Mortality in over 350,000 insured Swedish dogs from 1995-2000: II. Breed-specific age and survival patterns and relative risk for causes of death

Abstract

This study continues analysis from a companion paper on over 350,000 insured Swedish dogs up to 10 years of age contributing to more than one million dog-years at risk during 1995-2000. The age patterns for total and diagnostic mortality and for general causes of death (trauma, tumour, locomotor, heart and neurological) are presented for numerous breeds. Survival estimates at five, eight and 10 years of age are calculated. Survival to 10 years of age was 75% or more in Labrador and golden retrievers, miniature and toy poodles and miniature dachshunds and lowest in Irish wolfhounds (91% dead by 10 years). Multivariable analysis was used to estimate the relative risk for general and more specific causes of death between breeds accounting for gender and age effects, including two-way interactions. Older females had tumour as a designated cause of death more often than males in most breeds, but not in the Bernese mountain dog. Information presented in this and the companion paper inform our understanding of the population level burden of disease, and support decision-making at the population and individual level about health promotion efforts and treatment and prognosis of disease events.

Figure 1

Mortality rates, per 10,000 dog-years at risk (DYAR), developed using Cox regression for six common breeds. Shown are mortality rates for total and diagnostic mortality, as well as for the categories tumours, trauma, locomotor problems and heart. Note that scales differ between breeds.

Figure 2

Mortality rates, per 10,000 dog-years at risk (DYAR), developed using Cox regression for six higher-risk breeds. Shown are mortality rates for total and diagnostic mortality, as well as for the categories tumours, trauma, locomotor problems and heart. Note that scales differ between breeds.

Figure 3

Mortality rate ratios (MRR) from the diagnostic mortality are shown by breed¹, age and gender (F-female, M-male) for a few selected breeds, as well as for the baseline. The data are on dogs with life insurance at Agria² during years 1995 to 2000. The MDR are derived using the estimates in table 2. For example, the MRR for female German shepherds (GS) at age category 9 < 10 is constructed taking the exponent of (the breed estimate+ the gender estimate + (the age estimate × 9) + (the gender-age estimate × 9) + (the age-breed estimate × 9) +gender-GS estimate). ¹GS- German shepherd, Golden retr- golden retriever ²Agria Insurance, PO 70306, SE-107 23 Stockholm, Sweden ³BL- baseline- all breeds not included in the model

Figure 4

Mortality rate ratios (MRR) from the tumour model are shown by breed¹, age and gender (F-female, M-male) for a few selected breeds, as well as for the baseline. The data are on dogs with life insurance at Agria² during years 1995 to 2000. The MRR are derived using the estimates in table 3. For example, the MRR for female Bernese mountain dogs (BMD) at age category 9 < 10 is constructed taking the exponent of (the breed estimate+ the gender estimate + (the age estimate × 9) + (the gender-age estimate × 9) + the gender-BMD estimate). ¹BMD- Bernese mountain dog, Irish wh- Irish wolfhound ²Agria Insurance, PO 70306, SE-107 23 Stockholm, Sweden ³BL- baseline- all breeds not included in the model

Figure 5

Mortality rate ratios (MRR) from the heart model are shown by breed¹, age and gender (F-female, M-male) for a few selected breeds, as well as for the baseline. The data are on dogs with life insurance at Agria² during years 1995 to 2000. The MRR are derived using the estimates in table 3. For example, the MRR for female Cavalier King Charles spaniel (CKC) at age category 9 < 10 is constructed taking the exponent of (the breed estimate + the gender estimate + (the age estimate × 9) + (the age-breed estimate × 9)). ¹CKC- Cavalier King Charles spaniel, Irish wh- Irish wolfhound ²Agria Insurance, PO 70306, SE-107 23 Stockholm, Sweden ³BL- baseline- all breeds not included in the model