Estimating the health burden of road traffic injuries in Malawi using an individual-based model

Manning Smith, Robert; Cambiano, Valentina; Colbourn, Tim; Collins, Joseph H.; Graham, Matthew; Jewell, Britta; Li Lin, Ines; Mangal, Tara D.; Manthalu, Gerald; Mfutso-Bengo, Joseph; Mnjowe, Emmanuel; Mohan, Sakshi; Ng’ambi, Wingston; Phillips, Andrew N.; Revill, Paul; She, Bingling; Sundet, Mads; Tamuri, Asif; Twea, Pakwanja D.; Hallet, Timothy B.

doi:10.1186/s40621-022-00386-6

Injury Epidemiology

Table 2 Parameters used in the model, with name, description, value and source/calibration process

From: Estimating the health burden of road traffic injuries in Malawi using an individual-based model

Parameter	Description	Value		Source
Who is injured in a road traffic crash this month?
Base_rate_injrti	The base rate of which the model population is involved in road traffic collisions per month. Specifically, a woman above the age of 80 who doesn’t drink	Run-specific, see Table 4		Calibrated to GBD estimated incidence of RTI
rr_injrti_age04	The risk factor for having a road traffic injury associated with being aged 0–4	0.145533		Calibrated to GBD estimated incidence of RTI
rr_injrti_age59	The risk factor for having a road traffic injury associated with being aged 5–9	0.551895		Calibrated to GBD estimated incidence of RTI
rr_injrti_age1017	The risk factor for having a road traffic injury associated with being aged 10–17	0.967017		Calibrated to GBD estimated incidence of RTI
rr_injrti_age1829	The risk factor for having a road traffic injury associated with being aged 18–29	1.184184		Calibrated to GBD estimated incidence of RTI
rr_injrti_age3039	The risk factor for having a road traffic injury associated with being aged 30–39	1.052843		Calibrated to GBD estimated incidence of RTI
rr_injrti_age4049	The risk factor for having a road traffic injury associated with being aged 40–49	1.074376		Calibrated to GBD estimated incidence of RTI
rr_injrti_age5059	The risk factor for having a road traffic injury associated with being aged 50–59	1.336449		Calibrated to GBD estimated incidence of RTI
rr_injrti_age6069	The risk factor for having a road traffic injury associated with being aged 60–69	2.308514		Calibrated to GBD estimated incidence of RTI
rr_injrti_age7079	The risk factor for having a road traffic injury associated with being aged 70–79	4.031226		Calibrated to GBD estimated incidence of RTI
rr_injrti_male	The risk factor for having a road traffic injury associated with being male	2.7		Calibrated to GBD estimated incidence of RTI
rr_injrti_excessalcohol	The risk factor for having a road traffic injury associated with consuming excessive amounts of alcohol	6.53		Staton et al. (2018)
Did the injured persons die on the scene of the crash
imm_death_proportion_rti	The proportion of persons injured in a road traffic collision who experience pre-hospital mortality	0.018		Mulima et al. (2021)
What injuries did each injured person receive?
number_of_injured_body_regions_distribution	The distribution used to assign the number of injured body regions for each injured person	Run-specific, see Table 4		Distributions calibrated to the results of Sundet et al. (2018)
injury_location_distribution	The distribution used to assign the anatomic location of the person’s injuries	Location	Probability	Ranti et al. (2015), Otieno et al. (2004)
		Head	14.38
		Face	13.25
		Neck	2.1
		Thorax	9.45
		Abdomen	6.12
		Spine	1.55
		Upper Extremity	16.85
		Lower Extremity	36.3
head_prob_112	Probability of an unspecified skull fracture	0.0455		Eaton et al. (2017), Global Health Data (2017)
head_prob_113	Probability of a basilar skull fracture	0.0045		Eaton et al. (2017), Global Health Data (2017)
head_prob_133a	Probability of a subarachnoid hematoma	0.09149906		Carroll et al. (2010), Global Health Data (2017), Eaton et al. (2017)
head_prob_133b	Probability of a brain contusion	0.301946898		Carroll et al. (2010), Global Health Data (2017), Eaton et al. (2017)
head_prob_133c	Probability of an intraventricular haemorrhage	0.013724859		Carroll et al. (2010), Global Health Data (2017), Eaton et al. (2017)
head_prob_133d	Probability of a subgaleal hematoma	0.050324483		Carroll et al. (2010), Global Health Data (2017), Eaton et al. (2017)
head_prob_134a	Probability of an epidural hematoma	0.086670324		Carroll et al. (2010), Global Health Data (2017), Eaton et al. (2017)
head_prob_134b	Probability of a subdural hematoma	0.080003376		Carroll et al. (2010), Global Health Data (2017), Eaton et al. (2017)
head_prob_135	Probability of a diffuse axonal injury/midline shift	0.061731		Carroll et al. (2010), Global Health Data (2017), Eaton et al. (2017)
head_prob_1101	Probability of a laceration to the head	0.253536		Malm et al. (2008), Global Health Data (2017)
head_prob_1114	Probability of a burn to the head	0.010564		Tian et al. (2018), Global Health Data (2017)
face_prob_211	Probability of a facial fracture (nasal/unspecified)	0.158585		Hassan (2016)
face_prob_212	Probability of a facial fracture (mandible/zygomatic)	0.294515		Hassan (2016)
face_prob_241	Probability of a soft tissue injury to face	0.339		Hassan (2016)
face_prob_2101	Probability of a laceration to the face	0.194845		Malm et al. (2008), Global Health Data (2017)
face_prob_2114	Probability of a burn to the face	0.010255		Tian et al. (2018), Global Health Data (2017)
face_prob_291	Probability of an eye injury	0.0028		Hassan (2016)
neck_prob_3101	Probability of a laceration to the neck	0.06972		Malm et al. (2008), Global Health Data (2017)
neck_prob_3113	Probability of a burn to the neck	0.01428		Tian et al. (2018), Global Health Data (2017)
neck_prob_342	Probability of a soft tissue injury in neck (vertebral artery laceration)	0.004		Kasantikul et al. (2003)
neck_prob_343	Probability of a soft tissue injury in neck (pharynx contusion)	0.004		Kasantikul et al. (2003)
neck_prob_361	Probability of a Sternomastoid m. haemorrhage/Haemorrhage, supraclavicular triangle/Haemorrhage, posterior triangle/Anterior vertebral vessel haemorrhage/Neck muscle haemorrhage	0.495		Kasantikul et al. (2003)
neck_prob_363	Probability of a Hematoma in carotid sheath/Carotid sheath haemorrhage	0.405		Kasantikul et al. (2003)
neck_prob_322	Probability of an atlanto-occipital subluxation	0.00264		Kasantikul et al. (2003)
neck_prob_323	Probability of an atlanto-axial subluxation	0.00536		Kasantikul et al. (2003)
thorax_prob_4101	Probability of a laceration to the thorax	0.49036		Malm et al. (2008), Global Health Data (2017)
thorax_prob_4113	Probability of a burn to the thorax	0.04264		Tian et al. (2018), Global Health Data (2017)
thorax_prob_461	Probability of chest wall bruises/haematoma	0.0945		Okugbo et al. (2012)
thorax_prob_463	Probability of haemothorax	0.0945		Okugbo et al. (2012)
thorax_prob_453a	Probability of a lung contusion	0.0539		Okugbo et al. (2012)
thorax_prob_453b	Probability of a diaphragm rupture	0.0161		Okugbo et al. (2012)
thorax_prob_412	Probability of fractured ribs	0.0392		Okugbo et al. (2012)
thorax_prob_414	Probability of flail chest	0.0098		Okugbo et al. (2012)
thorax_prob_441	Probability of chest wall lacerations/avulsions	0.08586		Okugbo et al. (2012)
thorax_prob_442	Probability of surgical emphysema	0.01749		Okugbo et al. (2012)
thorax_prob_443	Probability of closed pneumothorax/open pneumothorax	0.05565		Okugbo et al. (2012)
abdomen_prob_5101	Probability of a laceration to the abdomen	0.11026		Malm et al. (2008), Global Health Data (2017)
abdomen_prob_5113	Probability of a burn to the abdomen	0.03874		Tian et al. (2018), Global Health Data (2017)
abdomen_prob_552	Probability of an injury to stomach/intestines/colon	0.047656		Global Health Data (2017), Ruhinda et al. (2008)
abdomen_prob_553	Probability of injury to spleen/Urinary bladder/Liver/Urethra/Diaphragm	0.77441		Global Health Data (2017), Ruhinda et al. (2008)
abdomen_prob_554	Probability of an injury to kidney	0.028934		Global Health Data (2017), Ruhinda et al. (2008)
spine_prob_612	Probability of fractured vertebrae	0.364		Biluts et al. (2015)
spine_prob_673a	Probability of a spinal cord injury at neck level with an AIS score of 3	0.015840216		Biluts et al. (2015), Stephan et al. (2015)
spine_prob_673b	Probability of a spinal cord injury below neck level with an AIS score of 3	0.040731984		Biluts et al. (2015), Stephan et al. (2015)
spine_prob_674a	Probability of a spinal cord injury at neck level with an AIS score of 4	0.074477731		Biluts et al. (2015), Stephan et al. (2015)
spine_prob_674b	Probability of a spinal cord injury below neck level with an AIS score of 4	0.116490809		Biluts et al. (2015), Stephan et al. (2015)
spine_prob_675a	Probability of a spinal cord injury at neck level with an AIS score of 5	0.134791137		Biluts et al. (2015), Stephan et al. (2015)
spine_prob_675b	Probability of a spinal cord injury below neck level with an AIS score of 5	0.210827163		Biluts et al. (2015), Stephan et al. (2015)
spine_prob_676	Probability of a spinal cord injury at neck level with an AIS score of 6	0.04284096		Biluts et al. (2015), Stephan et al. (2015)
upper_ex_prob_7101	Probability of a laceration to the upper extremities	0.43896		Malm et al. (2008), Global Health Data (2017)
upper_ex_prob_7113	Probability of a burn to the upper extremities	0.03304		Tian et al. (2018), Global Health Data (2017)
upper_ex_prob_712a	Probability of a fracture to Clavicle, scapula, humerus	0.10802		Global Health Data (2017)
upper_ex_prob_712b	Probability of a fracture to Hand/wrist	0.28969		Global Health Data (2017)
upper_ex_prob_712c	Probability of a fracture to Radius/ulna	0.09329		Global Health Data (2017)
upper_ex_prob_722	Probability of a dislocated shoulder	0.025		Global Health Data (2017)
upper_ex_prob_782a	Probability of an amputated finger	0.00750024		Global Health Data (2017)
upper_ex_prob_782b	Probability of a unilateral arm amputation	0.00102276		Global Health Data (2017)
upper_ex_prob_782c	Probability of a thumb amputation	0.002841		Global Health Data (2017)
upper_ex_prob_783	Probability of a bilateral upper extremity amputation	0.000636		Global Health Data (2017)
lower_ex_prob_8101	Probability of a laceration to the lower extremity	0.186094109		Malm et al. (2008), Global Health Data (2017)
lower_ex_prob_8113	Probability of a burn to the lower extremity	0.014007083		Tian et al. (2018), Global Health Data (2017)
lower_ex_prob_811	Probability of a foot fracture	0.023610948		Global Health Data (2017)
lower_ex_prob_813do	Probability of an open foot fracture	0.013281158		Global Health Data (2017), Court-Brown et al. (2012). Chagomerana et al. (2017)
lower_ex_prob_812	Probability of a fracture to patella, tibia, fibula, ankle	0.354164215		Global Health Data (2017)
lower_ex_prob_813eo	Probability of an open fracture to patella, tibia, fibula, ankle	0.199217371		Global Health Data (2017), Court-Brown et al. (2012), Chagomerana et al. (2017)
lower_ex_prob_813a	Probability of a hip fracture	0.029513685		Global Health Data (2017)
lower_ex_prob_813b	Probability of a pelvis fracture	0.023610948		Global Health Data (2017)
lower_ex_prob_813bo	Probability of an open pelvis fracture	0.005902737		Global Health Data (2017), Court-Brown et al. (2012), Chagomerana et al. (2017)
lower_ex_prob_813c	Probability of a femur fracture	0.076765094		Global Health Data (2017)
lower_ex_prob_813co	Probability of an open femur fracture	0.01177596		Global Health Data (2017), Court-Brown et al. (2012), Chagomerana et al. (2017)
lower_ex_prob_822a	Probability of a dislocated hip	0.037338982		Global Health Data (2017)
lower_ex_prob_822b	Probability of a dislocated knee	0.002383339		Global Health Data (2017)
lower_ex_prob_882	Probability of a amputation of toes	0.00731139		Global Health Data (2017)
lower_ex_prob_883	Probability of a unilateral lower leg amputation	0.007511491		Global Health Data (2017)
lower_ex_prob_884	Probability of a bilateral lower leg amputation	0.007511491		Global Health Data (2017)
Did they go on to seek health care for their injuries?
rt_emergency_care_ISS_score_cut_off	The ISS score above which people will automatically go to seek health care	Run-specific, see Table 4		Calibrated to the results of Zafar et al. (2018)
If they sought health care for their injuries, what do they need from the health system for their treatment?
mean_los_ISS_less_than_4	The mean length of stay for a person with an ISS score less than 4	4.97		Lee et al. (2016)
sd_los_ISS_4_to_8	Variation length of stay for those with an ISS score between 4 and 8	5.93		Lee et al. (2016)
mean_los_ISS_9_to_15	Mean length of stay for those with an ISS score between 9 and 15	15.46		Lee et al. (2016)
sd_los_ISS_9_to_15	Variation in length of stay for those with an ISS score between 9 and 15 (Lee et al. 2016)	11.16		Lee et al. (2016)
mean_los_ISS_16_to_24	Mean length of stay for those with an ISS score between 16 and 24	24.73		Lee et al. (2016)
sd_los_ISS_16_to_24	Variation in length of stay for those with an ISS score between 16 and 24	17.03,		Lee et al. (2016)
mean_los_ISS_more_than_25	Mean length of stay for those with an ISS score greater than 25	30.86		Lee et al. (2016)
sd_los_ISS_more_that_25	Variation length of stay for those with an ISS score greater than 25	34.03		Lee et al. (2016)
prob_dislocation_requires_surgery	Probability that a dislocation will require a surgery	0.01		Dummy variable used to account for the fact that some dislocations will require surgery
prob_depressed_skull_fracture	Probability that the person’s skull fracture is depressed and will require surgery	0.14		Eaton et al. (2017)
prob_open_fracture_contaminated	Probability that the person’s open fracture is contaminated	0.07		Chagomerana et al. (2017)
Based on their choice to seek or not seek health care what health outcomes did each person experience (mortality, morbidity or recovery)?
prob_death_iss_less_than_9	The probability of mortality associated with an ISS score less than 9 with medical treatment	Run-specific, see Table 4		Kuwabara et al. (2010), Tyson et al. (2015)
prob_death_iss_10_15	The probability of mortality associated with an ISS score between 10 and 15 with medical treatment	Run-specific, see Table 4		Kuwabara et al. (2010), Tyson et al. (2015)
prob_death_iss_16_24	The probability of mortality associated with an ISS score between 16 and 24 with medical treatment	Run-specific, see Table 4		Kuwabara et al. (2010), Tyson et al. (2015)
prob_death_iss_25_35	The probability of mortality associated with an ISS score between 25 and 35 with medical treatment	Run-specific, see Table 4		Kuwabara et al. (2010), Tyson et al. (2015)
prob_death_iss_35_plus	The probability of mortality associated with an ISS score greater than 35 with medical treatment	Run-specific, see Table 4		Kuwabara et al. (2010), Tyson et al. (2015)
prob_death_MAIS1	The probability of death associated with a MAIS score of 1	0		Champion et al. (2010)
prob_death_MAIS2	The probability of death associated with a MAIS score of 2	0		Champion et al. (2010)
prob_death_MAIS3	The probability of death associated with a MAIS score of 3	0.05		Champion et al. (2010)
prob_death_MAIS4	The probability of death associated with a MAIS score of 4	0.31		Champion et al. (2010)
prob_death_MAIS5	The probability of death associated with a MAIS score of 5	0.59		Champion et al. (2010)
prob_death_MAIS6	The probability of death associated with a MAIS score of 6	0.83		Champion et al. (2010)
prob_perm_disability_with_treatment_severe_TBI	The probability that a person with a traumatic brain injury will be left permanently disabled	0.199		Eaton et al. (2017)
prob_perm_disability_with_treatment_sci	The probability that a person with a traumatic brain injury will be left permanently disabled	0.436		Eaton et al. (2019)

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