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. 2026 Feb 7;26:64. doi: 10.1186/s12902-026-02190-5

Burden and predictors of risk for metabolic complications and risk for cardiovascular diseases among adults with noncommunicable diseases, Ethiopia

Alemneh Kabeta Daba 1,✉, Alemu Tamiso Debiso 1, Frehiwot Atsbeha 1, Freshet Assefa 1
PMCID: PMC12922397  PMID: 41654777

Abstract

Background

Metabolic complications and cardiovascular diseases are the leading contributors of noncommunicable disease (NCD) attributed mortality. But contextual regional data are not available to input to preventive initiatives’ design. Thus, we determined the burden of risks for metabolic complications and cardiovascular diseases, and identified their predictors among adults with noncommunicable diseases in Sidama National Regional State (SNRS) of Ethiopia.

Methods

A cross-sectional study was conducted among 882 adults with NCD (diabetes mellitus and/or hypertension) from February to June 2023 in SNRS of Ethiopia. Data were collected using interviewer administered questionnaire and anthropometric measurements. Descriptive statistics was used to describe participants and generate results for univariate outcomes. Regression analyses were applied to identify predictors of risk for metabolic complications (RMC) and cardiovascular diseases (RCVD).

Results

The burden of RMC determined by waist-to-hip circumference (WHpCR) and RCVD determined by waist-to-height (WHtR) ratios were 85% and 63%, respectively. Being a male [AOR = 0.49, (95%CI:0.29, 0.81), p = 0.01] decreased odds of RMC while no formal education [AOR = 2.45 (95%CI:1.24, 4.86), p = 0.01], no nutrition education or counseling attendance [AOR = 5.10 (95%CI: 1.55, 16.83), p = 0.00], poor NCD risk score [AOR = 2.59 (95%: 1.64, 4.10), p = 0.00] increased it. Likewise, no formal education [AOR = 1.83 (95%CI: 1.11, 3.01), p = 0.01], poor NCD protect score [AOR = 1.55 (95%CI: 1.10, 2.18), p = 0.01], poor NCD risk score [AOR = 1.55 (95CI:1.04, 2.32), p = 0.03] and higher body mass index (BMI) [AOR = 1.62 (95%CI:1.13, 2.31), p = 0.00] increased odds of RCVD.

Conclusions

RMC and RCVD are widespread among adults with NCD in SNRS of Ethiopia. Poor NCD protect and risk scores, no attendance of nutrition education and high BMI contributed to the increased of risks for RMC and RCVD.

Clinical trial number

Not applicable.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12902-026-02190-5.

Keywords: Risk for metabolic complication, Risk for cardiovascular disease, Noncommunicable disease, Noncommunicable disease protect and risk scores, Ethiopia

Introduction

Health burden attributed to noncommunicable diseases (NCD) is a growing global concern contributing to considerable share of undesired outcomes including morbidity, mortality and disability-adjusted life years (DALYs) disproportionately affecting low and middle income countries [1] with uneven rising trend in Sub-Saharan Africa [2]. Globally, NCD is estimated to contributed to more than seven trillion cases, forty-four million deaths, and to about two billion DALYs in 2021 [3]. Likewise, in Ethiopia, in 2019, about two hundred thousand NCD incident cases are estimated with a contribution to about 550 deaths and 12,200 DALYs per 100,000 population. Moreover, percentage contribution of NCD to deaths (from 8% to 35%) and DALYS (from 6% to 29%) considerably increased in the country over a decade, from 2010 to 2019 [4].

Most NCD related undesired outcomes are attributed to a range of factors [5, 6]. And, in Ethiopia, facing the worst because of NCD like diabetes (DM), hypertension (HTN) and others, considerable proportion (15%) of individuals with NCD died while being under medical care at tertiary hospitals [7]. Metabolic [8, 9] and cardiovascular [10] complications are among the occurrences contributing to NCD related mortality and DALYs.

According to the clinical and programmatic NCD management guideline of Ethiopia, the comprehensive prevention, treatment and care of NCD is suggested to balance medical and behavioral interventions with a patient centric approach [11]. Connectedly, interventions aiming the management of DM and HTN shall consider metabolic and cardiovascular related risks and complications as they signal possible occurrence of NCD related undesired outcomes [12]. For this, though scarce, context specific evidence on the burden of risk for metabolic complications (RMC) and risk for cardiovascular disease (RCVD) possibly with their contributors among adults with confirmed DM and/or HTN could add value to the planning of NCD related programs, development of case management protocols, designing of public health interventions, and revision of guidelines. To this end, this study analyzed the burden and predictors of risks for RMC and RCVD among adults with NCD in Sidama National Regional State (SNRS) of Ethiopia.

Materials and methods

Study design, area and period

This study was conducted using a cross-sectional study design in SNRS of Ethiopia. It is a subnational state where NCD is reported prevalent with an increasing trend. The region has twenty-one hospitals with clinics/units for NCD care. Data were collected from February to June 2023.

Sampling

Individuals with NCD (DM and/or HTN) under medical follow up at hospitals in the SNRS were the source population. Randomly selected adults with NCD who had a regular medical follow up and visited the health institutions during the data collection period were included. Sample size was calculated using single population proportion formula [13] considering metabolic and/or cardiovascular risk as the primary outcomes. And multistage sampling technique was applied to select study participants. Details on sample size determination and sampling technique are available in our previous publication [14].

Data collection

Data collection questionnaire was developed by reviewing available literature. The data collection tool had three parts. The first part of the questionnaire focused on socio demographic and socio-economic characteristics. Second section was composed of questions to assess dietary quality and nutrition education/counselling attendance [15–18]. The third section contained anthropometric measurements (Supplementary Material 1).

Training was given to ten data collectors and an orientation to two supervisors about objectives of the study, data collection tool, data collection techniques and basics of research ethics. Pre-test was performed at hospitals in the subnation but not selected for the actual data collection namely Hawassa University Comprehensive and Specialized Hospital, Adare General Hospital and Tula Primary Hospital. Following the pre-test, a discussion was held with data collectors and supervisors to effect correction based on feedbacks from the pre-test. Based on feedback from the pre-test, notes for clarification were added and corrections were made to the data collection tool. Data were collected using interviewer administered questionnaire deployed to KoboToolbox. Supervisors and co-principal investigator closely followed data collection on a daily basis.

Data analysis

The data were exported to statistical package for social science (SPSS) window version 24 for analysis. After checking for completeness and integrity, data re-coding, categorization and transformation were performed. Principal component analysis (PCA) was used to generate wealth index quantiles. Response options of all variables considered for PCA were dichotomized and iterative factor analysis was applied to reduce dimensions and produce acceptably interpretable five levels of wealth statuses. Descriptive statistical analyses were performed to describe the participants and generate results on univariate outcome interests. Nutritional status was determined using body mass index (BMI) and MUAC. Risk for metabolic complications and RCVD were computed from height, weight, waist circumference (WC) and hip circumference (HC) measurements and judged using cutoff suggested by world health organization [19]. Bivariate logistic regression analysis was applied to select variable eligible for multivariable analysis, a model used to statistically identify predictors of RMC and RCVD. Then, based on the results from the bivariate analysis, variables with p-values < 0.25 [20] were entered into multivariable logistic regression model to identify predictors of RMC and RCVD. Results were judged statistically significant at p-values < 0.05 in consideration of 95% confidence intervals for the adjusted odds ratios (AOR).

Results

Socio-demographic and socio-economic characteristics of the study participants

Table 1 presents sociodemographic and socioeconomic characteristics of the participants. About two of every three participants are male (63%) and from the rural areas (63%). Majority of the participants (84%) were married. More than three-fourths (77%) of the participants were 36years or older with mean score of 45.26(12.8) years. Half of the participants (51%) never attended formal education. Nonetheless, slightly more than one-third (36%) achieved primary or secondary education resulting with mean score of 7.5(2.4) for grades achieved. Two-thirds (66%) were either farmers (34%) or housemakers (28%) (Table 1).

Table 1.

Socio-demographic and socio-economic characteristics of the study participants, SNRS, Ethiopia

Characteristics N %
Sex Male 558 63.3
Female 324 37.7
Age 18-25Yrs 87 9.9
26-35Yrs 113 12.8
36-45Yrs 252 28.6
46-55Yrs 235 26.6
≥ 56Yrs 195 22.1
Education Never Attended Formal Education 450 51
Attended Primary or Secondary 320 36.3
Attended Higher Education 112 12.7
Marital Status Married 742 84.1
Single 87 9.9
Widowed/Divorced 53 6
Occupation Farmer 303 34.4
Housemaker 245 27.8
Merchant 134 15.2
Government Employ 71 8.0
Student 73 8.3
No Occupation 21 2.4
Others 35 4.0
Residence Rural 558 63.3
Urban 324 36.7
Wealth Index 1st ; Poorest 176 20
2nd ; Lower Middle 177 20.1
3rd ; Middle 179 20.3
4th ; Upper Middle 178 20.2
5th ; Richest 172 19.5
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Dietary quality and nutritional status

Consumption practice of the participants was assessed using dietary quality questionnaire over a 24-hour recall period and was operationally judged for noncommunicable diseases protect score and noncommunicable disease risk score indicators. Accordingly, nearly three-fourths of the participants (71%) were found being with poor noncommunicable disease protect score while slightly less than one-third (29%) achieved good noncommunicable disease protect score. On the other hand, most of the participants (85.5%) consumed no food or beverage product (good NCD risk score) from the risky groups whereas one in fifteen (14.5%) did consume at least one food or beverage product (poor NCD risk score) from the risk category. Detail is available somewhere [14].

Nutritional status was determined using BMI and MUAC. Based on the results from BMI, slightly more than three-fourths of the participants (77%) were with healthy nutritional status, BMI in 18 to 24.99 kg/m2 range. However, nearly one-fifth (19%) of the participants were overweight (16%) or obese (3%) and 4% were underweight. According to the results from MUAC-based nutritional status assessment, most of the participants (82%) were with health nutrition nutritional status while 12% were undernourished (Fig. 1).

Fig. 1.

Fig. 1

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Nutritional status of adults with NCD in SNRS, Ethiopia

Risk for metabolic complication and risk for cardiovascular diseases

Risk for metabolic complications was determined using WC and WHpCR. Based on the results from WC, about half (47%) of the participants were RMC. According to the results from WHpCR, most of the participants (85%) were at substantial RMC. Increased RCVD was judged using WHtR and about two-thirds (63%) of the participants were at increased risk (Fig. 2).

Fig. 2.

Fig. 2

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Burden of RMC and RCVD among adults with NCD in SNRS, Ethiopia

Contributors to risk for metabolic complications and risk for cardiovascular diseases

Variable selection was done through background scientific logical assumptions and statistical screening through bivariate logistic regression. And then fitted into the final multivariable logistic regression to identify characteristics contributing to risks for metabolic complications and risks for cardiovascular diseases. Sex, residence, marital status, educational status, wealth status, ever nutrition education or counseling attendance and noncommunicable disease risk score were characteristics fitted to the multivariable logistic regression model to identify characteristics contributing to risks for metabolic complications as they resulted with p-values of 0.25 or less for their respective bivariate logistic regression analysis. According to the results from the final multivariable logistic regression model, sex, educational status, wealth status, ever nutrition education or counseling attendance and noncommunicable disease risk score predicted risk for metabolic complications. The odd of risk for metabolic complications is by 51% lesser for males [AOR = 0.49, (95%CI:0.29, 0.81), p = 0.01] than females. Participants who never attended formal education were about two and half times [AOR = 2.45 (95%CI:1.24, 4.86), p = 0.01] riskier for metabolic complications than participants who attended higher education. Participants who did not attend nutrition education or counseling in three months duration were two and half times (AOR = 2.59 (95%CI: 1.64, 4.10), p = 0.00) riskier for metabolic complications than participants who attended nutrition education or counseling. Similarly, participants with poor noncommunicable risk score were two and half times [AOR = 2.59 (95%: 1.64, 4.10), p = 0.00] riskier to risk for metabolic complications than participants with good noncommunicable risk score.

On the other hand, sex, residence, educational status, wealth status, noncommunicable disease protect score, noncommunicable disease risk score and body mass index were characteristics fitted to the multivariable logistic regression model to identify predictors of risk for metabolic complication as they resulted with p-values of 0.25 or less for their respective bivariate logistic regression analysis. As a result, educational status, wealth status, noncommunicable disease protect score, noncommunicable disease risk score and body mass index identified to predict risk for cardiovascular diseases. Similar to its association with risk for metabolic complications, no formal education increased risk for cardiovascular diseases by about two times [AOR = 1.83 (95%CI: 1.11, 3.01), p = 0.01] as compared to higher education achievement. Correspondingly, the odds of risk for cardiovascular diseases were one and half times higher for participants with poor noncommunicable disease protect score [AOR = 1.55 (95%CI: 1.10, 2.18), p = 0.01] and poor noncommunicable risk [AOR = 1.55 (95CI:1.04, 2.32), p = 0.03] score than their counter parts. Risk for cardiovascular diseases is more than one and half times higher for participants with higher body mass index (overweight | obese) [AOR = 1.62 (95%CI:1.13, 2.31), p = 0.00] than participants with health body mass index (Table 2).

Table 2.

Characteristics contributing to risks for metabolic complications and cardiovascular diseases among adults with noncommunicable diseases in SNRS, Ethiopia

Characteristics Risk for Metabolic Complications Risk for Cardiovascular Diseases
COR (95CI), p AOR (95CI), p COR (95CI), p AOR (95CI), p
Sex Male 0.35 (0.22, 0.57), 0.000 0.49 (0.29, 0.81), 0.01 0.67 (0.50, 0.89), 0.00 0.81 (0.59, 1.09), 0.17
Female Ref Ref Ref Ref
Residence Urban 1.15 (0.77, 1.74), 0.49 0.89 (0.42, 1.92), 0.74 1.24 (0.93, 1.65), 0.14 0.92 (0.55, 1.56), 0.76
Rural Ref Ref Ref Ref
Educational Status No Formal Education 2.12 (1.23, 3.65), 0.007 2.45 (1.24, 4.86), 0.01 1.38 (0.90, 2.10), 0.14 1.83 (1.11, 3.01), 0.01
Primary or Secondary 1.54 (0.88, 2.68), 0.13 1.49 (0.80, 2.78), 0.20 1.09 (0.70, 1.68), 0.72 1.22 (0.76, 1.95), 0.40
Higher Education Ref Ref Ref Ref
Marital Status Married Ref Ref - -
Single 1.14 (0.59, 2.22), 0.70 1.04 (0.50, 2.18), 0.91 - -
Widowed/Divorced 4.20 (1.01, 17.53), 0.05 2.39 (0.55, 10.67), 0.24 - -
Wealth status 1st ; Poorest 2.29 (1.27, 4.11), 0.01 3.65 (1.38, 9.71), 0.01 1.94 (1.26, 2.99), 0.00 2.35 (1.17 4.69), 0.01
2nd ; Lower Middle 2.18 (1.22, 3.89), 0.01 3.25 (1.41, 7.50), 0.01 2.17 (1.40, 3.35), 0.00 2.56 (1.41, 4.64), 0.00
3rd ; Middle 1.99 (1.13, 3.50), 0.02 2.10 (1.14, 3.86), 0.01 1.80 (1.17, 2.76), 0.00 1.81 (1.15, 2.84), 0.01
4th ; Upper Middle 3.19 (1.68, 6.03), 0.00 3.07 (1.57, 5.99), 0.00 1.58 (1.03, 2.42), 0.04 1.62 (1.04, 2.52), 0.03
5th ; Richest Ref Ref Ref Ref
Nutrition Education Attendance Yes Ref Ref - -
No 5.38 (1.68, 17.27) 0.01 5.10 (1.55, 16.83), 0.00 - -
NCD Protect Score Good - - Ref Ref
Poor - - 1.55 (1.14, 2.11), 0.00 1.55 (1.10, 2.18), 0.01
NCD Risk Score Good Ref Ref Ref Ref
Poor 2.59 (1.64, 4.10), 0.00 2.23 (1.41, 3.75), 0.00 1.47 (1.01, 2.14), 0.05 1.55 (1.04, 2.32), 0.03
Body mass index Healthy - - Ref Ref
Overweight/obese - - 1.71 (1.21, 2.40), 0.00 1.62 (1.13, 2.31), 0.00
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Discussion

Chronic noncommunicable diseases and nutrition are highly interlinked [21]. A healthy nutritional status and practice is highly suggested to be considered in the prevention and management of nutrition attributed NCD and avoidance of related life threatening complications [22]. In view of this, this study analyzed the burden of nutrition related metabolic complications and cardiovascular diseases in the case of under medical follow-up adults with DM and/or HTN. The analysis primarily focused on risks for metabolic complications and cardiovascular diseases through anthropometric indices. It found that majority of the participants were with poor dietary quality as measured by NCD protect score (71%) though comparable proportion (85.5%) did not report consumption of food and beverage products from the unhealthy food groups. And, more of the participants were with healthy nutritional status as measured by BMI and MUAC. However, body fat distribution indices [23] showed that majority of the participants were at substantial risk of metabolic complications or increased risk of cardiovascular diseases as determined by WHpCR or WHtR.

Results of this analysis figured that nearly eight in ten participants were with a healthy nutritional status as determined using BMI (77%) or MUAC (82%) though the proportion determined by MUAC is slightly higher than that of BMI. Even if studies discuss that MUAC and BMI are correlated [24, 25], the observed slight difference in the findings of this analysis could be due to the fact that the indices could be affected by multiple factors including recent consumption of food or beverage products, fluid retention, and uneven body weight (fat and muscle) distribution. Further, compared with BMI, the suggested corresponding MUAC cut off (26.91 cm) to identify overnutrition is considerably high [26].

The primary interest of this analysis is anthropometrically [27] to determine burden of risks for metabolic complications and cardiovascular diseases. The risk for metabolic complications was determined using waist circumference and waist-to-hip circumference ratio while waist circumference-to-height ratio was used to judge risk for cardiovascular diseases [28]. In this analysis the proportion of risk for metabolic complications was 47% as determined by waist circumference and the proportion of substantial risk for metabolic complications was 85% as judged by waist-to-hip circumferences ratio. The observed prevalence of risk for metabolic complications determined by waist circumference by this analysis is lower than a proportion (58%) of central obesity in Dessie town, Northeastern-Ethiopia [29]. Difference in studied populations and related characteristics could have contributed to the observed difference in magnitude of risk for metabolic complications. All participants of the study conducted in Dessie town-Ethiopia are urban dweller bank employees who are prone to sedentary life style while about two-thirds (62%) of respondents of this analysis are rural dweller (63%) farmers (34%) or housemakers (28%). Also, the magnitude of substantial risk for metabolic complications (85%) as determined by waist-to-hip circumferences ratio is much lower than magnitude determined for women civil servants (30%) in Addis Ababa-Ethiopia [30]. Similarly, difference in the studied population and divergence in residency, lifestyle, demographic and economic factors might have contributed to the observed considerable difference in magnitude of central obesity and interpreted risk for metabolic complications.

Sex, educational achievement and wealth status were the sociodemographic and socioeconomic factors identified being associated with risk for metabolic complications. According to the results, male participants were by 65% less likely to be at risk for metabolic complication than female participants. Similarly, though it is with a different reference category, a study conducted in Adama town-Ethiopia (ten times) [31] and Woldia town-Ethiopia (thirteen times) [32] identified that females are more prone to risk for metabolic complications as it was measured by central obesity indicators. Moreover, comparably having the same reference group for the analysis, a study conducted in Mizan-Aman town-Ethiopia found that male are by 78% less at risk for metabolic complications than females [33]. Also, this analysis found that participants who never attended formal education are about two times more at risk for metabolic complications (AOR = 2.12, p.=0.007) and at risk for cardiovascular diseases (AOR = 1.83, p.=0.01) compared to participants who attended higher education. This could be because of multidimensional capacity that attending education builds to shape personal lifestyle [34].

According to the results of this analysis, participants from households in the lower four wealth quantiles (Poorest, Lower Middle, Middle and Upper Middle) are more at risk for metabolic complications and at risk for cardiovascular diseases than participants from richest (fifth wealth quantile) households. The finding is in agreement with findings of studies conducted in Adama town-Ethiopia [31] and Woldia town-Ethiopia [32]. This could be related to the collective economic challenge individuals from poor households might shoulder to sustainably practice healthy meals [35, 36] and cover healthcare related charges [37, 38].

Behavioral change interventions, counseling and/or education, for nutritional outcomes are expected to contribute to the reduction of health risks through shaping practices [39]. Connectedly, in agreement with the expected benefit from the nutrition counseling and/or education interventions integrated with the routine medical follow up and programmatic activities, this analysis found that participants who did not attend nutrition education or counseling are more than five times at risk for metabolic complications than who attended. Moreover, the result is in line with reported positive effect of nutrition education interventions on nutrition related outcomes [40–42].

Unhealthy food and beverage products are discussed to fasten weight gain and increase probability of unhealthy metabolic profiles [43, 44]. And, dietary quality scores are indicators used to assess quality of meals individuals consume from health protection (NCD protective score) and risk (NCD risk score) dimensions. In agreement to the scientific arguments and findings on the contribution of unhealthy food consumption to ill health [45], this study found that individuals with poor NCD risk score are more at risk for metabolic complications and at risk for cardiovascular diseases. This could be because that food and beverage products categorized under NCD risk score are identified being associated with nutrition related ill health. Similarly, participants with poor NCD protect score are at increased risk for cardiovascular diseases. This also could be subjected to the NCD protective contribution of food products considered for NCD protect score [18]. Being a finding from multi-health facility in a region covering wider geography and addressing multiple outcomes (metabolic complications, cardiovascular diseases, dietary quality and anthropometric status) among individuals with DM and/or HTN are among the strengths of this analysis. However, further extrapolation and usage of the findings shall be with due consideration of possible limitation of this study including data from a single observation of cross-sectional design and determination of risks for metabolic complications and cardiovascular diseases only using anthropometric measurement. Moreover, pharmacologic treatment regimen and DM and/or HTN controlling status were not assessed and considered in the model to identify predictors. As to its significance, the finding is with multidimensional implications to shape the routine medical services to individuals with DM and/or HTN so that the related life-threatening metabolic complications and cardiovascular diseases are potentially avoided. Moreover, the results could be used in the to the design, development and refinement of public health intervention for the prevention and community level management of NCD like DM and HTN, metabolic complications and cardiovascular diseases.

Conclusions

Risk for metabolic complications and risk for cardiovascular diseases are widespread among individuals with NCD in SNRS of Ethiopia. Being male decreases the probability of risk for metabolic complications while no formal education, no nutrition education or not attending nutrition counseling/education and poor noncommunicable disease risk score increased its likelihood. Poor dietary quality as determined by noncommunicable disease protect and noncommunicable disease risk scores and unhealthy BMI increases the probability of risk for cardiovascular diseases. Collective initiatives to improve consumption practice and nutritional status of adults with noncommunicable diseases, and to integrate comprehensive practicable nutrition behavioral change communication in the routine NCD health care are highly recommended.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1 (667.5KB, docx)

Acknowledgements

Authors would like to acknowledge Hawassa University for covering the field implementation and data collection costs, and the study participants for being willing and their time.

Abbreviations

AOR

Adjusted odds ratio

BMI

Body mass index

DALYs

Disability-adjusted life years

DM

Diabetes mellitus

HC

Hip circumference

HTN

Hypertension

MUAC

Mid-upper arm circumference

NCD

Noncommunicable diseases

PCA

Principal component analysis

RCM

Risk for metabolic complication

RCVD

Risk for cardiovascular diseases

SNRS

Sidama national regional state

SPSS

statistical package for social science

WC

Waist circumference

WHpCR

waist-to-hip circumference

WHtR

waist-to-height ratio

Author contributions

AKD: Conceptualization, design, coordinating and supervision of field implementation, data analysis, interpretation of results, drafting manuscript FA: Conceptualization, design, coordinating preparation for field implementation, supervision of field implementation, and reviewing the manuscript ATD: Conceptualization, fund curation, overall coordination and manuscript reviewing. FA: Conceptualization, result interpretation and manuscript reviewing. All authors read and approved the final manuscript.

Funding

Seed money in Ethiopian Birr was obtained from Hawassa University to support field implementation and data collection. The funder has no specific role in the conceptualization, design, data collection, analysis, result interpretation, decision to publish, or preparation of the manuscript except assessment and approval of the research protocol eligibility for funding.

Data availability

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The present study was conducted according to the guidelines laid down in the Declaration of Helsinki. Ethical clearance (Ref. No.: IRB/140/14) was obtained from the Institutional Review Board of College of Medicine and Health Sciences, Hawassa University. Also, letters requesting for permission and cooperation were issued from Research and Community Service Directorate of College of Medicine and Health Sciences, Hawassa University and submitted to the respective hospitals’ administrative bodies. Informed consents were secured from all study participants.

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.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (667.5KB, docx)

Data Availability Statement

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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