Study area

This paper reviews studies from Pakistan, a developing country located in South Asia that covers an area of 881,913 km² (33^rd largest country in the world). Pakistan has four provinces (Punjab, Sindh, Balochistan, and Khyber Pakhtunkhwa), one federal territory (Islamabad), and two autonomous territories (Gilgit-Baltistan and Azad Jammu and Kashmir). According to the latest census Figs, the total population of Pakistan was 197 million in 2017 (2.6% of the world’s population), making it the sixth most populous country in the world. In 2012–14, the general government expenditure on health in Pakistan was only 1% of the GDP [24].

Search strategy

Between May 2019 and August 2019, we searched the electronic databases of PubMed, EMBASE, PsycINFO, Elsevier, Scopus, and ISI Web of Science for all relevant and retrievable literature. The emphasis was on identifying all studies in Pakistan that investigated the factors associated with hypertension. We searched the following terms: “predictors”, “factors”, “determinants”, “characteristics”, “component”, “psychological”, “sociodemographic”, “clinical”, “health-related behavior”, “health-related outcomes” (such as diabetes, liver disease, and heart disease), and family-related variables such as “father or sibling history of hypertension”. Based on these terms, we created computer syntaxes for the online searches (S5 Table) and searched for combinations of the above-mentioned in the reference lists of retrieved articles. There was no restriction on language, but we found all the literature to be in English.

Study selection

We included all studies that were conducted in Pakistan, reported factors associated with hypertension (self-reported or clinically diagnosed), and/or considered hypertension as the main analysis outcome. There were no restrictions on the study design, age of the study population, and criteria for diagnosing hypertension. Studies that used statistical analysis to assess the association of various factors with hypertension, regardless of design, were included in this review. However, if a study was designed to assess a particular intervention and report it as a predictor of hypertension, it was not considered. Studies with narratives and case reports were also excluded.

We assessed all the identified studies for eligibility. Initially, reviewers G.S. and M.R. independently screened the identified studies by their titles first and then by the titles and abstract simultaneously. In the next step, they discussed the discrepancies and conducted a full-text review of the selected articles. Additionally, reviewer M.A. screened 2/3^rd of the selected studies and extracted data independently. All the reviewers reached a consensus after addressing any disagreement or uncertainty in the study selection or data extraction processes.

Data extraction

After screening the studies for inclusion, we developed a pre-designed data-extraction form to create a summary table of the important characteristics using the full text of the included studies. According to the form, G.S. initially performed data extraction under the supervision of M.R. The data was then verified by M.R. and M.A. (for 2/3^rd of the studies). The data collection form retrieved information on the author’s name, year and region (city) of the study cohort, aim and design of the study, patient characteristics (including age and anthropometric measurements), sample size and prevalence of hypertension, main outcome measure (hypertension), clinical validation of the outcome if applicable, factors associated with hypertension, statistical methods used, and effect estimates (e.g., odds ratios) for the factors significantly associated with hypertension. The process of screening and data extraction was carried out over 3 months (September 2019 to November 2019).

Quality assessment of the studies

To assess the quality of the included research, we adopted the Newcastle Ottawa Quality Assessment Scale (NOQAS) developed to assess the quality of cohort studies and case-control studies [25] and its modified version that assesses cross-sectional studies [26,78]. The studies we included were either case-control or cross-sectional.

For case-control studies, the NOQAS is composed of three major components: a selection of the study groups (4 items), comparability of the groups (2 items), and ascertainment of exposure (3 items). Hence, by allocating one star per item, the maximum possible score could be 9 stars for one study. The Modified Newcastle Ottawa Scale (MNOQS) is composed of three main sections: selection of the study groups (4 items), comparability of the groups (2 items), and ascertainment of the outcome of interest (2 items). Hence, the maximum possible score for this scale could be 8 for one study.

Two reviewers (G.S. and M.R.) independently assessed the quality of the included studies and scored them. An MNOQS score of ≥ 7 indicated a high-quality study; any study with a lower score was considered low-quality.

Meta-analyses

Details on the statistical analyses used in the included studies are described briefly in the results section of this manuscript. We identified several factors associated with hypertension in Pakistan, and pooled the effect estimates, and quantified the amount of heterogeneity to obtain valuable information about the direction and size of effects for the association of a factor. We conducted the meta-analysis for a factor if its effect estimates from at least three studies could be extracted or computed (based on the raw aggregated data available in the published manuscript). In all the included studies, the effect estimates (if available) were reported as odds ratios (ORs) 95% confidence intervals (95% CIs).

To ensure consistency of the various effect estimates in our meta-analysis, we changed the effect estimates for some factors so that we could use the appropriate reference category of the factor to assess its association with hypertension. For example, to perform a meta-analysis on older age as a categorical variable, we extracted or computed the ORs of hypertension for older age groups versus the youngest age group (<30 years). If a study reported an effect estimate for a continuous factor, we reported it separately.

Since the studies were mostly heterogeneous, we used the DerSimonian and Laird random-effects model to compute the pooled effect estimates and quantify the heterogeneity using the index I² [27]. This allowed us to compute the pooled effect estimates as OR (95% CI) along with I². We used the standardized normal statistic (z-score) to compute a p-value for testing the null hypothesis such that there was no effect of a factor on hypertension (effect estimate-OR = 1). A significance level of p<0.05 indicated a statistically significant association.

To adjust for multiple testing, we calculated the false discovery rate of controlling the multiple testing error rate using the Benjamini-Hochberg procedure [28]. We created a forest plot to display the results and used funnel plots to assess publication bias. To assess if the studies' quality affects the pooled results, we conducted sensitivity analyses by excluding low-quality studies while carrying out meta-analyses of factors reported by more than three good-quality studies (NOQAS>7).

This systematic review was completed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [29] (S4 Table).

Literature search and study selection

Our primary search of the electronic databases returned 17,570 studies. We also manually identified 25 studies in the literature (using citations) that were not picked by the search as they were published before 1980. Thus, we identified a total of 17,595 studies for evaluation. However, 19 out of the 25 manually identified studies were not retrievable from online resources and some did not exist in the journal they were reported to have been published, as per the citations. On contacting the main authors or corresponding journals of these studies, we obtained only 8 out of 19 studies.

Initially, we identified 1,984 studies based on their titles. Of these, we excluded 1,919 after screening their abstracts. We then conducted a full-text review of 65 articles and further excluded 28 studies as they did not fulfill our inclusion criteria (Fig 1). Two reviewers (M.R. and G.S.) agreed (100%) to include the remaining 37 studies (7 case-control and 30 cross-sectional) in the present analysis.

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Fig 1. Flow diagram showing the basis of inclusion and exclusion of studies in the review.

https://doi.org/10.1371/journal.pone.0246085.g001

Characteristics of the included studies

Detailed characteristics of the included studies are described in S1 Table. We investigated 99,391 participants from 37 studies. All the studies were observational in nature; 7 (18.9%) were case-control [30–36] and 30 (81.1%) were cross-sectional. They were conducted in various regions across Pakistan and were published between 1980 and 2019. The sample sizes of the included studies ranged from 120 to 43,943 individuals. The participants’ ages ranged from 5–80 years, and the study populations included medical students, university teachers, in-service/retired adults, businessmen, drivers/conductors, factory workers, clerks, shopkeepers, all working individuals, elderly individuals, and school children. Six studies were conducted in rural areas [37–42], 8 were carried out in urban populations [43–50], and the remaining 23 (62.2%) sampled a combination of the two. Five (13.5%) studies included only women [37,51–54], 1 study [36] included only men, and 2 [48,50] were based on schoolchildren.

Measurement of blood pressure and hypertension

In the included studies, BP was measured by trained investigators, attending physicians, community health workers, medical technicians, medical students, medical practitioners, and/or trained nurses using a manual mercury sphygmomanometer on the right arm with the patients in a sitting position. Three studies [39,42,55] employed an aneroid sphygmomanometer and 1 study [45] used the digital BP monitor OMRON. Out of 32 studies that described their BP measurement technique, 13 (40.6%) studies [31,38,39,42–44,52,56–61] did not report the frequency of BP readings, 8 (25.0%) [37,40,53–55,62–64] considered the average of two readings, 9 (28.1%) used the mean of three readings [36,41,45–48,51,65,66], and only 2 (6.3%) [30,50] used more than three BP readings. The initial rest time in all studies ranged from 2–5 minutes and the interval between two or more readings ranged from 2–20 minutes. All studies performed BP measurements in a single visit except 2 studies [30,66] that scheduled three and at least two visits, respectively.

Almost all the included studies followed the guidelines made by the Seventh Report of Joint National Committee (JNC-VII) [67] to diagnose hypertension (i.e., BP ≥ 140/90 or the use of medication for lowering BP), except for 2 studies [49,53]. In one study [49], self-reported hypertension or use of hypertensive medication was considered, and in the other [53], the reference range of BP to identify hypertension was not reported. Another study [46] used a cut-off value of BP ≥ 160/90 mm Hg for children aged 5–10 years and BP ≥ 165/90 mm Hg for adults aged 20–60 years. Four studies enrolled previously diagnosed cases of hypertension without specifying the standard methods used for diagnoses [32–35] (S1 Table).

Study quality

Of 37 studies, 13 (35.1%; 7 cross-sectional: [40,41,45,52,57,61,66] and 7 case-control: [30,31,33–36,46]) scored at least 7 points out of 9 on NOQAS [25] and were considered high-quality. The median score was 6 (range: 4–8). All the case-control studies except one [30] and 7 cross-sectional studies were considered to be high-quality (score ≥7). The low-quality studies failed to control potential confounders in the design and statistical analysis, did not describe the sampling strategy, and lacked appropriate statistical methods (S2 and S3 Tables).

Statistical methods used in the included studies

All the included studies contained statistical analyses that assessed the level of association between the outcome (hypertension) as a dependent variable and other factors as independent variables. Of the 37 studies, 17 (45.9%) studies (12 cross-sectional [30,36,39,41,42,45,50–52,57,58,61] and 5 case-control [31–35]) reported unadjusted ORs (95% CI) using logistic regression. Seven (18.9%) studies (5 cross-sectional [40,41,57,61,66] and 2 case-control [31,36]) reported adjusted ORs (95% CI) using multiple logistic regression analyses. Six (16.2%) studies (cross- sectional [38,43,46,55,58,64]) had descriptive analyses and the remaining 7 (18.9%) studies analyzed data using the Chi-Square test, t-test, or correlation analyses (S1 Table).

Prevalence of hypertension

The overall prevalence of hypertension in the 37 included studies ranged from 3.0–77.5%. In cross-sectional studies and case-control studies, it ranged from 3.0–65.1% and 27.5–77.5%, respectively.

Risk factors associated with hypertension

We extracted 46 factors that were significantly associated with hypertension from 37 studies conducted on the Pakistani population. There were 8 socio-demographic, 14 lifestyle, 14 health-related, and 10 psychological factors. The most frequently reported factors were: old age (15 studies), gender (14), urban vs. rural (4), level of education (5), physical activity (6), tobacco use (6), family history of hypertension (5), BMI categories (9), diabetes (6).

These and other less-frequently reported factors are presented in Table 1 along with their extracted estimates (95% CI), pooled effect estimates (95% CI) with p-values, and the heterogeneity index I². The meta-analysis results are displayed in Figs 2 and 3 and S1–S28 Figs. The sensitivity analyses (conducted by excluding low-quality studies) of several factors that were reported by more than three good-quality studies showed similar results with reduced heterogeneity (Table 1 and S28 Fig).

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Fig 2. Forest plot from the meta-analysis (using the random effect model) of different age groups.

https://doi.org/10.1371/journal.pone.0246085.g002

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Fig 3. Forest plot of the meta-analysis (using the random effect model) of different BMI groups.

https://doi.org/10.1371/journal.pone.0246085.g003

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Table 1. Factors significantly associated with hypertension: A meta-analysis.

https://doi.org/10.1371/journal.pone.0246085.t001

Socio-demographic factors

The socio-demographic factors significantly associated with hypertension were: age in 16 studies [31,37–41,46–48,51,55,57,61,63–65], gender in 14 [38–41,46–49,57,60,61,63–65], income status in 3 [33,57,62], level of education in 5 [31,47,49,57,65], marital status in 3 [39,47,65], financial dependency in 1 study [49], and occupational noise level [66] and ethnicity (i.e., Muhajir, Pashtun, Baluchi vs. Sindhi) [57] in 1 study. The pooled effect estimates from our meta-analysis revealed that the risk of hypertension was increasingly significantly higher in older individuals (30–39, 40–49, 50–59 and ≥ 60 years) as compared with an age of <30 years and pooled ORs (95% CIs) of 2.65 (1.82–3.87), 4.32 (3.24–5.76), 6.85 (4.77–9.85) and 9.68 (5.49–17.10), respectively (Table 1, Fig 2, and S1 Fig). The risk was significantly higher among participants living in urban areas as compared with those living in rural areas with an OR of 1.87 (1.27–2.76). Married participants [2.70 (1.88–3.88)] and those with a higher income status [1.34 (1.16–1.55)] were more likely to be hypertensive.

On the other hand, the risk of hypertension was significantly lower in participants with a negatively associated socio-economic factor, i.e., a higher level of education (college and above) as compared with illiterate or primary education [0.58 (0.38–0.88)] (Table 1, Fig 2, and S2–S8 Figs).

Lifestyle factors

We identified 3 lifestyle factors that were reported by at least three studies; they included physical activity (reported by 4 studies) [44,47,55,65], tobacco use (6 studies) [31,41,44,47,55,57], and use of unrestricted salt in the diet (3 studies) [41,53,57]. Physical activity and unrestricted salt use were negative factors, i.e., they were significantly associated with a lower risk of hypertension, with pooled ORs (95% CI) of 0.16 (0.07–0.35) and 0.24 (0.12–0.47), respectively (Table 1 and S9–S12 Figs). On the other hand, tobacco use was significantly associated with a higher risk of hypertension, with an OR of 1.48 (1.19–1.83) (Table 1 and S13 and S14 Figs).

Only one study reported other diet- and work-related lifestyle factors, including a high intake of meat, daily intake of ghee/butter, and daily intake of calcium [57]; housewives who ate three meals a day vs. those who followed no eating pattern, working women who consumed fast food once a week vs. those who consumed it sometimes, and working women and housewives in general [53]; male civil-servants and factory-workers [46]; those working in an environment with a high level of occupational noise [66]; and consumption of wine [41]. Among these variables, the daily intake of ghee/butter, daily intake of calcium, working women, male factory workers, exposure to a high level of occupational noise, and consumption of wine were significantly associated with a lower risk of hypertension, and the other variables were significantly associated with a higher risk of hypertension (Table 1).

Health-related factors

Three health-related factors were significantly related to hypertension and were reported by at least three studies were: a family history of hypertension [31,40,41,44,55], BMI (overweight and obesity vs. normal-weight) [36,40,41,43,44,47,48,52–55,57,59,61,64,65], and diabetes [36,40,42,49,51,57,60]. The pooled ORs (95% CI) of the above factors were 1.91 (1.51–2.42), 1.95 (1.55–2.44), 2.95 (2.26–3.84) and 2.94 (1.88–4.59), respectively (Table 1, Fig 3 and S15–S20 Figs).

Other significantly associated but less-reported health-related factors were: a history of kidney disease [31,40], cardiovascular disease [40,51], dyslipidemia (TC, LDL-C, HDL-C, TG) [30], hyperleptinemia [36], abnormal fasting blood sugar [36], frequent headaches [58], and the blood group O [52]. The extracted effect estimates of these variables are provided in Table 1.

Psychological factors

The psychological factors that were reported by at least three studies and were significantly associated with a higher risk of hypertension were: anxiety [32,33,35], stress [32,33,35,55], and anger-in and/or anger-control issues [32,34,35]. The pooled ORs of these factors (95% CI) was 1.67 (1.34–2.09), 1.39 (1.20–1.61), 1.42 (1.16–1.74), and 1.16 (1.09–1.23), respectively (Table 1 and S21–S26 Figs). Additionally, depression [32,33], state-anger [34], trait-anger [34], anger-expression [34], total anger [34], and psychological distress [32] were reported to be significantly associated with a higher risk of hypertension (Table 1).

Correction for multiple testing

Table 1 reports a meta-analysis of p-values for 21 variables. On applying the Benjamini-Hochberg procedure of false discovery rate [28] on the complete set of p-values, we found the corrected threshold of significance level to be p≤0.011, which was attained by 19 of the 21 p-values in our analysis (Table 1).

Heterogeneity

The meta-analyses of all the factors that were reported by at least three studies revealed that there was a significantly high level of heterogeneity among all the studies in terms of the factors described, except for 4 (income status, anxiety, stress, and anger-control). The heterogeneity coefficient I² ranged from 0.0–97.6%, where no heterogeneity was indicated by I² = 0.0% for income status, anxiety, stress, and anger-control (Table 1, Figs 2 and 3, and S1–S3 Figs). The index was was very high (≥75.0%) for 12 factors (e.g., I² = 97.6% for the age groups of >60 years), moderate (50.0% <I² <75.0%) for a family history of hypertension and the age group of 30–39 years, and very low (I² = 18.0%) for marital status. However, the meta-analysis of a factor with low or no heterogeneity included very few studies.

Heterogeneity among the studies may be due to differences in the study sample, sample size, study quality, use of different statistical methods (in some cases), BP measurement techniques, the definition of hypertension, measurement methods of a factor, and rates of hypertension observed. For example, by running meta-analyses for several factors based on good-quality studies, we observed that the heterogeneity index (I²) among the pooled study had reduced considerably (Table 1, S27 Fig).

Publication bias

The supplementary document contains the funnel and forest plots for each factor, which graphically represent the publication bias (S1–S27 Figs). Although the number of studies available for each factor is very small and it is difficult to form an opinion on the publication of each factor, the funnel plots for the results of some of the factors show evidence of some publication bias (S1–S3 and S10 Figs).

Limitations

There are several limitations to this review. Firstly, we included only observational studies, the majority (65.0%) of which had poor quality due to varying sample sizes, a lack of adjustment for potential confounders in the design and statistical analysis, no description of the sampling strategy, and the improper use of statistical methods. A few studies [39,46,51,63] were not specifically conducted to assess factors associated with hypertension, some published studies were not available even after we contacted the corresponding authors or journals, and some did not report proper statistics for the association or had errors in the reported statistics. Despite these inconsistencies, we have extracted relevant data from descriptive studies and corrected erroneous results wherever applicable.

Secondly, we could not conduct meta-analyses for several less-reported factors that were identified by only one or two studies in this review. The meta-analyses for other factors produced pooled effect estimates that provided information about the strength of associations and heterogeneity among the studies. For example, following our meta-analysis of gender, we observed that its effect on hypertension was not statistically significant and that there was a high level of heterogeneity regarding gender among the studies. A majority of the factors (65.0%) that underwent meta-analysis showed a high level of heterogeneity, but we also used random-effects models with inverse-variance weighted methods to further account for the heterogeneity. In the results section, we have provided a brief description of the sources of heterogeneity among the included studies, which can be explored in the future using meta-regression when enough studies are available. Further, the definition of hypertension varied among different studies, as did the coding or thresholds used to categorize explanatory variables. This further contributed to heterogeneity among the results.

Thirdly, all the included studies only considered a linear effect of the explanatory variables on hypertension, either through logistic regression or through other methods, such as correlations. However, it is important to recognize that some variables can have non-linear effects [79]. For example, essential dietary ingredients, such as salt and calcium, can be insufficient when taken in very small quantities but are harmful when the intake is higher than recommended. In such cases, the estimated ORs will not truly represent the relationship between the variable and hypertension.

Lastly, the variables included in this review may suffer from reporting bias because some studies may have omitted the results of variables having a non-significant association with hypertension. This could lead to an inflated value of the aggregate OR and indicate a false significant association in the meta-analysis. While we attempted to reduce the publication bias by using statistical techniques such as funnel plots and the Eggers test, it was difficult to form an opinion on the same because the number of studies was small and their analytical methods varied widely [80]. For example, some studies included additional covariates in multiple logistic regression analysis, in which case the estimated standard error might be lower than what would be obtained from a simple logistic regression analysis. In light of the above-mentioned limitations, the results of this review should be carefully interpreted when they are used.

This review is based on studies from one economically underdeveloped country, and we believe that there is much scope for this work to be extended to other countries worldwide, which can facilitate global generalizability of these findings. In this respect, our review can be considered as a protocol for further comprehensive systematic reviews. Further investigations are required to clarify inconsistent results (e.g., the effect of gender not being significant or the protective effect of the excessive use of salt) and to re-examine the factors from a few good-quality and several poor-quality studies. In this section and the results section, we have provided a list of factors that were reported by only one or two studies, which can be investigated in further rigorously conducted research.