Abstract
Abstract
Background and objectives
Hypertension (HTN) is one of the leading risk factors of cardiovascular diseases and accounts for substantial morbidity and mortality worldwide. We aimed to estimate the prevalence of HTN and its associated factors among healthcare workers (HCWs) at the Gaza Strip’s governmental hospitals and primary healthcare centres (PHCs).
Design
Cross-sectional study.
Settings, participants and methods
The study with multistage stratified random sampling was conducted in 10 hospitals and 15 PHCs of the Ministry of Health in Palestine from February to May 2020. Self-administered face-to-face interview questionnaires were used to collect information on sociodemographics, lifestyles, health profiles and health-related risk factors. The anthropometric parameters were measured, including height, weight, waist circumference (WC), hip circumference (HC), and systolic and diastolic blood pressure. HTN was diagnosed by taking any antihypertensive medication, or the mean blood pressure ≥140/90 mm Hg. The SPSS V.26.0 software was used for data analyses.
Results
A total of 1850 participants, with a mean (SD) age of 36.6 (7.9) years, including 12.2% physicians, 65.3% nurses, 18.1% paramedics and 4.4% non-medical personnel, were included in this study. The prevalence of HTN among HCWs was 8.4%. The associated modifiable factors of HTN were body mass index (BMI), smoking, coffee intake and physical activity (p<0.05). The anthropometric parameters were considerably higher in HTN than in non-HTN (p<0.05). In adjusted models, age, BMI, WC, HC, type of work, workplace, working experience, smoking, coffee intake, physical activity and family history of HTN showed statistically significant associations with HTN (p<0.05).
Conclusions
The modifiable factors, including smoking, coffee intake, physical activity and BMI, were associated with the risk of HTN. These findings indicate that effective efforts in maintaining a healthy lifestyle are needed to prevent HTN among HCWs.
Keywords: Hypertension, EPIDEMIOLOGY, PUBLIC HEALTH
STRENGTHS AND LIMITATIONS OF THIS STUDY.
Multistage stratified random sampling was employed to recruit the study participants.
Cross-sectional design could not address the question of causality and further studies are needed to develop effective strategies for risk assessment, prevention and intervention of hypertension (HTN).
Although detailed assessments on health-related risk factors were conducted, we cannot fully rule out potential confounders of other healthy lifestyle factors that may exist to attenuate or exaggerate the associations between modifiable factors and HTN.
The self-reported data on sociodemographic characteristics and lifestyle behaviours were vulnerable to recall bias, which might lead to misclassification.
Even though sphygmomanometer is the most widely used method in the measurement of systolic and diastolic blood pressure, potential measurement error might also be possible.
Introduction
Hypertension (HTN) is a worldwide public health concern and one of the leading clinical risk factors for cardiovascular diseases (CVDs), cerebrovascular disease, chronic kidney disease and premature death.1 2 According to the Non-communicable Disease Risk Factor Collaboration, the total number of cases of HTN has doubled since 1990 and over 1 billion people worldwide were affected by HTN in 2019.3 Although the prevalence of HTN remains stable in some high-income countries,4 5 it substantially increases over time across many low-income and middle-income countries (LMICs). HTN has become a public health priority in LMICs, including countries in the Eastern Mediterranean region. Palestine is confronted with an epidemiological transition from communicable diseases to non-communicable diseases (NCDs), such as CVDs and HTN.6 According to the annual report of the Palestinian Ministry of Health in 2016, the total number of deaths related to HTN accounted for 8%, with a rate of 21.8/100 000 of the population.7 In 2017, the prevalence of HTN among Palestinians aged 25 and older was up to 28.4% in the Gaza Strip.8 Therefore, an improved understanding of the modifiable factors of HTN is of important clinical significance.
People in Palestine are facing poor surroundings and critical situations with the Israeli siege and political conditions that have led to widespread unemployment, malnutrition, food insecurity and a lack of income for Palestinians, especially for the Palestinian refugees. A continuous shortage of medical care and treatments also leads to the deterioration of all categories of patients.9 The Palestinian health system is frequently confronted with serious difficulties in managing health issues,10 making it difficult to establish an effective healthcare intervention. As a vital group in the health system, healthcare workers (HCWs) serve as role models for the public in living a healthy lifestyle and are in charge of advocating suitable lifestyle modifications that affect illness prevention.11,15 Evidence proposes a strong and consistent relationship between HCWs’ choices and their recommendations to patients.16 Growing evidence indicates that many modifiable risk factors, including unhealthy foods, sedentary lifestyles, being overweight or obese, cigarette use, alcohol abuse and chronic stress, are associated with an increased risk of HTN.17,19 For HCWs, work-related variables such as shift work and mental and physical stress may place them at a high risk of certain illness situations.14 Considering the essential role of HCWs in HTN prevention and management, it is thus critical to understand the prevalence of HTN in healthcare practitioners.10 Preventing and controlling metabolic risk factors of HTN in HCWs is, therefore, a major policy goal for achieving a healthy workforce in the general population.20 Regrettably, research on the management of HTN in HCWs has mainly been neglected and the prevalence and characteristics of HTN among HCWs in Palestine remain unknown.
According to the Palestinian Central Bureau of Statistics, the Gaza Strip’s population was 1.93 million, accounting for 39.8% of Palestine’s overall population.21 The Gaza Strip is the second largest governorate of the Palestinians, accounting for 13.6% of the population. The aim of this study was to estimate the prevalence and the associated risk factors for HTN among HCWs in Palestinian hospitals and primary healthcare centres (PHCs).
Materials and methods
Study design and participants
A cross-sectional study was conducted from February to May 2020 in the Gaza Strip, Palestine, among a representative sample of Palestinian HCWs (physicians, nurses, paramedics and non-medical personnel) who work in hospitals and PHCs of the Ministry of Health. The sample size was calculated to be 1009 based on the previous data using the formula: n=Z2 P (1–P)/d2 at 95% confidence intervals (CIs).8 22 The participants were selected by a multistage stratified random sampling technique. The Gaza Strip was divided into five governorates on the basis of geographical location, which included North Gaza, Gaza City, Mid Zone, Khan Younis and Rafah. To obtain a representative sample of HCWs, stratification of the population was performed with a consideration of the population distribution in all five areas, and then two hospitals and three PHCs from each governorate were randomly selected. Finally, a total of 10 hospitals and 15 PHCs were selected for this study. Online supplemental figure 1 shows the number of participants for each governorate.
Participants were recruited from 10 governmental hospitals and 15 PHCs via advertisements. The inclusion criteria of this study were: (1) HCWs who were working at the Ministry of Health in the Gaza Strip; (2) HCWs who had at least 1 year of working experience; and (3) HCWs who were aged 18 years or older. Pregnant and lactating women, in addition to those workers under an unemployment programme, were excluded. A total of 2000 questionnaires were distributed, and 1900 responses were collected. The final analysis had 1850 responses after excluding 50 individuals who provided insufficient or implausible information.
Data collection
Data were collected by trained assessors in accordance with the WHO’s STEP approach to surveillance chronic disease risk factors. A self-constructed face-to-face interview was used to assess demographic and socioeconomic characteristics (nine items (age, sex, marital status, governorate, educational attainment, profession, workplace, work experience and monthly income)), lifestyle habits ((five items (smoking, tea consumption, coffee intake, physical activity and work routine)) and health profile information (two items (family history of HTN and medical records)). Smoking status was categorised into never- and former- or current-smokers. Tea and coffee consumption were dichotomised into yes or no. Physical activity was assessed by self-report and was dichotomised into yes (≥1 day/month) or no (<1 day/month). Furthermore, information about work routines was obtained by questioning the participants’ type of work and work time per day. After analysing the literature on the subject, the questionnaire was well-prepared. The questionnaire’s content validity was tested by sending the completed questionnaire and a cover letter explaining the intentions of the study to 10 experts in various health professions who were asked to comment on the research questions. From the original English edition, all questions were translated into Arabic (English speakers performed forward translations, while Arabic mother-tongue speakers performed backward translations). The study was carried out by five well-trained personnel, including clinic physicians and nurses, by filling out the printed questionnaire.
Anthropometric assessments of weight, height, waist circumference (WC), hip circumference (HC), systolic blood pressure (SBP) and diastolic blood pressure (DBP) were performed by expert nurses with standardised procedures and calibrated equipment. Weight was determined to the nearest 0.1 kg using weighing scales, and height was determined to the nearest 0.1 cm using stadiometers.23 WC and HC were measured to the nearest 0.1 cm at the position of 1 cm below the umbilicus and at the iliac crest level with a plastic, non-stretchable measuring tape.24 The body mass index (BMI) was calculated as a person’s weight in kilograms divided by the height in metres squared (kg/m2).25 According to the WHO criteria, participants were classified as underweight (BMI<18.5 kg/m2), normal weight (BMI 18.5 to <25 kg/m2), overweight (BMI 25 to <30 kg/m2) and obese (BMI≥30 kg/m2).26
SBP and DBP were taken in duplicate with a sphygmomanometer and the average of duplicate measures was recorded as the value for each measurement with results rounded to the nearest 0.1 mm Hg. After participants rested in a sitting position for at least 10 min, experienced nurses took the measurement of the right arm with a properly sized cuff at 1-minute intervals, with the arm supported at heart level and feet flat on the floor.27 HTN was diagnosed by a physician, taking any antihypertensive medication, or the mean SBP exceeding 140 mm Hg and/or mean DBP exceeding 90 mm Hg.28
Statistical analysis
All data analyses were performed using SPSS V.26. The descriptive analysis comprised mean and SD for continuous variables, and frequency (n) and percentages (%) for categorical variables. The differences in demographic, lifestyle, health profile and anthropometric parameters by individuals with and without HTN were examined with a χ2 test for categorical variables and t-tests for continuous variables. If three or more groups were studied, a one-way variance analysis (analysis of variance) was used to compare means between groups based on demographic characteristics. Multiple logistic regression analysis was conducted to estimate the OR and 95% CI of HTN across the associated factors. The model was adjusted for potential confounders (age, BMI, WC, HC, type of work, work experience, smoking status, coffee consumption, physical activity and family history of HTN) since they were associated with HTN in the bivariate data analysis (p<0.05). The statistical significance level was set as a two-sided p<0.05.
Patient and public involvement
Patients and the public were not involved in the study design, or conduct, or reporting or dissemination plans of this research.
Results
Characteristics of the study participants
A total of 1850 participants were recruited for the survey, with a response rate of 92.5%. Characteristics of the study participants are presented in table 1. The participants included 226 physicians (12.2%), 1208 nurses (65.3%), 334 paramedics (18.1%) and 82 non-medical personnel (4.4%). Of the participants, 1146 (61.9%) were males and 704 (38.1%) were females. About 49.3% of the respondents were in the age group between 30 and 39 years. Most of them were married (78.1%), while 68.6% had a first degree of education (Bachelor’s). Most of the participants lived in the Gaza governorate (40.4%). Additionally, most of the participants worked in the hospital (78.1%). Those whose working experience ranged from 10 to 14.9 years comprised 33.8%, while those with working hours of 6–7 hours per day comprised 66.4%. Finally, the monthly income of most participants was New Israeli Shekel 1501–1999 per month at 54.5%.
Table 1. Characteristics of the study participants by profession (n=1850).
| Variable | Profession | Totaln=1850 (100%) | |||
| Physiciann=226 (12.2%) | Nursen=1208 (65.3%) | Paramedicn=334 (18.1%) | Non-medical*n=82 (4.4%) | ||
| Gender | |||||
| Male | 154 (68.1) | 752 (62.3) | 184 (55.1) | 56 (68.3) | 1146 (61.9) |
| Female | 72 (31.9) | 456 (37.7) | 150 (44.9) | 26 (31.7) | 704 (38.1) |
| Age (years) | |||||
| 22–29 | 26 (11.5) | 260 (21.5) | 40 (12.0) | 8 (9.8) | 334 (18.1) |
| 30–39 | 102 (45.1) | 624 (51.7) | 154 (46.1) | 32 (39.0) | 912 (49.3) |
| 40–49 | 70 (31.0) | 232 (19.2) | 104 (31.1) | 34 (41.5) | 440 (23.8) |
| 50–61 | 28 (12.4) | 92 (7.6) | 36 (10.8) | 8 (9.8) | 164 (8.9) |
| Marital status | |||||
| Unmarried | 30 (13.3) | 289 (23.9) | 70 (21.1) | 17 (20.7) | 406 (21.9) |
| Married | 196 (86.7) | 919 (76.1) | 264 (78.9) | 65 (79.3) | 1444 (78.1) |
| Governorate | |||||
| North-Gaza | 44 (19.5) | 294 (24.3) | 42 (12.6) | 18 (22.0) | 398 (21.5) |
| Gaza | 92 (40.7) | 462 (38.2) | 148 (44.3) | 44 53.7) | 746 (40.4) |
| Mid zone | 26 (11.5) | 182 (15.1) | 70 (21.0) | 2 (2.4) | 280 (15.1) |
| Khan Younis | 28 (12.4) | 142 (11.8) | 44 (13.1) | 12 (14.6) | 226 (12.2) |
| Rafah | 36 (15.9) | 128 (10.6) | 30 (9.0) | 6 (7.3) | 200 (10.8) |
| Educational level | |||||
| Diploma | – | 276 (22.8) | 51 (15.3) | 27 (32.9) | 354 (19.1) |
| Bachelor | 180 (79.6) | 803 (66.5) | 237 (71.0) | 49 (59.8) | 1269 (68.6) |
| Master | 36 (15.9) | 128 (10.6) | 44 (13.2) | 6 (7.3) | 214 (11.6) |
| Doctoral | 10 (4.4) | 1 (0.1) | 2 (0.6) | – | 13 (0.7) |
| Workplace | |||||
| Hospital | 189 (83.6) | 1031 (85.3) | 187 (56) | 38 (46.3) | 1445 (78.1) |
| PHCs | 35 (15.5) | 129 (10.7) | 101 (30.2) | 20 (24.4) | 285 (15.4) |
| Management | 2 (0.9) | 40 (3.3) | 40 (12.0) | 22 (26.8) | 104 (5.6) |
| Others | – | 8 (0.7) | 6 (1.8) | 2 (2.4) | 16 (0.9) |
| Experience (years) | |||||
| <5 | 43 (19.0) | 282 (23.3) | 38 (11.4) | 18 (22.0) | 381 (20.6) |
| 5–9.9 | 83 (36.7) | 388 (32.1) | 96 (28.7) | 8 (9.8) | 575 (31.1) |
| 10–14.9 | 64 (28.3) | 384 (31.8) | 136 (40.7) | 42 (51.2) | 626 (33.8) |
| ≥15 | 36 (15.9) | 154 (12.7) | 64 (19.2) | 14 (17.1) | 268 (14.5) |
| Work time (hours) | |||||
| 6–7 | 130 (57.5) | 758 (62.7) | 264 (79.0) | 76 (79.0) | 1228 (66.4) |
| 8–12 | 74 (32.7) | 336 (27.8) | 50 (15.0) | 4 (15.0) | 464 (25.1) |
| >12 | 22 (9.7) | 114 (9.4) | 20 (6.0) | 2 (2.4) | 158 (8.5) |
| Monthly income (NIS) | |||||
| <1500 | 6 (2.7) | 112 (9.3) | 10 (3.0) | 8 (9.8) | 136 (7.4) |
| 1501–1999 | 52 (23.0) | 712 (58.9) | 190 (56.9) | 54 (56.9) | 1008 (54.5) |
| 2000–2499 | 46 (20.4) | 228 (18.9) | 64 (19.2) | 10 (12.2) | 348 (18.8) |
| 2500–3499 | 44 (19.5) | 142 (11.8) | 56 (16.8) | 8 (9.8) | 250 (13.5) |
| >3500 | 78 (34.5) | 14 (1.2) | 14 (1.2) | 2 (2.4) | 108 (5.8) |
1 NIS is equivalent to 0.27 dollars ($).
The non-medical profession includes admin officers, accountants, and reporters. Abbreviations: NIS, , one NIS is equivalent to 0.27 dollars ($); PHCs, centers.
NISNew Israeli ShekelPHCsprimary health care centres
Univariate analysis of the associated factors of HTN in HCWs
The prevalence of HTN among HCWs was 8.4%. There was a high prevalence in nurses (9.0%), while the lowest percentage was found in non-medical personnel (6.1%). HCWs working in the hospital had a lower prevalence of HTN than those in PHCs (7.3% vs 12.3%, p<0.001). In addition, HCWs with full-time contracts had a higher prevalence of HTN than those with part-time contracts (8.8% vs 2.4%, p<0.031). The HCWs with more years of working experience had a higher prevalence of HTN (p<0.001). The prevalence of HTN demonstrated no statistically significant differences in terms of profession (p=0.563) and educational level (p=0.330), as shown in table 2.
Table 2. Comparison of the work-related factors between HTN and non-HTN groups (n=1850).
| Variable | Non-HTNn=1695 (91.6%) | HTNn=155 (8.4%) | χ2 | P value |
| Occupation | ||||
| Physician | 209 (92.5) | 17 (7.5) | 2.04 | 0.563 |
| Nurse | 1099 (91.0) | 109 (9.0) | ||
| Paramedics | 310 (92.8) | 24 (7.2) | ||
| Non-medical* | 77 (93.9) | 5 (6.1) | ||
| Workplace | ||||
| Hospitals | 1340 (92.7) | 105 (7.3) | 25.66 | <0.001 |
| PHCs | 250 (87.7) | 35 (12.3) | ||
| Management | 95 (91.3) | 9 (8.7) | ||
| Other | 10 (62.5) | 6 (37.5) | ||
| Educational level | ||||
| Diploma | 330 (93.2) | 24 (6.8) | 3.43 | 0.330 |
| Bachelor | 1163 (91.6) | 106 (8.4) | ||
| Master | 190 (88.8) | 24 (11.2) | ||
| Doctoral | 12 (92.3) | 1 (7.7) | ||
| Type of work | ||||
| Full-time | 1566 (91.2) | 152 (8.8) | 6.96 | 0.031 |
| Part-time | 121 (97.6) | 3 (2.4) | ||
| Unfixed contract | 8 (100.0) | – | ||
| Experience (years) | ||||
| <5 | 378 (99.2) | 3 (0.8) | 125.54 | <0.001 |
| 5–9.9 | 549 (95.5) | 26 (4.5) | ||
| 10–14.9 | 564 (90.1) | 62 (9.9) | ||
| ≥15 | 204 (76.1) | 64 (23.9) |
The non-medical profession includes admin officers, accountants, and reporters. Abbreviations: HTN, hypertension; HCWs, healthcare workers; PHCs, centers.
HTNhypertensionPHCsprimary healthcare centres
We investigated the associations of HTN with lifestyles, anthropometric indices and family history. Results indicated statistically significant associations between HTN and smoking (χ2=4.29, p=0.039), coffee consumption (χ2=4.46, p=0.035), physical activity (χ2=4.89, p=0.027), family history (χ2=10.72, p=0.001) and BMI (χ2=40.67, p<0.001). However, there was no statistically significant difference between tea intake and HTN (χ2=0.03, p=0.850) (table 3). Table 4 shows the means of the physiological and anthropometric indices by categories of HTN. All anthropometric parameters (weight, height, BMI, WC and HC) were significantly greater in HTN patients than in non-HTN patients (p<0.05).
Table 3. Comparison of the lifestyle and health profiles between HTN and non-HTN groups (n=1850).
| Variable | Non-HTNn=1695 (91.6%) | HTNn=155 (8.4%) | χ2 | P value |
| Smoking | ||||
| No | 1384 (92.3) | 116 (7.7) | 4.29 | 0.039 |
| Yes | 311 (88.9) | 39 (11.1) | ||
| Tea intake | ||||
| No | 239 (91.9) | 21 (8.1) | 0.03 | 0.850 |
| Yes | 1456 (91.6) | 134 (8.4) | ||
| Coffee intake | ||||
| No | 286 (94.7) | 16 (5.3) | 4.46 | 0.035 |
| Yes | 1409 (91) | 139 (9.0) | ||
| Physical activity | ||||
| No | 1097 (90.6) | 114 (9.4) | 4.89 | 0.027 |
| Yes | 598 (93.6) | 41 (6.4) | ||
| Family history of hypertension | ||||
| No | 568 (94.7) | 32 (5.3) | 10.72 | <0.001 |
| Yes | 1127 (90.2) | 123 (9.8) | ||
| BMI (kg/m2)* | ||||
| Underweight | 18 (100.0) | – | 40.67 | <0.001 |
| Normal | 601 (95.4) | 29 (4.6) | ||
| Overweight | 672 (92.4) | 55 (7.6) | ||
| Obese | 404 (85.1) | 71 (14.9) |
BMI where underweight people had a BMI<18.5 kg/m2, normal weight had a BMI of 18.5 kg/m2 to 24.9 kg/m2, overweight had a BMI of 25.0 kg/m2 to 29.9 kg/m2 and obese ≥30.0 kg/m2.
BMIbody mass indexHTN, hypertension
Table 4. Comparison of the anthropometrics measurements between HTN and non-HTN (n=1850).
| Variables | Non-HTN (mean±SD) | HTN(mean±SD) | P value | MD | 95% CI | |
| Lower | Upper | |||||
| Height (cm) | 170.25±8.92 | 171.97±8.18 | 0.013 | −1.72 | −3.09 | −0.36 |
| Weight (kg) | 77.84±14.84 | 87.24±12.97 | <0.001 | −9.39 | −11.57 | −7.22 |
| BMI (kg/m2) | 26.84±4.61 | 29.80±5.60 | <0.001 | −2.96 | −3.87 | −2.04 |
| WC (cm) | 106.85±11.74 | 110.25±9.96 | <0.001 | −2.78 | −4.03 | −1.54 |
| HC (cm) | 99.59±6.92 | 102.38±7.55 | <0.001 | −2.96 | −3.87 | −2.04 |
BMI, body mass index; HC, hip circumference; HTNhypertensionMD, mean difference; WC, waist circumference
Multivariate analysis of the associated factors of HTN in HCWs
Multiple logistic analysis indicated that age, BMI, WC, HC, type of work, workplace, work experience, smoking, coffee intake and family history of HTN were significantly associated with increased risk of HTN among HCWs, as presented in table 5.
Table 5. Multivariate analysis of the associated factors of HTN in HCWs.
| Variables | Model 1OR (95% CI) | Model 2OR (95% CI) |
| Age | 1.13 (1.11 to 1.16) | 1.13 (1.11 to 1.15) |
| BMI (kg/m2) | 1.12 (1.08 to 1.15) | 1.11 (1.08 to 1.15) |
| WC (cm) | 1.03 (1.01 to 1.04) | 1.02 (1.01 to 1.04) |
| HC (cm) | 1.06 (1.03 to 1.08) | 1.05 (1.03 to 1.08) |
| Type of work | ||
| Full-time | Ref | Ref |
| Part-time | 4.17 (1.31 to 13.27) | 4.17 (1.31 to 13.27) |
| Workplace | ||
| Hospitals | Ref | Ref |
| Clinics | 1.78 (1.19 to 2.68) | 1.78 (1.19 to 2.68) |
| Management | 1.82 (1.03 to 3.24) | 1.82 (1.02 to 3.24) |
| Experience (years) | ||
| <5 | Ref | Ref |
| 5–9.9 | 5.97 (1.79 to 19.86) | 5.96 (1.79 to 19.85) |
| 10–14.9 | 13.85 (4.32 to 44.45) | 13.81 (4.31 to 44.44) |
| ≥15 | 39.53 (12.27 to 127.40) | 39.52 (12.26 to 127.39) |
| Smoking | ||
| No | Ref | Ref |
| Yes | 1.49 (1.02 to 2.20) | 1.49 (1.02 to 2.19) |
| Coffee intake | ||
| No | Ref | Ref |
| Yes | 1.76 (1.04 to 3.00) | 1.76 (1.03 to 3.00) |
| Physical activity | ||
| No | Ref | Ref |
| Yes | 0.66 (0.46 to 0.96) | 0.66 (0.45 to 0.95) |
| Family history of HTN | ||
| No | Ref | Ref |
| Yes | 2.44 (1.75 to 3.42) | 1.93 (1.29 to 2.89) |
The p value was considered statistically significant when p<0.05. Model 1: unadjusted model; model 2: adjusted for age, BMI, WC, HC, type of work, work experience, smoking status, coffee consumption, physical activity and family history of HTN.
BMI, body mass index; HChip circumferenceHCWs, healthcare workers; HTN, hypertension; WC, waist circumference
The OR of HTN significantly increased 0.13 times for each year increase of age (95% CI 1.10 to 1.15, p<0.001). On average, a one-unit increase in BMI (kg/m2) was associated with an increase of 11% in the occurrence of HTN (OR=1.11; 95% CI 1.08 to 1.15; p<0.001). Similarly, increases in WC (OR=1.02; 95% CI 1.01 to 1.04; p<0.001) or HC (OR=1.05; 95% CI 1.03 to 1.08; p<0.001) were both associated with an increased risk of HTN. Compared with participants working full-time, those who worked part-time were at greater risk of HTN (OR=4.17; 95% CI 1.13 to 13.27, p=0.015). The OR of HTN among individuals who work in the management workplace was almost two times greater than that of those working in hospitals (OR=1.82; 95% CI 1.02 to 3.24; p<0.041). Moreover, compared with the minimal category of work experience, the OR of HTN was 5 times higher for HCWs with 5–9.9 years of experience (OR=5.96; 95% CI 1.79 to 19.85, p<0.001), 13 times higher for those with 10–14.9 years of experience (OR=13.81; 95% CI 4.31 to 44.44, p<0.001) and more than 39 times higher for those with more than 15 years of experience (OR=39.52; 95% CI 12.26 to 127.39, p<0.001). Smoking was associated with a higher risk of HTN by 1.49 times than those who were non-smokers (OR=1.49; 95% CI 1.02 to 2.19; p=0.039). Furthermore, coffee intake was associated with a higher risk of HTN by 1.76 times (OR=1.76; 95% CI 1.03 to 3.00). This relationship was statistically significant (p=0.037). On the other hand, practicing regular physical exercise was a significant protective factor (OR=0.66, 95% CI 0.45 to 0.95) against the development of HTN. Finally, a family history of HTN was associated with HTN (OR=1.93; 95% CI 1.29 to 2.89; p<0.001).
Discussion
Our research is the first study to show the prevalence of HTN among HCWs in the Gaza Strip, Palestine, and the Middle East. Moreover, most studies explore the disease among the ill population, but we sought to explore the prevalence of HTN and other NCDs among people who provide health services. In addition, this study will be a baseline for further research in the future.
The overall prevalence of HTN in this study was 8.4% among HCWs. The profession-specific prevalence of HTN was higher among nurses (9.0%), and the family history of HTN was a non-modifiable risk factor that was significantly associated with HTN. This prevalence of HTN among hospital employees was lower than in the general population of Palestinians living in the Gaza Strip. Based on a recent study, the prevalence of HTN among individuals aged 25 and older was 28.4% in the Gaza Strip.8 In another study involving individuals aged 20–60 years, the rate of HTN was 28%.29 Additionally, other studies found that the prevalence of HTN among the population in Gaza was 24.4%. However, compared with other countries, the prevalence of HTN among health workers in the Gaza Strip is the lowest.8 29 30 In the current study, the frequency of HTN among hospital and PHCs workers was lower than that of HCWs in South Nigeria, Thailand, Singapore and Bangladesh31,36; this difference is perhaps due to the variance in the geographical distribution of population and the numbers of health workers in those countries.
Four behavioural factors, including physical inactivity, coffee and tea consumption, smoking and obesity, were common among the study population. Around 65.5% of the HCWs were physically inactive; there was a significant association between physical activity and HTN. We observed that irregular physical activity increased the risk of HTN. Consistent with our study, previous studies for the population considered physical inactivity as one of the associated factors of HTN that increases the risk of HTN.37,39 In addition, reduced physical activity led to obesity. Such changes in lifestyle and dietary habits contribute to the excess prevalence of abdominal obesity in participants, which eventually results in the increased prevalence of HTN. We found that coffee consumption was strongly related to decreasing the risk of HTN. A recent meta-analysis study included prospective cohort studies; high coffee consumption was associated with a small reduction in HTN. Additionally, this study mentioned that smoking might play a role in coffee consumption and the risk of HTN.40 On the other hand, in our study, there is no significant association between tea consumption and the risk of HTN. A systematic review showed that regular tea consumption decreased blood pressure.41 A meta-analysis of the data from 11 randomised controlled intervention studies with 378 participants indicated that daily black tea consumption for 1 week is linked to a statistically significant decrease in SBP and DBP of 2 and 1 mm Hg, respectively. Even though these BP reductions are small from the perspective of the individual, they are significant from the perspective of the population as a whole and may result in significant reductions in the risk of CVD.42
Smoking behaviours are regarded as an environmental risk factor influenced directly by social networks; about 18.9% of health professionals are smokers. There is a statistically significant relationship between smoking and the risk of HTN. Similarly, our findings agree with previous studies that confirmed smoking to be a modifiable risk factor for HTN.32 34 43 44 Smoking habits are considered an environmental risk factor that is directly affected by social network influences and smoking, and obesity represent the current leading preventable health risk.
Compared with a normal BMI, we found that being overweight and obese is strongly associated with HTN in HCWs. This is agreed with previous studies that revealed that any increase in BMI might reduce life expectancy and increase the risk of HTN and chronic diseases.42 45 46 This might be due to fast-growing urbanisation and industrialisation associated with changes in dietary habits and reduced physical activity, which leads to unbalanced energy gains and results in a high prevalence of overweight/obesity, indicating fat accumulation. This overweight/obesity with excess body fat accumulation and cholesterol level eventually results in the increased prevalence of HTN.47 Non-HTN people had lower anthropometric measurements, but hypertensive people had significantly higher BMI and WC for both genders. Anthropometric measurements had a weak but positive correlation with blood pressure. Moreover, the previous studies showed increased anthropometric measurement for the hypertensive group, especially increasing BMI.29 30 32 34 36
A familial history of HTN was present in most responders and hypertensive individuals; however, a family history of HTN was significantly associated with HTN among participants. This is agreed with previous studies that revealed.42 43 Although a high frequency among health workers who are not at risk of HTN is concerning, a family history of HTN was substantially associated with HTN in their sons and daughters. They have a high percentage of positive family history, which is a proven predictor of HTN, but they also work stressful jobs and rarely have time to exercise regularly.34
However, the current study showed significant predictors associated with an increase in the prevalence of HTN among HCWs. These associated factors included age, BMI, WC, HC, type of work, workplace, experience, smoking, coffee intake, physical activity and a family history of HTN. Evidence supports that HCWs with knowledge of HTN risk factor prevention would have a lower prevalence of risk factors than the general population. However, this discovery is equivocal, necessitating more research into the subject to better understand the current findings.
To the best of our knowledge, it was the first study with stringent quality control conducted on a large representative sample in Palestine to provide a nationwide estimate of HTN among HCWs. Inevitably, this study had several potential limitations. First, the study was cross-sectional, which could not address the question of causality regarding the effects of modifiable factors on HTN, and further studies are needed to develop effective strategies for risk assessment, prevention and intervention of HTN. Second, although detailed assessments on health-related risk factors were conducted, we cannot fully rule out potential confounders of other healthy lifestyle factors that may exist to attenuate or exaggerate the associations between modifiable factors and HTN. Third, the self-reported data on social demography and lifestyle behaviours were vulnerable to recall bias, which might lead to misclassification. Fourth, even though sphygmomanometer is the most widely used method in the measurement of SBP and DBP, potential measurement error might also be possible. Furthermore, the intervals of BP measurement were relatively short, and thus measurement error was inevitable. In addition, we did not perform a pilot study, which could improve research methodology and provide estimates for sample size calculation, both of which are crucial to enhancing the quality and efficiency of the primary study. A further pilot study and prospective study to assess the potential causality between modifiable factors and HTN as well as gain a better understanding of the underlying mechanisms is warranted.
Conclusions
This study estimated the prevalence of HTN and its associated factors. Our study indicated that 8.4% of HCWs are at risk for HTN. Furthermore, our findings also showed that smoking, irregular physical activity, a family history of HTN, increased BMI and coffee consumption are associated with a higher risk of HTN. Thus, effective strategies should be undertaken to reduce the risk of HTN among HCWs, focusing on lifestyle, physical activity, avoiding stressors and a safe work environment. A wellness programme should be developed by decision-makers through mass-level educational awareness campaigns for HCWs to prevent and manage the modifiable risk factors that increase the risk of HTN.
supplementary material
Acknowledgements
We would like to thank all participants who accepted to participate in this study and the administrators of facilities who participated in the study for their generous support, nurses, research assistants and nutritionists who helped in data collection.
Footnotes
Funding: This work was supported by the Key Research and Development Programme of Shaanxi (2022SF-185), and the Fundamental Research Funds for the Central Universities (qngz2016004, xzy032019008). The funders had no role in the study design, implementation, analysis, decision to publish or reparation of the manuscript.
Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2023-076577).
Provenance and peer review: Not commissioned; externally peer reviewed.
Patient consent for publication: Consent obtained directly from patient(s).
Ethics approval: This study involves human participants and approved by the Ethics Committee of Research Department at the Directorate General of Human Resources Development, Ministry of Health, Gaza (PHRC/HC/663/19). Informed consent forms were obtained from all subjects before participating in the study. The whole study complied with the Declaration of Helsinki. The data were collected and analysed in an anonymous and non-linked manner, with no participant names being used. In addition, physical risks, such as blood sampling, were not involved in our study, as no intervention was performed.
Data availability free text: The dataset generated and analysed during the current study is not publicly available. The project still running and future publications may be generated however data are available from the corresponding author on a resealable request.
Map disclaimer: The inclusion of any map (including the depiction of any boundaries therein), or of any geographic or locational reference, does not imply the expression of any opinion whatsoever on the part of BMJ concerning the legal status of any country, territory, jurisdiction or area or of its authorities. Any such expression remains solely that of the relevant source and is not endorsed by BMJ. Maps are provided without any warranty of any kind, either express or implied.
Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Author note: The study was conducted following the Declaration of Helsinki and approved by the Palestinian Health Research Committee (Number: PHRC/HC/663/19).
Contributor Information
Joma Younis, Email: jomaa_you@yahoo.com.
Lina Wang, Email: wln305@stu.xjtu.edu.cn.
Majed Jebril, Email: majed_art@hotmail.com.
Hong Jiang, Email: jiangh0926@outlook.com.
Yahui Fan, Email: fyh14042166@stu.xjtu.edu.cn.
Zhaofang Li, Email: 13821175362@163.com.
Mei Ma, Email: wysun2013195@stu.xjtu.edu.cn.
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Data availability statement
Data are available upon reasonable request.
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