Edge effects in spatial infectious disease models.

Epidemic models serve as a useful analytical tool to study how a disease behaves in a given population. Individual-level models (ILMs) can incorporate individual-level covariate information including spatial information, accounting for heterogeneity within the population. However, the high-level data required to parameterize an ILM may often be available only for a sub-population of a larger population (e.g., a given county, province, or country). As a result, parameter estimates may be affected by edge effects caused by infection originating from outside the observed population. Here, we look at how such edge effects can bias parameter estimates for within the context of spatial ILMs, and suggest a method to improve model fitting in the presence of edge effects when some global measure of epidemic severity is available from the unobserved part of the population. We apply our models to simulated data, as well as data from the UK 2001 foot-and-mouth disease epidemic.

Exploring the determinants of under-five mortality and morbidity from infectious diseases in Cambodia-a traditional and machine learning approach.

Cambodia has made progress in reducing the under-five mortality rate and burden of infectious diseases among children over the last decades. However the determinants of child mortality and morbidity in Cambodia is not well understood, and no recent analysis has been conducted to investigate possible determinants. We applied a multivariable logistical regression model and a conditional random forest to explore possible determinants of under-five mortality and under-five child morbidity from infectious diseases using the most recent Demographic Health Survey in 2021-2022. Our findings show that the majority (58%) of under-five deaths occurred during the neonatal period. Contraceptive use of the mother led to lower odds of under-five mortality (0.51 [95% CI 0.32-0.80], p-value 0.003), while being born fourth or later was associated with increased odds (3.25 [95% CI 1.09-9.66], p-value 0.034). Improved household water source and higher household wealth quintile was associated with lower odds of infectious disease while living in the Great Lake or Coastal region led to increased odds respectively. The odds ratios were consistent with the results from the conditional random forest. The study showcases how closely related child mortality and morbidity due to infectious disease are to broader social development in Cambodia and the importance of accelerating progress in many sectors to end preventable child mortality and morbidity.

Healthcare-seeking behavior and out-of-pocket payments in Erbil, Kurdistan Region of Iraq.

BACKGROUND: Understanding healthcare-seeking behavior and examining health expenditures can help determine possible barriers to accessing healthcare and direct more effective and inclusive healthcare systems. This study aimed to evaluate healthcare-seeking behavior and out-of-pocket healthcare expenditure in a sample of the population in Erbil, Iraq. METHODS: We conducted this cross-sectional study in Erbil, Kurdistan Region of Iraq, from October to December 2023. A convenience sample of 414 adults completed a self-administered online survey. The following data were collected: recent illness, sociodemographic characteristics, type of healthcare received, and cost of healthcare. RESULTS: The most common health conditions reported were communicable diseases (16.3%), musculoskeletal problems (13.1%), and noncommunicable diseases (12.7%). Approximately 85% of patients with health conditions requiring care sought healthcare; most visited private clinics (46.3%) and private hospitals (18.6%). The median total out-of-pocket healthcare expenditure in US dollars was 117.3 (interquartile range (IQR) = 45.6-410.0). The median total cost was much greater for participants who first visited a private health facility (USD 135.5, IQR = 57.3-405.6) than those who first visited a public facility (USD 76.8, IQR = 16.1-459.7). Participants ≥ 60 years spent significantly more than those < 14 years (USD 332, 95% CI = 211-453, p < 0.001). Evermarried participants spent significantly more than unmarried (USD 97, 95% CI = 1 to 192, p = 0.047). Health expenditures were significantly greater for noncommunicable diseases than infectious diseases (USD 232, 95% CI = 96-368, p = 0.001). After adjusting for covariates, age ≥ 60 years was independently associated with higher spending (USD 305, 95% CI = 153-457, p < 0.001). CONCLUSIONS: Most participants sought care from formal health services, preferring the private sector. Seeking care from private facilities incurred significantly higher costs than seeking care from public ones, which suggests potential barriers to accessing healthcare, particularly affordability. The findings underscore the importance of evaluating existing healthcare policies to enhance effectiveness and identify areas for improvement. This study can help policymakers and healthcare providers design effective interventions, allocate resources efficiently, and improve healthcare delivery.

The impact of population influx on infectious diseases - from the mediating effect of polluted air transmission.

The global population influx during the COVID-19 pandemic poses significant challenges to public health, making the prevention and control of infectious diseases a pressing concern. This paper aims to examine the impact of population influx on the spread of infectious diseases, with a specific emphasis on the mediating role of air pollution in this process. A theoretical analysis is conducted to explore the relationship between population influx, air pollution, and infectious diseases. Additionally, we establish a series of econometric models and employ various empirical tests and analytical techniques, including mediation effect test, threshold effect test, and systematic GMM test, to evaluate our hypotheses. The results indicate that: (1) Population influx directly and indirectly impacts infectious diseases. Specifically, population influx not only directly elevates the risk of infectious diseases, but also indirectly increases the incidence rate of infectious diseases by intensifying air pollution. (2) The impact of population inflow on infectious diseases exhibits regional heterogeneity. Compared to central and western China, the eastern regions exhibit a significantly higher risk of infectious diseases, exceeding the national average. (3) External factors influence the relationship between population influx and infectious diseases differently. Personal income and medical resources both help mitigate the risk of infectious diseases due to population influx, with medical resources having a more substantial effect. Contrary to expectations, abundant educational resources have not reduced the risk, instead, they have exacerbated the risk associated with population influx. This paper provides a scientific basis for formulating effective strategies for the prevention and control of infectious diseases.

Effect of individual activity level heterogeneity on disease spreading in higher-order networks.

The active state of individuals has a significant impact on disease spread dynamics. In addition, pairwise interactions and higher-order interactions coexist in complex systems, and the pairwise networks proved insufficient for capturing the essence of complex systems. Here, we propose a higher-order network model to study the effect of individual activity level heterogeneity on disease-spreading dynamics. Activity level heterogeneity radically alters the dynamics of disease spread in higher-order networks. First, the evolution equations for infected individuals are derived using the mean field method. Second, numerical simulations of artificial networks reveal that higher-order interactions give rise to a discontinuous phase transition zone where the coexistence of health and disease occurs. Furthermore, the system becomes more unstable as individual activity levels rise, leading to a higher likelihood of disease outbreaks. Finally, we simulate the proposed model on two real higher-order networks, and the results are consistent with the artificial networks and validate the inferences from theoretical analysis. Our results explain the underlying reasons why groups with higher activity levels are more likely to initiate social changes. Simultaneously, the reduction in group activity, characterized by measures such as "isolation," emerges as a potent strategy for disease control.

rtestim: Time-varying reproduction number estimation with trend filtering.

To understand the transmissibility and spread of infectious diseases, epidemiologists turn to estimates of the instantaneous reproduction number. While many estimation approaches exist, their utility may be limited. Challenges of surveillance data collection, model assumptions that are unverifiable with data alone, and computationally inefficient frameworks are critical limitations for many existing approaches. We propose a discrete spline-based approach that solves a convex optimization problem-Poisson trend filtering-using the proximal Newton method. It produces a locally adaptive estimator for instantaneous reproduction number estimation with heterogeneous smoothness. Our methodology remains accurate even under some process misspecifications and is computationally efficient, even for large-scale data. The implementation is easily accessible in a lightweight R package rtestim.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases.

BACKGROUND: Describing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection. METHODS: This study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China. RESULTS: When the spatio-temporal variation was small (time delay coefficient: 0.1-0.2, spatial sparsity:0.1-0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2-0.3, spatial sparsity: 0.6-0.9). CONCLUSION: This study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators-the time delay coefficient and spatial sparsity-into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Differences between males and females in infectious diseases notifications in the EU/EEA, 2012 to 2021.

BackgroundThere are differences between males and females for most diseases both for exposure and course of illness, including outcome. These differences can be related to biological sex or gender i.e. socio-cultural factors that may impact exposure and healthcare access.AimWe aimed to quantify differences between males and females in infectious disease notifications in Europe and identify countries with these differences significantly different from the European Union and European Economic Area (EU/EEA) average.MethodsNotifiable infectious disease surveillance data are reported by EU/EEA countries to ECDC. We retrieved surveillance data for 2012-2021. Using a cut-off median of annual disability-adjusted life years above 1 per 100,000 population, we included 16 infectious diseases. We calculated median male proportion and interquartile range by disease, year, country and age group and used boxplots to identify outliers.ResultsFor campylobacteriosis, acute hepatitis B, Legionnaires' disease, malaria and HIV and AIDS, all countries had male proportion above 50%. Most countries had a male proportion below 50% for pertussis (25/28 countries), STEC infection (21/28 countries) and Chlamydia trachomatis infection (16/24 countries). Chlamydia trachomatis infection and listeriosis showed the greatest dispersion of male proportion across age groups. Most outliers were countries reporting few cases.ConclusionWe observed important differences in male proportion across infectious disease notifications in EU/EEA countries. For some diseases with high male proportions in all countries, such as HIV and hepatitis B, behaviours play a role in disease transmission. Screening offered to specific populations may explain differences across countries for example for C. trachomatis infection.

Lessons from ancient pathogens.

Ancient infectious diseases and microbes can be used to address contemporary disease.

Distribution of Infectious Disease Outbreaks in India before and after the COVID-19 Pandemic: Analysis of National Weekly Surveillance Data.

ABSTRACT: National surveillance data were collected to study the outbreak trends of infectious diseases/syndromes before and during the COVID-19 pandemic period, spanning from 2018 to 2022. The study found that out of 4208 outbreaks, 2972 occurred before the COVID-19 period, affecting 147,425 people, while 1236 outbreaks affected 52,324 people during the pandemic. The number of outbreaks for diseases such as acute flaccid paralysis, fever with rashes, leptospirosis, rabies, and scrub typhus increased during the pandemic. The geographic distribution of outbreaks remained similar for some reemerging diseases in both periods. The trends for dengue, Japanese encephalitis, and cholera remained consistent with peaks mostly from July to September in both periods. We observed a considerable reduction in morbidity and mortality due to outbreaks in India during the pandemic. Despite similar distributional patterns, the study indicates a strong suspicion of persistent outbreak-initiating factors, necessitating an efficient and vigilant surveillance system in the country.

Impact of twelve immunization-preventable infectious diseases on population health using disability-adjusted life years (DALYs) in Spain.

BACKGROUND: The objective of this study is to estimate the burden of selected immunization-preventable infectious diseases in Spain using the Burden of Communicable Diseases in Europe (BCoDE) methodology, as well as focusing on the national immunization programme and potential new inclusions. METHODS: The BCoDE methodology relies on an incidence and pathogen-based approach to calculate disease burden via disability-adjusted life year (DALY) estimates. It considers short and long-term sequelae associated to an infection via outcome trees. The BCoDE toolkit was used to populate those trees with Spanish-specific incidence estimates, and de novo outcome trees were developed for four infections (herpes zoster, rotavirus, respiratory syncytial virus [RSV], and varicella) not covered by the toolkit. Age/sex specific incidences were estimated based on data from the Spanish Network of Epidemiological Surveillance; hospitalisation and mortality rates were collected from the Minimum Basic Data Set. A literature review was performed to design the de novo models and obtain the rest of the parameters. The methodology, assumptions, data inputs and results were validated by a group of experts in epidemiology and disease modelling, immunization and public health policy. RESULTS: The total burden of disease amounted to 163.54 annual DALYs/100,000 population. Among the selected twelve diseases, respiratory infections represented around 90% of the total burden. Influenza exhibited the highest burden, with 110.00 DALYs/100,000 population, followed by invasive pneumococcal disease and RSV, with 25.20 and 10.57 DALYs/100,000 population, respectively. Herpes zoster, invasive meningococcal disease, invasive Haemophilus influenza infection and hepatitis B virus infection ranked lower with fewer than 10 DALYs/100,000 population each, while the rest of the infections had a limited burden (< 1 DALY/100,000 population). A higher burden of disease was observed in the elderly (≥ 60 years) and children < 5 years, with influenza being the main cause. In infants < 1 year, RSV represented the greatest burden. CONCLUSIONS: Aligned with the BCoDE study, the results of this analysis show a persisting high burden of immunization-preventable respiratory infections in Spain and, for the first time, highlight a high number of DALYs due to RSV. These estimates provide a basis to guide prevention strategies and make public health decisions to prioritise interventions and allocate healthcare resources in Spain.

Study on health education methods based on rural residents' infectious disease-specific health literacy in Shandong, China.

Adequate infectious disease-specific health literacy (IDSHL) is of benefit to residents in dealing with infectious diseases. This study aimed to investigate the methods by which residents acquire knowledge about infectious disease prevention and control (IDPC knowledge) so as to find effective health education methods used to improve residents' IDSHL level. In 2022, a cross-sectional study was conducted in Shandong Province, China. Participants were recruited from rural areas by multistage sampling. The IDPC knowledge cognitive questionnaire, as a reliable and valid tool, was applied to data collection and to investigate the participants' IDPC knowledge. Chi-square analysis was utilized to analyze the differences in possession level of IDSHL between different subgroups. The relationship between demographic factors and methods to acquire IDPC knowledge was also examined by chi-square analysis. The possession rate of adequate IDSHL among the total 2283 participants was 31.80%. There was a significant association between IDSHL level and socio-demographic factors, including age (P < .001), sex (P = .02), education (P < .001), occupation (P < .001), annual family income (P < .001), whether to use smartphones (P < .001), whether to browse WeChat on smartphones (P < .001), and whether to browse apps on smartphones except WeChat (P < .001). Univariate analysis showed that whether to adopt specific methods, including television (P = .02), WeChat on smartphones (P < .001), propaganda of infectious disease prevention and control (P < .001), and doctor's advice (P < .001) to acquire IDPC knowledge had significant associations with IDSHL level. Age (P < .001), education (P < .05), occupation (P < .05), and annual family income (P < .01) were associated with methods to acquire IDPC knowledge. The rural residents' adequate IDSHL in Shandong Province, China, was not optimistic. The combination of traditional methods and Internet publicity platforms should take greater responsibility for IDSHL health education among rural populations.

The Burden and Risk Factors Associated with Infectious Diseases among Refugees in a Camp for Migrants in Porto Alegre: A Cross-Sectional Survey.

Refugees usually face a disproportionate burden of infectious diseases. Recently, Brazil has experienced an influx of refugees which demands the need for scaling up public health efforts to address the challenges. The research sought to study the burden and risk factors associated with infectious diseases among refugees received in the city of Porto Alegre. This was a cross-sectional study of 261 newly arrived refugees. The study sample was predominantly composed of Venezuelans (50.6%) and Haitians (44%), male (146: 56.7%), single (30.7%), with an average age of 33.38 (± 7.30) years. The average schooling was 10.42 (± 2.09) years. Diseases with the highest prevalence were influenza, whooping cough, diphtheria, and tuberculosis. There was significant association between the country of origin and presence of symptoms for infectious and contagious diseases, which warrants targeted interventions for reducing the incidence of these diseases among refugees in Brazil.

How to correctly fit an SIR model to data from an SEIR model?

In epidemiology, realistic disease dynamics often require Susceptible-Exposed-Infected-Recovered (SEIR)-like models because they account for incubation periods before individuals become infectious. However, for the sake of analytical tractability, simpler Susceptible-Infected-Recovered (SIR) models are commonly used, despite their lack of biological realism. Bridging these models is crucial for accurately estimating parameters and fitting models to observed data, particularly in population-level studies of infectious diseases. This paper investigates stochastic versions of the SEIR and SIR frameworks and demonstrates that the SEIR model can be effectively approximated by a SIR model with time-dependent infection and recovery rates. The validity of this approximation is supported by the derivation of a large-population Functional Law of Large Numbers (FLLN) limit and a finite-population concentration inequality. To apply this approximation in practice, the paper introduces a parameter inference methodology based on the Dynamic Survival Analysis (DSA) survival analysis framework. This method enables the fitting of the SIR model to data simulated from the more complex SEIR dynamics, as illustrated through simulated experiments.

Possible counter-intuitive impact of local vaccine mandates for vaccine-preventable infectious diseases.

We modeled the impact of local vaccine mandates on the spread of vaccine-preventable infectious diseases, which in the absence of vaccines will mainly affect children. Examples of such diseases are measles, rubella, mumps, and pertussis. To model the spread of the pathogen, we used a stochastic SIR (susceptible, infectious, recovered) model with two levels of mixing in a closed population, often referred to as the household model. In this model, individuals make local contacts within a specific small subgroup of the population (e.g., within a household or a school class), while they also make global contacts with random people in the population at a much lower rate than the rate of local contacts. We considered what would happen if schools were given freedom to impose vaccine mandates on all of their pupils, except for the pupils that were exempt from vaccination because of medical reasons. We investigated first how such a mandate affected the probability of an outbreak of a disease. Furthermore, we focused on the probability that a pupil that was medically exempt from vaccination, would get infected during an outbreak. We showed that if the population vaccine coverage was close to the herd-immunity level, then both probabilities may increase if local vaccine mandates were implemented. This was caused by unvaccinated pupils possibly being moved to schools without mandates.

Measuring preparedness to infectious diseases among people exposed to climate disasters in Cabo Delgado, Mozambique: a cross-sectional study.

INTRODUCTION: Climate change is contributing to increase the frequency and severity of climate disasters in Mozambique, leading, since 2019, to extensive damage to infrastructure and displacement 1.3 million people. Aim of this study is to evaluate baseline preparedness to vector-borne and water-borne infections among households and internally displaced people exposed to climate disasters in Mozambique. METHODS: This was a cross-sectional, community-based survey assessing the preparedness to infectious diseases outbreaks among people exposed to climate disasters in six districts in Mozambique. Structured form was delivered via face-to-face between October 15th and November 7th, 2022. Study outcome was defined as a seven-point score of preparedness to infectious disease outbreaks. Multivariable analysis of the score was conducted using Conway-Maxwell-Poisson regression. RESULTS: This study included 2,140 households and 11,239 people, with IDPs accounting for 30% of them. Overall, 1,186 (55.4%) households were overcrowded. Median score of preparedness was 3 points (IQR 2-4). At multivariable analyses, districts with low preparedness were Montepuez and Mueda. Higher preparedness was associated with family planning (p < 0.0001), access to primary education for all children living in the household (p < 0.001) and possession of a birth certificate for all children aged < 5y (p < 0.0001), while preparedness was heterogeneous among the districts (p < 0.05). Households composed by IDPs were not associated with a lower preparedness score. CONCLUSIONS: In climate-vulnerable communities in Mozambique, households practicing family planning, providing access to primary education and birth certificate for all children were less vulnerable to water-borne and vector-borne infectious disease outbreaks. Being family planning and childcare primarily performed by women, our findings can inform policymakers and practitioners on the importance of addressing women to mitigate the impact of climate disasters and reduce the risk of infectious disease outbreaks.

Focus, vigilance, resilience: towards stronger infectious disease surveillance, threat detection and response in the EU/EEA.

This perspective summarises and explains the long-term surveillance framework 2021-2027 for infectious diseases in the European Union/European Economic Area (EU/EEA) published in April 2023. It shows how shortcomings in the areas of public health focus, vigilance and resilience will be addressed through specific strategies in the coming years and how these strategies will lead to stronger surveillance systems for early detection and monitoring of public health threats as well as informing their effective prevention and control. A sharper public health focus is expected from a more targeted list of notifiable diseases, strictly public-health-objective-driven surveillance standards, and consequently, leaner surveillance systems. Vigilance should improve through mandatory event reporting, more automated epidemic intelligence processing and increased use of genomic surveillance. Finally, EU/EEA surveillance systems should become more resilient by modernising the underlying information technology infrastructure, expanding the influenza sentinel surveillance system to other respiratory viruses for better pandemic preparedness, and increasingly exploiting potentially more robust alternative data sources, such as electronic health records and wastewater surveillance. Continued close collaboration across EU/EEA countries will be key to ensuring the full implementation of this surveillance framework and more effective disease prevention and control.

Outbreak detector: a web application to boost disease surveillance systems and timely detection of infectious disease epidemics.

OBJECTIVE: Digital technologies have improved the performance of surveillance systems through early detection of outbreaks and epidemic control. The aim of this study is to introduce an outbreak detection web application called OBDETECTOR (Outbreak Detector), which as a professional web application has the ability to process weekly or daily reported data from disease surveillance systems and facilitates the early detection of disease outbreaks. RESULTS: OBDETECTOR generates a histogram that exhibits the trend of infection within a time range selected by the user. The output comprises red triangles and plus signs, where the former denotes outbreak days determined by the algorithm applied to the data, and the latter represents days identified as outbreaks by the researcher. The graph also displays threshold values and its symbols enable researchers to compute evaluation criteria for outbreak detection algorithms, including sensitivity and specificity. OBDETECTOR allows users to modify algorithm parameters based on their research objectives immediately after loading data. The implementation of automatic web applications results in immediate reporting, precise analysis, and prompt alert notification. Moreover, Public Health authorities and other stakeholders of surveillance can benefit from the widespread accessibility and user-friendliness of these tools, enhancing their knowledge and skills for better engagement in surveillance programs.

Interacting particle models on the impact of spatially heterogeneous human behavioral factors on dynamics of infectious diseases.

Human behaviors have non-negligible impacts on spread of contagious disease. For instance, large-scale gathering and high mobility of population could lead to accelerated disease transmission, while public behavioral changes in response to pandemics may effectively reduce contacts and suppress the peak of the outbreak. In order to understand how spatial characteristics like population mobility and clustering interplay with epidemic outbreaks, we formulate a stochastic-statistical environment-epidemic dynamic system (SEEDS) via an agent-based biased random walk model on a two-dimensional lattice. The "popularity" and "awareness" variables are taken into consideration to capture human natural and preventive behavioral factors, which are assumed to guide and bias agent movement in a combined way. It is found that the presence of the spatial heterogeneity, like social influence locality and spatial clustering induced by self-aggregation, potentially suppresses the contacts between agents and consequently flats the epidemic curve. Surprisedly, disease responses might not necessarily reduce the susceptibility of informed individuals and even aggravate disease outbreak if each individual responds independently upon their awareness. The disease control is achieved effectively only if there are coordinated public-health interventions and public compliance to these measures. Therefore, our model may be useful for quantitative evaluations of a variety of public-health policies.

Alcohol use disorder in people with infectious and chronic diseases and mental disorders: Brazil, 2015. / Transtornos relacionados ao uso de álcool entre pessoas com doenças infecciosas, crônicas e mentais: Brasil, 2015.

The study aimed to estimate the prevalence of alcohol use disorder (AUD) and associated factors in Brazilian adults that reported chronic noncommunicable diseases (NCDs), mental disorders (MDs), and infectious diseases (IDs). This was a secondary analysis of the 3rd National Survey on Drug Use by the Brazilian Population in which the principal outcome was presence of AUD. Prevalence of AUD was estimated for three subgroups: individuals that reported NCDs, MDs, and IDs. Factors associated with AUD in each group were analyzed using logistic regression models. Of the 15,645 adults interviewed, 30.5% (95%CI: 29.4-31.5) reported NCDs, 17.6% (95%CI: 16.5-18.7) MDs, and 1.6% (95%CI: 1.2-1.9) IDs. Considering comorbidities, the analytical sample was 6,612. No statistically significant difference was found in the prevalence of AUD between individuals with NCDs (7.5% [95%CI: 6.1- 8.7]), MDs (8.4% [95%CI: 6.7-10.2]), and IDs (12.4% [95%CI: 7.0-17.8]). The main factors associated with AUD in all the groups were male sex and young adult age. Considering the high prevalence of AUD in all the groups, systematic screening of AUD is necessary in health services that treat NCDs, MDs, and IDs.

O objetivo do artigo é estimar a prevalência de transtornos relacionados ao uso de álcool (TRA) e fatores associados entre indivíduos da população brasileira que reportaram doenças crônicas não-transmissíveis (DCNT), transtornos mentais (TM) e doenças infeciosas (DI). Análise secundária do III Levantamento Nacional sobre o Uso de Drogas pela População Brasileira, cujo desfecho principal foi a presença de TRA. A prevalência de TRA foi estimada para três subgrupos: indivíduos que reportaram DCNT, TM e DI. Os fatores associados a TRA dentro de cada grupo foram analisados utilizando modelos de regressão logística. Dos 15.645 adultos entrevistados, 30,5% (IC95%: 29,4-31,5) reportaram DCNT, 17,6% (IC95%: 16,5-18,7) TM e 1,6% (IC95%: 1,2-1,9) DI. Considerando as comorbidades, a amostra analítica foi de 6.612. Não foi encontrada diferença estatisticamente significativa na prevalência de TRA entre indivíduos com DCNT (7,5% [IC95% 6,1-8,7]), TM (8,4% [IC95% 6,7-10,2]) e DI (12,4% [IC95% 7,0-17,8]). Os principais fatores associados a TRA, em todos os grupos, foram ser do sexo masculino e jovem. Considerando a alta prevalência de TRA em todos os grupos é necessário seu rastreio sistemático em serviços de saúde que atendam DCNT, TM e DI.