Factors Associated with Having a
Boda boda Injury
MAB7203 Conditional Logistics Assignment: (Instructor:
Prof. Nazarius Mbona Tumwesigye)
Group 3
2025-04-08
Table of contents
1. Introduction....................................................................................................................... 2
2. Methodology ..................................................................................................................... 2
3. Descriptives ...................................................................................................................... 3
3.1 Boxplot by Age ............................................................................................................ 3
3.2 Characteristics table ................................................................................................... 4
4. Conditional Logit Model .................................................................................................... 5
4.1 Model diagnostics ....................................................................................................... 5
4.1.1 Area under the curve ............................................................................................ 6
4.1.2 ROC Curve for Alcohol with interaction of ownership ........................................... 6
4.1.3 Likelihood ratio test .............................................................................................. 7
4.1.4 Residual plots ....................................................................................................... 8
4.2 Conditional logistic table ............................................................................................. 9
5. Exploring Mcnemar’s test ............................................................................................... 10
5.0.1 Macnemar’s - Alcohol ......................................................................................... 10
5.0.2 Macnemar’s - Passengers .................................................................................. 11
5.0.3 Macnemar’s - Ownership ................................................................................... 11
Name
SERUNJOGI ROBERT
STUDENT # REGISTRATION # SIGNATURE
2400722352 2024/HD07/22352U
NAMBOZO BRIANTA ANNA 2400722346 2024/HD07/22346U
NABULYA OLIVIA
2400722351 2024/HD07/22351U
1. Introduction
Boda boda motorcycles have become a widespread mode of transportation in many East
African countries, especially in Uganda, due to their affordability, flexibility, and ability to
navigate congested urban areas. Despite their convenience, boda bodas are increasingly
associated with high rates of road traffic injuries and fatalities. These injuries impose a
significant burden on public health systems and the broader economy, particularly affecting
young, economically active males. The nature of boda boda operations—often informal,
poorly regulated, and with minimal adherence to safety practices—contributes to the
heightened risk of crashes and injuries.
Several factors have been linked to the occurrence of boda boda injuries, ranging from
rider behavior, such as alcohol consumption and risky driving practices, to systemic issues
like lack of licensing, poor enforcement of traffic laws, and inadequate infrastructure. Sociodemographic factors including age, marital status, and experience also play a critical role.
Moreover, carrying multiple passengers, riding without protective gear, and operating
during night hours have all been associated with increased injury risk.
Understanding the factors associated with boda boda injuries is crucial for informing
targeted interventions that can reduce the incidence and severity of such events. This
study aims to identify and analyze key socio-demographic, behavioral, and environmental
factors linked to the likelihood of being involved in a boda boda-related injury, thereby
contributing to the design of evidence-based road safety policies and public health
strategies.
2. Methodology
This study investigated factors associated with boda boda injuries using data from a
matched case-control study. This data set contained a number of matching (one case and
one control). To investigate the association between boda-boda accidents and various risk
factors, including alcohol consumption, age group, permit possession, and owenership of
the boda boda, we employed conditional logistic regression. This method was chosen to
account for the matched case-control design of the study by a stratification variable.
Several conditional logistic regression models were fitted for each explanatory variable. A
base model included alcohol consumption, stratified by the matched case. For comparison
and assessment of individual predictor effects, separate models were also fitted for alcohol
consumption alone, age group alone, religion, and the number of passengers, all stratified
by the matched case.
To evaluate the predictive performance of the models involving alcohol consumption and
age group, Receiver Operating Characteristic (ROC) curves were generated, and the Area
Under the Curve (AUC) was calculated. In a matched case-control study, matching controls
confounding, therefore for a better fit model of the alcohol model an interaction term was
explored to assess whether the effect of alcohol consumption varied by boda boda
ownership, and also assess goodness-of-fit of the model.
In addition, to assess the goodness-of-fit of the models involving religion and the number of
passengers, residual plots were examined. The deviance residuals were plotted against
the fitted values for both the religion and passenger models. The absence of any clear
patterns or trends in the scatter of residuals around the zero horizontal line suggested that
these models adequately fit the data, indicating no significant departures from the
assumptions of the logistic regression. All analyses were conducted using R version 4.2.3.
3. Descriptives
3.1 Boxplot by Age
The Controls (median age = 30) were significantly older compared to the cases (median
age = 28).
3.2 Characteristics table
3.2.0.1 Table 1. Demographic and Behavioral Characteristics of Boda-Boda Riders: A
Case-Control Study
Characteristic
Control N = 2891 Case N = 2891 Overall N = 5781 p-value2
Marital Status
<0.001
Divorced/Separated
4 (1.4%)
20 (6.9%)
24 (4.2%)
Married/Cohabitting
235 (81.3%)
191 (66.1%)
426 (73.7%)
Single/Widowed
50 (17.3%)
78 (27.0%)
128 (22.1%)
Religion
0.012
Catholic
95 (32.9%)
122 (42.7%)
217 (37.7%)
Muslim
87 (30.1%)
54 (18.9%)
141 (24.5%)
Others
5 (1.7%)
2 (0.7%)
7 (1.2%)
Pentocostal
29 (10.0%)
28 (9.8%)
57 (9.9%)
Protestant
73 (25.3%)
80 (28.0%)
153 (26.6%)
(Missing)
0
3
3
Age Group
<0.001
18-24
39 (13.5%)
79 (27.3%)
118 (20.4%)
25-29
99 (34.3%)
86 (29.8%)
185 (32.0%)
30-34
64 (22.1%)
77 (26.6%)
141 (24.4%)
35-39
48 (16.6%)
29 (10.0%)
77 (13.3%)
40+
39 (13.5%)
18 (6.2%)
57 (9.9%)
Has Permit
109 (37.7%)
124 (42.9%)
233 (40.3%)
Boda boda Ownership
0.2
<0.001
No
99 (34.3%)
163 (56.4%)
262 (45.3%)
Yes/Co-owner
190 (65.7%)
126 (43.6%)
316 (54.7%)
52 (18.0%)
86 (29.8%)
138 (23.9%)
<0.001
2 (0.9%)
5 (1.7%)
7 (1.4%)
0.7
75
0
75
Takes Alcohol
drugs
(Missing)
Passengers
0.004
1 Passenger
135 (63.4%)
145 (50.3%)
280 (55.9%)
2+ Passengers
78 (36.6%)
143 (49.7%)
221 (44.1%)
76
1
77
(Missing)
1
n (%)
2
Pearson's Chi-squared test; Fisher's exact test
The results indicate significant differences in several socio-demographic and behavioral
characteristics between cases and controls. Marital status was notably associated with
being a case. Divorced or separated individuals made up 6.9% of cases compared to only
1.4% of controls. Married or cohabiting individuals were the majority in both groups, they
were more common among controls (81.3%) than cases (66.1%). Additionally, a greater
proportion of cases were single or widowed (27.0%) compared to controls (17.3%).
Religious affiliation also showed a significant association between case and controls.
Catholics were more among cases (42.7%) than among controls (32.9%), while Muslims
were more common among controls (30.1%) than cases (18.9%).
Younger individuals, particularly those aged 18–24, were more likely to be cases (27.3%)
compared to controls (13.5%). On the other hand, older age groups, particularly those
aged 40 and above, were more represented among controls.
Alcohol consumption was significantly more prevalent among cases (29.8%) than controls
(18.0%), underscoring a possible link between substance use and risky behavior or
impaired judgment. In contrast, drug use was low and not significantly different between
the groups. Additionally, the number of passengers showed significant association. Cases
more frequently reported carrying two or more passengers (49.7%) than controls (36.6%),
while those carrying one passenger were more common among controls (63.4%).
Having a permit did not show any significant association, indicating that formal licensing
alone may not be protective if not paired with safe practices.
4. Conditional Logit Model
4.1 Model diagnostics
Model1a: logit[𝑃(𝑐𝑎𝑠𝑒𝑐𝑜𝑛𝑡𝑖 = 1|𝑚𝑎𝑡𝑐ℎ𝑒𝑑𝑐𝑎𝑠𝑒𝑗 )] = 𝛽0 + 𝛽1 ⋅ 𝑡𝑎𝑘𝑒𝑎𝑙𝑐𝑜ℎ𝑜𝑙𝑖
Model1b: The conditional logistic regression model used to assess the association
between boda-boda accidents (casecont) and the predictors of alcohol consumption
(takealcohol) and controlling for age group, while accounting for the matched case-control
design (strata(matchedcase)):
logit[𝑃(𝑐𝑎𝑠𝑒𝑐𝑜𝑛𝑡𝑖 = 1|𝑚𝑎𝑡𝑐ℎ𝑒𝑑𝑐𝑎𝑠𝑒𝑗 )]
𝐾
= 𝛽0 + 𝛽1 ⋅ 𝑡𝑎𝑘𝑒𝑎𝑙𝑐𝑜ℎ𝑜𝑙𝑖 + ∑ 𝛽2𝑘 ⋅ 𝐼(𝑎𝑔𝑒𝑔𝑟𝑝𝑖 = 𝑙𝑒𝑣𝑒𝑙𝑘 )
𝑘=2
Model2: logit[𝑃(𝑐𝑎𝑠𝑒𝑐𝑜𝑛𝑡𝑖 = 1|𝑚𝑎𝑡𝑐ℎ𝑒𝑑𝑐𝑎𝑠𝑒𝑗 )] = 𝛽0 + 𝛽1 ⋅ 𝑎𝑔𝑒𝑔𝑟𝑜𝑢𝑝𝑖
Model3: logit[𝑃(𝑐𝑎𝑠𝑒𝑐𝑜𝑛𝑡𝑖 = 1|𝑚𝑎𝑡𝑐ℎ𝑒𝑑𝑐𝑎𝑠𝑒𝑗 )] = 𝛽0 + 𝛽1 ⋅ 𝑟𝑒𝑙𝑖𝑔𝑖𝑜𝑛𝑖
Model4: logit[𝑃(𝑐𝑎𝑠𝑒𝑐𝑜𝑛𝑡𝑖 = 1|𝑚𝑎𝑡𝑐ℎ𝑒𝑑𝑐𝑎𝑠𝑒𝑗 )] = 𝛽0 + 𝛽1 ⋅ 𝑝𝑎𝑠𝑠𝑒𝑛𝑔𝑒𝑟𝑠𝑖
Model5: logit[𝑃(𝑐𝑎𝑠𝑒𝑐𝑜𝑛𝑡𝑖 = 1|𝑚𝑎𝑡𝑐ℎ𝑒𝑑𝑐𝑎𝑠𝑒𝑗 )] = 𝛽0 + 𝛽1 ⋅ 𝑜𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝𝑖
4.1.1 Area under the curve
The AUC value is slightly above 0.5, which indicates that the model has some predictive
power but is not very strong. The AUC value for the Age model (0.68) indicates moderate
predictive power. The AUC value for the Ownership model (0.66) indicates moderate
predictive power.
The model for Age Group has a reasonable level of predictive ability and is more reliable
than the Alcohol Consumption model.
4.1.2 ROC Curve for Alcohol with interaction of ownership
Setting levels: control = 0, case = 1
Setting direction: controls < cases
Area under the curve: 0.7
4.1.3 Likelihood ratio test
Analysis of Deviance Table
Cox model: response is Surv(rep(1, 578L), casecont)
Model 1: ~ takealcohol + strata(matchedcase)
Model 2: ~ takealcohol + agegrp + strata(matchedcase)
loglik Chisq Df Pr(>|Chi|)
1 -194.66
2 -178.62 32.08 4 1.843e-06 ***
--Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
The likelihood ratio test compares Model 2, which includes control for Age group, to Model
1, which does not. The very small p-value (1.84^{-6}) indicates that Model 2 provides a
significantly better fit to the data than Model 1. Therefore, based on the ROC AUC curve
and the Likelihood ratio test, we used model 2 to investigate the association between Boda
boda accidents and Alcohol.
4.1.4 Residual plots
TableGrob (2 x 1) "arrange": 2 grobs
z
cells
name
grob
1 1 (1-1,1-1) arrange gtable[layout]
2 2 (2-2,1-1) arrange gtable[layout]
Both residual plots suggest that the models fit the data well, as the residuals are randomly
scattered around the horizontal line at 0 without any clear patterns or trends.
4.2 Conditional logistic table
4.2.0.1 Table 2. Association of factors associated with Boda-Boda accidents
Characteristic
OR
95% CI
p-value
No
—
—
Yes
2.29
1.47, 3.55
25-29
—
—
18-24
2.57
1.53, 4.32
<0.001
30-34
1.48
0.94, 2.35
0.094
35-39
0.74
0.43, 1.28
0.3
40+
0.57
0.30, 1.08
0.084
Catholic
—
—
Muslim
0.46
0.29, 0.73
0.001
Others
0.30
0.06, 1.58
0.2
Pentocostal
0.76
0.43, 1.33
0.3
Protestant
0.82
0.54, 1.24
0.3
1 Passenger
—
—
2+ Passengers
1.79
1.21, 2.66
No
—
—
Yes/Co-owner
0.38
0.27, 0.55
Takes Alcohol
<0.001
Age Group
Religion
Passengers
0.003
Boda boda Ownership
<0.001
Abbreviations: CI = Confidence Interval, OR = Odds Ratio
The odds of men who consumed alcohol having an accident were 2.29 times (OR = 2.29,
95% CI: 1.47, 3.55, p < 0.001) those who do not drink alcohol. This indicates that alcohol
consumption is a significant risk factor for accidents.
Men aged 18-24 had 2.57 times higher odds of having an accident (OR = 2.57, 95% CI:
1.53, 4.32, p < 0.001) compared to those aged 25-29. This suggests that younger age
groups are at a significantly higher risk of accidents.
The odds of Muslim men having an accident were 0.46 times (OR = 0.46, 95% CI: 0.29,
0.73, p = 0.001) those of Catholic men. This implies that Muslim men had a significantly
lower risk of accidents compared to Catholic men. Muslim men had a 54% lower risk of
having an accident compared to Catholic men.
Men with 2+ passengers had 1.79 times higher odds of having an accident (OR = 1.79,
95% CI: 1.21, 2.66, p = 0.003) compared to those with only 1 passenger. Meaning that
Men with 2+ passengers had a 79% higher risk of having an accident compared to those
with only 1 passenger. This indicates that carrying more passengers is associated with a
significantly higher risk of accidents.
5. Exploring Mcnemar’s test
5.0.1 Macnemar’s - Alcohol
$data
Controls
Cases
Unexposed Exposed Total
Unexposed
168
69
237
Exposed
35
17
52
Total
203
86
289
$mcnemar_chi2
McNemar's Chi-squared test
data: mcc_table
McNemar's chi-squared = 11.115, df = 1, p-value = 0.0008561
$mcnemar_exact_p
Exact McNemar significance probability
0.001108621
$proportions
Proportion with factor
Cases Controls
0.8200692 0.7024221
$statistics
estimate [95% CI]
statistic
estimate
lower
upper
difference 0.1176471 0.04636803 0.1889261
ratio
1.1674877 1.06580258 1.2788743
rel. diff. 0.3953488 0.21462362 0.5760741
odds ratio 1.9714286 1.29443899 3.0515292
McNemar’s Chi-squared Test: McNemar’s test is used to compare paired proportions. In
this context, it tests whether the proportion of exposed is different between cases and
controls.
The small p-value (0.0008561) indicates that there is a statistically significant difference in
exposure status between cases and controls, meaning cases were significantly more likely
to have been exposed to accidents compared to controls.
McNemar’s test, which is more accurate for small sample sizes. The p-value is also
statistically significant, confirming the finding from the chi-squared test.
A higher proportion of cases was exposed (82.01%) compared to controls (70.24%). The
proportion of exposed is higher in cases by 11.76%. The confidence interval shows the
range where the true difference likely lies.
Ratio of proportions: 1.1675 (95% CI: 1.0658, 1.2789). The proportion of exposed in cases
is 1.1675 times the proportion of exposed in controls.
The odds of exposure among cases was 1.9714 times (95% CI: 1.2944, 3.0515) the odds
of exposure among controls. This is a key measure of association in case-control studies.
5.0.2 Macnemar’s - Passengers
$data
Controls
Cases
Unexposed Exposed Total
Unexposed
65
70
135
Exposed
39
39
78
Total
104
109
213
$mcnemar_chi2
McNemar's Chi-squared test
data: mcc_table
McNemar's chi-squared = 8.8165, df = 1, p-value = 0.002985
$mcnemar_exact_p
Exact McNemar significance probability
0.003854108
$proportions
Proportion with factor
Cases Controls
0.6338028 0.4882629
$statistics
estimate [95% CI]
statistic
estimate
lower
upper
difference 0.1455399 0.04678566 0.2442942
ratio
1.2980769 1.09219525 1.5427678
rel. diff. 0.2844037 0.12559694 0.4432104
odds ratio 1.7948718 1.19646139 2.7277629
5.0.3 Macnemar’s - Ownership
$data
Controls
Cases
Unexposed Exposed Total
Unexposed
59
40
99
Exposed
104
86
190
Total
163
126
289
$mcnemar_chi2
McNemar's Chi-squared test
data: mcc_table
McNemar's chi-squared = 28.444, df = 1, p-value = 9.643e-08
$mcnemar_exact_p
Exact McNemar significance probability
9.454908e-08
$proportions
Proportion with factor
Cases Controls
0.3425606 0.5640138
$statistics
estimate [95% CI]
statistic
estimate
lower
upper
difference -0.2214533 -0.3021874 -0.1407192
ratio
0.6073620 0.5047067 0.7308969
rel. diff. -0.5079365 -0.7371554 -0.2787177
odds ratio 0.3846154 0.2601469 0.5588389