Impact of modifiable factors on sex and age disparities for unfavorable outcomes in Peruvian population with tuberculosis

Impact of modifiable factors on sex and age disparities for unfavorable outcomes in Peruvian population with tuberculosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of modifiable factors on sex and age disparities for unfavorable outcomes in Peruvian population with tuberculosis Claudio Intimayta-Escalante, Roman Jandarov, Moises A. Huaman This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9124924/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background Tuberculosis remains a major public health problem in Peru, with persistent unfavorable outcomes driven by comorbidities, harmful behaviors, and structural barriers to care. These factors are unevenly distributed across populations, contributing to disparities by sex and age. The aim of this study was to evaluate the impact of modifiable factors on sex- and age-related disparities in unfavorable outcomes among the Peruvian population with tuberculosis. Methods Retrospective cohort study with national data from the Peruvian Tuberculosis Management Information System (SIGTB) between 2016 and 2023. Adults with tuberculosis, newly registered and complete clinical evaluations were included. Unfavorable outcomes comprised death, dropout or loss to follow-up, and treatment failure. Disparities were assessed by sex and age group (< 60 vs. ≥60 years). Modifiable factors included region and area of residence, health insurance, HIV/AIDS coinfection, diabetes mellitus, alcohol consumption, smoking, and drug use. The impacts were quantified using Population Attributable Fractions (PAF%). Results Among 177,185 adults with tuberculosis, 14.69% experienced an unfavorable outcome, more frequently among males (16.66%) and older adults (21.35%). HIV/AIDS coinfection showed the largest impact overall (PAF%=40.85), followed by alcohol consumption (PAF%=10.84) and drug use (PAF%=8.19). HIV/AIDS consistently exhibited the highest PAF% across sex and age groups, particularly among females and younger adults. Drug use showed marked sex disparities, with higher impacts among females, especially for dropout or loss to follow-up. Annual analyses indicated persistently high impacts of HIV/AIDS and increasing disparities related to substance use after the pandemic. Conclusion Modifiable factors like HIV/AIDS coinfection, alcoholism, and drug use account for a substantial proportion of unfavorable tuberculosis outcomes in Peru, with pronounced disparities by sex and age. These findings highlight the need for equity-focused TB strategies integrating comorbidity management, substance-use interventions, and resilient health systems. Health Impact Assessment Tuberculosis Healthcare Disparities Health Status Disparities Socioeconomic Disparities in Health Peru Figures Figure 1 Figure 2 Figure 3 Figure 3 Figure 4 Figure 5 BACKGROUND Tuberculosis (TB) remains a major global public health challenge 1 . In 2022, the World Health Organization reported approximately 1.25 million TB-related deaths worldwide, with a disproportionate burden borne by low- and middle-income countries or LMICs 2 , 3 . In Latin America, Peru ranks second in TB incidence and experienced an 8.54% increase in reported cases in 2022 following the COVID-19 pandemic 4 . This resurgence poses a substantial challenge given the country’s constrained healthcare resources and limited capacity to support a growing population affected by TB 5 . Tuberculosis unfavorable outcomes are closely linked to multiple factors, including comorbidities, and harmful behaviors such as alcohol consumption and drug use 6 – 8 . These determinants are unevenly distributed across populations, particularly by sex, age, and geographic location, contributing to persistent disparities in TB outcomes 9 , 10 . In Peru, health system centralization and fragmentation further restrict timely diagnosis, treatment initiation, and continuity of care, especially among vulnerable and rural populations 11 , 12 . Assessing disparities within TB-affected populations is essential for informing targeted public health strategies. The presence of modifiable sociodemographic, behavioral, and clinical determinants underscores the need for comprehensive analyses to better understand their contribution to TB-related mortality and other unfavorable outcomes in Peru 13 , 14 . Therefore, this study aimed to evaluate the impact of modifiable factors on sex- and age-related disparities in unfavorable outcomes among the Peruvian population with tuberculosis. METHODS Population and Study Design We conducted a retrospective cohort study using secondary data from the Tuberculosis Management Information System (SIGTB, in Spanish), which records TB treatment and follow-up information for patients in Peru between 2016 and 2023. Peru is a high TB-burden country with cases distributed across all regions, including Lima, the capital city, which concentrates nearly one-third of the national population 15 . We included all adult patients (≥ 18 years) newly registered in SIGTB with complete clinical evaluations at health centers. Records of children and individuals who did not initiate TB treatment were excluded (Appendix 1) . Evaluation of Outcomes Treatment outcomes were classified as favorable outcomes (cured or complete treatment). While the unfavorable outcomes were three possible scenarios, which includes death (without starting treatment, before or during treatment for tuberculosis), dropouts or lost to follow-up (even before starting treatment), and treatment failure for tuberculosis. In this way, we addressed how some factors can modify the impact on unfavorable outcomes. Modifiable Risk Factors Disparities were assessed across non-modifiable factors (sex, and age groups). This for compared to modifiable factors, grouped into demographic conditions such as areas (urban/rural) or region of residence (capital/outside). Also, some social conditions like alcoholism, smoking or drug use are based on self-reporting. In addition, health conditions such as having health insurance, HIV/AIDS coinfection, and diabetes mellitus comorbidity which were evaluated based on medical records and assessments at the health center part of the SIGTB. Statistical Analysis Analyses were conducted using RStudio version 4.4.2 ( https://cran.r-project.org/ ). Descriptive statistics were summarized using frequencies and percentages for categorical variables and means with standard deviations for continuous variables. Poisson regression models with robust variance were used to estimate adjusted risk ratios (aRRs) for unfavorable outcomes, overall and by outcome type. The impact of modifiable factors was quantified using the Population Attributable Fraction percentage (PAF%) with 95% confidence intervals (95% CI), calculated as: $$\varvec{P}\varvec{A}\varvec{F}\varvec{\%}=\frac{{p}_{e}\left(aRR-1\right)}{{p}_{e}\left(aRR-1\right)+1}x100\%$$ where \({p}_{e}\) represents the proportion of exposure 16 . In addition, annual variations in PAF% were assessed by sex and age group. Imputation of Missing Data Missing values for HIV/AIDS, diabetes, alcohol use, smoking, and drug use were handled using multiple imputations by chained equations, assuming a missing-at-random mechanism. Logistic regression models were used for binary variables, incorporating predictors of missingness (sex, age group, residence in Lima, and rurality). The characteristics for cases with incomplete data are detailed in Appendix 2 . Ethical Aspects This study used anonymized secondary data obtained from SIGTB through an official request. No personally identifiable information was included. Data collection followed informed consent procedures at health facilities, and confidentiality was maintained throughout. No additional ethics committee approval was required. RESULTS Characteristics of the Population In the 177185 Peruvians adults with tuberculosis selected from SIGTB for the study, the mean age was 40.92 years (DE: 18.51) with the 62.75% as male and the 19.44% as elderly (60 + years). In addition, more than half live in urban areas and the capital ( Table 1 ) , while the 14.69% showed an unfavorable outcome, but this was more frequent in males (16.66%) and elderly population (21.35%). Furthermore, the more frequent outcome was dropouts or lost to follow-up (7.16%), followed by death (6.79%), and treatment failure (0.73%). Table 1 Characteristics of the Peruvian population with tuberculosis and imputated estimations according to their outcome, 2017–2023 Variables Total Imputed Estimations* Unfavorable Outcome Decead Abandoned or Lost in Follow Failure in Treatment (n = 177,185) (n = 26,032) (n = 12,037) (n = 12,693) (n = 1,092) n (%) %* (95%CI) %* (95%CI) %* (95%CI) %* (95%CI) %* (95%CI) Sex Males 111,184 (62.75) 62.75 (62.53–62.98) 16.66 (16.44–16.88) 8.24 (8.07–8.4) 9.2 (9.02–9.38) 0.87 (0.81–0.93) Females 66,001 (37.25) 37.25 (37.02–37.47) 11.38 (11.14–11.62) 5.98 (5.8–6.17) 5.34 (5.17–5.52) 0.83 (0.76–0.9) Age Group (years) 18–59 142,738 (80.56) 80.56 (80.37–80.74) 13.08 (12.91–13.26) 4.85 (4.74–4.97) 8.36 (8.21–8.5) 0.83 (0.78–0.88) 60+ 34,447 (19.44) 19.44 (19.26–19.63) 21.35 (20.92–21.78) 17.4 (16.99–17.81) 4.85 (4.6–5.1) 0.98 (0.86–1.09) Place of Residence Others Regions 85,896 (48.48) 48.48 (48.25–48.71) 15.51 (15.27–15.76) 8.92 (8.72–9.11) 7.22 (7.04–7.4) 0.79 (0.72–0.85) Capital 91,289 (51.52) 51.52 (51.29–51.75) 13.92 (13.69–14.14) 5.91 (5.75–6.07) 8.23 (8.05–8.41) 0.92 (0.85–0.98) Area of Residence Urban 153,920 (86.87) 86.87 (86.71–87.03) 14.52 (14.35–14.7) 7.1 (6.96–7.23) 7.81 (7.67–7.95) 0.87 (0.82–0.92) Rural 23,265 (13.13) 13.13 (12.97–13.29) 15.8 (15.34–16.27) 9.21 (8.82–9.6) 7.29 (6.94–7.64) 0.75 (0.63–0.88) Health Insurance? No 40,013 (7.08) 7.76 (7.64–7.88) 13.77 (13.2–14.35) 4.66 (4.29–5.03) 9.62 (9.11–10.12) 0.45 (0.33–0.56) Yes 525394 (92.92) 92.24 (92.12–92.36) 14.77 (14.6–14.94) 7.6 (7.47–7.73) 7.58 (7.45–7.72) 0.89 (0.84–0.94) DM Comorbidity? No 162149 (93.2) 93.21 (93.1–93.33) 14.67 (14.49–14.84) 7.25 (7.11–7.38) 7.88 (7.75–8.02) 0.81 (0.77–0.86) Yes 11832 (6.8) 6.79 (6.67–6.9) 15.05 (14.4–15.71) 9.15 (8.6–9.69) 5.85 (5.4–6.3) 1.42 (1.19–1.65) HIV/AIDS Coinfection? No 166910 (95.76) 95.76 (95.67–95.86) 13.68 (13.52–13.84) 6.49 (6.37–6.61) 7.46 (7.33–7.59) 0.84 (0.79–0.89) Yes 7390 (4.24) 4.24 (4.14–4.33) 37.53 (36.42–38.63) 28.54 (27.44–29.64) 15.81 (14.85–16.78) 1.33 (1–1.66) Alcohol Consumption? No 149673 (92.14) 92.03 (91.89–92.16) 13.86 (13.69–14.03) 7.19 (7.06–7.32) 6.99 (6.86–7.12) 0.83 (0.78–0.87) Yes 12775 (7.86) 7.97 (7.84–8.11) 24.32 (23.58–25.06) 9.8 (9.25–10.34) 16.69 (16.01–17.37) 1.23 (1.01–1.45) Cigarette Consumption? No 152335 (93.78) 93.67 (93.56–93.79) 14.28 (14.11–14.45) 7.41 (7.28–7.54) 7.24 (7.11–7.37) 0.84 (0.8–0.89) Yes 10108 (6.22) 6.33 (6.21–6.44) 20.74 (19.97–21.52) 6.79 (6.27–7.3) 15.14 (14.42–15.87) 1.03 (0.81–1.26) Drug Consumption? No 163054 (92.29) 92.29 (92.16–92.41) 14.06 (13.89–14.22) 7.53 (7.4–7.67) 6.86 (6.73–6.99) 0.83 (0.78–0.88) Yes 13616 (7.71) 7.71 (7.59–7.84) 22.31 (21.61–23.01) 5.22 (4.81–5.64) 18.05 (17.39–18.72) 1.16 (0.95–1.36) *Estimates were derived using multiple imputations by chained equations, assuming missing at random. Note : The first two column has vertical estimates, while the rest of the columns have horizontal estimates A. Impact of modifiable factors on sex disparities for unfavorable outcome B. Impact of modifiable factors on age disparities for unfavorable outcome A. Impact of modifiable factors on sex disparities for deceased B. Impact of modifiable factors on age disparities for deceased A. Impact of modifiable factors on sex disparities for abandoned or lost in follow B. Impact of modifiable factors on age disparities for abandoned or lost in follow A. Impact of modifiable factors on sex disparities for failure in treatment B. Impact of modifiable factors on age disparities for failure in treatment A. Annual variation on impact of modifiable factors on sex disparities for unfavorable outcome B. Annual variation on impact of modifiable factors on age disparities for unfavorable outcome Only a small proportion of Peruvians adults with tuberculosis don’t have a health insurance (7.76%), and showed DM (6.79%), HIV/AIDS (4.24%), alcoholism (7.97%), smoking (6.22%), or drug use (7.71%). The unfavorable outcome was more frequent in those with HIV/AIDS (37.53%), especially for death and dropouts or lost to follow-up ( Table 1 ) . In contrast, this unfavorable outcome was less frequent among those who live in the capital. Impact of Modifiable Factors In the assessment of association between demographic, social and health conditions with unfavorable outcomes, it was identified that males (aRR = 1.29), and elderly (aRR = 1.87) showed a considerable increased risk. While those Peruvians adults with tuberculosis with HIV/AIDS (aRR = 2.84), alcoholism (aRR = 1.50), and drug use (aRR = 1.40) showed higher risk for unfavorable outcomes. The variables included in Poisson regression models don’t show multicollinearity (Appendix 5) . However, these models and imputated estimations allow assess the impact measures in HIV/AIDS (PAF%=40.85; 95%CI: 33.50 to 48.19), alcoholism (PAF%=10.84; 95%CI: 5.15 to 16.53), and drug use (PAF%=8.19; 95%CI: 3.03 to 13.36) for unfavorable outcomes in Peruvian populations with tuberculosis. Disparities in Modifiable Factors The estimations of PAF% showed that modifiable factors accounted for a substantial proportion of unfavorable outcomes among the Peruvian population with tuberculosis ( Fig. 1 A ) . The HIV/AIDS coinfection presented the highest impact in males (PAF%=38.24; 95%CI: 30.65 to 45.83), and females (PAF%=43.08; 95%CI: 28.00 to 58.17), followed by alcohol consumption (PAF% males =10.63, 95%CI: 4.88 to 16.39; PAF% females =13.06%, 95%CI: 5.11 to 21.01). This was similar for death, in HIV/AIDS coinfection, residence outside of capital and alcohol consumption ( Fig. 2 A ) . Treatment failure was associated with comparatively lower PAF magnitudes, including DM and alcohol consumption, with higher PAF% values observed among females ( Fig. 3 A ) . For dropout or loss to follow-up, HIV/AIDS coinfection and drug consumption showed positive PAF% values in both sexes, with markedly higher estimates among females, particularly for drug use ( Fig. 4 A ) , whereas several structural and behavioral factors presented negative or near-null PAF% values across outcomes. The disparity by age showed heterogeneity impact of modifiable factors to unfavorable outcomes among the Peruvian population with tuberculosis. For unfavorable outcomes overall, HIV/AIDS coinfection presented the highest values in younger (PAF% <60years =41.47%; 95%CI: 34.12 to 48.83) and older adults (PAF% 60+years =35.32; 95%CI: 26.12 to 44.52), with similar patterns observed for alcohol consumption in both age groups ( Fig. 1 A ) . In disparity by age for death, HIV/AIDS coinfection showed the highest PAF, whereas residence outside the capital and rural residence presented smaller positive values across age groups ( Fig. 2 B ) . Treatment failure showed low PAF magnitudes, including diabetes mellitus and alcohol consumption, with higher values among younger adults ( Fig. 3 B ) . For dropout or loss to follow-up, positive values were observed for HIV/AIDS coinfection, alcohol and drug use in both age groups, with higher estimates for drug consumption among younger adults ( Fig. 4 B ) . Annual Variation in Modifiable Factors In the study period, annual PAF% estimates showed marked variation by sex for the main modifiable factors ( Fig. 5 ) . The impact of HIV/AIDS coinfection was high and declined over time in the total population (PAF% 2016 =49.96% to PAF% 2023 =36.17%), with similar patterns in males (PAF% 2016 =47.72 to PAF% 023 =33.51) and females (PAF% 2016 =60.04 to PAF% 2023 =45.07). In addition, alcohol consumption showed positive values throughout the period in the total population, with comparable values in males and higher estimates for females after pandemic. Other conditions like smoking showed values fluctuating around zero, shifting from negative values in (PAF% 2016 = − 5.96 to PAF% 2020 = − 0.97) to positive values (PAF% 2021 =1.04 to PAF% 2023 =4.42); this pattern was similar in younger and older adults ( Fig. 5 ) . The drug use showed a positive impact in all years (PAF% 2016 =3.93 to PAF% 2023 =10.16), with consistently lower values in males and substantially higher values in females, particularly after 2021. DISCUSSION Main Findings of the Study In this study, we assessed the contribution of modifiable factors to sex- and age-related disparities in unfavorable outcomes among the Peruvian population with tuberculosis. HIV/AIDS coinfection consistently exhibited the largest impact across outcomes, particularly for death, with higher effects observed among females and younger adults compared with males and older adults. Drug use also emerged as a major contributor, showing pronounced sex disparities and substantially higher impacts among females, especially for dropout or loss to follow-up. Alcohol consumption contributed positively to several unfavorable outcomes, with generally higher impacts among females and heterogeneous patterns across age groups. In contrast, smoking and diabetes mellitus showed smaller and less consistent effects, with variability in both direction and magnitude across sex and age strata. Overall, these findings indicate that disparities in unfavorable tuberculosis outcomes are largely driven by HIV/AIDS coinfection and substance use, with marked differences by sex and age that became more relevant after pandemic period. Comparison with previous studies Our findings are consistent with prior cohort studies identifying HIV/AIDS coinfection as one of the strongest determinants of unfavorable tuberculosis outcomes, reflecting the heightened vulnerability of immunocompromised patients 17 , 18 . HIV/AIDS also contributed substantially to sex- and age-related disparities, in line with evidence on TB–HIV syndemics that amplify adverse outcomes across demographic groups 7 . Alcohol consumption showed patterns concordant with previous studies linking substance use to non-adherence, loss to follow-up, and poor tuberculosis outcomes, with marked disparities by sex and age in LMIC settings such as Peru 19 , 20 . Drug use similarly mirrored established associations with worse tuberculosis outcomes, with greater disparities observed among younger adults and women, likely related to higher risk of treatment interruption. In contrast, diabetes mellitus was more strongly associated with treatment failure, consistent with evidence on the adverse impact of metabolic comorbidity on tuberculosis prognosis 7 , 21 . The COVID-19 pandemic further modified these associations, exposing limitations in health-system resilience and continuity of care, particularly among populations already vulnerable to severe SARS-CoV-2 infection in South America countries like Peru 22 , 23 . Plausibility of the findings The disparities by sex and age groups observed in the Peruvian population with tuberculosis for unfavorable outcomes can be explained through a combination of biological, behavioral, and healthcare system factors. Biologically, men are more likely to experience worse tuberculosis outcomes due to higher prevalence of risk behaviors to infected by HIV, smoking, and alcoholism, which impair immune function and reduce adherence to treatment, failure and risk of death 24 . The age disparities are also biologically plausible, especially for those with younger adults with HIV/AIDS that show particularly higher rates of death on tuberculosis population, because the coinfection severely weakens the immune system and accelerates the progression from latent to active tuberculosis, which is prevalent in Peru 25 . Also, antiretroviral treatment cannot be initiated in people with tuberculosis due to the risk of immune reconstitution syndrome and drug interactions 26 , 27 . The higher proportion of deaths in Peruvian population with tuberculosis and diabetes comorbidity, could be explained because the tuberculosis can worsen hyperglycemia 28 , and the antituberculosis treatment with rifampicin is known to interact with oral hypoglycemic agents 29 . However is reasonable that diabetes increase treatment failure rates, even when some evidence suggests that metformin may have a protective effect against tuberculosis in diabetic patients 30 . Public health implications The findings of this study highlight the sex- and age-related disparities in tuberculosis unfavorable outcome, driven by modifiable factors such as comorbidities, alcoholism, smoking and drug use. These results underscore the urgent need to strengthen health system equity by improving access and quality to comprehensive tuberculosis care, especially for vulnerable populations such as men, older adults, and individuals living outside the capital 31 , 32 . Furthermore, the disproportionate impact of alcohol use and the exacerbation of disparities during the COVID-19 pandemic reveal weaknesses in the health system’s capacity to sustain essential tuberculosis services during public health emergencies 22 , 33 . The interplay between behavioral and structural factors requires targeted interventions, including mitigation strategies tailored to comorbid populations and those with harmful habits 34 , 35 . Strengths, Limitations and Recommendation The study has strengths because it was developed using nationwide data on the Peruvian population with tuberculosis over a long period of time. However, it also has some limitations that should be acknowledged for a judicious interpretation of our findings. First, because it is a secondary database developed in health centers, there is less representation of some groups and errors in data quality, but estimations are consistent in evaluated outcomes. Second, some socioeconomic variables (education, income, occupation), health variables (adherence to treatment, type of tuberculosis, and immune system status) were not recorded, and necessary for future investigations. Third, estimations among insured tuberculosis population may be overestimated because the database is recorded from health centers and could determine some selection bias. Future research should address the impact of public policies on tuberculosis control in LMICs, based on longitudinal registries with clinical, social, and environmental information. Conclusion In conclusion, modifiable factors like HIV/AIDS coinfection, alcohol consumption, and drug use drive substantial sex- and age-related disparities in unfavorable tuberculosis outcomes in Peru. These disparities disproportionately affect women and younger adults and were exacerbated during the COVID-19 pandemic. Addressing these inequities requires integrated, equity-focused TB care strategies that prioritize comorbidity management, substance use interventions, and health system resilience to reduce preventable adverse outcomes in high-burden settings. Abbreviations • TB Tuberculosis • WHO World Health Organization • LMICs Low—and middle—income countries • COVID 19—Coronavirus disease 2019 • SIGTB Tuberculosis Management Information System (Peru) • RStudio RStudio statistical software (environment for R) • aRR Adjusted Risk Ratio • PAF% Population Attributable Fraction (percentage) • 95% CI 95% Confidence Interval • HIV/AIDS Human Immunodeficiency Virus / Acquired Immunodeficiency Syndrome • DM Diabetes Mellitus • SARS CoV—2—Severe Acute Respiratory Syndrome Coronavirus 2 Declarations Conflicts of interest: The authors declare that they have no conflict of interest in the development of this research. Ethics approval: The study was conducted using secondary public data, anonymized and without compromising the integrity of the participants, who gave their consent for their data to be recorded in Peru's national tuberculosis system. Funding: None Author Contribution CIE conceptualization, methodology, formal analysis, writing - review & editing, visualization, and supervision. RJ formal analysis, writing - original draft, visualization, and writing - review and editing. MAH methodology, formal analysis, writing - original draft, visualization, and writing - review and editing. Acknowledgement None Data Availability The data is available to healthcare personnel registered in Tuberculosis Information System (https:/appsalud.minsa.gob.pe/sigtbdata/wflogin.aspx). References WHO. Global tuberculosis report 2024. Published online 2024. https://iris.who.int/bitstream/handle/10665/379339/9789240101531-eng.pdf?sequence=1 WHO, Tuberculosis. 2024. https://www.who.int/es/news-room/fact-sheets/detail/tuberculosis Martinez L, Warren JL, Harries AD, et al. Global, regional, and national estimates of tuberculosis incidence and case detection among incarcerated individuals from 2000 to 2019: a systematic analysis. Lancet Public Health. 2023;8(7):e511–9. 10.1016/S2468-2667(23)00097-X . National Center for Epidemiology, Disease Prevention and Control. Epidemiological situation of tuberculosis in Peru, 2018–2022. Published online 2023. https://www.dge.gob.pe/epipublic/uploads/boletin/boletin_202320_28_163316.pdf Yang H, Ruan X, Li W, Xiong J, Zheng Y. Global, regional, and national burden of tuberculosis and attributable risk factors for 204 countries and territories, 1990–2021: a systematic analysis for the Global Burden of Diseases 2021 study. BMC Public Health. 2024;24(1):3111. 10.1186/s12889-024-20664-w . Reid M, Roberts G, Goosby E, Wesson P. Monitoring Universal Health Coverage (UHC) in high Tuberculosis burden countries: Tuberculosis mortality an important tracer of UHC service coverage. Silva JP. ed PLOS ONE. 2019;14(10):e0223559. 10.1371/journal.pone.0223559 . Ugarte-Gil C, Curisinche M, Herrrera-Flores E, Hernandez H, Rios J. Prevalence of tuberculosis and diabetes comorbidity in adults in Peru, 2016–2018. Rev Peru Med Exp Salud Publica. 2021;38(2):254–60. 10.17843/rpmesp.2021.382.6764 . Raviglione M, Poznyak V. Targeting harmful use of alcohol for prevention and treatment of tuberculosis: a call for action. Eur Respir J. 2017;50(1):1700946. 10.1183/13993003.00946-2017 . Adekoya N, Chang MH, Wortham J, Truman BI. Disparities in Rates of Death From HIV or Tuberculosis Before Age 65 Years, by Race, Ethnicity, and Sex, United States, 2011–2020. Public Health Reports®. 2024;139(5):557–65. 10.1177/00333549231213328 . Alves LS, Berra TZ, Alves YM, et al. Geographic inequalities and factors associated with unfavorable outcomes in diabetes-tuberculosis and diabetes-covid comorbidities in Brazil. Sci Rep. 2025;15(1):8353. 10.1038/s41598-025-93476-6 . Moyano LM, Brunette MJ, Díaz-Vélez C. Afrontando los retos del fragmentado sistema de salud peruano: ¿Puede la academia realmente convertirse en un agente de cambio? Rev Med Hered. 2024;35(1):3–6. 10.20453/rmh.v35i1.5304 . Montalvo-Otivo R, Ramírez-Breña M, Bruno-Huamán A, Damián-Mucha M, Vilchez-Bravo S, Quisurco-Cárdenas M. Geographic distribution and risk factors for multidrug-resistant tuberculosis in central Peru. Rev Fac Med. 2020;68(2). 10.15446/revfacmed.v68n2.71715 . MINSA. Technical Health Standard for Comprehensive Care of Persons Affected by Tuberculosis. Published online 2023. https://bvs.minsa.gob.pe/local/MINSA/6344.pdf Montesinos EV. Health equity in Peru in light of its social determinants (DSS): past, present and future. 2023;40(4):291–3. 10.35663/amp.2023.404.2782 National Institute of Statistics and Informatics. Peru: Population Estimates and Projections by Department, Sex and Five-Year Age Groups 1995–2025. Published online 2009. https://proyectos.inei.gob.pe/web/biblioineipub/bancopub/Est/Lib0846/libro.pdf Newson RB. Attributable and Unattributable Risks and Fractions and other Scenario Comparisons. Stata J Promot Commun Stat Stata. 2013;13(4):672–98. 10.1177/1536867X1301300402 . Velásquez GE, Cegielski JP, Murray MB, et al. Impact of HIV on mortality among patients treated for tuberculosis in Lima, Peru: a prospective cohort study. BMC Infect Dis. 2015;16(1):45. 10.1186/s12879-016-1375-8 . Lelisho ME, Wotale TW, Tareke SA, et al. Survival rate and predictors of mortality among TB/HIV co-infected adult patients: retrospective cohort study. Sci Rep. 2022;12(1):18360. 10.1038/s41598-022-23316-4 . Trzcińska H. Alcoholism and other socio-demographic risk factors for adverse TB-drug reactions and unsuccessful tuberculosis treatment – data from ten years’ observation at the Regional Centre of Pulmonology, Bydgoszcz, Poland. Med Sci Monit. 2014;20:444–53. 10.12659/MSM.890012 . Wigger GW, Bouton TC, Jacobson KR, Auld SC, Yeligar SM, Staitieh BS. The Impact of Alcohol Use Disorder on Tuberculosis: A Review of the Epidemiology and Potential Immunologic Mechanisms. Front Immunol. 2022;13:864817. 10.3389/fimmu.2022.864817 . Gautam S, Shrestha N, Mahato S, Nguyen TPA, Mishra SR, Berg-Beckhoff G. Diabetes among tuberculosis patients and its impact on tuberculosis treatment in South Asia: a systematic review and meta-analysis. Sci Rep. 2021;11(1):2113. 10.1038/s41598-021-81057-2 . Rojas-Bolivar D, Intimayta‐Escalante C, Cardenas‐Jara A, Jandarov R, Huaman MA. COVID‐19 case fatality rate and tuberculosis in a metropolitan setting. J Med Virol. 2021;93(5):3273–6. 10.1002/jmv.26868 . Wang Q, Cao Y, Liu X et al. Systematic review and meta-analysis of Tuberculosis and COVID-19 Co-infection: Prevalence, fatality, and treatment considerations. Ediriweera D, ed. PLoS Negl Trop Dis . 2024;18(5):e0012136. 10.1371/journal.pntd.0012136 Pape S, Karki SJ, Heinsohn T, Brandes I, Dierks ML, Lange B. Tuberculosis case fatality is higher in male than female patients in Europe: a systematic review and meta-analysis. Infection. 2024;52(5):1775–86. 10.1007/s15010-024-02206-z . Sullivan ZA, Wong EB, Ndung’u T, Kasprowicz VO, Bishai WR. Latent and Active Tuberculosis Infection Increase Immune Activation in Individuals Co-Infected with HIV. EBioMedicine. 2015;2(4):334–40. 10.1016/j.ebiom.2015.03.005 . Navasardyan I, Miwalian R, Petrosyan A, Yeganyan S, Venketaraman V. HIV–TB Coinfection: Current Therapeutic Approaches and Drug Interactions. Viruses. 2024;16(3):321. 10.3390/v16030321 . Lanzafame M, Vento S. Tuberculosis-immune reconstitution inflammatory syndrome. J Clin Tuberc Mycobact Dis. 2016;3:6–9. 10.1016/j.jctube.2016.03.002 . Bisht MK, Dahiya P, Ghosh S, Mukhopadhyay S. The cause–effect relation of tuberculosis on incidence of diabetes mellitus. Front Cell Infect Microbiol. 2023;13:1134036. 10.3389/fcimb.2023.1134036 . Niemi M, Kivistö KT, Backman JT, Neuvonen PJ. Effect of rifampicin on the pharmacokinetics and pharmacodynamics of glimepiride. Br J Clin Pharmacol. 2000;50(6):591–5. 10.1046/j.1365-2125.2000.00295.x . Marupuru S, Senapati P, Pathadka S, Miraj SS, Unnikrishnan MK, Manu MK. Protective effect of metformin against tuberculosis infections in diabetic patients: an observational study of south Indian tertiary healthcare facility. Braz J Infect Dis. 2017;21(3):312–6. 10.1016/j.bjid.2017.01.001 . Huang CC, Trevisi L, Becerra MC, et al. Spatial scale of tuberculosis transmission in Lima, Peru. Proc Natl Acad Sci. 2022;119(45):e2207022119. 10.1073/pnas.2207022119 . Mendoza-Hisey EC, Dier A, Marquez NV, Bumanglag LV, Cadiao SBA, Guirgis SF. Gender-related factors affecting access to TB services and treatment outcomes in the Philippines. Public Health Action. 2023;13(3):107–11. 10.5588/pha.23.0021 . Cardenas-Escalante J, Fernandez-Saucedo J, Cubas WS. Impact of the COVID-19 pandemic on tuberculosis in Peru: are we forgetting anyone? Infect Dis Clin Microbiol. 2022;40(1):46–7. 10.1016/j.eimc.2021.07.014 . Chikovore J, Pai M, Horton KC, et al. Missing men with tuberculosis: the need to address structural influences and implement targeted and multidimensional interventions. BMJ Glob Health. 2020;5(5):e002255. 10.1136/bmjgh-2019-002255 . Osei-Wusu S, Asare P, Danso EK, et al. Addressing key risk factors hindering tuberculosis control activities in West Africa - progress in meeting the UN sustainable development goals. IJID Reg. 2025;14:100594. 10.1016/j.ijregi.2025.100594 . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers invited by journal 16 Mar, 2026 Editor assigned by journal 16 Mar, 2026 Submission checks completed at journal 16 Mar, 2026 First submitted to journal 14 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9124924","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606853475,"identity":"8d10523c-3642-40d6-8a9f-c3570fc348e1","order_by":0,"name":"Claudio Intimayta-Escalante","email":"data:image/png;base64,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","orcid":"","institution":"National University of San Marcos","correspondingAuthor":true,"prefix":"","firstName":"Claudio","middleName":"","lastName":"Intimayta-Escalante","suffix":""},{"id":606853479,"identity":"756dae0f-1ea0-4aa6-a82e-8f2ccd790319","order_by":1,"name":"Roman Jandarov","email":"","orcid":"","institution":"University of Cincinnati","correspondingAuthor":false,"prefix":"","firstName":"Roman","middleName":"","lastName":"Jandarov","suffix":""},{"id":606853480,"identity":"fb794b5c-6472-4337-a9c1-83dca759f7c6","order_by":2,"name":"Moises A. Huaman","email":"","orcid":"","institution":"University of Cincinnati","correspondingAuthor":false,"prefix":"","firstName":"Moises","middleName":"A.","lastName":"Huaman","suffix":""}],"badges":[],"createdAt":"2026-03-14 20:38:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9124924/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9124924/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104929522,"identity":"42e6db51-a49b-4c29-9e31-d33bcdc8b033","added_by":"auto","created_at":"2026-03-18 20:54:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1868199,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of modifiable factors on sex and age disparities for unfavorable outcome in Peruvian population with tuberculosis since 2016 to 2023\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9124924/v1/1df815146648020cd1f8e609.png"},{"id":104929525,"identity":"af706cea-2dae-476c-830e-8fe100748dac","added_by":"auto","created_at":"2026-03-18 20:54:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1808215,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of modifiable factors on sex and age disparities for deceased in Peruvian population with tuberculosis since 2016 to 2023\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9124924/v1/09e5002c9d5309e0a99de052.png"},{"id":105034649,"identity":"bddad20f-2cbb-4da0-b73b-4725c992be61","added_by":"auto","created_at":"2026-03-20 07:23:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1851905,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of modifiable factors on sex and age disparities for abandoned or lost in follow in Peruvian population with tuberculosis since 2016 to 2023\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9124924/v1/fb7a7000ca47e2a786177dd0.png"},{"id":104929527,"identity":"56259c55-5af9-4ae1-befb-3401725e246a","added_by":"auto","created_at":"2026-03-18 20:54:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1940840,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of modifiable factors on sex and age disparities for failure in treatment in Peruvian population with tuberculosis since 2016 to 2023\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9124924/v1/6ca639b96fb3e6ba68c1af12.png"},{"id":105007829,"identity":"a26a9206-2afc-46b8-a3c8-b05a357bbdb9","added_by":"auto","created_at":"2026-03-19 19:05:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1851905,"visible":true,"origin":"","legend":"\u003cp\u003eImpact of modifiable factors on sex and age disparities for abandoned or lost in follow in Peruvian population with tuberculosis since 2016 to 2023\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-9124924/v1/814d60cd1ca1174cbecac0d5.png"},{"id":104929524,"identity":"2fd48e65-da57-4443-838f-51ecff4cdef5","added_by":"auto","created_at":"2026-03-18 20:54:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2507225,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual variation of impact for modifiable factors by sex and age disparities in Peruvian population with tuberculosis since 2016 to 2023\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-9124924/v1/6eade138de67a261d8b834ee.png"},{"id":106401574,"identity":"0703e541-d6b0-4961-a264-c4cece754122","added_by":"auto","created_at":"2026-04-08 09:07:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13344843,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9124924/v1/174c7478-2c81-421a-b610-23863f69d5ff.pdf"},{"id":104929523,"identity":"686c7bcd-87f9-4739-b072-05d737e24d03","added_by":"auto","created_at":"2026-03-18 20:54:36","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":148354,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9124924/v1/d7969aec379ff6e1b82b9039.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of modifiable factors on sex and age disparities for unfavorable outcomes in Peruvian population with tuberculosis","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eTuberculosis (TB) remains a major global public health challenge \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. In 2022, the World Health Organization reported approximately 1.25\u0026nbsp;million TB-related deaths worldwide, with a disproportionate burden borne by low- and middle-income countries or LMICs \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In Latin America, Peru ranks second in TB incidence and experienced an 8.54% increase in reported cases in 2022 following the COVID-19 pandemic \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. This resurgence poses a substantial challenge given the country\u0026rsquo;s constrained healthcare resources and limited capacity to support a growing population affected by TB \u003csup\u003e5\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTuberculosis unfavorable outcomes are closely linked to multiple factors, including comorbidities, and harmful behaviors such as alcohol consumption and drug use \u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. These determinants are unevenly distributed across populations, particularly by sex, age, and geographic location, contributing to persistent disparities in TB outcomes \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In Peru, health system centralization and fragmentation further restrict timely diagnosis, treatment initiation, and continuity of care, especially among vulnerable and rural populations \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAssessing disparities within TB-affected populations is essential for informing targeted public health strategies. The presence of modifiable sociodemographic, behavioral, and clinical determinants underscores the need for comprehensive analyses to better understand their contribution to TB-related mortality and other unfavorable outcomes in Peru \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Therefore, this study aimed to evaluate the impact of modifiable factors on sex- and age-related disparities in unfavorable outcomes among the Peruvian population with tuberculosis.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePopulation and Study Design\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective cohort study using secondary data from the Tuberculosis Management Information System (SIGTB, in Spanish), which records TB treatment and follow-up information for patients in Peru between 2016 and 2023. Peru is a high TB-burden country with cases distributed across all regions, including Lima, the capital city, which concentrates nearly one-third of the national population \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. We included all adult patients (\u0026ge;\u0026thinsp;18 years) newly registered in SIGTB with complete clinical evaluations at health centers. Records of children and individuals who did not initiate TB treatment were excluded \u003cb\u003e(Appendix 1)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEvaluation of Outcomes\u003c/h3\u003e\n\u003cp\u003eTreatment outcomes were classified as favorable outcomes (cured or complete treatment). While the unfavorable outcomes were three possible scenarios, which includes death (without starting treatment, before or during treatment for tuberculosis), dropouts or lost to follow-up (even before starting treatment), and treatment failure for tuberculosis. In this way, we addressed how some factors can modify the impact on unfavorable outcomes.\u003c/p\u003e\n\u003ch3\u003eModifiable Risk Factors\u003c/h3\u003e\n\u003cp\u003eDisparities were assessed across non-modifiable factors (sex, and age groups). This for compared to modifiable factors, grouped into demographic conditions such as areas (urban/rural) or region of residence (capital/outside). Also, some social conditions like alcoholism, smoking or drug use are based on self-reporting. In addition, health conditions such as having health insurance, HIV/AIDS coinfection, and diabetes mellitus comorbidity which were evaluated based on medical records and assessments at the health center part of the SIGTB.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAnalyses were conducted using RStudio version 4.4.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Descriptive statistics were summarized using frequencies and percentages for categorical variables and means with standard deviations for continuous variables. Poisson regression models with robust variance were used to estimate adjusted risk ratios (aRRs) for unfavorable outcomes, overall and by outcome type.\u003c/p\u003e \u003cp\u003eThe impact of modifiable factors was quantified using the Population Attributable Fraction percentage (PAF%) with 95% confidence intervals (95% CI), calculated as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\varvec{P}\\varvec{A}\\varvec{F}\\varvec{\\%}=\\frac{{p}_{e}\\left(aRR-1\\right)}{{p}_{e}\\left(aRR-1\\right)+1}x100\\%$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({p}_{e}\\)\u003c/span\u003e\u003c/span\u003erepresents the proportion of exposure \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. In addition, annual variations in PAF% were assessed by sex and age group.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eImputation of Missing Data\u003c/h3\u003e\n\u003cp\u003eMissing values for HIV/AIDS, diabetes, alcohol use, smoking, and drug use were handled using multiple imputations by chained equations, assuming a missing-at-random mechanism. Logistic regression models were used for binary variables, incorporating predictors of missingness (sex, age group, residence in Lima, and rurality). The characteristics for cases with incomplete data are detailed in \u003cb\u003eAppendix 2\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthical Aspects\u003c/h2\u003e \u003cp\u003eThis study used anonymized secondary data obtained from SIGTB through an official request. No personally identifiable information was included. Data collection followed informed consent procedures at health facilities, and confidentiality was maintained throughout. No additional ethics committee approval was required.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eCharacteristics of the Population\u003c/h2\u003e\n \u003cp\u003eIn the 177185 Peruvians adults with tuberculosis selected from SIGTB for the study, the mean age was 40.92 years (DE: 18.51) with the 62.75% as male and the 19.44% as elderly (60\u0026thinsp;+\u0026thinsp;years). In addition, more than half live in urban areas and the capital \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e, while the 14.69% showed an unfavorable outcome, but this was more frequent in males (16.66%) and elderly population (21.35%). Furthermore, the more frequent outcome was dropouts or lost to follow-up (7.16%), followed by death (6.79%), and treatment failure (0.73%).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the Peruvian population with tuberculosis and imputated estimations according to their outcome, 2017\u0026ndash;2023\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eImputed\u003c/p\u003e\n \u003cp\u003eEstimations*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnfavorable Outcome\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDecead\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAbandoned or\u003c/p\u003e\n \u003cp\u003eLost in Follow\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFailure in Treatment\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;177,185)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;26,032)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12,037)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;12,693)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,092)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e%* (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e%* (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e%* (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e%* (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e%* (95%CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111,184 (62.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.75 (62.53\u0026ndash;62.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.66 (16.44\u0026ndash;16.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.24 (8.07\u0026ndash;8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.2 (9.02\u0026ndash;9.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.81\u0026ndash;0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66,001 (37.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.25 (37.02\u0026ndash;37.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.38 (11.14\u0026ndash;11.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.98 (5.8\u0026ndash;6.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.34 (5.17\u0026ndash;5.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.76\u0026ndash;0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge Group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u0026ndash;59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142,738 (80.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.56 (80.37\u0026ndash;80.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.08 (12.91\u0026ndash;13.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.85 (4.74\u0026ndash;4.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.36 (8.21\u0026ndash;8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.78\u0026ndash;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60+\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34,447 (19.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.44 (19.26\u0026ndash;19.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21.35 (20.92\u0026ndash;21.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17.4 (16.99\u0026ndash;17.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.85 (4.6\u0026ndash;5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.98 (0.86\u0026ndash;1.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of Residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers Regions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85,896 (48.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48.48 (48.25\u0026ndash;48.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.51 (15.27\u0026ndash;15.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.92 (8.72\u0026ndash;9.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.22 (7.04\u0026ndash;7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.79 (0.72\u0026ndash;0.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCapital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91,289 (51.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51.52 (51.29\u0026ndash;51.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.92 (13.69\u0026ndash;14.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.91 (5.75\u0026ndash;6.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.23 (8.05\u0026ndash;8.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.92 (0.85\u0026ndash;0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eArea of Residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e153,920 (86.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.87 (86.71\u0026ndash;87.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.52 (14.35\u0026ndash;14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.1 (6.96\u0026ndash;7.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.81 (7.67\u0026ndash;7.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.82\u0026ndash;0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23,265 (13.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.13 (12.97\u0026ndash;13.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.8 (15.34\u0026ndash;16.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.21 (8.82\u0026ndash;9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.29 (6.94\u0026ndash;7.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75 (0.63\u0026ndash;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth Insurance?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40,013 (7.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.76 (7.64\u0026ndash;7.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.77 (13.2\u0026ndash;14.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.66 (4.29\u0026ndash;5.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.62 (9.11\u0026ndash;10.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45 (0.33\u0026ndash;0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e525394 (92.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.24 (92.12\u0026ndash;92.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.77 (14.6\u0026ndash;14.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6 (7.47\u0026ndash;7.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.58 (7.45\u0026ndash;7.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89 (0.84\u0026ndash;0.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDM Comorbidity?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e162149 (93.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.21 (93.1\u0026ndash;93.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.67 (14.49\u0026ndash;14.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.25 (7.11\u0026ndash;7.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.88 (7.75\u0026ndash;8.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.81 (0.77\u0026ndash;0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11832 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.79 (6.67\u0026ndash;6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.05 (14.4\u0026ndash;15.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.15 (8.6\u0026ndash;9.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.85 (5.4\u0026ndash;6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.42 (1.19\u0026ndash;1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV/AIDS Coinfection?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166910 (95.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95.76 (95.67\u0026ndash;95.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.68 (13.52\u0026ndash;13.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.49 (6.37\u0026ndash;6.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.46 (7.33\u0026ndash;7.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84 (0.79\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7390 (4.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.24 (4.14\u0026ndash;4.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.53 (36.42\u0026ndash;38.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28.54 (27.44\u0026ndash;29.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.81 (14.85\u0026ndash;16.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.33 (1\u0026ndash;1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlcohol Consumption?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e149673 (92.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.03 (91.89\u0026ndash;92.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.86 (13.69\u0026ndash;14.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.19 (7.06\u0026ndash;7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.99 (6.86\u0026ndash;7.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.78\u0026ndash;0.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12775 (7.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.97 (7.84\u0026ndash;8.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.32 (23.58\u0026ndash;25.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.8 (9.25\u0026ndash;10.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.69 (16.01\u0026ndash;17.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23 (1.01\u0026ndash;1.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eCigarette Consumption?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e152335 (93.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.67 (93.56\u0026ndash;93.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.28 (14.11\u0026ndash;14.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.41 (7.28\u0026ndash;7.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.24 (7.11\u0026ndash;7.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84 (0.8\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10108 (6.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.33 (6.21\u0026ndash;6.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.74 (19.97\u0026ndash;21.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.79 (6.27\u0026ndash;7.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.14 (14.42\u0026ndash;15.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.81\u0026ndash;1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug Consumption?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163054 (92.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.29 (92.16\u0026ndash;92.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14.06 (13.89\u0026ndash;14.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.53 (7.4\u0026ndash;7.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.86 (6.73\u0026ndash;6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83 (0.78\u0026ndash;0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13616 (7.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.71 (7.59\u0026ndash;7.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22.31 (21.61\u0026ndash;23.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.22 (4.81\u0026ndash;5.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.05 (17.39\u0026ndash;18.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.16 (0.95\u0026ndash;1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"7\"\u003e\n \u003cp\u003e*Estimates were derived using multiple imputations by chained equations, assuming missing at random.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNote\u003c/strong\u003e: The first two column has vertical estimates, while the rest of the columns have horizontal estimates\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eA.\u003c/strong\u003e Impact of modifiable factors on sex disparities for unfavorable outcome\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eB.\u003c/strong\u003e Impact of modifiable factors on age disparities for unfavorable outcome\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eA.\u003c/strong\u003e Impact of modifiable factors on sex disparities for deceased\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eB.\u003c/strong\u003e Impact of modifiable factors on age disparities for deceased\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eA.\u003c/strong\u003e Impact of modifiable factors on sex disparities for abandoned or lost in follow\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eB.\u003c/strong\u003e Impact of modifiable factors on age disparities for abandoned or lost in follow\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eA.\u003c/strong\u003e Impact of modifiable factors on sex disparities for failure in treatment\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eB.\u003c/strong\u003e Impact of modifiable factors on age disparities for failure in treatment\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eA.\u003c/strong\u003e Annual variation on impact of modifiable factors on sex disparities for unfavorable outcome\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u003cstrong\u003eB.\u003c/strong\u003e Annual variation on impact of modifiable factors on age disparities for unfavorable outcome\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eOnly a small proportion of Peruvians adults with tuberculosis don\u0026rsquo;t have a health insurance (7.76%), and showed DM (6.79%), HIV/AIDS (4.24%), alcoholism (7.97%), smoking (6.22%), or drug use (7.71%). The unfavorable outcome was more frequent in those with HIV/AIDS (37.53%), especially for death and dropouts or lost to follow-up \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. In contrast, this unfavorable outcome was less frequent among those who live in the capital.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eImpact of Modifiable Factors\u003c/h2\u003e\n \u003cp\u003eIn the assessment of association between demographic, social and health conditions with unfavorable outcomes, it was identified that males (aRR\u0026thinsp;=\u0026thinsp;1.29), and elderly (aRR\u0026thinsp;=\u0026thinsp;1.87) showed a considerable increased risk. While those Peruvians adults with tuberculosis with HIV/AIDS (aRR\u0026thinsp;=\u0026thinsp;2.84), alcoholism (aRR\u0026thinsp;=\u0026thinsp;1.50), and drug use (aRR\u0026thinsp;=\u0026thinsp;1.40) showed higher risk for unfavorable outcomes.\u003c/p\u003e\n \u003cp\u003eThe variables included in Poisson regression models don\u0026rsquo;t show multicollinearity \u003cstrong\u003e(Appendix 5)\u003c/strong\u003e. However, these models and imputated estimations allow assess the impact measures in HIV/AIDS (PAF%=40.85; 95%CI: 33.50 to 48.19), alcoholism (PAF%=10.84; 95%CI: 5.15 to 16.53), and drug use (PAF%=8.19; 95%CI: 3.03 to 13.36) for unfavorable outcomes in Peruvian populations with tuberculosis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eDisparities in Modifiable Factors\u003c/h2\u003e\n \u003cp\u003eThe estimations of PAF% showed that modifiable factors accounted for a substantial proportion of unfavorable outcomes among the Peruvian population with tuberculosis \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. The HIV/AIDS coinfection presented the highest impact in males (PAF%=38.24; 95%CI: 30.65 to 45.83), and females (PAF%=43.08; 95%CI: 28.00 to 58.17), followed by alcohol consumption (PAF%\u003csub\u003emales\u003c/sub\u003e=10.63, 95%CI: 4.88 to 16.39; PAF%\u003csub\u003efemales\u003c/sub\u003e=13.06%, 95%CI: 5.11 to 21.01). This was similar for death, in HIV/AIDS coinfection, residence outside of capital and alcohol consumption \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eTreatment failure was associated with comparatively lower PAF magnitudes, including DM and alcohol consumption, with higher PAF% values observed among females \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e. For dropout or loss to follow-up, HIV/AIDS coinfection and drug consumption showed positive PAF% values in both sexes, with markedly higher estimates among females, particularly for drug use \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e, whereas several structural and behavioral factors presented negative or near-null PAF% values across outcomes.\u003c/p\u003e\n \u003cp\u003eThe disparity by age showed heterogeneity impact of modifiable factors to unfavorable outcomes among the Peruvian population with tuberculosis. For unfavorable outcomes overall, HIV/AIDS coinfection presented the highest values in younger (PAF%\u003csub\u003e\u0026lt;60years\u003c/sub\u003e=41.47%; 95%CI: 34.12 to 48.83) and older adults (PAF%\u003csub\u003e60+years\u003c/sub\u003e=35.32; 95%CI: 26.12 to 44.52), with similar patterns observed for alcohol consumption in both age groups \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n \u003cp\u003eIn disparity by age for death, HIV/AIDS coinfection showed the highest PAF, whereas residence outside the capital and rural residence presented smaller positive values across age groups \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. Treatment failure showed low PAF magnitudes, including diabetes mellitus and alcohol consumption, with higher values among younger adults \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e. For dropout or loss to follow-up, positive values were observed for HIV/AIDS coinfection, alcohol and drug use in both age groups, with higher estimates for drug consumption among younger adults \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eAnnual Variation in Modifiable Factors\u003c/h2\u003e\n \u003cp\u003eIn the study period, annual PAF% estimates showed marked variation by sex for the main modifiable factors \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. The impact of HIV/AIDS coinfection was high and declined over time in the total population (PAF%\u003csub\u003e2016\u003c/sub\u003e=49.96% to PAF%\u003csub\u003e2023\u003c/sub\u003e=36.17%), with similar patterns in males (PAF%\u003csub\u003e2016\u003c/sub\u003e=47.72 to PAF%\u003csub\u003e023\u003c/sub\u003e=33.51) and females (PAF%\u003csub\u003e2016\u003c/sub\u003e=60.04 to PAF%\u003csub\u003e2023\u003c/sub\u003e=45.07). In addition, alcohol consumption showed positive values throughout the period in the total population, with comparable values in males and higher estimates for females after pandemic.\u003c/p\u003e\n \u003cp\u003eOther conditions like smoking showed values fluctuating around zero, shifting from negative values in (PAF%\u003csub\u003e2016\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;5.96 to PAF%\u003csub\u003e2020\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.97) to positive values (PAF%\u003csub\u003e2021\u003c/sub\u003e=1.04 to PAF%\u003csub\u003e2023\u003c/sub\u003e=4.42); this pattern was similar in younger and older adults \u003cstrong\u003e(\u003c/strong\u003eFig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e. The drug use showed a positive impact in all years (PAF%\u003csub\u003e2016\u003c/sub\u003e=3.93 to PAF%\u003csub\u003e2023\u003c/sub\u003e=10.16), with consistently lower values in males and substantially higher values in females, particularly after 2021.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMain Findings of the Study\u003c/h2\u003e \u003cp\u003eIn this study, we assessed the contribution of modifiable factors to sex- and age-related disparities in unfavorable outcomes among the Peruvian population with tuberculosis. HIV/AIDS coinfection consistently exhibited the largest impact across outcomes, particularly for death, with higher effects observed among females and younger adults compared with males and older adults. Drug use also emerged as a major contributor, showing pronounced sex disparities and substantially higher impacts among females, especially for dropout or loss to follow-up.\u003c/p\u003e \u003cp\u003eAlcohol consumption contributed positively to several unfavorable outcomes, with generally higher impacts among females and heterogeneous patterns across age groups. In contrast, smoking and diabetes mellitus showed smaller and less consistent effects, with variability in both direction and magnitude across sex and age strata. Overall, these findings indicate that disparities in unfavorable tuberculosis outcomes are largely driven by HIV/AIDS coinfection and substance use, with marked differences by sex and age that became more relevant after pandemic period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eComparison with previous studies\u003c/h2\u003e \u003cp\u003eOur findings are consistent with prior cohort studies identifying HIV/AIDS coinfection as one of the strongest determinants of unfavorable tuberculosis outcomes, reflecting the heightened vulnerability of immunocompromised patients \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. HIV/AIDS also contributed substantially to sex- and age-related disparities, in line with evidence on TB\u0026ndash;HIV syndemics that amplify adverse outcomes across demographic groups \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Alcohol consumption showed patterns concordant with previous studies linking substance use to non-adherence, loss to follow-up, and poor tuberculosis outcomes, with marked disparities by sex and age in LMIC settings such as Peru \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDrug use similarly mirrored established associations with worse tuberculosis outcomes, with greater disparities observed among younger adults and women, likely related to higher risk of treatment interruption. In contrast, diabetes mellitus was more strongly associated with treatment failure, consistent with evidence on the adverse impact of metabolic comorbidity on tuberculosis prognosis \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The COVID-19 pandemic further modified these associations, exposing limitations in health-system resilience and continuity of care, particularly among populations already vulnerable to severe SARS-CoV-2 infection in South America countries like Peru \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003ePlausibility of the findings\u003c/h2\u003e \u003cp\u003eThe disparities by sex and age groups observed in the Peruvian population with tuberculosis for unfavorable outcomes can be explained through a combination of biological, behavioral, and healthcare system factors. Biologically, men are more likely to experience worse tuberculosis outcomes due to higher prevalence of risk behaviors to infected by HIV, smoking, and alcoholism, which impair immune function and reduce adherence to treatment, failure and risk of death \u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe age disparities are also biologically plausible, especially for those with younger adults with HIV/AIDS that show particularly higher rates of death on tuberculosis population, because the coinfection severely weakens the immune system and accelerates the progression from latent to active tuberculosis, which is prevalent in Peru \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Also, antiretroviral treatment cannot be initiated in people with tuberculosis due to the risk of immune reconstitution syndrome and drug interactions \u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe higher proportion of deaths in Peruvian population with tuberculosis and diabetes comorbidity, could be explained because the tuberculosis can worsen hyperglycemia \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, and the antituberculosis treatment with rifampicin is known to interact with oral hypoglycemic agents \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However is reasonable that diabetes increase treatment failure rates, even when some evidence suggests that metformin may have a protective effect against tuberculosis in diabetic patients \u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003ePublic health implications\u003c/h2\u003e \u003cp\u003eThe findings of this study highlight the sex- and age-related disparities in tuberculosis unfavorable outcome, driven by modifiable factors such as comorbidities, alcoholism, smoking and drug use. These results underscore the urgent need to strengthen health system equity by improving access and quality to comprehensive tuberculosis care, especially for vulnerable populations such as men, older adults, and individuals living outside the capital \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, the disproportionate impact of alcohol use and the exacerbation of disparities during the COVID-19 pandemic reveal weaknesses in the health system\u0026rsquo;s capacity to sustain essential tuberculosis services during public health emergencies \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. The interplay between behavioral and structural factors requires targeted interventions, including mitigation strategies tailored to comorbid populations and those with harmful habits \u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eStrengths, Limitations and Recommendation\u003c/h2\u003e \u003cp\u003eThe study has strengths because it was developed using nationwide data on the Peruvian population with tuberculosis over a long period of time. However, it also has some limitations that should be acknowledged for a judicious interpretation of our findings. First, because it is a secondary database developed in health centers, there is less representation of some groups and errors in data quality, but estimations are consistent in evaluated outcomes. Second, some socioeconomic variables (education, income, occupation), health variables (adherence to treatment, type of tuberculosis, and immune system status) were not recorded, and necessary for future investigations. Third, estimations among insured tuberculosis population may be overestimated because the database is recorded from health centers and could determine some selection bias. Future research should address the impact of public policies on tuberculosis control in LMICs, based on longitudinal registries with clinical, social, and environmental information.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, modifiable factors like HIV/AIDS coinfection, alcohol consumption, and drug use drive substantial sex- and age-related disparities in unfavorable tuberculosis outcomes in Peru. These disparities disproportionately affect women and younger adults and were exacerbated during the COVID-19 pandemic. Addressing these inequities requires integrated, equity-focused TB care strategies that prioritize comorbidity management, substance use interventions, and health system resilience to reduce preventable adverse outcomes in high-burden settings.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; TB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; WHO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; LMICs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLow\u0026mdash;and middle\u0026mdash;income countries\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; COVID\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e19\u0026mdash;Coronavirus disease 2019\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SIGTB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTuberculosis Management Information System (Peru)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; RStudio\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRStudio statistical software (environment for R)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; aRR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdjusted Risk Ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; PAF%\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePopulation Attributable Fraction (percentage)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; 95% CI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; HIV/AIDS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus / Acquired Immunodeficiency Syndrome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; DM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiabetes Mellitus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u0026bull; SARS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCoV\u0026mdash;2\u0026mdash;Severe Acute Respiratory Syndrome Coronavirus 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of interest:\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest in the development of this research.\u003c/p\u003e\n\u003ch2\u003eEthics approval:\u003c/h2\u003e\n\u003cp\u003eThe study was conducted using secondary public data, anonymized and without compromising the integrity of the participants, who gave their consent for their data to be recorded in Peru\u0026apos;s national tuberculosis system.\u003c/p\u003e\n\u003ch2\u003eFunding:\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eCIE conceptualization, methodology, formal analysis, writing - review \u0026amp;amp; editing, visualization, and supervision. RJ formal analysis, writing - original draft, visualization, and writing - review and editing. MAH methodology, formal analysis, writing - original draft, visualization, and writing - review and editing.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data is available to healthcare personnel registered in Tuberculosis Information System (https:/appsalud.minsa.gob.pe/sigtbdata/wflogin.aspx).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO. Global tuberculosis report 2024. Published online 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/bitstream/handle/10665/379339/9789240101531-eng.pdf?sequence=1\u003c/span\u003e\u003cspan address=\"https://iris.who.int/bitstream/handle/10665/379339/9789240101531-eng.pdf?sequence=1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWHO, Tuberculosis. 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/es/news-room/fact-sheets/detail/tuberculosis\u003c/span\u003e\u003cspan address=\"https://www.who.int/es/news-room/fact-sheets/detail/tuberculosis\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartinez L, Warren JL, Harries AD, et al. Global, regional, and national estimates of tuberculosis incidence and case detection among incarcerated individuals from 2000 to 2019: a systematic analysis. Lancet Public Health. 2023;8(7):e511\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S2468-2667(23)00097-X\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(23)00097-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Center for Epidemiology, Disease Prevention and Control. Epidemiological situation of tuberculosis in Peru, 2018\u0026ndash;2022. Published online 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dge.gob.pe/epipublic/uploads/boletin/boletin_202320_28_163316.pdf\u003c/span\u003e\u003cspan address=\"https://www.dge.gob.pe/epipublic/uploads/boletin/boletin_202320_28_163316.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang H, Ruan X, Li W, Xiong J, Zheng Y. Global, regional, and national burden of tuberculosis and attributable risk factors for 204 countries and territories, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Diseases 2021 study. BMC Public Health. 2024;24(1):3111. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-024-20664-w\u003c/span\u003e\u003cspan address=\"10.1186/s12889-024-20664-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReid M, Roberts G, Goosby E, Wesson P. Monitoring Universal Health Coverage (UHC) in high Tuberculosis burden countries: Tuberculosis mortality an important tracer of UHC service coverage. Silva JP. ed PLOS ONE. 2019;14(10):e0223559. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0223559\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0223559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUgarte-Gil C, Curisinche M, Herrrera-Flores E, Hernandez H, Rios J. Prevalence of tuberculosis and diabetes comorbidity in adults in Peru, 2016\u0026ndash;2018. Rev Peru Med Exp Salud Publica. 2021;38(2):254\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.17843/rpmesp.2021.382.6764\u003c/span\u003e\u003cspan address=\"10.17843/rpmesp.2021.382.6764\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaviglione M, Poznyak V. Targeting harmful use of alcohol for prevention and treatment of tuberculosis: a call for action. Eur Respir J. 2017;50(1):1700946. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1183/13993003.00946-2017\u003c/span\u003e\u003cspan address=\"10.1183/13993003.00946-2017\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdekoya N, Chang MH, Wortham J, Truman BI. Disparities in Rates of Death From HIV or Tuberculosis Before Age 65 Years, by Race, Ethnicity, and Sex, United States, 2011\u0026ndash;2020. Public Health Reports\u0026reg;. 2024;139(5):557\u0026ndash;65. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/00333549231213328\u003c/span\u003e\u003cspan address=\"10.1177/00333549231213328\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlves LS, Berra TZ, Alves YM, et al. Geographic inequalities and factors associated with unfavorable outcomes in diabetes-tuberculosis and diabetes-covid comorbidities in Brazil. Sci Rep. 2025;15(1):8353. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-025-93476-6\u003c/span\u003e\u003cspan address=\"10.1038/s41598-025-93476-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoyano LM, Brunette MJ, D\u0026iacute;az-V\u0026eacute;lez C. Afrontando los retos del fragmentado sistema de salud peruano: \u0026iquest;Puede la academia realmente convertirse en un agente de cambio? Rev Med Hered. 2024;35(1):3\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20453/rmh.v35i1.5304\u003c/span\u003e\u003cspan address=\"10.20453/rmh.v35i1.5304\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontalvo-Otivo R, Ram\u0026iacute;rez-Bre\u0026ntilde;a M, Bruno-Huam\u0026aacute;n A, Dami\u0026aacute;n-Mucha M, Vilchez-Bravo S, Quisurco-C\u0026aacute;rdenas M. Geographic distribution and risk factors for multidrug-resistant tuberculosis in central Peru. Rev Fac Med. 2020;68(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.15446/revfacmed.v68n2.71715\u003c/span\u003e\u003cspan address=\"10.15446/revfacmed.v68n2.71715\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMINSA. Technical Health Standard for Comprehensive Care of Persons Affected by Tuberculosis. Published online 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bvs.minsa.gob.pe/local/MINSA/6344.pdf\u003c/span\u003e\u003cspan address=\"https://bvs.minsa.gob.pe/local/MINSA/6344.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontesinos EV. Health equity in Peru in light of its social determinants (DSS): past, present and future. 2023;40(4):291\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.35663/amp.2023.404.2782\u003c/span\u003e\u003cspan address=\"10.35663/amp.2023.404.2782\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Institute of Statistics and Informatics. Peru: Population Estimates and Projections by Department, Sex and Five-Year Age Groups 1995\u0026ndash;2025. Published online 2009. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proyectos.inei.gob.pe/web/biblioineipub/bancopub/Est/Lib0846/libro.pdf\u003c/span\u003e\u003cspan address=\"https://proyectos.inei.gob.pe/web/biblioineipub/bancopub/Est/Lib0846/libro.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNewson RB. Attributable and Unattributable Risks and Fractions and other Scenario Comparisons. Stata J Promot Commun Stat Stata. 2013;13(4):672\u0026ndash;98. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/1536867X1301300402\u003c/span\u003e\u003cspan address=\"10.1177/1536867X1301300402\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVel\u0026aacute;squez GE, Cegielski JP, Murray MB, et al. Impact of HIV on mortality among patients treated for tuberculosis in Lima, Peru: a prospective cohort study. BMC Infect Dis. 2015;16(1):45. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12879-016-1375-8\u003c/span\u003e\u003cspan address=\"10.1186/s12879-016-1375-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLelisho ME, Wotale TW, Tareke SA, et al. Survival rate and predictors of mortality among TB/HIV co-infected adult patients: retrospective cohort study. Sci Rep. 2022;12(1):18360. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-022-23316-4\u003c/span\u003e\u003cspan address=\"10.1038/s41598-022-23316-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrzcińska H. Alcoholism and other socio-demographic risk factors for adverse TB-drug reactions and unsuccessful tuberculosis treatment \u0026ndash; data from ten years\u0026rsquo; observation at the Regional Centre of Pulmonology, Bydgoszcz, Poland. Med Sci Monit. 2014;20:444\u0026ndash;53. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12659/MSM.890012\u003c/span\u003e\u003cspan address=\"10.12659/MSM.890012\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWigger GW, Bouton TC, Jacobson KR, Auld SC, Yeligar SM, Staitieh BS. The Impact of Alcohol Use Disorder on Tuberculosis: A Review of the Epidemiology and Potential Immunologic Mechanisms. Front Immunol. 2022;13:864817. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.864817\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.864817\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGautam S, Shrestha N, Mahato S, Nguyen TPA, Mishra SR, Berg-Beckhoff G. Diabetes among tuberculosis patients and its impact on tuberculosis treatment in South Asia: a systematic review and meta-analysis. Sci Rep. 2021;11(1):2113. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41598-021-81057-2\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-81057-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojas-Bolivar D, Intimayta‐Escalante C, Cardenas‐Jara A, Jandarov R, Huaman MA. COVID‐19 case fatality rate and tuberculosis in a metropolitan setting. J Med Virol. 2021;93(5):3273\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/jmv.26868\u003c/span\u003e\u003cspan address=\"10.1002/jmv.26868\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Cao Y, Liu X et al. Systematic review and meta-analysis of Tuberculosis and COVID-19 Co-infection: Prevalence, fatality, and treatment considerations. Ediriweera D, ed. \u003cem\u003ePLoS Negl Trop Dis\u003c/em\u003e. 2024;18(5):e0012136. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pntd.0012136\u003c/span\u003e\u003cspan address=\"10.1371/journal.pntd.0012136\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePape S, Karki SJ, Heinsohn T, Brandes I, Dierks ML, Lange B. Tuberculosis case fatality is higher in male than female patients in Europe: a systematic review and meta-analysis. Infection. 2024;52(5):1775\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s15010-024-02206-z\u003c/span\u003e\u003cspan address=\"10.1007/s15010-024-02206-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSullivan ZA, Wong EB, Ndung\u0026rsquo;u T, Kasprowicz VO, Bishai WR. Latent and Active Tuberculosis Infection Increase Immune Activation in Individuals Co-Infected with HIV. EBioMedicine. 2015;2(4):334\u0026ndash;40. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ebiom.2015.03.005\u003c/span\u003e\u003cspan address=\"10.1016/j.ebiom.2015.03.005\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNavasardyan I, Miwalian R, Petrosyan A, Yeganyan S, Venketaraman V. HIV\u0026ndash;TB Coinfection: Current Therapeutic Approaches and Drug Interactions. Viruses. 2024;16(3):321. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/v16030321\u003c/span\u003e\u003cspan address=\"10.3390/v16030321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLanzafame M, Vento S. Tuberculosis-immune reconstitution inflammatory syndrome. J Clin Tuberc Mycobact Dis. 2016;3:6\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jctube.2016.03.002\u003c/span\u003e\u003cspan address=\"10.1016/j.jctube.2016.03.002\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBisht MK, Dahiya P, Ghosh S, Mukhopadhyay S. The cause\u0026ndash;effect relation of tuberculosis on incidence of diabetes mellitus. Front Cell Infect Microbiol. 2023;13:1134036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2023.1134036\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2023.1134036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNiemi M, Kivist\u0026ouml; KT, Backman JT, Neuvonen PJ. Effect of rifampicin on the pharmacokinetics and pharmacodynamics of glimepiride. Br J Clin Pharmacol. 2000;50(6):591\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1046/j.1365-2125.2000.00295.x\u003c/span\u003e\u003cspan address=\"10.1046/j.1365-2125.2000.00295.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarupuru S, Senapati P, Pathadka S, Miraj SS, Unnikrishnan MK, Manu MK. Protective effect of metformin against tuberculosis infections in diabetic patients: an observational study of south Indian tertiary healthcare facility. Braz J Infect Dis. 2017;21(3):312\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bjid.2017.01.001\u003c/span\u003e\u003cspan address=\"10.1016/j.bjid.2017.01.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang CC, Trevisi L, Becerra MC, et al. Spatial scale of tuberculosis transmission in Lima, Peru. Proc Natl Acad Sci. 2022;119(45):e2207022119. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1073/pnas.2207022119\u003c/span\u003e\u003cspan address=\"10.1073/pnas.2207022119\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendoza-Hisey EC, Dier A, Marquez NV, Bumanglag LV, Cadiao SBA, Guirgis SF. Gender-related factors affecting access to TB services and treatment outcomes in the Philippines. Public Health Action. 2023;13(3):107\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5588/pha.23.0021\u003c/span\u003e\u003cspan address=\"10.5588/pha.23.0021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardenas-Escalante J, Fernandez-Saucedo J, Cubas WS. Impact of the COVID-19 pandemic on tuberculosis in Peru: are we forgetting anyone? Infect Dis Clin Microbiol. 2022;40(1):46\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.eimc.2021.07.014\u003c/span\u003e\u003cspan address=\"10.1016/j.eimc.2021.07.014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChikovore J, Pai M, Horton KC, et al. Missing men with tuberculosis: the need to address structural influences and implement targeted and multidimensional interventions. BMJ Glob Health. 2020;5(5):e002255. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjgh-2019-002255\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2019-002255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsei-Wusu S, Asare P, Danso EK, et al. Addressing key risk factors hindering tuberculosis control activities in West Africa - progress in meeting the UN sustainable development goals. IJID Reg. 2025;14:100594. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.ijregi.2025.100594\u003c/span\u003e\u003cspan address=\"10.1016/j.ijregi.2025.100594\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Health Impact Assessment, Tuberculosis, Healthcare Disparities, Health Status Disparities, Socioeconomic Disparities in Health, Peru","lastPublishedDoi":"10.21203/rs.3.rs-9124924/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9124924/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTuberculosis remains a major public health problem in Peru, with persistent unfavorable outcomes driven by comorbidities, harmful behaviors, and structural barriers to care. These factors are unevenly distributed across populations, contributing to disparities by sex and age. The aim of this study was to evaluate the impact of modifiable factors on sex- and age-related disparities in unfavorable outcomes among the Peruvian population with tuberculosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRetrospective cohort study with national data from the Peruvian Tuberculosis Management Information System (SIGTB) between 2016 and 2023. Adults with tuberculosis, newly registered and complete clinical evaluations were included. Unfavorable outcomes comprised death, dropout or loss to follow-up, and treatment failure. Disparities were assessed by sex and age group (\u0026lt;\u0026thinsp;60 vs. \u0026ge;60 years). Modifiable factors included region and area of residence, health insurance, HIV/AIDS coinfection, diabetes mellitus, alcohol consumption, smoking, and drug use. The impacts were quantified using Population Attributable Fractions (PAF%).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong 177,185 adults with tuberculosis, 14.69% experienced an unfavorable outcome, more frequently among males (16.66%) and older adults (21.35%). HIV/AIDS coinfection showed the largest impact overall (PAF%=40.85), followed by alcohol consumption (PAF%=10.84) and drug use (PAF%=8.19). HIV/AIDS consistently exhibited the highest PAF% across sex and age groups, particularly among females and younger adults. Drug use showed marked sex disparities, with higher impacts among females, especially for dropout or loss to follow-up. Annual analyses indicated persistently high impacts of HIV/AIDS and increasing disparities related to substance use after the pandemic.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eModifiable factors like HIV/AIDS coinfection, alcoholism, and drug use account for a substantial proportion of unfavorable tuberculosis outcomes in Peru, with pronounced disparities by sex and age. These findings highlight the need for equity-focused TB strategies integrating comorbidity management, substance-use interventions, and resilient health systems.\u003c/p\u003e","manuscriptTitle":"Impact of modifiable factors on sex and age disparities for unfavorable outcomes in Peruvian population with tuberculosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-18 20:54:27","doi":"10.21203/rs.3.rs-9124924/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"212512471231191256633710529596616916700","date":"2026-04-11T15:49:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116320447704801640884571374123067280834","date":"2026-03-16T11:15:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-16T06:10:54+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-16T04:46:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-16T04:45:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Infectious Diseases of Poverty","date":"2026-03-14T20:21:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"infectious-diseases-of-poverty","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"idop","sideBox":"Learn more about [Infectious Diseases of Poverty](http://idpjournal.biomedcentral.com)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/idop/default.aspx","title":"Infectious Diseases of Poverty","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c1740ed-826e-4647-b14c-31d149359e47","owner":[],"postedDate":"March 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-19T19:07:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-18 20:54:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9124924","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9124924","identity":"rs-9124924","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}