3.1. Overview of Potential Sources of Road Crash and Casualty Data in Uganda
A broad analysis of road crash costs in Uganda relied on data from various sources, including the police, health sector, insurance companies, road authorities, government institutions, legal services sector, automotive repair centres, and formal employers. However, challenges in accessing and integrating these data arose due to fragmentation and inconsistent reporting practices. This section reviews these data sources, their contributions, and the obstacles to improving data quality.
3.1.1. The Police
The Uganda Police Force plays a central role in road crash data collection, ensuring extensive coverage across diverse crash scenarios in both urban and rural areas. Coordination between the Directorate of Traffic and Road Safety and the Directorate of Fire Prevention and Rescue Services enhances their response capabilities, particularly in severe crashes. Despite their emergency assistance, limited ambulance services, especially in remote areas, could delay medical care for victims, which is consistent with findings by Alanazy et al. [
22]. Several studies have also underscored the significance of the police as the primary source of road crash data [
23,
24,
25,
26]. In Uganda, police data are obtained through various specialised units, including the following:
Each unit contributes distinct, yet interconnected data that form the basis of road crash statistics and cost analyses. A summary of the data types collected from each police unit is presented in
Table 5.
The Directorate of Traffic and Road Safety serves as the primary unit for documenting road crashes across Uganda [
27]. As first responders, they collect essential data, forming the foundation of national crash statistics. These data are important for estimating the economic impacts of road crashes, including medical costs, property damage, administrative costs, and productivity losses.
Underreporting remains a persistent issue, especially in rural areas where follow-up mechanisms are limited. Police records may fail to document fatalities if victims succumb to their injuries weeks or months after a crash, unless families notify authorities. This was especially common where relatives handled fatalities privately, bypassing official procedures. This aligns with findings by Muni et al. [
26]. Additionally, towing data were incomplete due to the reliance on private service providers, whose activities were not consistently documented, creating data gaps and reducing the reliability of crash statistics.
The Inspectorate of Vehicles (IOV) assesses the mechanical condition of vehicles involved in road crashes, determining the extent of damage, identifying potential mechanical failures, and verifying compliance with roadworthiness standards. The data collected are essential for estimating property damage costs and identifying the technical causes of road crashes. However, in Uganda, the IOV provides limited data on these crash specifics, such as the extent of each vehicle’s damage. Furthermore, minor incidents often go unreported due to private settlements or efforts to avoid inspections [
26], leading to gaps in crash data.
The Fire Brigade responds to incidents involving vehicle fires, trapped victims, and hazardous materials. While relevant data were provided, personnel noted that underreporting remains a significant issue, particularly for less severe crashes where local police handle basic rescues without involving the fire brigade, leading to omissions in official records. The fire brigade is typically utilised in complex cases, such as trapped victims, high-risk fires, or overturned vehicles in water. Additionally, the uneven distribution of fire rescue services, especially in rural areas, often results in delayed responses, reducing survival chances and worsening injuries, a common issue in LMICs [
1].
3.1.2. Health Sector
The health sector is a fundamental source of road crash data in Uganda, essential for estimating medical costs associated with road crashes.
Table 6 summarises different data categories collected from the health sector. Several studies have recommended linking police and hospital data to obtain improved data, especially for serious injuries, as hospitals have been found to possess more accurate injury-related data [
23,
25,
28]. Key sources include the following:
Hospitals;
Ministry of Health;
Mortuaries.
Hospitals, particularly Mulago National Referral Hospital, provide data essential for estimating direct medical costs of road crashes and understanding the healthcare system’s burden. As Uganda’s largest referral hospital, Mulago receives a significant portion of road crash victims, offering valuable data on injury types, treatment costs, and patient outcomes. However, its Accident and Emergency (A&E) unit frequently operates at twice its capacity [
29], impacting both quality of care and data accuracy, as overburdened staff prioritise treatment over detailed record-keeping. The lack of specialised trauma centres and rehabilitation units further limits the hospital’s ability to track long-term crash-related outcomes.
Limitations in emergency response capacity exacerbate these issues, as ambulance services are scarce and distant from crash scenes, leading to delayed and poorly documented emergency responses, a concern also noted in previous studies [
22,
30]. Additionally, Mulago’s reporting system categorises patient data into broad demographic groups (for example, 0–4 years and 5+ years), which restricts analytical depth and hinders a comprehensive understanding of crash-related injuries and their economic implications. This limitation is particularly significant as the HC method assigns zero productivity to individuals outside the working population, further affecting cost estimations [
31].
The Ministry of Health (MoH) gathers health data from hospitals, clinics, and healthcare facilities across Uganda, providing nationwide insights into road crash injuries and fatalities. However, manual record-keeping systems, especially in rural areas, increase the risk of errors, data loss, and inconsistencies. Additionally, the absence of a dedicated road crash injury database necessitates manual extraction from general medical records, often hindered by bureaucratic barriers and limited archival access.
Mortuaries provide data on road crash fatalities, helping to resolve discrepancies in death statistics and enhance data accuracy, especially in countries where death certificates are not mandatory. Their records are essential for estimating direct medical and funeral costs and understanding the broader economic impacts of road crashes.
3.1.3. Insurance Sector
The insurance sector provides valuable data presented in
Table 7 for estimating property damage, medical, and administrative costs associated with crashes. Key data sources within the insurance sector include the following:
Motor insurance companies;
Insurance Regulatory Authority (IRA);
Uganda Insurers Association (UIA).
Insurance companies record comprehensive data on claims, payouts and premiums, which are needed for estimating road crash costs. However, data sensitivity and confidentiality concerns pose significant hindrances to data collection. Many insurance providers were reluctant to share detailed or even anonymised information due to regulatory restrictions and proprietary concerns. Additionally, aggregated claims data often combined various claim types, making it difficult to isolate crash-specific records, thereby reducing the accuracy of cost analysis.
The Insurance Regulatory Authority (IRA) oversees Uganda’s insurance sector by collecting, auditing, and compiling data from various insurance companies. While the IRA aggregates insurance-related data from these companies, individual insurance providers are recommended for more detailed records. However, a significant proportion of vehicles remain uninsured [
32], leaving many road crash costs unrecorded, which distorts the economic impact assessment of road crashes [
17]. A centralised, comprehensive database with standardised and up-to-date insurance data would enhance the accuracy of road crash cost estimation.
The Uganda Insurers Association (UIA) serves as an umbrella organisation representing the collective interests of insurance companies. While the UIA aggregates data from its members, it currently lacks detailed, crash-specific data. Therefore, individual insurance companies remain the recommended source for the detailed data required for road crash cost analysis.
3.1.4. Road Authorities
Road authorities collect data essential for estimating property damage and environmental costs resulting from road crashes. However, incompleteness and lack of specificity in the recorded data limit its effectiveness for comprehensive analysis. A summary of the data collected from road authorities is presented in
Table 8. Key institutions include the following:
The Uganda National Roads Authority (UNRA) manages and maintains the country’s road infrastructure, recording essential data to estimate infrastructure-related damage costs and assess the impact of road crashes. However, much of the required road crash data had not been previously recorded, and the available data were limited to national roads, making it unrepresentative of the entire country. Efforts to obtain additional data from city councils were constrained by time limitations and the absence of a centralised database. It is recommended that future efforts prioritise collecting additional data from city councils to enhance comprehensiveness.
The Ministry of Works and Transport (MoWT) oversees infrastructure development, transport policy, and regulatory frameworks in Uganda. It collects data on vehicle ownership, road design, maintenance, and safety regulations, collaborating with stakeholders like the police and UNRA to produce road safety reports for evidence-based policy decisions. Initially regarded as a potential source for comprehensive road crash data, the ministry was found to lack the specific data required for detailed analysis. Consequently, UNRA and the police were recommended as alternative sources; however, the data obtained from these entities were also incomplete, underscoring significant gaps in the availability and integration of road safety data across the country.
3.1.5. Government Statistical and Financial Institutions
These institutions, such as the Uganda Bureau of Statistics (UBOS), the Ministry of Finance, and the Bank of Uganda, provide macro-level data for road safety economics. Their datasets offer key insights into economic indicators, demographic trends, and national expenditure, essential for estimating productivity and consumption losses, which help assess the broader economic impacts of road crashes. However, data collection is hindered by several challenges, including discrepancies with international datasets, ambiguities in classification, and bureaucratic approval processes. UBOS, which compiles data from multiple institutions such as the central bank, the police, government authorities, and various ministries, produces comprehensive reports, requiring significant effort to extract relevant road safety data.
3.1.6. Legal Services Sector
This sector, consisting of legal courts and prisons, records data essential for understanding the legal and administrative costs of road crashes.
Legal courts, such as chief magistrates’ courts, record judicial processes related to road crashes, providing insights into the effectiveness of the legal system in managing traffic violations and their implications for road safety studies. However, inconsistencies in record-keeping across different courts, due to varying documentation formats, hinder the standardisation and reliability of crash-related case data. Additionally, confidentiality and data privacy concerns limit access to sensitive personal and financial information, further restricting data availability.
Furthermore, delays and inefficiencies in judicial proceedings also affect the quality and timeliness of court data. Road crash-related cases often experience significant backlogs, leading to data lag in reflecting current trends. Discussions with key personnel highlighted high levels of underreporting, partly due to the substantial costs of legal representation and court fees, which discourage victims from seeking legal redress.
Prisons contribute valuable data on the legal and punitive aspects of traffic-related offences, detaining individuals convicted of violations such as reckless driving, driving under the influence, and vehicular manslaughter. However, most offenders opt to pay fines rather than serve prison sentences, resulting in limited records on detained offenders. Furthermore, the absence of a centralised database for road crash-related offences across prisons restricts efforts to compile comprehensive legal data for road crash analysis.
3.1.7. Auto Garages
Auto garages record data regarding vehicle repairs and maintenance costs due to road crashes, highlighting the extent and nature of vehicle damage. These insights are relevant in calculating property damage costs, a significant portion of the total economic impact of road crashes [
2]. However, most garages in Uganda, especially small independent ones, rely on paper-based systems with limited emphasis on detailed record-keeping. Available records primarily focus on technical repair details rather than the circumstances of the crash. For instance, while the cost of replacing a damaged bumper may be documented, information on whether the damage resulted from a major or minor collision is rarely recorded. The absence of contextual details reduces the usefulness of garage data for comprehensive cost analysis.
Additionally, auto garages in Uganda operate independently, with minimal coordination with other key institutions involved in road crash data collection, such as police and insurance companies. This lack of integration results in fragmented, siloed data, limiting opportunities for cross-referencing or validation against other sources and ultimately reducing the reliability of garage data for road crash analysis.
3.1.8. Formal Employers
Formal employers, defined as organisations with structured employment arrangements, provide useful data for assessing friction costs and productivity losses from road crashes involving employees. These figures highlight the economic impact of workforce disruptions caused by injuries, fatalities, or incapacitation, which imposed financial burdens on businesses.
However, many organisations are hesitant to share sensitive information due to concerns over data security breaches and competitive risks. Additionally, some do not recognise the relevance of HR data to road crash research, leading to low participation. As a result, data remains incomplete and insufficiently detailed, limiting the scope and accuracy for comprehensive economic analyses.
3.3. Overcoming Road Crash Data Collection Challenges from Key Institutions
Based on the challenges observed during data collection, improving the quality of road crash data in Uganda will necessitate a collaborative approach among key stakeholders, including the police, health sector, insurance sector, road authorities, statistical and financial bodies, legal sector, automotive repair centres, and formal employers. Adopting uniform data collection protocols across these institutions would be fundamental to achieving consistency, reliability, and comprehensive coverage of data.
Transitioning to digital systems from manual record-keeping should improve the accuracy and accessibility of road crash records. Digital solutions reduce errors, safeguard data from potential loss, and facilitate quicker retrieval of information. Establishing a unified, real-time database where stakeholders can seamlessly input and retrieve data would greatly enhance the comprehensiveness and effectiveness of road crash data management.
Fostering inter-agency cooperation and formal data-sharing frameworks is essential to bridge existing data gaps and gain a more complete understanding of road crashes. Additionally, incorporating data from informal sources, such as independent healthcare providers and small vehicle repair shops, could complement official records and provide a broader perspective on road crash impacts.
Adequate resource allocation is crucial for upgrading infrastructure, including the implementation of electronic health records (EHR) with specific provisions for road traffic crashes and the deployment of enforcement technologies such as traffic surveillance cameras. Capacity-building initiatives, such as training personnel across law enforcement, healthcare, and insurance sectors in standardised data entry and reporting protocols, should further enhance the accuracy and reliability of records.
Ensuring data confidentiality through robust privacy policies would be important for fostering stakeholder confidence while enabling data sharing. Additionally, simplifying administrative procedures for data access could enhance availability for researchers and policymakers.
Implementing these strategies will enable Uganda to address current data collection challenges and establish a more coordinated framework for analysing road crashes.
3.4. Policy and Developmental Implications of Accurate Safety Valuation
The implementation of road safety interventions depends on various factors, including public awareness and governmental accountability. When citizens understand the negative consequences of road crashes on their lives and livelihoods, they are more likely to request effective safety measures and accountability from their governments. Accurate road safety valuation has significant and multifaceted implications for policy and development [
35,
36,
37]. For instance, accurate safety valuation enables policymakers to allocate resources more effectively [
11]. By understanding the economic costs associated with different safety measures or interventions, governments could prioritise investments in initiatives that offer the greatest potential for reducing crash-related costs [
35,
37]. Additionally, this valuation facilitates robust cost–benefit analysis for proposed infrastructure projects or safety initiatives. With a clearer understanding of the anticipated benefits relative to the costs, decision-makers can make more informed and strategic policy choices [
35].
Consequently, the ability to value road crashes could lead to an informed regulation and infrastructure planning. With a precise understanding of the economic implications of safety measures, policymakers can enact regulations that strike an optimal balance between safety benefits and regulatory compliance costs. This approach promotes improved safety outcomes without imposing excessive burdens on businesses or society. Furthermore, urban and transportation planners could use safety valuation data to inform the design and layout of infrastructure. This includes decisions about road geometry, traffic control devices, pedestrian facilities, and bicycle lanes. By prioritising safety enhancements in areas with the highest potential for crash-related costs, planners could create safer environments for all road users [
37].
Beyond infrastructure, safety valuation directly informs behavioural interventions targeting road user behaviour. By quantifying the monetary value of safety improvements, policymakers can tailor interventions such as education campaigns, enforcement efforts, or incentives to encourage safer practices effectively [
37]. Additionally, accurate valuation could influence insurance premiums and liability assessments. Insurers may adjust premiums based on the expected safety performance of insured entities, while liability judgements may reflect the economic impacts of safety lapses [
38]. These mechanisms incentivise individuals and organisations to prioritise safety and mitigate financial risks.
Enhanced safety could contribute to economic development by reducing productivity losses associated with crashes, injuries, and fatalities [
37]. Businesses may benefit from lower healthcare costs, reduced absenteeism, and improved employee morale in safer transportation environments. Ultimately, accurate safety valuation serves as a cornerstone for evidence-based policymaking, enabling governments, businesses, and communities to implement targeted interventions that maximise safety benefits while optimising resource utilisation and promoting sustainable development.
However, the availability of complete, accurate, and comprehensive data on road crashes, alongside relevant socio-economic indicators, remains a critical challenge, particularly in LMICs [
5,
10,
21]. The absence of reliable data limits the understanding of the true economic and social impacts of road crashes. Similarly, insurance companies may avoid paying the full compensation if the value of life and property destroyed in a crash are underestimated [
17]. This challenge is more pronounced in LMICs, hindering meaningful citizen-driven efforts to demand proactive government approaches to the road safety crisis. Addressing these data deficiencies is essential for unlocking the full potential of safety valuation in shaping effective and equitable road safety interventions.