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Research Article
Socio-economic, Demographic, and Clinical Predictors of Diabetic Kidney Disease Progression (Renal Function Decline) Among Adults with Diabetes: A Retrospective Cohort Study in Kenya
Issue:
Volume 15, Issue 2, April 2026
Pages:
27-39
Received:
1 February 2026
Accepted:
20 February 2026
Published:
5 March 2026
Abstract: Diabetic kidney disease (DKD) represents a major global health burden, yet predictive models often overlook socio-economic determinants that may independently influence disease progression. This retrospective cohort study aimed to identify socio-economic, demographic, and clinical predictors of diabetic kidney disease development among diabetic patients in Kenya and compare Cox regression with support vector machine (SVM) models for risk prediction. Data were collected from 756 adult diabetic patients attending Meru Teaching and Referral Hospital and Kerugoya Level 5 Hospital between January 2018 and July 2024 through medical record review and semi-structured questionnaires. Survival analysis employed Kaplan-Meier estimation, log-rank tests, multivariable Cox proportional hazards regression, and survival SVM modeling. During follow-up, 286 participants (37.8%) developed diabetic kidney disease. Multivariable Cox analysis identified seven significant predictors of diabetic kidney disease progression: older age at diabetes diagnosis (adjusted HR=1.023, p=0.002), male gender (HR=1.282, p=0.041), family history of chronic kidney disease (HR=6.919, p<0.001), alcohol consumption (HR=1.556, p=0.001), and financial hardship (HR=4.524, p<0.001) increased risk, while secondary/higher education (HR=0.593, p<0.001) and ever being employed (HR=0.635, p=0.011) were protective. The SVM model demonstrated marginally superior predictive accuracy (C-index=0.775) versus Cox regression (C-index=0.770). These findings underscore that socio-economic factors function as independent risk modifiers beyond traditional clinical parameters, challenging conventional prediction paradigms that focus exclusively on biomedical indicators. The high incidence of diabetic kidney disease observed highlights an urgent public health challenge requiring integrated screening protocols that assess both clinical and socio-economic risk profiles at diabetes diagnosis. We recommend implementing targeted public health interventions that address financial barriers, promote educational attainment, and support employment opportunities for diabetic patients to mitigate diabetic kidney disease progression in resource-limited settings.
Abstract: Diabetic kidney disease (DKD) represents a major global health burden, yet predictive models often overlook socio-economic determinants that may independently influence disease progression. This retrospective cohort study aimed to identify socio-economic, demographic, and clinical predictors of diabetic kidney disease development among diabetic pat...
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Research Article
Logistic Regression Analysis of Factors Influencing Tuberculosis Prevalence in Nairobi Embakasi Sub-Counties
Ouma Calvince Odhiambo*
,
Idah Orowe
Issue:
Volume 15, Issue 2, April 2026
Pages:
39-46
Received:
23 December 2025
Accepted:
19 January 2026
Published:
18 March 2026
DOI:
10.11648/j.ajtas.20261689.12
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Abstract: This paper has examined the factors explaining prevalence of tuberculosis (TB) in the Embakasi sub-counties of Nairobi through logistic regression analysis. Patient records at the chest clinic of Mama Lucy Kibaki Teaching and Referral Hospital, which is the primary health facility of the Embakasi area, were used to gather the data. Tuberculosis is an infectious disease that is caused by Mycobacterium tuberculosis and normally attacks the lungs but may also propagate to other organs as a significant public health problem of the world. Although the infection has been managed through early diagnosis and treatment in developed countries, tuberculosis (TB) is still very widespread in most areas with low income levels such as sub-Saharan Africa. Kenya is in the 13th position in the list of 22 countries that have contributed approximately 80 percent of the world tuberculosis (TB) burden with the majority of the infections falling within the 15-44 years age group. The research was conducted to determine the major socio-demographic and environmental factors that have a relationship with the prevalence of tuberculosis (TB) in Embaksi. The logistic regression analysis showed that alcoholism and congestion in the household were the most significant predictors of tuberculosis (TB) infection. The five sub-counties had similar prevalence rates and the likelihood of being infected with tuberculosis (TB) in Embaksi was, on the whole, some 4.66 times greater than the national one. This evidence highlights the necessity of specific interventions that should be delivered to mitigate behavioral and environmental risk factors in order to reduce tuberculosis (TB) transmission in urban low-income environments.
Abstract: This paper has examined the factors explaining prevalence of tuberculosis (TB) in the Embakasi sub-counties of Nairobi through logistic regression analysis. Patient records at the chest clinic of Mama Lucy Kibaki Teaching and Referral Hospital, which is the primary health facility of the Embakasi area, were used to gather the data. Tuberculosis is ...
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Research Article
Change-Point Detection with ARIMA of Inflation Rate in Ghana
Issue:
Volume 15, Issue 2, April 2026
Pages:
47-58
Received:
8 March 2026
Accepted:
30 March 2026
Published:
15 April 2026
DOI:
10.11648/j.ajtas.20261502.13
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Abstract: Change-point detection is the point or location in the series where the observations of that series are shifted to another point. Inflation is considered as one of the most important determinants of economic growth and also a key macroeconomic indicator which shows how prices of goods change from one period to another and this plays a critical role in economic stability and growth. The study therefore, aimed to determine structural change-point(s) of the inflation rate in Ghana, which will serve as an essential source of information to guide policy direction. Annual data on Ghana’s inflation rate were sourced from the World Bank website covering the years 1965-2025. To remove the effect of serial correlation since the inflation data was collected over time, an ARIMA model was considered and the errors which were independent and identically distributed, were extracted for multiple change point procedures. Change point methods considered were the Cumulative Sum (CUSUM) Test, the Binary Segmentation (BS) Method and the Pruned Exact Linear Time (PELT) Algorithm. We sought to determine change points in mean, variance (risk) and mean-variance jointly since they are the basic measured quantities for econometric analysis. Results show that the mean change point was detected at time (index) 12, which represents the year 1976, corresponding to Ghana’s mid-1970s macroeconomic instability. Variance (risk) change points were detected at time points 37 and 56, which represent the years 2001 and 2020, respectively corresponding to times of fiscal stress (Ghana joining the Heavily Indebted Poor Countries (HIPC)), electoral spending and COVID-19 shock. The mean-variance change points were also detected at time points 10 and 20, which represent the years 1974 and 1984, respectively aligning with the oil shock era and the economic recovery programme (ERP) regime. The study showed that Ghana’s inflation process has experienced multiple structural shifts associated with major economic shocks and policy transitions. It is highly recommended that credible macroeconomic management and fiscal discipline be adhered to during structural changes.
Abstract: Change-point detection is the point or location in the series where the observations of that series are shifted to another point. Inflation is considered as one of the most important determinants of economic growth and also a key macroeconomic indicator which shows how prices of goods change from one period to another and this plays a critical role...
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Research Article
Spatial Modeling of Cardiovascular Disease in Kenya
Grace Wanjiku Mwangi*,
Anthony Kibira Wanjoya
Issue:
Volume 15, Issue 2, April 2026
Pages:
59-71
Received:
20 March 2026
Accepted:
3 April 2026
Published:
16 April 2026
DOI:
10.11648/j.ajtas.20261502.14
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Abstract: The study conducts an assessment of the spatial distribution of cardiovascular diseases (CVD) in Kenya by integrating spatial modeling techniques and spatial autocorrelation measures. CVDs, which refer to disorders of the heart and blood vessels, have surpassed communicable diseases as the leading cause of morbidity and mortality worldwide, posing a critical public health concern, especially in low- and middle-income countries (LMICs) where resources remain limited. A growing body of global evidence has revealed marked geographical disparities in CVD incidence, prompting investigations into small-area spatial distribution patterns. This study employed both global and local spatial autocorrelation measures to analyze CVD prevalence across Kenyan counties. The Global Moran’s I statistic was used to assess the overall degree of spatial clustering, while the Local Moran’s I identified significant clusters of high and low prevalence, alongside spatial outliers. Additionally, the Getis-Ord Gi* statistic was applied to detect statistically significant hotspots and coldspots, revealing important spatial patterns in disease prevalence. Spatial regression models were compared using the Lagrange Multiplier (LM) test, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) for model selection. The Spatial Lag Model (SLM) demonstrated superior performance over the Spatial Error Model (SEM) and the Spatial Durbin Model (SDM), achieving a Rao’s score (RSlag) of 16.449 and an adjusted score (adjRSlag) of 12.181, both statistically significant at the 5% level. The SLM also recorded the lowest AIC and BIC values at -380.09 and -361.80, respectively, confirming its suitability in capturing spatial dependence in the data. The findings revealed significant spatial clustering of CVD prevalence, with distinct high-risk and low-risk regions across the country. High body mass index (HBMI), tobacco use, and poor dietary habits emerged as major risk factors driving CVD prevalence, while urbanization and economic development were associated with lower disease burdens. The study highlights the importance of incorporating spatial analysis in public health planning to inform targeted interventions, optimize resource allocation, and enhance community health education campaigns aimed at promoting heart-healthy lifestyles.
Abstract: The study conducts an assessment of the spatial distribution of cardiovascular diseases (CVD) in Kenya by integrating spatial modeling techniques and spatial autocorrelation measures. CVDs, which refer to disorders of the heart and blood vessels, have surpassed communicable diseases as the leading cause of morbidity and mortality worldwide, posing ...
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