Transcript Impact of economic activity

The impact of the economy on
health in France & Brittany
Martine M. BELLANGER
& A. JOURDAIN
[email protected]
Contents
• Background
• Some issues to be discussed
• Further work to be done
Background
• Starting point: findings from a previous research:
– Regional health programmes (PRS) on suicide
prevention in France (Bellanger, Jourdain & Batt in Social
science &Medicine 65 (2007): 431-441)
– An ecological approach (i.e. contexts may influence
health) was adopted to analyse factors having an
impact on suicide rate in the regions with PRS.
– Were included, among variables, those related to
“economic, social and health capital, employment and
living conditions, state of health, mental health….
Were adding some variables reflecting social
fragmentation together with health care provision.
Some regional features
• Among economic variables likely to have a
relationship with regional population health:
– Regional wealth: GDP per inhabitant
– Economic activity: rate of unemployment (proxy)
– ‘Social assistance for depraved population’: rate of
minimum revenue beneficiaries
• Health state: Standardized mortality rate
• In addition, weight of ageing population
• See table 1 below
Table 1 Some data related to economic activity and health, in some of the 22 regions
Variables
GDP/per
inhabitant
2004, in €
Unemployment
rate in 2005
(rank 1 to 7
levels)
Minimum
revenue (rate for
100 people from
18 to 59
(rank 1 to 5
levels)
27, 123
10%
3%
100
16.3%
25,661(3)
9% (2)
2% (1)
105 (11)
15% (3)
Brittany (D)
23,653 (11)
8% (1)
2% (1)
106 (12)
19% ( 13)
Pays-de-la-Loire (D)
24,547 (6)
8% (1)
2% (1)
97 (5)
17% (10)
North-Pas Calais (C)
21,076 (20)
13% (6)
5% (4)
121(16)
15% (2)
Provence - Alps – Azur
Cost (F)
25,073 (4)
12% (5)
5% (4)
96 (4)
19% (14)
Rhone-Alps (F)
26,988 (2)
9% (2)
2% (1)
95 (3)
16% (6)
Ile de France (Paris -A)
41,662 (1)
10% (3)
3% (2)
90 (1)
13% (1)
Limousin (B)
21,799 (18)
8% (1)
2% (1)
99 (7)
24% (22)
Regions
France (Metropolitan)
Alsace (E)
(Letter) for region classification in the principal component analysis
SMR
In 2002
(rank 1 to
16)
Population
65 and
above, in %
(Number) for the rank among the regions
Some relationships between
variables
• In our research, the above mentioned
variables were included in addition to other
ones (e.g. Gini coefficient measuring
income inequality gap)
• A principal component analysis (PCA) on
the descriptive variables showed:
– Some relationships between economic
variables and population health at the
regional level,
– But, some results were not as expected
• Inverse correlation between unemployment
and SMR in some regions
– In some regions with low unemployment rate , i.e
good level of economic activity, high SMR was
found (Brittany)
– Some regions with high unemployment rate
enjoyed low SMR, conversely (Mediterranean
regions)
– See figure 1 below
Figure 1 External factors of suicide
Axis 1
Total fertility
rate 2000
Gross birth
rate 2000
SMR
Child mortality
rate
Female suicide
mortality Rate
Axis 2
GDP /inhab.
/.
Density 2000
inhab / km²
Natural growth
rate-1999 en %
Male suicide
mortaliy rate
Ageing rate
2000
inter-quartiles (€)
inter-deciles
Gini
Source Social Science an Medicine p. 437
Gross mortality
rate 2000
Unemployment
Rate ILO 2001
• In addition, the higher the revenue
inequalities were founded, the lower the
SMR were
– e.g. pretty good health in South & South east
crescent). Those specific regions have the
highest level of CMU& RMA beneficiaries
– e.g. Paris region
• Conversely, high SMR were found in more
‘egalitarian’ regions
•
Regions with low GDP/inhabitant were found older
relatively (high rate of ageing population):
– E.g. Limousin, Auvergne
• Conversely, regions with high level of GDP/inhabitant
were also found younger (but also with large income
inequalities:
– E.g. Alsace and to a less extent Rhône-Alpes
• Thus a clear-cut opposition was observed among the
French regions, which were grouped, with some
exceptions:
– Paris region (A) and North-Pas de Calais (C). See above figure 2
Figure 2 The typology of the regions from group A to F
Axis 1
Bretagne(D)
Haute-Normandie(D)
)
Centre 7 Bourgogne(D)
Pays De Loire(D)
Franche-Comte(D)
Picardie Poitou - (D)
Champagne-Ardenne
1
Lorraine (D)
Limousin(B)
Auvergne(D)
Axis 2
Nord-Pas-De-Calais (c)
Alsace (E) Rhône-Alpes(F)
Aquitaine(D)
Midi-Pyrénées(D)
Ile De France(A)
Languedoc-Roussillon (F)
Prov.-Alpes-côtes-d'azur(F)
Corse(F)
Source Social Science an Medicine p. 437
Type of activity & health status
• Relationship between employment area
and cancer mortality level in France:
– Type of economic activity and its impact on
health state
– E.g. Cancer mortality rates in France, in 19731977 and 1995-1999 (Rican et al. 2004 see
map below)
– See map of Gross mortality rate in France (T
in U)
Map 1- Cancer mortality rate according to employment area, in France
Map 2-SMR (all death causes, Male and Female)
Further work to be done
• Further research have to be carried out,
nevertheless, at this stage, some issues could be
raised:
– Population components need to be included in the
variables
• Large discrepancies between social groups (6.5 year
difference in terms of LE at age 35 were found between
skilled workers and executives and members of intellectual
professions during 1992-1996 period.
• See for instance Brittany (good level of employment does not
mean good population health. The later depends on the
weight of social categories, e.g. skilled workers
• Brittany is the ‘first’ region in terms of suicide & accident male
mortality rate in France (in some departments, high rates for
persons aged 65 and over
Further work to be done
• Disparities in health state (e.g.
morbidity/mortality) can be due to
differences in income activity, but also, to
educational level, information access and
social environment.
• The variables have to be disentangled
• Thus multilevel modeling approach could
be used