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2017.5.28' יום א

405חדר | מאירסדורףבית

האוניברסיטה העברית בירושלים| הר הצופים קמפוס

THE LANCET Health in Israel 4 Coming of age: health-care challenges of an ageing population in Israel Tzvi Dwolatzky, Jenny Brodsky, Faisal Azaiza, A Mark Clarfield, Jeremy M Jacobs, Howard Litwin

The Analysis

• Performed specifically for this article

• We used SHARE data from 2009-2010

• Analytic sample: respondents aged 65 and older

• We compared older Israelis with respondents from 16 SHARE countries (N = @ 30,000)

• We considered ten health status indicators.

Variable Range Examples of items/ Explanation

Chronic conditions 0-9 diabetes, osteoporosis, hypertension

Bothersome symptoms 0-12 chest pain, dizziness, breathlessness

Mobility limitations 0-10 100 meters, climbing one or several flights of

stairs

Activities of daily living 0-6 dressing, bathing, getting in/ out of bed

Instrumental activities of daily

living

0-7 managing money, taking medications, shopping

for groceries

Euro-D scale 0-12 loss of appetite, excessive guilt feelings, no hopes

for the future

Doctor visits during last 12 months 0-98 including emergency room but not dentist visits

or hospital stays

Medications taken at least weekly 0-12 drugs for hypertension, chronic bronchitis, joint

inflammation

Body Mass Index 11.72-68.01

Maximum grip strength

1-99

static force the hand can squeeze around a

dynamometer, measured in kilograms

Health status measures

EUROPE

-------------

ISRAEL

----------------------------------

Indicator Mean (SD) Mean (SD) ba

Age 74.56 (6.78) 75.15 (6.71) ----

Chronic conditionsb 1.86 (1.51) 2.38 (1.71) 0.59***

Bothersome symptomsc 2.34 (2.18) 2.83 (2.64) 0.60***

Mobility limitationsd 2.33 (2.66) 2.91 (3.04) 0.64***

ADL difficultiese 0.40 (1.11) 0.78 (1.64) 0.36***

IADL difficultiesf 0.65 (1.44) 1.44 (2.02) 0.74***

Table 2: Health status indicators from the Survey of Health, Ageing and

Retirement in Europe (2009-2010): Israel and Europe compared (65+)

a Unstandardized regressions from OLS regressions that control for: age, gender, income, education, work status, marital status, and number of children. The numbers shown (the b scores) are the deviation from the mean of each indicator after considering the control variables.

EUROPE

-------------

ISRAEL

----------------------------------

Indicator Mean (SD) Mean (SD) ba

Depressive symptomsg 2.78 (2.38) 3.29 (2.80) 0.67***

Doctor visitsh 7.63 (10.18) 12.76 (16.58) 5.05***

Medicationsi 2.03 (1.74) 2.92 (2.14) 0.94***

BMIj 26.84 (4.56) 27.00 (4.49) 0.37 **

Grip strengthk 30.40 (11.04) 27.43 (10.27) -3.86***

Table 2: Health status indicators from the Survey of Health, Ageing and

Retirement in Europe (2009-2010): Israel and Europe compared (65+)

(continued)

a Unstandardized regressions from OLS regressions that control for: age, gender, income, education, work status, marital status, and number of children. The numbers shown (the b scores) are the deviation from the mean of each indicator after considering the control variables.

Separate analyses were executed on the Israeli sample. We looked at the relative health of the three main population groups :

1) veteran Jewish Israelisa (n=787) 2) Arab Israelis (n=125) 3) recent FSU immigrants (n=313)

We found that that older Arab-Israelis were less healthy than the

veteran Jewish population, according to most of the health status indicators.

The recent immigrants from the FSU, had poorer health than

veteran Jews on all the indicators (except for maximum grip strength).

a Jews who were either born in the area that became the State of Israel or immigrated to it (except for the recent immigrants from the Former Soviet Union)

We also examined gender differences

Israeli women (veteran Jews, Arabs and FSU immigrants combined) were worse off than men on three of the six self- reported health status indicators, showing greater positive associations with:

bothersome symptoms mobility limitations depressive symptoms

In comparison, the women in the full European sample were significantly less healthy than their male counterparts on all six of the health status indicators.

Thus, the gender differences in relation to health were less pronounced in Israel than they were in Europe.

A few last points: The poorer health status of older Israelis compared to elders in Europe was driven, at least partly:

by the greater health risks of the Arab minority, and by the poor health of the recent immigration from the FSU.

Nevertheless, additional analysis showed that the veteran Jewish Israelis scored relatively poorly compared with their European counterparts, even after the Arab respondents and the FSU immigrants were removed from the analysis.

The results also indicated that while older Israeli women were less healthy than older Israeli men, this gender gap is less pronounced than that found in Europe.

Family and social networks constitute the interpersonal

environments in which people are embedded.

They modify the effects of stressors on health and

wellbeing in older age, and are important factors for

economic decisions such as the choice of retirement

age.

Social networks at different points in life and under

differing circumstances shape the paths to key

outcomes in the lives of older persons.

Introduction

11

12

Introduction

Economics: Income, wealth, pensions

Social

Network:

Family and

social support

Medicine: Physical and

mental health, longevity

dynamic

longitudinal

13

• Beginning of SN module: sn001

• (Introduction): Questions about

relationships with whom respondents

discuss important things

Outline of module

14

• Data from the name generator:

• The first block collects information about names and role relationship

of social network members (up to six):

– sn002a_x → Start of loop, resp. is asked if he/she wants to name

the first person

– sn002_x → Name of persons in network

– sn005_x → Relationship to SN members

– sn002a_x → “Are there any more persons with whom you often

discuss things that are important to you?”

– If no more SN members or after six persons: sn003a_ → “Is there

anyone (else) who is very important to you for some other

reason?”

– If yes to sn003a_: sn002_7 (first name) & sn005_7 (relationship),

meaning this person appears as 7th person of the network.

Outline of module

15

• Social Network Member details:

– sn005a_x → Gender of SN members

– sn006_x → Proximity of SN members. Skip for

parents (information found in DN).

– sn007_x → Contact with SN members. Skip if living in

the same HH (assume daily contact); skip for parents

(information found in DN).

– sn009_x → Emotional closeness of SN members.

Outline of module

16

• Overall satisfaction from social network /

lack of a social network (sn012 / sn017).

Outline of module

The aim of this inquiry was to clarify the role of social networks in relation to late life activity, taking into account mobility limitations.

The study was based upon data from two waves of the SHARE project.

Wave 4 2011

Wave 5 2013

It focused on respondents aged 65+

The initial analyses underscored previous findings that have appeared in the literature:

social networks enhance activity

mobility limitations restrain activity.

Examination of the interaction between social network and mobility showed a unique, and perhaps unexpected, association with activity at follow-up.

IRR 95% CI

Control variables

Age: 70-79 1.03** (1.02, 1.05)

80+ 0.99 (0.97, 1.02)

Gender (female) 1.09*** (1.07, 1.11)

Married/ partnered 0.95*** (0.94, 0.97)

Secondary education 1.17*** (1.14, 1.19)

Postsecondary education 1.22*** (1.20, 1.25)

Employed 0.87*** (0.85, 0.90)

Income: Quintile 2 1.06*** (1.03, 1.08)

Quintile 3 1.07*** (1.05, 1.10)

Quintile 4 1.10*** (1.07, 1.13)

Quintile 5 1.10*** (1.07, 1.14)

Memory 1.03*** (1.02, 1.03)

CASP 1.01*** (1.01, 1.02)

Mobility limitations 0.98*** (0.98, 0.98)

Social network 1.09*** (1.08, 1.10)

Log-likelihood -34733.15 _____________________________________________________________________________________________________________

N=23,985. ***p < .001. **p < .01. *p < .05. The analysis is adjusted for country

Table 2. Activity participation, mobility limitations and social network:

Cross-sectional baseline analysis (wave 4), Poisson regression

Social network resources at baseline were positively and independently related to activity level measured at the same point in time

This reinforces the assumption that social capital is an important resource in older age (Forsman and Nyqvist 2015).

The cross sectional findings suggest that social ties might increase social activity (although it might equally be inferred that social activity augments social ties).

The cross-sectional analysis also showed a negative association of mobility limitations with activity level, again at baseline.

This means that persons who were more functionally challenged were also less active (Golinowska et al. 2016; Galenkamp et al. 2016).

This can be stated with some degree of confidence, insofar as it is less likely that the extent of activity was what lead to the mobility limitation.

We also considered the factors associated with activity level at follow-up, two years later.

This part of the analysis examined short term activity change because the procedure looked at extent of activity at follow-up after controlling for the effect of baseline activity level scores.

Activity change (and particularly activity decline) is an important topic in gerontology.

Short term changes in activity may presage more serious subsequent declines in other areas.

Decline may have negative implications for quality of life.

IRR 95% CI IRR 95% CI

Mobility limitations 0.98*** (0.98, 0.99) 0.96*** (0.95, 0.97)

Social network (SN) 1.01** (1.01, 1.02) 1.00 (1.00, 1.01)

Mobility limitations X SN 1.01*** (1.01, 1.01)

Log-likelihood -31680.18 -31667.71 ______________________________________________________________________________________________________________________________

N=23,985. ***p < .001. **p < .01. *p < .05. The analysis is adjusted for Age, Gender, Married/ partnered, Education, Employed, Income, Country, Memory, and CASP.

Table 3. Activity participation, mobility limitations and social network: Longitudinal analysis, Poisson regression

Model 1 Model 2

Model 1 supports the cross-sectional analysis.

Mobility limitations were negatively associated with activity at follow-up, all else considered

Social network was positively related, albeit rather weakly.

The entry of the interaction term in Model 2 changed the picture, however.

The interaction was significant

The analysis showed that social network had a positive effect on activity primarily among those with mobility limitations.

The effect of social network among those with no mobility limitations was negligible.

Figure 1: The interaction of mobility limitation and social network in relation to activity

Adjusted for baseline activity, age group, gender, marital status, education, work status, income, memory, CASP and country.

The analysis suggests that the positive interaction of social network and mobility limitations enhances activity participation, principally among older adults who are in greater need.

The negative implication of this finding is that those without a

resourceful social network and who suffer from mobility limitation are at double jeopardy.

Not only must they deal with the challenges of disability, they must contend with lesser activity as well.

This accentuates their risk of beginning a downward spiral that may propel them towards yet greater decline.

IMPLICATIONS

The policy aim of active ageing must pay attention to the growing proportion of older adults who are subject to mobility limitations and who lack a sufficient social network to help them compensate for their physical decline.

The promotion of active ageing cannot be left to the families, friends and neighbors of persons who are challenged mobility-wise, because there are many older adults who lack the necessary social network ties that help to keep them socially engaged.

Whether public social services can effectively intervene in the informal social networks of older adults and, indeed, whether they should, and through what methods, is a topic that requires further attention.

Some literature suggests that the entry of formal services into the domain of the private interpersonal milieu leads the network to cease functioning rather than encouraging it to continue (Pickard 2012).

Other studies hint that a functional complementarity between the two domains can exist (Litwin and Attias-Donfut 2009).

IN CONCLUSION:

This study underscores that social networks are especially important in the promotion of activity participation among older adults with mobility limitations.

Second, it sensitizes us to the risk and, indeed, to the double jeopardy that mobility restricted older adults who are not embedded in resourceful social networks face.

This latter group should, therefore, have high priority in efforts to increase active ageing.

This study examined whether fear of falling (FOF) is a risk factor for subsequent falling among older adults, independent of the effects of other factors.

This is difficult to confirm because FOF is related to other variables, particularly prior falls.

Moreover, it is complicated to determine what comes first, falls or fear of falls

Both of these phenomena are part of the same downward spiral of functionality that increases one's vulnerability to falling in late life.

Here too we focused on community-dwelling Europeans aged 65 and older who participated in both the fourth (2011) and fifth waves of the survey (2013).

The analytical sample numbered 20,654 respondents.

Our analysis considered two hypotheses: 1. FOF is an independent risk factor for falling,

beyond the effects of sociodemographic background, health/frailty and having experienced a prior fall.

2. Mobility limitation moderates the effect of FOF on falling.

As will be seen in the next slide, the unadjusted bivariate analyses showed that falling at follow-up was related to all the study variables.

VARIABLES OR

Age 1.074***

Female 1.946***

Married/Partnered .513***

Secondary educationA 0.623***

Post-secondary educationA 0.603***

Cognition score. 0.550***

Depressive symptoms 1.291***

BMI- underweightA 1.838***

BMI- overweightA 0.844**

BMI- obeseA 1.164**

Poor eyesight 3.260***

Poor hearing 2.556***

Medications 1.402***

Frailty (pre-frail or frail)A 3.308***

Alzheimer's disease, dementia, senility 3.041***

Cataracts 2.045***

Diabetes 1.637***

Heart attack 1.912***

Hip or femoral fracture 3.518***

Osteoarthritis/other rheumatism 2.095***

Parkinson 3.679***

Rheumatoid arthritis 2.184***

Mobility limitation 1.339***

Fell in W4 5.293***

FOF (W4) 3.722***

Table 2: Association of study variables with falling in the past 6 months: Unadjusted odds ratios ______________________________ * p<0.05, ** p<0.01, *** p<0.001

The first hypothesis posited that FOF is an independent risk factor for falling, beyond the effects of sociodemographic background, health/frailty and having had experienced a prior fall.

The multivariate analyses confirmed that baseline FOF was predictive of reported having fallen two years later, beyond the respective effects of the other study variables.

In the regression model that examined this hypothesis (model 3), the effect of FOF was significant, albeit weak.

The amount of explained variance in this model was also modest.

Model 3 Model 4

VARIABLES OR OR

Mobility limitation 1.171*** 1.195***

Fell in W4A 2.280*** 2.294***

FOF (W4)A 1.172* 1.711***

FOF (W4) X activity reduction (W5) 0.921***

Pseudo R-squared 0.150 0.151

Adjusted for Age, Female, Married/Partnered, Education, Cognition, Depressive symptoms, BMI, Poor eyesight, Poor hearing, Medications, AD/dementia, Cataracts, Diabetes, Heart attack, Hip or femoral fracture, Osteoarthritis/other rheumatism, Parkinson, Rheumatoid arthritis, and Country

* p<0.05, ** p<0.01, *** p<0.001

Table 3: Predictors of Falling in the past 6 months: Logistic regression

The second hypothesis maintained that mobility limitation moderates the effect of FOF on falling.

This was confirmed.

In Model 4, both the mobility limitation variable and the FOF measure were significant.

Moreover, the strength of FOF as a predictor increased meaningfully with the entry of the interaction term between these two variables.

The strength of mobility limitation as a predictor in the respective models remained about the same with and without the interaction.

The graph shows the dynamics behind the association between FOF and mobility limitation in relation to overall fall probability.

As may be seen, FOF predicted subsequent falls when mobility limitation was low to moderate.

It was only when mobility limitation was at a high level that the effect of FOF on fall probability became reversed.

When mobility limitation was high, lack of FOF was a stronger predictor of subsequent falls.

The graph underscores the complex association between FOF and mobility limitation in relation to late-life falls.

IMPLICATIONS FOF is an independent risk factor for falling.

People who are worried about falling fall more often.

There seems to be, therefore, a self-fulfilling prophecy.

This stresses the importance of addressing the self-confidence of older people as a preventive factor in the attempt to reduce falling among older populations.

Self-confidence is a malleable feature that can certainly be strengthened through appropriate interventions.

• Those who have a high level of mobility limitation but lack FOF are a group at risk that has received less attention.

• In the case of considerable mobility limitation, FOF acts as a protective buffer, limiting the most vulnerable from taking actions that may precipitate falls.

• The less worried may not be as careful, however and may be subject to greater falling.

• This topic requires more research and greater professional attention.

IN CONCLUSION: The association between FOF and mobility limitation in relation to late-life falls is a complex dynamic. With proper understanding of these dynamics, the downward spiral of FOF and falling might be altered to some degree