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Multimorbidity as an important issue among women: results of a gender difference investigation in a large population-based cross-sectional study in West Asia
  1. Masoomeh Alimohammadian1,2,3,
  2. Azam Majidi2,3,
  3. Mehdi Yaseri4,
  4. Batoul Ahmadi5,
  5. Farhad Islami2,6,
  6. Mohammad Derakhshan3,7,
  7. Alireza Delavari2,3,
  8. Mohammad Amani2,3,
  9. Akbar Feyz-Sani2,3,
  10. Hossein Poustchi2,3,
  11. Akram Pourshams2,3,8,
  12. Amir Mahdi Sadjadi2,
  13. Masoud Khoshnia3,9,
  14. Samad Qaravi3,9,
  15. Christian C Abnet10,
  16. Sanford Dawsey10,
  17. Paul Brennan11,
  18. Farin Kamangar2,12,
  19. Paolo Boffetta13,
  20. Alireza Sadjadi2,3,
  21. Reza Malekzadeh2,3
  1. 1 Department of Human Ecology, School of Public Health, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
  2. 2 Digestive Diseases Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
  3. 3 Digestive Oncology Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
  4. 4 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
  5. 5 Department of Health Management and Economics, School of Public Health, Tehran University of Medical Sciences, Tehran, Islamic Republic of Iran
  6. 6 Surveillance and Health Service Research, American Cancer Society, Atlanta, GA, USA
  7. 7 Section of Gastroenterology, Division of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK
  8. 8 Liver and Pancreatic Biliary Research Center, Digestive Diseases Research Institute, Tehran University of Medical SCiences, Tehran, Islamic Republic of Iran
  9. 9 Golestan Research Center of Gastroentreology and Hepatology, Golestan University of Medical Sciences, Gorgan, Golestan, Islamic Republic of Iran
  10. 10 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, Washington, MD, USA
  11. 11 Genetic Epidemiology Group, International Agency for Research on Cancer, Lyon, France
  12. 12 Department of Public Health Analysis, School of Community Health and Policy, Morgan State University, Baltimore, USA
  13. 13 Mount Sinai School of Medicine, The Tisch Cancer Institute, New York, NY, USA
  1. Correspondence to Batoul Ahmadi; ahmadiba{at} and Dr Alireza Sadjadi; sadjadia{at}


Objectives To investigate the impact of gender on multimorbidity in northern Iran.

Design A cross-sectional analysis of the Golestan cohort data.

Setting Golestan Province, Iran.

Study population 49 946 residents (age 40–75 years) of Golestan Province, Iran.

Main outcome measures Researchers collected data related to multimorbidity, defined as co-existence of two or more chronic diseases in an individual, at the beginning of a representative cohort study which recruited its participants from 2004 to 2008. The researchers utilised simple and multiple Poisson regression models with robust variances to examine the simultaneous effects of multiple factors.

Results Women had a 25.0% prevalence of multimorbidity, whereas men had a 13.4% prevalence (p<0.001). Women of all age-groups had a higher prevalence of multimorbidity. Of note, multimorbidity began at a lower age (40–49 years) in women (17.3%) compared with men (8.6%) of the same age (p<0.001). This study identified significant interactions between gender as well as socioeconomic status, ethnicity, physical activity, marital status, education level and smoking (p<0.01).

Conclusion Prevention and control of multimorbidity requires health promotion programmes to increase public awareness about the modifiable risk factors, particularly among women.

  • Multimorbidity
  • Gender differences
  • Golestan Cohort Study
  • Iran

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Strengths and limitations of this study

  • This is a large, population-based cross-sectional study, with detailed data provided on sociodemographic, lifestyle and behavioural risk factors.

  • Trained, qualified medical researchers collected all of the data by interactive face-to-face interviews.

  • Cross-sectional data analyses are susceptible to residual confounding and cannot determine the direction of an association.

  • Another limitation of this investigation was possible recall bias resulting from the method of gathering medical history (self-report).


Chronic diseases not only induce physical complications and social hardships for patients, but also present a heavy burden for healthcare systems. This issue can be further intensified by multimorbidity, the simultaneous occurrence of two or more chronic diseases in one person.1–3 Multimorbidity is associated with a higher mortality risk and increased utilisation of healthcare services; therefore, it is a demanding situation for patients, their families and healthcare providers.4–8

Despite the increase in life expectancy among lower and middle income countries over the last decades, the growing prevalence of multimorbidity has led to a decreased quality of life in patients with chronic diseases, especially in populations with limited resources.9 10 Patients with multimorbidity require specific medical care; however, the current clinical practice lacks practical guidelines to manage and treat these patients.11–14 Current clinical practices for patients with multiple chronic diseases routinely deal with each individual disease rather than multiple diseases, and can result in increased expenses for patients and healthcare systems, and possibly lead to unwanted adverse effects.13 15 16

Previous studies have shown a gender difference in patterns of multimorbidity. A systematic review of most previous studies indicated that women had a greater prevalence of multimorbidity compared with men.17 This difference might be related to biological, sociocultural, environmental or economic factors. As these factors vary globally, their associations with multimorbidity might differ across populations.18–22 Therefore, more research should be conducted on this topic.

We used baseline data from the Golestan Cohort Study (GCS), a large-scale prospective study in Western Asia, to explore the epidemiology of multimorbidity.23 In this article we used detailed statistical analyses to examine the variability of the prevalence and determinants of multimorbidity among different age groups. We also investigated interactions between gender and multiple sociodemographic and lifestyle factors potentially associated with multimorbidity.


This cross-sectional study analysed baseline data from the GCS. We analysed data from 49 946 Iranians, aged 40–75 years, who resided in Golestan Province in northeastern Iran. Participants had no current or previous diagnosis of any upper gastrointestinal cancers. The original cohort recruited its participants from 2004 to 2008.24 The details of this study have been described elsewhere.23

Body mass index (BMI) was defined as underweight (<18.5 kg/m2), normal (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (>30 kg/m2). We divided participants into the following age clusters (age at the time of the interviews): 40–49, 50–60, and 61–75 years. In this mainly rural population, physical activity was defined based on occupational activity as follows: physically active (heavy or intense activity during employment) or physically inactive (all other participants). Participants’ socioeconomic status (SES) comprised three levels (low, middle or high) according to the two-step cluster analysis25 with the use of similarities for family assets, ethnicity, sex, employment status, age at onset of the first job, home ownership status and house size (surface area), and age.

Based on the possibility of gathering valid self-reported data during the feasibility phase of the GCS, we collected self-reported information for the following chronic diseases: cardiovascular disease, diabetes (types I and II), chronic obstructive pulmonary disease, chronic kidney disease, chronic liver disease, tuberculosis, gastro-oesophageal reflux disease (GORD), and cancers.24 Respondents reported the presence of regurgitation or heartburn during the past 1–2 years, via a standard GORD questionnaire.26 27 Participants who reported any symptoms during the mentioned time period were considered to have GORD.

In this study, we defined multimorbidity as the simultaneous occurrence of two or more of the above mentioned chronic diseases (non-acute conditions)23 in the same person.7 8 28

Statistical analysis

We estimated multimorbidity among participants according to several sociodemographic and lifestyle factors. The Student’s t-test, Mann-Whitney or χ2 tests evaluated differences in the distribution of respondents according to sociodemographic factors and lifestyle factors between men and women whenever appropriate.

Occasions exist in which the OR can be misleading. Hence, we have used a model that could provide the prevalence ratio. Simple and multiple Poisson regressions were used with robust variances according to the method presented by Barros and Hirakata.29 We sought to examine the possibility of a gender-based association between the studied factors and multimorbidity, calculated crude and adjusted prevalence ratios and tested it with the interaction (effect modification) test, and 95% CIs. The gender differences between determinants of multimorbidity (ie, interaction or effect modification) were evaluated in separate multiple Poisson regression models.

We have considered the design effects according to the generalised estimating equations method for all analyses. This method is a generalisation of the generalized linear model (GLM) that can handle the correlation of observation. The assumption of the GLM is the independence of observation which could be violated in studies that have intra-cluster correlations in cluster sampling or randomisation, as well as in longitudinal analysis of repeated measurements obtained from a subject.30 31 All statistical analyses were performed using SPSS software (IBM Corporation, released 2013, IBM SPSS Statistics for Windows, version 22.0; Armonk, New York, USA). Two-sided p-values <0.05 were considered statistically significant.


This cross-sectional study enrolled 49 946 participants (aged 40–75 years) who predominantly resided in rural areas. Women comprised 28 748 (57.6%) participants. The Supplementary table shows the prevalence of self-reported chronic diseases in those with multimorbidity and the gender differences.

Supplementary Material

Supplementary data

The results indicated an overall age-sex standardised prevalence for multimorbidity of 19.4% (95% CI 19.1% to 19.8%). Women had almost twice the prevalence (25.0%) compared with men (13.4%), with a difference of 11.7% (95% CI 11.0% to 12.4%) (p <0.001). Table 1 shows the prevalence of multimorbidity in all study participants according to gender, sociodemographic and lifestyle factors. Women had evidence of more multimorbidity in all age groups. Of note, compared with men, multimorbidity in women began at an earlier age (40–49 years). In this age group, 17.3% of women had multimorbidity compared with 8.6% for men (p <0.001).

Table 1

Prevalence of multimorbidity by gender according to sociodemographic and lifestyle factors

Table 2 shows the results from multiple Poisson regression models. Men who were ex-smokers, non-Turkmen, married and physically inactive had a significantly higher chance of multimorbidity. Being in lower socioeconomic groups showed an association with multimorbidity only in women. Higher educational levels (compared with illiteracy) showed an inverse association with multimorbidity, mainly in women (p<0.001). Based on interaction analysis, a significantly stronger association existed between poor/middle SES with multimorbidity in women compared with men (p=0.033). The associations between non-Turkmen ethnicity groups (p=0.003), married status (p=0.041), physical inactivity (p=0.009) and ex- smoking (p=0.033) and the prevalence of multimorbidity was statistically significant in men only, or stronger in men compared with women. There was an inverse association between education and multimorbidity mainly in women (p for interaction <0.001).

Table 2

Crude and adjusted prevalence ratio (PR) based on simple and multiple Poisson regression.


This study examined gender-related determinants of multimorbidity in a cross-sectional analysis of a large cohort study that had more than 49 000 participants. Women of all ages had a higher overall prevalence of multimorbidity. Multimorbidity was not only prevalent in elderly people, it also affected participants aged 40–49 years. Gender interacted with sociodemographic factors of ethnicity, marital status, educational level, SES, physical activity and smoking in terms of multimorbidity prevalence. Past smoking history, non-Turkmen ethnic groups, married status and physical inactivity showed significant associations with multimorbidity in men. Lower socioeconomic groups were associated with multimorbidity, but only in women.

Despite current perceptions, this study revealed that multimorbidity was not confined to elderly people. The results showed that middle-age cohort participants (age <50) of both genders also suffered from this condition, and this observation has been supported by another study.32 We observed that women had a greater burden of multimorbidity which supported the results from a study by Zielinski et al on a Swedish population.33 A potential cause for more significant prevalence of multimorbidity among women could be their relatively higher tendency to share their conditions in self-reports.34 35 There is evidence that women use more healthcare facilities, particularly public funded healthcare, compared with men.36–38 Possible influences for this excess multimorbidity in women could be higher exposure to common risk factors for chronic diseases or gender inequality in access to healthcare.7 8 39–42 It is worth mentioning that this information is related to data collected approximately 10 years ago at a subnational level.

This study showed a significant association between low SES and multimorbidity among women, a finding suggested by earlier studies.43 44 This might partially be explained by differences in lifestyle attributed to SES. Low SES might reduce care-seeking in patients with chronic diseases.43 This might produce a paradox, as lower healthcare use might translate to lower documented morbidity. This study’s findings indicated that low and middle SES had an association with multimorbidity in women, which has supported the assertion that women are at higher risk from the adverse effects of poverty, payment inequality and health disparities.45 Khanam et al concluded that gender differences in SES, living and working environments, lifestyle factors and life-events might affect the occurrence and outcome of multimorbidity among women.46

A previous study revealed that inactivity might increase the risk of breast and colorectal cancers, diabetes mellitus and ischaemic heart diseases.47 Autenrieth et al discovered an inverse association between physical activity and multimorbidity among men.35 Another study reported no association between multimorbidity and physical activity for either men or women.48 We observed an association between sedentary occupations that involved decreased physical activity and multimorbidity, which was more evident in men. This might be due to the proportionately longer hours that men spend in sedentary occupations.

In this study the prevalence ratio of multimorbidity significantly decreased with increased education level; the association was more obvious in women. Similarly, other studies showed a decreased likelihood of multimorbidity among better-educated populations.39 42 49

The GCS enrolled participants above 40 years of age whose cultural, educational and social basis was shaped around the 1970s – a time of limited access to primary healthcare facilities, particularly in rural areas.50 However, after the 1980s, there was a substantial improvement in health, life expectancy, control of communicable diseases and SES in Iran.25 50 Iran was among the few countries that reached WHO defined millennium development goals before 2015.51 We observed that education was defined as a protective factor for multimorbidity. In this population, women had a literacy rate below 20%.24 The latest report, released in 2014, has shown a tremendous increase in women’s literacy in Golestan Province, which is over 80%.52 The improvements in health, education and other SES have decreased health inequalities which may influence disease patterns, including multimorbidity, in succeeding generations.

Access to affordable health services, as a human right, is a necessity for improved quality of life. Despite the considerable progress in community health in recent decades, there is an increasing prevalence of chronic diseases.53 Some global movements related to healthy lifestyles have underlined the need for multi-dimensionality collaborations for health promotion as stated in the Ottawa Charter.53–57 The findings of this study and recommendations by WHO53 indicate that specific national and sub-national health policies for men and women of different age groups and SES should be implemented.

The possible changes in demographic characteristics and lifestyle habits of the Iranian population over the past decades and the current study sampling methods (regional vs national) should be taken into consideration. Future studies that assess more representative samples or at the national level are recommended.


The findings of this study confirm that multimorbidity is an important health issue for all individuals above 40 years of age, with particular emphasis on women with lower SES and educational levels. Men with decreased physical activity who are married and are former smokers also have a higher risk for multimorbidity.

Defining priority interventions and multisectorial policies that tackle multimorbidity in both men and women, as well as focusing increased attention on middle-aged populations, are required. In order to control multimorbidity, particularly in women, we recommend the use of health promotion and educational methods to enhance public awareness about modifiable risk factors such as physical activity and smoking.

Supplementary Material

Supplementary data


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  • Acknowledgements We wish to thank the study participants for their cooperation over a number of years and the primary healthcare workers (Behvarz) employed in the study areas for their help.

  • Contributors MY analysed the data. MA, AM, BA, AS, MY, AMS and RM, drafted the manuscript. AS, AM, MA, AFS, and MHD provided scientific input. RM, AP, HP, SQ, MK, AD, and MA provided expert clinical advice and data interpretation. RM, PB, FK, BA, CCA, SD, AP, FI, PB, and SMD provided expert guidance on health policy and delivery of healthcare and interpretation of the data. RM, AS, MA, AM, BA, MY, FK, SD, PB, PB, MHD, CCA, and FI were involved as methodologists in research interpretation and manuscript writing. All authors made critical revisions and provided intellectual content to the manuscript, approved the final version to be published, and agreed to be accountable for all aspects of this work. AS and BA are the guarantors for this study.

  • Competing interests None declared.

  • Ethics approval The ethics committee of the Digestive Diseases Research Institute, Tehran University of Medical Sciences (OHRP-IRB-00001641)

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data sharing statement Data are available and can be accessed by contacting Dr. Alireza Sadjadi (