Looking at Israel’s morbidity rates by SES (Fig. 1), we observe a general trend, suggesting that a rise in SES is associated with a decrease in morbidity rates, i.e., higher morbidity in lower ranking communities and lower morbidity in wealthier ones.
Confirmed morbidity by SES
Notably, the SES levels 2 and 3 sustained the highest morbidity rates in Israel. These categories include numerous Jewish orthodox communities (e.g. Bnei Brak, Elad, Kiryat Yearim, Emanuel, Rechasim in rank 2 and Beit Shemesh, and Zefat in rank 3), which have indeed suffered the highest morbidity in the country. SES level 1 is also populated by orthodox communities. However, it also includes numerous peripheral Arab Bedouin communities. These communities were relatively less affected than communities in the two subsequent levels, as illustrated in Fig. 1. SES category 4 does not include as many orthodox communities, but rather traditional peripheral Jewish as well as Arab settlements. These communities have apparently been relatively less affected than their poorer and wealthier counterparts, possibly due to their peripheral location and distance from major sites of contagion.
When assessed by the communities’ population density (Fig. 2), the morbidity distribution appears to follow an even clearer trend, generally increasing with the population density: the higher the density, the higher the morbidity. At the upper right hand corner, the city of Bnei Brak, with the highest population density in Israel – roughly 50% denser than the subsequent town (See Table 1) – sustained the highest morbidity rate in Israel during the epidemic’s first wave: 14.72 per 1000 persons. At the upper left corner, the small Arab community of Deer El Assad is an outlier, where morbidity soared for a relatively short period of time in April, but was contained within a few days.
Morbidity Rates by Community Population Density
Nonetheless, as suggested by Table 1, communities differed greatly. For example, though Elad’s population density is roughly 3/4 that of Givataim, morbidity rate in the former was 10 times that of the latter. Visibly, orthodox cities suffered much greater morbidity than denser non-orthodox communities. Morbidity rates thus increased with population density. Yet, it was clearly not a sole determining factor.
The third factor is the size of elderly population as measured by the proportion of people aged 65+ in the community. The distribution (Fig. 3) depicted an inverse relation between the variables, i.e., the higher the percentage of elderly persons in a community, the lower the morbidity. The two outliers are once again, the communities of Bnei Brak and Deer El Assad, which stood out in the density distribution above (Fig. 2).
Morbidity rate by number of residents aged 65+
The descrpetive statisitics thus suggest 1. Inverse association between a community’s SES and its morbidity rate; 2. Positive association between population density and morbidity; 3. Inverse relation between percentage of elderly population and morbidity.
The last result, which goes somewhat against findings reported from other settings, requires some more attention. We start with the city of Bnei Brak, that had the highest morbidity rate in Israel (14.7 per 1000) but a mere 8.9% of elderly residents. Jerusalem and Haifa, in contrast, with evidently larger proportions of elderly residents (12.3 and 27% respectively), had strikingly lower morbidity rates: 4.16 and 0.61 per 1000 respectively. A more systematic look into the communities with the highest morbidity rates (Table 2), shows that the proportions of elderly population in these communities were all below the average for the Jewish sector (13%) and even below the national average (11%), thereby illustrating the inverse association:
Notably, all four cities are orthodox Jewish communities.
Exploring the opposite direction, reveals similar findings. As shown in Table 3, the morbidity rates in Israel’s ‘oldest communities’, namely those with the largest proportions of people older than 65 (19-21%), were consistently below (0.32–1.51) the national average of 1.64 per 1000 at the time (June 2, 2020). None of these communities is orthodox.
At a glance, the low proportion of elderly people in Israel might be taken as a protective factor that helped moderate morbidity rates in the country. However, the findings suggest the opposite, as illustrated in Tables 2 and 3. The key factor explaining this unusual distribution is the exceptional fertility rates in Israel’s orthodox Jewish communities that stand on 7.1 children per woman, as opposed to roughly 3 children per woman in other Jewish and Arab communities. This difference drastically reduces the percentage of the orthodox communities’ elderly cohorts. At the same time, these very large families are mostly poor, often crowding in small apartments, and as such, are prone to contagion.
In order to gain a closer insight into the association of each variable with morbidity rates, we conducted a linear regression, wherein the dependent variable is the morbidity rate. The following results were obtained (Table 4):
The regression model thus predicts 38.1% (R2 = 0.381) of the inter-community morbidity rate variance in Israel at the community level, by SES, density, elderly population and minority status. As shown, SES turned out to be statistically insignificant, but population density, size of elderly population and minority status emerged as significantly associated with morbidity rates. The association of each variable with COVID-19 communities’ morbidity rates is presented in Fig. 4, which shows the respective regression lines.
Prediction linear regression of Morbidity in Israel (1:1000)
The nearly horizontal SES line visualizes the lack of statistically significant association between this variable and the rate of morbidity in Israel. Population density, in contradistinction, emerged as significant, affirming the public discourse that linked crowdedness with increased morbidity. Indeed, population density emerged as the strongest association with communities’ morbidity rates (β = 0.439). A 100 person increase per km2, raises morbidity by 0.024 patients per thousand (b = 0.00024), namely, 2.4 additional patients per 100,000 persons. As for the percentage of a community’s elderly residents, as suggested by the descriptive statistics, the regression shows a significant inverse association between this variable and communities’ morbidity rates, i.e., the smaller the elderly population, the higher the local rate of morbidity (b = − 9.16). In terms of minority status, despite their lower SES and minority status, Arab communities sustained lower morbidity rates, i.e., Jewish communities suffered 1.68 higher morbidity rates compared to Arab communities (b = 1.68, β = 0.437). Moreover, the Jewish-Arab gap in morbidity proved persistent. Figures 5 and 6 present the marginal association of population density and percent of elderly population – the two variables that were found to be statistically significant – with morbidity rates in the Jewish and Arab populations.
Morbidity in Jewish and Arab cities by community population density
Morbidity rates in Jewish and Arab communities by the number of residents aged 65 +
The calculation is based on the regression model, assuming that all other variables are held constant. The figures demonstrate that the higher the population density, the higher the community’s morbidity rates and the larger the elderly population, the lower the morbidity rate. Equally important: at any given value of population density or size of elderly population, the gap in morbidity rate between the Jewish and Arab cities persists, with morbidity rates consistently lower in Arab communities.
In the light of the emergent gap in morbidity between Arab and Jewish communities, we divided our calculation and produced the average values of the scrutinized sociodemographic variables alongside Covid-19 morbidity rates in Jewish vs. Arab communities. Table 5 presents the averge values in each sector.
The table reveals a clear Jewish-Arab difference: with communities twice or more richer, denser and older, Israel’s Jewish cities sustained 2.6 times the morbidity of their Arab counterparts. Whereas these figures accord with international findings regarding population density and proportion of elderly residents, the higher morbidity in richer cities is exceptional. In other words, Israel’s Arab communities, despite being much poorer and despite belonging to an underprivileged minority, sustained less than half the morbidity of their wealthier Jewish neighbors. Therefore, we examined how these variables are associated with morbidity in Jewish vs. Arab communities. The linear regression analysis presented in Table 6, reveals further details and differences:
The analysis shows that when considered on its own, SES (Model 1) becomes significant in the Jewish communities though not in the Arab ones. Thus, in Israel’s Jewish communities SES was associated with morbidity in a manner similar to that observed elsewhere, i.e., poorer communities were more vulnerable to the virus than wealthier ones. More concretely, a rise in one SES rank in a Jewish community was associated with a reduction of 0.373 sick person per 1000. In the Arab sector, no such association was found. However, once the other variables are added (Model 2), the association of SES with morbidity once again becomes statistically insignificant also in the Jewish sector. Population density (Model 2), however, proved significant and equally influential in both sectors.
The third variable, proportion of elderly population, also emerged as statistically insignificant in the Arab communities but significant and inversely associated with morbidity in Jewish communities, i.e., a smaller elderly population was associated with higher morbidity. Another substantial inter-sectorial difference related to the explained variance. While explaining nearly 38% of the variance in morbidity rates in Jewish communities, Model 2 accounts for less than 11% in Arab settlements. The model thus reaffirmed the lay perception that population density was positively associated with morbidity rates. However, in contrast to international findings, SES was statistically insignificant to morbidity rates in the Arab communities and even in the Jewish sector, the association was weak.
Finally, some Israeli commentators attributed the high morbidity rates in some ultra-orthodox communities to the scarcity of home and mobile Internet access in these communities, that wish to maintain but minimal contact with their non-orthodox surrounding. (‘Kosher mobile phones’ have only pre-designated internet access, e.g., to banks, HMO etc.) This virtual detachment was assumed to underlie the communities’ lack of awareness and belated application of the protective measures and lockdown guidelines. We therefore zoomed in on this specific component of the SES index and conducted a specific linear regression.
The general regression revealed no association with internet access. Sector specific analyses (Table 7) found that home Internet access followed the SES pattern, namely, significant association with morbidity only in Jewish communities and only when considered on its own.
However, the latter association was substantial: a 10% increase in home internet access in Jewish communities was associated with a morbidity decline of 0.3 person per 1000. The resemblance to SES patterns is reasonable, given the fact that internet access is a component of the SES index.
Figure 7 further supports Table 7 findings, showing that as internet access rises, the communities’ morbidity rates decrease.
Morbidity in the Jewish sector by community rate of home Internet access
We now turn to discuss the observed trends and particularities and propose some lines of explanation.
