The current data is part of a Ravansar Non-Communicable Disease (RaNCD) cohort study conducted in Kermanshah, Iran. RaNCD was one of 19 centers in the Prospective Epidemiological Research studies in IrAN (PERSIAN) cohort studies started in March 2015. The PERSIAN cohort studies profile, its objectives, and data collection protocol has been described in detail elsewhere . In brief, RaNCD was a population-based cohort study which its main phase started in March 2015. The study recruited only permanent 35–65 years old resident of Ravansar. We used the base-line data of (RaNCD) cohort study which is the first cohort study to investigate non-communicable diseases in a Kurdish population in Iran . In sum, 9826 adults (aged 35–65 year) were analyzed in this cross-sectional analysis as a base-line data of RaNCD. The ethics committee of Kermanshah University of Medical Sciences approved this study (IR.KUMS.REC.1397.187).
There were 15,000 eligible 35–65 years old residents in Ravansar district (urban and rural areas). Annually updated registration information for all residents has routinely available in all Iranian rural areas utilizing local health units (Health Houses). In the urban areas, trained research assistants selected for their communication skills who were native in Kurdish and local languages, conducted a door-to-door survey of all residents for obtaining registry information i.e. demographic (age, sex, ..), home address, contact phone numbers and assigning a unique code to each household. At the start of each interview, the whole process including the main objective of the study was clarified for the family members. In agreement with central PERSIAN team, and by the end of February 2017, 10,065 participants proportional to corresponding urban and rural area sizes were recruited. This was including 1,100 participants recruited in the pilot phase.
When one or more members of the household met the inclusion criteria and agreed in principle to participate in the project (93.2% of participation rate), a pamphlet was provided that contained information about the research plan, methods, standard testing conditions (e.g. what was meant by “fasting” in relation to blood and urine sampling). They were also given a personal invitation, and a date scheduled for attendance at the cohort research center.
All residents in the included areas of Ravansar District aged 35–65 years, who were willing to participate, were invited to join the study. If people were unwilling to participate, the reason was recorded. From total invitations, 738(7.3%) people(347 women and 391men) declined to participate for reasons such as: do not have enough time to participate (54.2%); no intention to have health assessment at all as they thought they were healthy enough (32.5%); drug misuse (7.8%); and unwillingness to provide blood for the biobank (5.5%).
Data collection protocol
Trained interviews with trained staff were employed for data collection stage. They were systematically qualified to employ standardized data collection procedures and were generally selected based on their interview skills. The main study goals were clarified at the start of each interview. The study was approved by the ethical committee of the Kermanshah University of Medical Sciences.
Data on sleep duration, socioeconomic as well as lifestyle factors i.e. smoking, drug abuse, alcohol intake was obtained. Medical history i.e. diabetes, high blood pressure and anthropometric measures including BMI categories along with demographic factors i.e. age, marital status and place of residence was also gathered.
Sleep duration was used as a proxy for sleep quality [5, 31]. We asked the following questions to obtain the participants’ sleep duration in the past three months: ‘What time did you in general go to bed? ‘How long it takes you to fall asleep?’, and ‘What time did you wake up to start your day?’ we calculated total sleep time [6, 23].
Socioeconomic status: Using polychoric principal component analysis (PPCA) we constructed an index of household’s socioeconomic status (SES). The variables used in the PPCA analysis were the following: having a personal computer, having a personal laptop, having a color TV, color TV type, freezer, motorcycle, mobile phone, car, having a washing machine, having a dish washer, bathroom, vacuum cleaner, traveling abroad, traveling at home, educational years, have access to internet, house ownership, family number, house area and number of rooms. The constructed SES index was classified into quintiles and used in the subsequent analysis.
Participants were asked whether they ever smoked > 100 cigarettes during their lives. Information relating cigarette smoking history (total duration (years), average amount smoked per day, and converted to pack-years) was also obtained. We categorized participants as: i) current smokers, if they were regularly smoking ≥ 1 cigarette per day, ii) never smokers, if they have never smoked or smoked < 100 cigarettes during their lifetime, and finally as former smokers if participants have smoked > 100 cigarettes during their lives and did not smoke regularly or even occasionally during the last year.
Data on drug use was collected using following question: “Have you ever used any type of substance during your lifetime?”
Participants were asked about their lifetime history of alcohol consumption, including beer, wine and wine coolers, and liquor.
For obtaining more valid anthropometric indices i.e. weight, height and BMI all participants were asked to remove extra layers and heavy clothes i.e. their shoes, socks, hat, jewelry, accessories (e.g. watch, keys, cell phone). The precision of weight and height utilized measures was 0.1 kg and 0.1 cm, respectively. The Body Mass Index (BMI) was calculated as weight (kg)/ (height [m] × height [m]) (kg/m2).
The questionnaire also included demographic questions (age, sex and marital status).
Descriptive statistics were used for describing the basic features of the data. Moreover, the slope index of inequality (SII) and relative index of inequality (RII) as regression-based measures of socio-economic inequality were used to assess absolute and relative socio-economic inequality, respectively . To calculate RII and SII on grounds of SES the individuals were ranked (from the highest to the lowest SES index); the highest and lowest values ranked zero and one, respectively. RII represents the ratio of short sleep among individuals at the highest relative inequality related to SES rank to those who are ranked at zero taking into account the whole entire distribution of socioeconomic status . An RII greater than 1 indicates that the prevalence of short sleep among people with low SES is greater. SII is a measure of the difference in short sleep among individuals at the highest relative inequality related to assets rank to those who are ranked at zero considering the whole entire distribution of socioeconomic status.
Gender, age, marital status, place of residence, BMI categories, diabetes, hypertension, smoking, drug abuse and alcohol intake has been demonstrated to be associated with sleep quality in epidemiological studies [22, 32–34]. For obtaining the adjusted indices, we entered enumerated covariates as potential confounder to the final model. In model 1, unadjusted SII and RII were calculated. In model 2, SII and RII were adjusted for age, sex, marital status, and place of residence. Model 3 was further adjusted for cigarette smoking, alcohol use, substance use as covariates. All statistical analysis was performed using Stata 12 (STATA Corp., Texas, USA).