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ItemMulti-dimensional poverty measurement and its determinants in peri-urban areas of Uganda.(Makerere University, 2026)This study estimated the incidence and intensity of multidimensional poverty among peri-urban households in Uganda, examined its associated factors, and compared multidimensional and monetary poverty estimates. Secondary data from the 2016/17 Uganda National Household Survey were analysed. Multidimensional poverty was constructed using the Alkire–Foster counting approach and a predefined weighted deprivation threshold. Given by household i, the weighted deprivation score was cᵢ = Σⱼwⱼgᵢⱼ, and the binary outcome was ρₖ(cᵢ)=1(cᵢ≥0.40). The aggregate adjusted headcount was M₀ = H×A, where H is the weighted proportion classified as multidimensionally poor and A is their weighted mean deprivation intensity The analysis applied descriptive statistics, pairwise correlations, bivariate tests and Determinants were estimated using log[pᵢ/(1−pᵢ)] = β₀ + Xᵢβ. A survey-weighted linear probability model and design-adjusted Wald (F)-test were additionally used to compare multidimensional-poverty headcounts between peri-urban and gazetted urban households. The survey design incorporated sampling weights, 15 strata and 279 primary sampling units. Of the 1,945 peri-urban households in the analytic sample, 486 (25.0%) were multidimensionally deprived and 1,459 (75.0%) were not deprived. At the bivariate level, deprivation was significantly associated with the household head’s age, marital status, educational attainment and industry of employment, as well as distance to the nearest telephone service. Sex of the household head, geographical region and borrowing goods on credit were not significantly associated with deprivation. Correlations among deprivation indicators were generally weak to moderate, with the strongest association observed between housing and toilet deprivation ((r=0.578)), indicating no evidence of excessive inter-indicator correlation. In the adjusted logistic model, age, sex, region and borrowing status were not statistically significant. Households located 5–<8 kilometres from the nearest telephone service had 4.80 times the odds of multidimensional poverty compared with households located less than 3 kilometres away (OR=4.80; 95% CI: 2.47–9.31; (p<0.001)). Each additional income source increased the odds of deprivation by approximately 18% (OR=1.179; 95% CI: 1.019–1.363; (p=0.026)), suggesting that multiple income sources may reflect fragmented, seasonal or poorly remunerated livelihood activities rather than effective diversification. Relative to agriculture, forestry and fishing, employment in construction (OR=0.044; (p<0.001)), professional, scientific and technical activities (OR=0.163; (p=0.006)), administrative and support activities (OR=0.173; (p=0.040)), and education (OR=0.059; (p=0.002)) was associated with significantly lower odds of deprivation. The national survey-weighted comparison showed that the multidimensional-poverty headcount was 23.7% among peri-urban households (95% CI: 16.8%–32.4%) and 14.1% among gazetted urban households (95% CI: 11.9%–16.7%). The 9.7-percentage-point difference was statistically significant (95% CI: 1.4–12.1%; (F(1,264)=5.34), (p=0.022)); therefore, the null hypothesis of equal headcounts was rejected. The MPI also produced higher poverty estimates than the national monetary poverty-line approach, although MPI intensity and the monetary poverty gap measure conceptually different quantities and are not directly comparable. The study concludes that peri-urban poverty is strongly associated with infrastructure access and livelihood quality and recommends integrating multidimensional measures into poverty monitoring while prioritizing essential services and stable, adequately remunerated employment.
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ItemComparing the frequentist and Bayesian estimation approaches for cox regression in modelling cervical cancer patient outcomes in Uganda.(Makerere University, 2026)The objective of this paper was to compare the Frequentist and Bayesian estimation approaches for Cox regression in modelling cervical cancer patient outcomes in Uganda by evaluating the predictive performance of the two approaches, and assessing the association of clinical and sociodemographic factors with patient survival. The Cox PH, Exponential PH, and Weibull PH models were run under both frameworks, and their model performance and predictive accuracy was compared to determine which framework best fits survival analysis for cervical cancer patients in Uganda. The models were applied on dataset containing 454 women diagnosed with Cervical Cancer which was primarily collected by the Uganda Cancer Institute (UCI) between June 2019 to February 2020. The study also observed how different covariates such as age at diagnosis, morphology, initial disease stage, region of treatment, and other clinical and sociodemographic factors influence the time until death for the cervical cancer patients involved in the study. The non-parametric Cox PH model, and the Exponential PH model demonstrated the best overall performance based on model fit statistics under the Frequentist and Bayesian approaches respectively. Both models yielded nearly identical predictive outcomes when evaluated against each other with C-index ≈ 0.8 and AUC ≈ 0.79. Further findings based on the Bayesian Exponential model revealed that, diagnosis at stage 3 (HR=5.259; CrI: 1.446, 25.798) and stage 4 (HR=6.678; CrI: 1.63, 35.196), treatment by palliation (HR=4.940; CrI: 1.473, 20.503), and unspecified morphology (HR=2.174; CrI: 1.231, 3.785), were significantly associated with lower chances of survival. While women with negative metastases (HR=0.505; CrI: 0.284, 0.944), and those who were treated from the Eastern (HR=0.115; CrI: 0.029, 0.36) and Central (HR=0.498; CrI: 0.249, 0.985) regions, exhibited reduced hazard of death. Nevertheless, age at diagnosis, occupation and marital status had no significant effect on the survival outcomes of the women with involved in this study. In conclusion, the study underscores the critical role of stage at diagnosis in determining cervical cancer survival, it therefore calls upon the Ministry of Health to strengthen routine screening initiatives to promote early detection. The findings of the study also showed that both the Frequentist and Bayesian approaches perform effectively and can be applied in survival analysis. The study therefore recommends that future research should evaluate Frequentist and Bayesian frailty survival models to account for unobserved heterogeneity among cervical cancer patients.
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ItemDeterminants of survival time among breast cancer patients in Uganda: a survival analysis.(Makerere University, 2026)Breast cancer remains a leading cause of cancer-related mortality among women in Uganda, with survival outcomes substantially lower than those reported in high-income countries. Despite the increasing burden, limited context-specific evidence exists quantifying the independent socio demographic and economic, clinical, and treatment-related determinants of survival time. This study sought to identify and quantify determinants of survival time among breast cancer patients treated at the Uganda Cancer Institute between 2015 and October 2025. A retrospective cohort design was employed using registry data for 400 patients with confirmed primary breast cancer. The primary outcome was survival time (in days) from diagnosis to death, with right-censoring for patients alive at last follow-up. Survival functions were estimated using the Kaplan–Meier method and compared using log-rank tests. Multivariable analysis was conducted using the Stratified Cox Proportional Hazards regression model. Model adequacy was assessed through Schoenfeld residuals, deviance residual diagnostics, Cox–Snell residuals, and Harrell’s concordance statistic. The overall 365 days (1-year), 1,095 days (3-year), and 1,825 days (5-year) survival probabilities were 75.7%, 45.3%, and 21.6%, respectively, with a median survival time of 966 days. Earlier stage at diagnosis was strongly protective, with patients diagnosed at Stage I experiencing an 85.8% lower hazard of death compared to Stage IV patients (aHR = 0.142; 95% CI: 0.02–0.97). HIV comorbidity significantly increased mortality risk (aHR = 1.45; 95% CI: 1.01–2.11). Treatment-related factors were also critical determinants of survival. Receipt of surgery, radiotherapy, and hormonal therapy significantly reduced mortality risk, while failure to complete treatment increased the hazard of death by more than twofold (aHR = 2.532; 95% CI: 1.729–3.71). Longer delays between diagnosis and treatment initiation were also associated with poorer survival outcomes. The global Schoenfeld test showed no significant violation of the proportional hazards assumption, confirming the validity of the model. In conclusion, advanced stage at diagnosis, HIV comorbidity, and incomplete treatment were found to be key contributors to poor survival outcomes in Uganda. Strengthening early detection strategies, integrating HIV and oncology care, and improving treatment adherence may substantially enhance survival.
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ItemThe effect of foreign direct investment on economic growth in Uganda: a sectoral analysis(Makerere University, 2026)This study examined the effect of Foreign Direct Investment (FDI) on economic growth in Uganda, with a focus on sectoral effects over the period 1984–2023. The main objective was to analyze the effect of FDI on aggregate GDP growth, with a more detailed analysis of its effects on agricultural, industrial, and services sector growth. The study employed the Seemingly Unrelated Regression (SUR) technique, which accounts for contemporaneous correlation among the error terms across the aggregate and sectoral equations. The key findings suggest that during the period under study, FDI did not significantly influence Uganda’s aggregate economic growth contemporaneously but exerted a positive lagged effect, and positively influenced the growth of both the industrial and services sectors, both contemporaneously and with a two-year lag. The latter effects suggest that FDI may take time to influence growth, possibly because foreign-invested projects require time to become operational and generate productive linkages. In relation to agricultural sector growth, FDI had a negative and significant contemporaneous effect, but its interaction with financial sector development was positive and significant both contemporaneously and with a two-year lag, underscoring the importance of domestic absorptive capacity in shaping sectoral outcomes. Overall, the findings indicate that the contribution of FDI to Uganda’s economic growth is sector specific, time-dependent, and conditioned by domestic factors. The study therefore highlights the need to strengthen financial sector development, labour capacity, and productive linkages between foreign and domestic enterprises to enhance the growth contribution of FDI.
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ItemAre large-scale farmers better than small-scale farmers? evidence from maize farmers using Uganda National Panel Survey (UNPS), 2010-2018(Makerere University, 2026)This study investigates the comparative effectiveness of small-scale and large-scale maize farmers in addressing maize production in Uganda, utilizing data from five waves of the Uganda National Panel Survey (UNPS) spanning 2010 to 2018. The primary aim was to assess the factors influencing maize production among small-scale and large-scale farmers, with specific objectives examining the effects of farm inputs, demographic, biophysical and regional factors on maize harvest quantities. Employing fixed effects regression models, the analysis was conducted separately for small-scale farmers (n=5,485), large-scale farmers (n=206), and a pooled sample (n=5,691). Results reveal significant disparities in production patterns and input effectiveness between the two groups. Small-scale farmers dominate Uganda’s maize production sector (over 96% of producers) but achieve lower average annual harvests (268.1 kg) compared to large-scale farmers (520.3 kg). However, when small-scale farmers access fertilizers and pesticides, their production increases substantially by 52.8 kg and 71.5 kg respectively. Among small-scale farmers, regional location significantly influences production, with Northern and Western regions outperforming Central Uganda by 55.1 kg and 63.7 kg annually. Customary land tenure reduces production by approximately 25 kg compared to freehold tenure. Counterintuitively, farmers using traditional seeds produce significantly more maize than those using improved seeds across both categories, small-scale farmers using traditional seeds harvest 67.9 kg more, while large-scale farmers harvest 322.2 kg more annually. For large-scale farmers, sandy loam soils increase production by 373.4 kg compared to sandy clay loam soils. The study concludes that small-scale farmers are not inherently less productive but are constrained by limited access to inputs, insecure land tenure, and inadequate institutional support. Recommendations include revising agricultural support programs to include small-scales, strengthening land tenure security, improving fertilizer subsidy distribution, expanding integrated pest management training, and developing regionally targeted interventions. These findings contribute to evidence-based agricultural policymaking aligned with Sustainable Development Goal 2 (Zero Hunger) and highlight the need for context-specific strategies that recognize the complementary roles of both small-scale and large-scale farmers in achieving maize production in Uganda.