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Eastern Mediterranean Health Journal [2019; Vol.25, Issue 11]

30 novembre 2019 Organisation mondiale de la santé

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La Revue de Santé de la Méditerranée orientale

Eastern Mediterranean Health Journal

EMHJ – Vol. 25 No. 11 – 2019

Volume 25 / No. 11 November/Novembre

المجلد الخامس والعشرون / عدد 10 2019نوفمبر/تشرين الثاني

Eastern M editerranean H

ealth Journal Vol. 25 N o. 11 – 2019

Cancer is one of the major contributing causes of morbidity and mortality in the Eastern Mediterranean Region, with 676 500 new cases and 419 000 cancer deaths recorded during 2018. Projections indicate a doubling of incidence within the coming decades, driven by population growth, ageing and the rise of risk factors. To address the issue, the World Health Organization endorsed a regional framework on cancer prevention and control in 2017, guiding policy-makers on how to adopt an integrated public health approach to cancer prevention and control.

Editorial

Strengthening the early detection of common cancers in the Eastern Mediterranean Region Nasim Pourghazian, Rengaswamy Sankaranarayanan, Samar Alhomoud and Slim Slama .................................................................... 767

Research articles

Unsupervised neural network for evaluating the ability of the SF-36 instrument to differentiate individuals Saeedeh Pourahmad, Peyman Jafari and Sara Ghodsi .......................................................................................................................................... 769

Health-related quality of life in informal settlements in Kermanshah, Islamic Republic of Iran: role of poverty and perception of family socioeconomic status Seyed Ramin Ghasemi, Alireza Zangeneh, Nader Rajabi-Gilan, Sohyla Reshadat, Shahram Saeidi and Arash Ziapour ............... 775

Validation study of the Arabic version of the Brief Fatigue Inventory (BFI-A) Khaled Suleiman, Mahmoud Al Kalaldeh, Loai Abu Shahroor, Bernice Yates, Ann Berger, Tito Mendoza, Malakeh Malak, Ayman Bani Salameh, Charles Cleeland and Ahmed Menshawi .....................................................................................784

Advance directive preferences of patients with chronic and terminal illness towards end of life decisions: a sample from Saudi Arabia Salim Baharoon, Mohsen Alzahrani, Eiman Alsafi, Laila Layqah, Hamdan Al-Jahdali and Anwar Ahmed ..........................................791

Psychosocial factors of deliberate self-harm in Afghanistan: a hospital based, matched case-control study Mohammad Paiman, Murad Khan, Tazeen Ali, Nargis Asad and Iqbal Azam ..............................................................................................798

Substance use among high school students in Erbil City, Iraq: prevalence and potential contributing factors Nazar Mahmood, Samir Othman, Namir Al-Tawil and Tariq Al-Hadithi .......................................................................................................806

Implementation of surveillance systems to determine the burden of communicable diseases in a facility in Qatar Humberto Guanche Garcell, Tania Fernandez Hernandez, Elmousbasher Baker and Ariadna Arias ...................................................813

Rapid assessment of marketing of unhealthy foods to children in mass media, schools and retail stores in Oman Samia Al-Ghannami,1 Saleh Al-Shammakhi, Ayoub Al Jawaldeh, Fatma Al-Mamari, Ibtisam Al Gammaria, Jokha Al-Aamry, and Ruth Mabry ....................................................................................................................................820

Health literacy among Iranian adults: findings from a nationwide population-based survey in 2015 Ali Haghdoost, Mohammad Karamouzian, Ensiyeh Jamshidi, Hamid Sharifi, Fatemeh Rakhshani, Nadia Mashayekhi, Hamid Rassafiani, Fatemeh Harofteh, Mansoor Shiri, Mohammad Aligol, Hossein Sotudeh, Atoosa Solimanian, Fatemeh Tavakoli and Abedin Iranpour .................................................................................................................................................................................................................828

Review

Determinants of over and underuse of caesarean births in the Eastern Mediterranean Region: an updated review Bismeen Jadoon, Ramez Mahaini and Karima Gholbzouri ................................................................................................................................. 837

WHO events addressing public health priorities

Regional workshop on healthy diet with a focus on trans-fatty acid elimination .....................................................................847

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Eastern Mediterranean Health Journal

IS the official health journal published by the Eastern Mediterranean Regional Office of the World Health Organization. It is a forum for the presentation and promotion of new policies and initiatives in public health and health services; and for the exchange of ideas, concepts, epidemiological data, research findings and other information, with special reference to the Eastern Mediterranean Region. It addresses all members of the health profession, medical and other health educational institutes, interested NGOs, WHO Collaborating Centres and individuals within and outside the Region.

المجلة الصحية لشرق المتوسط هى المجلة الرسمية التى تصدر عن المكتب الإقليمى لشرق المتوسط بمنظمة الصحة العالمية. وهى منبر لتقديم السياسات والمبادرات الجديدة في الصحة العامة والخدمات الصحية والترويج لها، ولتبادل الآراء والمفاهيم والمعطيات الوبائية ونتائج الأبحاث وغير ذلك من المعلومات، وخاصة ما يتعلق منها بإقليم شرق المتوسط. وهى موجهة إلى كل أعضاء المهن الصحية، والكليات الطبية وسائر المعاهد التعليمية، وكذا المنظمات غير الحكومية المعنية، والمراكز المتعاونة مع منظمة

الصحة العالمية والأفراد المهتمين بالصحة فى الإقليم وخارجه.

La Revue de Santé de la Méditerranée Orientale

EST une revue de santé officielle publiée par le Bureau régional de l’Organisation mondiale de la Santé pour la Méditerranée orientale. Elle offre une tribune pour la présentation et la promotion de nouvelles politiques et initiatives dans le domaine de la santé publique et des services de santé ainsi qu’à l’échange d’idées, de concepts, de données épidémiologiques, de résultats de recherches et d’autres informa- tions, se rapportant plus particulièrement à la Région de la Méditerranée orientale. Elle s’adresse à tous les professionnels de la santé, aux membres des instituts médicaux et autres instituts de formation médico-sanitaire, aux ONG, Centres collaborateurs de l’OMS et personnes concernés au sein et hors de la Région.

Correspondence

Editor-in-chief

Eastern Mediterranean Health Journal WHO Regional Office for the Eastern Mediterranean P.O. Box 7608 Nasr City, Cairo 11371 Egypt Tel: (+202) 2276 5000 Fax: (+202) 2670 2492/(+202) 2670 2494 Email: emrgoemhj@who.int

Members of the WHO Regional Committee for the Eastern Mediterranean Afghanistan . Bahrain . Djibouti . Egypt . Islamic Republic of Iran . Iraq . Jordan . Kuwait . Lebanon Libya . Morocco . Oman . Pakistan . Palestine . Qatar . Saudi Arabia . Somalia . Sudan . Syrian Arab Republic Tunisia . United Arab Emirates . Yemen

البلدان أعضاء اللجنة الإقليمية لمنظمة الصحة العالمية لشرق المتوسط الأردن . أفغانستان . الإمارات العربية المتحدة . باكستان . البحرين . تونس . ليبيا . جمهورية إيران الإسلامية

الجمهورية العربية السورية . جيبوتي . السودان . الصومال . العراق . عُمان . فلسطين . قطر . الكويت . لبنان . مصر . المغرب المملكة العربية السعودية . اليمن

Membres du Comité régional de l’OMS pour la Méditerranée orientale Afghanistan . Arabie saoudite . Bahreïn . Djibouti . Égypte . Émirats arabes unis . République islamique d’Iran Iraq . Libye . Jordanie . Koweït . Liban . Maroc . Oman . Pakistan . Palestine . Qatar . République arabe syrienne Somalie . Soudan . Tunisie . Yémen

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EMHJ is a trilingual, peer reviewed, open access journal and the full contents are freely available at its website: http://www/emro.who.int/emhj.htm

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EMHJ is abstracted/indexed in the Index Medicus and MEDLINE (Medical Literature Analysis and Retrieval Systems on Line), ISI Web of knowledge, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), Embase, Lexis Nexis, Scopus and the Index Medicus for the WHO Eastern Mediterranean Region (IMEMR).

Disclaimer The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement.

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ISSN 1020-3397

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Editorial

Research articles

Review

WHO events addressing public health priorities

Regional workshop on healthy diet with a focus on trans-fatty acid elimination .....................................................................847

Vol. 25.11 – 2019

La Revue de Santé de la Méditerranée orientale

Eastern Mediterranean Health Journal

Ahmed Al-Mandhari Editor-in-Chief Arash Rashidian Executive Editor Ahmed Mandil Deputy Executive Editor Phillip Dingwall Managing Editor

Editorial Board Zulfiqar Bhutta Mahmoud Fahmy Fathalla Rita Giacaman Ahmed Mandil Ziad Memish Arash Rashidian Sameen Siddiqi Huda Zurayk

International Advisory Panel Mansour M. Al-Nozha Fereidoun Azizi Rafik Boukhris Majid Ezzati Hans V. Hogerzeil Mohamed A. Ghoneim Alan Lopez Hossein Malekafzali El-Sheikh Mahgoub Hooman Momen Sania Nishtar Hikmat Shaarbaf Salman Rawaf

Editorial assistants Nadia Abu-Saleh, Suhaib Al Asbahi (graphics), Diana Tawadros (graphics)

Editorial support Guy Penet (French editor) Eva Abdin, Fiona Curlet, Cathel Kerr, Marie-France Roux (Technical editors) Ahmed Bahnassy, Abbas Rahimiforoushani, Manar El Sheikh Abdelrahman (Statistics editors)

Administration Iman Fawzy, Marwa Madi

Web publishing Nahed El Shazly, Ihab Fouad, Hazem Sakr

Library and printing support Hatem Nour El Din, Metry Al Ashkar, John Badawi, Ahmed Magdy, Amin El Sayed

Cover and internal layout designed by Diana Tawadros and Suhaib Al Asbahi Printed by WHO Regional Office for the Eastern Mediterranean, Cairo, Egypt

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EMHJ – Vol. 25 No. 11 – 2019Editorial

Strengthening the early detection of common cancers in the Eastern Mediterranean Region

1Technical Officer, Department of Noncommunicable Diseases and Mental Health, World Health Organization Regional Office for the Eastern Med- iterranean, Cairo, Egypt. 2 Senior Visiting Scientist, International Agency for Research on Cancer, Lyon, France & Senior Medical Advisor, Research Triangle Institute India, New Delhi, India. 3Consultant Colorectal Surgeon at King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia & Chair of The Ethics Committee at International Agency for Research on Cancer (IARC) Lyon, France. 4Regional Advisor, Noncommunicable Diseases Prevention, World Health Organization Regional Office for the Eastern Mediterranean, Cairo, Egypt. (Correspondence to: Nasim Pourghazian: pourg- haziann@who.int).

Citation: Pourghazian N; Sankaranarayanan R; Alhomoud S; Slama S. Strengthening the early detection of common cancers in the Eastern Mediterra- nean Region. East Mediterr Health J. 2019;25(11):767–768. https://doi.org/10.26719/2019.25.11.767

Cancer is the fourth leading cause of death in the Eastern Mediterranean Region (EMR) with an estimated 676 500 new cases and 419 000 cancer deaths in 2018 (1,2). Population growth, ageing and the rise of risk factors may lead to double the incidence within the coming decades (2). Based on GLOBOCAN 2018 the most common cancers in the region are breast, colorectal, lung, liver and bladder cancer, closely followed by Non-Hodgkin lymphoma and leukemia. The most common cancers among men in the Region are lung (10.4%), liver (8.4%) and prostate cancer (8%), while the most common cancers among women are breast (34.7%), colorectal (5.7%) and cervical cancer (4.6%) (2).

In 2017, a regional framework on cancer prevention and control was endorsed, guiding policy-makers on how to adopt an integrated public health approach to cancer prevention and control, while taking into consideration the health system building blocks and multi-sectoral integration. The framework proposes strategic interventions divided into six key areas of work across the continuum of care, of which one is specifically on early detection. Another document, “Early Detection of Cancers Common in the Eastern Mediterranean Region,” guiding policy-makers on how to prioritize and differentiate between appropriate early detection approaches was published in 2017 (3). Global commitments such as the WHO Global NCD Action Plan 2013-2030 and the recently launched global initiative on cervical cancer elimination highlighted the critical importance of early detection and treatment of noncommunicable diseases (NCDs) to rapidly reduce premature mortality. Moreover, technical consultations and global policy dialogue on the scaling up of early detection are planned to be initiated in 2020.

Early detection of cancer aims to discover the disease at an early curable stage when treatment is more effective and affordable. The two main strategies for early detection include early diagnosis and screening programmes (4). Early diagnosis is defined as the early identification of cancer in symptomatic patients and is applicable to all contexts and cancers. This contrasts with cancer screening programmes, which seeks to identify

the disease in its pre-clinical stage among asymptomatic and seemingly healthy target populations, thus suitable for selected cancers and settings. Screening is much more complex and resource intensive, requiring considerable investment and a strong health care infrastructure to have an impact (5). There is sufficient evidence to support organized, quality assured, population-based screening programmes for breast, colorectal and cervical cancer in countries where the disease burden is high, resources are available and the health system is able to deliver effective services in a timely manner (5–8). On the other hand, poorly organized screening programmes can cause harm to individuals and lead to inappropriate use of health care resources, which in turn can have implications on the rest of the health care system. Thus, it is essential to anticipate the various health system requirements when planning a national early detection strategy in the Region.

To date, most screening activities in the Region remain opportunistic and are in some instances initiated with limited planning and health system assessment, resulting in high costs, inefficiencies and increasing burden on the health services (9). Opportunistic breast, cervical and colorectal cancer screening have been implemented in some EMR countries, but this is still on a small scale since only a limited proportion of the population participate. The 2017 country capacity survey for the prevention and control of NCDs reported that 76% and 73% of countries globally had a national screening programme for cervical cancer or breast cancer respectively, and just over a third of the programmes reached up to 10–50% of the target population – confirming the challenge of ensuring high participation and detection rates (10).

Breast cancer is the most common cancer in all countries of the region, and is associated with risk factors such as obesity and physical inactivity which are highly prevalent among the female population in EMR. Although the age standardized incidence rate of breast cancer for the Region (42.6 per 100 000 population) is lower than that of Europe (69.5) and the Americas (66.5), the EMR still has the highest mortality rate of all WHO regions (2). This discrepancy is partially explained by

Nasim Pourghazian,1 Rengaswamy Sankaranarayanan,2 Samar Alhomoud 3 and Slim Slama4

EMHJ – Vol. 25 No. 11 – 2019Editorial

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the lack of effective early detection mechanisms in the Region and deficiencies in treatment uptake, leading to a significant proportion of breast cancer cases being discovered at a late stage when the condition is less susceptible to available treatment.

Therefore, countries in the Region should make use of available tools and guidance documents to strengthen an early diagnosis approach based on evidence-informed assessments of national cancer burden and health system readiness, while addressing financial, geographical, logistic and sociocultural barriers relevant to the national context. Improving population awareness and primary health care capabilities to suspect and refer breast cancer for timely diagnosis and treatment, while developing adequate human resources and infrastructure to manage breast cancer and other priority cancers amenable to cure is crucial. Countries also need to ensure a robust monitoring and evaluation system that identifies gaps in early diagnosis, assesses quality, programme performance and guides mid-course corrections. Furthermore, preventive strategies such as the use of HPV-vaccination for cervical cancer and tobacco control

interventions should be scaled-up. With every third cancer case in adults attributable to eight potentially modifiable risk factors (smoking, alcohol, high BMI, insufficient physical activity, unhealthy diet, suboptimal breastfeeding, infection, air pollution), implementation of strategic population-level preventive interventions are essential but remain underutilized in the EMR (11).

While some of the common cancers in the region, such as lung and liver, are better tackled by primary preventive interventions such as population-level tobacco control measures and vaccination, other cancer forms such as breast, colorectal and cervical cancer can be more effectively managed through adoption of the suitable national early detection strategy. Given the health system resource constraints that most countries in the EMR are facing, countries and policy-makers of the Region should prioritize an early diagnosis approach as a solid foundation before considering the introduction of organized, systematic population-based screening programmes.

References 1. Kulhánová I, Bray F, Fadhil I, Al-zahrani AS, El-basmy A, Anwar WA, et al. Profile of cancer in the Eastern Mediterranean region :

The need for action. Int J Cancer Epidemiol Detect Prev. 2017;47:125–32.

2. Global Cancer Observatory (GLOBOCAN). Estimated number of cancer cases in 2018, Worldwide. Lyon: IARC; 2019 (http://gco. iarc.fr/).

3. WHO Regional Office for the Eastern Mediterranean (WHO/EMRO). Early detection of cancers common in the Eastern Med- iterranean Region. Cairo: WHO/EMRO; 2017 (https://apps.who.int/iris/bitstream/handle/10665/258889/emropub_2017_19206. pdf?sequence=1&isAllowed=y).

4. World Health Organization. Guide to cancer early diagnosis. Geneva: World Health Organizaation; 2017 (http://apps.who.int/ bookorders).

5. International Agency for Research on Cancer (IARC). Handbooks of Cancer Prevention - Breast Cancer Screening, Volume 15. Lyon: IARC; 2016.

6. International Agency for Research on Cancer (IARC). Handbooks of Cancer Prevention - Colorectal cancer screening, Volume 17. Lyon: IARC; 2019.

7. World Health Organization. Comprehensive cervical cancer control -aA guide to essential practice (2nd edition). Geneva: World Health Organization; 2015 (https://apps.who.int/iris/bitstream/handle/10665/144785/9789241548953_eng.pdf?sequence=1)

8. World Health Organization. WHO position paper on mammography screening. Geneva: World Health Organization; 2014. (https://apps.who.int/iris/bitstream/handle/10665/137339/9789241507936_eng.pdf?sequence=1).

9. Lyons G, Sankaranarayanan R, Miller AB, Slama S. Scaling up cancer care in the WHO Eastern Mediterranean Region. East Med- iterr Health J. 2018;24(1):104-110. https://doi.org/10.26719/2018.24.1.104

10. World Health Organization. Assessing national capacity for the prevention and control of noncommunicable diseas- es - Report of the 2017 global survey. Geneva: World Health Organization; 2018. (https://apps.who.int/iris/bitstream/hand le/10665/276609/9789241514781-eng.pdf?ua=1).

11. Kulhánová I, Znaor A, Shield KD, Arnold M, Vignat J, Charafeddine M, et al. Proportion of cancers attributable to major lifestyle and environmental risk factors in the Eastern Mediterranean region. Int J Cancer. 2019 (Epub ahead of print)

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Unsupervised neural network for evaluating the ability of the SF-36 instrument to differentiate individuals Saeedeh Pourahmad,1,2 Peyman Jafari2 and Sara Ghodsi2

1Bioinformatics and Computational Biology Research Center, Shiraz University of Medical Sciences, Shiraz, Islamic Republic of Iran. 2Biostatistics Department, Shiraz University of Medical Sciences, Shiraz, Islamic Republic of Iran. (Correspondence to: S. Pourahmad: pourahmad@sums.ac.ir).

Abstract Background: Health-related quality of life (HRQoL) and well-being refer to the positive, subjective state that is contrary to illness. HRQoL instruments include some common questionnaires, which may often be understood differently de- pending on the level of individuals’ knowledge. Aims: To investigate the ability of 36 Short Form Health Survey (SF-36) as a well-known questionnaire in evaluating peo- ple’s well-being. Methods: We compared unsupervised artificial neural networks with a self-organized map learning algorithm and k-means clustering method. Understanding of the content of the questionnaire was also checked according to age group and sex. The study included 1087 people aged > 18 years (640 healthy individuals and 447 patients with chronic diseases) in Shiraz, Islamic Republic of Iran between 2011 and 2013. Results: The eight subscale scores of the SF-36 instrument were not able to evaluate the well-being of people. The ability of all 36 items in the questionnaire was > 60% in both self-organized map and k-means methods. The self-organized map learning algorithm evaluated people better than the k-means clustering method, based on the accuracy rate in prediction. The SF-36 instrument was better understood by young people. Conclusions: Differences in people’s health conditions may not appear on the SF-36 subscale scores; therefore, the find- ings from the subscale scores of SF-36 should be cautiously interpreted. Keywords: SF-36 instrument, unsupervised artificial neural network, k-means clustering, self-organized map, learning algorithm Citation: Pourahmad S; Jafari P; Ghodsi S. Unsupervised neural network for evaluating the ability of the SF-36 instrument to differentiate individuals. East Mediterr Health J. 2019;25(11):769-774. https://doi.org/10.26719/2019.25.11.769 Received: 03/04/17; accepted: 28/11/17 Copyright © World Health Organization (WHO) 2019. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO license (https:// creativecommons.org/licenses/by-nc-sa/3.0/igo).

Introduction Health-related quality of life (HRQoL) and well-being refer to the positive, subjective state that is contrary to illness. These concepts are often described in terms of a multidimensional index including physical, social, emo- tional or psychological, intellectual, and spiritual com- ponents (1). Well-being is defined as the combination of positive–negative affect balance and satisfaction with life. Measures of HRQoL and well-being comprise meas- urement of overall functioning. The World Health Or- ganization (WHO) has developed a number of question- naires to measure these terms. These instruments have been translated into many languages (1).

An important question in this field includes whether these questionnaires are able to evaluate and distinguish individuals’ well-being. HRQoL measure is an independent predictor of health conditions and it can be used as a screening tool in clinical practice (1). Hence, it would be valuable to assess the ability of these instruments to evaluate individuals’ well-being. To investigate this ability, a wide range of statistical methods may be utilized, including clustering and classification approaches (2). Recently, soft computing techniques such as artificial neural networks have been applied to HRQoL research. artificial neural networks are used to solve

uncertain or vague problems and are helpful for analysis of massive data. These methods and other similar data- mining techniques are widely used in different medical fields. Some recent works include disease diagnosis (3), imaging analysis (4,5), predicting disease status (6), identification of important risk factors for disease (7), and modelling survival of patients (8). A few studies with artificial neural networks have included finding cut-off scores for HRQoL of people with incontinence problems (9), identifying heart failure (10), HRQoL in diabetes (11), HRQoL after breast cancer surgery (12), HRQoL of Parkinson’s disease (13), and predicting the response to a standard 4-week interdisciplinary pain programme (14).

Accordingly, the main objective of the present study was to investigate the ability of the 36 Short Form Health Survey (SF-36) to differentiate healthy and unhealthy individuals. Two different statistical methods were compared for this purpose: an unsupervised artificial neural networks with a self-organization map learning algorithm and the k-means clustering method. The accuracy rate and the area under the receiver operating characteristic (ROC) curve were used as the evaluation criteria. In addition, the present study investigated whether comprehension of the questions differed by age and sex.

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Methods Participants The study included 1087 people (56.9% female) aged >18 years [mean age 49.3 (standard deviation 13.8) years]. There were 640 healthy individuals and 447 patients with chronic diseases. Participants were selected randomly from patients referred to clinics affiliated with Shiraz University of Medical Sciences, Islamic Republic of Iran, during 2011–2013. The validity and reliability of the trans- lated Persian language version of the SF-36 question- naire (Cronbach’s α 0.7–0.9 in diverse studies) have been demonstrated in previous research (15). In the current study, one of the authors (PJ) was responsible for clari- fying the possible questions of participants about the in- strument and purposes of the research. The participants all gave signed informed consent. In addition, the study was approved by the Ethics Committee of Shiraz Univer- sity of Medical Sciences. Healthy people were selected randomly from the parents of high-school students with- out any physical or psychological diseases according to their own admission.

HRQoL instrument The SF-36 includes 36 items and covers 8 domains: physical functioning; role limitations due to physical problems; bodily pain; general health perceptions; vital- ity; social functioning; role limitations due to emotion- al problems; and perceived mental health. Most of the items were scored on a Likert response scale. However, this questionnaire has multiple choice questions as well, and higher total scores represent better HRQoL. All the domains were transformed to a 0–100 scale. The health status of participants (healthy/unhealthy) was consid- ered as the output and 36 items of SF-36 were applied as the predicting variables.

Statistical analysis Artificial neural networks are a simulated version of human biological neural systems and generally consist of layers. Each layer is composed of the smaller units linked together named neurons. Typically, 3 layers are considered for a network including an input layer, an in- termediate layer (it may be >1 layer) and an output layer. The number of neurons in the input layer is equal to the number of predicting variables. For the output layer, it depends on the output structure or target variable. Each neuron in a layer connects to 1 or more neurons of the next layer with different weights. The magnitude of the weights (w_(i,j)) represents the influence of 2 neurons on each other. These weights and some values named bias (b_i) are treated as the system parameters. Estimating the network parameters is done in the learning process. Indeed, the network learns the relations among inputs and outputs by updating the initial weights based on the learning algorithm. The training process stops when the mean square error among the system outputs and the target outputs are minimized (16). The arrangement of various elements of the network including neurons, lay-

ers and the links is called topology. Learning in neural networks is based on 2 approaches,

supervised and unsupervised. In the supervised method, the input and output data are both given to the system, while in the unsupervised method, only the inputs are at hand. The network attempts to discover the pattern of the input data.

The self-organized map is a well-known learning algo- rithm in unsupervised artificial neural networks. It works based on winner neuron logic. The model in this type of network is produced by a learning algorithm that automat- ically orders the inputs on a 1 or 2D grid according to their mutual similarity. In a self-organized map, the winner neuron is determined and then updated in iterative steps. In each step, the neighbouring neurons of the winner neu- ron are also updated based on the Kohenen rule (17). the self-organized map learns to recognize clusters of similar inputs in such a way that neighbouring neurons in the lay- er respond to similar inputs. Each neuron in a self-organ- ized map is represented by a d-dimensional weight vector (Equation 1):

mi=(mi1,mi2,…,mid )

In each training step, one sample x from the input data set is chosen. Then, the distances between x and all the weight vectors of the self-organized map are computed. The neuron whose weight vector is closest to the input vector is called the winner neuron (mc) (Equation 2): ‖x—mc

‖=mini ‖x—mi ‖, where ‖.‖ represents the distance value

After finding the winner neuron, the weight vectors are updated so that the winner neuron is moved closer to the input vector in the input space. Also, the topological neighbours of the winner neuron are treated similarly.

The self-organized map update rule of the weight vector of unit i is (Equation 3):

mi (t+1)=mi (t)+α(t) hci (t)[x(t)-mi (t)]

Where, x(t) is input vector chosen at time t and hci (t) is the neighbourhood function that defines the kernel around the winner neuron c (Equation 4):

hci=exp(-‖rc-ri ‖2/2σ2 (t))

Also, σ(t) is the neighbourhood radius at time t and α(t) is the learning rate at time t. It is a linear function such as: α(t)=α0 (1- t

T ) where α0 is the initial learning rate and T is triangular length inversely proportional to time α(t)= A

t+B, with A,B as the suitable constants (17). The basic characteristics of the network include

map size and topology, weight initialization, type of training algorithm (batch or sequential), learning rate, neighbourhood and distance functions, and radius. In the present research, the self-organized map utilized for evaluating the wel-lbeing of the people with and without health conditions was a 3-layer network (with 1 intermediate layer) in which linear and hex top topologies were compared with each other. In addition, cosine and Euclidian distance functions were applied and learning rate was set at 0.02. For the other

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characteristics, the default values in MATLAB version 7.1 were used. The self-organized map ability to investigate the well-being of the people was also compared with the results of the k-means clustering method, which is a well-known clustering approach in classical statistics. MATLAB version 7.1 was used for both methods.

In the present research, the input layer of the self- organized map model included individuals’ answers to the SF-36 questionnaire. These inputs were converted to weighted inputs on the intermediate layer. During the learning process, these weighted inputs were connected to 1 of the 2 neurons in the output layers that were labelled as healthy or unhealthy.

Results A total of 1087 participants answered all the items in SF- 36. Table 1 describes the participants based on their sex and type of disease. The scores of the 8 subscales of SF- 36 were considered as the predicting variables (inputs) at first. However, these scores were approximately equal for all individuals in the sample study (data not shown). Therefore, 36 items were used as the predicting obser- vations. k-means clustering and self-organized map ap- proaches were utilized to categorize participants into 2 groups, with or without health conditions, according to their answers to the items. Table 2 summarizes the per- formance of both methods compared with true status of the people (unhealthy or healthy status as the target output). As a result, k-means correctly identified 64.9% and self-organized map 62.4% of unhealthy individuals. For healthy people, these values were 67% and 80.6%, re- spectively (Table 2 and Table 3).

For the self-organized map method, different network structures with respect to the topology, number of intermediate layers and neurons in each layer, and distance function were examined, and the result of the best one according to the accuracy rate is shown in Table 3. Accordingly, the accuracy (the proportion of true

results) of the self-organized map in predicting the true status of individuals (healthy or unhealthy) based on the SF-36 instrument was higher than that with k-means clustering (73.1% vs 66.1%). In addition, positive and negative predictive values were both higher in the self- organized map. However, k-means clustering was more sensitive than the self-organized map in predicting unhealthy status. The characteristics of the selected network involved 2 intermediate layers with 5 neurons in each, Euclidian distance function, and hex top topology. Table 4 represents the ROC curves for both methods. The area under the ROC curve was higher for k-means clustering than the self-organized map (0.794 vs 0.699). In the other words, k-means clustering had a 79.4% chance to distinguish between unhealthy and healthy individuals, compared with 69.9% for the self-organized map. In order to determine the effect of age and sex on answering the items, the performance of these methods was compared according to age and sex groups (Table 5). Both methods showed more accuracy in individuals aged 25–35 years. In addition, k-means clustering had more accuracy than the self-organized map in men.

Discussion To the best of our knowledge, this is the first study to evaluate ability of SF-36 to investigate the well-being of healthy subjects versus patients with a specific disease. Therefore, we could find no comparable studies in the literature. However, some studies have investigated the application of artificial neural network methods in HRQoL (7–14); however, their main objectives were dif- ferent from those of the present study. Indeed, no one has evaluated the sensitivity and specificity of HRQoL instruments.

Our findings revealed that the SF-36 instrument is moderately able to evaluate the well-being of individuals with and without a health condition (sensitivity, specificity and the accuracy rate were generally >60%). An interesting result based on a preliminary analysis (not reported in the present study) was that the subscale scores were not informative enough to be used to evaluate people’s health condition. Accordingly, considerable caution is warranted when using the SF-36 subscale scores for clustering people in different groups. This fact was confirmed by previous studies based on differential item functioning (18,19) and was investigated by different approaches in the

Table 1 Participants separated by sex and health status

Health status Female (%) Male (%) Healthy 392 (61.2) 248 (38.8)

Unhealthy people with diabetes 119 (73.9) 42 (26.1)

Unhealthy people with kidney disease 53 (40.4) 78 (59.5)

Unhealthy people with liver disease 55 (35.5) 100 (64.5)

Table 2 Results of 2 methods and their target output

Target output k-means clustering Predicted

SOM Predicted

Unhealthy people Healthy Unhealthy people Healthy True status people

Unhealthy 290 157 279 168

Healthy 211 429 124 516 SOM = self-organized map.

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present study. The self-organized map neural network and k-means

clustering had similar sensitivity, but the former had significantly higher specificity. Furthermore, the results of the 2 methods revealed that SF-36 differentiated younger people (aged < 35 years) more accurately. However, the accuracy was higher for women using the self-organized map method and for men using the k-means clustering method.

Despite the strengths of the present study in methodology and application, this study had a potential limitation. In order to obtain sufficient sample size, people with different chronic diseases were considered in the unhealthy group. This may have led to some heterogeneity among the unhealthy group. Hence, diseases should be assessed separately for future studies. Moreover, evaluating the performance of other HRQoL instruments may be valuable, and different methods in classification approaches, such as decision trees, regression models, and hierarchical clustering methods, are suggested.

Conclusion The main objective of the present study was to evalu- ate the ability of SF-36 to differentiate people with and without health conditions. Two clustering methods were compared in terms of sensitivity and specificity values. The results indicated that the subscale scores of SF-36 were not able to evaluate health condition. In- stead, better performance was achieved based on all 36 questions in the SF-36 instrument. Our results reveal that differences in health conditions may not appear on the SF-36 subscale scores; therefore, such scores should be interpreted with caution.

Acknowledgements This article was extracted from Sara Ghodsi’s Master of Science thesis. The authors are thankful to Z. Sharafi, S. Rafatti and M. Safe for their help in data gathering. Also, we would like to thank Dr. N. Shoukrpour from the Research Consultant Center, Shiraz University of Medical Sciences for editing this manuscript.

Funding: Grant number 92-6894 from Shiraz University of Medical Sciences Research Council.

Competing interests: None declared.

Utilisation de réseaux de neurones non supervisés pour l’évaluation de la capacité du questionnaire SF-36 en tant qu’instrument de différenciation individuelle Résumé Contexte : la qualité de vie liée à la santé et le bien-être font référence à l’état subjectif positif qui est contraire à la maladie. Les instruments relatifs à la mesure de la qualité de vie liée à la santé incluent des questionnaires communs, qui peuvent souvent être interprétés différemment selon le niveau de connaissance de l’individu. Objectifs : Examiner l’aptitude de la forme abrégée du questionnaire généraliste SF-36 (qualité de vie) en tant qu’outil bien connu pour l’évaluation du bien-être des individus.

Table 3 Description of methods’ accuracy based on results of Table 2

Index (%) k-means clustering

SOM

Sensitivity 64.9a 62.4

Specificity 67.0b 80.6

Accuracy 66.1c 73.1

PPV 57.8d 69.3

NPV 73.2e 75.4 a 290

290+157 ×100 b 429

429+211 ×100 c 290+429

290+157+211+429 ×100

d PPV= sensitivity × prevalance sensitivity × prevalance +(1-specificity) × (1-prevalance) (prevalence of unhealthy individuals= 0.411)

e NPV= sensitivity × (1-prevalance) (1-sensitivity) × prevalance + specificity × (1-prevalance) NPV = negative predictive value; PPV = positive

predictive value; SOM = self-organized map.

Table 5 Comparison of k-means and SOM methods according to age and sex of participants

Variable Accuracy (%)

k-means SOM Age, years

< 25 70.7 69.7

25–35 72.4 74.9

35–45 63.9 63.2

45–55 65.5 65.1

55–65 66.1 66.7

> 65 60.6 59.1

Sex

Female 60.4 63.1

Male 74.6 52.9 SOM = self-organized map.

Table 4 Description of methods’ accuracy based on results of Table 2

Method AUC SD P 95 % CI k-means 0.794 0.014 < 0.001 (0.767–0.82)

SOM 0.699 0.016 < 0.001 (0.667–0.731) AUC = area under receiver operating characteristic curve; CI = confidence interval; SD = standard deviation; SOM = self-organized map.

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)36-SF( "ًشبكة عصبية غير خاضعة للرقابة من أجل تقييم قدرة أداة "المسح الصحي القصير المكون من 36 بندا على التمييز بين الأفراد بناء على حالتهم الصحية

سعيدة بور أحمد، بيمان جعفري، سارا قدسي الخلاصة

الخلفية: تشير جودة الحياة الصحية والرفاه إلى الحالة الشخصية الإيجابية التي تتنافى مع الاعتلال. وتشتمل أدوات قياس جودة الحياة الصحية على بعض الاستبيانات الشائعة، التي قد تُفهم في الغالب فهمًا مختلفاً حسب المستوى المعرفي للأفراد.

ن من 36 بنداً )SF-36(، بوصفه استبياناً مشهوراً، على تقييم رفاه الأهداف: هدفت هذه الدراسة إلى استقصاء قدرة المسح الصحي القصير الُمكوَّ الناس.

)SOM( "التنظيم ذاتية "خريطة تعلم ذات للرقابة مع خوارزمية البحث: جرى استخدام ومقارنة شبكة عصبية اصطناعية غير خاضعة طرق وطريقة التقسيم العنقودي k-means. وجرى أيضاً التحقق من فهم محتوى الاستبيان حسب الفئة العمرية ونوع الجنس.

النتائج: أظهرت نتائج هذه الدراسة التي أُجريت على 1087 شخصاً تجاوزت سنه 18 عاماً )640 شخصاً مُعَافًى و447 مريضاً بمرض مزمن في مدينة شيراز بجمهورية إيران الإسلامية خلال الفترة من 2011 إلى 2013( أن درجات المقاييس الفرعية الثمانية في أداة SF-36 لا تستطيع تقييم )SOM( رفاه الناس، في حين أن قدرة البنود الستة والثلاثين كانت أكثر من 60% بكلتا الطريقتين. وإضافةً إلى ذلك، قدمت الخريطة الذاتية التنظيم

تقييمًا أفضل للأشخاص استناداً إلى معدل الدقة في التنبؤ. وعلاوة على ذلك، فهم الشباب هذه الأداة فهمًا أفضل. الاستنتاجات: يُستنتج من ذلك أن فروق الأحوال الصحية بين الأشخاص قد لا تظهر في درجات المقاييس الفرعية، ولذلك ينبغي توخي الحذر عند

.)SF-36( ًن من 36 بندا تفسير النتائج المأخوذة من المقياس الفرعي في المسح الصحي القصير الُمكوَّ

References 1. Michalos AC (editor). Encyclopedia of quality of life and well-being research. Springer Reference; 2014.

2. Dumuid D, Olds T, Lewis LK, Martin-Fernández JA, Katzmarzyk PT, Barreira T, et al. Health-related quality of life and life- style behavior clusters in school-aged children from 12 countries. J Pediatr. 2017 Apr;183:178-183.e2. http://dx.doi.org/10.1016/j. jpeds.2016.12.048 PMID: 28081885

3. Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA. Computer aided decision making for heart disease de- tection using hybrid neural network-Genetic algorithm. Computer Methods Programs Biomed. 2017 Apr;141:19–26. https://doi. org/10.1016/j.cmpb.2017.01.004

4. Arunprasath T, Rajasekaran M P, Kannan S, George SM. Performance evaluation of PET image reconstruction using radial basis function networks. In: Suresh LP, Dash SS, Panigrahi BK, editors. Artificial intelligence and evolutionary algorithms in engineer- ing systems. Springer India; 2015:481–9.

5. Payan A, & Montana G. Predicting Alzheimer’s disease: a neuroimaging study with 3D convolutional neural networks. 2015; arXiv:1502.02506. https://arxiv.org/pdf/1502.02506.pdf

6. Licastro F, Ianni M, Ferrari R, Campo G, Buscema M, Grossi E et al. A new risk chart for acute myocardial infarction by a innova- tive algorithm. Curr Pharmacogenomics Pers Med. 2014;12(3):159–66. http://dx.doi.org/10.2174/1875692113666150116232904

7. Pourahmad S, Khosravi B, Mohamadianpanah M. Effective attributes in colorectal cancer relapse using artificial neural network and cox proportional hazards regression. Ann Colorectal Res. 2014 Jun 30;2(2):e22329. http://dx.doi.org/10.17795/acr-22329

8. Khosravi B, Pourahmad S, Bahreini A, Nikeghbalian S, Mehrdad G. Five years survival of patients after liver transplantation and its effective factors by neural network and cox poroportional hazard regression models. Hepat Mon. 2015 Sep 1;15(9):e25164.

Méthodes : Nous avons comparé des réseaux de neurones non supervisés artificiels à l’aide d’un algorithme d’apprentissage par carte auto-adaptive et de la méthode du partitionnement en K-moyennes. La compréhension du contenu du questionnaire a également été vérifiée en fonction du groupe d’âge et du sexe. L’étude regroupait 1087 personnes âgées de plus de 18 ans (640 individus en bon état de santé et 447 patients souffrant de maladies chroniques) à Shiraz (République islamique d’Iran) entre 2011 et 2013. Résultats : Les scores des huit sous-échelles du questionnaire SF-36 ne permettaient pas d’évaluer le bien-être des individus. La capacité des 36 items du questionnaire était supérieure à 60 % dans les méthodes de la carte auto-adaptive et du partitionnement en K-moyennes. L’algorithme d’apprentissage par carte auto-adaptive a mieux évalué les individus par rapport au partitionnement en K-moyennes, en fonction du taux de précision de la prédiction. Le questionnaire SF-36 était mieux assimilé par les jeunes. Conclusions : La différence entre les états de santé des individus peuvent ne pas apparaître dans les scores des sous-échelles du questionnaire SF-36 ; les résultats doivent donc être interprétés avec prudence.

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http://dx.doi.org/10.5812/hepatmon.25164 PMID:26500682

9. Corcos J, Behlouli H, Beaulieu S. Identifying cut-off scores with neural networks for interpretation of the incontinence impact questionnaire. Neurourol Urodyn. 2002;21(3):198–203. PMID:11948712

10. Behlouli H, Feldman D, Ducharme A, Frenette M, Giannetti N, Grondin F et al. Identifying relative cut-off scores with neu- ral networks for interpretation of the Minnesota Living with Heart Failure questionnaire. Conf Proc IEEE Eng Med Biol Soc. 2009;2009:6242–6. http://dx.doi.org/10.1109/IEMBS.2009.5334659 PMID:19965089

11. Rao MR, Sridhar GR, Madhu K, Rao AA. A clinical decision support system using multi-layer perceptron neural network to predict quality of life in diabetes. Diabetes Metab Syndr Clin Res Rev. 2010 Jan–Mar;4(1):57-9. https://doi.org/10.1016/j. dsx.2009.04.002

12. Shi HY, Tsai JT, Chen YM, Culbertson R, Chang HT, Hou MF. Predicting two-year quality of life after breast cancer surgery using artificial neural network and linear regression models. Breast Cancer Res Treat. 2012 Aug;135(1):221–9. http://dx.doi. org/10.1007/s10549-012-2174-6 PMID:22836876

13. Borchani H, Bielza C, Martı P, Larranaga P. Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: an application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39). J Biomed Inform. 2012 Dec;45(6):1175–84. http://dx/doi.org/10.1016/j.jbi.2012.07.010 PMID:22897950

14. Salgueiro M, Basogain X, Collado A, Torres X, Bilbao J, Doñate F et al. An artificial neural network approach for predicting func- tional outcome in fibromyalgia syndrome after multidisciplinary pain program. Pain Med. 2013 Oct;14(10):1450–60. http://dx.doi. org/10.1111/pme.12185 PMID:23915306

15. Montazeri A, Goshtasebi A, Vahdaninia M, Gandek B. The Short Form Health Survey (SF-36): translation and validation study of the Iranian version. Qual Life Res. 2005 Apr;14(3):875–82. PMID:16022079

16. Hagan MT, Demuth HB, Beale MH. Neural network design. Boston: PWS Publishing; 1996.

17. Kohonen T. Self-organizing maps. Springer Science & Business Media; 2001.

18. Horner-Johnson W, Krahn GL, Suzuki R, Peterson JJ, Roid G, Hall T et al. Differential performance of SF-36 items in healthy adults with and without functional limitations. Arch Phys Med Rehabil. 2010 Apr;91(4):570–5. http://dx.doi.org/10.1016/j. apmr.2009 PMID:20382289

19. Yu YF, Yu AP, Ahn J. Investigating differential item functioning by chronic diseases in the SF-36 health survey: a latent trait analysis using MIMIC models. Med Care. 2007 Sep;45(9):851–9. PMID:17712255

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Health-related quality of life in informal settlements in Kermanshah, Islamic Republic of Iran: role of poverty and perception of family socioeconomic status Seyed Ramin Ghasemi,1 Alireza Zangeneh,1 Nader Rajabi-Gilan,1 Sohyla Reshadat,1 Shahram Saeidi1 and Arash Ziapour1

1Social Development and Health Promotion Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah, Islamic Republic of Iran (Correspondence to: Alireza Zangeneh: ali.zangeneh88@gmail.com).

Abstract Background: Quality of life is an important indicator for measuring health status, and information on quality of life of different groups in society can be used to assess the effect of interventions on health. Aims: This study aimed to assess the relationship between urban poverty and perception of family socioeconomic status, and health-related quality of life in residents of informal settlements. Methods: A cross-sectional study was conducted among 432 residents of two neighbourhoods of informal settlements in Kermanshah in 2015. To measure poverty, the 16 indicators of 2011 Iranian census were used. The neighbourhoods were classified into three groups: high poverty (9.3%), middle poverty (49.2%) and low poverty (41.5%) levels. Health-related qual- ity of life was assessed with the SF-36 questionnaire. The Pearson correlation coefficient was calculated and regression and ANOVA analyses were done. Results: There were no statistically significant differences between the SF-36 scores for the three poverty levels, and no relationship between poverty and the health-related quality of life subscales (P > 0.05). A significant positive correlation was found between perception of family socioeconomic status and health-related quality of life (P < 0.05). In regression analysis, having a chronic illness, perception of family socioeconomic status, age and sex predicted the physical health domain of the SF-36, whereas perception of family socioeconomic status and having a chronic illness predicted the mental health domain. Conclusions: Subjective perception of family socioeconomic status can explain differences in health-related quality of life of low-income people. Keywords: quality of life, health status, urban poverty, Iran Citation: Ghasemi SR; Zangeneh A; Rajabi-Gilan N; Reshadat S; SaeidiS; Ziapour A. Health-related quality of life in informal settlements in Kerman- shah, Islamic Republic of Iran: role of poverty and perception of family socioeconomic status. East Mediterr Health J. 2019;25(11):775–783. https://doi. org/10.26719/emhj.19.013 Received: 15/08/16; accepted: 06/12/17 Copyright © World Health Organization (WHO) 2019. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO license https:// creativecommons.org/licenses/by-nc-sa/3.0/igo

Introduction Over the past few decades, promotion of quality of life, one of the main goals of human development, has affect- ed policy-making in many countries (1–3). As well as the increasing importance of social goals and formulating them into development plans, sociological and human attitudes about quality of life have gradually found their way into planning and policy-making for health care systems. As a general term, quality of life is a concept to show how human needs are fulfilled and a benchmark for understanding the satisfaction or dissatisfaction of individuals and groups with the different aspects of their lives (4,5). The World Health Organization (WHO) defines quality of life as “the individuals’ perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards and concerns” (6).

Quality of life is one of the main indicators for measuring health status (3); therefore, information on the quality of life of different groups in society can be used to assess the

effect of interventions and implemented programmes on health (7).

It is predicted that by 2030 the urban population will be larger than the rural population in developing areas of the world (8). One of the outcomes of this population mobility and growing urbanization is increasing urban poverty (9). The main manifestation of urban poverty is informal settlements (10). According to estimates of the United Nations, more than one billion people, about 14% of the world’s population, live in slums and this could double by the year of 2030 (11,12). In the Islamic Republic of Iran, as with other middle-income countries, rapid urbanization has led to the expansion of informal settlements in the areas surrounding big cities (13). Kermanshah city, the capital of Kermanshah Province in the west of country, has a population of 946 651 (2016 data) (14), most of whom are of Kurdish ethnicity. The province is faced with the problem of informal settlements, especially in Kermanshah city (15).

Inequality in access to health promotion opportunities and increasing health risks are the main health consequences of living in informal settlements (16). Apart

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from the environmental and physical problems of these areas, people living in informal settlements are poor and deprived (17). Both the unfavourable environment and the poverty can have a destructive effect on the quality of life.

Although, previous studies have shown the negative effect of poverty on health (18–20), few studies have been conducted on the relationship between poverty and health- related quality of life in the Islamic Republic of Iran (21). In addition, most studies have calculated income poverty and compared poor groups with non-poor groups. However, in the present study, the sample was drawn from poor areas (informal settlements) and participants were grouped into poverty levels. In addition, the capability approach was used to measure poverty using social, cultural, economic and physical indicators. The capability approach considers that freedom to achieve well-being is a matter of what people are able to do and to be, and thus the kind of life they are effectively able to lead (22,23). The aim of our study was to explore the relationship between urban poverty and perception of family socioeconomic status, and health-related quality of life in people living in informal settlements in Kermanshah city.

Methods Study design This was a cross-sectional study carried out in 2015.

Study population and sample selection The study population was residents of 13 informal settle- ments in Kermanshah city (15). Using multistage strati- fied cluster sampling, two neighbourhoods (Dowlatabad and Jafarabad) were selected because 70% of people liv- ing in the informal settlements of Kermanshah live there (Figure 1) (14,15).

The selected neighbourhoods were divided into the statistical blocks used by the 2011 population and housing census, and the poverty status was assessed using 16 indicators of the census from the Statistical Centre of Iran (Table 1). We examined poverty through the social justice (24,25) and capability approach. Therefore, to evaluate the poverty status across the statistical blocks, the economic, social, cultural and physical indicators (Table 1) used in other studies (17,26) were considered the poverty index. Using cluster analysis in Arc/GIS software, the poverty index was evaluated and the statistical blocks of the two selected neighbourhoods were classified into three groups: high poverty level (most deprived group), middle poverty level and low poverty level.

The results of statistical data analysis for measurement of poverty showed that from a total of 453 blocks, 42 blocks (9.72%) with 5160 residents, 223 blocks (49.23%) with 35 104 residents and 118 blocks (41.50%) with 27 927 residents were classified as high, middle and low poverty levels respectively (Figure 1). Three blocks were selected randomly from each poverty level in the two neighbourhoods (18 blocks in total). After this, study samples were selected

from the 18 blocks using a multistage stratified cluster sampling method.

Data collection Trained investigators visited the selected households. At each house, one of the residents (over 15 years) was selected randomly. After the investigator explained the purpose of the study, she/he completed the question- naire. The questionnaire was filled by the investigator for illiterate respondents.

Perception of family socioeconomic status

This was evaluated using a subjective social status scale (27), which is a measure designed to capture an individ- ual’s subjective evaluation of her/his social status rel- ative to society (28). In an easy pictorial format, it pre- sents 10 rungs, a “social ladder”, and asks individuals to place an “X” on the rung on which they feel they stand. The respondent’s selection is based on her/his family education level, employment status and wealth. The ladder translates to a 10-point continuum (1–10) where 1 represents the worst socioeconomic status and 10 the best (best education, wealth and employment status).

Health-related quality of life tool

The short form health survey (SF-36) was used to measure health-related QOL. It has two main domains (physical and mental/emotional health) and eight subscales [phys- ical functioning (limit or no limit in performing all types of physical activity), role physical (problem or no problem with work or other daily activities as a result of physical health), bodily pain (very severe and extremely limiting pain or no pain or limitations because of pain), gener- al health (believes personal health is poor or excellent), vitality (feels tired and worn out all of the time or not at

Figure 1 Location of the two informal settlements selected in the city of Kermanshah, Islamic Republic of Iran.*

*The green parts of the map represent the rest of the city

Meters 0 1,250 2,500 5,000

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all), social functioning (extreme and frequent interfer- ence with normal social activities because of physical and emotional problems or normal performance), role emo- tional (problem or no problem with work or other daily activities as a result of emotional problems) and mental health (feelings of nervousness and depression all of the time or feels peaceful and happy) (29).

SF-36 is a valid and reliable questionnaire. The Cronbach alpha for SF-36 subscales are between 0.69 and0.93 (29). In addition, Montazeri and colleagues showed that the Farsi version of the SF-36 has acceptable validity in the Iranian population: Cronbach alphas for the Farsi version of SF- 36 subscales were between 0.77 to 0.9 (30). The domains and subscales of the SF-36 questionnaire are scored from 0–100 where zero and 100 scores indicate the worst and best quality of life status respectively.

Demographic variables

Data on age, sex, marital status, education and neighbour- hood were collected. Also respondents answered a ques- tion about whether or not they had any chronic illnesses.

Statistical analysis Data were analysed using Arc/GIS and SPSS, version 18 software. The level of significance was set at P < 0.05. The Pearson correlation coefficient was calculated to assess the linear relationship between age and perception of fam- ily socioeconomic status, and quality of life. ANOVA was used to examine the relationship between marital status and educational level, and quality of life. The independent

t-test was used to analyse the relationship between sex and neighbourhood, and quality of life. Linear regression anal- ysis using the backward step method was done to deter- mine the adjusted associations of all variables with quality of life.

Ethical considerations The study was approved by the review board of the Ker- manshah University of Medical Sciences (project code: 93493). All procedures performed involving human par- ticipants were done in accordance with the ethical stand- ards of the institutional research committee and the 1964 Helsinki declaration and its later amendments. Informed consent was obtained verbally from all participants in the study.

Results A total of 450 people (over 15 years old) were selected and 432 filled the questionnaires completely (response rate: 96%). Of the 432 respondents, 54.2% were female. The mean age of the respondents was 29.93 (SD 11.34) years, and ranged from 15 to 72 years. Most of the par- ticipants were married (56.7%). Almost half of the re- spondents had not completed high school (48.1%), 31.1% had a high-school diploma and 20.80% had a university degree. Significant differences were found between the physical health domain of SF-36 and marital status, ed- ucational level and neighbourhood (P < 0.05) (Table 2).

The results for the eight subscales of the SF-36 health-

Table 1 Indicators used for poverty measurement and their formula

Social indicators Cultural indicators

Ageing rate = Number over 65 years

Total population x 100 Literacy rate = Number of literate people aged ≥7 years Total aged ≥7 years x 100

Household dimension = Total population Total households Enrolment in education rate = Number of students

Total 7-18 years old population x 100

Disability rate = Households that have at least 1 disabled member Total households

x 100 Adult literacy rate = Number of literate people aged 14-65 years Number aged 14-65 years x 100

Economic indicators Physical indicators

Dependency rate = Number aged 0-14 years + over 65 years Number aged 14-65 years x 100 Population density =

Number of people Land area of city

Unemployment rate = Number employed Active population over 10 years old x 100 Residential unit density = Number of residential units

Land area of city—Non-residential and open areas)

Employment rate = Number employed Active population over 10 years old x 100 Household density in residential unit = Number of households

Number of residential units

Economic participation rate = Active population over 10 years old Total number of over 10 years x 100 Individual density in residential unit = Total population

Number of residential units

Overhead rate = Total population Number aged 14-65 years x 100 Individual density in room = Total resident population

Number of rooms

The active population is persons (males/females) who are economically active and produce economic goods and services. The active population age in the Islamic Republic of Iran has been determined to start at 11 years.

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related quality of life are given in Table 3. The mean scores of the physical functioning (76.23) and role emotional (49.61) subscales were the highest and lowest respectively. SF-36 subscale scores was generally higher in males, but this was only statistically significant for the bodily pain subscale (P = 0.026) (Table 3).

There was a negative correlation between age and all the subscales of the physical health domain of SF-36 (P < 0.05); older people had poorer physical health-related quality of life. However, no correlation was found between age and subscales of the mental health domain, except for vitality (P = 0.002).

The results of ANOVA showed no statistically significant differences between the SF-36 scores of the three poverty levels (P > 0.05) (Table 4).

The mean (standard deviation) scores of individuals’ perceptions of their family socioeconomic status were 3.90 (SD 1.88). In addition, the mean scores for the low poverty level, middle poverty level and high poverty level groups were 4.12 (SD 1.84), 3.90 (SD 1.88) and 3.52 (SD 1.84) respectively. The results suggest that members of the high poverty level group perceived their socioeconomic status level significantly more positively than the other two groups (P = 0.023).

There was a positive correlation between perception of family socioeconomic status and SF-36 subscales (P < 0.05). The highest and the lowest correlation coefficients were between perception of family socioeconomic status and vitality subscale (r = 0.376) and physical functioning (r = 0.095) respectively.

Table 3 Mean scores of health-related quality of life subscales by sex

Main domains Subscales Total score Females Males P-value

Mean (SD) Mean (SD) Mean (SD) Physical health Physical functioning 76.23 (24.69) 74.32 (24.67) 78.48 (24.60) 0.081

Role physical 58.34 (36.83) 57.11 (36.50) 59.79 (37.24) 0.452

Bodily pain 61.31 (26.94) 58.67 (26.93) 64.43 (26.69) 0.026

General health 60.41 (15.56) 59.45 (15.67) 61.55 (15.38) 0.164

Mental health Vitality 54.93 (18.33) 53.57 (18.92) 56.53 (17.53) 0.095

Social functioning 65.24 (23.10) 64.36 (22.63) 66.29 (23.65) 0.386

Role emotional 49.61 (39.26) 49.64 (40.57) 49.56 (37.75) 0.098

Mental health 58.67 (19.70) 58.59 (21.01) 58.78 (18.09) 0.092 SD = standard deviation.

Table 2 Characteristics of the study sample

Characteristic Males Females Total HRQoL domains

Physical health Mental health

No. (%) No. (%) No. (%) Mean (SD) Mean (SD) Number 198 (45.8) 234 (54.2) 432 (100) – –

Age [mean (SD)] 30.87 (12.18) 29.14 (10.54) 29.93 (11.34) – –

Marital status

Married 109 (55.6) 133 (57.6) 242 (56.7) 62.31 (19.83) 55.44 (19.72)

Single 82 (41.8) 78 (33.8) 160 (37.5) 68.38 (18.82) 60.28 (20.74)

Divorced/widowed 5 (2.6) 20 (8.6) 25 (5.8) 53.05 (19.46) 54.31 (20.46)

P-value < 0.001 0.048

Educational level

Illiterate 13 (6.7) 18 (7.7) 31 (7.2) 49.48 (22.06) 49.81 (19.07)

< High-school diploma

72 (36.9) 103 (44.2) 175 (40.9) 61.26 (19.32) 56.01 (19.43)

High-school diploma 58 (29.7) 75 (32.2) 133 (31.1) 67.68 (18.44) 59.40 (20.59)

University degree 52 (26.7) 37 (15.9) 89 (20.8) 68.76 (18.90) 57.70 (20.89)

P-value < 0.001 0.095

Neighbourhood

Dowlatabad 100 (50.5) 104 (44.4) 204 (47.2) 66.80 (19.19) 56.95 (20.17)

Jafarabad 98 (49.5) 130 (55.6) 228 (52.8) 61.63 (20.02) 57.26 (20.21)

P-value 0.007 0.872 HRQoL = health-related quality of life, SD = standard deviation.

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Two backward linear regression analyses were conducted in which the physical and mental health domains of SF-36 were the dependent variables and perception of family socioeconomic status, age, sex, marital status, education and having a chronic illness were the independent variables. Having a chronic illness, perception of family socioeconomic status, age and sex predicted the physical health domain, whereas only perception of family socioeconomic status and having a chronic illness predicted the mental health domain (Table 5). As shown in Table 5, the adjusted R2 for physical and mental health domains of health-related quality of life were 0.189 and 0.121 respectively. This means that 18.9% of the variation in the physical health domain can be explained by changes in the chronic disease, perception of family socioeconomic status, age and sex variables, and 12.1% of the variation in the mental health domain can be explained by changes in the perception of family socioeconomic status and chronic disease variables.

Discussion We found no statistically significant differences between SF-36 health-related quality of life scores of the different poverty groups (high, middle and low poverty level). This finding differs from other studies that found a signifi-

cant relationship between poverty and health (12,16,19,20). For example Tampubolon and Hanandita (2014) found that higher levels of poverty were associated with more mental health problems (20). This difference in find- ings may be because the previous studies compared the health status of poor and non-poor people, while we com- pared poor people categorized into three poverty levels. Furthermore, our poverty measurement was based on a capability approach using factors such as literacy and not only income-based factors. This approach considers poverty not just income shortage but defines it as dep- rivation of individual and social capabilities (21). A poor person is someone who has no or a low power of choice, and it is the individual capabilities of a person as well as environmental capabilities that can give him/her power (21). This difference between our approach to poverty measurement and that of the previous studies is another possible explanation for the difference in results. None- theless, similar to our findings, other studies have also not found an association between poverty and health (31,32).

We found a significant positive correlation between an individual’s perception of their family socioeconomic status and health-related quality of life scores; people who had more positive perception of their family socioeconomic status had higher health-related quality

Table 4 Mean scores of health-related quality of life subscales by poverty level

Main domains Subscales Poverty status P-value

High poverty level

Moderate poverty level

Low poverty level

Mean (SD) Mean (SD) Mean (SD) Physical health Physical functioning 72.45 (25.30) 79.47 (22.20) 76.57 (26.10) 0.055

Role physical 54.92 (39.22) 60.09 (35.81) 59.83 (35.49) 0.414

Bodily pain 62.48 (25.94) 60.01 (27.46) 61.49 (27.47) 0.737

General health 59.71 (15.28) 60.73 (14.90) 60.76 (16.51) 0.813

Mental health Vitality 54.62 (19.29) 55.17 (17.38) 54.98 (18.45) 0.968

Social functioning 66.37 (21.78) 64.96 (22.80) 64.45 (24.67) 0.769

Role emotional 49.52 (40.86) 49.87 (37.98) 49.42 (39.22) 0.995

Mental health 58.59 (20.89) 58.72 (18.67) 58.71 (19.69) 0.998 SD = standard deviation.

Table 5 Results of regression analyses of the effect of variables on the physical health and mental health domains

Main domains B Beta SE t Adjusted R2 P-value Physical healtha Constant 63.53 – 3.260 19.49

0.189

< 0.001

Chronic disease –14.38 –0.285 2.47 –5.82 < 0.001

Perception of family SES 1.90 0.179 0.486 3.91 < 0.001

Age –0.229 –0.133 0.083 2.76 0.006

Sex 5.37 0.135 1.77 3.01 0.003

Mental healthb Constant 48.25 – 2.30 20.96

0.121

< 0.001

Perception of family SES 2.70 0.252 0.511 5.29 < 0.001

Chronic disease –10.23 –0.201 2.42 –4.22 < 0.001 SE = standard error, SES = socioeconomic status. aR = 0.444, R2 = 0.197. bR = 0.354, R2 = 0.126.

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Acknowledgements We thank our colleagues working in the Social Development and Health Promotion Research Centre, Kermanshah University of Medical Sciences. Funding: This study was funded by Kermanshah University of Medical Sciences, Kermanshah, Islamic Republic of Iran (grant number 93493). Competing interests: None declared.

of life scores. Similarly, in other studies, subjective socioeconomic status was associated with better physical and mental health, often more strongly than objective measures (27,33,34).

Based on our data, the perception of the high poverty level group of their family socioeconomic status was significantly more positive than other groups. In the other words, the low poverty level group gave lower scores for their family socioeconomic status. Based on the capability approach, a person’s living environment can or cannot give power of choice to people and this power has an influence on his/her actual poverty status (35). Accordingly, this finding suggests that the low poverty level group have a greater desire for a higher standard of living; therefore, living in marginalized neighbourhoods with fewer facilities and many environmental problems creates a sense of dissatisfaction about one’s socioeconomic status. On the other hand, members of the high poverty level group, because they have lower expectations about standard of living, have less conflict with their living environment and its deprivations; therefore their expectations for a desired life are low and this leads to a more positive perception of their status in life. It may be that the poorer class has a limited conception and perceptual map of their environment because of their socioeconomic roots (25), and this leads to different impressions about the environment, even in poor neighbourhoods.

The mean score of the physical and mental dimensions of health-related quality of life of our respondents were 64.07 and 57.11 respectively. In contrast, the study of Rajabi-Gilan et al. (2014) in a sample of Iranian women living in informal settlements of Kermanshah city, using the WHO quality of life-BREF questionnaire, reported lower scores in these two dimensions (55.6 and 48.7 respectively) (36). This difference is probably a result of the different study tools used. The study of Ghafari et al. (2013) in the general population of Qom city, Islamic Republic of Iran reported similar findings to ours (37). We found no significant difference between the health-related quality of life mean scores of males and females except for the bodily pain subscale. The study of Ghafari et al. also found no sex differences except in the physical performance subscale, which is consistent with our findings (37).

Age was negatively associated with the subscales of the physical health domain of health-related quality of life, which is similar to the findings of Rajabi-Gilan et al. for

female residents of informal settlements in Kermanshah city (36). However, in the mental health domain, only the vitality subscale was significantly associated with age, which is inconsistent with the findings of Rajabi-Gilan et al. (36).

The results of the regression analyses indicated that having a chronic illness, perception of family socioeconomic status, sex and age explained 18.4% of the health-related quality of life of respondents. Similarly, in the study of Ghasemi et al. among rural women in Kermanshah city, having a chronic illness was associated with and explained quality of life changes (7). These results indicate that medical interventions and improvements in quality of life are needed for residents of Kermanshah city informal settlements.

A limitation of our study was to calculate poverty without measuring income directly, even though it was addressed indirectly with other variables. However, assessing income variables directly using open or closed questions is not very valid and usually it is not possible to get reliable answers from respondents (38).

Eradication of poverty is the ultimate goal for policy-makers around the world, but it is out of reach in most societies. Because poverty is an important social determinant of health, reducing the effects of poverty on health, especially in poor neighbourhoods, is a health policy priority. The perception of individuals of their family socioeconomic status was strongly associated with their health-related quality of life in our study. Therefore, as a step towards reducing the effects of poverty on health, policy-makers should work on controlling the factors that affect people’s perception of family socioeconomic status in poor neighbourhoods.

Conclusion Based on the results of our study, the subjective percep- tion of socioeconomic status partly explains health-re- lated quality of life among poor people. Therefore, positive perceptions may control some of the nega- tive effects of poverty on health-related quality of life . Therefore, in order to try and reduce the effect of pover- ty on health, it may be worthwhile and less difficult for policy-makers to consider trying to influence subjective perceptions of socioeconomic status rather than objec- tive poverty.

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Qualité de vie liée à la santé dans les quartiers informels de Kermanshah (République islamique d'Iran) : rôle de la pauvreté et perception du statut socio-économique de la famille Résumé Contexte : La qualité de vie est un indicateur important pour mesurer l'état de santé et les informations sur la qualité de vie de différents groupes de la société peuvent être utilisées pour évaluer l'effet des interventions sur la santé. Objectifs : La présente étude visait à évaluer la relation entre la pauvreté urbaine et la perception du statut socio- économique de la famille et la qualité de vie liée à la santé des résidents des quartiers informels. Méthodes : Une étude transversale a été menée auprès de 432 résidents de deux quartiers informels à Kermanshah en 2015. Pour mesurer la pauvreté, les 16 indicateurs du recensement iranien de 2011 ont été utilisés. Les quartiers ont été classés en trois groupes : pauvreté élevée (9,3 %), moyenne (49,2 %) et faible (41,5 %). La qualité de vie liée à la santé a été évaluée à l'aide du questionnaire SF-36. Le coefficient de corrélation de Pearson a été calculé et des analyses de régression et de la variance ont été effectuées. Résultats : Aucune différence statistiquement significative entre les scores du SF-36 pour les trois niveaux de pauvreté et aucune relation entre la pauvreté et les sous-échelles de qualité de vie liée à la santé n’ont été établies (p > 0,05). Une corrélation positive significative a été observée entre la perception du statut socio-économique de la famille et la qualité de vie liée à la santé (p < 0,05). À l'analyse de régression, le fait d'être atteint d’une maladie chronique, la perception du statut socio-économique de la famille, l'âge et le sexe permettent de prédire le score du domaine de la santé physique du SF-36, tandis que la perception du statut socio-économique de la famille et le fait d'être atteint d'une maladie chronique permettent de prédire le score du domaine de la santé mentale. Conclusions : La perception subjective du statut socio-économique de la famille peut expliquer les différences de qualité de vie liées à la santé chez les personnes à faible revenu.

جودة الحياة الصحية في الأحياء العشوائية في كرمانشاه، بجمهورية إيران الإسلامية: دور الفقر وتصور الحالة الاجتماعية الاقتصادية للأسرة

سيد رامين قاسمي، علي رضا زنجنة، نادر رجبي-جيلان، سهيلة رشادات، شهرام سعيدي، آرش ضيابور الخلاصة

تأثير لتقييم المجتمع فئات شتى حياة بجودة الخاصة المعلومات استخدام ويمكن الصحية، الحالة لقياس مهمًا مؤشراً الحياة جودة تُعدّ الخلفية: التدخلات على الصحة.

الأهداف: هدفت هذه الدراسة إلى تقييم العلاقة بين الفقر في المناطق الحضرية وتصور الحالة الاجتماعية الاقتصادية للأسرة من ناحية، وجودة الحياة الصحية لسكان الأحياء العشوائية من ناحية أخرى.

طرق البحث: أُجريت دراسة مقطعية على 432 شخصاً من سكان اثنين من الأحياء العشوائية في كرمنشاه في عام 2015. ولقياس مستوى الفقر، استُخدمت المؤشرات الستة عشر للتعداد السكاني الإيراني لعام 2011. وصُنِّفت الأحياء إلى ثلاث مجموعات حسب مستوى الفقر: مستوى الفقر المرتفع )9.3%(، ومستوى الفقر المتوسط )49.2%(، ومستوى الفقر المنخفض )41.5%(. وخضعت جودة الحياة الصحية للتقييم باستخدام

ن من 36 بنداً )SF-36(. وحُسب معامل ارتباط بيرسون، وأُجري تحليلا الانحدار والتباين. استبيان المسح الصحي القصير الُمكوَّ النتائج: لم تكن هناك فروق ذات دلالة إحصائية بين درجات استبيان المسح الصحي SF-36 الخاصة بمستويات الفقر الثلاثة، ولم تكن هناك علاقة بين الفقر والمقاييس الفرعية لجودة الحياة الصحية )p > 0.05(. ووُجِد ارتباط إيجابي كبير بين تصور الحالة الاجتماعية الاقتصادية للأسرة وجودة الاقتصادية للأسرة، والسن، والجنس بمجال الحالة الاجتماعية المزمن، وتصور المرض تنبأ الانحدار، الصحية )p > 0.05(. وفي تحليل الحياة

الصحة البدنية في استبيان المسح الصحي SF-36، بينما تنبأ تصور الحالة الاجتماعية الاقتصادية للأسرة، والمرض المزمن بمجال الصحة النفسية. الاستنتاجات: يمكن أن يقدم التصور الذاتي للحالة الاجتماعية الاقتصادية للأسرة تفسيراً للفروق الموجودة في جودة حياة الفقراء الصحية.

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References 1. Bache I. Measuring quality of life for public policy: an idea whose time has come? Agenda-setting dynamics in the European

Union. J European Public Policy. 2013;20(1):21–38.

2. Rajabi-Gilan N., Ghasemi SR., Reshadat S., Rajabi S. The relationship between social capital and health-related quality of life among teachers. J Zanjan Univ Med Sci. 2013;21(88):95–107.

3. Mirghafourvand M, Charandabi SM-A, Jafarabadi MA, Tavananezhad N, Karkhane M. Predictors of health-related quality of life in Iranian women of reproductive age. Applied Res Quality Life. 2016;11(3):723–37.

4. Pourtaheri M., Eftekhari A., Fatahi A. Assessment quality of life (QOL) in rural areas (Case study: Northern Khaveh village, Lor- estan province). Human Geog Res. 2011;43(76):13–31.

5. Theofilou P. Quality of life: definition and measurement. Eur J Psychol. 2013;9(1):150–62.

6. World Health Organization. Health statistics and information systems. WHOQOL: measuring quality of life (https://www.who. int/healthinfo/survey/whoqol-qualityoflife/en/,accesses 17 February 2019).

7. Ghasemi SR., Rjabi Gilan N., Reshadat S. The survey of health-related quality of life in Kermanshah rural women and some relat- ed factors. J Mazandaran Univ Med Sci. 2014;24(109):186–94.

8. UN-HABITAT. State of the world’s cities 2010/2011: bridging the urban divide. London: Earthscan; 2010.

9. Analytical perspective of pro-poor slum upgrading frameworks. Nairobi: UN-HABITAT; 2006.

10. Irandoost K, Azami M, Tavallaei R. Defining indicators of informal settlements in Iran. J Urban–Regional Studies Res. 2014;6(21):43–60.

11. UN-HABITAT. State of the world’s cities. The Millennium Development Goals and urban sustainability. London: Earthscan; 2006.

12. UN-Habitat. The challenge of slums: global report on human settlements 2003. Management of Environmental Quality: An International Journal. 2004;15(3):337–8.

13. Tilaki MJM, Justafa RA, Marzbali MH, Abdullah A, Ariffin J. Challenges of the informal settlements in developing countries’ cities: a case study of Iran. World Applied Sci J. 2001;12:160–9.

14. Statistical Center of Iran. Population and housing censuses; 2016 (https://www.amar.org.ir/english/Population-and-Hous- ing-Censuses, accessed 17 February 2019).

15. Rustaee SH, Ahadnejad M, Asgharizamani A, Zangeneh A, Saeedi SH. Evaluation of urban poverty in informal settlements across Kermanshah, Iran through factor analysis (a case study of Dolat Abad & Shater Abad Settlements in 2006). J. Geog Plan. 2015;19(53):137–66.

16. Zangane Shahraki S, Sharifdini JG, Hassanzadeh D, Salary Moghadam Z. Evaluation of the quality of life in informal settle- ments, Tehran metropolitan (case study: Islamabad Salehabad). Human Geog Res. 2014;46(1):177–96.

17. Roustaei S, Ahadnezhad M, Zamani AA, Zangene A. The comparative pattern of poverty spread in Kermanshah City in 1996–2006 using factor analysis technique. Urban–Regional Studies and Research (University Of Isfahan). 2012;3(12):5–8.

18. Adena M, Myck M. Poverty and transitions in health in later life. Soc Sci Med. 2014;116:202–10.

19. Raphael D. Poverty in childhood and adverse health outcomes in adulthood. Maturitas. 2011;69(1):22–6.

20. Tampubolon G., Hanandita W. Poverty and mental health in Indonesia. Soc Sci Med. 2014;106:20–7.

21. Heidarnia MA, Ghaemian T, Abadi A, Fathian S, Montazeri A. Health-related quality of life and poverty. Payesh. 2012;11(4):491–5.

22. Sen A. Inequality reexamined. New York: Clarendon Press; 1992.

23. Robeyns I. Capabilitarianism. J Human Develop Capabilities. 2016;17(3):397–414.

24. Reshadat S, Zangeneh A, Saeidi S, Khademi N, Izadi N, Ghasemi SR, et al. The spatial clustering analysis of HIV and poverty through GIS in the metropolis of Kermanshah, western Iran. Acta Medica Mediterranea. 2016;32:1995–9.

25. Harvey D. Social justice and the city. Atlanta: University of Georgia Press; 2010.

26. Weeks JR, Getis A, Hill AG, Agyei-Mensah S, Rain D. Neighborhoods and fertility in Accra, Ghana: An AMOEBA-based approach. Ann Assoc Am Geogr. 2010;100(3):558–78.

27. Operario D, Adler NE, Williams DR. Subjective social status: reliability and predictive utility for global health. Psychology & Health. 2004;19(2):237–46.

28. Odéen M, Westerlund H, Theorell T, Leineweber C, Eriksen HR, Ursin H. Expectancies, socioeconomic status, and self-rated health: use of the simplified TOMCATS questionnaire. Int J Behav Med. 2013;20(2):242–51.

29. Ware JE, Gandek B. Overview of the SF-36 health survey and the international quality of life assessment (IQOLA) project. J Clin Epidemiol. 1998;51(11):903–12.

30. Montazeri A., Goshtasebi A., Vahdaninia M. Translating, reliability and validity of Persian form of SF-36 standard tool. Payesh. 2005;5(1):49–56.

31. Das J, Do Q-T, Friedman J, McKenzie D, Scott K. Mental health and poverty in developing countries: Revisiting the relationship. Soc Sci Med. 2007;65(3):467–80.

783

Research article EMHJ – Vol. 25 No. 11 – 2019

32. Das J, Do Q-T, Friedman J, McKenzie D. Mental health patterns and consequences: results from survey data in five developing countries. The World Bank Economic Review. 2008;23(1):31–55.

33. Miyakawa M, Magnusson Hanson LL, Theorell T, Westerlund H. Subjective social status: its determinants and association with health in the Swedish working population (the SLOSH study). Eur J Public Health. 2011;22(4):593–7.

34. Singh-Manoux A, Adler NE, Marmot MG. Subjective social status: its determinants and its association with measures of ill- health in the Whitehall II study. Soc Sci Med. 2003;56(6):1321–33.

35. Mahmoudi V, Samimifar G. Poverty as capability deprivation. Social Welfare Quarterly. 2005;17(5):9–32.

36. Rajabi-Gilan N, Ghasemi S, Reshadat S, Zanganeh A, Saeidi S. Health-related quality of life of women in marginal areas of Ker- manshah and some related factors. J Kermanshah Univ Med Sci. 2014;18(9):547–56.

37. Ghafari R., Rafiei M., Taheri-Nejad MR. Assessment of health related quality of life by SF-36 Version 2 in general population of Qom city. Arak Med Univ J. 2014;16(80):62–71.

38. Sahn DE, Stifel D. Exploring alternative measures of welfare in the absence of expenditure data. Review of income and wealth. 2003;49(4):463–89.

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Validation study of the Arabic version of the Brief Fatigue Inventory (BFI-A) Khaled Suleiman,1 Mahmoud Al Kalaldeh,1 Loai AbuSharour,1 Bernice Yates,2 Ann Berger,2 Tito Mendoza,3 Malakeh Malak,1 Ayman Bani Salameh,1 Charles Cleeland3 and Ahmed Menshawi4

1School of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan (Correspondence to: K. Suleiman: ksuleiman@zuj.edu.jo). 2College of Nursing, University of Nebraska Medical Centre, Omaha, United States of America. 3MD Anderson Cancer Center, Houston, United States of America. 4King Hussein Cancer Center, Amman, Jordan.

Abstract Background: Fatigue is the most reported and most distressing symptom among patients with cancer. However, no questionnaire that measures fatigue and fatigue interference with life has been translated into Arabic. Aims: This study aimed to translate and validate the Arabic version of the Brief Fatigue Inventory (BFI-A). Methods: The BFI was translated into Arabic using the forward–backward translation technique. This cross-sectional study collected data from cancer patients through a self-administered questionnaire that included the BFI-A, Insomnia Severity Index (ISI), Zung Depression Scale (ZDS), MD Anderson Symptom Inventory total score (MDASI), and Medical Outcome Study Short Form 36 (SF-36) Vitality Subscale. Descriptive and inferential statistics were used including mean, standard deviation, internal consistency, and correlation coefficient using Pearson’s correlation. Results: A total of 79 patients were recruited in Amman, Jordan, in 2015. Mean of the total BFI-A was 4.01 (2.4), showing that 83.5% had nonsevere fatigue. Cronbach’s α coefficient of the BFI-A was 0.93. The correlations between total BFI-A scores and BFI-A items were significant (P < 0.05) and ranged from 0.75 to 0.86. BFI-A showed a significant correlation (P< 0.05) with the following tools: ISI = 0.70, ZDS = 0.69, MDASI = 0.75, and SF-36 Vitality Subscale = −0.57. Conclusions: This study suggests that the BFI-A is a reliable and valid tool to assess fatigue among Arab cancer patients. Keywords: Arabic, cancer, fatigue, validation. Citation: Suleiman K; Al Kalaldeh M; Shahroor LA; Yates B; Berger A; Mendoza T; et al. Validation study of the Arabic version of the Brief Fatigue Inventory (BFI-A). East Mediterr Health J. 2019;25(11):784–790. https://doi.org/10.26719/emhj.19.032 Received: 19/10/16; accepted: 30/01/18 Copyright © World Health Organization (WHO) 2019. Some rights reserved. This work is available under the CC BY-NC-SA 3.0 IGO license https:// creativecommons.org/licenses/by-nc-sa/3.0/igo

Introduction Fatigue is the most frequent and distressing symptom reported by patients with cancer (1). In addition, fatigue reduces functional status (2), social functioning and qual- ity of life (3). Patients with cancer experience fatigue as a multidimensional, subjective feeling of physical, emo- tional and cognitive exhaustion that is not relieved by rest (4). Fatigue is contributed to by the disease process itself and during and after treatment such as chemother- apy and radiotherapy (5). Fatigue is experienced with oth- er symptoms such as insomnia (6) and depression (7).

Many instruments have been constructed to measure fatigue. Some of these instruments are long and time consuming, while others include expressions or idioms that are hard to translate into another language (8). The Brief Fatigue Inventory (BFI) (8) is one of the most widely used measures of fatigue among patients with cancer. The BFI assesses severity of fatigue and the interference of fatigue with daily functioning in the past 24 hours. The BFI was developed to be a brief screening measure of fatigue among patients with cancer that is easy to comprehend, score and translate into other languages. The BFI is a reliable and valid tool that has been used to measure fatigue among different cancer populations such as lung and breast cancer (7,9).

The BFI has been translated into other languages including German (10), Greek (9), Italian (3), Japanese (2), Chinese (5) and Taiwanese (11). The translated versions of the BFI have demonstrated acceptable internal consistency reliability for a newly translated tool for cancer patients. The reliability ranges from 0.92 for the German (10) and Chinese (5) versions to 0.96 for the Japanese (2) and Taiwanese (11) versions. An Arabic translation of the BFI will provide a standardized fatigue questionnaire for Arab researchers to investigate the subjective aspects of fatigue among Arab patients with cancer. Most of the investigators who translated the BFI into other languages used the back- translation method (2,3,5,9), which involves translation (from source to target language) and back-translation (to source language) using independent translators at each step (12). Also, the majority of investigators used self- report measures to examine convergent validity between the newly translated measure and other fatigue measures or other concepts related to fatigue such as depression or vitality (2,3,5,9).

Little is known about fatigue among Arab patients with cancer. This is mainly due to the lack of Arabic instruments that measure fatigue. Currently, the Chandler Fatigue Scale (CFS) (13) is the only instrument that has been translated into Arabic (14). The CFS is hard to score and does not measure the impact of fatigue on

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Research article EMHJ – Vol. 25 No. 11 – 2019

daily functioning. In addition, it was not constructed to measure fatigue specifically among patients with cancer. Only 2 studies have examined fatigue among the Arab population (14, 15). In these studies, fatigue was measured using the CFS. In the first study, the researchers validated the CFS (14). The internal consistency reliability of the CFS was 0.74. They also reported significant inter-rater reliability. Additionally, for criterion validity purposes, no significant differences were found between the Arabic and English versions of the CFS in 46 bilingual students (14). In another study testing the CFS in Arabic, no psychometric information was reported (15).

An additional measure of fatigue in the Arabic language is needed that captures the impact of cancer on patients’ daily lives and detects the effects of interventions designed to improve fatigue and daily functioning in Arab patients with cancer. In addition, measuring fatigue using an Arabic version of the BFI will provide the opportunity to measure fatigue cross- culturally among different Arab populations with cancer. Thus, the aims of this study were 1) to translate the BFI into Arabic (Fusha dialect), which is the education dialect in all Arab countries; and 2) to obtain psychometric data including convergent validity and internal consistency reliability for the newly translated tool.

Methods Instrument translation The final version of the BFI-A (Figure 1) in this study was obtained by the back-translation method (12). The trans- lation process started by translating the English version of the BFI into Arabic by a bilingual Arabic translator. A second bilingual Arab researcher then blindly back-trans- lated the Arabic version of the BFI into English. Finally, the back-translated version and the original BFI version were compared for equivalence by a monolingual English speaker with a PhD in nursing (fourth author). Vocabu- lary equivalence

Informations clés

Organisation Organisation mondiale de la santé (OMS)

Type de document Journal articles

Date d'adoption 30 novembre 2019

Source Organisation mondiale de la santé