# Prevalence and determinants of non-communicable diseases among child-bearing women in Somaliland from a 2020 nationwide survey in Somaliland

Mustafe Abdillahi Ali, Abdirahman Omer Ali, Abdirizak Hassan Abokor, Omer Adam Farih, Abdisalam Hassan Muse

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Mustafe Abdillahi Ali, Abdirahman Omer Ali, Abdirizak Hassan Abokor, Omer Adam Farih, Abdisalam Hassan Muse. Prevalence and determinants of non-communicable diseases among child-bearing women in Somaliland from a 2020 nationwide survey in Somaliland. Discover Public Health, 2024, 21 (229), 10.1186/s12982-024-00371-y. doi:10.1186/s12982-024-00371-y

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# Prevalence and determinants of non-communicable diseases among child-bearing women in Somaliland from a 2020 nationwide survey in Somaliland

Mustafe Abdillahi Ali¹ · Abdirahman Omer Ali¹,² · Abdirizak Hassan Abokor¹ · Omer Adam Farih¹ · Abdisalam Hassan Muse¹

Received: 17 June 2024 / Accepted: 5 December 2024

Published online: 11 December 2024

© The Author(s) 2024 OPEN

## Abstract

**Background** Chronic diseases contribute significantly to global morbidity and mortality, with their prevalence projected to rise due to lifestyle changes and socioeconomic factors. This study examines the prevalence and determinants of chronic diseases in Somaliland, utilizing data from the 2020 Somaliland Demographic and Health Survey (SLDHS) data.

**Methodology** The SLDHS employed a stratified multi-stage probability cluster sampling technique to select 5,994 households. Data was collected using computer-assisted personal interviewing (CAPI) through smartphones. The study analyzed data from 5,709 individuals to assess the prevalence and determinants of chronic diseases.

**Results** The study revealed a significant prevalence rate of 8.25% for chronic diseases in Somaliland. Several risk factors were identified, including smoking, age, obesity, regional disparities, marital status, and Khat chewing. Individuals residing in the Sool region had a significantly higher risk of developing chronic diseases (Adjusted Odds Ratio (AOR) = 2.31; 95% CI 1.594–3.348) compared to those in the Awdal region. Individuals who were abandoned had a significantly higher likelihood of experiencing chronic diseases (AOR = 2.631; 95% CI 1.042–6.646) compared to those who were married. Widowed individuals also had a significantly higher likelihood of experiencing chronic diseases (AOR = 1.759; 95% CI 1.006–3.076) compared to those who were married. Individuals classified as obese had a significantly higher odds ratio of having a chronic disease (AOR = 2.013; 95% CI 1.354–2.994) compared to those who were underweight.

**Conclusion** The significant prevalence of chronic diseases in Somaliland necessitates immediate preventive interventions and targeted strategies. Addressing risk factors such as smoking, age-related vulnerabilities, obesity, regional disparities, marital status, and Khat chewing is crucial to reduce the burden of chronic diseases. Future research should focus on longitudinal designs and broader risk factor analysis to inform effective prevention and control strategies.

**Keywords** Chronic diseases · Risk factors · Smoking · Age · Obesity · Regional disparities · Marital status · Khat chewing

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<footnote>Abdirahman Omer Ali, Abdirahman.Omer@amoud.edu.so | ¹Faculty of Science and Humanities, School of Postgraduate Studies and Research (SPGSR), Amoud University, Borama 25263, Somalia. ²School of Medicine and Surgery, College of Health Sciences, Amoud University, Amoud Valley, Borama 25263, Somalia.</footnote>
# 1 Introduction

## 1.1 Background of the study

Chronic diseases play a significant role in causing illness and death on a global scale [1, 2]. According to a report by the World Health Organization (2002), chronic diseases are responsible for 60% of worldwide morbidity, mortality, and disability [2]. The anticipated increase in the near future of individuals affected by chronic illnesses is a direct result of rapid changes in lifestyle and broader factors such as social, economic, and environmental influences that contribute to chronic conditions [3]. In low- and middle-income countries, chronic diseases account for 80% of all deaths [4].

In the developed world, chronic diseases have become the primary motivation for seeking healthcare and are the leading cause of disease burden globally. Currently, approximately 2.8% of the world's population is affected by diabetes, but this figure is projected to increase to 6.5% (around 366 million people) by 2030. Consequently, cardiovascular diseases have assumed paramount significance as the primary contributor to global disability [5].

World Health Organization's (WHO) South-East Asia Region, chronic disease which include heart disease, stroke, cancer, chronic respiratory diseases and diabetes are estimated to account for half of annual mortality (54%) and burden of disease (47%) [6]. Chronic diseases accounted for 46% of all fatalities in developing nations, and this ratio is expected to rise to 59% by 2030, or more than 37 million lives per year (a 64% increase). Chronic diseases are expected to be the top cause of death in every part of the world, including low-income countries [7]. These trends highlight the critical need for global efforts to address the growing burden of chronic diseases, particularly in resource-limited settings. For instance, the incidence and prevalence of NCDs are rising in Ethiopia and are mentioned as a cause for 42% of deaths (27% are early deaths before 70 years of age) [8].

Asthma affects nearly a quarter of a billion people globally, is the most frequent chronic ailment in childhood, and causes over 1000 fatalities each day, the majority of which are preventable [9]. Moreover, hypertension emerges as a prominent risk factor for health challenges in low- and middle-income countries [10, 11].

Recent studies have observed an escalation in the prevalence of hypertension among children and adolescents in numerous nations [12–16].

Furthermore, projections suggest that the population increase between 2015 and 2030 will result in approximately 17.4 million individuals developing hypertension [14].

African health systems are underdeveloped and incapable of dealing with the accumulated burden of infectious and chronic diseases. There is an insufficient number of non-communicable disease (NCD) specialists, health facilities are inadequate, medications are either inaccessible or expensive, and few countries have devised strategies to address chronic illness care [15–17].

Several reports from regions such as eastern, southern, and south-eastern Asia, including countries like China, India, and Vietnam, indicate a significant disparity in the prevalence of chronic kidney disease (CKD) across the area. The prevalence rates range from 4.7% to 17.4% [17, 18]. Primary headache disorders are neurological conditions that cause disabling symptoms. Alongside the head pain, these disorders also result in various sensory disturbances [19, 20].

In 2011, information regarding the societal impact of headaches was available in only 6% of African countries, while the economic burden associated with these disorders has not been adequately assessed so far [21]. While Chronic Low back pain is the second most common cause of disability in US adults [22]. More than 80% of the population will experience an episode of Chronic Low back pain at some time during their lives [23].

Although communicable diseases remain the leading cause of death, non-communicable diseases (NCDs) were responsible for approximately 42% of mortality in Somalia in 2019 [24]. In Somaliland, at least 7% of the population has a chronic disease, with the most prevalent conditions being high blood pressure (41%), diabetes (19%), kidney disease (9%), and heart disease (7%) [25].

This study focuses on understanding the prevalence and determinants of six specific chronic diseases in Somaliland: hypertension, diabetes, asthma, kidney problems, liver problems, and chronic headaches. By examining the occurrence and risk factors associated with these conditions, we aim to inform public health initiatives and interventions aimed at reducing the burden of chronic diseases and promoting healthier lifestyles in Somaliland, contributing to the attainment of the Sustainable Development Goals (SDGs) that emphasize the importance of global health and well-being [25].
## 1.2 Study objective

This study identified the prevalence of chronic diseases among child-bearing women in Somaliland and determined the factors associated with these conditions. We utilized data from the 2020 Somaliland Demographic and Health Survey (SLDHS) to examine the relationships between demographic characteristics, lifestyle factors, and the presence of chronic diseases within this specific population group.

## 1.3 Study purpose

The findings of this study contribute to a better understanding of the burden of chronic diseases among child-bearing women in Somaliland. This knowledge is crucial for informing public health policy, program development, and interventions aimed at reducing the incidence and prevalence of these conditions. By identifying key risk factors, we hope to have facilitated the development of targeted interventions that address these factors and promote healthier lifestyles among child-bearing women in Somaliland.

# 2 Methodology

## 2.1 Study setting

The Somaliland Health and Demographic Survey (SLHDS) 2020 was conducted across the entirety of the Republic of Somaliland, covering all six official regions: Awdal, Marodijeh, Sahil, Togdheer, Sool, and Sanaag. The survey aimed to capture a nationally representative picture, encompassing the diverse populations of urban, rural, and nomadic communities within Somaliland. Data collection took place over two distinct phases: the listing/MMR phase, which ran from February 2018 to December 2019, and the main survey phase, which spanned August 2018 to December 2019 and it was officially published 2020.

## 2.2 Data source

The SLHDS 2020 relied on primary data collection through four meticulously designed questionnaires: the Maternal Mortality Questionnaire, the Household Questionnaire, the Ever-married Woman's Questionnaire, and the Never-married Woman's Questionnaire. These questionnaires provided a comprehensive framework for gathering information on a wide range of health indicators, demographic characteristics, and socioeconomic factors focusing on sections relevant to chronic disease prevalence and determinants.

## 2.3 Data collection

The survey employed Computer-Assisted Personal Interviewing (CAPI) using smartphones for data collection, allowing for efficient data transfer via Bluetooth. Interviewing teams were comprised of one supervisor, three enumerators, and a driver for the main survey, and one supervisor, four enumerators, and a driver for the listing/MMR phase. Stringent quality assurance measures were implemented throughout the data collection process, including consultations with stakeholders, extensive training, independent third-party monitoring, GPS tracking, consistency checks, peer review, and validation forums.

## 2.4 Sampling techniques

The SLHDS 2020 utilized a stratified multi-stage probability cluster sample design to ensure representative sampling across the country's diverse communities. The design differed slightly for urban/rural areas, utilizing a three-stage stratified cluster sample design, and nomadic areas, which employed a two-stage stratified cluster sample design. The primary sampling units (PSUs) were selected with probability proportionate to the number of dwelling structures. Secondary sampling units (SSUs) were chosen using PPS sampling of listed households. Finally, households were systematically selected as ultimate sampling units (USUs) from the listed SSUs. This robust sampling approach, coupled with the careful construction of a comprehensive sampling frame, ensured that the SLHDS 2020 yielded valuable and reliable data.
## 2.5 Study participants

After excluding missing values and duplicates, a total of 5,709 individuals interviewed for if they ever had chronic disease one or more than one were included in the analysis. This resulted in a sample size of 5,709 after applying sampling weights.

## 2.6 Study variables

### 2.6.1 Outcome variables

The outcome variable addressed in this study was chronic disease. To determine Suffered from one or more chronic diseases and whether this had been diagnosed by a physician. (Yes/No) variable.

### 2.6.2 Explanatory variables

Independent variables used as determinants and associate factors were chosen based on causal assumptions drawn from the subject's knowledge. These included Age of household, region, place of residence, highest education level, body mass index, wealth index, sex, chew khat, smoke cigarette.

## 2.7 Data analysis

Data analysis was conducted using Statistical Package for Social Sciences (SPSS) version 26 and STATA version 17. Descriptive statistics were computed to assess the prevalence of chronic diseases, including frequencies, percentages, and means. Bivariate analysis was performed using Chi-squared tests to explore the association between chronic disease status and each explanatory variable. A p-value < 0.05 was considered statistically significant, indicating a potential relationship between the variables. Multivariate analysis was conducted using binary logistic regression to identify the independent determinants of chronic disease, controlling for confounding factors. Odds ratios (OR) and their 95% confidence intervals (CI) were calculated for each explanatory variable, representing the odds of having chronic disease for individuals with a given characteristic compared to those without. P-values < 0.05 were considered statistically significant. Statistical significance was set at a p-value of 0.05. All analyses were adjusted for sampling weights to ensure representativeness of the data to the Somaliland population.

# 3 Results

This study investigated the prevalence and determinants of chronic diseases in Somaliland, utilizing data from the 2020 Somaliland Demographic and Health Survey (SLDHS). The analysis considered all chronic diseases reported in the SLDHS questionnaire, providing a comprehensive overview of the burden of chronic disease in Somaliland.

## 3.1 Demographic characteristics

Table 1 presents the demographic characteristics of the study participants. The majority of respondents (25%) resided in the Togdheer region, followed by Sanaag, Waqooyi-Galbeed, and Sool (around 20% each). Awdal was the least represented region. Urban residents comprised the largest proportion (38%) of the sample, followed closely by rural and nomadic residents (31% each). Male respondents constituted 69% of the sample, while female respondents comprised 31%. In terms of wealth index, the majority (40%) of respondents were categorized in the lowest wealth quintile. The highest and second-highest wealth quintiles were similarly represented (around 37% each). The majority of respondents were married (62.88%), with the second-largest group being never-married (31.14%). A vast majority of respondents (99.05%) reported not chewing Khat, and 98.04% reported not smoking cigarettes/tobacco. The age distribution of respondents was relatively evenly spread across the categories, with the largest proportions falling
<table><caption>**Table 1** Descriptive Statistics for Demographic Variables in the Study</caption><thead><tr><th>Variable</th><th>Levels</th><th>Frequency</th><th>Percentage (%)</th></tr></thead><tbody><tr><td rowspan="5">Region</td><td>Awdal</td><td>765</td><td>13.40</td></tr><tr><td>Woqooyi-Galbeed</td><td>1192</td><td>20.88</td></tr><tr><td>Togdheer</td><td>1409</td><td>24.68</td></tr><tr><td>Sool</td><td>1147</td><td>20.09</td></tr><tr><td>Sanaag</td><td>1196</td><td>20.95</td></tr><tr><td rowspan="3">Type of residence</td><td>Urban</td><td>2164</td><td>37.91</td></tr><tr><td>Rural</td><td>1753</td><td>30.71</td></tr><tr><td>Nomadic</td><td>1792</td><td>31.39</td></tr><tr><td rowspan="2">Sex</td><td>Male</td><td>3963</td><td>69.42</td></tr><tr><td>Female</td><td>1746</td><td>30.58</td></tr><tr><td rowspan="5">Wealth Index</td><td>Lowest</td><td>2312</td><td>40.50</td></tr><tr><td>Second</td><td>565</td><td>9.90</td></tr><tr><td>Middle</td><td>716</td><td>12.54</td></tr><tr><td>Fourth</td><td>1069</td><td>18.72</td></tr><tr><td>Highest</td><td>1047</td><td>18.34</td></tr><tr><td rowspan="5">Marital Status</td><td>Married</td><td>3590</td><td>62.88</td></tr><tr><td>Divorced</td><td>220</td><td>3.85</td></tr><tr><td>Abandoned</td><td>27</td><td>0.47</td></tr><tr><td>Widowed</td><td>94</td><td>1.65</td></tr><tr><td>Never Married</td><td>1778</td><td>31.14</td></tr><tr><td rowspan="2">Smoke Cigarettes/Tobacco</td><td>No</td><td>5597</td><td>98.04</td></tr><tr><td>Yes</td><td>112</td><td>1.96</td></tr><tr><td rowspan="2">Chew Khat/Miraa</td><td>No</td><td>5655</td><td>99.05</td></tr><tr><td>Yes</td><td>54</td><td>0.95</td></tr><tr><td rowspan="6">Age of household head</td><td>18-24</td><td>343</td><td>6.01</td></tr><tr><td>25-34</td><td>1240</td><td>21.72</td></tr><tr><td>35-44</td><td>1652</td><td>28.94</td></tr><tr><td>45-54</td><td>1285</td><td>22.51</td></tr><tr><td>55-64</td><td>701</td><td>12.28</td></tr><tr><td>Above 64</td><td>488</td><td>8.55</td></tr><tr><td rowspan="4">Body Mass Index</td><td>Under Weight</td><td>960</td><td>16.82</td></tr><tr><td>Normal</td><td>3091</td><td>54.14</td></tr><tr><td>Over Weight</td><td>1082</td><td>18.95</td></tr><tr><td>Obesity</td><td>576</td><td>10.09</td></tr></tbody></table>

between 35 and 44 years old (28.94%) and 45-54 years old (22.51%). Finally, the majority of respondents (54.14%) had a normal body mass index, while 18.95% were overweight, 16.82% were underweight, and 10.09% were obese.

## 3.2 Prevalence of chronic diseases

Table 2 displays the prevalence of chronic diseases among the study participants. A significant proportion (8.25%) of respondents reported suffering from at least one chronic disease. \* MERGEFORMAT Fig. 1 presents the prevalence of the most common chronic diseases reported by participants. Hypertension was the most prevalent condition, accounting for almost 23% of reported cases. Kidney problems, chronic headaches, diabetes, liver problems, and asthma followed, with prevalence rates

<table><caption>**Table 2** Prevalence of Chronic Diseases</caption><thead><tr><th>Variable</th><th>Levels</th><th>Frequency</th><th>Percentage (%)</th></tr></thead><tbody><tr><td rowspan="2">Suffering from chronic disease</td><td>No</td><td>5238</td><td>91.75</td></tr><tr><td>Yes</td><td>471</td><td>8.25</td></tr></tbody></table>

<figure><img src="image_3.png" /></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 1** Prevalence of the Most Common Chronic Diseases Reported by Study Participants in Somaliland</figcaption></figure>

of 10%, 7%, 6%, 5%, and 5%, respectively. Notably, 11% of respondents indicated "other" chronic diseases, highlighting the need for further investigation into less common but potentially significant health issues.

## 3.3 Bivariate analysis

To explore associations between demographic variables and the presence of chronic diseases, a chi-squared test was conducted (Table 3). The findings revealed significant associations between chronic disease status and region ($\chi^2$ = 32.0902, p = 0.000), place of residence ($\chi^2$ = 6.3145, p = 0.043), wealth index ($\chi^2$ = 13.1605, p = 0.011), marital status ($\chi^2$ = 48.9710, p = 0.000), Khat chewing ($\chi^2$ = 5.1018, p = 0.024), smoking ($\chi^2$ = 9.2324, p = 0.002), and body mass index ($\chi^2$ = 30.5641, p = 0.000). No statistically significant associations were found between sex ($\chi^2$ = 0.1703, p = 0.680) or age ($\chi^2$ = 5.9705, p = 0.309) and chronic disease status.

## 3.4 Multivariable analysis

A multivariable logistic regression model was used to examine the independent associations between demographic variables and chronic disease status (\* MERGEFORMAT Table 4).

The results indicate that individuals residing in the Sool region had a significantly higher odds of experiencing chronic diseases (OR = 2.31, CI 1.594–3.348, p = 0.000) compared to those living in the Awdal region (reference group). Similarly, the odds ratios for Woqooyi-Galbeed and Togdheer regions were higher than Awdal, but only statistically significant for Woqooyi-Galbeed. While the odds ratio for Sanaag was higher than Awdal, it was not statistically significant (p = 0.32).

Regarding place of residence, the results suggest that nomadic settings may have lower odds of experiencing chronic diseases compared to urban settings, but this effect was not statistically significant. No significant difference in the likelihood of having chronic diseases was observed between urban and rural areas.

Analysis of wealth index revealed varied odds ratios across the categories, but none were statistically significant.

In terms of marital status, abandoned individuals had significantly higher odds of experiencing chronic diseases compared to married individuals (OR = 2.631, CI 1.042–6.646, p = 0.041). Widowed individuals also had significantly higher odds of experiencing chronic diseases compared to married individuals (OR = 1.759, CI 1.006–3.076, p = 0.047). Conversely, individuals who were never married had significantly lower odds of experiencing chronic diseases compared to married individuals (OR = 0.553, CI 0.428–0.714, p = 0.000). The odds ratio for divorced individuals was lower than married individuals, but this effect was not statistically significant.

Regarding smoking and Khat chewing, while smokers had higher odds of experiencing chronic diseases compared to non-smokers (OR = 1.764, CI 0.975–3.193, p = 0.061), and Khat chewers had higher odds compared to non-chewers (OR = 1.499, CI 0.649–3.462, p = 0.343), neither effect was statistically significant.

Finally, individuals with a normal body mass index had higher odds of experiencing chronic diseases compared to those who were underweight, but this effect was not statistically significant (OR = 1.305, CI 0.955–1.783, p = 0.094). Similarly, overweight individuals had higher odds compared to underweight individuals, but this effect was also not
<table><caption>**Table 3** Bivariate Analysis of Association Between Study Variables</caption><thead><tr><th rowspan="3">Factor<br>Levels<br>Region</th><th colspan="2">Suffering from chronic disease</th><th rowspan="3">Chi-Squared (X<sup>2</sup>)</th><th rowspan="3">Degree of Freedom (Df)</th><th rowspan="3">P-Value</th></tr><tr><th colspan="2">Frequency (Percentage)</th></tr><tr><th>No</th><th>Yes</th></tr></thead><tbody><tr><td>Awdal</td><td>41 (5.36)</td><td>724 (94.64)</td><td>32.0902</td><td>4</td><td>0.0000</td></tr><tr><td>Woqooyi-Galbeed</td><td>107 (8.98)</td><td>1085 (91.02)</td><td></td><td></td><td></td></tr><tr><td>Togdheer</td><td>117 (8.30)</td><td>1292 (91.70)</td><td></td><td></td><td></td></tr><tr><td>Sool</td><td>132 (11.51)</td><td>1015 (88.49)</td><td></td><td></td><td></td></tr><tr><td>Sanaag</td><td>74 (6.19)</td><td>1122 (93.81)</td><td></td><td></td><td></td></tr><tr><td>Place of residency</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Urban</td><td>171 (7.90)</td><td>1993 (92.10)</td><td>6.3145</td><td>2</td><td>0.043</td></tr><tr><td>Rural</td><td>168 (9.58)</td><td>1585 (90.42)</td><td></td><td></td><td></td></tr><tr><td>Nomadic</td><td>132 (7.37)</td><td>1660 (92.63)</td><td></td><td></td><td></td></tr><tr><td>Sex of household Head</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Male</td><td>323 (8.15)</td><td>3640 (91.85)</td><td>0.1703</td><td>1</td><td>0.680</td></tr><tr><td>Female</td><td>148 (8.48)</td><td>1598 (91.52)</td><td></td><td></td><td></td></tr><tr><td>Wealth quintile</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Lowest</td><td>183 (7.92)</td><td>2129 (92.08)</td><td>13.1605</td><td>4</td><td>0.011</td></tr><tr><td>Second</td><td>69 (12.21)</td><td>496 (87.79)</td><td></td><td></td><td></td></tr><tr><td>Middle</td><td>57 (7.96)</td><td>659 (92.04)</td><td></td><td></td><td></td></tr><tr><td>Fourth</td><td>83 (7.76)</td><td>986 (92.24)</td><td></td><td></td><td></td></tr><tr><td>Highest</td><td>79 (7.55)</td><td>968 (92.45)</td><td></td><td></td><td></td></tr><tr><td>Marital Status</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Married</td><td>342 (9.53)</td><td>3248 (90.47)</td><td>48.9710</td><td>4</td><td>0.000</td></tr><tr><td>Divorced</td><td>18 (8.18)</td><td>202 (91.82)</td><td></td><td></td><td></td></tr><tr><td>Abandoned</td><td>6 (22.22)</td><td>21 (77.78)</td><td></td><td></td><td></td></tr><tr><td>Widowed</td><td>16 (17.02)</td><td>78 (82.98)</td><td></td><td></td><td></td></tr><tr><td>Never Married</td><td>89 (5.01)</td><td>1689 (94.99)</td><td></td><td></td><td></td></tr><tr><td>Smoke Cigarettes/Tobacco</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>453 (8.09)</td><td>5144 (91.91)</td><td>9.2324</td><td>1</td><td>0.002</td></tr><tr><td>Yes</td><td>18 (16.07)</td><td>94 (83.93)</td><td></td><td></td><td></td></tr><tr><td>Chew Khat/Miraa</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>No</td><td>462 (8.17)</td><td>5193 (91.83)</td><td>5.1018</td><td>1</td><td>0.024</td></tr><tr><td>Yes</td><td>9 (16.67)</td><td>45 (83.33)</td><td></td><td></td><td></td></tr><tr><td>Body Mass Index</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Under Weight</td><td>54 (5.63)</td><td>906 (94.38)</td><td>30.5641</td><td>3</td><td>0.000</td></tr><tr><td>Normal</td><td>246 (7.96)</td><td>2845 (92.04)</td><td></td><td></td><td></td></tr><tr><td>Over Weight</td><td>93 (8.60)</td><td>989 (91.40)</td><td></td><td></td><td></td></tr><tr><td>Obesity</td><td>78 (13.54)</td><td>498 (86.46)</td><td></td><td></td><td></td></tr><tr><td>Age</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>18-24</td><td>18 (5.25)</td><td>325 (94.75)</td><td>5.9705</td><td>5</td><td>0.309</td></tr><tr><td>25-34</td><td>99 (7.98)</td><td>1141 (92.02)</td><td></td><td></td><td></td></tr><tr><td>35-44</td><td>143 (8.66)</td><td>1509 (91.34)</td><td></td><td></td><td></td></tr><tr><td>45-54</td><td>115 (8.95)</td><td>1170 (91.05)</td><td></td><td></td><td></td></tr><tr><td>55-64</td><td>60 (8.56)</td><td>641 (91.44)</td><td></td><td></td><td></td></tr><tr><td>Above 64</td><td>36 (7.38)</td><td>452 (92.62)</td><td></td><td></td><td></td></tr></tbody></table>

statistically significant (OR = 1.284, CI 0.888-1.856, p = 0.185). Individuals who were considered obese had significantly lower odds of experiencing chronic diseases compared to those who were underweight (OR = 0.497, CI 0.334-0.739, p = 0.001).

<figure><img src="image_5.png" /></figure>
<table><caption>**Table 4** Multivariable Logistic Regression</caption><thead><tr><th>Variable</th><th>Levels</th><th>Odds ratio (OR)</th><th>Coefficient SE</th><th>95% Confidence interval (CI)</th><th>P-value</th></tr></thead><tbody><tr><td rowspan="5">Region</td><td>Awdal</td><td>Ref</td><td></td><td></td><td></td></tr><tr><td>Woqooyi Galbeed</td><td>1.669</td><td>0.323</td><td>1.142-2.439</td><td>0.008</td></tr><tr><td>Togdheer</td><td>1.617</td><td>0.307</td><td>1.115-2.346</td><td>0.011</td></tr><tr><td>Sool</td><td>2.31</td><td>0.437</td><td>1.594-3.348</td><td>0</td></tr><tr><td>Sanaag</td><td>1.228</td><td>0.254</td><td>0.819-1.84</td><td>0.32</td></tr><tr><td rowspan="3">Place of residence</td><td>Urban</td><td>Ref</td><td></td><td></td><td></td></tr><tr><td>Rural</td><td>1.001</td><td>0.137</td><td>0.766-1.308</td><td>0.996</td></tr><tr><td>Nomadic</td><td>0.747</td><td>0.149</td><td>0.505-1.106</td><td>0.145</td></tr><tr><td rowspan="5">Wealth Index</td><td>Lowest</td><td>Ref</td><td></td><td></td><td></td></tr><tr><td>Second</td><td>1.284</td><td>0.235</td><td>0.897-1.838</td><td>0.172</td></tr><tr><td>Middle</td><td>0.816</td><td>0.164</td><td>0.55-1.209</td><td>0.311</td></tr><tr><td>Fourth</td><td>0.814</td><td>0.16</td><td>0.554-1.195</td><td>0.293</td></tr><tr><td>Highest</td><td>0.845</td><td>0.173</td><td>0.566-1.261</td><td>0.409</td></tr><tr><td rowspan="5">Marital Status</td><td>Married</td><td>Ref</td><td></td><td></td><td></td></tr><tr><td>Divorced</td><td>0.784</td><td>0.201</td><td>0.475-1.294</td><td>0.341</td></tr><tr><td>Abandoned</td><td>2.631</td><td>1.244</td><td>1.042-6.646</td><td>0.041</td></tr><tr><td>Widowed</td><td>1.759</td><td>0.501</td><td>1.006-3.076</td><td>0.047</td></tr><tr><td>Never Married</td><td>0.553</td><td>0.072</td><td>0.428-0.714</td><td>0</td></tr><tr><td rowspan="2">Smoke Cigarettes</td><td>No</td><td>Ref</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.764</td><td>0.534</td><td>0.975-3.193</td><td>0.061</td></tr><tr><td rowspan="2">Chew Khat/Miraa</td><td>No</td><td>Ref</td><td></td><td></td><td></td></tr><tr><td>Yes</td><td>1.499</td><td>0.64</td><td>0.649-3.462</td><td>0.343</td></tr><tr><td rowspan="4">Body Mass Index</td><td>Underweight</td><td>Ref</td><td></td><td></td><td></td></tr><tr><td>Normal</td><td>1.305</td><td>0.208</td><td>0.955-1.783</td><td>0.094</td></tr><tr><td>Over Weight</td><td>1.284</td><td>0.242</td><td>0.888-1.856</td><td>0.185</td></tr><tr><td>Obesity</td><td>2.013</td><td>0.408</td><td>1.354-2.994</td><td>0.001</td></tr></tbody></table>

This study provides valuable insights into the prevalence and determinants of chronic diseases in Somaliland. Further research focusing on specific chronic conditions and incorporating longitudinal designs would enhance our understanding of these health issues and inform the development of effective prevention and control strategies.

## 4 Discussions

This study aimed to assess the prevalence and determinants of chronic diseases in Somaliland using data from the 2020 Somaliland Demographic and Health Survey (SLDHS). The study represents a crucial first step in understanding the burden of chronic diseases in Somaliland, as it utilizes the first-ever DHS data collected in the country. This provides a valuable baseline for future research and public health interventions.

The findings revealed a significant prevalence of chronic diseases in Somaliland, with 8.25% of the study participants reporting suffering from at least one chronic disease. This is consistent with previous studies in Somaliland %) [25]. Which reported a similar prevalence of chronic conditions, although those studies focused on a broader range of chronic diseases. While our findings are consistent with the global trend of increasing chronic disease prevalence (2, 4, 7), the specific prevalence rates for the six chronic diseases included in this study—hypertension, diabetes, asthma, kidney problems, liver problems, and chronic headaches—require further investigation.

Our findings also align with previous research on the association between smoking and chronic diseases (10–13). The study found that smokers had a higher likelihood of having chronic diseases compared to non-smokers, a finding consistent with studies in Nepal (10–13). though the observed prevalence of smoking in this study (1.96%) was lower than in the Nepali studies (17.1%). Similarly, this study aligns with previous research indicating age as a significant, unmovable
risk factor for chronic diseases (10–13, 25). Our findings confirmed that older adults and younger adults have a higher likelihood of chronic disease compared to middle-aged individuals.

Regarding regional disparities, our study found that individuals residing in the Sool region had a significantly higher risk of developing chronic diseases. This finding is consistent with previous research, which reported higher prevalence of chronic diseases in the Sool region (25). This could be attributed to several factors, including limited access to healthcare, socioeconomic differences, and environmental factors. Future research is needed to better understand these regional variations and their underlying causes.

The study also found that abandoned and widowed individuals had significantly higher odds of experiencing chronic diseases compared to married individuals. These findings highlight the importance of considering social and emotional factors in chronic disease prevention and management. Abandoned and widowed individuals may experience greater stress, social isolation, and limited access to support systems, which could contribute to their increased risk.

Our findings also suggest that obesity is associated with a significantly higher odds ratio of having a chronic disease, confirming the importance of promoting healthy lifestyle choices and addressing the growing obesity problem in Somaliland (15–17). However, the study did find a lower odds ratio for obesity compared to underweight individuals, suggesting a need for further investigation into the complex relationship between body mass index and chronic disease in this context.

This study adds to the growing body of literature on chronic disease in Somaliland, providing valuable insights into the prevalence, risk factors, and potential determinants of specific chronic conditions. However, the study also highlights several limitations that need to be addressed in future research.

First, the cross-sectional design of the study does not allow for establishing causal relationships between risk factors and chronic diseases. Longitudinal studies are needed to capture temporal associations and better understand the trajectory of chronic diseases over time. Second, the study examined only a limited set of risk factors, and further investigation into additional lifestyle behaviors, such as physical activity, dietary patterns, and alcohol consumption, is needed to develop a more comprehensive understanding of the determinants of chronic diseases. Third, the study did not include information on treatment and control of chronic diseases, limiting our ability to assess the effectiveness of healthcare interventions and the overall burden of disease.

Despite these limitations, the study makes a significant contribution to public health by providing valuable insights into the prevalence and determinants of chronic diseases in Somaliland. The findings underscore the urgent need for preventive interventions and targeted strategies to address the growing prevalence of chronic diseases in Somaliland. Addressing risk factors such as smoking, obesity, and Khat chewing is crucial. Furthermore, strengthening primary health-care systems, improving access to healthcare services, and implementing targeted health education programs are critical steps towards reducing the burden of chronic diseases and promoting healthier lifestyles in Somaliland.

## 4.1 Strengths of the study

* **Rigorous Sampling:** The study utilized data from the Somaliland Demographic and Health Survey 2020, which employed a rigorous sampling design to ensure the representativeness of the sample. This enhances the generalizability of the findings to the adult population in Somaliland.

* **Comprehensive Analysis:** The study conducted univariate, bivariate, and multivariate analyses, allowing for a comprehensive examination of the factors associated with chronic diseases. This approach strengthens the validity of the results and helps identify independent associations between risk factors and chronic diseases.

## 4.2 Limitations of the study

* **Cross-sectional Design:** The study employed a cross-sectional design, which limits the ability to establish causal relationships between risk factors and chronic diseases. Longitudinal studies would be valuable in capturing temporal associations and investigating the trajectory of chronic diseases over time.

* **Limited Risk Factor Assessment:** The study only examined a limited set of risk factors, such as smoking, age, obesity, marital status, and Khat chewing. Additional lifestyle behaviors, such as physical activity, dietary patterns, and alcohol consumption, could provide a more comprehensive understanding of the determinants of chronic diseases.

* **Lack of Treatment Data:** The study did not include information on the treatment and control of chronic diseases among the study population. Assessing the management and control of chronic diseases would provide insights into the effectiveness of healthcare interventions and the overall burden of disease.

<figure><img src="image_6.png" /></figure>
* Generalizability: While the study provides valuable insights into the prevalence and determinants of chronic diseases in Somaliland, the findings may not be directly applicable to other regions or countries. Socioeconomic, cultural, and healthcare system differences can influence the patterns of chronic diseases in different populations.

# 5 Conclusions and recommendations

This study investigated the prevalence and determinants of chronic diseases in Somaliland using data from the Somali-land Demographic and Health Survey 2020 (SLDHS). The findings revealed a prevalence rate of 8.25% for chronic diseases in Somaliland, highlighting the significant burden these conditions pose on the population. Several risk factors were identified, including smoking, age, obesity, regional disparities, marital status, and Khat chewing. These findings underscore the urgent need for preventive interventions and targeted strategies to address the growing prevalence of chronic diseases in Somaliland.

To effectively tackle this challenge, the following recommendations are proposed:

Firstly, comprehensive public health interventions are crucial. These interventions should focus on health promotion, awareness campaigns, and behavioral change communication to address modifiable risk factors such as smoking, obesity, and Khat chewing. Secondly, improving access to healthcare services and facilities, particularly in resource-challenged settings, is essential. Enhancing the availability and affordability of diagnostic and treatment services for chronic diseases will facilitate early detection and management, leading to better outcomes.

Strengthening primary healthcare systems is another critical aspect. This can be achieved through capacity building of healthcare providers, improving infrastructure, and ensuring the availability of essential medications for chronic disease management. Effective primary healthcare systems are vital for the early detection, diagnosis, and management of chronic conditions.

Targeted health education programs are crucial for raising awareness about the risk factors and consequences of chronic diseases among the population. This is particularly important for women, who may be at increased risk. Empowering individuals to make informed decisions regarding their lifestyle choices and encouraging regular health check-ups are essential components of this effort.

Investing in longitudinal studies and surveillance systems is necessary to monitor the prevalence and trends of chronic diseases in Somaliland. This will provide valuable data for evidence-based decision-making, policy formulation, and the evaluation of interventions. Collaborative efforts between the government, healthcare providers, non-governmental organizations, and international partners are vital for developing and implementing comprehensive strategies for the prevention, control, and management of chronic diseases in Somaliland. This includes sharing best practices, resources, and expertise.

Addressing socioeconomic determinants of chronic diseases, such as poverty, education, and employment, is essential. Promoting socioeconomic development initiatives that improve living conditions, access to nutritious food, and opportunities for physical activity can have a significant impact on the prevalence of chronic diseases.

By implementing these recommendations, Somaliland can reduce the burden of chronic diseases, improve the quality of life for individuals affected by these conditions, and promote a healthier population overall.

**Declarations** We declare that this work has not been submitted as a manuscript to any other journal.

**Author contributions** Mustafe Abdillahi Ali, Dr. Abdirahman Omer Ali, Abdirizak Hassan Abokor, Omer Adam Farih contributed to the patient's care by taking her history and providing medical care throughout her hospital stay. Dr. Abdisalam Hassan Muse and Dr. Abdirahman Omer Ali contributed to the development of the manuscript. All authors reviewed and approved the final version of the manuscript for submission.

**Funding** Not applicable.

**Data availability** The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

## Declarations

**Competing interests** The authors declare no competing interests.

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