Epidemiology and Health

Original Articles

The probability of diagnostic delays for tuberculosis and its associated risk factors in northwest Iran from 2005 to 2016: a survival analysis using tuberculosis surveillance data: Reza Ebrahimoghli, Hassan Ghobadi, Davoud Adham, Parviz Jangi, Abbas Abbasi-Ghahramanloo, Eslam Moradi-Asl; Epidemiol Health. 2022;44:e2022060. Published online July 18, 2022; DOI: https://doi.org/10.4178/epih.e2022060

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Abstract PDF: Abstract
OBJECTIVES
Early diagnosis is essential for effective tuberculosis (TB) control programs. Therefore, this study examined the risk of delays in TB diagnosis and associated factors in Ardabil Province in northwest Iran from 2005 to 2016.
METHODS
This longitudinal retrospective cohort study was conducted using data obtained from the Iranian National Tuberculosis Control Program at the provincial level between 2005 and 2016. The total delay in diagnosis was defined as the time interval (days) between the onset of symptoms and TB diagnosis. Survival analysis was conducted to analyze the delay in diagnosis. Associated factors were identified using a Cox proportional hazards model.
RESULTS
A total of 1,367 new TB cases were identified. The 12-year median diagnostic delay was 45 days (interquartile range [IQR], 30-87). The annual median diagnostic delay decreased from 68 days (IQR, 33-131) in 2005 to 31 days (IQR, 30-62) in 2016. The probability of a delay in TB diagnosis decreased by 5.0% each year (hazard ratio [HR], 1.05; 95% confidence interval [CI], 1.04 to 1.07). Residence in a non-capital county (HR, 0.83; 95% CI, 0.74 to 0.92) and referral from the private health system (HR, 0.74%; 95% CI, 0.65 to 0.84) were significantly associated with an increased risk of delay in TB diagnosis over the 12-year study period.
CONCLUSIONS
The median delay decreased during the study period. We identified factors associated with a longer delay in TB diagnosis. These findings may be useful for further TB control plans and policies in Iran.; Summary

Korean summary

Key Message
A longitudinal analysis of tuberculosis surveillance data from northwest Iran showed that during the 12-year period from 2005 to 2016, a total of 1,367 tuberculosis cases were registered, of whom 942 patients had a prolonged diagnostic delay (>30 days) and the probability of a delay in tuberculosis diagnosis decreased by 5.0% each year.

Patterns and predictors of smoking relapse among inpatient smoking intervention participants: a 1-year follow-up study in Korea: Seung Eun Lee, Chul-Woung Kim, Hyo-Bin Im, Myungwha Jang; Epidemiol Health. 2021;43:e2021043. Published online June 9, 2021; DOI: https://doi.org/10.4178/epih.e2021043

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Abstract

OBJECTIVES
This study aimed to identify relapse patterns in smokers who participated in an inpatient treatment program and to investigate factors related to relapse.

METHODS

The participants comprised 463 smokers who participated in an inpatient treatment operated by the Daejeon Tobacco Control Center from 2015 to 2018. Participants received high-intensity smoking cessation intervention for 5 consecutive days, including pharmacotherapy and behavioral support, and continued with follow-up for 1 year to determine whether they maintained smoking cessation after discharge from inpatient treatment. Kaplan-Meier and Cox proportional hazard models were used in the analysis.

RESULTS

Participants’ relapse rate within 1 year was 72.8%, and 59.8% of participants smoked again within 6 months after participation. A higher number of counseling sessions was significantly associated with a lower risk of relapse (hazard ratio [HR], 0.23; 95% confidence interval [CI], 0.17 to 0.32 for ≥9 vs. ≤5 counseling sessions). Conversely, higher relapse rates were significantly associated with the use of nicotine replacement therapy (NRT) (HR, 1.91; 95% CI, 1.43 to 2.55 for use vs. no use), and higher levels of baseline expired carbon monoxide (CO) (HR, 1.58; 95% CI, 1.21 to 2.06 for expired CO concentrations of 10-19 ppm vs. expired CO concentrations <10 ppm).

CONCLUSIONS

High-intensity smoking cessation interventions in hospital settings can be effective for smoking cessation in smokers with high nicotine dependence. In addition, the results suggest that for quitters to maintain long-term abstinence, they should receive regular follow-up counseling for 1 year after completing a high-intensity smoking cessation intervention.

Summary

Korean summary
• 한국은 국가금연정책의 일환으로 중증고도흡연자를 위해 고강도의 입원형 금연 중재 프로그램을 운영하고 있다. • 고강도 금연 중재를 받은 후 첫 6개월은 재흡연이 가장 빈번히 일어나는 시기였다. • 장기 금연을 유지하기 위해서는 고강도 금연 중재 후에도 1년간 지속적인 추후 상담이 이루어져야 한다.

Key Message
• South Korea operates a high-intensity inpatient treatment program for heavy smokers. • Relapse typically occurred in the first 6 months after completing the intervention. • Continuous 1 year follow-up counseling is important for long-term abstinence.

Citations

Citations to this article as recorded by

Factors associated with long-term smoking relapse in those who succeeded in smoking cessation using regional smoking cessation programs
Seung Hun Lee, Yu Hyeon Yi, Young In Lee, Hyo Young Lee, Kyoung-Min Lim
Medicine.2022; 101(31): e29595. CrossRef

Application of a non-parametric non-mixture cure rate model for analyzing the survival of patients with colorectal cancer in Iran: Mehdi Azizmohammad Looha, Mohamad Amin Pourhoseingholi, Maryam Nasserinejad, Hadis Najafimehr, Mohammad Reza Zali; Epidemiol Health. 2018;40:e2018045. Published online September 17, 2018; DOI: https://doi.org/10.4178/epih.e2018045

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Abstract

OBJECTIVES
Colorectal cancer (CRC) patients are considered to have been cured when the mortality rate of individuals with the disease returns to the same level as expected in the general population. This study aimed to assess the impact of various risk factors on the cure fraction of CRC patients using a real dataset of Iranian CRC patients with a non-mixture non-parametric cure model.

METHODS

This study was conducted on the medical records of 512 patients who were definitively diagnosed with CRC at Taleghani Hospital, Tehran, Iran from 2001 to 2007. A non-mixture non-parametric cure rate model was applied to the data after using stepwise selection to identify the risk factors of CRC.

RESULTS

For non-cured cases, the mean survival time was 1,243.83 days (95% confidence interval [CI], 1,174.65 to 1,313.00) and the median survival time was 1,493.00 days (95% CI, 1,398.67 to 1,587.33). The 1- and 3-year survival rates were 92.9% (95% CI, 91.0 to 95.0) and 73.4% (95% CI, 68.0 to 79.0), respectively. Pathologic stage T1 of the primary tumor (estimate=0.58; p=0.013), a poorly differentiated tumor (estimate=1.17; p<0.001), a body mass index (BMI) between 18.6 and 24.9 kg/m2 (estimate=−0.60; p=0.04), and a BMI between 25.0 and 29.9 kg/m2 (estimate=−1.43; p<0.001) had significant impacts on the cure fraction of CRC in the multivariate analysis. The proportion of cured patients was 64.1% (95% CI, 56.7 to 72.4).

CONCLUSIONS

This study found that the pathologic stage of the primary tumor, tumor grade, and BMI were potential risk factors that had an impact on the cure fraction. A non-mixture non-parametric cure rate model provides a flexible framework for accurately determining the impact of risk factors on the long-term survival of patients with CRC.

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Assessment of prognostic factors in long-term survival of male and female patients with colorectal cancer using non-mixture cure model based on the Weibull distribution
Mehdi Azizmohammad Looha, Elaheh Zarean, Fatemeh Masaebi, Mohamad Amin Pourhoseingholi, Mohamad Reza Zali
Surgical Oncology.2021; 38: 101562. CrossRef
Actual survival after resection of primary colorectal cancer: results from a prospective multicenter study
Inge van den Berg, Robert R. J. Coebergh van den Braak, Jeroen L. A. van Vugt, Jan N. M. Ijzermans, Stefan Buettner
World Journal of Surgical Oncology.2021;[Epub] CrossRef

Spatial inequalities and predictors of HIV/AIDS mortality risk in Hamadan, Iran: a retrospective cohort study: Somayeh Momenyan, Amir Kavousi, Jalal Poorolajal, Narges Momenyan; Epidemiol Health. 2018;40:e2018038. Published online August 5, 2018; DOI: https://doi.org/10.4178/epih.e2018038

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4 Citations

Abstract PDF

Abstract

OBJECTIVES
Understanding the geographic variation of HIV/AIDS mortality risk and human immunodeficiency virus (HIV) infection could help identify high-burden areas. The aim of our study was to evaluate the effects of predictors of the time interval between HIV diagnosis to death, while accounting for spatial correlations across counties, and to assess patterns of spatial inequalities in the risk of HIV/AIDS mortality in Hamadan Province, Iran.

METHODS

This retrospective study was conducted on 585 patients. The outcome in this study was the time period between the date of HIV/AIDS diagnosis and the date of death. A Weibull regression model with spatial random effects was used.

RESULTS

According to multivariate analysis, there were significant associations between age, tuberculosis co-infection, and marital status and the risk of death. In terms of spatial inequalities, a cluster of counties was identified with a somewhat higher death hazard in the north, northwest, northeast, and central regions. Additionally, a cluster with a somewhat lower hazard was identified in the south, southwest, southeast, and west regions.

CONCLUSIONS

The spatial pattern of HIV/AIDS death risk could reflect inequalities in access to antiretroviral therapy and public health services. Our results underscore the importance of attention to vulnerable groups in urban areas.

Summary

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Citations

Citations to this article as recorded by

Análise da tendência da mortalidade por doenças definidoras e não definidoras de HIV/aids segundo características sociodemográficas, por Unidade da Federação e Brasil, 2000-2018
Ana Paula da Cunha, Marly Marques da Cruz
Epidemiologia e Serviços de Saúde.2022;[Epub] CrossRef
Spatial variability of mother-to-child human immunodeficiency virus transmission in a province in the Brazilian Rainforest: An ecological study
Marcus Matheus Quadros Santos, Bianca Alessandra Gomes do Carmo, Taymara Barbosa Rodrigues, Bruna Rafaela Leite Dias, Cleyton Abreu Martins, Glenda Roberta Oliveira Naiff Ferreira, Andressa Tavares Parente, Cíntia Yollete Urbano Pauxis Aben-Atha, Sand
Geospatial Health.2022;[Epub] CrossRef
Clinical, health systems and neighbourhood determinants of tuberculosis case fatality in urban Blantyre, Malawi: a multilevel epidemiological analysis of enhanced surveillance data
McEwen Khundi, Peter MacPherson, Helena R. A. Feasey, Rebeca Nzawa Soko, Marriott Nliwasa, Elizabeth L. Corbett, James R. Carpenter
Epidemiology and Infection.2021;[Epub] CrossRef
Spatial and temporal analysis of the human immunodeficiency virus in an area of social vulnerability in Northeast Brazil
Géssyca Cavalcante de Melo, Emilia Carolle Azevedo de Oliveira, Iane Brito Leal, Carolina Piedade Morais de Freitas Soares Silva, Roberta Andrade Beltrão, Allan Dantas dos Santos, Renata Karina Reis, Marco Antônio Prado Nunes, Karina Conceição Gomes Macha
Geospatial Health.2020;[Epub] CrossRef

Brief Communication

Introduction of Relative Survival Analysis Program: Using Sample of Cancer Registry Data with Stata Software.: Kyu Won Jung, Hyun Joo Kong, Seon Hee Yim, Young Joo Won, Joohon Sung, Hai Rim Shin; Korean J Epidemiol. 2007;29(2):222-229.

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Abstract PDF: Abstract
Patient survival is one of the most important measures for the evaluation of progress in cancer patient care across the wide spectrum from diagnosis to treatment. The optimal monitoring method for cancer patient survival is to estimate survival based on representative data from cancer patients in the population, which is only achievable through using population-based cancer registration data. Relative survival is used to compare the survival experience in a study cohort that expected to result from background population mortality rates. This technique is useful when the cause of death is not accurate or not available, since it provides a measure of excess mortality in a group of patients with a certain disease. The purpose of this article is to demonstrate the procedures for estimating relative survival using the statistical software Stata. For this survival analysis to show the procedure, the example data set was randomly selected from the National Cancer Incidence Database, which was used in a recent article reporting the overall relative survival of cancer patients diagnosed during 1993-2002 in Korea.; Summary

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