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Child mortality estimates from complete birth histories from Demographic and Health Surveys (DHS) surveys and similar surveys are a chief source of data used to track Millennium Development Goal 4, which aims for a reduction of under-five mortality by two-thirds between 1990 and 2015. Based on the expected sample sizes when the DHS program commenced, the estimates are usually based on 5-y time periods. Recent surveys have had larger sample sizes than early surveys, and here we aimed to explore the benefits of using shorter time periods than 5 y for estimation. We also explore the benefit of changing the estimation procedure from being based on years before the survey, i.e., measured with reference to the date of the interview for each woman, to being based on calendar years.
Methods and Findings
Jackknife variance estimation was used to calculate standard errors for 207 DHS surveys in order to explore to what extent the large samples in recent surveys can be used to produce estimates based on 1-, 2-, 3-, 4-, and 5-y periods. We also recalculated the estimates for the surveys into calendar-year-based estimates. We demonstrate that estimation for 1-y periods is indeed possible for many recent surveys.
The reduction in bias achieved using 1-y periods and calendar-year-based estimation is worthwhile in some cases. In particular, it allows tracking of the effects of particular events such as droughts, epidemics, or conflict on child mortality in a way not possible with previous estimation procedures. Recommendations to use estimation for short time periods when possible and to use calendar-year-based estimation were adopted in the United Nations 2011 estimates of child mortality.
Editors' Summary
In 2000, world leaders set, as Millennium Development Goal 4 (MDG 4), a target of reducing global under-five mortality (the number of children who die before their fifth birthday to a third of its 1990 level (12 million deaths per year) by 2015. (The MDGs are designed to alleviate extreme poverty by 2015.) To track progress towards MDG 4, the under-five mortality rate (also shown as 5q0) needs to be estimated both "precisely" and "accurately." A "precise" estimate has a small random error (a quality indicated by a statistical measurement called the coefficient of variance), and an "accurate" estimate is one that is close to the true value because it lacks bias (systematic errors). In an ideal world, under-five mortality estimates would be based on official records of births and deaths. However, developing countries, which are where most under-five deaths occur, rarely have such records, and under-five mortality estimation relies on "complete birth histories" provided by women via surveys. These are collected by Demographic and Health Surveys (DHS, a project that helps developing countries collect data on health and population trends) and record all the births that a surveyed woman has had and the age at death of any of her children who have died.
Why Was This Study Done?
Because the DHS originally surveyed samples of 5,000–6,000 women, estimates of under-five mortality are traditionally calculated using data from five-year time periods. Over shorter periods with this sample size, the random errors in under-five mortality estimates become unacceptably large. Nowadays, the average DHS survey sample size is more than 10,000 women, so it should be possible to estimate under-five mortality over shorter time periods. Such estimates should be able to track the effects on under-five mortality of events such as droughts and conflicts better than estimates made over five years. In this study, the researchers determine appropriate time periods for child mortality estimates based on full birth histories, given different sample sizes. Specifically, they ask whether, with the bigger sample sizes that are now available, details about trends in under-five mortality rates are being missed by using the estimation procedures that were developed for smaller samples. They also ask whether calendar-year-based estimates can be calculated; mortality is usually estimated in "years before the survey," a process that blurs the reference period for the estimate.
What Did the Researchers Do and Find?
The researchers used a statistical method called "jackknife variance estimation" to determine coefficients of variance for child mortality estimates calculated over different time periods using complete birth histories from 207 DHS surveys. Regardless of the estimation period, half of the estimates had a coefficient of variance of less than 10%, a level of random variation that is generally considered acceptable. However, within each time period, some estimates had very high coefficients of variance. These estimates were derived from surveys where there was a small sample size, low fertility (the women surveyed had relatively few babies), or low child mortality. Other analyses show that although the five-year period estimates had lower standard errors than the one-year period estimates, the latter were affected less by bias than the five-year period estimates. Finally, estimates fixed to calendar years rather than to years before the survey were more directly comparable across surveys and brought out variations in child mortality caused by specific events such as conflicts more clearly.
What Do These Findings Mean?
These findings show that although under-five mortality rate estimates based on five-year periods of data have been the norm, the sample sizes currently employed in DHS surveys make it feasible to estimate mortality for shorter periods. The findings also show that using shorter periods of data in estimations of the under-five mortality rate, and using calendar-year-based estimation, reduces bias (makes the estimations more accurate) and allows the effects of events such as droughts, epidemics, or conflict on under-five mortality rates to be tracked in a way that is impossible when using five-year periods of data. Given these findings, the researchers recommend that time periods shorter than five years should be adopted for the estimation of under-five mortality and that estimations should be pegged to calendar years rather than to years before the survey. Both recommendations have already been adopted by the United Nations Inter-agency Group for Child Mortality Estimation (IGME) and were used in their 2011 analysis of under-five mortality.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001289.
- This paper is part of a collection of papers on Child Mortality Estimation Methods published in PLOS Medicine
- The United Nations Childrens Fund (UNICEF) works for children's rights, survival, development, and protection around the world; it provides information on Millennium Development Goal 4, and its Childinfo website provides detailed statistics about child survival and health, including a description of the United Nations Inter-agency Group for Child Mortality Estimation; the 2011 IGME report on Levels and Trends in Child Mortality is available
- The World Health Organization also has information about Millennium Development Goal 4 and provides estimates of child mortality rates (some information in several languages)
- Further information about the Millennium Development Goals is available
- Information is also available about Demographic and Health Surveys of infant and child mortality
Citation: Pedersen J, Liu J (2012) Child Mortality Estimation: Appropriate Time Periods for Child Mortality Estimates from Full Birth Histories. PLoS Med 9(8): e1001289. https://doi.org/10.1371/journal.pmed.1001289
Academic Editor: Peter Byass, Umeå Centre for Global Health Research, Umeå University, Sweden
Received: November 8, 2011; Accepted: June 11, 2012; Published: August 28, 2012
Copyright: © Pedersen, Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: The funding for this paper originated from UNICEF through a grant from Stanton-Hill Research. The funders had no role in study design, data collection and analysis, or preparation of the manuscript. However, UNICEF did participate in the decision to publish the paper as part of the TAG/IGME collection on child mortality estimation.
Competing interests: The authors have declared the following competing interests: Stanton-Hill Research supported this study. Kenneth Hill of Stanton-Hill Research is also the chair of the Technical Advisory Group (TAG) to the UN Inter Agency group of Child Mortality Estimation. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS Medicine policies on sharing data and materials, as detailed online in the guide for authors.
Abbreviations: 5q0, under-five mortality rate; DHS, Demographic and Health Surveys; PSU, primary sampling unit
Provenance: Submitted as part of a sponsored Collection; externally reviewed.
Tracking Millennium Development Goal 4, which sets a target of reducing child mortality by two-thirds between 1990 and 2015, requires that the under-five mortality rate (also denoted in the literature as U5MR or 5q0), be estimated precisely and accurately. Moreover, child mortality is an important indicator of the performance of the public health system of a country. Demographic and Health Surveys (DHS) surveys and similar surveys that collect full birth histories are one of the chief sources of estimates of under-five mortality, deriving such estimates from women's own reports of their complete birth histories.
The DHS surveys were originally designed for samples of 5,000–6,000 eligible women [1]. Measures of child mortality have traditionally been reported for 5-y time periods, since time periods shorter than 5 y often result in sampling errors that are too large because estimates are based on the deaths that occur during the particular time spans. Many recent surveys have had sample sizes much larger than required by the original design, and these larger sample sizes may make it possible to use time periods shorter than 5 y for child mortality estimates.
Estimates for short time periods have the benefit that trends and deviations from trends may be ascertained with some detail. The mortality effects of particular events, such as a drought, hurricane, or the outbreak of war, may be tracked if the time period is sufficiently short. Moreover, recent major changes in the pace of change of child mortality may be discovered earlier than if long time periods are used for estimation.
The tracking of the Millennium Development Goals is partly global, but the responsibility for meeting the goals lies with each country. Moreover, regardless of international goal setting, countries need to monitor the development of their child mortality level as an important indicator of the performance of their public health system. Global tracking of child mortality can be considered to be served by the current 5-y period of estimation, since short-term and local effects might obscure the overall trend. However, the overall trends for the different countries are constructed by smoothing the data from several surveys [2], so the trend estimates can be relatively smooth even if the estimates from particular surveys reflect specific events or random fluctuations observed in a country. Thus, using short estimation periods in the estimation from individual surveys can serve both the overall trend analysis and the short-term concerns that are of interest for individual countries.
The term "time period" in the context of child mortality estimation actually has two meanings. The first is the duration of the period for which an estimate is derived, 1 y, 2 y, and so on. The second is somewhat subtle. The usual practice in DHS surveys is to estimate mortality based on the concept of "years before the survey." That is, the estimation period is measured with reference to the time of the interview of each individual woman. As a consequence, the survey estimates for a period will actually cover a longer period than the apparent number of years in the period since the fieldwork may last from 2 mo to nearly a year. An alternative is to measure time in calendar years independent of when each woman was interviewed. The benefit of the traditional DHS approach is that it allows estimates into the fieldwork period of the survey, while the benefit of the calendar year approach is that it is provides a much clearer time reference. Thus, this paper will explore the consequences of using calendar-year-based estimates rather than estimates based on the time of the interview of each woman.
We proceed by considering criteria for when the sampling variability of an estimate is sufficiently small for tracking the levels and trends in child mortality, as well as the theory behind how to select appropriate time periods when sampling phenomena that fluctuated in time. We then give an overview of how DHS surveys are designed and how child mortality estimates and their sampling errors are computed. We then compute child mortality measures and their sampling errors for available DHS surveys in order to ascertain the conditions that allow the use of short time periods.
One way to determine appropriate time periods is to consider the standard errors of the estimates, and then reject any estimate if its standard error is too high. Since the standard error is scaled to what is measured, a common approach used in survey statistics and in other fields is to consider the coefficient of variation, i.e., the standard error divided by the estimate, expressed as a percentage or as the unmodified ratio [3],[4]. Strictly speaking, the coefficient of variation may be thought of as a population parameter, and as "relative standard error" or "coefficient of variation estimate" when calculated from samples [5]. Common usage is nevertheless to use the term "coefficient of variation" also for sample estimates.
It is commonly assumed that an estimate can be accepted for use or publication if its coefficient of variation is less than a given value, usually less than 5% or 10% of the estimate. Nevertheless, the rule of thumb of 5% or 10% is somewhat arbitrary, as is the case with many statistical rules of thumb, such as the 95% confidence interval [6]. Kish [7] agrees with Hansen et al. [8] that coefficients of variation larger than 0.2 (i.e., 20%) indicate a danger of unstable estimates, and notes that in his own practice "whenever the program found cv(x)>0.1 it printed out in red letters STOP, LOOK, AND DO SOMETHING" ("cv" is coefficient of variation) [7]. Hansen et al. [8] adopt 10% as a "working rule." In a somewhat more bureaucratic tone, the Eurostat sampling guidelines require the national statistics offices of member states to ensure that the coefficient of variation is lower than 8% in reported estimates from national surveys [9].
Despite the fact that what exact level one should choose as a cutoff may be uncertain, the reason for not accepting high coefficients of variation is clear cut. Like the estimate of child mortality itself, the standard error as observed in a survey is a sample statistic. A large standard error (and correspondingly large coefficient of variation), means that one does not really know where within the confidence interval the true value lies, but only that the confidence interval is wide, because the statistics used to compute it are uncertain. When samples are small, both the estimates and their variance estimates become unstable. This was to a large extent Kish's concern in the quotation above. But when the coefficient of variation is smaller than the level implying instability, there is no obvious statistical reason for choosing between the conventional 5% or 10% or the Eurostat's 8%.
In addition to the purely statistical issue, there is a substantive issue of importance in choosing an acceptable level for the coefficient of variation. For a child mortality rate of 100 deaths per 1,000, a 10% coefficient of variation would give a 95% confidence interval of roughly 80–120, while a 20% coefficient of variation would give a 95% confidence interval of 60–140. The latter is way too large if one wants to estimate change from one period of estimation to the next, or indeed for estimating the level itself. But, ultimately, the question is one that revolves around what is needed for specific purposes.
The appropriate time period for analysis is not simply the smallest that the sample size can support. If one assumes the absence of bias in a given child mortality measure estimate, in principle the appropriate time period is determined by the level of child mortality, the level of fertility, the sample size (and the sample design), and the variation in child mortality over time. The Shannon-Nyquist sampling theorem [10] describes the relationship between the frequency of a signal and how often the signal needs to be measured in order to be accurately represented. The more frequent the variation, the shorter the period needed in order to accurately portray it. In general, if there is no fluctuation with a frequency higher than B, then one should sample at intervals that are shorter than 1/(2B). For example, to accurately describe mortality fluctuations that occur every second year, i.e., with a frequency equal to 0.5, one would need to sample with an interval no longer than a year (1/(2×0.5) = 1). The frequent sampling required by the sampling theorem is clearly unrealistic given the sampling error in the case of child mortality estimates.
The Shannon-Nyquist sampling theorem assumes measurements at instants. In the case of child mortality measurements, that is not possible, as child mortality estimates are, again for sample size reasons, not measured at instants, but for periods that are long compared to possible fluctuations in mortality level. If child mortality varies regularly with 5 y between peaks, then a series of 1-y periods of estimation is able to reconstruct the curve form relatively well, as shown by the step curve in Figure 1.
The figure shows a cosine curve (green lines) with a 5-y periodicity and average estimates based on 1- to 5-y periods (blue lines).
Even with a 1-y estimation period there is some distortion in terms of peaks and troughs, if one considers that child mortality is a continuous smooth function of time. With a 2-y period more major distortions appear, because the periodicity of the measurements does not match the periodicity of the variation. This is even more pronounced with 3- and 4-y periods, and with 5-y periods the original variation is not visible at all. The scenario shown in Figure 1, with a regular fluctuation of mortality, may be that which will be most distorted, since the regularity forces long periods to average out fluctuation. With a 5-y period, a peak during a single year or two followed by a return to a baseline will be detectable, although the importance of the peak will be understated.
The Rwanda 2000 DHS survey provides an empirical example of the issues at hand. The birth history recorded in the survey covers the 1994 genocide. Although the massacres took place mainly from April to July, effects on child mortality may have lasted longer. As can be seen in Figure 2, while the 1994 events are somewhat detectable on the 5-y period plot, the effect is clearly visible on the 1-y period plot, showing that the 5-y estimates seriously understate the mortality increase, even when one disregards selection effect issues such as that children whose mothers were killed, and who therefore were not interviewed in 2000, must have had a much higher risk of death than other children. The 2-y trend line also shows a substantial flattening of the trend compared to the 1-y trend, illustrating empirically the theoretical plot in Figure 1.
Estimates based on years before the survey.
The distortion of the estimates is a problem that pertains not only to the shape of the trend lines of the mortality measures, but also—as the literature within economics indicates—to the issue that the coefficients of models based on the long time periods may be biased [11], in a way that is similar to misspecification error or ecological fallacy. This is a problem for models that use aggregated child mortality measures, such as attempts to create models of the relationship between gross domestic product per capita and child mortality for countries (e.g., [12],[13]) or models that aim to describe the relationship between conflict and child mortality levels (e.g., [14],[15]), but not for models focusing on finding covariates for individual child deaths.
A few observations follow from this exposition. First, sample sizes and mortality levels constrain how short a period can be used for estimation of child mortality measures. Second, having a large sample and using it to estimate child mortality measures with long observation periods is not optimal, because if mortality fluctuates, then significant bias may be a result. Thus, it may be better to trade lower precision (higher variance) in the estimate for higher accuracy (reduced bias) than to assume that a 5-y estimation period is always preferable. The problem remains, however, how that trade-off should be calibrated, and that will be addressed in the rest of this article. The purpose of this article is to determine appropriate time periods for child mortality estimates from full birth histories, given various sample sizes. Thus, it tries to answer the question of whether, given the increased sample sizes in recent years, we are wasting information by sticking to the estimation procedures that were developed for smaller samples.
Data Sources
The data used in this analysis were datasets that were publicly available from DHS, i.e., they could be downloaded from the Measure DHS website (http://www.measuredhs.com/data/available-datasets.cfm) without special permission from the country in question. Surveys that were carried out after 1 January 1985 and had data released before 31 October 2010 were included (surveys before 1990 do not appear to include sampling stratum information; a complete list of the surveys used is in Table S1). The data were not edited or altered in any way, except in the case where a dataset had one or more sampling strata consisting of only one cluster. In such cases each stratum in question was merged with an adjacent stratum or with a nearby stratum that also had only one cluster, i.e., employing the so-called collapsed stratum technique [16]. This will tend to overstate variance slightly. In some cases we did not feel confident that we had understood the stratification of the survey properly (e.g., Egypt 2005), and such surveys were dropped from the analysis. In total, 207 surveys were retained for analysis.
Rather than using exact dates in the estimation, DHS uses so-called century month coding, in which dates are converted into the number of months since January 1900. Some surveys, such as the Nepali and Ethiopian ones, use national calendars. These were recalculated according to the standard DHS coding and the Gregorian calendar. DHS data files do not contain the day part of dates, and therefore the conversion can be only approximate.
DHS Survey Design
The DHS surveys generally employ a two-stage stratified sampling design [1]. The sample is usually explicitly stratified by major administrative regions of the country in question, and also by urban and rural location. The allocation of the sample varies, and may be disproportionate in order to ensure adequate reporting domains, although proportionate allocation is preferred. The samples are very often implicitly stratified by geographic location within the explicit strata. Primary sampling units (PSUs) are most often census enumeration areas, consisting of 50 to 300 households. They are selected with probability proportionate to their size, as measured by their number of households, usually derived from the latest census of the country in question. If the PSUs are large then they are usually segmented, and only one segment is selected with probability proportionate to its number of households. PSUs (or segments) are always mapped, and a current list of households is created before the selection of households takes place by linear systematic sampling. Depending on the survey, and sometimes on geographic location within a specific survey, about ten to 20 households are selected in each PSU. As noted above, DHS surveys were designed for a sample size of 5,000–6,000 eligible women or approximately the same number of households, but many surveys have had larger samples, with those of India and Pakistan being the largest, with more than 100,000 households. Since 2005, the median sample size has been over 10,000 households, corresponding to an increase of roughly 3,000 households since 1990–1994 (Table 1).
Sampling weights are calculated so that they account for the details of the design. Although the DHS design is in principle self-weighting within the explicit strata, weight variations do occur because the estimate of the PSU sizes from the sampling frame may differ from the actual number found in the PSUs during the mapping and listing. Moreover, weight variation naturally occurs between strata that have different overall sampling rates.
Mortality Estimation from Full Birth Histories
Full birth histories are taken from all women aged 15–49 y in most surveys, although some, principally in the Middle East and southern Asia, define only married women in the 15- to 49-y-old group as eligible. Both usual residents of the households and visitors are used in the estimation.
The DHS estimates of child mortality are calculated using a synthetic cohort approach [17]. The age span from 0 to 5 y is split into eight groups, 0 to 30 d and 1 to 2, 3 to 5, 6 to 11, 12 to 23, 24 to 35, 36 to 47, and 48 to 59 mo. Survival probabilities from the beginning to the end of each age group are calculated, and then the appropriate measures are calculated as the product of the composite age rates.
DHS Variance Estimates
Estimates of variance for child mortality estimates from full birth histories collected through a typical household survey are somewhat involved. That is both because the various mortality ratios are computed in a complex fashion as described above, so that working out their variance estimator would be practically impossible, and because the sampling design is complex, i.e., includes unequal weights, clustering, and stratification, resulting in estimators that are not linear.
The variance estimates in the DHS reports have been calculated with a jackknife procedure. The basic premise of this method is that the realized sample can be seen as a good approximation of the population distribution, and that repeated samples from the sample can be made. The distribution of the estimates made from these subsamples will then reflect the sampling variability. While several methods based on this premise exist, the unique characteristic of the jackknife approach is that subsamples are formed by deleting one sampling unit from the sample, calculating the estimate, replacing the deleted unit, and then repeating the procedure with the next sampling unit. Thus, a number of replicate estimates equal to the number of sampling units is formed, and the variance is basically the variance of the distribution of replicates.
In jackknife cluster samples, the ultimate clusters, rather than the last stage sampling units, are used. Thus, for example, with a sample of 368 clusters and 24,000 births, 368 replicates are computed, not 24,000.
According to the DHS sampling manual [1] and the various country reports, the formula used for the variance estimate is(1)where k is the number of ultimate clusters, r is the estimate from the full sample, and r(i) is the estimate from the reduced sample of k−1 clusters, when cluster i is excluded.
A characteristic of the variance estimator as employed by DHS is that it disregards stratification. Therefore, it likely overestimates variance, at least in samples where strata allocations are more or less proportional to the stratum sizes. A test on the 2002 DHS survey for Eritrea confirms that it was indeed the unstratified estimator that was used.
An alternative variance estimator is the JKn variance estimator used by Westat's WesVar software [18] and by SUDAAN [19], and described by Wolter [16]. The stratified JKn procedure for estimating variance is very similar to that of the unstratified version, i.e., it deletes and replaces each ultimate cluster in turn. However, when a cluster is omitted, the weights of the other clusters in the stratum are adjusted by a factor of nh/(nh−1), where nh is the number of clusters in the stratum. Then, the actual variance estimate is calculated as given below, where hk is (nh−1)/nh:(2)Note that in this case ri is the actual estimate from the i-th replicate, i.e., without the transformation applied in the formula used by DHS. The DHS formula and Equation 2 are in fact equivalent: if the adjustment to the weights and the hk factor are not applied, the two formulas yield the same result. Thus, the difference is the adjustment for stratification. The formula that DHS uses is the original version as described by Quenouille, the inventor of the method [16]. Neither the DHS version of the variance estimate nor the stratified version includes a finite population correction. In general, sampling rates in DHS surveys are too small for this to matter.
The differences in standard errors between the estimates that DHS presents and those taking stratification into account are on the order of 10% or less, since the variances of the child mortality measures usually do not differ all that much between strata.
The mortality estimates and standard errors were produced by a program written in Delphi (Embarcadero RAD Studio XE). For each data source, estimates for 1-, 2-, 3-, 4-, and 5-y periods were calculated as far back in time as was possible for each survey, given the length of the birth histories. The estimates of childhood mortality for 5-y periods before the survey match exactly those produced by the Measure DHS online statistics compiler (STATcompiler) or the DHS final reports for datasets that are not represented in the compiler output. Delphi was chosen because the system produces compiled code. It is therefore much faster than the interpreted code used by R, Stata, or SPSS. The program reads comma-separated or Excel files exported from the original DHS data files. It is available from the authors on request. Computed estimates from the DHS surveys used in our analysis are found in Tables S2 and S3.
Regardless of the length of the estimation period, 75% of the estimates of 5q0 have a coefficient of variation below 14%, and half below 10%. For estimation for periods of 3 y and more, the estimate of the coefficient of variation is less than 10% in 75% of the cases (Figure 3; Table 2). Nevertheless, the distribution within each period is highly skewed, with a few very high coefficients. In general, the outliers are surveys that have small sample sizes, represent estimation periods long before the survey date, have low fertility, or have low mortality, but there are some exceptions to this pattern.
Only estimates for periods 20 y or less before the survey were included. Two extreme outliers have been excluded from the graph.
The results are highly determined by sample size, mortality level, and fertility (Figures 4 and 5; Table S1). To some extent, fertility can be used as a proxy for mortality level, since countries with high fertility usually also have high child mortality. In general, with the total fertility rate at 5.5 births per woman or higher, all realistic sample sizes, including small ones, make the estimation of 5q0 for single-year periods at least 10 y back from the survey date feasible if a coefficient of variation of less than 10% is the criterion. At medium fertility levels, 3.5–5.5 births per woman, one would need a sample of at least 8,000 to be able to estimate 5 y back with 1-y periods, while 12,000 would be required for estimating 10 y back. At fertility levels lower than 3.5 births per woman, estimation of 5q0 with samples of less than 8,000 households appears to be ill-advised, regardless of the period for estimation. At low fertility levels and with small samples, the only estimate that can be made with confidence in most cases is 5q0 for 0–4 y before the survey, and sometimes not even that (e.g., Kazakhstan).
Estimates based on years before the survey. The number of surveys used for the estimates in each panel is indicated in the upper left corner of each.
Estimates based on years before the survey. Estimates based on calendar years. The number of surveys used for the estimates in each panel is indicated in the upper left corner of each.
One should note, however, that there are exceptions to the general rules of sample size and fertility. Sierra Leone 2008 has, for example, no estimate with a coefficient of variation lower than 10%. It is unclear why this is so, since Sierra Leone 2008 has a standard DHS sample design, with a sample size of 7,572 households and a total fertility rate of 5.1 births per woman.
Similar considerations pertain to neonatal mortality (formally, the number of deaths during the first 28 d after birth per 1,000 births, but estimated in DHS surveys and here as the number of deaths during the first month) and infant mortality (the number of deaths during the first year after birth per 1,000 births) estimates. Since the number of deaths observed in a period is smaller for these estimates, the number of years with low coefficients of variation is also fewer than for the 5q0 estimates. Nevertheless, if a coefficient of variation of less than 10% is the standard, then it is possible to produce mortality estimates 5 to 10 y back, even for neonatal mortality in high-fertility populations for most samples, but estimation further back than the most recent 5 y is likely to be unstable.
Not surprisingly in the case of neonatal mortality, the number of continuous years before the survey that can provide estimates with acceptable coefficients of variation is quite small regardless of the period used. For 1-y estimation periods, one needs samples of more than 15,000 households to be able to estimate back much in time.
If a coefficient of variation of less than 5% is the standard, then samples of more than 15,000 households are needed in order to make estimates for 1-y periods for 5q0, and neonatal and infant mortality can seldom be estimated at all (Figure 5; Table S1).
The results described above can also be seen graphically for each individual country survey. The general trend, regardless of sample size, is that the confidence intervals around the curves are quite narrow close to the survey date, and they then become wider the further away from the survey date the estimate is located. Mali 2001 (Figure 6) is an example of a survey of a high-mortality and high-fertility situation (5q0 of 200/1,000 or more; total fertility 5 y before the survey was 6.8 births per woman), with a relatively large sample (13,429 completed household interviews). Such a survey allows estimation for 1-y periods more than 20 y back in time using the 10% criterion. It is also a case where the period of estimation makes little difference, because the development of mortality has been relatively smooth.
Sample size 12,439 households; total fertility rate 6.8 (births per woman). Estimates based on years before the survey.
In contrast to Mali 2001, the Kazakhstan 1999 survey had a small sample, and the country had relatively low mortality and quite low fertility (Figure 7). The survey estimates have generally wide confidence intervals. The 1- and 2-y estimates suggest a peak in child mortality around 1997 and then a decline, while the 5-y line suggests a slight recent increase.
Sample size 5,841households; total fertility rate 2.0 (births per woman). Estimates based on years before the survey.
Moldova 2005 (Figure 8) has twice the sample size of Kazakhstan 1999, and this is reflected in the more narrow confidence bands. Still, it is difficult to achieve really small coefficients of variation for surveys in countries with relatively low mortality and very low fertility.
Sample size 11,095 households; total fertility rate 1.7 (births per woman). Estimates based on calendar years.
The Zimbabwe 1999 data show a characteristic trumpet shape of the confidence bands, which is seen in many surveys: the further the estimate is from the survey date, the wider the estimate of the confidence band (Figure 9). The reason for this is that for estimates back in time, only a portion of the women interviewed provide data. Thus, children reported for 20 y ago were born to women who at the time were under 30 y. This reduces the sample size, and also introduces selection biases, since such estimates reflect the mortality of children born only to young mothers, and because it excludes children of women who have died.
Sample size 6,372 households; total fertility rate 4.0 (births per woman). Estimates based on calendar years.
Issues of Bias
As noted in the Introduction, focusing on the coefficient of variation overlooks the combined effects of bias due to long periods and sampling error. In principle, one can consider the mean square error, rather than the sampling error in isolation. If the bias and sampling error are uncorrelated, then the mean square error (MSE) is given by:(3)The square root of the mean square error, usually termed root mean square error, is then comparable to a standard error, but includes the effect of the bias. Thus, the root mean square error is a measure of the total uncertainty associated with the estimate.
The problem is to estimate the bias. A simple approach is to assume that the difference between two estimates, e.g., for a 5-y period and a 1-y period, constitutes the bias of the longer period. That is certainly inaccurate, not the least because both estimates are subject to sampling error, and the 1-y estimate also may have some long-period bias. Moreover, the 1-y estimate is also biased because of selection effects, as noted before. With these caveats, one may reconsider the calculation of the root mean square error for the 5q0 mortality trend during the genocide period in Rwanda, as recorded by the 2000 DHS survey.
The root mean square error for the 5-y estimate (using the difference between the 1-y estimate for February 1994 and the 5-y period centered on February 1993 as a proxy for bias) is 51.2, while that for the February 1994 1-y period estimate remains 10.4, as the bias is assumed to be 0 (Table 3).
The large root mean square error of the 5-y estimate appears credible when considered together with a graphical view of the development of 5q0 in Rwanda (Figure 10), with the data from the 2000 and 2005 DHS surveys (the 2007–2008 DHS survey shows a very different trend, and is disregarded here, while the 1992 DHS survey is consistent with the 2000 and 2005 surveys but irrelevant for the genocide period). A first observation that can be made from Figure 10 is that the 1-y period estimates from both the 2000 and 2005 surveys show clear peaks in 1994, although the 2005 survey shows one peak between the two peaks of the 2000 survey—most likely because of the difference in exactly when the time period of the estimate is centered. A second observation from the graph is that to some extent 1-y periods may remove some of the inconsistencies associated with trends based on the 5-y period estimates. The 5-y periods from both surveys show a leveling off of increasing mortality around the time of the genocide. The 2000 DHS survey suggests that this happened before the genocide, while the 2005 survey suggests that it happened after.
The preceding analysis implicitly makes three points. First, even though the 5-y period estimates in general have lower standard errors than the 1-y period estimates, the 1-y period estimates may be preferable, because of the potential bias that may occur in using the 5-y estimates. Second, one possible decision criterion is that when the mean square error (as calculated above) is larger for the 5-y estimate than for the 1-y estimate, the 1-y estimate is preferable. Third, if the coefficient of variation of the 1-y estimate is sufficiently low (i.e., at least lower than 10%), then the 1-y estimate should be preferred. In cases where the differences between the 5-y period estimates and the 1-y estimates are slight, the choice is largely irrelevant.
A further consideration is the time location of the estimates. As noted, the DHS estimates cover a longer calendar period than the estimation period, because the time reference is with respect to each individual interview date. This is a relatively minor consideration for 5-y periods, especially if the fieldwork period is short, but becomes an important consideration for the short estimation periods, such as 1- and 2-y periods, especially in the context of rapidly changing mortality. The consideration is also becoming increasingly important as samples increase in size, since large surveys require longer fieldwork periods.
The effect of the time location can easily be seen by recomputing the estimates for calendar years rather than years before the survey. The midyear estimates of 5q0 computed from the Rwanda 2000 and 2005 surveys were, respectively, 208 and 192 for 1993, 281 and 260 for 1994, and 248 and 235 for 1995, i.e., a match that puts the yearly estimates squarely within each other's confidence intervals, and with peaks in the same years (Figure 11).
Since the Rwanda massacres took place within a calendar year, it is likely that estimates based on calendar year are more accurate than estimation based on years before the survey. But what works best for sudden changes in mortality is dependent on the actual time location of the sudden change. Nevertheless, fixing the estimates to calendar years rather than to years before the survey, which is anchored in the individual interview dates of the women providing the birth histories, has the benefit of making the estimates more directly comparable across surveys. The apparent inconsistencies between the 1-y estimates from the two Rwanda surveys, as well as between the 5-y period estimates, disappear with calendar-year-based estimation.
Our analysis shows that whereas childhood mortality estimates based on 5-y periods of data have previously been the norm, the sample sizes presently employed in DHS surveys make it feasible to base the estimates on shorter periods. It also shows that using calendar years rather than years before the survey as a basis for estimation is useful because it brings out variation in child mortality in a clearer and more realistic manner.
Apart from the benefit in relation to tracking the Millennium Development Goals and the general benefit of avoidance of bias, there are three main benefits of this approach in relation to monitoring of public health. The first is that changes in mortality rates may be detected earlier. The second is that the relationship between child mortality rates and other events may be elucidated. One example is that of Rwanda, where the use of 5-y rates based on years before the survey seriously distorts the effect of the genocide on child mortality. Another example is the relationship between child mortality and outbreaks of some epidemic diseases, such as measles, which may be detected with the use of 1-y rates, but which will be undetectable with the use of 5-y rates. Finally, using short time periods for estimates, survey data may be used to establish consistency with other sources of child mortality estimates. Vital registration is often incomplete in developing countries, but one often assumes that the trends in vital registration data can be used for tracking fluctuation in child mortality [20]. This assumption is difficult to check with 5-y estimates from survey data, since vital registration typically provides data for single years, but the check can be carried out with single-year estimates.
For surveys with sample sizes larger than 8,000 households, it is possible in most cases to estimate child mortality for single years about 10 y back before the survey, although for low-fertility countries this cannot be done. For really large sample sizes, i.e., 20,000 or more, the factor limiting estimation for single-year periods is more the inherent selection bias when estimating back in time, than the sample size itself.
How short should the estimation period be? The results for countries such as Rwanda, with a very abrupt mortality change, suggest that 2-y periods do not bring large improvement to the estimation compared to 5-y periods, because somewhat unpredictable misestimation may still occur.
A practical rule for when short time periods should be chosen is when the mean square error (as calculated above) for the 5-y estimates becomes large compared to that of the short-period estimates. Large mean square errors are likely to occur when there are rapid surges or drops in mortality. In practice, one can use the same decision rule for the relative mean square error as for the coefficient of variation. A simpler decision rule is to use estimates with a coefficient of variation lower than 10%.
Some of the variation in child mortality estimates between different surveys in a population with changing rates of change in mortality stems from the way time periods are calculated. Both for 5-y periods and shorter ones, there is merit in changing estimation to fixed calendar years, rather than years before the survey. This is particularly the case for short time periods, but may likely also resolve some of the differences between survey estimates from different surveys in the same country, even for 5-y periods.
The two main recommendations given here—adopting shorter time periods than 5 y for estimation and using calendar years—have been adopted by the United Nations Inter-agency Group for Child Mortality Estimation in the 2011 estimates of child mortality [21].
Supporting Information
Table S1.
Number of contiguous years from survey date allowing estimates of mortality with coefficient of variation less than 10%.
Table S2.
Estimates of mortality and standard errors, part 1: Albania to Jordan.
Table S3.
Estimates of mortality and standard errors, part 2: Jordan to Zimbabwe.
The authors would like to thank the members of the United Nations Inter-agency Group for Child Mortality Estimation and its Technical Advisory Group for comments on initial versions of the paper.
Author Contributions
Analyzed the data: JP JL. Wrote the first draft of the manuscript: JP. Contributed to the writing of the manuscript: JP JL. ICMJE criteria for authorship read and met: JP JL. Agree with manuscript results and conclusions: JP JL. Designed the software used in the analysis: JP. Managed datasets: JL.
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text:
Earl Webb
Since 1931, Earl Webb has held the major-league record for the most doubles hit in a single season – 67. It is one of the longest-lasting records in baseball, despite the fact that when it was set, the season was eight games shorter than today's 162-game schedule. As it happens, Earl's team that year – the Boston Red Sox –played only 152 games, not the scheduled 154. Webb also drove in 103 runs and batted an even .333. The Red Sox finished the year 62-90, in sixth place. And Webb himself was someone who had twice been waived by major-league teams. He was on his fifth club when he set the record. The Sporting News said, "Webb topped off a checkered baseball career by placing his name in the record books with a mark that is likely to stand for some time to come." [The Sporting News, September 24, 1931] It did earn him a nickname: The Earl of Doublin'.
William Earl Webb was born on a farm in Blue Spring Cove, White County, Tennessee, right near the community of Bon Air, more or less halfway between Nashville and Knoxville, and less than 10 miles from Sparta. He was born on September 17, 1897, or 1898 (his death certificate gives the later date). His father, William Matthew Webb, worked as a "coal digger," in Earl's words, and when Earl was 6, the family moved to Ravenscroft, another seven miles or so from Sparta. Earl's father not only mined but served as assistant deputy sheriff. And he pitched and played the outfield for the Ravenscroft baseball team, and also taught singing in the local school. Earl's mother was the former Helen Victoria Palmer, and she is listed in the 1920 Census as a "matron" in a hotel; she ran the coal company's boardinghouse. (The death certificate named his mother Ellen, but she apparently went by the nickname Vic.) By 1920, William Webb was a foreman in the mine. Both Earl, then 22, and his brother Clifford, 18, were coal miners. There were seven other children in the family: sisters Alice, Willie Bell, Ina, Betty, and Margaret, and younger brothers named Carl and Duncan.
Earl's daughter Iris Webb Glebe wrote a biography of her father, in which she lets us know that there was another ballplayer in the family – Alice Webb. Glebe recounts her Aunt Alice's memory of winding string to make their own baseballs while using hickory sticks for bats, adding, "Alice eventually captained the Ravenscroft girls' ball team, and still chuckles over their uniforms – black bloomers and stockings, white middie blouses with red ties." [Iris Webb Glebe, The Earl of Dublin, p. 3]
Webb was involved with coal mining before baseball and after baseball. He claimed to have begun working in the mines at the age of 11, for 5 cents an hour. A profile by Harry Brundidge in The Sporting News was where Earl characterized his dad as a coal digger. He spoke with Brundidge at length, and said that he himself had started working in the mines at an early age. "I roamed the mountains, haunted the mines, and attended school," he said. "In those days I could think of no work as thrilling as laboring in the coal mines, and during my school days I idled away my time, waiting until I would be big enough to go down in the ground. I was eldest of nine children and at the age of 11 quit school and went down into the Ravenscroft mine to labor 10 hours a day, at five cents an hour, as a trapper, a boy who looks after the doors and switches." [The Sporting News, February 18, 1932. Glebe suggests that going to work at age 11 may have referred to a summer job, and perhaps was a bit of hyperbole, in that Earl did attend school for quite a few more years.] Webb was introduced to baseball a year later: "I didn't begin to play baseball until I was about 12 years old, maybe 13, when the mine doctor advised me to get exercise and fill my lungs with fresh air after breathing coal dust all day. At 14 years of age, I began to drive a blind mule in the mine that had not been up in the daylight for 12 years. I was paid eight cents an hour for that. About this time I began pitching for the Ravenscroft team." [Ibid]
Ravenscroft's rival was the Pleasant Hill team. Left-hander Walter Stewart, who hailed from the Sparta area, too, and who'd begun pitching minor-league ball in 1919 (and made the St. Louis Browns in 1921), was the star pitcher for Pleasant Hill and the two often faced off. "We used to walk eight miles to pitch against each other," Webb told Brundidge. [Ibid] By the time he was 17, Earl began mine work as a coal digger himself but on Sundays was beginning to pick up a little extra cash as a country pitcher. In 1918, he signed with the Memphis Chickasaws (Southern Association), but he never reported. He cited two reasons: "I was scared stiff at the idea of going to a big city, [and] I was in love with Miss Blanche Matthews, and I didn't want to leave Blanche or my native mountains." [Ibid. It is worth noting that Iris Webb Glebe reports that Earl's sister Willie Bell suggested that Earl sometimes embroidered accounts he gave the press. For instance, he stayed in school much longer than his account would indicate. Glebe's book is an exceptionally well-written account packed into one slim volume.]
A couple of years later, Earl bit. He'd taken another job in 1919, at a mine in Jellico, and pitched there for two years, playing against Earle Combs during the summers. He and Blanche married at the end of 1920 and his father took Earl aside, suggesting that coal mining was a rough life and he should pursue the possibilities of playing baseball professionally. There was still interest from Memphis, so he signed on again and was farmed out to to the Clarksdale Cubs in the Class D Mississippi State League. We don't know his earned run average in 1921, but he was 12-8 and surrendered only 1.075 walks and hits per inning pitched. It was a good beginning and Webb impressed New York Giants manager John McGraw in an April 1, 1922, exhibition game against the Giants. The Giants purportedly paid $10,000 and a couple of players to the Memphis club for the rights to his contract, though he continued to play with Memphis (8-6, with a 4.78 ERA). [The Sporting News, May 11 and 25, 1922] He hit well, .282 and .284 those first two years. There was another hitch in his progress, though. His contract was sold to the New York Giants in August 1923 but once again he refused to report. Over the wintertime, a Giants scout visited Earl at home and convinced him that he would not be alone; there were other greenhorns in a similar position and he'd be matched up with them.
In 1922, Earl and Blanche had their first child, a daughter they named Earlene. The couple had five children in all, the others being Jessie Iris (1925), Helen (1928), Patricia (1930), and Gilda (1932).
Earl traveled to New York and became completely lost, claiming (no doubt hyperbolically) that it took him two full hours to find his way out of Penn Station. When he finally emerged, he coincidentally ran into Babe Ruth, who drove him to his hotel. Giants manager John McGraw optioned Webb out to play with the Eastern League's Pittsfield Hillies in Massachusetts. When he wasn't pitching, Webb was asked to play the outfield – and he excelled at hitting, putting up a .323 average at the plate (and an 8-18, 4.64 record on the mound). Webb was recalled to the Giants on September 14, but saw no action.
The last year in which Webb pitched was 1924, again he was optioned out to Pittsfield after a look with the big-league club in spring training. With the Hillies that year, he was 9-15 (4.22 ERA) but he hit .343 in 114 games with 14 homers. He was sold to the Toledo Mud Hens on September 6, departing Pittsfield about a week later, and hit .333 in 17 games with Toledo (Double A), where he pitched not at all.
Webb told Brundidge that he had declared, "I'm an outfielder or nothing from now on and if you don't like it, I'll go home." [Brundidge article, op. cit.] He was making a mark with his bat. The Hartford Courant observed in April 1925 that the Hillies had "no slugger to take the place of Earl Webb." [Hartford Courant, April 19, 1925] He was a right-handed pitcher but a left-handed hitter. Earl stood 6-feet-1 and weighed 185 pounds. The Courant's Albert Keane called him a "hustling aggressive ballplayer … a gentleman on and off the field" who made a lot of friends who were pulling for him when he returned to the majors in 1927. [Courant, May 26, 1927. Keane even went so far a month later as to compare the Hack Wilson/Earl Webb duo to Gehrig and Ruth, who were tearing up the American League that year.]
Webb was given a chance to make the Giants (in fact, traded by Toledo for Hack Wilson and a player to be named later). "The Giants need new faces," said McGraw. [New York Times, August 9, 1925] Webb appeared in four games beginning with a fruitless pinch-hitting role on August 13, 1925, but he fell short, hitless with one walk and one strikeout in four plate appearances. He otherwise spent 1925 with Toledo (.329) and all of 1926 with Louisville (.333) – the Giants had sent him there in December 1925. There was one odd game in September, in which he and two other Giants played under assumed names for the semipro Englewood (New Jersey) Madonnas, helping them defeat their Hackensack rivals, with permission from the Giants. He wasn't making a lot of progress, but he was making a lot more money than he would have in the Tennessee coal mines.
On August 28, 1926, Webb was sold to the Chicago Cubs, as Chicago president Bill Veeck looked ahead to the 1927 season. [Chicago Tribune, August 29, 1926] It was the break he needed. Playing for manager Joe McCarthy, who cited the "hard-socking outfielder" (per the February 2, 1927, Los Angeles Times) as part of his hopes to contend for the pennant, Earl spent all of 1927 and 1928 with the Cubs, and acquitted himself pretty well the first year. After training on Santa Catalina Island off the Southern California coastline, and outhitting all the other Cubs, he played in 102 major-league games and batted .301, hitting 14 homers and 18 doubles, and driving in 52 runs. Two of those home runs came on Opening Day as he homered twice and walked twice against the Cardinals to kick off the season. Needless to say, Webb immediately won favor among the fans at Wrigley Field. His first major-league home run was hit off future Hall of Famer Grover Cleveland Alexander, in his first Cubs at-bat, and the second off Eddie Dyer, who later managed the Cardinals for five years after World War II. The Cubs placed fourth again, 8½ games behind the pennant-winning Pirates. Interestingly, but coincidentally, one of Cubs owner William Wrigley's investments included the Bon Air and Ravenscroft mines; he was the chairman of the board. Earl lived in company-owned housing until 1929, when he bought his first house. He worked during the offseasons both in the mines and as a motorman.
The Cubs added a future Hall of Famer, the established Kiki Cuyler, in November 1927, and Webb was reduced to a backup role. In 1928, he got into only 62 games and hit only .250. (Cuyler had an offyear and his .285 average wasn't as good as Webb had hit in 1927, though he did lead the league in stolen bases. He recovered his form and then some for the next three seasons. The team as a whole improved, finishing in third place just four games out of first.)
In 1929, Webb played for Los Angeles in the Pacific Coast League. They'd been after him before the '28 campaign, and were pleased that Earl wasn't dealt to the Boston Braves to help acquire Rogers Hornsby, but stayed in the Wrigley fold instead. He had an excellent season, batting .357 and banging out 37 homers and 58 doubles. He played in 188 games of LA's 202-game PCL season that year, so the extra-base totals might appear inflated, but to maintain that high an average that long against Triple-A competition was a real achievement. Earl had to follow the action from afar as the Cubs won the pennant; they lost the World Series to the Philadelphia Athletics in five games.
Webb was drafted in the fall of 1929 by the Cincinnati Reds, who looked him over in spring training but then placed him on waivers and traded him to the Washington Senators near the end of the exhibition season. Just 22 days later, on April 26, Washington (with a surfeit of left-handed hitters) sent Webb to the Red Sox for Whispering Bill Barrett. Webb said that Washington, "after a look, told me to be on my way, and I started the 1930 season with the Boston Red Sox." He'd been with three major-league ballclubs in the month of April, but never appeared in a game for either the Reds or the Senators. John Kieran joked about Webb's travels: "Every time he sees a club secretary he starts packing his bag." [New York Times, May 4, 1930]
Webb homered in his first game of 1930, against the Yankees at the Stadium. He felt he'd finally found the right home. "I've found myself at last because I am working for a man who understands me, sympathizes with me, and does his best to make me a better ballplayer. That man is John "Shano" Collins, manager of the Red Sox, who, like myself, was just an average player, a steady, hard worker, who never became a great star. Other managers had little, if any, interest in me, and until I went to work for Collins, I was never inspired." [Ibid]
With Boston, Webb joined Tom Oliver and Russ Scarritt as the primary outfielders for the Red Sox in 1930. Webb played right field, and he really improved his outfield play. It hadn't been good. It was hitting that had kept him in the game. "Earl always could hit," wrote The Sporting News. "but his fielding left so much to be desired that it over-balanced his ability with the stick." [September 24, 1931] One reporter that Glebe quotes wrote, "He looked the worst of all the bad right fielders who have been tried out at Fenway Park. … Webb dropped them, misjudged them, and almost had his head knocked off by fly balls. He was hooted by the fans, and it seemed as if he might not last a month with the Red Sox." [Glebe, p. 23] Webb talked about his switch from pitching: "As an outfielder … I became the All-American Stumbler. I have seen some mighty bad outfielders but none of them had anything on me until I landed in Boston. Even then, I could do a pretty fair job of stumbling. Seems queer that none of the managers ever had been able to make me a better outfielder. …. It came to me all at once. I had tried and tried to discover why it was that fly balls were just falling a yard or two out of my reach and I finally tumbled to the fact that I was not getting the jump on the ball." [Hartford Courant, March 6, 1932] He taught himself how to react at the crack of the bat. "I can no longer boast of being baseball's worst outfielder." [Ibid]
Webb appeared in 127 games for the Red Son in 1930 and accumulated 449 at-bats. His .323 average led the team by a sizable margin. Not one of the other regulars hit over .293. Sixteen of Webb's hits were homers, helping drive in 66 runs. He hit 30 doubles. For the "hero-starved fans" in Boston (the phrase is Iris Glebe's), he was a breath of fresh air.
One massive blow helped kick off Webb's 1931 season, a reported 550-foot home run on March 22 that struck the outfield wall at Pensacola in a game against the House of David team. It was in 1931 that Webb set his record for doubles in a single season. He appeared in all but one of the games the Red Sox played, wearing No. 15. (It was the first year that the Red Sox wore uniform numbers.) Webb had 154 more plate appearances than in 1930, but hit more than twice as many two-baggers: 67 to 1930's 30. He drove in 103 runs. His .333 batting average (ranking seventh in the league) was supplemented by a career-high 70 walks, boosting his on-base percentage for the year to .404. His 14 homers ranked him 10th in the league, the same rank he'd held in 1930. It was a remarkable season and he was well aware of the doubles record as the season unfolded.
There were some who suggested that Webb went for doubles at the expense of some triples, pulling up at second base instead of trying to get three bases out of a long drive to the outfield. In 1930, he'd tripled six times, while in 1931 he tripled just three times. Given the additional 140 at-bats he enjoyed, perhaps the law of averages would suggest that he might have hit eight or nine triples in 1931 instead of the three he recorded. He was, of course, a year older but he hit nine triples in 1932, so speed on the basepaths is probably not an issue. Of course, it was well into the season before Webb would have ever dreamed of setting a single-season record for doubles. It's not the sort of thing one goes into a season thinking about, particularly when his career high was 30 and the existing record was more than twice that amount. He didn't double until the third game of the year, and he probably didn't really notice the accumulation of two-base hits until the end of May when he hit two doubles in the May 26 game against the visiting Washington Senators, and then doubled twice in both games of the May 27 doubleheader against Washington. (Despite Earl's four doubles, and a triple, the Red Sox lost both games, however.) Six doubles in two days helped bring him to 20 for the still-young season, and attracted some notice (particularly in the Washington Post, which noted the devastation he had wreaked against the Senators – seven doubles, one triple, and two homers in one series in Boston. The paper miscalculated his number of doubles so far as 22, and said that he batted right-handed, but advised readers that the league record was 64, set by George Burns. [Washington Post, May 29, 1931]
Then Webb hit only one double in the next nine games.
There never was a game in which he hit more than two. There were eight games, aside from the May 26-27 games, when he hit a pair. Fans began turning out in bigger numbers at Fenway Park, noted the Christian Science Monitor on July 19, in part because of the growing number of doubles Webb was accumulating. He missed the July 21 game because he mistakenly put iodine into his eye instead of the eyewash he'd reached for, but he came back and hit a double the following day. His daughter Iris Glebe suggests that this was not a mistake, but that an envious unknown Red Sox player had deliberately placed iodine in the eyewash bottle.
The Associated Press began to tally the two-base hits. When Webb hit two as part of a 4-for-4 day on July 28, the subhead in the New York Times read "46th, 47th doubles for Webb." One of the doubles everyone thought he had was taken away on August 4 when American League officials realized that a single on May 1 had mistakenly been counted as a double. That still left him with an even 50 and August was only beginning. There were nearly two months left to play.
Webb might have hit a lot more doubles had he not slumped badly in August. It wasn't a bad slump overall – he entered the month hitting .369 and exited it batting .344 – but there were 14 games in a row, from August 5 through the 21st when he didn't double even once. He had 10 other hits, but no doubles. He didn't have a hit on August 8, though – Bobby Burke no-hit the Red Sox. There had been one game earlier in the year when Webb's single denied Willis Hudlin a no-hitter. This time he joined the rest of the Red Sox, going hitless in Washington. Frank H. Young of the Washington Post said there was a lot of talk around town as to whether Webb deliberately took strike three to help out Burke (Webb was the last Boston batter of the game), but Young noted the slump Webb was in and suspected he had not let up. [Washington Post, August 10, 1931]
For the entire month of August, Webb hit only six doubles, down from 18 in July. Fortunately for the record, he laid it on and doubled 13 times in September. On the 15th, he doubled twice and helped the Red Sox beat Chicago, 2-1, bringing Boston out of last place in the process. He was one double away from tying Burns's record. The very next day – his birthday – he doubled twice more, both tying and then surpassing the record, hitting one in each game of the doubleheader against the visiting Indians. And he hit No. 66 the next game. Webb failed to double in any of the next seven games, but hit No. 67 in the last game of his year.
The Red Sox finished the 1931 season in sixth place, just barely behind the fifth-place Browns. They had no chance at reaching the first division, 15 games behind fourth-place Cleveland. Should one fault Webb if he pulled up two or three times at second base instead of tearing on toward third base in hopes of stretching a double into a triple?
His splits are worth looking at. Most of his doubles were hit at home – 39 of the 67. Of those, 33 were hit at Fenway Park, and six were hit at Braves Field, where the Red Sox played all of their Sunday home games because of an old Blue Law forbidding playing baseball near a church. The doubles on the road were fairly evenly distributed; the six hit at Yankee Stadium were the most in any one ballpark outside Boston.
Was Webb's mind on second base? He led the league in errors at his position, 16. He'd made nine the year before, and 11 the year after; though he did play many more games in 1931 than in either of the seasons before or after, it was still a statistically significant increase, albeit with rather small numbers. Shano Collins said, "The reason he hits so many doubles is that he's hitting a long, hard ball this year and he's too darned slow on the bases to get to third." [Numerous sources, including Glebe, p. 37]
Webb's record remains an oddity – doubling 30 times in 1930 and 28 times in 1932, he hit more doubles in 1931 than in those two years combined. The 67 he hit in his big year were more than 43 percent of the 155 doubles he hit over his seven seasons of major-league ball.
After the season, Webb declined the opportunity you go on a baseball tour of Japan. He signed his 1932 contract without a hint of hesitation.
At the end of April 1932, he'd hit three doubles. At the end of May, he had a total of six. The year was not shaping up like 1931. He had nine by the time he was traded to the Tigers on June 12 for Dale Alexander and Roy Johnson. Webb was pleased. He correctly perceived that the Tigers were on the way up, and stood a better chance to finish in the money than did the Red Sox that year. [See Glebe, p. 45] Once more, the Red Sox lucked out by getting a player in a dramatically anomalous year: Alexander won the American League batting championship in 1932, hitting .367. The next year, he suffered a burn and hit only .281. It was his final year. Webb himself only had one more major-league season in him.
For the 1932 Red Sox, Webb hit .281. For the Tigers, he had a terrific start for manager Bucky Harris (who also had experience as a coal miner), with nine hits in his first five games and his arrival "inspired the attack of the whole team." [The Sporting News, June 23, 1932] In the end, he hit .287. At year's end, he had 28 doubles. Tigers management was disappointed, but understood that his "decline was a more or less natural reaction to his big year" and that he would return to his former power numbers. [The Sporting News, October 13, 1932]
In 1932, Paul Waner set a new National League record, hitting 62 doubles. The following year, 1933, Webb hit only five – all for the White Sox who claimed him on May 9 after Detroit placed him on waivers. He'd been with the team as a reserve outfielder, but now was deemed expendable. Fortunately, White Sox skipper Lew Fonseca really wanted a left-handed hitter, and Webb did hit .290 but with only six extra-base hits – the doubles and one home run. It was a sharp decline. On February 20, 1934, the White Sox released him to Milwaukee in the American Association. He had a career major-league batting average of .306 and had driven in 333 runs.
At Milwaukee, Webb hit a grand slam on Opening Day and hit throughout the year, including one 31-game hit streak. He tore some cartilage in his knee in late July, but came back late in the year and won the league's batting crown by the narrowest of margins (.3679 to Vernon "George" Washington's .3672) on the strength of several hits on the last day of the season. Earl hit .338 for the Brewers in 1935. In November, his contract was sold to Knoxville of the Southern Association, and he was able to play just 85 miles from his home in east Tennessee. Webb had another good year for the Smokies, a Boston Braves-affiliated ballclub, batting .348 with a league-leading 20 homers. He struggled a bit in 1937, and though he was hitting .278, he quit the club on July 29, announcing, "I asked to be suspended because I haven't done the club any good. I hope to get back with the boys next year if I feel I can help them win." [Atlanta Constitution, July 10, 1937] It wasn't to be. He'd returned to his home to farm "after 17 years of hearing the crowds cheer" and never re-entered the ranks of organized baseball. [The phrase is from Ed Harris in the July 30, 1937 Knoxville Journal.]
Webb took a position with the Consolidated Coal Company of Jenkins, Kentucky, both working as a foreman in the mines and managing the company baseball team. A number of interviews show him as a perceptive student of the game, and he managed teams in Kentucky and West Virginia into his 50s. He died of coronary thrombosis on the night of May 23, 1965. The death certificate described him as a "laborer" in the coal mines. His daughter reported, "He had spent the day fishing a stream that meanders through the hills that he loved."
Glebe, Iris Webb. The Earl of Dublin (Ann Arbor, Michigan: McNaughton & Gunn, 1988)
Earl Webb player file at the National Baseball Hall of Fame
In addition to the sources indicated within the text, the author also consulted the online SABR Encyclopedia, Retrosheet.org, and Baseball-Reference.com. Thanks to Kathleen Ragsdale and Ed Washuta for assistance with this biography.
Full Name
William Earl Webb
September 17, 1897 at White County, TN (USA)
May 23, 1965 at Jamestown, TN (USA)
If you can help us improve this player's biography, contact us.

subset: commoncrawl


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Apotheca Soy Coconut Candle | Spiced Tonkla Cacao Pod
Apotheca Soy Coconut Candle | Spiced Tonkla Cacao Pod
Handcrafted in Australia using pure, natural soy & coconut wax & natural fibre wicks, this highly fragrant, luxurious candle draws on a rich blend of scents specifically designed to amplify your sensory state. Embrace the simple ritual of lighting a scented candle & create a fragrant sanctuary that will awaken your spirit & ignite your heart & mind.
Burn time approximately 80 hours.
400 gm / 14.1 oz
Ignite a sense of otherworldly pleasure & delight. An opululent and intoxicating fragrance, featuring black-rose top notes, gracefully leading to precious sandalwood. Velvet base notes of cacao pod & tonka bean finish this sultry blend.
- Top Notes: pink pepper, orange, black rose
- Middle Notes: vanilla, sandalwood, tobacco
- Base Notes: cacao pod, tonka, patchouli
Fragrance Family: Woody
Candle Care
Get the most "life" out of your candles by following these easy tips:
- Before each burn, trim wick to 5 mm. This promotes an even burn, steady flame and reduces sooting & mushrooming of the wick.
- When lighting for the first time, burn until wax melts across the entire surface to the sides of jar. (This may take up to 2-4 hours) This creates an even burn for the duration of the candle's life.
- Keep wicks centred, standing upright and trimmed to prevent soot from forming on the glass.
- If soot does form on the inside of your jar, extinguish candle, wait for it to cool, then wipe with a clean damp cloth. Trim wick and relight.
- Burn away from draughts. Draughts decrease overall burn time, cause uneven burning, smoking and diminished scent throw.
- Extinguish candle with a snuffer. This prevents smoking and hot wax from splattering. If you do blow it out, do so very gently. Then straighten wick & ensure wick is centre of jar.
- Extinguish your candle if it smokes, flickers, or the flame becomes too high. These signs indicate the candle is not burning properly and the flame isn't controlled. Let the candle cool, trim the wick, then check for drafts before re-lighting. Best burning means flame is not flickering, is teardrop shaped and standing centred & upright.
- Always read and follow the safety instructions carefully.
Please note: Burn times will vary depending on the environmental conditions.


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We're into the final days before members of the European Parliament vote on the Copyright and the Digital Single Market Directive, home of the censoring Article 13, and the anti-news Article 11. Europeans are still urging their MEPs to vote down these articles (if you haven't already, call now, and stepping up the visibility of their complaints in this final week.
Stop Article 13
The first salvo drawing attention to the damage the directive will cause has come from the European Wikipedias. German Wikipedia has gone completely dark for today, along with the Czech, Slovak and Danish Wikipedias, German OpenStreetMap, and many more.
With confusing rhetoric, the Directive's advocates have always claimed that they mean no harm to popular, user-driven sites like Wikipedia and OpenStreetMap. They've said that the law is aimed only at big American tech giants, even as drafters have scrambled to address the criticism that it affects all of the Internet. Late in the process, the drafters tried to carve out exceptions for "online encyclopedias," and the German government and European Parliamentarians fought hard – though ultimately failed – to put in effective exceptions for European start-ups and other competitors.
Very few of the organizations and communities for whom these exceptions are meant to protect are happy with the end result. The Wikimedia Foundation, which worked valiantly to improve the Directive over its history, came out last week and declared that it could not support its final version. Even though copyright reform is badly needed online, and Wikipedians fought hard to include positive fixes in the rest of the Directive, Article 13 and Article 11 have effectively undermined all of those positive results.
As Wikimedia's experts write:
Despite some good intentions, the wholly problematic inclusion of Articles 11 and 13 mean that fundamental principles of knowledge sharing are overturned: in practice users and projects will have to prove they are allowed to share knowledge before a platform permits an upload. The EU Copyright Directive envisions a technical and legal infrastructure that treats user generated content with suspicion unless proved legal. We cannot support this—it is better to have no reform at all, than to have one including these toxic provisions.
The European lawmakers who see Article 13 and Article 11 as a simple fix for the woes of entertainment and news media companies still don't get that the Internet isn't a competing "industry" – it's an ecosystem. Companies like Google and Facebook are certainly supported by that ecosystem – but so too are the billions of individuals, thousands of European companies, families, and ad-hoc communities of creators, coders, and services. As Wikimedia says, this Directive turns the simplest basic actions of those Internet users - sharing and linking - suspect. Websites must check everything that users upload, because if they upload something that another person decided is their own, the website can be liable for unbounded costs. If Article 11 passes, everyone will have to make a legal assessment when linking to the news, out of fear the text accompanying their link contains one too many words, and triggers Article 11's licensing requirements.
The sites that are shutting down today in protest are, without question, sites that are home to European creators: the very people that Article 13 and 11 adherents claim to be protecting. That these parts of the European creative community are so concerned about their own future, and the wider ecology of the Net, should be a giant, flashing, warning sign to all MEPs.
If you're in Europe, contact your MEP, and join the protests this weekend. The future doesn't have to be as dark as it looks today.


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John Moran • Riverboat Lounge
December 15th 8PMBio: Singer/Songwriter/Guitarist/Harmonica Player. John's deep, rich, blues inflected voice, both powerful and passionate, was honed in the honky-tonks and bars up and down and east and west of the Mississippi River. His relentless harmonica wailing is amazing, intricate and original. His guitar playing runs the gamut from wild and raucous to pure and distinctive. He is also a founding member of two midwestern bands that performed for many years in the Tri-State area: Longshot and Saddletramp. John is a veteran rocker who spent 13 years in Nashville, Tennessee sharpening his songwriting and performing skills. He's had his songs recorded by artists in California, New York and Nashville.


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Yoga Modifications to Protect Your Joints
Fitness crazes come and go, but yoga is one program that truly stands the test of time. This ancient art form originated in northern India nearly 5,000 years ago, and it has continued to grow in popularity throughout the centuries. A 2008 market study in Yoga Journal estimates that 16 million Americans currently practice yoga, and they spend at total of $5.7 billion annually on yoga gear (Source: The Better India).
More than 21 million Americans have osteoarthritis, a degenerative joint disease characterized by inflammation and loss of cartilage within the joints. However, some individuals may need to take extra precautions to protect their bodies – specifically their joints – when practicing yoga.
Pay close attention to the position of your front knee when doing poses like Warrior I, Warrior II, and High Crescent Lunge. Your knee should form a 90 degree angle and stay vertically in line with your ankle. Make sure your knee does not extend beyond your toes or cave inward, as this can stress the knee ligaments and cause pain.
When doing plank positions, keep your elbows about shoulder width apart and tuck them firmly against your rib cage. Allowing your elbows to buckle outward can contribute to joint pain.
Downward dog is an excellent pose to stretch and strengthen the shoulders, arms and legs, but it can be quite hard on the wrists if you aren't careful. When doing this pose, press through your palms to evenly distribute your weight throughout your hands. If your wrists are particularly sensitive, you can modify this pose by lowering onto your forearms.
Keep shoulders relaxed and away from your ears when doing yoga or any other form of exercise. If you notice your shoulders beginning to creep upwards, take a deep breath and roll them back, pinning your shoulder blades down and towards each other.
Resist the urge to crane your neck when trying to get into some of the more difficult yoga poses. This creates unnecessary strain on your muscles and could cause pain. Keep your head in line with your spine, and release any neck tension by moving your head left to right or rolling it side to side (Source: Huffington Post).


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Check out more Rock and Roll Hall of Fame Class of 2018 cover features here.
This week we've posted tributes to three of this year's six Rock and Roll Hall of Fame inductees: The Cars, Dire Straits, and Nina Simone. And lord knows we've posted plenty of covers of the other three over the years: Bon Jovi, The Moody Blues, and "Early Influence" inductee Sister Rosetta Tharpe. But to celebrate them all in one place in advance of this weekend's induction ceremony, we thought we'd round up a few of the best covers we didn't include in all those other features. Continue reading »


text: Taraba Illustration Art, LLC is an independent small business, specializing in original works of art by American illustrators and spanning the period from 1850 to 1990. Fred Taraba was the Director of Illustration House in New York City from 1991 through August of 2007. At that time, he moved to Casper, Wyoming to start his own business. Being a small operation, you can rest assured that clients --- whether buying or selling are given a level of personal service not found elsewhere. Being my own boss keeps me light on my feet. There are a thousand stories to tell about my 30-plus years in business … don't be surprised to see some of them pop up here from time to time.


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Monument to Rahn in Burg Waldeck (Hunsrück). Rahn and Wandervogel
V.: I have a question. Richard mentions the monument to Otto in the forest of the Hunsruck. Have anyone some pictures? Or the precise coordinates? I have not found any mentions about.
By the way - I don't like the inscription on it "Caution – these ways lead astray!"
It's not so simple)
Richard Stanley: Ave V. - You are right, mon frere. It is not so simple! The monument is a round stone bank in the courtyard of the seat of the Nerother Bund, a deeply conservative branch of the German youth movement known as the 'Wandervogel' whose headquarters can be found in Burg Waldeck in the forest of Hunsruck. As far as I know there are no photographs available on the web of this curious construction.
The fact of the monument's existence however turns out to be merely the tip of a genuinely esoteric iceberg. Work on the monument ( under the direction of chaplain Pater Martin Kuhn ) began in 1964. The monument was constructed entirely from stones removed from the castle of Montsegur by members of the Bund, personally supervised by their leader Karl Oelbermann ( otherwise known as 'King Oelb' ) who happens to have been a close friend of Otto's former landlady Madame Couquet.
In fact during their frequent visits to Montsegur they based themselves in Madame Couquet's auberge where Otto had lodged back in 1929. Karl Oelbermann's motives in building this memorial seem to have been, at last in part, to celebrate the memory of his twin brother, Robert, who died in Dachau in 1941 after the movement was driven underground by the Nazis. Robert Oelbermann was a known homosexual with a deep interest in the history of Montsegur. I suspect that Otto first became involved with the Wandervogel as a teenager in Giessen and that they may well have helped underwrite his travels. It is pure speculation, of course, but there is a good case to be made that Robert Oelbermann was the prisoner that Otto was accused of fraternizing with during his time at Dachau ( circa 1937 ). A further 46 kg of stones were removed from the castle and carried back to the forest of Hunsruck in 1995 by members of the Bund who continue to hold the summer solstice as their major festival.
The badge of the Nerother Bund - the conservative branch of the 'Wandervogel' referenced below. Could this be the explanation behind the mysterious lightning bolt/sieg rune logo that appears on the jumper knitted by Otto's mother?
Journalist Christian Bernadac speculates in 'The Secret Glory' that Otto Rahn must have come to Montsegur as an envoy of the Thule Society. How else, he reasons, can we we explain his knowledge of the runes? In point of fact the Thule Society (based in Munich) had little interest in rune lore. The Nerother Bund on the other hand plainly use runes such as the wolf's engel on their badges - hence presumably the reasons behind the symbol on Otto's jumper. Rather charmingly I note that one division of the bund are known as the 'Wolfen'. The 'wandervogel' were directly responsible for introducing the celebration of the main pagan dates ( ie Beltane, Lammas, Samhain, Yuletide, Imbolc etc ) into the scouting movement and hence into the modern new age/neopagan community...
On the upper photo: Otto as a teenager posing with what appear to be members of the Wandervogel (Giessen - circa 1919).
Here - Subdivisions of Wandervogel
From our conversation with Richard Stenley
Similar By Terms


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What is Love!?
What is Love!? |
Original YTMND: What is Love!? |
by ligamentx |
June 2, 2005 |
Worthy spinoffs: |
Used Music: |
What Is Love is a YTMND-turned-fad created by user ligamentx based on the song "What Is Love" by Haddaway. The original YTMND shows a scene from a skit from Saturday Night Live wherein Chris Kattan, Jim Carrey and Will Ferrell go clubbing to many dance clubs and fail to pick up chicks.
The Song
What is Love was originally a song by dance artist Haddaway, recorded in 1993. It only reached #11 in the US, but was #2 in the UK and Germany and #1 in several other countries. It was Haddaway's only US hit. The chorus was samples in 2010 for the song No Love by Eminem featuring Lil Wayne.
The Skit
During the nineties, Saturday Night Live did a series of skits featuring the song called 'A Night at the Roxbury'. In them, Will Ferrell, Chris Kattan, and a guest star would go clubbing at many dance clubs and fail to pick up chicks. Guest stars for this skit included Jim Carrey, Tom Hanks, Martin Short, Alec Baldwin, and Sylvester Stallone. It was made into a movie in 1998.
The Fad
In 2005, user ligamentx made a site with an image from on of the skits, featuring Will Ferrell, Chris Kattan, and Jim Carrey bobbing their heads up and down. The domain for this site is ckjcwf, which stand for Chris Kattan, Jim Carrey, and Will Ferrell. This YTMND has spread into its own fad, in which other people's faces are photoshopped over the original YTMND, and the YTMND plays a parody on "What Is Love" by Haddaway. Some other sites may take clips from different TV shows/movies where the drivers and passengers may be moving their heads up and down as seen in the original video. The fad also achieved (relative) mainstream success when a flash movie compilation of the fad circulated around the internet.
What is Love was included in Volume 5 of the YTMND Soundtrack, while the "goth version" by MDP was included in Volume 2. A similar song, "What Is Love" by Howard Jones, is used in PTKFGS instead. There is also one version including Queen's Bohemian Rhapsody.
See Also
KHAN! · Barrel Roll · Picard Song · Stapler · Conan is... · Future Conan · NSMB · Nooo! · Sonic says · What is Love · Burger King · Ualuealuealeuale · Brian Peppers · ...has one weakness. · O RLY? · OMG, secret Nazi... · I love bell peppers · CATCH THAT MAN! · Facial Expressions · lol, internet · Safety Not Guaranteed · PTKFGS · Nintendo 64 kid · Bill Cosby · Epic Maneuver · NEDM · Cake Song


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Himalaya Herbals Fairness Kesar Face Wash 150 ml
Category: Face
Brand: Himalaya Herbals
What it does: Himalaya's Fairness Kesar Face Wash is a soap-free, natural formulation that brightens and rejuvenates your skin, giving it a healthy, natural glow. Enriched with Kesar, proven for its complexion enhancing properties, this face wash gives you a fairer and clearer complexion. Pomegranate nourishes, Mint cools, and Cucumber soothes your skin, leaving it refreshed. Key ingredients: Kesar is a luxurious herb that has been used in skin care for centuries for a fairer, brighter complexion. The antibacterial property of Kesar kills bacteria, helping to treat infections. Cucumber makes an excellent toner as it immediately tightens open pores. The abundant antioxidants in Cucumber rejuvenate the skin and leave it feeling soft and smooth. Pomegranate is a rich source of polyphenols. It is reported to possess potent antioxidant and protective benefits against free radicals generated by the environment. Pomegranate softens, hydrates and nourishes the skin. Mint is an antiseptic and an excellent skin cleanser. It soothes the skin, cures infections and alleviates itching. It is also an effective treatment for pimples and can be used for treating insect bites. Directions for use: Moisten face, apply a small quantity of Fairness Kesar Face Wash and gently work up a lather using a circular motion. Wash off and pat dry. Use twice daily. Suitable for all skin types.


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NAACP Sues President Trump over 2020 Census
By William J. Ford
The NAACP announced that the group has filed a lawsuit against President Donald Trump, the U.S. Census Bureau and Secretary of Commerce Wilbur Ross, "to combat the imminent threat that the 2020 Census will substantially undercount African Americans and other people of color in communities throughout the United States," a press release about the lawsuit said.
Prince George's County, the NAACP's Prince George's County branch and two county residents (branch President Bob Ross and Elizabeth Johnson), also joined the suit. Prince George's County experienced one of the highest undercounts in the nation at 2.3 percent during the 2010 Census, according to the suit. The figures are based on counties with a population of at least 100,000.
"Such a dramatic undercount will especially dilute the votes of racial and ethnic minorities, deprive their communities of critical federal funds and undervalue their voices and interests in the political arena," the suit alleges.
During a press conference about the lawsuit at the National Press Club in Northwest D.C., Bradford Berry, general counsel of the NAACP said that this lawsuit is unique, because the plaintiffs seek action before work on the 2020 Census begins.
For instance, the suit claims the federal government has decreased resources and manpower for the 2020 Census and "cancelled crucial, pre-Census field tests and is rushing to digitize the Census without adequate cybersecurity protections, thus undermining public confidence in the privacy of Census data" the press release said.
The lawsuit also states that the Census Bureau doesn't have sufficient staffing; the agency's acting director, Ron Jarmin, was also named as a defendant in the suit.
On Capitol Hill last week, the U.S. House of Representatives approved $2.8 billion for the bureau, an increase more than double the amount of the Trump administration's request of $1.1 billion.
"Proposing a bill and passing a bill are two different things," said NAACP President Derrick Johnson. "Once the final bill passes, we would like to evaluate to see if it's sufficient. We simply need the political will to make sure we have an accurate count for this [upcoming] Census."
Prince George's County Executive Rushern L. Baker III said his jurisdiction has lost about $200 million in federal money, because of Census undercounts. The Maryland jurisdiction of nearly 900,000 people borders Washington, D.C., with 65 percent of the population African American.
Federal law requires that citizens are counted in a decennial census that not only helps redraw political boundaries, but also for counties and states to receive federal money for improvement of schools, roads and other needs.
Critics have argued that a proposed citizenship question in the 2020 Census will deter legal immigrants from responding and decrease the number of people counted in those communities. The Hispanic population in Prince George's County stands at about 18 percent.
"What's more frightening about this Census count, more than in the past, is the rhetoric from the Trump administration," Baker said after the press conference. "With a growing Latino population in the county, this is a direct assault on those folks participating in the Census. If it's happening here, then it's happening everywhere."
William J. Ford is a staff writer for The Washington Informer. You can follow him on Twitter @jabariwill. The Washington Informer is a member publication of the National Newspaper Publishers Association.
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