5):337–45. Epub 2011/07/13. doi: 10.2188/jea.JE20100142 [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Chetty R. The association between income and life expectancy in the United States, 2001–2014. JAMA. 2016;315(16):1750-. doi: 10.1001/jama.2016.4226 [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Cochran SD, Bandiera FC, Mays VM. Sexual orientation-related differences in tobacco use and secondhand smoke exposure among US adults aged 20 to 59 years: 2003–2010 National Health and Nutrition Examination Surveys. Am J Public Health. 2013;103(10):1837–44. Epub 2013/08/21. doi: 10.2105/AJPH.2013.301423 [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Cochran SD, Mays VM. Mortality risks among persons reporting same-sex sexual partners: evidence from the 2008 General Social Survey-National Death Index data set. Am J Public Health. 2015;105(2):358–64. Epub 2014/07/18. doi: 10.2105/AJPH.2014.301974 [DOI] [PMC free article] [PubMed] [Google Scholar]
23.National Health and Nutrition Examination Survey analytic guidelines, 2011–2014 and 2015–2016 2018 [cited 2022 October 31]. https://www.cdc.gov/nchs/data/nhanes/analyticguidelines/11-16-analytic-guidelines.pdf.
24.National Center for Health Statistics. The Linkage of National Center for Health Statistics Survey Data to the National Death Index—2019 Linked Mortality File (LMF): Linkage Methodology and Analytic Considerations, June 2022. Hyattsville, Maryland [cited 2023 June 15]. https://www.cdc.gov/nchs/data-linkage/mortality-methods.htm.
25.Skinner C. Analysis of categorical data for complex surveys. INSR International Statistical Review. 2019;87:S64–S78. [Google Scholar]
26.Bosh KA, Johnson AS, Hernandez AL, Prejean J, Taylor J, Wingard R, et al. Vital Signs: Deaths among persons with diagnosed HIV infection, United States, 2010–2018. MMWR Morbidity and Mortality Weekly Report. 2020;69(46):1717–24. doi: 10.15585/mmwr.mm6946a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Ingle SM, May MT, Gill MJ, Mugavero MJ, Lewden C, Abgrall S, et al. Impact of risk factors for specific causes of death in the first and subsequent years of antiretroviral therapy among HIV-infected patients. Clin Infect Dis. 2014;59(2):287–97. Epub 2014/04/29. doi: 10.1093/cid/ciu261 [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Lindström M, Rosvall M. Sexual orientation and all-cause mortality: A population-based prospective cohort study in southern Sweden. Public Health in Practice. 1:100032. doi: 10.1016/j.puhip.2020.100032 [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Lynch KE, Gatsby E, Viernes B, Schliep KC, Whitcomb BW, Alba PR, et al. Evaluation of suicide mortality among sexual minority US veterans from 2000 to 2017. JAMA Network Open. 2020;3(12):e2031357. doi: 10.1001/jamanetworkopen.2020.31357 [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Mathers BM, Degenhardt L. Examining non-AIDS mortality among people who inject drugs. AIDS. 2014;28 Suppl 4:S435–44. Epub 2014/11/20. doi: 10.1097/QAD.0000000000000435 [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Lopez-Quintero C, Roth KB, Eaton WW, Wu LT, Cottler LB, Bruce M, et al. Mortality among heroin users and users of other internationally regulated drugs: A 27-year follow-up of users in the Epidemiologic Catchment Area Program household samples. Drug Alcohol Depend. 2015;156:104–11. Epub 2015/09/21. doi: 10.1016/j.drugalcdep.2015.08.030 [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Gjersing L, Bretteville-Jensen AL. Gender differences in mortality and risk factors in a 13-year cohort study of street-recruited injecting drug users. BMC Public Health. 2014;14:440. Epub 2014/06/03. doi: 10.1186/1471-2458-14-440 [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Kimber J, Stoové M, Maher L. Mortality among people who inject drugs: Ten-year follow-up of the hepatitis C virus cohort. Drug Alcohol Rev Drug and alcohol review. 2019;38(3):270–3. doi: 10.1111/dar.12884 [DOI] [PubMed] [Google Scholar]
34.Mattson CL, Tanz LJ, Quinn K, Kariisa M, Patel P, Davis NL. Trends and geographic patterns in drug and synthetic opioid overdose deaths—United States, 2013–2019. MMWR Morbidity and Mortality Weekly Report. 2021;70(6):202–7. doi: 10.15585/mmwr.mm7006a4 . [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Operario D, Gamarel KE, Grin BM, Lee JH, Kahler CW, Marshall BD, et al. Sexual minority health disparities in adult men and women in the United States: National Health and Nutrition Examination Survey, 2001–2010. Am J Public Health. 2015;105(10):e27–34. Epub 2015/08/14. doi: 10.2105/AJPH.2015.302762 [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Rosario M, Corliss HL, Everett BG, Reisner SL, Austin SB, Buchting FO, et al. Sexual orientation disparities in cancer-related risk behaviors of tobacco, alcohol, sexual behaviors, and diet and physical activity: pooled Youth Risk Behavior Surveys. Am J Public Health. 2014;104(2):245–54. Epub 2013/12/18. doi: 10.2105/AJPH.2013.301506 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Stringhini S, Carmeli C, Jokela M, Avendano M, Muennig P, Guida F, et al. Socioeconomic status and the 25 x 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women. Lancet. 2017;389(10075):1229–37. Epub 2017/02/06. doi: 10.1016/S0140-6736(16)32380-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Bundy JD, Mills KT, He H, LaVeist TA, Ferdinand KC, Chen J, et al. Social determinants of health and premature death among adults in the USA from 1999 to 2018: a national cohort study. Lancet Public Health. 2023;8(6):e422–e31. Epub 2023/05/28. doi: 10.1016/S2468-2667(23)00081-6 . [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Fewell Z, Davey Smith G, Sterne JA. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol. 2007;166(6):646–55. Epub 2007/07/07. doi: 10.1093/aje/kwm165 . [DOI] [PubMed] [Google Scholar]
40.VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019;34(3):211–9. Epub 2019/03/07. doi: 10.1007/s10654-019-00494-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
41.Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009;20(4):488–95. Epub 2009/06/16. doi: 10.1097/EDE.0b013e3181a819a1 [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:i1981. Epub 2016/04/29. doi: 10.1136/bmj.i1981 . [DOI] [PubMed] [Google Scholar]

PLoS One. doi: 10.1371/journal.pone.0290113.r001

Decision Letter 0

Veincent Christian Pepito

9 Jun 2023

PONE-D-23-05219The association between all-cause mortality and HIV acquisition risk groups in the United States, 2001-2014PLOS ONE

Dear Dr. Shebl,

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.

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Additional Editor Comments:

Dear Dr. Shebl,

Thank you very much for your submission to PLOS ONE. Here are some of my comments to your manuscript:

1. While everyone knows HIV, NHANES might be unfamiliar to those who do not live in the United States. Please write in full for first use even in the Abstract.

2. What is the rationale for excluding women who have sex with women?

3. What paper/s served as basis for your choice of covariates?

4. Why was a chi-square test done instead of a log-rank test?

5. Justify use of Cox regression over Poisson regression, considering that mortality rates are not expected to vary significantly during the time period.

5. It is unclear what effect measure modifications were assessed and why they were done. What variables were considered as effect measure modifiers?

6. There had been issues with the change in estimate criterion in more recent published literature, which you have used in your paper. What is the justification for this choice of modelling strategy, and how will your model fare if you controlled for all known covariates regardless? I think your number of deaths is sufficient even if you controlled for all covariates which you have data for.

7. How was the proportional hazards assumption assessed? Please indicate in the methods.

8. Any results about your assessment of effect modification?

9. Line 284-288: "The attenuation of the magnitude of difference in mortality between male injection drug users and heterosexually active males at average risk for HIV after adjusting for confounders indicates that the excess mortality could be attributed to the health risk behaviors such as smoking and alcohol consumption, as well as racial disparity". I thought you have adjusted for smoking, alcohol consumption and race? What made you say these statements?

10. Itemize what variables were not controlled for in the analysis.

Kindly address the comments of the reviewers as well. Thanks and I look forward to your revision.

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

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Review Comments to the Author

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

Reviewer #1: The authors present a well-performed and well-written analysis of the NHANES datasets to assess the inherent risks of all-cause mortality in defined groups of HIV-negative persons at higher risk of HIV acquisition. The authors are asked to address the following comments:

1. Mortality was followed-up through 2015, but obviously more data might be available in the Linked Mortality File. Please explain and defend this end date for the analysis. If more data do exist, then the analyses should be redone to account for the longer follow-up period.

2. Please defend the exclusion of women who have sex with women. If they are not at higher risk for HIV acquisition, then why not include them in the analyses?

3. Although the risk groups were stratified by sex, please clarify if there was a statistically significant interaction between risk group and sex on all-cause mortality.

4. Please use ‘sex’ or ‘sex at birth’ instead of ‘gender’ throughout, unless gender is the truly assessed variable.

Reviewer #2: The authors should consider the following.

1. Unless certain that they are PWID it should be PWUD.

2. Limitations should mention the time update variables -- eg antiretroviral therapy, viral load, nadir CD4 etc.

3. Figure 1 should be a table -- it is not clear.

4. I would like the analyses repeated by race.

5. The discussion around the social determinants of health could be expanded.

**********

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Reviewer #1: No

Reviewer #2: No

**********

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PLoS One. 2023 Aug 17;18(8):e0290113. doi: 10.1371/journal.pone.0290113.r002

Author response to Decision Letter 0

24 Jul 2023

Dear Academic Editor,

Thank you for the opportunity to revise and resubmit our manuscript. Below is a point-by-point response to the editor’s and reviewers’ comments. Changes made in the manuscript are bolded.

Regards

Fatma Shebl

Editor’s comments:

1. While everyone knows HIV, NHANES might be unfamiliar to those who do not live in the United States. Please write in full for first use, even in the Abstract.

Response: We added the full name as suggested.

Abstract, p.3

Objective: To investigate associations between all-cause mortality and human immunodeficiency virus (HIV) acquisition risk groups among people without HIV in the United States.

Methods: We used data from 23,657 National Health and Nutrition Examination Survey (NHANES) participants (2001-2014) and the Linked Mortality File...

2. What is the rationale for excluding women who have sex with women?

Response: We excluded women who have sex with women (WSW) because the CDC does not include this population as one of the HIV acquisition risk groups; additionally, women who have sex with women are not in the comparator group for this study because they are not "heterosexually active.” We revised the text to clarify the rationale for the exclusion.

Methods, p.7-8

We also excluded women who have sex with women without a history of injection drug use (N=1,008) because they are not defined as an HIV acquisition risk group by the CDC [7] and are not "heterosexually active at low risk for HIV acquisition."

3. What paper/s served as the basis for your choice of covariates?

Response: We added citations for the papers that served as a basis for the choice of covariates.

Methods, p.11

To examine mortality rates among individuals in the HIV acquisition risk groups, we assessed an a priori list of potential effect modifiers and confounders based on the published literature, including age, race/ethnicity, education, health insurance, health status, at-risk alcohol use, tobacco use, body mass index, condom use, lifetime sexual partners, and history of sexually transmitted infections [11, 12, 19-22].

4. Why was a chi-square test done instead of a log-rank test?

Response: We have generally used log-rank tests for the comparison of survival curves [1]. Since we were interested in comparing proportions, i.e., comparing the proportions of population characteristic variables between the risk groups, we used the chi-square test. We have clarified this in the manuscript methods.

Methods, p.11

For bivariate associations, we used the Rao-Scott F adjusted chi-square statistic to assess the relationship between covariates and the primary exposure (HIV acquisition risk groups) (i.e., to assess whether the distribution of the proportions of HIV acquisition risk groups is independent of the covariate) [25].

5. Justify the use of Cox regression over Poisson regression, considering that mortality rates are not expected to vary significantly during the time period.

Response: As noted by the reviewer, several options are available to model survival, including Cox, Poisson, and discrete-time logistic regression models [1, 2]. Historically, before the wide availability of Cox regression in statistical software, Cox models were approximated using Poisson regression [3]. Several studies have shown that the estimates are similar [3, 4]. Therefore, our decision regarding which model to use was based on two factors: 1) the Cox model is a semiparametric model; thus, we do not need to assume the baseline hazard, and 2) the availability of the statistical model in SAS. Given SAS does not include a procedure that employs Poisson regression in complex survey design but has procedures that can employ the Cox model or discrete-time logistic regression model in complex survey data, we decided to use the Cox model.

Methods, pp. 11-12

We assessed the relationship between covariates and the outcome of interest (all-cause mortality) using Cox proportional hazards regression models, accounting for the complex survey design [25]. Cox proportional hazards regression model is a semiparametric model; therefore, no assumption of the baseline hazard function is needed.

6. It is unclear what effect measure modifications were assessed and why they were done. What variables were considered as effect measure modifiers?

Response: We clarified that we assessed age as an effect modifier and why we selected this variable to assess as an effect measure modifier.

Methods, p.12

We then assessed whether "age at risk" is a potential effect modifier by including the HIV acquisition risk group, age, and the interaction term between age and HIV acquisition risk group in a Cox proportional hazards model. Age would be considered an effect modifier if the p-value of the interaction term was less than the alpha level of 0.05.

Results, p.16

We detected no significant interaction between age and HIV acquisition risk groups, F(6,104) = 1.88, p = 0.091 (S3 Table 3).

7. There had been issues with the change in estimate criterion in more recent published literature, which you have used in your paper. What is the justification for this choice of modeling strategy, and how will your model fare if you controlled for all known covariates regardless? I think your number of deaths is sufficient even if you controlled for all covariates which you have data for.

Response: We used the change in estimate criterion as a complementary method to evaluate a list of a priori potential confounders. It is advised not to control for variables that are not confounders, "unnecessary adjustment," since the adjustment can introduce bias [5]. Therefore, we elected not to adjust except for variables associated with both outcome and exposure that meaningfully changed the estimate. Although the dataset is large, we observed a sparse data problem because the outcome is relatively rare, which precluded us from including all the potential confounders in the model [6]. Our objective was to select the most parsimonious model; thus, we used both a priori list and the change in estimate criterion.

Discussion, pp.24-25

The use of the change in estimate approach to identify confounders has been criticized [41]. However, since it is advised not to control for variables that are not confounders to avoid introducing "unnecessary adjustment" bias [42], we used the change in estimate criterion as a complementary method to evaluate a list of a priori potential confounders. In identifying the list of a priori potential confounders, we followed the recommendations of VanderWeele 2019 [41] by selecting variables that could be causes of the exposure, outcome, or both, but excluding colliders, mediators, and instrumental variables. Although the dataset is large, we observed a sparse data problem because the outcome is relatively rare, which precluded us from including all the potential confounders in the model [43]. Therefore, we elected not to adjust except for variables associated with both outcome and exposure that meaningfully changed the estimate as determined by the change in estimate approach.

8. How was the proportional hazards assumption assessed? Please indicate in the methods.

Response: The proportional hazards assumption assessment method was provided in supplemental appendix 1, and we have now added it to the main manuscript.

Methods, p.13

“Additionally, we assessed the Cox proportional hazard assumption using two standard approaches: 1) to visually assess the parallelisms of the lines of the graph of the log of -log survival versus the log of survival time (log-negative log plot), and 2) to include and assess the significance of the time-dependent variable in the model. If the lines were parallel and the time-dependent variable was not significant, then we considered the proportional hazard assumption to not be violated.”

9. Any results about your assessment of effect modification?

Response: We included the results of effect modification in the main text.

Results, p.16

We detected no significant interaction between age and HIV acquisition risk groups, F(6,104) = 1.88, p = 0.091 (S3 Table 3).

10. Line 284-288: "The attenuation of the magnitude of difference in mortality between male injection drug users and heterosexually active males at average risk for HIV after adjusting for confounders indicates that the excess mortality could be attributed to the health risk behaviors such as smoking and alcohol consumption, as well as racial disparity." I thought you have adjusted for smoking, alcohol consumption and race? What made you say these statements?

Response: We have adjusted for these variables, but residual confounding can exist after adjusting for covariates. Residual confounding can happen if the confounder is not perfectly measured; therefore, even after adjusting for the confounder, the effect of the confounder is not entirely removed. We have added a reference to this concept where we address this limitation.

Discussion p.24

Although we adjusted for multiple confounders, including smoking, alcohol use, health status, and race/ethnicity, residual confounding may persist due to missing data, measurement error, unreported confounders, or incomplete self-report especially because NHANES does not include data regarding time-varying covariates [40].

Reviewers’ comments:

Response: After this project's conception, analysis, and manuscript development, the linked mortality data then became available through 2019. Based on the reviewer's request, we revised the analysis to include the latest release of the mortality data and updated the text accordingly, including revising the methods. All results have been revised throughout the manuscript.

Methods, p.7

2. Please defend the exclusion of women who have sex with women. If they are not at higher risk for HIV acquisition, then why not include them in the analyses?

Methods, p.7-8

“We also excluded women who had sex with women without a history of injection drug use (N=1,008) because they are not defined as an acquisition risk group by the CDC [7] and are not “heterosexually active at low risk for HIV acquisition.”

3. Although the risk groups were stratified by sex, please clarify if there was a statistically significant interaction between risk group and sex on all-cause mortality.

Response: We revised the methods to clarify this.

Methods, p.12

We stratified risk groups by gender for two reasons, 1) there was a statistically significant interaction between the four-level risk group and gender on all-cause mortality, 2) it is more appropriate to compare women to women and men to men to account for unmeasured confounders that are related to self-reported gender, and the difference in mortality rates between women and men.

4. Please use 'sex' or 'sex at birth' instead of 'gender' throughout, unless gender is the truly assessed variable.

Response: We used the term "gender" because the NHANES questionnaire used the following verbatim question: "Gender of the participant."

Methods, p. 10

“The remaining participants met the criteria for heterosexually active persons at average risk for HIV acquisition (HAH), stratified by self-reported gender as used in NHANES questionnaires.”

Reviewer #2: The authors should consider the following.

1. Unless certain that they are PWID it should be PWUD.

Response: We used the terminology, “PWID,” because we defined this subgroup as people who self-reported in NHANES ever using a needle (Supplemental appendix 1).

Methods, pp.8-9

“We categorized participants as “ever-PWID” if they self-reported ever using a needle to inject illegal or street drugs based on their answers to drug use questionnaires available for NHANES participants aged 20-59 years (Supplemental Methods). All other participants were classified as “never-PWID” or unknown PWID status based on their self-response to the PWID questions.”

2. Limitations should mention the time updated variables – e.g. antiretroviral therapy, viral load, nadir CD4 etc.

Response: Because this population is restricted to people without HIV, data regarding antiretroviral therapy, viral load, and nadir CD4 are not available or relevant.

3. Figure 1 should be a table – it is not clear.

Response: The data in Figure 1 require a very long table, which we include in the supplement and now reference in the main text.

Results, p.14

The associations between demographic characteristics and HIV acquisition risk groups are presented in Fig 1, S2 Results and S3 Table 1.

4. I would like the analyses repeated by race.

Response: When we repeated the analysis by race, we ran into a sparse data bias issue because there were not enough case numbers for some of the combinations of the outcome and predictor, which was evident by very wide confidence intervals, unreasonable values, and convergence problems [6]. Special analytic methods are needed to address this issue, which is beyond the scope of this work. We have added this to the limitations.

Discussion, p. 24

“Given the small number of outcomes, we were not able to perform the analyses stratified by race.”

5. The discussion around the social determinants of health could be expanded.

Response: We have expanded the discussion around social determinants of health to underscore that this is a critical issue to address among people at increased risk for HIV.

Discussion, p.24

“A recently published analysis underscored that increasing numbers of disadvantageous social determinants of health were associated with all-cause mortality in the United States with a significant linear trend; among people with six or more disadvantageous social determinants of health, all-cause mortality was increased more than 7-fold [39]. Our findings highlight that heterosexually active people at increased risk for HIV acquisition also experience increased mortality, which further highlights the need to address social determinants of health and improve health outcomes among people with low income who are at risk for HIV acquisition.”

References

1. Riffenburgh RH. Chapter 23 - Survival, Logistic Regression, and Cox Regression. In: Riffenburgh RH, editor. Statistics in Medicine (Third Edition). San Diego: Academic Press; 2012. p. 491-508.

2. Suresh K, Severn C, Ghosh D. Survival prediction models: an introduction to discrete-time modeling. BMC Med Res Methodol. 2022;22(1):207. Epub 2022/07/27. doi: 10.1186/s12874-022-01679-6. PubMed PMID: 35883032; PubMed Central PMCID: PMCPMC9316420.

3. Dickman PW. Replicate a Cox model using Poisson regression 2019 [6/15/2023]. Available from: https://www.pauldickman.com/software/stata/compare-cox-poisson/.

4. Golden A, Ellis E, Le H. 0163 Comparison of risk estimates from cox proportional hazards and poisson modeling for association of occupational titanium dioxide exposure and selected causes of death. Occupational and Environmental Medicine. 2017;74(Suppl 1):A49. doi: 10.1136/oemed-2017-104636.131.

5. Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009;20(4):488-95. Epub 2009/06/16. doi: 10.1097/EDE.0b013e3181a819a1. PubMed PMID: 19525685; PubMed Central PMCID: PMCPMC2744485.

6. Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:i1981. Epub 2016/04/29. doi: 10.1136/bmj.i1981. PubMed PMID: 27121591.

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PLoS One. doi: 10.1371/journal.pone.0290113.r003

Decision Letter 1

Veincent Christian Pepito

2 Aug 2023

The association between all-cause mortality and HIV acquisition risk groups in the United States, 2001-2014

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Acceptance letter

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8 Aug 2023

PONE-D-23-05219R1

The association between all-cause mortality and HIV acquisition risk groups in the United States, 2001-2014

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Data Availability Statement

[pone.0290113.ref001] 1.Antiretroviral Therapy Cohort Collaboration. Causes of death in HIV-1-infected patients treated with antiretroviral therapy, 1996–2006: collaborative analysis of 13 HIV cohort studies. Clin Infect Dis. 2010;50(10):1387–96. Epub 2010/04/13. doi: 10.1086/652283 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref002] 2.Samji H, Cescon A, Hogg RS, Modur SP, Althoff KN, Buchacz K, et al. Closing the gap: increases in life expectancy among treated HIV-positive individuals in the United States and Canada. PLoS One. 2013;8(12):e81355. Epub 2013/12/25. doi: 10.1371/journal.pone.0081355 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref003] 3.Croxford S, Kitching A, Desai S, Kall M, Edelstein M, Skingsley A, et al. Mortality and causes of death in people diagnosed with HIV in the era of highly active antiretroviral therapy compared with the general population: an analysis of a national observational cohort. Lancet Public Health. 2017;2(1):e35–e46. Epub 2017/12/19. doi: 10.1016/S2468-2667(16)30020-2 . [DOI] [PubMed] [Google Scholar]

[pone.0290113.ref004] 4.Hutton HE, McCaul ME, Chander G, Jenckes MW, Nollen C, Sharp VL, et al. Alcohol use, anal sex, and other risky sexual behaviors among HIV-infected women and men. AIDS Behav. 2013;17(5):1694–704. Epub 2012/05/09. doi: 10.1007/s10461-012-0191-4 . [DOI] [PubMed] [Google Scholar]

[pone.0290113.ref005] 5.Shirley DK, Kaner RJ, Glesby MJ. Effects of smoking on non-AIDS-related morbidity in HIV-infected patients. Clin Infect Dis. 2013;57(2):275–82. Epub 2013/04/11. doi: 10.1093/cid/cit207 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref006] 6.Hart BB, Nordell AD, Okulicz JF, Palfreeman A, Horban A, Kedem E, et al. Inflammation-related morbidity and mortality among HIV-positive adults: How extensive is it? J Acquir Immune Defic Syndr. 2018;77(1):1–7. Epub 2017/10/11. doi: 10.1097/QAI.0000000000001554 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref007] 7.Centers for Disease Control and Prevention (CDC). HIV surveillance report, 2018 (updated); vol. 31 2020 [cited 2022 October 31]. http://www.cdc.gov/hiv/library/reports/hiv-surveillance.html.

[pone.0290113.ref008] 8.Walker ER, Pratt LA, Schoenborn CA, Druss BG. Excess mortality among people who report lifetime use of illegal drugs in the United States: A 20-year follow-up of a nationally representative survey. Drug Alcohol Depend. 2017;171:31–8. Epub 2016/12/25. doi: 10.1016/j.drugalcdep.2016.11.026 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref009] 9.Centers for Disease Control and Prevention (CDC). HIV infection, risk, prevention, and testing behaviors among heterosexually active adults at increased risk for HIV infection—National HIV Behavioral Surveillance, 23 U.S. cities, 2019. HIV surveillance special report 26 2021 [cited 2022 October 31]. https://www.cdc.gov/hiv/pdf/library/reports/surveillance/cdc-hiv-surveillance-special-report-number-26.pdf.

[pone.0290113.ref010] 10.Nandi A, Glymour MM, Subramanian SV. Association among socioeconomic status, health behaviors, and all-cause mortality in the United States. Epidemiology (Cambridge, Mass). 2014;25(2):170–7. doi: 10.1097/EDE.0000000000000038 [DOI] [PubMed] [Google Scholar]

[pone.0290113.ref011] 11.Cochran SD, Mays VM. Sexual orientation and mortality among US men aged 17 to 59 years: results from the National Health and Nutrition Examination Survey III. Am J Public Health. 2011;101(6):1133–8. Epub 2011/04/16. doi: 10.2105/AJPH.2010.300013 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref012] 12.Cochran SD, Björkenstam C, Mays VM. Sexual orientation and all-cause mortality among US adults aged 18 to 59 Years, 2001–2011. Am J Public Health 2016;106(5):918–20. Epub 2016/03/18. doi: 10.2105/AJPH.2016.303052 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref013] 13.Frisch M, Bronnum-Hansen H. Mortality among men and women in same-sex marriage: a national cohort study of 8333 Danes. Am J Public Health. 2009;99(1):133–7. Epub 2008/11/15. doi: 10.2105/AJPH.2008.133801 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref014] 14.Centers for Disease Control and Prevention (CDC). National Center for Health Statistics (NCHS). National Health and Nutrition Examination Survey sample design, survey questionnaire, examination protocol, laboratory protocol, survey data, and analytic guidelines [2001–2014] [cited 2022 October 31]. https://www.cdc.gov/nchs/nhanes/index.htm.

[pone.0290113.ref015] 15.National Center for Health Statistics Division of Analysis and Epidemiology. Continuous NHANES Public-use Linked Mortality Files, 2019. Hyattsville, Maryland. https://www.cdc.gov/nchs/data-linkage/mortality-public.htm. [cited 2023 June 15].

[pone.0290113.ref016] 16.Nitika, Mishra SS, Lohani P. Lexis expansion: a prerequisite for analyzing time changing variables in a cohort study. Nepal J Epidemiol. 2017;7(2):681–4. Epub 2017/11/29. doi: 10.3126/nje.v7i2.17974 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref017] 17.Miles IJ, Le Binh C, Wejnert C, Oster A, DiNenno E, Paz-Bailey G. HIV infection among heterosexuals at increased risk United States, 2010. MMWR Morbidity and Mortality Weekly Report. 2013;62(10):183–8. [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref018] 18.United States Census Bureau. How the Census Bureau mesaures poverty [cited 2022 October 31]. https://www.census.gov/topics/income-poverty/poverty/guidance/poverty-measures.html#:~:text=The%20total%20family%20income%20divided,Ratio%20of%20Income%20to%20Poverty.&text=The%20difference%20in%20dollars%20between,(for%20families%20above%20poverty.

[pone.0290113.ref019] 19.Suresh S, Sabanayagam C, Shankar A. Socioeconomic status, self-rated health, and mortality in a multiethnic sample of US adults. J Epidemiol. 2011;21(5):337–45. Epub 2011/07/13. doi: 10.2188/jea.JE20100142 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref020] 20.Chetty R. The association between income and life expectancy in the United States, 2001–2014. JAMA. 2016;315(16):1750-. doi: 10.1001/jama.2016.4226 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref021] 21.Cochran SD, Bandiera FC, Mays VM. Sexual orientation-related differences in tobacco use and secondhand smoke exposure among US adults aged 20 to 59 years: 2003–2010 National Health and Nutrition Examination Surveys. Am J Public Health. 2013;103(10):1837–44. Epub 2013/08/21. doi: 10.2105/AJPH.2013.301423 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref022] 22.Cochran SD, Mays VM. Mortality risks among persons reporting same-sex sexual partners: evidence from the 2008 General Social Survey-National Death Index data set. Am J Public Health. 2015;105(2):358–64. Epub 2014/07/18. doi: 10.2105/AJPH.2014.301974 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref023] 23.National Health and Nutrition Examination Survey analytic guidelines, 2011–2014 and 2015–2016 2018 [cited 2022 October 31]. https://www.cdc.gov/nchs/data/nhanes/analyticguidelines/11-16-analytic-guidelines.pdf.

[pone.0290113.ref024] 24.National Center for Health Statistics. The Linkage of National Center for Health Statistics Survey Data to the National Death Index—2019 Linked Mortality File (LMF): Linkage Methodology and Analytic Considerations, June 2022. Hyattsville, Maryland [cited 2023 June 15]. https://www.cdc.gov/nchs/data-linkage/mortality-methods.htm.

[pone.0290113.ref025] 25.Skinner C. Analysis of categorical data for complex surveys. INSR International Statistical Review. 2019;87:S64–S78. [Google Scholar]

[pone.0290113.ref026] 26.Bosh KA, Johnson AS, Hernandez AL, Prejean J, Taylor J, Wingard R, et al. Vital Signs: Deaths among persons with diagnosed HIV infection, United States, 2010–2018. MMWR Morbidity and Mortality Weekly Report. 2020;69(46):1717–24. doi: 10.15585/mmwr.mm6946a1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref027] 27.Ingle SM, May MT, Gill MJ, Mugavero MJ, Lewden C, Abgrall S, et al. Impact of risk factors for specific causes of death in the first and subsequent years of antiretroviral therapy among HIV-infected patients. Clin Infect Dis. 2014;59(2):287–97. Epub 2014/04/29. doi: 10.1093/cid/ciu261 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref028] 28.Lindström M, Rosvall M. Sexual orientation and all-cause mortality: A population-based prospective cohort study in southern Sweden. Public Health in Practice. 1:100032. doi: 10.1016/j.puhip.2020.100032 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref029] 29.Lynch KE, Gatsby E, Viernes B, Schliep KC, Whitcomb BW, Alba PR, et al. Evaluation of suicide mortality among sexual minority US veterans from 2000 to 2017. JAMA Network Open. 2020;3(12):e2031357. doi: 10.1001/jamanetworkopen.2020.31357 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref030] 30.Mathers BM, Degenhardt L. Examining non-AIDS mortality among people who inject drugs. AIDS. 2014;28 Suppl 4:S435–44. Epub 2014/11/20. doi: 10.1097/QAD.0000000000000435 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref031] 31.Lopez-Quintero C, Roth KB, Eaton WW, Wu LT, Cottler LB, Bruce M, et al. Mortality among heroin users and users of other internationally regulated drugs: A 27-year follow-up of users in the Epidemiologic Catchment Area Program household samples. Drug Alcohol Depend. 2015;156:104–11. Epub 2015/09/21. doi: 10.1016/j.drugalcdep.2015.08.030 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref032] 32.Gjersing L, Bretteville-Jensen AL. Gender differences in mortality and risk factors in a 13-year cohort study of street-recruited injecting drug users. BMC Public Health. 2014;14:440. Epub 2014/06/03. doi: 10.1186/1471-2458-14-440 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref033] 33.Kimber J, Stoové M, Maher L. Mortality among people who inject drugs: Ten-year follow-up of the hepatitis C virus cohort. Drug Alcohol Rev Drug and alcohol review. 2019;38(3):270–3. doi: 10.1111/dar.12884 [DOI] [PubMed] [Google Scholar]

[pone.0290113.ref034] 34.Mattson CL, Tanz LJ, Quinn K, Kariisa M, Patel P, Davis NL. Trends and geographic patterns in drug and synthetic opioid overdose deaths—United States, 2013–2019. MMWR Morbidity and Mortality Weekly Report. 2021;70(6):202–7. doi: 10.15585/mmwr.mm7006a4 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref035] 35.Operario D, Gamarel KE, Grin BM, Lee JH, Kahler CW, Marshall BD, et al. Sexual minority health disparities in adult men and women in the United States: National Health and Nutrition Examination Survey, 2001–2010. Am J Public Health. 2015;105(10):e27–34. Epub 2015/08/14. doi: 10.2105/AJPH.2015.302762 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref036] 36.Rosario M, Corliss HL, Everett BG, Reisner SL, Austin SB, Buchting FO, et al. Sexual orientation disparities in cancer-related risk behaviors of tobacco, alcohol, sexual behaviors, and diet and physical activity: pooled Youth Risk Behavior Surveys. Am J Public Health. 2014;104(2):245–54. Epub 2013/12/18. doi: 10.2105/AJPH.2013.301506 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref037] 37.Stringhini S, Carmeli C, Jokela M, Avendano M, Muennig P, Guida F, et al. Socioeconomic status and the 25 x 25 risk factors as determinants of premature mortality: a multicohort study and meta-analysis of 1.7 million men and women. Lancet. 2017;389(10075):1229–37. Epub 2017/02/06. doi: 10.1016/S0140-6736(16)32380-7 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref038] 38.Bundy JD, Mills KT, He H, LaVeist TA, Ferdinand KC, Chen J, et al. Social determinants of health and premature death among adults in the USA from 1999 to 2018: a national cohort study. Lancet Public Health. 2023;8(6):e422–e31. Epub 2023/05/28. doi: 10.1016/S2468-2667(23)00081-6 . [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref039] 39.Fewell Z, Davey Smith G, Sterne JA. The impact of residual and unmeasured confounding in epidemiologic studies: a simulation study. Am J Epidemiol. 2007;166(6):646–55. Epub 2007/07/07. doi: 10.1093/aje/kwm165 . [DOI] [PubMed] [Google Scholar]

[pone.0290113.ref040] 40.VanderWeele TJ. Principles of confounder selection. Eur J Epidemiol. 2019;34(3):211–9. Epub 2019/03/07. doi: 10.1007/s10654-019-00494-6 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref041] 41.Schisterman EF, Cole SR, Platt RW. Overadjustment bias and unnecessary adjustment in epidemiologic studies. Epidemiology. 2009;20(4):488–95. Epub 2009/06/16. doi: 10.1097/EDE.0b013e3181a819a1 [DOI] [PMC free article] [PubMed] [Google Scholar]

[pone.0290113.ref042] 42.Greenland S, Mansournia MA, Altman DG. Sparse data bias: a problem hiding in plain sight. BMJ. 2016;352:i1981. Epub 2016/04/29. doi: 10.1136/bmj.i1981 . [DOI] [PubMed] [Google Scholar]

PERMALINK

The association between all-cause mortality and HIV acquisition risk groups in the United States, 2001–2014

Fatma M Shebl

Yiqi Qian

Julia H A Foote

Nattanicha Wattananimitgul

Krishna P Reddy

Anne M Neilan

Andrea L Ciaranello

Elena Losina

Kenneth A Freedberg

Emily P Hyle

Roles

Abstract

Objective

Methods

Results

Conclusions

Introduction

Methods

Study population and data sources

Ethical approval

Outcome

All-cause mortality

Exposures

People who ever injected drugs (ever-PWID)

Men who have sex with men (MSM)

Heterosexually active people at increased risk for HIV (HIH)

HIV acquisition risk group categorization

Covariates of interest

Statistical analysis

Results

HIV acquisition risk groups

Fig 1. Weighted percentages of risk groups by participants’ characteristics.

Mortality

Mortality association with HIV acquisition risk groups

Table 1. All-cause mortality rates by mutually exclusive HIV acquisition risk groups*.

Multivariable regression models of the association between mortality and HIV acquisition risk groups

Table 2. Unadjusted and adjusted association of mutually exclusive HIV acquisition risk group and all-cause mortality.

Discussion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Veincent Christian Pepito

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Author response to Decision Letter 0

Decision Letter 1

Veincent Christian Pepito

Roles

Acceptance letter

Veincent Christian Pepito

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Associated Data

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Data Availability Statement

ACTIONS

PERMALINK

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