Prevalence, spatial pattern, and predictors of cardiovascular and chronic diseases: A cross-sectional study

doi:10.21203/rs.3.rs-2861281/v1

Aim: To analyze the prevalence, spatial pattern, and predictors of cardiovascular disease (CVD) and chronic health conditions (CHCs) in Afghanistan.

Subject and Methods: This cross-sectional study of 3,956 adults aged 18-69, using population-based data from the World Health Organization's STEPwise Approach to Surveillance (STEPS) survey. The study determined the single and group prevalence of noncommunicable diseases based on sociodemographic and location characteristics. Logistic regression was used to estimate the association between physical activity levels and non-communicable diseases.

Results: The prevalence rates for CVD, obesity, hypertension, diabetes, and high cholesterol were 8.90%, 42.60%, 32.40%, 7.99%, and 11.87%, respectively. The prevalence of CVD increased with age and was notably higher among men, rural residents, and Zabul province. The prevalence of chronic conditions also increased with age, with higher rates among women and urban residents varying across different provinces. Spatial analysis showed a hotspot of CVD in the southern region, while other chronic diseases were clustered in the northern region. Multivariate logistic analysis showed that moderate occupational physical activity (PA) is linked to an increased risk of CVD and high cholesterol levels while being inversely associated with obesity. In contrast, moderate leisure-related PA is positively associated with obesity. Vigorous leisure-related PA, on the other hand, is linked to a decreased risk of CVD, obesity, and high cholesterol levels, while being positively associated with hypertension.

Conclusion: Findings provide valuable insights for healthcare providers and health coaches to design interventions to reduce the burden of CVD and CHCs for specific groups.

Epidemiology

Cardiovascular diseases

Stroke

Heart attack

Chronic diseases

Health disparities

Spatial disparities

Physical activity levels

Noncommunicable diseases (NCDs) are a major global public health concern, especially in economically less developed countries with a shift from communicable to noncommunicable diseases (Bloom et al. 2012; World Health Organization 2018). In 2022, NCDs accounted for 74% of all deaths globally, causing 41 million deaths (World Health Organization 2022a). Although NCDs are usually associated with older age groups, evidence shows that 17 million premature NCD-related deaths occur before age 70, with 86% of these deaths occurring in low- and middle-income countries (Qiao et al. 2022). Unhealthy diets, lack of physical activity (PA), and other multidimensional forces such as rapid urbanization, globalization of unhealthy lifestyles, and aging populations (Mahmood et al. 2013; Stringhini et al. 2016; Xi et al. 2020; Qiao et al. 2022) are NCD risk factors that may result in elevated blood pressure, increased blood glucose, raised blood lipids, and obesity, all of which contribute to cardiovascular diseases (CVDs). This study uses the spatial-social epidemiology of diseases framework to analyze the prevalence, spatial pattern, and predictors of CVD and chronic health conditions (CHCs) in Afghanistan. Notably, we aim to understand how levels of PA are associated with NCDs.

CVD encompasses heart and blood vessel conditions, including coronary artery disease, heart failure, stroke, and arrhythmia, and can lead to severe complications such as brain damage, kidney failure, and death (D’Andrea et al. 2015; Roth et al. 2020). CVD is the primary cause of death globally, accounting for an estimated 17.9 million deaths in 2019, with 85% resulting from heart attack and stroke. While CVD risks have been primarily studied in developed countries, over three-quarters of CVD-related deaths occur in LMICs (Roth et al. 2020). The economic burden of NCDs is staggering, affecting productivity, human capital, and healthcare expenditure. NCDs are both a cause and a consequence of poverty, with an estimated 100 million people worldwide being pushed into extreme poverty each year due to out-of-pocket healthcare expenses (NCDAlliance 2015).

The spatial-social epidemiology of diseases framework integrates spatial and social factors to analyze disease patterns and determinants, acknowledging the varying disease burden based on contextual and compositional factors of a place or region (Iyanda et al. 2020; Albery et al. 2021). Spatial factors refer to disease distribution, environmental influences, and contextual factors, while social factors refer to demographic, socioeconomic, behavioral, and cultural characteristics (Chaix et al. 2009; Kestens et al. 2017). This approach provides a more comprehensive understanding of the complex interactions between people, places, and health.

According to this framework, spatial and social factors are not independent but rather interrelated and mutually reinforcing. For example, spatial factors may shape social factors by creating opportunities or barriers for social interactions, access to resources, exposure to hazards or stressors, and mobility patterns. Conversely, social factors may shape spatial factors by influencing the choice of residence, the use of space, the creation of social networks, and the collective action for environmental change. Therefore, spatial and social factors may have direct or indirect effects on health outcomes and synergistic or antagonistic effects when combined (Sabater et al. 2019; Oviedo 2021). Using the spatial-social epidemiology of diseases framework, we examined the prevalence and disparity of CVD and selected chronic diseases in Afghanistan.

Cardiovascular and chronic diseases in Afghanistan

CVD is a significant health problem in Afghanistan, with increasing rates in recent years. By 2030, CVD will be the primary cause of death in developing countries such as Afghanistan, surpassing even the long-standing leading cause of mortality, maternal and neonatal conditions, and other communicable diseases (Roth et al. 2020). Recent global analysis indicates that CVD, cancer, and diabetes account for about 18% of all deaths in the country, with ischemic heart disease and stroke accounting for most deaths related to CVD (Li et al. 2021). Recent trend analysis of stroke prevalence in the Middle East and North Africa indicates that the prevalence in Afghanistan was relatively lower than in many other countries in the region but higher than in neighboring countries like Iran (Jaberinezhad et al. 2022). The gravity of the problem is further highlighted by the fact that Afghanistan is among the countries experiencing political unrest and economic hardship (Islam et al. 2022). Afghanistan has the fourth-highest prevalence of CVD in Central Asia, with a prevalence rate of 12.2% in adults over 30 (Aringazina et al. 2018), and the recent pandemic, war, and climate change can compound the problem.

The high rate of CVD is attributed to a combination of factors, including poor healthcare infrastructure, lack of health education, poverty, and a high burden of risk factors. Nevertheless, the risk factors for CVD in Afghanistan are similar to those in other countries, with some unique characteristics. In addition, in recent decades, several studies have found increasingly significant evidence linking the risk of CVD to lifestyle and built and natural environments (e.g., street walkability and green infrastructure, respectively) (Richardson et al. 2013; Nieuwenhuijsen 2018; Seo et al. 2019). Additionally, a sedentary lifestyle and lack of PA (PA) are significant risk factors for CVD that must be thoroughly investigated as urbanization increases in developing countries.

Association between levels of physical activity and chronic conditions

Research on the adult population has established the relationship between light intensity and moderate-vigorous PA and CVD risk factors (LaMonte et al. 2017; Liu et al. 2020). In addition, engaging in regular PA has been shown to correlate with reduced risk for chronic diseases and in maintaining body physiology. Therefore, engaging in moderate- and vigorous-intensity work and recreational activities is a recommended strategy for overall health (World Health Organization 2022b). However, the relationship between vigorous and moderate PA and CVD among different age groups and populations in different geographic settings remains unclear. This study intends to fill this gap by examining the prevalence and risk of CVD among different age categories.

In spite of the fact that vigorous and moderate PA have been extensively studied for their social and health benefits (Thompson 1996; Wannamethee and Shaper 2001; Rennie et al. 2003b, a; Richardson et al. 2013; Valero-Elizondo et al. 2016; Liu et al. 2020; Ahmadi et al. 2022), the evidence regarding their effects is mixed. Additionally, while research in developed countries has suggested a link between occupation-related PA trends and chronic health issues like obesity, the impact of levels of PA on health in developing countries is not clear. For instance, a recent study conducted in the United States (Zhang et al. 2021) found that higher levels of work-related vigorous PA for men and recreational-related moderate PA for women were associated with a reduced risk of abdominal obesity. Similarly, a study conducted in the United Kingdom by Stamatakis and colleagues found that standing/walking occupations were linked to a lower risk of all-cause and cancer mortality as compared to sitting occupations, but no such association was observed in men (Stamatakis et al. 2013).

Furthermore, studies have established the relationship between the risk of CVD and other chronic health conditions such as diabetes, hyperlipidemia, elevated low-density lipoprotein (LDL), obesity, and a family history of CVD (Abdullah et al. 2018; Carr et al. 2019; Opoku et al. 2019; Castañer et al. 2020; Deprince et al. 2020; Santos et al. 2020; Xi et al. 2020). These risks are not peculiar to Afghanistan; they are similar to those in other countries, with some unique factors. In addition, chronic conditions could be identified as confounders in understanding the link between levels of PA and CVD. The Afghan government and international organizations are taking conscious steps to address the issue, including improving healthcare infrastructure, increasing health education, and implementing measures to control risk factors. However, more efforts are needed to reduce the burden of CVD in the country by tracking the socio-spatial patterns of CVD.

Epidemiological studies have reported the link between urbanization and CVD risk factors such as diabetes and hypertension (Young 2019; Tai et al. 2020; Chen et al. 2020; Pinchoff et al. 2020; Boakye et al. 2021; Ostchega et al. 2022). Urbanization directly impacts the food environment, choices, and behaviors, with a greater likelihood of adopting Western-style diets and sedentary lifestyles contributing to the prevalence and disparities of cardiometabolic risks. In addition, urban green infrastructure can facilitate leisure-related PA, promoting general well-being and improving health outcomes like chronic health (e.g., asthma, hyperlipidemia, and high blood pressure). Therefore, health risks do vary by place of residence.

Based on this background, this study aims to assess the association between levels of PA and a range of NCDs, including chest pain or stroke, as a proxy for CVD, hypertension, diabetes, high body mass index (BMI), and high cholesterol level among the diverse population in Afghanistan. In addition, we explore the age-sex-place prevalence of CVD for target intervention. Lastly, the objective is to determine the benefit of work-related and leisure-related vigorous and moderate PA on CVD and selected chronic health conditions.

We conducted a cross-sectional study using population data from the World Health Organization (WHO) STEPwise Approach to Surveillance (STEPS) survey for Afghanistan. The survey, aimed at identifying the risk factors of NCD among the adult population, was carried out from February 2018 to October 2018 (World Health Organization. 2021). A detailed description of the STEPS survey's methods, challenges and opportunities has been published elsewhere (Riley et al. 2016). Briefly, the WHO STEPS data was a population-based survey of adults aged 18–69 collected in three phases. The sociodemographic and behavioral information was collected in the first step; physical measurements such as height, weight, and blood pressure were collected in the second step; and biochemical measurements were collected to assess blood glucose and cholesterol levels in the last step. The survey used a multi-cluster sample design to produce representative data from 3,956 adults in Afghanistan (Afghan Ministry of Public Health and World Health Organization 2020), and the trained interviewers received consent from all adult participants.

Measure

Outcome variables: Cardiovascular disease (CVD) was measured based on those who answered 'yes' to the question on the lifetime history of CVD, which asked, "Have you ever had a heart attack or chest pain from heart disease (angina) or a stroke (cerebrovascular accident or incident)?" In addition to the single measure of CVD, we also examined the risk factors of CVD as additional health outcomes, including hypertension, diabetes, cholesterol, and obesity measured as high body mass index (BMI > 30 kg/m²). Hypertension is closely linked with CVD and was measured based on the question that asked "Have you ever been told by a doctor or other health worker that you have raised blood pressure or hypertension?" and diabetes was based on the question, "Have you ever been told by a doctor or other health worker that you have raised blood sugar or diabetes?" We computed BMI based on body weight (kg) and height in centimeters (cm) converted to meters (m²) and categorized into nonobese = < 30 kg/m² and obese = ≥ 30.0 kg/m². High cholesterol was measured based on the total cholesterol level above 200 mg/dL, denoted as ‘1’ and less than 200 mg/dL as 0.

Measure of physical activity

This study classified vigorous and moderate PA as work-related and leisure-related PA, referred to as situational PA based on the survey questions. Vigorous PA was based on the questions that asked, "Do you do any vigorous-intensity sports, fitness, or recreational (leisure) activities that cause large increases in breathing or heart rate, like [running or football] for at least 10 minutes continuously?" Moderate PA was based on the question that asked, "Do you do any moderate-intensity sports, fitness, or recreational (leisure) activities that cause a small increase in breathing or heart rate such as brisk walking, [cycling, swimming, volleyball] for at least 10 minutes continuously?"

Measure of sociodemographic variables

We controlled for age, sex (defined as biological sex at birth), and rural-urban residence as proxies for spatial variables in this study. Age was reclassified based on WHO's adult age categorization into Young Adults (18–29, reference group), Middle-aged adults (29–44), and Older adults (44 and above) to examine CVD disparity by age group. Even though we treated CVD risk factors as part of the outcome variables, we also controlled for them in the CVD model as possible confounders.

Statistical analysis

We computed the single variable and multivariable prevalence of CVD. The single prevalence of CVD was calculated based on participants’ sociodemographic characteristics (i.e., age groups, sex at birth, place of residence, and provincial administrative boundaries). We also computed the multivariable prevalence of CVD and selected chronic health conditions based on age, sex, and rural-urban characteristics of the participants resulting in six groups: A1 = Aged 18–29#Men; A2 = Aged 18–29#Women; B1 = Aged 29–44#Men; B2 = Aged 29–44#Men; C1 = Aged ≥ 44 #Men; and C2 = Aged ≥ 44 #Women in rural and urban communities. We used the logit transformation to determine the multivariate prevalence using the proportion command and the 95% confidence interval (CI). The results were visualized graphically in the form of tables and bar graphs.

Local spatial analysis

To assess the spatial significance of the individual prevalence of the five chronic health conditions investigated in this study, we conducted a local spatial analysis with Getis-Ord Gi* in ArcMap Pro (Esri Inc.) to identify significant hotspots of health outcomes based on surrounding neighboring features with high values or cold spots with low values. The contiguity of edges and corners informed spatial relationships. We present the map of the local analysis with significant levels classified into 90%, 95%, 99%, and not significant for both cold and hot spots.

Multivariate analysis

The study used a logistic regression model to examine the relationship between levels of PA and various health outcomes, including chest pain or stroke as a proxy for CVD, diabetes, hypertension, high cholesterol, and obesity (BMI ≥ 30.0 kg/m²). Three models were developed for each health outcome. Model 1 only included levels of PA, while Model 2 adjusted for age, sex, and urbanization. Model 3 included potential mediators of chronic health conditions such as obesity, hypertension, high cholesterol, and diabetes in the CVD health outcome model. The same steps were repeated for each of the four chronic condition health outcomes, but we substituted each one for CVD. In total, 15 models were developed.

Model diagnostic

Two measures were used to evaluate the models: discrimination and calibration. Discrimination measures the model's ability to differentiate between observations with and without the outcome of interest, while calibration measures how well the predicted probabilities match the observed probabilities. The area under the receiver operating characteristic (ROC) curve, also known as the Harrell C-statistic, was used to measure discrimination, and the Hosmer-Lemeshow statistic was used to measure calibration. The ROC curve area was derived by running the -lroc- command after each logistic command, while the Hosmer-Lemeshow statistic was calculated using the -estat gof- command.

To determine statistical significance, the alpha level was set to 0.05, and a significant association was reached if the estimated coefficients fell within the acceptable 95% confidence interval (CI). The data from the World Health Organization (WHO) STEPs survey was analyzed using Stata v17 (STATA Inc., College Station, TX).

Table 1

The distribution of cardiovascular disease and chronic health conditions by age, rural-urban residence, sex, and province
	CVD (Prevalence = 8.90)	Obesity (Prevalence = 42.60)	Hypertension (Prevalence = 32.40)	Diabetes (Prevalence = 7.99)	High cholesterol (Prevalence = 11.87)
Age
< 29	6.13	31.98	25.12	3.77	9.82
29–44	8.96	49.33	30.26	8.22	12.06
> 44	14.37	52.71	46.27	16.13	15.74
Residence
Rural	9.49	39.59	34.66	5.53	9.88
Urban	8.44	44.89	30.64	9.91	13.38
Sex
Men	11.58	37.44	27.66	6.44	6.76
Women	5.99	49.15	36.22	9.64	17.39
Province
Badakhshan	4.24	34.11	26.37	3.86	18.15
Badghis	11.01	11.28	47.5	0.74	0.86
Baghlan	7.04	41.56	38.72	2.62	8.63
Balkh	5.53	38.52	36.68	9.13	16.68
Bamyan	20.04	78.85	56.34	0.00	13.45
Hilmand	19.44	42.37	29.73	4.75	14.09
Hirat	5.09	44.83	31.04	6.37	4.69
Kabul	16.73	56.21	42.57	9.99	10.00
Kandahar	3.55	57.11	23.81	10.15	17.32
Kapisa	17.78	23.00	48.6	0.00	8.91
Khost	1.72	42.16	11.11	6.48	7.63
Kunar	16.78	36.92	47.75	5.33	0.00
Kunduz	13.99	48.38	36.4	26.09	12.77
Laghman	5.80	45.15	46.31	13.47	10.8
Nangahar	2.05	58.83	53.18	8.10	14.84
Nuristan	0.00	24.57	0.00	0.00	0.00
Paktya	2.34	44.97	42.32	11.7	18.36
Parwan	2.53	38.75	52.4	5.93	9.19
Samangan	4.76	30.04	45.93	2.68	15.31
Sari Pul	2.48	38.66	16.76	9.96	14.99
Takhar	0.00	45.91	5.12	9.81	2.27
Zabul	24.32	24.34	20.84	5.33	0.00

Descriptive results

The study examined 3,924 participants with an average age of 37.2 ± 14. Table 1 displays the proportional distribution of CVD and chronic conditions in Afghanistan. The prevalence rates for CVD, obesity, hypertension, diabetes, and high cholesterol were 8.90%, 42.60%, 32.40%, 7.99%, and 11.87%, respectively. The study found that the prevalence of CVD increased with age and was exceptionally high among men, rural residents, and those living in Zabul province. Furthermore, the prevalence of CVD risk factors (chronic conditions) also increased with age, with higher rates observed among women and urban residents and varying across different provinces.

Group prevalence

According to the analysis of group prevalence (Table 2), there are significant disparities in the prevalence of CVD among middle-aged urban men (aged 29–44) and older men (C1) compared to their rural counterparts. Conversely, older women residing in rural areas have higher CVD rates than older women in urban areas. The prevalence of obesity was observed to be higher among younger women, middle-aged women, older women, and older men in urban areas than in rural areas, whereas middle-aged men in rural areas have a higher obesity rate than their urban counterparts.

Furthermore, the rate of hypertension was found to be higher among younger men and women, middle-aged women, and older women residing in rural areas than those residing in urban areas. On the other hand, the rate of hypertension was higher among older men in urban areas than their rural counterparts. Additionally, the diabetes rate was higher among all groups residing in urban areas than among rural men. Notably, high cholesterol levels were found to be higher among young people living in rural areas than their urban counterparts, while the rest of the urban group had higher cholesterol levels than their rural counterparts.

Hotspot analysis of cardiovascular and chronic diseases

To analyze the local spatial patterns of CVD and CHCs, we performed a local analysis using the Getis-Ord Gi* technique. The results are presented in Figs. 1–5 for hotspot analysis. Figure 1 shows highly significant hotspots of CVD in Kandahar province. The prevalence of obesity was significantly clustered in Samangan and Baghlan provinces, as shown in Fig. 2. Figure 3 displays a pocket of diabetes in Nangarhar province in the eastern region, while northwestern provinces, including Balkh, Kunduz, Samangan, and Takhar, show significant clusters of the disease.

Table 2

Disparities in cardiovascular diseases and chronic health conditions (Group prevalence)
	Rural			Urban
	Prevalence (%)	[95% conf. interval]		Prevalence (%)	[95% conf. interval]
		Lower	Upper		Lower	Upper
CVD
A1	11.14	5.66	20.75	10.67	5.14	20.86
A2	1.86	0.66	5.13	0.4	0.17	0.95
B1	6.46	2.79	14.27	11.95	5.36	24.52
B2	13.23	4.47	33.15	4.71	2.16	9.98
C1	9.03	4.57	17.07	21.46	10.85	38.04
C2	20.70	10.56	36.60	5.38	0.22	12.54
Obesity
A1	27.04	18.95	37.02	27.23	19.07	37.28
A2	34.26	25.56	44.17	42.14	32.78	52.10
B1	49.75	38.85	60.67	45.02	31.90	58.88
B2	42.60	31.14	54.91	59.46	48.84	69.27
C1	40.03	31.18	49.59	47.79	35.21	60.65
C2	52.34	39.4	64.97	70.41	59.51	79.39
Hypertension
A1	34.41	19.37	53.38	23.22	12.62	38.77
A2	24.29	15.54	35.88	21.51	13.21	33.03
B1	19.28	10.9	31.79	20.52	10.64	35.89
B2	43.73	30.41	58.01	35.07	24.73	47.02
C1	31.54	21.29	43.97	39.87	26.79	54.58
C2	55.3	41.19	68.61	53.76	41.33	65.75
Diabetes
A1	1.52	0.33	6.65	3.96	1.88	8.13
A2	2.52	0.97	6.39	6.32	3.74	10.49
B1	1.97	0.76	5.02	9.81	3.79	23.09
B2	8.92	4.82	15.93	11.04	6.38	18.45
C1	10.64	5.74	18.87	15.68	9.31	25.20
C2	13.9	6.75	26.46	23.98	14.59	36.81
Cholesterol
A1	10.34	5.18	19.58	3.19	1.63	6.15
A2	8.63	4.47	16.00	18.00	11.60	26.85
B1	6.54	2.73	14.85	7.57	3.66	15.00
B2	11.81	7.19	18.78	21.47	14.24	31.05
C1	6.98	3.99	11.95	8.17	4.44	14.57
C2	16.48	9.49	27.08	31.86	21.9	43.79
A1 = Aged 18–29#Men; A2 = Aged 18–29#Women; B1 = Aged 29–44#Men; B2 = Aged 29–44#Men; C1 = Aged ≥ 44 #Men; C2 = Aged ≥ 44 #Women; CVD = Cardiovascular disease

Furthermore, significant hotspots of hypertension were noted in eastern provinces such as Parwan, Kabul, and Nangarhar, as well as in Samangan in the northern region of the country. On the contrary, a significant cold spot for hypertension was identified in the southern part of the country in Hilmand province (Fig. 4). Finally, the prevalence of cholesterol was clustered in Balkh and Samangan provinces. Overall, the hotspot analysis revealed specific regions in Afghanistan that have higher incidences of CVD and chronic diseases, which can be helpful for targeted interventions and resource allocation.

Multivariate logistic regression of cardiovascular and chronic diseases

Multivariate analysis results revealing predictors of CVD

The results from Table 3 indicate that (model 1), work-related PA (both vigorous and moderate) is positively associated with the odds of reporting lifetime chest pain or stroke (i.e., CVD) in the unadjusted model. On the other hand, vigorous leisure-based PA is significantly inversely associated with CVD. After adjusting for age, sex, and place of residence in model 2, only moderate work-related PA remains positively related to the odds of CVD. In model 3, controlling for sociodemographic factors, location, and chronic comorbidities, vigorous leisure-related PA is inversely associated with the odds of CVD. However, the test of goodness of fit indicates that model 3, which adjusted for demographic and comorbidities was reliable, based on the insignificant value of Chi-squared estimation (p > 0.05) and the AUC of 0.63.

Table 3

Logistic regression model of the association between physical activity and cardiovascular disease
	Model 1			Model 2			Model 3
CVD	OR	[95% CI]		AOR	[95% CI]		AOR	[95% CI]
Vigorous PA (work)	2.68	1.60	4.49	2.01	0.98	4.10	1.50	0.47	4.78
Moderate PA (work)	2.15	1.31	3.50	2.47	1.46	4.17	2.72	1.08	6.81
Vigorous PA (leisure)	0.35	0.13	0.94	0.42	0.15	1.16	0.82	0.01	0.57
Moderate PA (leisure)	0.97	0.60	1.50	0.91	0.33	2.47	0.77	0.22	2.71
Age (ref: <29)
29–44				1.42	0.68	2.97	2.35	0.82	6.70
44–70				2.55	1.26	5.17	1.65	0.50	5.53
Male				1.73	0.84	3.57	2.78	1.17	6.64
Rural				1.06	0.61	1.84	2.53	1.18	5.39
Obesity							0.47	0.22	1.04
High cholesterol							2.21	0.98	4.98
Current smoker							2.48	0.65	9.38
Hypertension							1.14	0.48	2.73
Diabetes							1.98	0.71	5.52
Goodness-of-fit
No. of observation	3,952			3,924			120
No. Covariate patterns	16			144			93
Pearson Chi²	14.13 (11)^ns			210.72 (135)**			95.68 (79)^ns
AUC	0.55			0.67			0.63
AUC indicates Area Under ROC Curve; ns = not significant; significant Chi-squared; AOR = adjusted odd ratio; Bold** values indicate significance at p < 0.05

Multivariate analysis results revealing predictors of chronic conditions

In Table 4, the unadjusted model showed that both vigorous work-related and leisure-related PA were significantly associated with reduced odds of reporting obesity. However, all the PA variables lost their significance in model 2, adjusting for age, sex, and place of residence. In model 3, moderate work-related and vigorous leisure-related PA were inversely associated with the risk of obesity (BMI > 30 kg/m²), controlling for hypertension, diabetes, and high cholesterol level. On the other hand, moderate leisure-related PA was positively associated with obesity (AOR = 12.28; 95% Confidence Interval 2.27–66.46). The goodness of fit assessment indicated that model 3 of Table 4 is the most reliable. The AUC values increased from model 1 to 3, indicating improvement in the logistic model.

Table 4

Logistic regression model of the association between physical activity and obesity
		Model 1			Model 2			Model 3
Obesity	OR	[95% CI]		AOR	[95% CI]		AOR	[95% CI]
Vigorous PA (work)	0.65	0.44	0.94	0.68	0.45	1.01	0.78	0.28	2.17
Moderate PA (work)	0.78	0.59	1.02	0.83	0.63	1.09	0.38	0.20	0.73
Vigorous PA (leisure)	0.49	0.28	0.87	0.70	0.40	1.24	0.14	0.03	0.53
Moderate PA (leisure)	0.94	0.55	1.61	1.11	0.64	1.94	12.28	2.27	66.46
Age (ref: <29)
29–44				2.07	1.46	2.92	1.00	0.43	2.33
44–70				2.29	1.63	3.23	0.84	0.38	1.82
Male				0.70	0.52	0.95	0.55	0.28	1.06
Rural				0.80	0.61	1.05	0.73	0.39	1.37
Obesity
High cholesterol							2.73	1.12	6.67
Current smoker							2.47	0.55	11.14
Hypertension							1.69	0.88	3.25
Diabetes							1.67	0.72	3.89
Goodness of fit
No. of Observation	3746			3720			120
No. of Covariate. patterns	16			143			85
Pearson’s Chi² (df)	32.26 (11)**			164.93 (134)**			82.46 (76)^ns
AUC	0.56			0.64			0.72
AUC indicates Area Under ROC Curve; ns = not significant; significant Chi-squared; AOR = adjusted odd ratio; Bold** values indicate significance at p < 0.05

In the model assessing the association between PA and diabetes in Table 5, only moderate leisure-related PA was inversely associated with diabetes (AOR = 0.42; 95% CI 0.20–0.88) after controlling for age, sex, and place of residence. Notably, increasing age was positively associated with the odds of diabetes, while rural residency reduced the odds of it (model 2). More importantly, the odds of diabetes increased by 3.32 times with every log-linear increase in cholesterol level. Current smoking status and hypertension reduced the odds of diabetes in Model 3 of Table 5. The AUC values for each model indicate that the model improves from model 1 (AUC = 0.60) to model 3 (AUC = 0.82).

Table 5

Logistic regression model of the association between physical activity and diabetes
		Model 1			Model 2			Model 3
Diabetes	OR	[95% CI]		AOR	[95% CI]		AOR	[95% CI]
Vigorous PA (work)	0.74	0.40	1.36	0.74	0.36	1.50	2.73	0.90	8.46
Moderate PA (work)	0.58	0.36	0.94	0.71	0.43	1.17	0.61	0.26	1.42
Vigorous PA (leisure)	0.43	0.15	1.29	0.85	0.28	2.61	1.99	0.42	9.37
Moderate PA (leisure)	0.36	0.19	0.70	0.42	0.20	0.80	0.80	0.21	3.11
Age (ref: <29)
29–44				2.22	1.22	4.02	2.11	0.66	6.77
44–70				4.71	2.79	7.95	2.61	0.82	8.35
Male				0.77	0.43	1.37	0.55	0.24	1.26
Rural				0.50	0.31	0.79	0.33	0.15	0.72
Obesity							1.77	0.77	4.07
High cholesterol							3.32	1.42	7.73
Current smoker							0.13	0.04	0.40
Hypertension							0.67	0.30	1.50
Goodness-of- fit
No. Observations	845			841			120
No. covariate patterns	15			98			82
Pearson Chi² (df)	19.44 (10)**			110.84 (89) ^ns			74.32 (69)^ns
AUC	0.60			0.71			0.82
AUC indicates Area Under ROC Curve; ns = not significant; significant Chi-squared; AOR = adjusted odd ratio; Bold** values indicate significance at p < 0.05

The results presented in Table 6 demonstrate the relationship between levels of PA and hypertension. After adjusting for sociodemographic and location covariates, vigorous leisure-related PA was found to be positively associated with hypertension. Conversely, moderate leisure-related PA was inversely associated with hypertension. However, after adjusting for comorbidities in the full model (model 3), the significant association observed between moderate leisure-related PA in model 2 dissipated, while vigorous leisure-related PA remained significant. Furthermore, the analysis revealed that other comorbidities, such as obesity, high cholesterol, and diabetes, were positively associated with the risk of developing hypertension. The AUC value for each model indicates that the models improved from model 1 (AUC = 0.56) to model 3 (AUC = 0.73).

Table 6

Logistic regression model of the association between physical activity and hypertension
	Model 1				Model 2			Model 3
Hypertension	OR	[95% CI]			AOR	[95% CI]		AOR	[95% CI]
Vigorous PA (work)	0.83	0.51	4.49		0.85	0.48	1.48	0.88	0.48	1.60
Moderate PA (work)	0.96	0.69	3.50		1.13	0.80	1.59	1.24	0.86	1.79
Vigorous PA (leisure)	2.20	0.86	5.65		3.24	1.17	9.01	3.26	1.07	10.00
Moderate PA (leisure)	0.27	0.60	0.54		0.30	0.15	0.61	0.32	0.15	0.68
Age (ref: <29)
29–44					1.37	0.88	2.14	1.12	0.70	1.83
44–70					2.84	1.85	4.36	2.18	1.38	3.46
Male					0.73	0.48	1.11	0.71	0.45	1.12
Rural					1.09	0.77	1.54	1.18	0.81	1.72
Obesity								1.78	1.21	2.63
High cholesterol								1.64	1.00	2.70
Current smoker								0.97	0.47	2.00
Diabetes								2.01	1.19	3.40
Goodness-of- fit
No. Observations	3,708				3,682			750
No. covariate patterns	16				141			288
Chi² (df)	22.99 (11)**			186.20 (132)**			272.68 (275)^ns
AUC	0.56				0.63			0.73
AUC indicates Area Under ROC Curve; ns = not significant; significant Chi-squared; AOR = adjusted odd ratio; Bold** values indicate significance at p < 0.05

Table 7 presents the associations between levels of PA and high cholesterol. In model 1, both vigorous work-related and moderate leisure PA showed significant inverse associations with high cholesterol. However, after adjusting for sociodemographic and location covariates, the significant association of vigorous PA remained, while moderate leisure-related PA lost its significance. In the full model (model 3), which also controlled for comorbidities, moderate work-related PA was positively associated with high cholesterol. On the other hand, vigorous leisure-related PA remained significantly inversely associated with cholesterol. Additionally, the three comorbidity covariates were positively associated with high cholesterol. According to the AUC values presented in Table 7, it can be deduced that models 2 and 3 achieved an accuracy of 65% and 76%, respectively, in classifying the probability of high cholesterol. On the other hand, model 1 did not accurately determine high cholesterol based on levels of PA .

Table 7

Logistic regression model of the association between physical activity and cholesterol
	Model 1			Model 2			Model 3
Cholesterol	Crude OR	[95% CI]		AOR	[95% CI]		AOR	[95% CI]
Vigorous PA (work)	0.32	0.19	0.53	0.42	0.24	0.74	0.46	0.11	1.95
Moderate PA (work)	1.03	0.71	1.50	1.01	0.75	1.61	2.59	1.22	5.48
Vigorous PA (leisure)	0.71	0.31	1.62	1.16	0.51	2.64	0.72	0.07	7.82
Moderate PA (leisure)	0.44	0.23	0.83	0.61	0.31	1.17	0.37	0.10	1.33
Age (ref: <29)
29–44				1.29	0.81	2.05	0.78	0.24	2.48
44–70				1.85	1.17	2.92	1.11	1.39	3.17
Male				0.45	0.29	0.69	0.71	0.31	1.61
Rural				0.67	0.45	0.99	1.31	0.58	3.00
Obesity							2.50	1.00	6.26
Current smoker							0.41	0.10	1.61
Hypertension							2.32	1.07	8.55
Diabetes							3.57	1.49	3.40
Goodness-of- fit
No. Observations	2,490			3,682			750
No. covariate patterns	16			141			288
Pearson’s Chi² (df)	18.09 (10)^ns			186.20 (132)**			272.68 (275)^ns
AUC	0.57			0.65			0.76
AUC indicates Area Under ROC Curve; ns = not significant; ** significant Chi-squared
Bold values indicate significance at p < 0.05

Summary of findings

In this study, we utilized the WHO's STEPS dataset to examine the prevalence and predictors of noncommunicable health outcomes in Afghanistan. Our research yielded several noteworthy findings. First, the prevalence of CVD (8.9%) was lower than in other countries in the region, such as Iran (20—23%) (Sarrafzadegan & Mohammmadifard, 2019), Pakistan (17.5%) (Zubair et al., 2018), and Central Asia (12.2%) (Aringazina et al., 2018). However, the risk of CVD was higher among older individuals, men, rural residents, and those living in Zabul province. Conversely, the prevalence of chronic health issues such as diabetes, obesity, cholesterol, and hypertension tended to be higher among women and urban residents, except for hypertension, which was more prevalent among those living in rural areas.

We also conducted a spatial analysis of chronic diseases and discovered a significant cluster of CVD prevalence in the southern region, while other chronic diseases were clustered in the northern part of the country. Additionally, our study revealed group disparities in the prevalence of CVD that were not previously reported in national health surveys (Peltzer and Pengpid 2021; Neyazi et al. 2023). Older rural women had the highest rate of CVD compared to their urban counterparts, while older men in urban areas had a higher rate than their rural counterparts.

Furthermore, compared to other countries in the region, the prevalence of diabetes in Afghanistan was relatively lower, while the prevalence of hypertension was higher. These findings were consistent with other studies that also reported geographic differences in the prevalence of CVD in the country (Shah et al. 2001; Saeed et al. 2014, 2016, 2020; Saeed 2015; Ghamri et al. 2019). The regional differences in the prevalence of CVD in Afghanistan may be attributed to various factors, such as political unrest, the impact of climate change on the food environment, the process of urbanization, and lifestyles (e.g., diet and PA ). However, investigating the association between diet and health outcomes was beyond the scope of our study. Overall, our research underscores the importance of considering group disparities in health outcomes when designing public health interventions in Afghanistan.

The multivariate analysis found that different types of PA have varying effects on various health outcomes. Vigorous and moderate work-related PA may be detrimental to cardiovascular health, but they play important protective roles in reducing the risk of obesity, diabetes, hypertension, and cholesterol. This suggests that while work-related PA may not be the best choice for reducing the risk of cardiovascular disease, defined as chest pain and incidence of stroke, it is still important for overall health and well-being. Our study also found that vigorous leisure-based and moderate work-based PA significantly reduces the odds of obesity. This is an important finding, as obesity is a significant risk factor for many chronic diseases, including cardiovascular disease. Therefore, engaging in these types of PA can have a positive impact on reducing the risk of developing these diseases.

On the other hand, our analysis found that moderate leisure-based PA is ineffective in reducing obesity. This highlights the importance of choosing the right type of PA to achieve specific health goals by consulting health professionals. The association between PA and chronic diseases is consistent with findings from other studies, indicating that PA can help reduce the risk of developing chronic diseases (Wannamethee and Shaper 2001; Rennie et al. 2003b, a; Liu et al. 2020; Ahmadi et al. 2022). However, the specific types and levels of PA that are most effective may vary depending on the population and the disease being studied.

According to the World Health Organization (WHO), adults should engage in at least 150 minutes of moderate or 75 minutes of vigorous PA per week to maintain good health (World Health Organization 2022b). However, local contextual factors, such as high-energy expenditure occupations, may hinder adherence to these guidelines in Afghanistan. This study found that different vigorous levels of PA (work or leisure) were significant determinants of CVD, especially among adult women. Interestingly, moderate PA was also a determinant factor for CVD, which may be attributed to the cultural interpretation of "moderate" in Afghanistan. Nevertheless, regular exercise can reduce the risk of developing CVD, improve cardiovascular health, lower blood pressure, and reduce stress. Inappropriate choice of levels of PA may contribute to regional differences in CVD prevalence, and limited access to safe public spaces for PA may also exacerbate this issue (Mushkani and Ono 2021). Therefore, health practitioners must consider cultural context when recommending a type of PA to those with chronic diseases. For example, traditional activities like walking or hiking may be more common in specific regions and may be recommended over other forms of PA.

Public health implication of the study

The study's findings have significant implications for public health in the context of developing countries, particularly in Afghanistan. The high prevalence of CVD in developing countries is an important public health challenge that requires prompt action from the government and other stakeholders. One crucial implication of this study is the need for targeted interventions to address the regional disparities in CVD prevalence and chronic health conditions. These interventions should be group-based and tailored to the sociodemographic characteristics and geography of the affected regions. The study's findings highlight that the burden of CVD is not distributed evenly across the country, and some parts are more affected than others. Therefore, interventions should consider the unique cultural, economic, and environmental factors contributing to CVD risk in each area.

Another crucial implication is improving access to prevention and treatment services for CVD. Promoting healthy lifestyle behaviors, such as a healthy diet and regular PA, as well as improving access to screening, diagnosis, and treatment services for CVD, is critical. Access to these services is crucial in reducing the burden of CVD in the country and improving health outcomes for individuals with CVD. Furthermore, the study's findings also highlight the need for enhanced surveillance and monitoring of CVD in Afghanistan. This includes collecting and analyzing data on CVD prevalence, risk factors, and outcomes to inform public health policy and interventions. It also includes strengthening the capacity of the healthcare system to diagnose and manage CVD, including training healthcare providers in the latest guidelines and best practices for CVD prevention and treatment. Overall, these findings are essential for policymakers and public health practitioners in Afghanistan to develop effective interventions and policies to reduce the burden of CVD in the country.

Strengths and limitations

The analysis of data WHO STEPS survey in Afghanistan has both strengths and limitations. One of the strengths is the standardized methodology used in the study, which allows for cross-country comparisons and helps identify global trends in CVD risk factors. Additionally, the survey involved a relatively large sample size, providing a representative sample of the adult population and allowing for more precise estimates of the prevalence of CVD and associated risk factors. Furthermore, the survey collects data on various CVD risk factors, providing a comprehensive picture of the burden of CVD in Afghanistan and identifying priority areas for intervention. However, relying on self-reported data from participants is a significant limitation of the STEPS survey, as it may be subjected to recall and social desirability bias. Additionally, the survey was conducted in only 21 of the 34 provinces, which may limit the generalizability of the findings to the entire Afghan population. Despite these limitations, the data source provides valuable insights for policy-makers, healthcare providers, and researchers to design interventions to reduce the burden of CVD in the country. However, interpreting the findings requires caution, and additional data sources should be used to validate and complement the STEPS survey data.

Vigorous levels of PA (work or leisure) significantly correlate with CVD and chronic health conditions (i.e., obesity, hypertension, diabetes, and cholesterol). Findings from this study provide valuable insights for policymakers, healthcare providers, and researchers to design interventions to reduce the burden of CVD in the country.

Acknowledgment

We acknowledge that the data used for this study was provided by the World Health Organization (WHO) for the purpose of research. Data is freely available with permission from the WHO’s NCD Microdata repository.

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Competing interests: The authors declare no competing interests.

Prevalence, spatial pattern, and predictors of cardiovascular and chronic diseases: A cross-sectional study

Status:

Version 1

Abstract

Figures

Introduction

Cardiovascular and chronic diseases in Afghanistan

Association between levels of physical activity and chronic conditions

Data and methods

Measure

Statistical analysis

Results

Group prevalence

Hotspot analysis of cardiovascular and chronic diseases

Multivariate logistic regression of cardiovascular and chronic diseases

Multivariate analysis results revealing predictors of CVD

Multivariate analysis results revealing predictors of chronic conditions

Discussion

Public health implication of the study

Strengths and limitations

Conclusion

Declarations

Acknowledgment

References

Additional Declarations

Status:

Version 1