A review of data needed to parameterize a dynamic model of measles in developing countries

Inclusion/Exclusion criteria

To identify relevant literature sources with useful data, Web of Science, Google Scholar, and PubMed were searched using terms including: "case report", "case study", "surveillance", "epidemiology", "seroepidemiology", "antibody", "seroprevalence", and variations on each term combined with the terms: "measles", "Africa" or "India", and sometimes specific country names within Central or Eastern Africa such as "Nigeria", "Niger", and "Uganda". The reference section in each paper was checked and any papers that had referenced them were also researched. We focussed on identifying papers dated before the era of widespread vaccination in Africa and India, i.e., from the 1970s and early 1980s. This is because vaccination influences infection transmission in the population and hence can affect seroprevalence and attack rates [19]. (However, we note that methods do exist for adjusting for such effects). We included several more recent papers as well to analyze the changes in epidemiology as a result of vaccination.

Each source was classified into one of three types: (1) epidemic case reporting, (2) endemic case reporting, and (3) seroprevalence. Epidemic case reporting papers generally describe the percent infected by age during a specific measles outbreak. Conversely, endemic case reporting papers describe reported cases over a longer period of time, during and outside of outbreaks. Case report studies can suffer from under- or over-reporting due to misdiagnosis, unreported cases, or subclinical infection. Seroprevalence studies do not suffer from under/over-reporting subclinical infection, or misdiagnosis and hence can prove very useful when trying to determine what proportion of the population remains susceptible. On the other hand, seroprevalence surveys do not always reflect long-term average conditions in a given population. For instance, a seroprevalence profile obtained just before an epidemic outbreak will differ from one obtained just after an outbreak, and this can influence estimates of the mean age at infection and the force of infection. This is especially a problem for smaller, isolated populations with long inter-epidemic intervals. Moreover, seroprevalence studies cannot distinguish between immunity due to natural infection and immunity due to vaccination. We included seroprevalence publications that used a representative, randomly chosen sample set with a relatively large number of children in each age category being tested. We included case report publications that relied on national surveillance systems or representative community-based surveys.

Many case report studies that we found only used hospital data to estimate incidence or attack rates (eg. [21, 22]). Unless some adjustment is made, this may create a sampling bias towards those children who have access to health care or those children sufficiently ill to be admitted to a hospital. Hence, these publications were excluded. Of the seroprevalence studies, many were only concerned with seroconversion rates after vaccination (eg. [23–25]) or collected samples from children and infants attending a health care centre (eg. [26–29]), which again may introduce bias by including only those with access to health care. Hence, these publications were also excluded.

A common occurrence among Indian case report studies was that the authors would report the number of cases in an age group but not give a breakdown by age of the surveyed population, making it impossible to calculate the attack rates (eg. [30–32]). In other publications, children were surveyed regarding the age when they had measles infection, but it was unclear when the population per age group was tabulated, again making attack rates impossible to calculate (eg. [33–35]).

Reporting and construction of age-stratified attack rate and seroprevalence profiles

We summarized the age-stratified attack rates from most included studies through several figures. The attack rates were either taken directly from the study, when available for each year of age, or found by calculating them from component data reported in the study. If the reported age group spanned more than one year, then the attack rate reported for the whole age group was also assumed to represent the attack rate for each individual year of age in that age group.

Similarly, from the seroprevalence studies, the percent of children seropositive for measles antibody was taken directly from the data, when available for each year of age. If the reported percent seropositive in an age group spanned more than one year, then the seropositive value was assumed to represent the percent seropositive of the midpoint age of the age group only.

To fit a "representative" seroprevalence profile, we first constructed an average profile from the surveys reviewed as follows. If only one of the reviewed studies reported seroprevalence at a given age, then that was used as the seroprevalence of the average profile at that age. If more than one reviewed study reported seroprevalence at a given age, then the average seroprevalence of those studies--weighted by their sample size--was used as the seroprevalence of the average profile at that age according to the equation:

(1)

where S_i is the average seroprevalence in age class i, s_ij is the seroprevalence in age class i from study j, n_ij is the number of samples in age class i of study j, and N_i = Σ _j n _ij . Finally, if no reviewed study reported seroprevalence at a given age, then the seroprevalence of the average profile was not given for that age. Below age 1, averaged seroprevalence data points were sought for 3, 6, and 9 months (or as close to that as possible), and it was assumed that all newborns had maternal immunity. Above and including age 1, averaged seroprevalence data points were sought for each year, although they were not always available, especially for some older ages where no included studies reported samples from that age.

This average seroprevalence profile was then fitted to the following equation to generate a "representative" or "typical" age-stratified seroprevalence profile:

(2)

where S(a) is the percent seropositive in age a, and k₁, k₂ and k₃ are constants. The last term exp(-k₃a) in Equation (2) describes an exponentially declining curve that captures the decline in seroprevalence attributable to maternal immunity in the first year of life. The rest of Equation (2) describes a curve that captures seroprevalence attributable to infection, where k₁ and k₂ control how quickly seroprevalence increases with age and how high seroprevalence can become in the oldest age groups. For realistic values of k₁ and k₂, one obtains an S-shaped seroprevalence profile with an inflection point at young ages, that approaches 100% seroprevalence asymptotically. By dropping the maternal immunity term in Equation (2) one can also define the seroprevalence attributable to infection as:

(3)

These equations were adapted from the approach described in Ref [36] and references therein. The values of k₁, k₂ and k₃ that yielded the smallest ordinary least squares error between S(a) and the average seroprevalence profile were deemed optimal and used to construct the representative seroprevalence profile. The squared error at each age was weighted according to how many adjacent ages (+/- 1 year) had known average seroprevalence data points, to mitigate over-sampling of age ranges for which there were many available average seroprevalence values. Because Equation (2) accounts for decay of maternal antibodies, we can fit it to seroprevalence data for all age categories, including those below 1 year of age. Although Equation (1) adjusts for variable sample sizes across studies within a given age class, we do not adjust for variable sample sizes across age classes when fitting Equation (2). We note that this introduces the potential for wayward estimates of seroprevalence to influence the best-fit representative seroprevalence profile.

Plots of age-stratified attack rates from included studies were constructed for both India and Africa. Separate plots were constructed for epidemic versus endemic reports. Plots were also constructed from included studies for age-stratified seroprevalence surveys for India and Africa. The representative seroprevalence profile from Equation (2) was included on the same plot.

Estimation of force of infection

To estimate the force of infection from the average seroprevalence profiles for Africa and India, we formulated several functions for an age-dependent force of infection, modelled the seroprevalence profiles resulting from those functional forms, and fitted the resulting seroprevalence model to the average seroprevalence profiles for India and Africa. A catalytic modelling approach adjusting for the presence of maternal antibodies allows us to relate the force of infection λ (a) at age a to seroprevalence via:

(4)

where k₃ is the maternal immunity component emerging from Equation (2). Equation (4) can be discretized with respect to age, giving:

(5)

where Δa is a (very small) age increment from j-1 to j, S_j is seroprevalence at age j, and λ_j is the force of infection at age j. Equation (5) can be solved for S_j yielding

(6)

From Equation (6), the seroprevalence arising from any assumed force of infection can be derived. The four functional forms for the force of infection that were tested were:

(7)

(8)

(9)

(10)

where S(a) and S_inf(a) in Equation (10) are taken from Equations (2) and (3) according to the seroprevalence model of Ref [36].

Values of k₁, k₂ (where applicable), and k₃ were tested with fine resolution across broad ranges and the sum-of-squares error between the seroprevalence computed from Equation (6) and the average seroprevalence profiles for India and Africa was computed. The criterion for best fitting parameters was the minimization of sum-of-squares error. The model parsimony of the linear and exponential forms relative to the constant forms were analyzed via an F-test in order to test the hypothesis that the force of infection declines with age. A declining force of infection with age would indicate that the eradication equation p_c = 1-1/R₀ overestimates the required proportion immune for eradication in those populations [19]. The F-ratio is the ratio of the increase in the sum-of-squares error (of the simpler model relative to the more complex model) to the increase in the degrees of freedom (of the more complex model relative to the simpler model). An F-ratio greater than 1 indicates that the more complex model may be parsimonious.

This method of computing the force of infection was repeated for each individual age-stratified seroprevalence profile from the included Indian and African studies, with the exceptions of Refs [37] and [38] in African populations, due to lack of data points below 1 year of age in those surveys. Hence, we computed force of infection for 6 studies in Indian populations and 2 studies in African populations, for the exponential and constant functional forms.

Estimation of R₀ and relation to population size

Studies have shown that R₀ increases with increasing population density, particularly via:

(6a)

where N is the total population, K is a scaling constant, and 0 ≤ c ≤ 1 is a constant that determines the impact of population density on R₀[20].

The relation between R₀ and population size was determined in both African and Indian populations using case reporting and seroprevalence data. In Africa, we estimated R₀ values for large cities or rural areas in 8 different countries, whereas in India, R₀ values were estimated for 11 different urban or rural regions. In one African study, the R₀ value was estimated by averaging over a statistical description of R₀ values reported directly by the authors and so no further calculation was necessary. For all others, the R₀ value was estimated using the formula:

(7a)

where G is the inverse of the per capita birth rate, A is the age at infection in years assuming no vaccination within the population, and D is the average duration of maternal antibodies [14]. D = 3.3 months was estimated from a study by Hartter et al [39]. G was estimated from demographic data made available in the study or, if not available in the study, from other various sources.

The mean age at infection A was calculated in two different ways depending on what data were available in the study. Both methods assume that the populations have experienced no previous history of vaccination, which of course is only approximately true in most cases. Therefore, the following methods estimate the mean age at infection in a partially vaccinated population, A'. If the force of infection (λ) was known through a pre-existing estimate, A' was estimated via [19]:

(8a)

If λ was not given, then A' was estimated via:

(9a)

where r _i is the attack rate at age a _i . We note that it is possible to underestimate A' using this formula if attack rates at higher ages are not given [14, 18]. Each age was taken at the midpoint of the year (i.e. a _i = 0.5 yr, 1.5 yrs, 2.5 yrs, etc). For case report studies, attack rates were determined as before. For seroprevalence studies, the attack rate at age a _i above one year of age was estimated as:

(10a)

where S_{inf, i}is the proportion seropositive at age a _i . The values of S_{inf, i} were the best-fit values of Equation (3) to the data from each individual study, above the age of 1.

Since vaccination can increase the mean age at infection, the values of A' computed from a population where vaccination is occurring must be adjusted before Equation (7) can be applied. To adjust for vaccination, we used the following relation between the mean age at infection A' with vaccination coverage and the mean age at infection A without vaccination:

(11a)

where p is the percent of the study population with vaccine derived immunity [19]. To find p, we first sought vaccination coverage data reported specifically for the populations studied. If this was not given, p was found using the total vaccination coverage by country [40] for the study year and the number of years previous equal to the number of age groups sampled (if possible) and averaged. We assumed a vaccine efficacy of 85% [41]. Thus p is just the product of the average vaccine coverage and vaccine efficacy. The resulting values of A were used in Equation (7).

Thus, with estimated values of R₀ for each population sampled, and with the corresponding population size N, we fitted a linear regression to Equation (6), rescaled on a ln-ln plot, and used ordinary least squares criterion to obtain the best-fit values for c and K.

Case report analysis: Africa

The age-stratified attack rates for African populations are summarized in Figure 1a (epidemic case reports) and 1b (endemic case reports). The age of peak attack rates is 1 year in almost all studies where sufficiently fine age stratification allows this observation to be made. As already noted, this is a much younger age than observed in the "developed" world before the advent of mass vaccination in the middle 20^th century [14, 18].

In Africa, three epidemic studies were analyzed and all took place within an urban setting. An epidemic took place in Kampala, Uganda [42] and Niamey, Niger [43] within the same year but the epidemics exhibited different characteristics. Though Kampala had almost twice the population of Niamey and lower vaccine coverage, the highest attack rate was found in the 12-23 month age group, compared to 6-8 months of age in Niamey. However, Niamey had an overall lower total percent infected in children under 5 years than in Kampala (11.65% vs. 16.94%). In 2004, outbreaks were investigated in representative regions in three separate countries: Nigeria, Niger, and Chad [44]. Comparing attack rates in Niger between this epidemic and the early epidemic in 1990 [43], we see that the reported attack rate under age 5 is higher. However, out of the three countries investigated by Grais et al [44], Niger had the lowest overall attach rate in children less than 5 years.

The variation in epidemic patterns between and within countries can be due to a variety of factors including geographical location, crowding effects, availability of health care, and changing birth rates which would change the rate of recruitment of susceptible individuals [45]. And even though vaccine coverage may seem quite high (73% in Niamey, 1990), there may be a high vaccine failure rate due to poor maintenance of the cold chain or because vaccinations were given to children already immune, or were given at the incorrect age [46].

Four endemic case reporting publications were also reviewed, three within an urban setting [46–48] and one in a rural setting [49]. In both the rural and urban settings, the highest attack rate was seen within the 6-11 month age group except in one study which reported the highest attack rate in children 9-23 months [47]. The papers by Guyer et al [46] and Heymann et al [47] combined describe the epidemiology of measles over a period of ten years within the city of Yaoundé, Cameroon. Yaoundé experienced a continuous decline in attack rates in all age groups from 1972 to 1979, even though surveillance was increased in 1974 and then again in 1977. There was a large decrease in attack rates in the younger age groups, but also a smaller decrease in older age groups, suggesting a possible herd immunity effect. Heymann et al report constant vaccination coverage of roughly 40% over most of this time; consistent vaccine coverage may have led to the decline in attack rate. The discrepancy between the two papers for attack rates in 1975 could be due to how the authors estimated the population per age group. Taylor et al [48] reported on Kinshasa, Zaire (now known as the Democratic Republic of the Congo) several years after Heymann et al published their paper. The cumulative percent infected calculated from their data showed a slightly higher percentage in children less than 1 year but lower in all age groups above that. Though Kinshasa had a much higher population than Yaoundé, they had a continual increase in vaccination coverage from 1977 to 1983 which probably allowed for a decrease in attack rate to those comparable to Yaoundé. However, Taylor et al explain that there was non-uniform vaccination throughout the city, which left pockets of susceptible individuals, allowing for continuing transmission despite overall high vaccine coverage. Furthermore, children under 9 months may have served as a reservoir for measles transmission because most were too young to be efficaciously vaccinated but had a high incidence rate. Although vaccinating at a younger age may suggest itself as a strategy to combat this, the vaccine is significantly less efficacious below 9 months of age due to interference from maternal antibodies, and vaccinating to sufficient levels in older age groups can provide herd immunity that protects younger age groups [43].

Case report analysis: India

Figure 1c (epidemic) and 1d (endemic) summarize the age-stratified attack rates for various Indian populations. From the studies that are stratified by year, the highest attack rate lies roughly between 1 and 2 years of age, which is slightly higher than that noted in the African populations but still lower than in developed countries in the mid-twentieth century. Furthermore, the most recent studies in each figure show that the peak attack rates have shifted to the older age groups. This may reflect changing demographics, social circumstances, or improving vaccine coverage.

Three endemic papers were analyzed for India. Garai et al [50] and Jain et al [51] both conducted community-based surveys in rural areas over a period of three years. Of the two, Jain et al indicated a distinctive lower cumulative % infected at every age up to age 14 yrs. Many factors could have influenced this, such as geographic location or timing of epidemics, but because both studies spanned three years a more likely cause is a higher vaccination coverage seen in the population studied by Jain et al. Though vaccination coverage was not mentioned in either paper, we know that mass routine immunization did not start until the mid 1980s so it is quite possible that Garai et al studied an almost vaccine free population while Jain et al were able to study a population with at least some vaccination. Bhaskaram et al [52] showed a higher cumulative % infected despite being conducted in between the previous two studies. However, their study was conducted in an urban slum and so crowding effects may have caused an increase in the number of children infected. Also, the study was conducted over a period of one year, so it is possible that it was an epidemic year.

We were able to find six outbreak papers for India [53–58], all within a rural setting with minimal or no vaccination at all, except Gupta et al [58]. There does not seem to be variation in the attack rates across varying vaccination coverage levels or population sizes. This is also seen in the paper by Narain et al [56] who surveyed 13 unvaccinated villages, where both large and small villages experienced high attack rates. Isolated populations can experience only intermittent but large outbreaks of measles that may be more difficult to predict [59], so the apparent lack of clear dependence on vaccine coverage or population sizes may reflect lesser predictability of measles outbreaks in rural populations. In all papers except Gupta et al, the highest attack rate during the outbreak was in children below the age of 4 years. Gupta et al was the only paper to have an increasing percent infected by age group, the lowest being in children 0-4 years and the highest being in children 10-14 years.

Though Chand et al [60] was not included in the table, we felt it necessary to discuss as it does give overall attack rates for children under 14 years of age and for the entire population of a representative village over a period of thirteen years. Before 1980, epidemics occurred every 2 years at varying levels of intensity, but after 1980, the inter-epidemic period lengthened to 3 years. This transition from outbreaks every 2 years to outbreaks every 3 years may reflect the introduction of vaccination [61]. It can also be seen that during the last 3 years of the study, the fluctuations in incidence rates becomes less dramatic. The authors suggest that these changes in the outbreak patterns of the disease were a result of the Measles Vaccination Programme.

Seroprevalence analysis: Africa

We identified five studies that give a reasonable to excellent age breakdown of seroprevalence in African populations; however we only included the three oldest in Figure 2a since they were likely to have limited vaccination coverage and thus reflect exposure to natural infection rather than immunization [62–64]. These three were thus used to estimate an average seroprevalence profile. The average seroprevalence profile computed from these studies indicates that about 60% of children exhibited seropositivity for measles antibody by the age of 2. Figure 2b shows the age-dependent force of infection as computed from the representative seroprevalence profile in Figure 2a, using Equation (5).

There was considerable variation in seroprevalence by age across the studies although it was quite evident that the two more recent studies [60, 37] showed a much higher seroprevalence rate among children over 1 year old. This is most likely due to increased vaccination (~80% in Addis Ababa and national coverage of 55% in Eritrea prior to when the study was carried out), but perhaps also the fact that both were conducted in an urban setting where crowding effects occur. The two older urban studies [63, 64] both show a low seropositivity in children aged 1 to 5 years, but Munube et al [38] show a high seropositivity by age 5 comparable to that reported by Enqusellassie et al [37]. This could be due to sampling location or the fact that their sample size was smaller than all the others. Unfortunately, vaccine coverage was not available in most studies, so we could not compare vaccine coverage to seroprevalence more systematically. All of these studies were in urban populations, except for a district-wide survey in Busoga district, Uganda, which included rural populations [38]. As noted in the Methods section, seroprevalence profiles can be less representative of average conditions in cases where measles epidemiology is characterized by intermittent outbreaks, as might occur for some isolated rural populations.

Seroprevalence analysis: India

We identified 6 seroprevalence studies in Indian populations [65–70], all conducted in an urban setting and published in the 1970s or early 1980s (Figure 2c). Seroprevalence increases less quickly with age than in the African studies, as seen in the modelled average seroprevalence curves. This suggests that the force of infection is higher at younger ages in African populations. However, the lowest seroprevalence occurs at the age of 8 months in both India and Africa, demonstrating similar waning patterns of maternal antibodies. Because all these studies were in urban populations, the lack of representativeness of the seroprevalence profile that can be caused by intermittent outbreaks in small, isolated populations should not be an issue here (see Methods). Figure 2d shows the age-dependent force of infection as computed from the representative seroprevalence profile in Figure 2c, using Equation (5).

Seroprevalence data do not usually distinguish between immunity from infection and immunity from vaccination but since mass routine vaccination did not start until the mid 1980s [40], the papers we have give us a good picture of how prevalent measles would be without vaccination, at least within an urban setting during those decades.

In almost all of the studies in Indian populations, roughly 80% of children are seropositive by age 6 years. The exception is Sehgal et al. [69], which incidentally had the highest sample size. In most papers, seroprevalence could either increase or decrease from one age to the next. This could be evidence of a cohort effect [71, 72], but is more likely a sample size effect. Interestingly, Sehgal et al was the only paper not to exhibit this effect, again perhaps because of its large sample size. Khare et al [70] report a lower seroprevalence in children under 9 months but a higher seroprevalence above 9 months, compared to other studies. This study is relatively recent and thus its higher seroprevalence at younger ages could reflect the initial effects of vaccination. Vaccination also shifts incidence to older children due to herd immunity, which protects younger children from acquiring measles infection. From the seroprevalence profile, we see that below the age of 8 months, the majority of the seroprevalence will be from maternal antibodies that continually decline. The rise again in seroprevalence after 8 months is then due to the acquisition of measles infection, or vaccination.

Estimation of force of infection

The unimodal force of infection predicts an increase in the force of infection with age, followed by a decrease. We note that the eradication equation p_c = 1-1/R₀ overestimates the critical proportion of immune only if the force of infection declines beyond the mean age at infection. For the exponential and linear functions which decline monotonically with age, this condition is automatically satisfied. However, for the unimodal function, it is only satisfied if the mean age at infection exceeds the age of peak force of infection. At the best-fit parameter values for the unimodal function for the 6 Indian and 2 African seroprevalence surveys of Table 2, this condition is always satisfied (results not shown).

R₀ estimates: Africa

The following populations in Africa were used to determine the relationship between R₀ and population size: Niakher (Senegal, 1983-2001) [73], Niamey (Niger, 2003-2004) [74], Machakos (Kenya, 1984) [17], Moshi (Tanzania, 1984) [17], Kinshasa (Zaire, 1983) [48], Kampala (Uganda, 1990) [42], Yaoundé (Cameroon, 1971, 1975) [46], and Lusaka (Zambia, 1996-1999) [75].

Data required to calculate R₀ are summarized in Table 3 and a log-log plot of R₀ versus N is given in Figure 3a whose slope is the constant c in Equation (6). The first two studies were the only two to provide R₀ estimates in the publication or estimates of the mean age at infection. The R₀ = 9.6 value for Niamey was found by averaging over all R₀ values reported for various districts of the city. For Niakher, Broutin et al reported a mean age at infection (with no vaccination coverage) of 4.6 years and a per capita birth rate of 47/1000 giving an R₀ value of 4.9. (The authors also estimated R₀ but did not adjust for maternal immunity).

City/Country	Birth Rate	p	A'	A	N	R 0
Niamey/Niger [74]	n/a*	n/a	n/a	n/a	750000	9.5641
Niakher/Senegal [73]	n/a*	n/a	n/a	n/a	23413	4.9194
Machakos/Kenya [17]	43/1000 [77]	0.2784	2.9070	2.0977	84320 [78]	12.7587
Moshi/Tanzania [17]	50.5/1000 [79]	0.0000	3.1153	3.1153	96838 [78]	6.9719
Kinshasa/Zaire [46]	49/1000 [80]	0.2040	1.6078	1.2798	3000000	20.3101
Kampala/Uganda [40]	50.1/1000 [79]	0.4080	2.5355	1.5010	800000	16.2804
Yaounde/Cameroon 1971 [44]	45/1000	0.6053	0.9736	0.3843	166000	203.3308
Yaounde/Cameroon 1975 [44]	45/1000	0.4316	1.0522	0.5981	260000	68.7812
Lusaka/Zambia [75]	40/1000	0.7395	5.0000	1.3025	1240000	24.3309

*n/a = not applicable since authors report A or R₀ directly

Equation (8) was used to find A' in Machakos, Moshi and Lusaka, and Equation (9) was used for the rest of the studies. Seroprevalence studies were not included due to the relatively few studies with sufficiently good age stratification to allow computation of A. The study by Enquselassie et al [37] was also not included because their data incorporated a high proportion of vaccinated individuals in all age groups. The recommended age for vaccination is 9 months and so vaccinated populations have a very high seropositivity rate from a very young age which would misrepresent the actual seroprevalence due to natural infection. Birth rates were reported in the studies for Yaoundé and Lusaka, but for the most part, birth rates were found from various other sources. Vaccination coverage for each study population was reported in all papers except Taylor et al and Scott et al. For those two studies, estimates from the World Health Organization (WHO) website were used to calculate p. R₀ values were calculated and reported in Table 3. The unusually high value of R₀ for Yaoundé, Cameroon, 1971, may reflect the fact that only cases below the age of 3 were reported (though these constituted 90% of all cases, according to the authors). The best fit value of a linear regression to a plot of log(R₀) versus log(N) yielded c = 0.1708, the R² value was 0.051, and the P < 0.6. This P-value suggests that it is more likely that there is no relation between population and R₀; however, we sampled over many different African countries so it is possible that this relation would be stronger within individual countries.

R₀ estimates: India

We estimated R₀ values for 11 different regions in India: Sathuvachari village (Tamil Nadu, 1969) [53], Ramgarh village (Rajasthan, 1986-1988) [51], Hyderbad city (Andhra Pradesh, 1982-1983) [52], Hooghly District (West Bengal, 1976-1978) [50], 13 villages of Tehri Garhwal (Uttar Pradesh, 1986) [56], Dumehar village (Himachal Pradesh, 2004) [58], Barbadi (Maharashtra, 1982) [54], Bombay (Maharashtra, 1972) [65], Vellore (Tamil Nadu, 1973) [66], Pondicherry (Puducherry, 1979) [68], and Delhi (1984-1985) [70].