YLL YLD 0 500 1000 1500 2000 2500 3000 DALY s
Age goup (5 year age bands)
Persons
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Table 13: Disability-adjusted life years due to TBI, 2010
YLL YLD Mild YLD Moderate/Severe DALY
Age Female Male Female Male Female Male Female Male Total
0-4 82 78 188 215 7 24 278 317 594 5-9 78 74 168 199 14 58 260 331 591 10-14 220 208 165 208 21 94 406 510 916 15-19 546 899 151 204 26 122 724 1225 1949 20-24 634 1605 107 158 27 132 769 1896 2664 25-29 351 1259 80 107 27 130 458 1496 1954 30-34 161 949 82 90 34 157 277 1196 1473 35-39 195 904 82 81 39 181 315 1166 1481 40-44 351 809 78 84 41 199 470 1092 1563 45-49 391 607 72 90 39 198 502 895 1397 50-54 206 498 64 82 35 181 305 760 1066 55-59 149 479 68 68 33 174 250 721 972 60-64 152 357 68 53 27 138 247 548 795 65-69 210 292 52 35 24 116 287 443 730 70-74 187 218 32 22 20 91 239 331 570 75-79 132 178 26 20 18 76 176 274 450 80-84 156 164 23 16 14 49 193 228 422 85+ 361 245 26 14 13 31 401 290 691
Table 14: Disability-adjusted life years due to TBI projected to 2020
YLL YLD Mild YLD Moderate/Severe DALY
Age Female Male Female Male Female Male Female Male Total
0-4 82 78 263 302 10 34 356 414 770 5-9 156 148 224 264 19 78 399 490 889 10-14 293 277 200 248 26 116 519 641 1160 15-19 615 1092 162 216 29 135 805 1443 2248 20-24 824 2141 124 187 33 165 981 2492 3473 25-29 468 1970 103 149 37 190 608 2308 2916 30-34 214 1249 91 105 39 194 344 1548 1892 35-39 195 1039 78 79 38 185 312 1303 1615 40-44 351 768 67 69 37 172 455 1009 1464 45-49 430 643 73 82 41 190 544 915 1459 50-54 275 622 75 85 42 199 392 906 1299 55-59 209 639 86 77 44 210 339 926 1265 60-64 228 558 97 69 41 195 367 822 1189 65-69 358 475 76 49 37 169 470 692 1162 70-74 323 406 52 35 33 149 408 590 997 75-79 198 267 33 26 24 104 255 397 651 80-84 195 253 26 21 17 68 238 342 581 85+ 4323 0 34 24 18 56 4374 81 4455
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Discussion
The aim of this study was to estimate first ever in lifetime TBI incidence, prevalence of TBI survivors in the population, TBI attributable deaths and TBI-attributable health loss (denominated in DALYs) for NZ in 2010. Combining information from the BIONIC study with NZ national health data, the modelled results suggest that there were approximately 11,300 first-ever TBI events in 2010 with a total prevalence of approximately 527,000. Males experienced higher rates of TBI than females. A high proportion of TBIs occurred among children (aged <16 years) and young adults (aged <34 years), accounting for about 75% of all first-ever TBI cases.
Health loss attributable to TBI in NZ was estimated to be approximately 20,300 DALYs in 2010. This is over one quarter (27%) of all health loss attributable to intentional and unintentional injuries in that year, and almost 2.4% of all health loss from all causes (i.e., all diseases and injuries) (Ministry of Health and Accident Compensation Corporation, 2013). Importantly, we found that most of the health loss attributable to TBI (71%) resulted from fatal injuries. However, nonfatal outcomes (i.e., disability) still accounted for a substantial share of the total TBI burden. While both moderate/severe and mild TBI contributed to this nonfatal burden, mild TBI made the greater contribution (56% of total TBI YLDs). Our findings are consistent with previously reported studies. Tagliaferri and colleagues (Tagliaferri, Compagnone, Korsic, Sevadei, & Kraus, 2006) reviewed 23 studies conducted in Europe and reported an aggregate hospitalised TBI incidence rate of 235 per 100,000 person-years. However, these estimates reflect episode rates of TBI (incident and recurrent) sourced from hospitalisation data. In the BIONIC study approximately 30% of incident TBI cases were never seen in hospital. Similarly Corrigan et al. (2010) reviewed TBI incidence and prevalence studies conducted in the US and abroad. The authors concluded that approximately 235,000 Americans are hospitalised for non-fatal TBI each year, but were unable to estimate the incidence of non-hospitalised events (Corrigan et al., 2010). In contrast, a prospective birth cohort study found the incidence of TBI to be much higher (McKinlay et al., 2008). The authors reported an incidence rate of 1,750 per 100,000 per year. The result was based on a population capture methodology which included non-hospitalised TBI (McKinlay et al., 2008). However McKinlay et al.’s (2008) findings were limited to a 0-25 year age group. It is of note that high quality epidemiological design and case ascertainment is lacking in most previous studies. TBI DALYs have previously been reported for New Zealand in the New Zealand Burden of Disease Study (Ministry of Health, 2012) these estimates are consistent with those reported here, despite differences in time period, methodology and data sources.
Strengths of this study include the use of data from a population-based TBI incidence study (Barker- Collo & Feigin, 2009; Feigin et al., 2013; Theadom et al., 2012). Unlike previous studies (Cassidy et al., 2004; Ribbers, 2007; Tate et al., 1998), the estimates reported here are based on investigating TBI in both hospital and community settings across all ages and severities of injury.
The main limitation of our study is the use of routinely collected mortality data to estimate TBI mortality. However, the multi-state life table model corrects for inaccuracy in routine cause of death coding. Thus we estimated 448 deaths from TBI in NZ in 2010, whereas 519 deaths were coded to this cause in the official mortality statistics. This correction is itself a useful output of our study and helps to inform policy makers of the true impact of TBI on our society; reasons why TBI appears to be over-
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reported as the underlying cause of death require further investigation to understand how underlying causes of death are coded, especially where multiple trauma is involved. Secondly, the data used on levels of disability (from the BIONIC Study) is subject to self-report bias, however it is the best available data.
Finally, the model as currently constructed does not disaggregate by ethnicity or by socio-economic status due to insufficient data. This clearly reduces the policy relevance and value of our findings. Despite these limitations the model still provides an internally consistent description of TBI epidemiology (including incidence, prevalence and survival) and current burden (including both YLL and YLD).
In conclusion, the current study quantifies the substantial population health impact of TBI in NZ. Further studies are needed to extend the findings to ethnic and socioeconomic subpopulations, and study trends in TBI epidemiology and impacts over time, including recurrent TBI. Such data is essential for planning and evaluating public health interventions and clinical TBI services.
Summary
The current study used DISMOD II and multi-state life table modelling to estimate the incidence and prevalence of TBI for NZ. The results suggest that the number of TBI sufferers in NZ is substantial and is expected to increase further by 2020. Further similar studies are needed to confirm the findings in other populations; to establish reliable estimates for monitoring TBI as more population-based longitudinal data becomes available. Taking further steps towards improving models will allow predicting the burden of TBI to be extended to a fuller and effective description. Good information on disability outcomes are lacking and current research make the best possible estimates as to the true disability burden.
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