Impact of demographic characteristics in pet ownership: Modeling animal count according to owners income and age

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Impact of demographic characteristics in pet ownership: Modeling animal
count according to owners income and age
Article in Preventive Veterinary Medicine · November 2012
DOI: 10.1016/j.prevetmed.2012.10.006 · Source: PubMed
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Preventive Veterinary Medicine 109 (2013) 213–218
Contents lists available at SciVerse ScienceDirect
Preventive Veterinary Medicine
journal homepage: www.elsevier.com/locate/prevetmed
Impact of demographic characteristics in pet ownership: Modeling
animal count according to owners income and age
Camila Marinelli Martins a,∗,1 , Ahmed Mohamed b,1 , Ana Marcia Sá Guimarães b ,
Cristiane da Conceição de Barros c , Raquel dos Santos Pampuch c , Walfrido Svoboda d ,
Rita de Cassia Maria Garcia e , Fernando Ferreira a , Alexander Welker Biondo d,2
a
b
c
d
e
Department of Preventive Veterinary Medicine, São Paulo University, São Paulo, SP 05508, Brazil
Department of Comparative Pathobiology, Purdue University, West Lafayette, IN 47907, USA
Department of Health Surveillance, City Secretary of Health, Pinhais, PR 83323, Brazil
Departament of Veterinary Medicine, Federal University of Paraná, Curitiba, PR 80035, Brazil
Executive Director of Technical Institute for Education and Animal Control, São Paulo, SP 06707, Brazil
a r t i c l e
i n f o
Article history:
Received 21 March 2012
Received in revised form 4 October 2012
Accepted 13 October 2012
Keywords:
Pet dog population
Cat population
Pet population management
Pet ownership
a b s t r a c t
Pet owner characteristics such as age, gender, income/social class, marital status,
rural/urban residence and household type have been shown to be associated with the
number of owned pets. However, few studies to date have attempted to evaluate these
associations in Brazil. Accordingly, the aim of this study was to evaluate the association
between age and income of owners and the number of owned dogs and cats in a Brazilian
urban center. Pinhais, metropolitan area of Curitiba, Southern Brazil, the seventh largest
city in Brazil, was chosen for this study. Questionnaires were administered door-to-door
between January and February 2007 and data were analyzed by zero-inflated negative binomial (ZINB) models. A total of 13,555 of 30,380 (44.62%) households were interviewed. The
majority (62.43%) of households reported having one or more dogs, with one or two dogs
being the most common (29.97% and 19.71%, respectively). Cat ownership per household
was much lower (P = 0.001) than dog ownership, with 90% of the households reported having no owned cats. ZINB analyses indicated that income is not associated with the number
of both dogs and cats among households that have pets. However, households from higher
income categories were more likely to have dogs (but not cats) when compared to the lowest income category (P < 0.05), contradicting a common belief that the poorer the family,
the more likely they have pets. Certain age categories were significantly associated with the
number of dogs or cats in households that have pets. In addition, most age categories were
significantly associated with having dogs and/or cats (P < 0.05). In conclusion, our study has
found that age but not household income is associated with the number of dogs or cats in
households that have pets; higher income households were more likely to have dogs when
compared to low-income households.
© 2012 Elsevier B.V. All rights reserved.
∗ Corresponding author at: Departamento de Medicina Veterinária Preventiva e Saúde Animal, Faculdade de Medicina Veterinária e Zootecnia,
Universidade de São Paulo – USP, Rua Prof Dr Orlando Marques de Paiva, 87 – Cidade Universitária, São Paulo, SP 05508-270, Brazil.
Tel.: +55 11 3091 9937; fax: +55 11 3091 7928.
E-mail address: cami.marinelli@gmail.com (C.M. Martins).
1
These authors equally contributed for this study.
2
He is also a visiting professor in the Department of Veterinary Pathobiology, University of Illinois, Urbana, IL 61802, USA.
0167-5877/$ – see front matter © 2012 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.prevetmed.2012.10.006
214
C.M. Martins et al. / Preventive Veterinary Medicine 109 (2013) 213–218
1. Introduction
2.2. Questionnaires
In recent years, several studies have been conducted
in Southern and Southeastern Brazil in an effort to estimate the number of owned dogs and cats in urban centers
(Serafini et al., 2008; Nunes et al., 1997; Lima Júnior, 1999;
Dias, 2001; Paranhos, 2002; Dias et al., 2004; Alves et al.,
2005). These estimates have been used to accurately plan
and monitor government investments in public health services, such as rabies and animal control. Besides being
lower than estimates of the World Health Organization
for developing countries, human:dog ratios vary greatly
among different areas of the country (3:1–13:1 for owned
dogs) (Serafini et al., 2008; Nunes et al., 1997; Lima Júnior,
1999; Dias, 2001; Paranhos, 2002; Dias et al., 2004; Alves
et al., 2005). The same variation has been observed among
different owned cat populations (7:1–86:1 for owned cats);
however, fewer studies have been undertaken on them cats
when compared to dogs (Paranhos, 2002; Dias et al., 2004;
Garcia, 2009). It is likely that various demographic and
socioeconomic characteristics of the human population in
different regions may be associated with and influence the
number of owned dogs and cats.
Human demographic and socioeconomic characteristics associated with the number of owned pets have
been extensively investigated worldwide (Endenburg et al.,
1990; Leslie et al., 1994; Downes et al., 2009; Murray et al.,
2010). Although the evaluated variables and associations
differ greatly among studies, factors such as age, gender,
income/social class, marital status, rural/urban residence
and household type may be associated with pet ownership (Marx et al., 1988; Endenburg et al., 1990; Leslie et al.,
1994; Downes et al., 2009). Similar studies in Brazil are
scarce; household type was recently found to be associated with the number of owned dogs and cats in a Brazilian
urban center (Serafini et al., 2008). In this previous study,
family income was suggested as responsible for differences
in human:pet ratios among neighborhoods (Serafini et al.,
2008). Since data on income and age of the human population may be routinely accessed by the Brazilian Department
of Population Demographics, the objective of this study was
to evaluate the association between these variables and the
number of owned dogs and cats per household in an urban
center.
Questionnaires were administered door-to-door
between January and February 2007 by trained health
agents of the Center of Zoonosis Control (CCZ) of Pinhais
City as part of a non-related study (National Dengue
Prevention Program). All houses were visited. Regarding
the three apartment building complexes, one apartment
in each of the 24 4-story buildings was randomly chosen
to be interviewed and counted as a household unit. All
visits occurred during work hours (08:00 AM–5:00 PM).
Questions asked per household for this study included the
age of each household member, household income, and
number of dogs and cats. Age information of household
members was recorded as 6 independent variables (<1,
1–<10, 10–<20, 20–<30, 30–<40, ≥40 years old). Interviewees were asked the age of every person living in the
household. The interviewers then recorded the number of
people in each age variable for each household. Based on
the Brazilian monthly minimum wage (MW) of R$380.00
(U$182.17) at the time, income was divided in 4 categories
(<1, 1–<2, 2–<4 and ≥4 MW). Pet ownership was defined
as claiming a dog or cat as one’s or family’s own instead
of just having a pet in the household at the time of the
study. Pet ownership was assessed irrespective of where
the pets were kept (such as house, backyard, barn) or if
they were allowed to roam or kept indoor or outdoor. Data
was recorded per household and organized in Excel sheets
separated by neighborhoods.
2. Materials and methods
2.1. Study site
Pinhais city is part of Curitiba County, capital of Paraná
State, southern Brazil, the 7th most populated city in Brazil
with a total of 3.16 million people within the metropolitan
area. At the time of the sampling, Pinhais had approximately 117,166 inhabitants (IBGE, 2010) distributed in
30,680 households in 15 neighborhoods. There were only
three 4-story apartment building complexes (complexes 1,
2 and 3 had nine, ten and five 4-story buildings, respectively); the remaining households were houses (R. Lacerda,
personal communication, Department of Urban Planning,
Pinhais City Hall).
2.3. Statistical analysis
Initial analysis of the data indicated that the distributions of dogs and cats per household were severely right
skewed for all neighborhoods and overall (Supplement 1).
Therefore, median and ranges of owned dogs and cats in
each neighborhood were calculated as descriptive statistics. A non-parametric method (Kruskal–Wallis) was used
to test the hypothesis of equal distribution. A post hoc
multiple comparison test of the number of pets/household
between neighborhoods was performed using the SAS
macro KW MC (Elliott and Hynan, 2011). This macro is a
convenient way to perform multiple comparisons analysis
for non-normally distributed data. It is designed to implement Dunn’s multiple comparison procedure (Dunn, 1964)
in cases of unequal sample sizes or when ties are present.
Using this procedure, a q statistics was calculated based on
the standard errors (SE) for the comparison between two
groups (Zar, 2010) and A type 1 error of 0.05 was used.
About 38% and 90% of the households had no dogs or
cats, respectively. In addition to this excess of zeros, an
overdispersed distribution of the data was also observed;
mean count and variance were 1.18, 1.83 and 0.17, 0.51
for dogs and cats per household, respectively. Therefore,
these distributions are unlikely to fit classical or zeroinflated Poisson distributions (PD). Although ignoring the
over-dispersion may not impact the parameters estimates,
standard errors may be underestimated and result in
inaccurate p-values. On the other hand, assuming PD for
a count data with negative binomial distribution may
lead to inconsistent parameter estimates. Zero-inflated
C.M. Martins et al. / Preventive Veterinary Medicine 109 (2013) 213–218
Table 1
Count frequency, proportion and central tendency parameters for the
owned pet count in the city of Pinhais, Brazil, 2007.
Dogs
Count
0
1
2
3
4
≥5
Total
Cats
Freq.
5092
4063
2672
1039
382
307
13,555
Percent
37.57
29.97
19.71
7.67
2.82
2.25
100
Count
Freq.
Percent
0
1
2
3
4
≥5
12,196
878
278
90
37
77
89.97
6.48
2.05
0.66
0.27
0.56
13,556
100
Central tendency parameters among all households
1
0
Median
0
0
Min
16
15
Max
Central tendency parameters among dogs, and cat owning households
2
1
Median
Min
1
1
16
15
Max
Poisson (ZIP) may account for excess zeroes beyond what
is expected by PD but does not account for over-dispersion.
Negative binomial model accounts for data overdispersion
but does not for the excess zeroes. Based on these criteria and the graphic illustration of prediction obtained from
Poisson, zero-inflated negative binomial (ZINB) was determined to be the best analytical approach. All the analyses,
except multiple comparison tests, were carried out using
Stata 11.2 (StataCorp, College Station, TX, USA), and a pvalue < 0.05 was considered significant.
3. Results
All 15 neighborhoods were visited with the exception of
one small condominium (300 households), which refused
the visits, and an environmentally protected area with
no residents (Parque das Águas neighborhood). All the
remaining households in the city were visited, resulting
in a total of 13,555/30,380 (44.62%) interviews. The other
16,825 households/families either refused to be interviewed or were absent at the time of the visit. The number
of owned pets per household ranged from 0 to 16 for dogs
and 0 to 15 for cats, with median of 1 and zero, respectively (Table 1). Among pet-owning households, median
number of owned dogs was 2 (1–16) and cats 1 (1–15). The
majority (62.43%) of households reported having at least
one dog, with one or two dogs being the most frequent scenario (29.97% and 19.71% of the households, respectively)
(Table 1). Cat ownership per household was much lower
(P = 0.0001) than dog ownership, with approximately 90%
of the households having no cats (Table 2). Households with
one or two cats were also more frequently observed (6.48%
and 2.05%, respectively). In contrast to cats, the number
of dogs per household was statistically different between
neighborhoods (P = 0.0001) (Table 2). The multiple comparisons test for the number of dogs per household showed
that 26 different combinations were statistically different
(Table S2).
Zero-negative binomial (ZINB) model were found to
be the appropriate approach for analyzing the count of
215
pet (dogs and cats) ownership per household. The Poisson
model poorly fit the data distribution (due to overdispersion) compared to the negative binomial regression.
The ZINB model was chosen based on the highly significant Voung statistic (Z = 6.74, p-value < 0.0001 for dogs,
Z = 3.18, p-value = 0.0007 for cats) and because the dispersion parameter alpha was significantly different from zero
in both models for owned dog 0.25 (0.22–0.29) and owned
cat counts 6.30 (5.43–7.39) (Table 3). The effect of neighborhood was considered but was not included in the final
model since no statistical significance was found in the final
count model (data not shown).
The ZINB consisted of two parts: a negative binomial
(NB) to model the pet count process among households
with owned pets, and a logistic regression (logit) to model
the probability of having zero owned pet counts (no dogs
or cats). The NB model showed that income categories
are not associated with the number of both dogs and cats
among households that have pets (P > 0.05). However, the
logit indicated that households in higher income categories
were more likely to have at least one dog (P < 0.05), but not
cats (P > 0.05), when compared with households of <1 MW.
In other words, the odds of household within the income
category <1 MW having a zero dog count were approximately 2.04, 2.38 and 2.13 times higher than the odds for
income categories 1–<2 MW, 2–<4 MW and >4 MW, respectively (Table 3).
When considering age, the NB model indicated that
the all age variables, except <1 and 20–<30 years old,
were associated with the number of dogs per household,
whereas age variables of 20 years and older were significantly associated with the number of cats per household
(P < 0.05). Or equivalently, the expected number of owned
dogs would increase on average by 1.03, 1.09, 1.1 and 1.12
for each additional member of 1–<10, 10–<20, 30–<40 and
≥40 years old, respectively. For owned cats, the expected
count per household would increase on average by 1.12,
1.22 and 1.22 cats for each additional member of 20–<30,
30–<40 and ≥40 years old.
The logit results showed that all age categories, with the
exception of category <1 year old, were significantly associated with having at least one dog (P < 0.05). The odds that
a household would have zero owned dogs decrease by 2,
3.12, 4.54, 10, and 12.5 for each additional household member in the age variables of 1–<10 to ≥40 years, respectively.
The relationship between age variables and pet count was
different for owned cats; only variables 1–<10, 10–<20 and
20–<30 years old were less likely to have zero cats. For
each additional household member in these age variables,
the odds that this household would have zero cats would
decrease by 1.39, 2.27, and 0.75, respectively (all exact pvalues of NB and logit analyses are shown in Table 3).
4. Discussion
Our study has shown a much higher ownership preference for dogs compared to cats, as previously observed
in Brazil (Paranhos, 2002; Dias et al., 2004; Garcia, 2009). A
total of 8463/13,555 (62%) households had at least one dog,
whereas only 1360/13,555 (10%) showed at least one cat.
This finding is in contrast to developed countries, which
216
C.M. Martins et al. / Preventive Veterinary Medicine 109 (2013) 213–218
Table 2
Summary statistics for the number of owned cats and dogs in the each neighborhood of Pinhais city, Brazil.
Neighborhood
Alto Tarumã
Atuba
Centro
Emiliano Perneta
Estância Pinhais
Jardim Karla
Jd. Amélia
Jd. Cláudia
Maria Antonieta
Parque das Nascentes
Pineville
Vargem Grande
Weissópolis
Total
Dogs
Cats
Median
Q1
Q3
Min
Max
Median
Q1
Q3
Min
Max
1
1
1
1
1
1
1
1
1
2
1
1
1
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
2
2
2
2
2
2
2
2
2
3
2
2
2
2
0
0
0
0
0
0
0
0
0
0
0
0
0
0
10
14
11
14
15
9
9
12
16
13
11
10
11
16
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
9
6
10
12
11
10
8
6
7
6
11
15
15
15
Kruskal–Wallis equality-of-populations rank, P-value = 0.0001.
reported similar distributions of dogs and cats, with 22% of
the households having dogs and 18% having cats in the UK
(PFMA, 2007), and 37% having dogs and 32% having cats in
the USA (SAUS, 2011). Although further studies should be
conducted to fully establish the pet species preference in
Brazil, cultural beliefs and background may play a role in
such a scenario (Downes et al., 2009).
The number of dogs per household, but not cats,
was different between various neighborhoods. A possible
explanation for this disparity is the fact that neighborhoods
from Pinhais city vary in respect to human population density and household occupation. Neighborhoods are more or
less densely populated (R. Lacerda, personal communication, Department of Urban Planning, Pinhais city), which
may result in differences in the number of dogs/household
(Serafini et al., 2008). Regardless of such speculations, studies should be performed to evaluate the impact of such
demographic differences between neighborhoods on the
Table 3
Results of ZNIB for owned pet count in the city of Pinhais, Brazil, 2007.
Negative Binomial Section
Dogs
Variable
Coef.
Cats
P>z
[95% Conf.
Coef.
P>z
[95% Conf.
Income (multiples of one minimum salary)
1.00
<1 MW
0.96
1–<2 MW
1.01
2–<4 MW
≥4 MW
1.04
NA
0.421
0.895
0.48
Reference
0.87–1.06
0.91–1.11
0.93–1.16
1.00
1.01
1.08
1.13
NA
0.95
0.68
0.57
Reference
0.71–1.45
0.75–1.57
0.75–1.70
Age (years)
<1
1–<10
10–<20
20–<30
30–<40
≥40
0.99
1.03
1.09
0.99
1.1
1.12
0.704
0.012
<0.001
0.501
<0.001
<0.001
0.91–1.06
1.01–10.5
1.07–1.12
0.97–1.01
1.07–1.12
1.09–1.16
1.03
1.03
1.05
1.12
1.22
1.22
0.83
0.52
0.3
0.01
0.01
<0.001
0.77–1.39
0.94–1.13
0.96–1.16
1.03–1.23
1.06–1.40
1.07–1.39
Logistic section
Income (multiples of one MW)
<1
1–<2
2–<4
≥4
1.00
0.49
0.42
0.47
NA
0.006
0.004
0.03
Reference
0.29–0.81
0.23–0.76
0.23–0.93
1.00
1.37
1.27
1.90
NA
0.66
0.75
0.4
Reference
0.34–5.51
0.29–5.48
0.43–8.53
Age (years)
<1
1–<10
10–<20
20–<30
30–<40
≥40
Cons
0.83
0.5
0.32
0.22
0.1
0.08
9.73
0.652
<0.001
<0.001
<0.001
<0.001
<0.001
<0.001
0.37–1.87
0.36–0.69
0.23–0.44
0.15–0.31
0.05–0.17
0.04–0.15
5.01–18.90
1.19
0.72
0.44
1.33
0.79
<0.001
0.44
0.62
0.04
0.01
0.02
0.39
0.98
0.33
0.60–2.39
0.53–0.98
0.23–0.84
1.04–1.70
0.46–1.35
0
0.09–2.25
/lnalpha
Alpha
−1.38
0.25
<0.001
−1.50–−1.25
0.22–0.29
1.84
6.3
<0.001
1.69–1.99
5.43–7.39
Vuong test
Z = 6.74
<0.0001
Z = 3.18
0.0007
C.M. Martins et al. / Preventive Veterinary Medicine 109 (2013) 213–218
number of dogs/household that may aid in developing targeted educational strategies for pet population control.
Our study also found that owner income was not associated with the number of dogs or cats in households that
already have pets. However, households within the income
category <1 MW were significantly more likely to have zero
dogs compared to households higher income categories
(1–<2, 2–<4 and ≥4 MW). Therefore, income may not be
a good indicator of the number of dogs or cats among pet
owners despite its association with having or not a dog. A
similar finding was observed in Netherlands, where animal
owners tended to have higher incomes than non-owners
(Endenburg et al., 1990), but this study did not report the
probability of zero pets. Most of the educational initiatives of animal control in this area are concentrated on
poor families that already have pets (AWB, personal communication). Our results show that, ideally, other income
categories should also be targeted.
In contrast to income, our study showed multiple age
variables associated with pet ownership/count. A similar report in the United States also found an association
between the number of dogs and owner’s age within a category of 25–44 years old (Ramón et al., 2010). The fact that
only the age variables between 1 and 30 years old were less
likely to have zero cats may imply increasing cat ownership
in this age group. Finally, age may aid in modeling pet count
in future studies, as different age groups were associated
with the number of dogs and cats owned. A potential limitation of this study is that age was recorded as categories.
Future studies should explore age as categories and take
into consideration the number and age of members in each
household.
It is noteworthy that the high number of owned dogs
and cats may not be necessarily correlated with the fact that
owners allow their animals to roam. The roaming of owned
dogs may be connected to income and education level and
needs to be further explored. Free-roaming owned dogs, as
well as loosely owned or community owned animals, may
be then responsible for increasing the stray dog population
size.
A limitation of this study is response bias with respect
to the response rate. Although the reason why certain
households refused to be interviewed was not registered,
interviewers later reported that certain people refused to
be interviewed once they were told the expected time to
complete the interview (sometimes more than 30 min – the
team was responsible for evaluating the conditions of the
household with respect to dengue prevention) (R. Lacerda,
personal communication, Department of Urban Planning,
Pinhais City Hall). The refusal and the absence of people
at the time of the visit may imply busy lifestyles, which
in turn, may somehow affect the presence and number
of pets in a household. Since vertical housing has been
associated with a low number of pets in one Brazilian
neighborhood (Serafini et al., 2008), the number of animals
in the 3 apartment building complexes may be lower than
the average for the city. However, the lack of representativeness of these apartment buildings with respect to the
overall household population (approximately 1%; data not
shown) is unlikely to bias with the results of the present
study.
217
5. Conclusion
The count data analysis methods used in this study may
be applied to data from other Brazilian cities, and probably in urban areas, as it takes into account people that have
and do not have pets. As the number of household members of given age categories increase in certain households,
one can expect an increase in the number of pets. Therefore, the results of this study can be used to plan public
health initiatives and educational programs in Pinhais city
based on age stratification. Interventions can target people
that still do not have pets and households in higher income
categories, so as to help in the decision to acquire or care
for a pet.
Conflict of interest
None.
Acknowledgements
Funding for Dr. Camila Martins’s research fellowship
was provided by the Tesouro Nacional – Universidade Federal do Paraná – UFPR. We kindly thank the help given by
the Pinhais Center of Zoonosis Control (CCZ) personnel for
the interviews and data collection.
Appendix A. Supplementary data
Supplementary data associated with this article can be
found, in the online version, at http://dx.doi.org/10.1016/
j.prevetmed.2012.10.006.
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