Abstract
Background
Hypertension is a major contributor to global disease burden, and the prevalence is associated to various social determinants, including area-level deprivation. However, the mechanisms underlying this association remain unclear, particularly in low- and middle-income countries (LMICs). This study explores how household food insecurity, individual behaviors (smoking and alcohol use), and nutritional status may mediate the relationship between area-level deprivation and hypertension in Nepal.
Methods
We used nationally representative data from the 2016 Nepal Demographic and Health Survey. Area-level deprivation was measured using a validated 15-item composite index. A two-level structural equation model was employed to analyze both the direct and indirect (mediated) relationships between deprivation and hypertension, with individuals (aged ≥ 15 years) and households nested within geographic clusters/areas.
Results
The overall prevalence of hypertension was 22.9%, with a higher rate in less deprived (more affluent) areas (27.5%) compared to highly deprived ones (17.9%). When mediators were not included, area-level deprivation was inversely associated with hypertension. However, when potential mediators—Body Mass Index (BMI; as a proxy for nutritional status), household food insecurity, and individual behaviors (smoking and alcohol use) were included, the direct effect of deprivation on hypertension was no longer significant. BMI emerged as the only significant mediator, accounting for approximately 70% of the total indirect effect. Food insecurity and individual behaviors did not significantly mediate the relationship; although, food insecurity was associated with lower hypertension risk and individual health behaviors (smoking and alcohol use) with higher risk at the individual level.
Conclusion
In the context of Nepal, hypertension is more prevalent in affluent areas, and BMI- a proxy for nutritional status significantly mediates the link between area-level deprivation and hypertension risk. These findings highlight the role of nutritional transitions in LMICs and the need for context-specific public health strategies targeting both environmental and individual-level factors.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12889-025-25471-5.
Keywords: Area-level deprivation, Hypertension, Multilevel-Mediation
Background
Hypertension is a leading preventable cause of morbidity and mortality worldwide [1]. Nearly one billion people are living with hypertension and is contributing to an estimated 7.5 million deaths annually [2]. Correlates and determinants of hypertension are multifactorial and potential causal effects may operate at multiple levels; individual, household, and area/community level factors. At the individual level these factors include age, sex, body mass index, health behaviors such as physical activity, smoking, dietary patterns and alcohol consumption [3–11]. At the household levels, factors may include food insecurity, wealth index, socio-economic status [12–16]. Green space, urbanicity, community or neighborhood level socioeconomic status are some of the area level determinants [17–20].
Area-level deprivation (AD); defined as a lack of access to essential material, social, and infrastructural resources has gained increasing attention as a determinant of health [21]. It is often correlated with poor access to health services [22], food insecurity [23], poorer health promoting-behaviors, poorly built environments such as recreational parks, walking space, health care facilities, and environmental pollutants [24, 25]. Individuals residing in socially and economically deprived areas often face an elevated risk of adverse health outcomes such as obesity [26, 27], diabetes [28], mental health disorders [29, 30]. Behavioral risk factors, including gambling, sedentary lifestyles, drug abuse, unhealthy dietary patterns, alcoholism, smoking, and inter-partner violence [31, 32] are relatively common in a deprived area.
However, the association varies between high income countries (HICs) and low- and middle-income countries (LMICs). In HICs, conditions such as hypertension and obesity are usually prevalent in deprived areas [33, 34]. Residing in deprived areas in HICs is linked with elevated social stress, poor diet, with higher intake of processed and refined foods, higher smoking rates, and sedentary lifestyle and limited opportunity for recreational facilities and green spaces [25, 28–38] In contrast, several studies from LMICs showed that individuals from the least deprived, or the affluent, areas may experience higher odds of hypertension compared to those living in highly deprived areas [39–41]. This contrasting association could be explained by the differential stages of the socio-demographic and nutritional transition in developing nations [42].
Nepal is experiencing a rapid socio-economic, epidemiological and nutritional transition, characterized by increased consumption of processed foods, fats, sugars, and salt, alongside shifts from agricultural to service-based occupations [36, 40, 43–45]. The dietary changes are particularly common among affluent population subgroups in LMICs [46, 47]. Despite these changes, less than 50% of households are food secure, and about 20% of adults are overweight or obese, reflecting growing nutritional challenges. Additionally, high smoking rates (27% in men and 6% in women) and notable alcohol use (Nepal Demographic and Health Survey, 2016) contribute to cardiovascular risk. Understanding how area-level deprivation influences hypertension and its modifiable risk factors is critical for designing effective, context-specific public health interventions in Nepal [48, 49]. This study investigates whether household food insecurity, behavioral factors (smoking and alcohol use), and nutritional status (as measured by BMI) mediate the relationship between area-level deprivation and hypertension risk in Nepal. The choice of these variables was based on the earlier studies and availability of the data [50–52].
Methods
Study design and data source
This analysis utilized data from the 2016 Nepal Demographic and Health Survey (NDHS), a nationally representative cross-sectional survey. The NDHS employed a multi-stage, stratified cluster sampling method designed to capture demographic, health, and socioeconomic indicators across Nepal. Detailed sampling procedures are available in the NDHS final report [53]. For this study, individuals aged 15 years and older with valid blood pressure measurements were included, excluding pregnant women, resulting in a final sample size of 14,652 individuals from 383 primary sampling units (PSUs).
Individual and household level variables
Hypertension was defined using the guidelines of the International Society of Hypertension [54]. Individuals were considered hypertensive if their systolic blood pressure was ≥ 140 mmHg and/or diastolic pressure was ≥ 90 mmHg. Blood pressure was measured three times using a UA-767 F/FAC digital monitor, and the average of the second and third readings was used. Additionally, participants who reported taking antihypertensive medication or had been diagnosed with hypertension were classified as hypertensive, regardless of current readings.
Key covariates included age (continuous) and sex (Male/Female). Behavioral risk factors included smoking or tobacco use (Yes/No) and alcohol consumption (Yes/No). Nutritional status was assessed using body mass index (BMI), calculated as weight (kg) divided by height (m²). Household food insecurity (HFI) was assessed using the household food insecurity access scale (HFIAS) [55]. Food insecurity, defined as limited or uncertain access to nutritionally adequate, safe, and socially acceptable food [50, 56]. Households were classified as food secure or food insecure based on the criteria established by the Food and Nutrition Technical Assistance (FANTA) project of the United States Agency for International Development (USAID) [57]. Households experiencing moderate or severe food insecurity were categorized as food insecure. The internal consistency of the HFIAS in this study was high, with a Cronbach’s alpha of 0.8, indicating good reliability.
Area level variables
Area-level deprivation was measured using a previously validated 15-item composite index [58]. The index incorporated indicators across material conditions, social characteristics, and infrastructure such as housing quality, education levels, employment, and access to roads or urban centers. The original index, scaled with a mean of 100 (SD = 20), was rescaled from 1 to 10 for interpretability, with higher scores indicating greater deprivation. The rescaled index was also divided into quartiles to represent increasing levels of deprivation.
Mediation model.
Given the hierarchical structure of the data, a two-level structural equation model was employed, with individuals nested within clusters. All eligible individuals within each household were included for blood pressure measurement. As the average number of individuals per household was fewer than two, both individual- and household-level variables were treated as level 1, while area-level deprivation (AD), representing the residential area, was modeled at level 2.
The study hypothesized 2-1−1 multilevel mediation model [59], meaning the predictor (AD) is assessed at level 2, and the mediators (food insecurity, body mass index and individual’s behavior), adjustment variables (sex, age) and the outcome variable (hypertension status; binary) are assessed at level 1. We hypothesized that AD, as a level 2 antecedent influences the level 1 mediators, which in turn affect the level-1 outcome, hypertension as described below.
Level 1: 
.
Level 2: 
= 
.
This represents the effect of level 2 independent variable (AD, denoted as 
on the level 1 mediator variables (food insecurity, body mass index and individual behavior, denoted as 
separately. The subscript ‘i’ and ‘j’ refers to individual (level 1) and area level (level 2), respectively. 
is the intercept for 
, ‘a’ is the effect of area level deprivation 
, 
and 
are the residuals at level 1 and level 2, respectively.
The remaining part of the multilevel mediation model related to the dependent variable (hypertension status,
) as described below.
Level 1: 
= 
-
) + 
.
Level 2: 
= 
+ c’
+ b
+ 
.
Level 2: 
.
The coefficient b indicates the effect of mediators (
) on hypertension (
) at the group (level 2).
refers to the group mean (aka latent variable) of the mediator (
) for the group j. Coefficient 
represents the effect of mediators (
) on (
) at the individual (level 1). Since 
is constant within a group (level 2), it cannot influence within-group variation. Therefore, the effect of area level deprivation (
) occur only at level 2. The mediating effect of area level deprivation (
) on hypertension (
) through the mediators (
) could take place only at level 2. The mediating effect of area-level deprivation on hypertension through the mediators is indicated by the product of the coefficients ‘a*b’. Additionally, 
is the residual for the random slope at Level 2, and 
is a random slope.
Briefly, in multilevel mediation models, particularly the 2-1−1 design used in this study, individual-level mediators (e.g., food insecurity, BMI, health behaviors) are decomposed into within-cluster (individual-level) and between-cluster (area-level) components. The between-cluster component is modeled as a latent variable; an unobserved group-level factor estimated via random effects rather than a simple observed group mean. This approach accounts for measurement error and provides a more reliable estimate of contextual influences. In our model, area-level deprivation (Level 2 predictor) affects this latent group-level component of the mediator, allowing us to examine how contextual factors influence individual health outcomes through indirect pathways.
Model coefficients were estimated adjusting for age and sex. In Mplus, weighted least square mean and variance (WLSMV) are associated with probit models when the dependent variable is binary, and the mediator is a latent variable (Mplus User’s Guide (8th ed.). However, interpretation of the probit coefficients in probit regression is not as straightforward as in linear regression or logit regression. The increase in probability attributed to a one-unit increase in each predictor is dependent both on the values of the other predictors and the starting value of the given predictors.
The total and mediated (indirect) effect estimates were calculated using MODEL CONSTRAINT command by multiplying the path coefficients. The comparative fit index (CFI), root mean square error of approximation (RMSEA) and standardised root mean square residual (SRMR) were used to assess goodness of fit, with CFI > 0.95 and RMSEA and SRMR < 0.05 indicating good model fit. These thresholds are general guidelines, and some flexibility might be needed depending on the complexity of the model. Multilevel Structure Equation modeling was conducted in Mplus (Version 7, Muthén & Muthén, Los Angeles, CA, 2017).
Results
Characteristics of the study population
A total of 14,652 individuals from 383 clusters were included in the study. Average cluster size was 40 individuals (range; 10–74). The mean age was 38.6 years (SD ± 17.6), with approximately 15% aged 60 years and older. Females comprised 57% of the study population. Regarding nutritional status, 18% of participants were classified as overweight or obese, while 19% were underweight. Additionally, approximately 6% of the population experienced severe food insecurity (Table 1).
Table 1.
Characteristics of the study population Nepal Demographic and Health Survey
| Variables | Overall (n=14,652)Mean (SD)/Frequency (%) |
|---|---|
| Age | 38.61±17.6 years |
| 15–39 | 8355 (57.0) |
| 40–60 | 4119 (28.1) |
| 60+ | 2178 (14.9) |
| Sex | |
| Male | 6246 (42.6) |
| Female | 8406 (57.4) |
| Smoke (Last 1 year) | |
| No | 13121 (89.5) |
| Yes | 1531 (10.5) |
| Alcohol Intake | |
| No | 14427 (98.6) |
| Yes | 225 (1.5) |
| Body Mass Index | |
| Underweight | 2783 (18.9) |
| Normal Weight | 9232 (63.1) |
| Overweight/Obesit | 2637 (18.0) |
| Food Security | |
| Secure | 6716 (46.37) |
| Mild insecure | 1670 (11.53) |
| Moderately Insecure | 5188 (35.8) |
| Severely food insecure | 909 (6.28) |
| Area level Deprivation and Hypertension prevalence | |
| Highly Deprived | 17.9 (n=631) |
| Moderate deprived | 23.1 (n=874) |
| Less deprived | 23.0 (n=885) |
| Least Deprived | 27.5 (n=961) |
| Overall | 22.9 (n=3,351) |
Prevalence and Spatial distribution of hypertension in Nepal
The overall prevalence of hypertension was 22.9% and was slightly higher amongst male. Individuals residing in the least deprived (more affluent) areas exhibited a higher prevalence (27.5%) compared to those in the most deprived areas (17.9%). The northern regions showed a relatively higher burden of hypertension. Further details on the prevalence, its distribution by the area level deprivation and the spatial distribution of hypertension have been reported in our previous study [39] and is also available in the supplementary file.
Mediation analysis
Direct Effect
The direct effect of area-level deprivation on hypertension, without considering mediators, was significant (β = 0.78, 95% CI: 0.73, 0.83). However, after adding mediators (BMI, food insecurity, and individual behaviors) and adjusting for age and sex, the direct effect was attenuated non-significant (0.33, 95% CI: −0.03, 0.09). This suggests that the previously observed strong association between AD and hypertension may be explained through indirect pathways, mediated by area-level factors such as overweight/obesity, food insecurity, and individual behaviors like smoking and alcohol consumption (Fig. 1).n
Fig. 1.
Multilevel SEM for 2-1−1 mediation showing the association between Area-level level Deprivation (AD) and Hypertension (HTN) as mediated by individual’s behaviour (IB), food insecurity (FI), and body mass index (BMI) in a Nationwide study from Nepal Demographic Health Survey-2016. Variables marked with a ‘j’ subscript are the observed variables at the area level, variables marked with an ‘ij’ subscript are observed at the individual level within the area. Square boxes represent the observed variables and round represents the latent variable. Arrows from between cluster variables or within cluster variables to the observed variables represents the decomposition of observed effect into between and within cluster effects, respectively. Short arrows entering endogenous variables indicate errors. Only between indirect effects exists because the exposure, mediator and outcome have between cluster variation
Indirect Effects
Nutritional status (BMI as a proxy measure) was the only significant mediator in the pathway between area-level deprivation and hypertension (indirect effect = −0.13, 95% CI: −0.18, −0.09). Paths mediated through food insecurity (Indirect effect = −0.01, 95% CI; −0.03, 0.02) and individual behaviors (Indirect effect = 0.016, 95% CI; −0.012, 0.04) were non-significant (Table 2).
Table 2.
Measure of direct and indirect effect (Estimates measures a probit coefficient)
| Area Level (Between) | Estimates | P- value |
|---|---|---|
| HTN regressed on | ||
| Body mass index | 0.87 (0.64, 1.1) | 0.001 |
| Food Insecurity | −0.05 (−0.23, 1.19) | 0.13 |
| Individual Behavior | 0.17 (−0.08, 0.42) | 0.32 |
| Deprivation (Direct effect) | 0.03 (−0.03, 0.09) | 0.13 |
| BMI regressed on deprivation | −0.83 (−0.87, −0.78) | 0.001 |
| Food insecurity regressed on deprivation | 0.68 (0.62, 0.74) | 0.001 |
| Individual Behavior regressed on deprivation | 0.53 (0.37, 0.70) | 0.001 |
| Individual Level (Within) | ||
| HTN regressed on | ||
| BMI | 0.29 (0.26, 0.31) | 0.001 |
| Food Insecurity | −0.034 (−0.06, −0.007) | 0.023 |
| Individual Behavior | 0.14 (0.07, 0.21) | 0.001 |
| Indirect effects | ||
| Deprivation-BMI-HTN | −0.13 (−0.18, −0.09) | 0.001 |
| Deprivation-Food Insecurity-HTN | −0.01 (−0.03, 0.02) | 0.13 |
| Deprivation-Individual Behavior-HTN | 0.016 (−0.012, 0.04) | 0.31 |
| All indirect effect | −0.12 (−0.18, −0.08) | 0.001 |
| All effect (total effect) | −0.09 (−0.11, −0.07) | 0.001 |
| Difference (Direct- Indirect) | 0.03 (−0.03, 0.90) | 0.26 |
*total effect excluding the direction of the association: Indirect effects (0.13 + 0.01 + 0.016) + direct effect (0.03) = 0.186
Individual level effects
Within clusters, BMI (0.29, 95% CI: 0.26, 0.31), food insecurity (−0.034, 95% CI: −0.06, −0.01), and individual behaviors (0.14, 95% CI: 0.07, 0.21) were significantly associated with hypertension (Table 2).
With Regards to the total effect, nearly 70% of the area-level effect (0.13/0.186) was mediated through BMI, a proxy for nutritional status. The model statistics indicated a reasonable fit: RMSEA: 0.029; CFI: 0.93; SRMR: (within clusters) and 0.09 (between clusters).
Discussion
This study examined the potential mediating role of household food insecurity, nutritional status, and behavioral characteristics on the previously established association between area level deprivation and hypertension using the 2016- Nepal Demographic and Health Survey [39]. Traditional multilevel modeling for mediation analysis does not fully separate between-cluster and within-cluster effects, i.e., the effects are conflated leading to a faulty indirect effect estimate [59]. Given that we are primarily concerned with area-level effects, any mediation by a variable at level 2 must occur at the between-area level, regardless of where the mediators and outcomes are measured. Multilevel Structural Equation Modeling offers a more robust approach by disentangling relationships at the two levels and estimating them separately, thus avoiding the conflation of effects. In MLSEM, the cluster-level component of a level 1 variable is treated as a latent variable, enabling more accurate assessment of indirect effects [60].
Body mass index, a robust proxy for nutritional status among adults, was the only significant mediator, accounting for a substantial portion (70%) of the indirect pathway, suggesting that obesity-related mechanisms are key contributors to the burden of hypertension. BMI is strongly correlated with physical activity levels, food access, and overall nutrition, thus explaining the significant mediated effect between AD and hypertension. This finding aligns with a previous study in Nepal, where BMI was identified as a key mediator in the relationship between household socioeconomic status and hypertension, mediating 89% of the indirect effect between household wealth quintiles and hypertension [61]. In recent years, Nepal has experienced a marked shift in dietary patterns, moving from a traditional agriculture-based diet to a more processed, calorie-dense food consumption, driven largely by rapid urbanization and trade liberalization. This dietary transition is further corroborated by findings from the most recent Nepal Demographic and Health Surveys, which show a growing positive association between wealthier communities, higher BMI, and increasing obesity rates [62].
While the mediating role of individual health behaviors (i.e., smoking and alcohol consumption) in the relationship between area deprivation and hypertension is well-documented [63], there was no significant association in the current study. Several factors may account for this discrepancy. First, the true prevalence of smoking, alcohol consumption may be underreported [64–66], leading to a potential underestimation of the association between area-level deprivation and these behaviors. Additionally, patterns of tobacco and alcohol use at the area level may exhibit limited variation across regions and wealth quintiles [67], which could attenuate observed associations. Moreover, it is plausible that other unmeasured and potentially crucial mediators in this relationship were not captured in the current analysis.
Household food insecurity did not emerge as a significant mediator in the relationship. It is possible that food insecurity may be associated with hypertension within specific clusters; however, access to refined and processed foods may not vary substantially across areas [68] rendering individuals from different quartiles of the area-level deprivation index equally susceptible to hypertension risk. Additionally, the magnitude of the indirect effect of deprivation on hypertension through BMI may vary depending on an individual’s food security status. Food insecurity could exacerbate the nutritional consequences of deprivation, such as increased reliance on calorie-dense, nutrient-poor foods, potentially amplifying the impact on BMI and related health outcomes. Future research using moderated mediation models could help clarify whether food insecurity alters the strength or direction of these indirect pathways.
These findings have important implications for public health policy and practice in Nepal. The identification of area-level deprivation as a key structural determinant of hypertension highlights the need for place-based interventions that address the broader socio-environmental contexts influencing health. Public health strategies should not only focus on individual behavior change but also target upstream factors, such as improving food environments, enhancing access to nutritious food, and reducing socio-economic disparities across regions. Integrating hypertension prevention into community-level programs, especially those addressing nutrition, urban planning, and social protection, may be critical. Moreover, the observed role of BMI as a mediator underscores the need for integrated NCD prevention efforts that combine healthy diet promotion, obesity prevention, and equitable access to healthcare.
From our earlier publication, we observed that the association between literacy a proxy for socio-economic status and hypertension varied depending on place of residence [39]. Specifically, literate individuals living in highly deprived areas were more likely to have higher odds of hypertension compared to those with no formal education. In contrast, literate individuals from the least deprived areas had lower odds of hypertension. Assessing the mediating factors stratified by socio-economic status in a larger data would further enhance understanding of how these pathways differ across populations.
Interpretation of the study results should be approached with caution, given several inherent limitations of this observational design. First, due to the cross-sectional nature of the data, causal inferences regarding the observed associations cannot be definitively drawn. The analysis assumes causality from area-level deprivation to mediators, and from mediators to hypertension, based on a hypothesized pathway, which cannot be fully verified. Additionally, the dataset lacks information on key potential determinants, including dietary patterns, family history, comorbidities, income, and certain other behavioral factor like physical activity, sleeping pattern, etc. The limited duration of available data on alcohol consumption and smoking likely underestimates the true prevalence of these risk factors. Finally, while the findings are specific to Nepal, caution is warranted in generalizing the results to other settings or populations.
Conclusion
This study highlights the complex relationship between area-level deprivation and hypertension, with a focus on potential mediators such as BMI, household food insecurity, and individual behavioral factors (smoking and alcohol consumption). While nutritional status emerged as a significant mediator in the pathway linking area-level deprivation to hypertension, individual behaviors like smoking and alcohol consumption, as well as food insecurity, did not show significant mediation in this context. The findings suggest that BMI, as a proxy for nutritional status, plays a pivotal role in the hypertension risk associated with area-level deprivation, a relationship likely influenced by changing dietary patterns and increasing urbanization in Nepal.
Supplementary Information
Acknowledgements
Ishor Sharma would like to thank Ontario Trillium Foundation for providing the scholarship for PhD Studies. Also, would thank the Demographic and Health Survey program; USAID for providing free access to the data.
Abbreviations
- AD
Area level deprivation
- ADI
Area level deprivation index
- BMI
Body mass index
- CFI
Comparative fit index
- EA
Enumeration areas
- HCIs
High income countries
- HTN
Hypertension
- LMICs
Low- and middle-income countries
- MLSEM
Multi level structure equation modeling
- NDHS
Nepal demographic and health survey
- PSUs
Primary sampling units
- RMSEA
Root mean square error of approximation
- SEM
Structure equation modeling
- SRMR
Standardized root mean square residual
Authors’ contributions
IS, MKC, YHC, INL, JMW, JCVG, and SS all contributed for the conception, design, interpretation of the data and revised the manuscript. IS performed the data analysis and drafting the report.
Funding
First Author received Ontario Trillium Scholarship for his PhD studies.
Data availability
Anonymous data is freely available at Demographic and Health survey program. [https://dhsprogram.com/].
Declarations
Ethics approval and consent to participate
Not Applicable.
Consent for publication
Not Applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Arima H, Barzi F, Chalmers J. Mortality patterns in hypertension. J Hypertens. 2011;29. 10.1097/01.hjh.0000410246.59221.b1. [DOI] [PubMed]
- 2.World Health Organization, Hypertension. Key Facts 2023. https://www.who.int/news-room/fact-sheets/detail/hypertension (Accessed Jun 6, 2024).
- 3.Kannel WB, Gordon T, Schwartz MJ. Systolic versus diastolic blood pressure and risk of coronary heart disease. Am J Cardiol. 1971;27:335–46. 10.1016/0002-9149(71)90428-0. [DOI] [PubMed] [Google Scholar]
- 4.Kannel WB, Gordon T, Schwartz MJ. Systolic versus diastolic blood pressure and risk of coronary heart disease. The Framingham study. Am J Cardiol. 1971. 10.1016/0002-9149(71)90428-0. [DOI] [PubMed] [Google Scholar]
- 5.Doumas M, Papademetriou V, Faselis C, Kokkinos P. Gender differences in hypertension: Myths and reality. Curr Hypertens Rep. 2013. 10.1007/s11906-013-0359-y. [DOI] [PubMed] [Google Scholar]
- 6.Diaz KM, Shimbo D. Physical activity and the prevention of hypertension. Curr Hypertens Rep. 2013. 10.1007/s11906-013-0386-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Exercise and Hypertension. Med Sci Sports Exerc. 2004. 10.1249/01.MSS.0000115224.88514.3A. [Google Scholar]
- 8.Cornelissen VA, Smart NA. Exercise training for blood pressure: a systematic review and meta-analysis. J Am Heart Assoc. 2013. 10.1161/JAHA.112.004473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Benowitz NL, Burbank AD. Cardiovascular toxicity of nicotine: implications for electronic cigarette use. Trends Cardiovasc Med. 2016. 10.1016/j.tcm.2016.03.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Rehm J, Gmel GE, Gmel G, Hasan OSM, Imtiaz S, Popova S, et al. The relationship between different dimensions of alcohol use and the burden of disease—an update. Addiction. 2017. 10.1111/add.13757. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Taylor B, Irving HM, Baliunas D, Roerecke M, Patra J, Mohapatra S, Addiction et al. 2009. 10.1111/j.1360-0443.2009.02694.x
- 12.Dinour LM < Bergen D, Ming CY. The food insecurity-obesity paradox: a review of the literature and the role food stamps May play. J Am Diet Assoc. 2007. [DOI] [PubMed]
- 13.South AM, Palakshappa D, Brown CL. Relationship between food insecurity and high blood pressure in a National sample of children and adolescents. Pediatr Nephrol. 2019. 10.1007/s00467-019-04253-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Meshram II, Arlappa NB, Rao KM, Laxmaiah A, Brahmam GNV. Prevalence of hypertension, its correlates and awareness among adult tribal population of Kerala state, India. J Postgrad Med. 2012;58255–61. 10.4103/0022-3859.105444. [DOI] [PubMed]
- 15.Agho KE, Osuagwu UL, Ezeh OK, Ghimire PR, Chitekwe S, Ogbo FA. Gender differences in factors associated with prehypertension and hypertension in nepal: A nationwide survey. PLoS ONE. 2018;13e0203278. 10.1371/journal.pone.0203278. [DOI] [PMC free article] [PubMed]
- 16.Moser KA, Agrawal S, Davey Smith G, Ebrahim S. Socio-demographic inequalities in the prevalence, diagnosis and management of hypertension in india: analysis of nationally-representative survey data. PLoS ONE. 2014;9e86043. 10.1371/journal.pone.0086043. [DOI] [PMC free article] [PubMed]
- 17.Milà C, Ranzani O, Sanchez M, Ambrós A, Bhogadi S, Kinra S, et al. Land-use change and cardiometabolic risk factors in an urbanizing area of South india: A population-based cohort study. Environ Health Perspect. 2020. 10.1289/EHP5445. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lane KJ, Stokes EC, Seto KC, Thanikachalam S, Thanikachalam M, Bell ML. Associations between greenness, impervious surface area, and nighttime lights on biomarkers of vascular aging in Chennai, India. Environ Health Perspect. 2017. 10.1289/EHP541. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Tazeen JH, Andrew LS, Fahim JH, Franklin W, Asma G, Rahbar MH, et al. Ethnic subgroup differences in hypertension in Pakistan. J Hypertens. 2003;21905–12. 10.1097/00004872-200305000-00014. [DOI] [PubMed]
- 20.Kibria GM, Al, Swasey K, Das Gupta R, Choudhury A, Nayeem J, Sharmeen A, et al. Differences in prevalence and determinants of hypertension according to rural-urban place of residence among adults in Bangladesh. J Biosoc Sci. 2019. 10.1017/S0021932018000366. [DOI] [PubMed] [Google Scholar]
- 21.Lamnisos D, Lambrianidou G, Middleton N. Small-area socioeconomic deprivation indices in cyprus: development and association with premature mortality. BMC Public Health. 2019. 10.1186/s12889-019-6973-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Singh GK, Claire Lin CC. Area deprivation and inequalities in health and health care outcomes. Ann Intern Med. 2019. 10.7326/M19-1510. [DOI] [PubMed] [Google Scholar]
- 23.Tarasuk V, Fafard St-Germain AA, Mitchell A. Geographic and socio-demographic predictors of household food insecurity in Canada, 2011-12. BMC Public Health. 2019. 10.1186/s12889-018-6344-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Nazmi A, Diez Roux A, Ranjit N, Seeman TE, Jenny NS. Cross-sectional and longitudinal associations of neighborhood characteristics with inflammatory markers: findings from the multi-ethnic study of atherosclerosis. Health Place. 2010. 10.1016/j.healthplace.2010.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Diez Roux AV. Residential environments and cardiovascular risk. J Urb Health. 2003. 10.1093/jurban/jtg065. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Powell-Wiley TM, Ayers C, Agyemang P, Leonard T, Berrigan D, Ballard-Barbash R, et al. Neighborhood-level socioeconomic deprivation predicts weight gain in a multi-ethnic population: longitudinal data from the Dallas heart study. Prev Med (Baltim). 2014. 10.1016/j.ypmed.2014.05.011. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Powell-Wiley TM, Cooper-McCann R, Ayers C, Berrigan D, Lian M, McClurkin M, et al. Change in neighborhood socioeconomic status and weight gain: Dallas heart study. Am J Prev Med. 2015. 10.1016/j.amepre.2015.01.013. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Keita AD, Judd SE, Howard VJ, Carson AP, Ard JD, Fernandez JR. Associations of neighborhood area level deprivation with the metabolic syndrome and inflammation among middle-And older-Age adults. BMC Public Health. 2014. 10.1186/1471-2458-14-1319. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Schulz AJ, Mentz G, Lachance L, Johnson J, Gaines C, Israel BA. Associations between socioeconomic status and allostatic load: effects of neighborhood poverty and tests of mediating pathways. Am J Public Health. 2012. 10.2105/AJPH.2011.300412. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Diez Roux AV. Investigating neighborhood and area effects on health. Am J Public Health. 2001. 10.2105/AJPH.91.11.1783. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Welte JW, Wieczorek WF, Barnes GM, Tidwell MC, Hoffman JH. The relationship of ecological and geographic factors to gambling behavior and pathology. J Gambl Stud. 2004. 10.1007/s10899-004-4582-y. [DOI] [PubMed] [Google Scholar]
- 32.Ribeiro AI, Fraga S, Kelly-Irving M, Delpierre C, Stringhini S, Kivimaki M, et al. Neighbourhood socioeconomic deprivation and allostatic load: a multi-cohort study. Sci Rep. 2019. 10.1038/s41598-019-45432-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Xu J, Lawrence KG, O’Brien KM, Jackson CL, Sandler DP. Association between neighbourhood deprivation and hypertension in a US-wide Cohort. J Epidemiol Community Health (1978). 2022. 10.1136/jech-2021-216445 [DOI] [PMC free article] [PubMed]
- 34.Adams J. Addressing socioeconomic inequalities in obesity: democratising access to resources for achieving and maintaining a healthy weight. PLoS Med. 2020. 10.1371/journal.pmed.1003243. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Gutiérrez OM. Contextual poverty, nutrition, and chronic kidney disease. Adv Chronic Kidney Dis. 2015. 10.1053/j.ackd.2014.05.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Bishwajit G. Trade Liberalization, urbanization and nutrition transition in Asian countries. J Nutrit Health Food Sci. 2014. 10.15226/jnhfs.2014.00109. [Google Scholar]
- 37.He YH, Jiang GX, Yang Y, Huang HE, Li R, Li XY, et al. Obesity and its associations with hypertension and type 2 diabetes among Chinese adults age 40 years and over. Nutrition. 2009. 10.1016/j.nut.2009.04.003. [DOI] [PubMed] [Google Scholar]
- 38.Liu X, Zhang D, Liu Y, Sun X, Han C, Wang B, et al. Dose-Response association between physical activity and incident hypertension: A systematic review and Meta-Analysis of cohort studies. Hypertension. 2017. 10.1161/HYPERTENSIONAHA.116.08994. [DOI] [PubMed] [Google Scholar]
- 39.Sharma I, Karen Campbell M, Choi YH, Luginaah I, Were JM, Vargas- Gonzalea JC, et al. Does the place of residence influence your risk of being hypertensive? A study-based on Nepal demographic and health survey. Hypertens Res. 2023. 10.1038/s41440-023-01217-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Allen L, Williams J, Townsend N, Mikkelsen B, Roberts N, Foster C, et al. Socioeconomic status and non-communicable disease behavioural risk factors in low-income and lower-middle-income countries: a systematic review. Lancet Glob Health. 2017. 10.1016/S2214-109X(17)30058-X. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Anchala R, Kannuri NK, Pant H, Khan H, Franco OH, Di Angelantonio E, et al. Hypertension in india: A systematic review and meta-analysis of prevalence, awareness, and control of hypertension. J Hypertens. 2014. 10.1097/HJH.0000000000000146. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Popkin BM, Adair LS, Ng SW. Global nutrition transition and the pandemic of obesity in developing countries. Nutr Rev. 2012. 10.1111/j.1753-4887.2011.00456.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Subedi YP, Marais D, Newlands D. Where is Nepal in the nutrition transition? Asia Pac J Clin Nutr. 2017. 10.6133/apjcn.112015.10. [DOI] [PubMed] [Google Scholar]
- 44.Hasan M, Khan MSA, Sutradhar I, Hossain MM, Hossaine M, Yoshimura Y, et al. Prevalence and associated factors of hypertension in selected urban and rural areas of Dhaka, bangladesh: findings from SHASTO baseline survey. BMJ Open. 2021. 10.1136/bmjopen-2020-038975. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Johnson C, Praveen D, Pope A, Raj TS, Pillai RN, Land MA, et al. Mean population salt consumption in india: A systematic review. J Hypertens. 2017. 10.1097/HJH.0000000000001141. [DOI] [PubMed] [Google Scholar]
- 46.Sharma K, Gupta R, Guptha S, Deedwania PC, Gupta A, Achari V, et al. Association of Educational, occupational and socioeconomic status with cardiovascular risk factors in Asian indians: A Cross-Sectional study. PLoS ONE. 2012;7:e44098. 10.1371/journal.pone.0044098. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Reddy KS, Prabhakaran D, Jeemon P, Thankappan KR, Joshi P, Chaturvedi V, et al. Educational status and cardiovascular risk profile in Indians. Proc Natl Acad Sci U S A. 2007. 10.1073/pnas.0700933104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Haider MR, Das Gupta R. Inequalities in undiagnosed hypertension among adult Nepalese population: evidence from a nationally representative survey. Int J Cardiol Hypertens. 2020. 10.1016/j.ijchy.2020.100026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Guo X, Zou L, Zhang X, Li J, Zheng L, Sun Z et al. Prehypertension: A meta-analysis of the epidemiology, risk factors, and predictors of progression. Tex Heart Inst J 2011. [PMC free article] [PubMed]
- 50.Santa-Ramírez HA, Otálvaro-Castro GJ, Joost S, Melgar-Quiñonez H, Bilal U, Stringhini S. Small area vulnerability, household food insecurity and child malnutrition in Medellin, colombia: results from a repeated cross-sectional study. Lancet Reg Health – Americas. 2023. 10.1016/j.lana.2023.100521. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Sreeramareddy CT, Pradhan PMS, Mir IA, Sin S. Smoking and smokeless tobacco use in nine South and Southeast Asian countries: prevalence estimates and social determinants from demographic and health surveys. Popul Health Metr. 2014. 10.1186/s12963-014-0022-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Shihab HM, Meoni LA, Chu AY, Wang NY, Ford DE, Liang KY, et al. Body mass index and risk of incident hypertension over the life course: the Johns Hopkins precursors study. Circulation. 2012. 10.1161/CIRCULATIONAHA.112.117333. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Ministry of Health and Population (MoHP). Nepal New ERA and, ICF International Inc. Nepal Demographic and Health Survey 2016. 2017.
- 54.Unger T, Borghi C, Charchar F, Khan NA, Poulter NR, Prabhakaran D, et al. 2020 international society of hypertension global hypertension practice guidelines. Hypertension. 2020;75:1334–57. 10.1161/HYPERTENSIONAHA.120.15026. [DOI] [PubMed] [Google Scholar]
- 55.Swindale A, Bilinsky P. Household Dietary Diversity Score (HDDS) for measurement of household food access: Indicator guide. Food and Nutrition Technical Assistance… 10.1017/CBO9781107415324.004.
- 56.Benjamin Neelon SE, Burgoine T, Gallis JA, Monsivais P. Spatial analysis of food insecurity and obesity by area-level deprivation in children in early years settings in England. Spat Spatiotemporal Epidemiol. 2017. 10.1016/j.sste.2017.07.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 57.Coates J, Swindale a, Bilinsky P. Household Food Insecurity Access Scale (HFIAS)for measurement of food access: indicator guide. Washington, DC: Food and Nutrition Technical ….
- 58.Sharma I, Campbell MK, Heisel MJ, Choi Y-H, Luginaah IN, Were JM, et al. Construction and validation of the area level deprivation index for health research: A methodological study based on Nepal demographic and health survey. PLoS ONE. 2023;18:e0293515. 10.1371/journal.pone.0293515. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Preacher KJ, Zhang Z, Zyphu MJ. Alternative methods for assessing mediation in multilevel data: the advantages of multilevel Sem. Struct Equ Model. 2011. 10.1080/10705511.2011.557329. [Google Scholar]
- 60.Zhang Z, Zyphur MJ, Preacher KJ. Testing multilevel mediation using hierarchical linear models: problems and solutions. Organ Res Methods. 2009. 10.1177/1094428108327450. [Google Scholar]
- 61.Rana J, Ahmmad Z, Sen KK, Bista S, Islam RM. Socioeconomic differentials in hypertension based on JNC7 and ACC/AHA 2017 guidelines mediated by body mass index: evidence from Nepal demographic and health survey. PLoS ONE. 2020;15:e0218767. 10.1371/journal.pone.0218767. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62.Acharya S. Nutrition transition and SES as a fundamental cause: an integrated approach to examine weight status among women in Nepal. 2020. https://digitalcommons.library.uab.edu/etd-collection/677/
- 63.Shohaimi S, Luben R, Wareham N, Day N, Bingham S, Welch A et al. Residential area deprivation predicts smoking habit independently of individual educational level and occupational social class. A cross sectional study in the Norfolk cohort of the European Investigation into Cancer (EPIC-Norfolk). J Epidemiol Community Health (1978). 2003. 10.1136/jech.57.4.270 [DOI] [PMC free article] [PubMed]
- 64.Hwang JH, Kim JY, Lee DH, Jung HG, Park SW. Underestimation of self-reported smoking prevalence in Korean adolescents: evidence from gold standard by combined method. Int J Environ Res Public Health. 2018. 10.3390/ijerph15040689. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Russell J, Flood V, Yeatman H, Mitchell P. Prevalence and risk factors of food insecurity among a cohort of older Australians. J Nutr Health Aging. 2014. 10.1007/s12603-013-0339-6. [DOI] [PubMed] [Google Scholar]
- 66.Ashraf A, Quaiyum MA, Ng N, Van Minh H, Razzaque A, Masud Ahmed S et al. Self-reported use of tobacco products in nine rural INDEPTH health and demographic surveillance systems in Asia. Glob Health Action 2009;2. [DOI] [PMC free article] [PubMed]
- 67.Khanal V, Adhikari M, Karki S. Social determinants of tobacco consumption among Nepalese men: findings from Nepal demographic and health survey 2011. Harm Reduct J. 2013. 10.1186/1477-7517-10-40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 68.Jomaa L, Naja F, Cheaib R, Hwalla N. Household food insecurity is associated with a higher burden of obesity and risk of dietary inadequacies among mothers in Beirut, Lebanon. BMC Public Health. 2017. 10.1186/s12889-017-4317-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
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Data Availability Statement
Anonymous data is freely available at Demographic and Health survey program. [https://dhsprogram.com/].