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| Journal of the New Zealand Medical Association,
17-October-2008, Vol 121 No 1284
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Rurality and pandemic influenza: geographic
heterogeneity in the risks of infection and death in Kanagawa, Japan
(1918–1919)
Hiroshi Nishiura, Gerardo Chowell
An increase in the number of outbreaks caused by highly
pathogenic avian influenza type A (H5N1) virus in poultry, and its transmission
in humans, has raised a considerable public health concern over the next
pandemic.1
Although it is difficult to offer valid prediction of the
forthcoming influenza pandemic, exploring previous pandemics is crucial for
identifying specific patterns of transmission and suggesting optimal
intervention strategies. Influenza caused by type A (H1N1) virus in
1918–19 is known to have caused the world’s worst-known influenza
pandemic, the so-called ‘Spanish influenza’ (which did not originate
in Spain), causing an estimated 50 million deaths worldwide.
Quantification of the spread and transmission of pandemic
influenza should provide valuable suggestions to improve the effectiveness of
future pandemic preparedness plans.2,3
The mechanisms of transmission that may be deduced from the
pattern of geographic spread of pandemic influenza have been demonstrated in
several recent studies.4–8 During the
influenza pandemic it has been reported that severity (particularly mortality)
differed considerably by geographic
locations.9,10
Recently, historical data of Spanish influenza in New
Zealand was revisited; it suggested that the mortality estimate was
significantly smaller in rural areas than cities and
towns.11 Similarly, mortality has been
suggested to be high in urban settings in other
countries,12,13 as supported by mathematical
models attributing the differential mortality to sociodemographic conditions and
public health measures.14,15
However, different epidemiologic outcomes have not been
comparatively explored to examine the impact of rurality on 1918–19
influenza pandemic (e.g. infection and death). This is mainly owing to limited
availability and scarce information of historical data which usually document
the number of deaths alone. It is therefore fruitful to discuss this issue,
explicitly distinguishing the implications of rurality between infection and
death.
In the present study, we uncover a historical record of
pandemic influenza in Kanagawa Prefecture, Japan, from October 1918 to April
1919, which precisely recorded both the numbers of cases and deaths by region.
This study was aimed at characterising the impact of rurality on influenza by
exploring three different outcomes—i.e. morbidity, mortality, and case
fatality.
MethodsWe extracted historical epidemiologic data of the
influenza pandemic in Kanagawa, Japan, from
1918–19.16 The historical data show
numbers of cases and deaths in 199 different administrative regions; the total
numbers between October 1918 and April 1919 were documented.
Prior to the pandemic, Kanagawa had suffered only
sporadic outbreaks of bubonic plague at different times and places; thus it was
believed that the prefectural government had been well trained and particularly
successful in precisely tracing the spread of Spanish influenza in the
prefecture.16 In addition to influenza data,
population sizes and mean household sizes (i.e. mean number of members per
household) by region, as of the end of 1917, were obtained from a census
report.17
Kanagawa is in the southern Kanto region of Honshu
Island; and lies to the north between Yokohama and Tokyo. Ninety years ago the
prefecture was very unique in that the capital city Yokohama played a key role
as the major port of Kanto region; the main railway lines from Tokyo to southern
Japan also passed through that city. Its population at the end of 1917 was
1,359,451, which covered 2415 km2.
Detailed statistical record was independently
summarised only in this prefecture in Japan, which was briefly revisited in a
historical study introducing the report as containing the higher quality
data.18
The present study used population size as a measure of
assessing geographic heterogeneity. The populations were categorised as cities
(population>20,000), larger towns (5,000<population≤20,000), smaller
towns (2,000<population≤5,000), and villages (population≤2,000).
The cut-off values 2000 and 20,000 followed a previous
study in New Zealand,11 and 5000 was the
minimum population size prerequisite to legally become a town as indicated by
Japanese law.
Since we have access to cases, deaths and population
sizes by region, morbidity (cases/population), mortality (deaths/population),
and case fatality (deaths/cases) were comparatively examined.
When
Dunnett’s method was applied, villages were set as a control variable.
Moreover, mean household sizes were similarly compared by population group,
followed by test of within-group correlation by means of the Pearson’s
product-moment correlation between outcome variables and household size.
All statistical data were analysed using JMP v7.0
statistical software (SAS Institute Inc., Cary, NC, USA).
ResultsIn total, 292,139 cases and 5021 deaths were recorded during
the period of observation, yielding overall morbidity, mortality, and case
fatality estimates of 214.9 (95% confidence interval (CI): 214.2–215.6)
per 1,000, 3.69 (3.59–3.79) per 1,000, and 1.72 (1.67–1.77)%,
respectively.
Estimating the outcomes by region, median (25–75%
quartile) morbidity and mortality were 182.7 (87.4–317.5) per 1,000 and
1.62 (0.84–3.06) per 1,000, respectively. Similarly, median (25–75%
quartile) case fatality was estimated as 3.1 (1.6–5.1)%, ranging from 0 to
14.2%. Table 1 shows crude estimates by population group.
Table 1. Epidemiologic outcomes of influenza
pandemic in Kanagawa, Japan: October 1918--April 1919
 † Morbidity and mortality are calculated as rate
per 1000 inhabitants for a period between October 1918 and April 1919;
¶Case fatality is proportion of deaths among the total number of cases;
‡ CI, confidence interval.
Morbidity was highest in villages, followed by smaller towns
and cities. The risk of infection (measured as IRR) in all cities and towns was
0.601 (95% CI: 0.600–0.602) times that in villages. On the contrary,
mortality was lowest in villages, and three other groups yielded a significantly
higher estimate [MRR = 1.12 (1.11, 1.12)].
Case fatality was highest in larger towns followed by
smaller towns and cities. Villages appeared to yield the lowest case fatality
with an estimated 0.96 (0.82–1.09)%. Comparison of detailed ratios is
summarised in Table 2.
In villages, crude estimates of mortality and case fatality
were significantly lower compared to other population groups, whereas morbidity
was significantly higher. Larger towns showed significantly higher case fatality
[RCF=1.23 (1.18–1.29)] than smaller towns, but morbidity and mortality
were significantly smaller in larger towns [IRR and MRR were 0.749 (0.744,
0.755) and 0.92 (0.89, 0.96), respectively]. Moreover, cities yielded higher
morbidity [IRR=1.257 (1.253, 1.261)] than larger towns.
Within cities, the capital Yokohama showed significantly
lower morbidity [IRR=0.990 (0.986, 0.993)] compared with five other cities.
Within each group, we did not find any significant correlation between the
outcomes and population size.
Table 2. Differential risks of influenza
pandemic by population groups in Kanagawa, Japan, from October 1918--April
1919
 Figure 1. Morbidity, mortality, and case
fatality of influenza pandemic as a function of population size, Kanagawa,
Japan: October 1918--April 1919
 Three outcomes (A) morbidity, (B) mortality and (C)
case fatality of Spanish influenza pandemic are shown in relation to the
population size. Each dot represents estimate of a single administrative region.
In each panel, three vertical dashed lines represent cut-off values of
population size for grouping (i.e. population sizes of 2,000, 5000, and 20,000).
Yokohama (n=469,868) and Yokosuka (n=75,325) are excluded from the figure as the
population sizes are large. Morbidity, mortality, and case fatality in these
cities were 215.5 per 1,000, 3.74 per 1,000, and 1.73 % and 180.0 per 1,000, 2.4
per 1,000, and 1.33 %, respectively.
Figure 1 shows the distributions of the outcomes by
population size and group. One-way ANOVA revealed that morbidity was
significantly different between population groups (p=0.01), where villages
appeared to have experienced significantly higher morbidity than larger towns
(p<0.01). Mortality did not differ significantly between population groups
(p=0.33), but we found a significant difference in case fatality (p=0.02)
between the groups.
Following the post-hoc test, smaller towns appeared to yield
higher case fatality than villages (p=0.01). Unlike the observation using the
crude case fatality, case fatality in larger towns was not significantly
different from that of villages (p=0.12).
Mean household size significantly differed by population
group (p<0.01), which was characterised by significantly smaller household
sizes in cities (p<0.01) and larger towns (p=0.02). However, we did not find
any significant within-group correlations between mean household size and
morbidity as well as mortality.
DiscussionThe present study analysed differences in the risks of
infection and death of Spanish influenza by population size. Using historical
data in Kanagawa, Japan, the numbers of cases and deaths as well as population
size were extracted, enabling us to analyse three different outcomes.
To the best of our knowledge, this study is the first to
investigate geographic differences in morbidity, mortality, and case fatality,
explicitly separating the role of outcomes. Although our case fatality estimates
were smaller than those of hospitalised cases among young adult armies in
Tokyo,19 the higher estimate in the hospital
most likely highlights more severe cases (i.e. those who were hospitalised) and
age (i.e. young adults who were at high risk of death), and our estimates are
consistent with that of entire Japan ranging from 0.5–13.7 % with the mean
estimate of 1.0%20 (mortality and morbidity
estimates for all prefectures in Japan are given in English in p. 397 of Rice
and Palmer21).
With regard to crude mortality estimates by population
group, MRRs were smaller than those in New
Zealand,11 but the consistent pattern was seen
with lowest estimate in villages and highest in smaller towns. However,
morbidity was highest in villages. Case fatality proportion bridges the
relationship between morbidity and mortality, and villages appeared to yield the
lowest estimate, which was significantly different by population group in both
comparisons of crude estimates and the corresponding distributions by region.
In other words, the low mortality in villages appeared to be
greatly influenced by case fatality, the conditional probability of death given
infection (or onset), at least in the unique dataset of Kanagawa. Moreover, when
we comparatively examined the distributions of mortality using ANOVA, no
significant difference in mortality was found by population group. That is,
although our analysis of crude mortality by population group implied a possible
protection of the population by remoteness, the difference reflected differing
fatality of disease by region, and rather, morbidity appeared highest in
villages.
Kanagawa was one of the prefectures where the administrative
regions were moderately affected by the influenza
pandemic.21 In a location where extreme
remoteness may not be expected,22 geographic
heterogeneity in the risk of infection (i.e. morbidity) revealed opposite
pattern of our expectation, showing higher risks of infection in smaller
population groups.14
Although the data in Kanagawa differs from that of New
Zealand in aspects such as the time period and areas of observation, the present
study suggests that rurality was not predictive of protection against pandemic
influenza when we measured both morbidity and mortality. Considering the similar
variations between smaller and larger towns, larger towns showed lower morbidity
than smaller towns, and accordingly, smaller towns were also not protected from
infection in Kanagawa.
It is difficult to intuitively suggest the definitive
reasons why significantly high incidence was seen in villages. Mean household
size tended to be higher as population size decreases, but this was not
correlated with the risk of infection. Heterogeneous patterns of transmission
would not be clarified unless the relevant social and biological backgrounds are
explicitly clarified.
As a potential mechanism of intensive within-regional
transmission in rural areas, it should be noted that each village was a small
community of farmers who lived closely together and were well-connected to each
other, and perhaps, this permitted the spread of the disease once the community
experienced the introduction of an influenza case.
In practical terms, in order to minimise the risk of
infection, high morbidity in rural areas highlights importance of social
distancing measures in the event of the forthcoming next pandemic. Provided that
rural areas are at high risk of transmission, and given that communities in the
present day are more densely connected to each other than those in
1918–19, it would be critically important to protect the community from
interregional introduction of cases.
If rural areas indeed prevent themselves from inter-regional
introduction of cases by means of social distancing, it will be possible to
expect lower risk of infection in these areas.
In addition, towns and cities could have been potentially
protected against influenza due to population and individual
countermeasures.18,23 Indeed, public health
authorities in Kanagawa were better-prepared for an epidemic than almost any
other prefecture in Japan.24 For example,
spinning (cotton) mills in Kanagawa initially suffered from outbreaks in October
1918 and thus the prefecture decided to close similar factories and restricted
the movements of individuals in crowded dormitories at an early phase of the
pandemic.16
The prefecture was also a leader in warning the public of
the dangers of influenza and its mode of transmission through the use of
pamphlets and posters.20,24 At the individual
level, the use of several different types of mask was recommended not only for
those participating in medical practices but also the general
population.20,25
Mathematical analysis of Spanish influenza data in the US
suggests not only that intervention effectively reduced the disaster size, but
also that individuals reactively reduced the number of infectious contacts,
perhaps by behavioural changes.14
Morbidity and mortality with time and place in addition to
any information of the timing of implementing public health measures would
permit explicit analyses of the effectiveness of countermeasures. To achieve
precise estimation of the effectiveness, it is essential to address
heterogeneous contact patterns and risk of severe manifestations, and thus
further studies are needed to precisely estimate the impact of interventions in
heterogeneously mixing populations with varying risks of death.
How about the lower case fatality in villages than in other
locations? As a possible reason, differential case fatality could be explained
by different levels of previous exposures. A historical study suggests low
frequency of previous exposures in town areas by previous pandemic of type A
(H1N1) influenza.18 The similar argument of the
impact of acquired immunity on the risk of influenza death (i.e. partial
protection) has been made historically.26
However, if this was the case, not only the risk of death but also that of onset
(given infection) should have been more or less inhibited by previous exposure
in villages.
In line with this, age-related heterogeneity and underlying
diseases have to be remembered as factors generating heterogeneous risks of
death. We postulate that some underlying diseases and sociodemographic
characteristics have modified case fatality, which in general varies widely by
region.10,27 For example, it is likely that
proportion of young adults were higher in cities and towns than that in villages
where middle-aged farmers constituted the core of rural population. Moreover, as
a potential reason, poorer health and nutrition in towns and cities as well as
limited social supports and healthcares in urban areas (e.g. limited nursing
care offered by neighbours) could have also contributed to higher case fatality
in urban areas.
Further data on socioeconomic status could be useful in
testing whether poverty levels in urban areas contributed to higher case
fatality than in rural areas.
A limitation must be noted in relation to the interpretation
of morbidity and case fatality. If historical survey included many false
diagnoses of influenza (e.g. febrile illness caused by different disease),
disease misclassification (i.e. non-differential misclassification) must have
been present.
Although the historical record in Kanagawa explicitly
documents clinical pictures of influenza with the characteristic flu-like
symptoms (e.g. fever, myalgia, severe
malaise),16 it is fairly difficult even today
to achieve population-based diagnoses of influenza with high sensitivity and
specificity. Therefore, if the diagnoses of cases in rural areas included more
false negatives than those in cities, reported estimates of morbidity and case
fatality in rural areas might be deemed, respectively, overestimate and
underestimate, which cannot be fully addressed using the historical record of
Spanish influenza alone. Besides, as we briefly discussed, the prefecture had
suffered from plague outbreaks prior to the pandemic, and Kanagawa was one of
the prefectures where the epidemiologic data by region were most precisely
recorded in Japan.
It is worth documenting that agreement between pneumonia and
influenza death with time were visually and implicitly examined in the original
report.16 Also, it should be remembered that it
is not rare to observe that the regional pattern of influenza morbidity goes in
the opposite direction to that of
mortality.28
As another technical issue, the present study did not
account for other variables except for population size. Investigations over age
and gender would be desirable, and analyses of similar data in other locations
are called for. In particular, historical record in a geographically isolated
area (e.g. small island) with both the numbers of cases and deaths has a
potential to inspire new knowledge to the world on this issue.
As an epidemiologic implication, the present study would be
deemed typical to indicate the critical importance in explicitly distinguishing
the roles of outcomes (e.g. infection and
death).28 It is usually the case that we can
obtain death data alone from historical literature. If this is the case, the
underlying assumption to make an interpretation and its validity would play key
roles to offer valid conclusions.
Specifically, although mortality data are frequently used
even for performing predictions,29 it should be
noted that mortality reflects two separate epidemiologic steps (i.e. infection
and death) which are differently modified by numerous factors. To decipher the
mechanisms of transmission using death data only, some reasonable adjustment or
additional case data are needed.
So, weren’t rural areas protected against pandemic
influenza? Unfortunately, the present study cannot offer explicit general
conclusion on this issue. At least, our analysis of the data in Kanagawa
suggests high incidence in rural areas, and in this prefecture rural areas were
not protected from pandemic influenza in terms of both mortality and morbidity.
Our result was suggestive of potential protectiveness of
individuals in rural areas from severe disease (i.e. death given infection), but
it has to be clarified more in detail with other
variables.30 Accordingly, the potential
importance of social distancing (to minimise the risk of infection) and an
epidemiologic need in measuring different outcomes were highlighted. Further
studies with different datasets measuring both the numbers of cases and deaths
are therefore crucial.
In addition, mathematical and statistical models with
spatiotemporal components can be useful tools for deciphering the mechanisms of
observing different outcomes by region.
In conclusion, the present study analysed the role of
rurality during the 1918–19 Spanish influenza pandemic in Kanagawa, Japan,
using numbers of cases and deaths by region. Villages had the highest reported
incidence.
If the geographic patterns of morbidity were valid, lower
morbidity in the towns and cities might be potentially explained by effective
preventive measures in urban areas. However, provided that morbidity data were
not sufficiently accurate, slightly smaller estimates of mortality in rural
areas still imply the potential protectiveness of remote areas.
In future studies, high resolution spatiotemporal morbidity
and mortality data in addition to any information on the timing of public health
measures would be crucial for offering the most effective pandemic preparedness
plans in heterogeneously mixing populations with varying risks of severe
manifestation.
Competing interests: None known.
Author information: Hiroshi Nishiura,
Postdoctoral Research Fellow, Theoretical Epidemiology, University of Utrecht,
Utrecht, The Netherlands; Gerardo Chowell, Assistant Professor, School of Human
Evolution and Social Change, Arizona State University, Tempe, Arizona, USA
Acknowledgement: GC received financial
support from the Office of the Dean of the College of Liberal Arts and Sciences
at Arizona State University.
Correspondence: Hiroshi Nishiura,
Theoretical Epidemiology, University of Utrecht, Yalelaan 7, 3584 CL, Utrecht,
The Netherlands. Fax: +31 30 2521887; email: h.nishiura@uu.nl
References:
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