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Producing estimates of infant (under age 1 y), child (age 1–4 y), and under-five (under age 5 y) mortality rates disaggregated by sex is complicated by problems with data quality and availability. Interpretation of sex differences requires nuanced analysis: girls have a biological advantage against many causes of death that may be eroded if they are disadvantaged in access to resources. Earlier studies found that girls in some regions were not experiencing the survival advantage expected at given levels of mortality. In this paper I generate new estimates of sex differences for the 1970s to the 2000s.
Methods and Findings
Simple fitting methods were applied to male-to-female ratios of infant and under-five mortality rates from vital registration, surveys, and censuses. The sex ratio estimates were used to disaggregate published series of both-sexes mortality rates that were based on a larger number of sources. In many developing countries, I found that sex ratios of mortality have changed in the same direction as historically occurred in developed countries, but typically had a lower degree of female advantage for a given level of mortality. Regional average sex ratios weighted by numbers of births were found to be highly influenced by China and India, the only countries where both infant mortality and overall under-five mortality were estimated to be higher for girls than for boys in the 2000s. For the less developed regions (comprising Africa, Asia excluding Japan, Latin America/Caribbean, and Oceania excluding Australia and New Zealand), on average, boys' under-five mortality in the 2000s was about 2% higher than girls'. A number of countries were found to still experience higher mortality for girls than boys in the 1–4-y age group, with concentrations in southern Asia, northern Africa/western Asia, and western Africa. In the more developed regions (comprising Europe, northern America, Japan, Australia, and New Zealand), I found that the sex ratio of infant mortality peaked in the 1970s or 1980s and declined thereafter.
The methods developed here pinpoint regions and countries where sex differences in mortality merit closer examination to ensure that both sexes are sharing equally in access to health resources. Further study of the distribution of causes of death in different settings will aid the interpretation of differences in survival for boys and girls.
Please see later in the article for the Editors' Summary.
Editors' Summary
In 2000, world leaders agreed to eradicate extreme poverty by 2015. To help track progress towards this global commitment, eight Millennium Development Goals (MDGs) were set. MDG 4, which aims to reduce child mortality, calls for a reduction in under-five mortality (the number of children who die before their fifth birthday) to a third of its 1990 level of 12 million by 2015. The under-five mortality rate is also denoted in the literature as U5MR and 5q0. Progress towards MDG 4 has been substantial, but with only three years left to reach it, efforts to strengthen child survival programs are intensifying. Reliable estimates of trends in childhood mortality are pivotal to these efforts. So, since 2004, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) has used statistical regression models to produce estimates of trends in under-five mortality and infant mortality (death before age one year) from data about childbearing and child survival collected by vital registration systems (records of all births and deaths), household surveys, and censuses.
Why Was This Study Done?
In addition to estimates of overall childhood mortality trends, information about sex-specific childhood mortality trends is desirable to monitor progress towards MDG 4, although the interpretation of trends in the relative mortality of girls and boys is not straightforward. Newborn girls survive better than newborn boys because they are less vulnerable to birth complications and infections and have fewer inherited abnormalities. Thus, the ratio of infant mortality among boys to infant mortality among girls is greater than one, provided both sexes have equal access to food and medical care. Beyond early infancy, girls and boys are similarly vulnerable to infections, so the sex ratio of deaths in the 1–4-year age group is generally lower than that of infant mortality. Notably, as living conditions improve in developing countries, infectious diseases become less important as causes of death. Thus, in the absence of sex-specific differences in the treatment of children, the sex ratio of childhood mortality is expected be greater than one and to increase as overall under-five mortality rates in developing countries decrease. In this study, the researcher evaluated national and regional changes in the sex ratios of childhood mortality since the 1970s to investigate whether girls and boys have equal access to medical care and other resources.
What Did the Researcher Do and Find?
The researcher developed new statistical fitting methods to estimate trends in the sex ratio of mortality for infants and young children for individual countries and world regions. When considering individual countries, the researcher found that for 92 countries in less developed regions, the median sex ratio of under-five mortality increased between the 1970s and the 2000s, in line with the expected changes just described. However, the average sex ratio of under-five mortality for less developed regions, weighted according to the number of births in each country, did not increase between the 1970s and 2000s, at which time the average under-five mortality rate of boys was about 2% higher than that of girls. This discrepancy resulted from India and China—the two most populous developing countries—having sex ratios for both infant and under-five mortality that remained constant or declined over the study period and were below one in the 2000s, a result that indicates excess female mortality. In China, for example, infant mortality was found to be 12% higher for boys than for girls in the 1970s, but 24% lower for boys than for girls in the 2000s. Finally, although in the less developed regions (excluding India and China) girls went from having a slight survival disadvantage at ages 1–4 years in the 1970s, on average, to having a slight advantage in the 2000s, girls remained more likely to die than boys in this age group in several Asian and African countries.
What Do These Findings Mean?
Although the quality of the available data is likely to affect the accuracy of these findings, in most developing countries the ratio of male to female under-five mortality has increased since the 1970s, in parallel with the decrease in overall childhood mortality. Notably, however, in a number of developing countries—including several each in sub-Saharan Africa, northern Africa/western Asia, and southern Asia—girls have higher mortality than boys at ages 1–4 years, and in India and China girls have higher mortality in infancy. Thus, girls are benefitting less than boys from the overall decline in childhood mortality in India, China, and some other developing countries. Further studies are needed to determine the underlying reasons for this observation. Nevertheless, the methods developed here to estimate trends in sex-specific childhood mortality pinpoint countries and regions where greater efforts should be made to ensure that both sexes have equal access to health care and other important resources during early life.
Additional Information
Please access these websites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.1001287.
- This paper is part of a collection of papers on Child Mortality Estimation Methods published in PLOS Medicine
- The United Nations Childrens Fund 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 UN IGME report Levels & 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
- A 2011 report by the United Nations Department of Economic and Social Affairs entitled Sex Differentials in Childhood Mortality is available
Citation: Sawyer CC (2012) Child Mortality Estimation: Estimating Sex Differences in Childhood Mortality since the 1970s. PLoS Med 9(8): e1001287. https://doi.org/10.1371/journal.pmed.1001287
Academic Editor: Peter Byass, Umeå Centre for Global Health Research, Umeå University, Sweden
Received: December 12, 2011; Accepted: June 11, 2012; Published: August 28, 2012
Copyright: © Cheryl Chriss Sawyer. 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 views expressed are those of the author and do not necessarily reflect those of the United Nations. No direct funding was received for this study. The author was personally salaried by her institution during the period of writing (though no specific salary was set aside or given for the writing of this paper).
Competing interests: The author has declared that no competing interests exist.
Abbreviations: CIS, Commonwealth of Independent States; DHS, Demographic and Health Surveys; UN IGME, United Nations Inter-agency Group for Child Mortality Estimation
Provenance: Submitted as part of a sponsored Collection; externally reviewed.
Sex is a key variable for disaggregation of childhood mortality rate estimates, both for monitoring and analytical purposes and as an input to other demographic estimates such as sex-specific life tables or population size and structure. However, the task of estimating trends in sex-specific child mortality, particularly for countries without reliable death registration statistics, is far from straightforward, and sex-specific data are frequently more limited or noisier than those available for both sexes combined. In this paper I outline the challenges in the estimation and interpretation of sex-specific childhood mortality rates, and develop simple methods to take advantage of available data on under-five or infant mortality by sex. The analysis updates previous United Nations work on sex differences in childhood mortality [1],[2].
Challenges in the Estimation of Mortality Trends Disaggregated by Sex
Reliably estimating even overall trends in childhood mortality—that is, without taking into account differences by sex—is a difficult task in many developing countries. In the absence of complete and reliable vital registration systems in much of the developing world, estimation of mortality rates for children primarily relies upon data from certain questions in household sample surveys and population censuses [3]. These questions elicit information from female respondents about their childbearing history, either in detail or in summary, and the survival status of their children. Estimates based on these questions are subject to sampling errors (for surveys) and non-sampling errors (for both surveys and censuses), with the outcome that multiple inquiries may produce quite different estimates for the same time period. Since 2004, the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME) has reconciled inconsistent data on overall (both-sexes) under-five mortality for each country using regression models to produce a best estimate of trends from the 1960s to the present [3]. The UN IGME also produces time series of infant mortality estimates.
One reason that the UN IGME has not taken sex-specific data into account to date is that some of the censuses and surveys on which United Nations estimates of both-sexes mortality are based did not collect the relevant data by sex. If estimates by sex were to be produced by fitting trend lines to sex-specific data, the estimates might not be consistent with estimates for both sexes combined that incorporate more data sources. A further complicating factor is that sampling error for mortality estimates from surveys—which is often large already for both sexes combined due to the relatively small number of child deaths even in a large sample—is increased when mortality estimates are disaggregated by sex or any other variable.
For countries with high-quality data from vital registration, computing mortality rates by sex from annual data is uncomplicated, but there is a further consideration pertaining to the analysis of sex differentials in mortality. In countries with low levels of mortality, ratios of male to female under-five and infant mortality can fluctuate substantially from year to year because of small numbers of deaths. For purposes of analysis and cross-national comparisons, some form of smoothing is desirable.
Interpreting Sex Differentials in Childhood Mortality
Boys and girls have different probabilities of death due to biological factors, and these differences vary between infancy and early childhood. If sex-disaggregated estimates are to be used for monitoring or advocacy purposes, it must be clearly explained to users (1) what the expected differences are, (2) when a given difference might indicate excessive disadvantage for one sex or the other, and (3) how to understand changes.
The under-five mortality rate, also denoted in the literature as U5MR or 5q0, is the probability of dying between birth and exact age 5 y. The components of the under-five mortality rate examined here are the infant mortality rate (the probability of dying between birth and exact age 1 y, denoted 1q0) and the child mortality rate (the probability of dying between exact ages 1 and 5 y, denoted as 4q1). The under-five mortality rate and its components are related as follows:(1)The measures of sex differences employed are the ratios of male to female rates of infant, child, and under-five mortality, multiplied by 100 for ease of presentation.
Equity in survival between females and males does not imply equal mortality rates (that is, male-to-female ratios equal to 100). Under circumstances where boys and girls have the same access to resources such as food and medical care, boys have higher mortality rates than girls during childhood, and the examined ratios would overall be expected to be greater than 100. Newborn girls have a biological advantage in survival over newborn boys, with lesser vulnerability to perinatal conditions (including birth trauma, intrauterine hypoxia and birth asphyxia, prematurity, respiratory distress syndrome, and neonatal tetanus), congenital anomalies, and such infectious diseases as intestinal infections and lower respiratory infections [4]. However, beyond early infancy, girls do not enjoy the same advantage in relation to certain infectious diseases, which are the primary causes of death in later infancy and early childhood in settings where overall mortality is high [5],[6]. Thus, the sex ratio of child mortality (that is, mortality at ages 1–4 y) is generally lower than the sex ratio of infant mortality (Figure 1). The sex ratio of under-five mortality is intermediate between the two, and will depend on the relative mortality levels of the infant and child age groups.
As living conditions improve, an "epidemiological transition" occurs during which infectious diseases recede as a cause of death [7]. As the decline in infectious disease proceeds, perinatal and congenital causes form an increasing share of total mortality among infants, while external causes, more typically affecting boys, form an increasing share of mortality for children between ages 1 and 5 y [8],[9]. Hence, as overall levels of mortality fall, female advantage in infant and child mortality would normally increase, assuming no sex-specific changes in the treatment of children. Figure 1 shows the historical change in sex ratios of infant, child, and under-five mortality for several developed countries where access of children to resources was not believed to differ greatly by sex [9]. The female advantage in survival, however, can be eroded if girls are deprived relative to boys in access to health care or to proper nutrition. If such deprivation occurs, the sex ratio of mortality might be substantially below the values shown in Figure 1 for a given level of mortality.
Because of the biologically based characteristics of differential survival by sex, it is difficult to construct a precise target of gender equity in survival in childhood. However, situations in which the survival of girls is lower than that of boys—that is, the sex ratio of mortality is less than 100—suggest that differential treatment or access to resources may be putting girls at a disadvantage. Earlier studies have found numerous countries in many regions of the world, particularly southern Asia, eastern Asia, and northern Africa/western Asia, where mortality at ages 1–4 y was higher for girls than for boys [1],[10],[11]. India and China in particular have a long-documented history of female disadvantage in mortality that is covered in an extensive literature [12]–[25].
The present study addresses the challenges outlined above in the estimation and interpretation of sex differentials. I estimate smoothed trends in the sex ratio of mortality for infants and young children to evaluate changes over time in the relative mortality of males and females for individual countries and world regions. I also examine regional differences in the relationship between the overall level of under-five mortality and sex ratios of infant and child mortality.
Data Sources
The three indicators of childhood mortality can be estimated directly or indirectly from demographic data sources. Direct estimates of 1q0, 4q1, and 5q0 are calculated from reported deaths and information on the population exposed to the risk of death, and do not require the use of models for their derivation. Direct estimates may be based either on vital events data—normally from a vital registration system or in some cases from information about household deaths collected in a census or large survey—or on survey questions posed to adult women about their childbearing histories. The childbearing histories collected in surveys comprise the date of each live birth and the age at death of children who have died before the survey date. Period-specific probabilities of dying calculated from birth histories are based on reported deaths and the numbers of children at risk of dying during a specific period, such as the 5 y preceding the survey [26].
Indirect estimates of mortality in childhood are derived from summary data on the total number of children ever born and number surviving to women of reproductive age. The method used to derive indirect estimates (also known as the Brass method) is described in detail in a United Nations manual [27]. A large number of censuses and surveys have collected the required data, although the data are not always collected or published by sex (that is, the questionnaires do not always ask about sons and daughters separately, or, if they do, the separate tabulations may not be published). The Brass method translates proportions of children surviving classified by age of mother at the time of data collection into measures of survival to various childhood ages, which in turn can be transformed into standard indicators of childhood mortality using model life tables. Under-five mortality is the preferred indicator derived via the Brass method, because 5q0 is more robust to the choice of model life table than 1q0, which can vary considerably according to the model selected. For this reason, indirect methods do not provide a satisfactory basis for estimating sex ratios of 1q0, since the sex ratios obtained for this indicator through indirect methods are more affected by the choice of mortality model used than are differentials in 5q0.
The dataset used for this study builds upon datasets [28]–[30] that were developed for a 1998 United Nations publication on sex differentials in childhood mortality [1] and expanded for a 2011 report [2] (data collection for [2] was completed in 2010; the present study incorporates additional or revised data obtained through November 2011). Microdatasets from Demographic and Health Surveys (DHS) were processed to produce a time series from each survey of direct estimates of 5-y mortality rates by sex, extending back to a period 20–24 y before each survey. In addition, tabulations of children ever born and children surviving by age of the mother were calculated by sex of the child for each DHS survey to produce indirect estimates of 5q0. An important new source of data since the mid-1990s is the Multiple Indicator Cluster Survey program, conducted by the United Nations Children's Fund, which has yielded additional sets of indirect estimates by sex, many for countries that had very limited data by sex from other sources. For other survey programs (including the World Fertility Survey, the Reproductive Health Survey, and the Pan Arab Project for Family Health), surveys not affiliated with the major survey programs, and censuses, the data used here are any direct or indirect estimates by sex available in published sources, or calculated from tabulations available therein. In addition, the number of data points from vital registration was greatly expanded. A large dataset of infant and under-five mortality by sex calculated from vital registration data was provided by the World Health Organization. These data were supplemented with registration data from the Human Mortality Database, the United Nations Demographic Yearbook, and other sources of life tables.
Table S1 lists the data sources considered for each country.
Data Issues
Vital registration data.
Data derived from the complete registration of births and deaths are the ideal basis for the estimation of mortality, since they cover the full set of events of interest and permit the estimation of trends. Unfortunately, in most developing countries the coverage and completeness of registration by vital registration systems is insufficient to produce accurate estimates of the level of childhood mortality. However, in the absence of evidence that reporting of births and deaths differs by sex of the child in a way that would affect the ratio of male to female mortality, such ratios derived from vital registration may usefully inform trends of sex differentials. The sex differentials in 1q0 and 5q0 calculated from vital registration data were used without adjustment, even when overall births and child deaths were known to be under-registered, on the assumption that under-registration in vital registration systems did not differ by sex of the child. More study is required to assess whether this assumption is valid. For most countries, however, sex differentials estimated from vital registration are consistent with those calculated from survey birth history data and often have considerably less variability. The same assumption of sex-neutral underreporting was made for data from census or survey questions on household deaths.
Survey data.
Compared to most measures estimated by sample surveys, deaths of children are relatively rare events. The sample sizes of typical household surveys are not large enough to produce very precise estimates of childhood mortality, even for both sexes combined at the national level. In a study of 50 DHS surveys, Curtis [31] showed that relative standard error for estimated infant and under-five mortality over a 5-y period for both sexes at the national level ranged from 0.04 to 0.08, implying that the 95% confidence interval ranged from 8% to 16% on each side of the point estimate. For child mortality, relative standard errors were higher, in the range of 0.06 to 0.15, because fewer deaths occur at ages 1 to 4 y.
Such large sampling errors, which are even larger when estimates are disaggregated for a subset of the sample, complicate the assessment of trends in differential mortality by sex. For example, the male-to-female ratio of infant mortality calculated from birth histories for the Haiti 2000 DHS survey was 141 for 1991–1995 and 93 for 1996–2000, while the corresponding ratios for child mortality were 88 and 105. If taken at face value, the reported ratios would imply that the situation in Haiti changed from one in which there was excess male mortality under age 1 y and excess female mortality between ages 1 and 4 y to a reverse situation in only 5 y. The trend estimates derived in this study smooth out such fluctuations through the application of regression techniques described below.
Some important potential non-sampling biases in survey reports of childbearing histories include errors in the dating of births and deaths or omission of events from the birth history. Incorrect assignment of dates to events—for example, the heaping of date of death on 12 mo of age—can have an effect particularly on the relative levels of 1q0 and 4q1. Fortunately, for the purposes of the present study, such misdating is unlikely to occur differentially for the deaths of boys and girls, so it is unlikely to have a major impact on the sex differentials in either of these indicators. Omission of children from the birth history, on the other hand, might be more likely to differ by sex of the child. In most cases, however, there was insufficient data from alternative sources to assess whether sex-differential omission from survey birth histories was occurring. The exception was in India, where examination of sex ratios of infant mortality (SR1) from the Sample Registration System and from the National Family Health Surveys revealed systematic differences in the sex ratio of infant mortality between the two sources, with SR1 estimates from the National Family Health Surveys being consistently higher than those from the Sample Registration System. For the sex ratio of child mortality, in contrast, the two sources produced consistent estimates. The discrepancy in SR1 could be due either to defects in the sample registration system that understate male mortality, or to omission from the survey birth histories of girls who died, thus inflating survey estimates of excess male mortality. The assessment was made that the difference in SR1 was most likely due to underreporting in the National Family Health Surveys birth histories of babies who died shortly after birth, with daughters who died more likely to be omitted than sons who died [2],[32].
Availability of recent data.
A final caveat refers to the availability of data for the 2000s. In many cases, the last available data point refers to 2005 or earlier (Table S1), and the estimates for the latter part of the decade are a projection of the earlier trend.
Estimation Methods
The estimation of sex differentials in under-five, infant, and child mortality proceeded in three basic steps: (1) estimate trend in the sex ratio of 5q0 (SR5); (2) estimate, and adjust if appropriate, trend in the sex ratio of 1q0 (SR1); and (3) apply those trends to both-sexes estimates of 5q0 and 1q0 to derive estimate and sex ratio of 4q1 (SR4).
In the first step, a weighted trend line SR5t was fitted to all available SR5 estimates. The weights for data points from surveys, censuses, and vital registration were determined using a weighting scheme used in previous work by the UN IGME [33],[34]. This weighting scheme assigns progressively lower weights to direct estimates from birth histories that refer to 5-y time periods more distant from the survey date, on the assumption that recall errors may affect distant periods more strongly. For indirect data, low or zero weights are assigned to indirect estimates that are based on reports of women in the early and late childbearing years, on the assumption that these estimates may be of lower quality or subject to systematic biases.
Because of variations between countries in the amount and consistency of data available, three different methods were ultimately employed to estimate sex ratios of mortality. Initially, loess regression was tested for all countries. The loess method fits a series of polynomials to localized subsets of the data centered on each point of the dataset. The weight of each data point in the localized regression is determined by its distance from the center. A bandwidth, denoted alpha, selected by the user, determines the proportion of the dataset used to fit each local regression. A number of different alpha values were tested, to impose varying degrees of smoothing. For countries where estimates were based on a time series of vital registration data, it was found that the loess with an alpha of 0.75 captured changes in trend without being overly sensitive to short-term variation. In addition, a re-descending M estimator with Tukey's biweight function was applied in the loess procedure in R (family = "symmetric") to reduce the influence of more extreme data points. The case of Bulgaria is shown in Figure 2A to illustrate the loess fitting method.
(A) Loess method, applied to Bulgaria. (B) Linear method, applied to the Dominican Republic. (C) Average method, applied to Lao People's Democratic Republic. Points shown in gray were assigned zero weight in the weighting scheme. The fitted SR1 was adjusted in the linear and average methods to account for the exclusion of indirect data. VR, vital registration.
For countries where the primary sources of data were sample surveys, however, the degree of smoothing required to achieve plausible results with the loess often was so strong that the results differed little from a simpler linear regression. The linear regression line had the additional benefit of being more straightforward to adjust for SR1, as described below. Therefore, for many developing countries the results are based on robust linear regression (implemented with the rlm function in the R MASS package and hereafter referred to as the "linear method"), shown for the Dominican Republic in Figure 2B.
There were a number of countries where neither loess nor robust linear regression fitted to all data points was able to produce a result that was satisfactory for purposes of analysis or disaggregation. In a few of these countries, where time series of vital registration data were available to fit a stable trend and survey data had high sampling error, the decision was made to exclude the survey data and fit the loess or robust linear regression to the vital registration data only. In countries where such a stable time series was not available, a simple weighted average of all available SR5 data was computed (as in Figure 2C for Lao People's Democratic Republic). Such an average may be useful for disaggregating both-sexes estimates if no other method is available, but it does not give any information on time trends in SR5. For this reason, results from the average method are not analyzed at length, and countries where it was used are excluded from the time trend analysis for regional aggregations presented below. Table S2 indicates which of the methods—loess, linear, or average—was used to produce estimates of sex differentials for each country.
The second step of the estimation process was to fit a trend line SR1t to available data on SR1. As was noted in the previous section on data sources, indirect data on SR1 were not included in the analysis because SR1 is less robust than SR5 to the choice of model life table. Yet, using only direct data for SR1 while using both direct and indirect data for SR5 could cause inconsistency between time series fitted for SR1 and SR5. This was particularly the case in countries where a number of surveys had collected indirect data only. Therefore, in order to best exploit the available information, direct and indirect estimates of SR5 were used to adjust direct estimates of SR1 in the linear and average methods. A preliminary trend line, SR1t*, was fitted to SR1 from direct data using the same fitting method that had been selected for SR5t. If no indirect data had been used to fit SR5t, then SR1t* was adopted as the final estimate SR1t. If both direct and indirect data had been used to fit SR5t, an additional trend line, SR5t*, was fitted to SR5 points coming from direct data only. The ratio of SR5t/SR5t* was used to adjust SR1t*, producing the final estimate SR1t. Figure 2B and 2C show the results of this adjustment for the Dominican Republic and Lao People's Democratic Republic, respectively.
Predicted SR5t and SR1t were applied to estimates of 5q0 and 1q0 for both sexes to produce time series of infant and under-five mortality levels by sex. Levels of 5q0 by sex for time period t were derived from the both-sexes estimates using the formulas(2)and(3)where SRB is the sex ratio at birth as estimated for each country for the period 2000–2005 in World Population Prospects: The 2010 Revision [35]. Corresponding formulas were applied for infant mortality. Then, 4q1male and 4q1female were derived via the relationship in Equation 1. The resulting 4q1male and 4q1female were used to compute estimates of SR4t. These derived estimates of SR4t were compared to direct data on SR4 from surveys or vital registration and generally found to be consistent.
Estimates of under-five and infant mortality rates for both sexes combined were taken from two United Nations sources, World Population Prospects: The 2010 Revision, produced by the Population Division [35], and Levels & Trends in Child Mortality: Report 2011, produced by the UN IGME [36],[37]. The estimates from these two sources are generally quite similar for 5q0—the indicator coordinated by the UN IGME—but can differ somewhat more for 1q0, usually because of the use of different model life tables. The both-sexes estimates for 5q0 and 1q0 from World Population Prospects [35] were used in this report for most developing countries (noting that for 5q0 the estimates referring to periods prior to 1980 are unpublished). The choice of which series of both-sexes estimates to use does not affect the estimated trends SR5t or SR1t, as those come from the data, but it does affect estimated trends in SR4t because the trend in the sex ratio of 4q1 derived from estimated 5q0 and 1q0 is dependent on the relative levels of 5q0 and 1q0 as well as the sex differentials in each. There were only a few cases where the choice of both-sexes estimate made an appreciable difference in SR4t. For countries of the more developed regions, estimates from Levels & Trends in Child Mortality [36] were used because levels of 5q0 and 1q0 from this set of estimates are taken directly from annual vital registration.
For countries where averages of SR5 and SR1 were employed, these average ratios were applied to the whole series of both-sexes estimates. It should be noted that applying constant SR5t and SR1t to changing both-sexes estimates results in SR4t values that change over time. However, these changes in SR4t should not be interpreted as trends and will not be presented as such. As noted above, estimates for countries where the average method was used are not included in the aggregated trends for regions and development groups presented below.
Estimates were attempted for all countries or areas (hereafter referred to as countries) that had a population of 1 million or more in 2010. Out of the 156 countries with such a population, estimates were generated for 153 countries (Table 1). Of these, 113 were in less developed regions, comprising Africa, Asia excluding Japan, Latin America/Caribbean, and Oceania excluding Australia and New Zealand (the lists of countries located in the less developed regions and more developed regions as well as the other geographical groupings used for this study—sub-Saharan Africa, northern Africa/western Asia, eastern/southeastern Asia, Commonwealth of Independent States [CIS] Asia, Latin America/Caribbean, and developing Oceania—are shown in Table S3). Ninety-two countries in the less developed regions, containing 92% of the population of those regions, had sufficient data to apply the methods developed for trend analysis. For an additional 21 countries, holding 6% of the population of the less developed regions, enough data were available to estimate average sex differentials in under-five or infant mortality, which were assumed to apply to the entire time span under consideration.
Trends were estimated for 39 countries in the more developed regions (comprising Europe, northern America, Japan, Australia, and New Zealand), while for one developed country (Bosnia and Herzegovina), only average sex differentials could be estimated.
The methods and results presented in this article were developed as an analytical study separate from the production of United Nations mortality estimates published in Levels & Trends in Child Mortality [36] or World Population Prospects [35]. The mortality estimates by sex presented here may differ from estimates in forthcoming editions of those publications due to differences in data availability or other methodological considerations.
Trends in the sex ratios of under-five, infant, and child mortality are summarized as decade averages for the 1970s, 1980s, 1990s, and 2000s. Two different approaches to examining global and regional trends are examined: first, considering each country as a unit of analysis for computing median country-specific ratios (Table 2) and, second, weighting country mortality rates by number of births to produce weighted regional averages (Table 3). The country results on which the regional analyses are based may be found in Table S2. Only the 131 countries for which trend estimates were produced were included in the computation of regional medians and averages.
Table 2 shows that in the less developed regions the median sex ratio of under-five mortality increased from the 1970s to the 2000s. For the 92 countries in the less developed regions for which trends were estimated in this study, the median sex ratio of under-five mortality increased from 111 in the 1970s to 117 in the first decade of the 2000s. Thus, in the majority of developing countries, females have an advantage in survival to age 5 y, and this advantage has increased, as expected from the historical experience of developed countries as described above, as mortality has declined. This increase is due primarily to increases in the sex ratio of mortality at ages 1–4 y in many countries, while increases in the sex ratio of the infant component of under-five mortality have been smaller.
However, when countries are weighted according to the number of births, no such rise in the sex ratio of under-five mortality is seen. On average, the sex ratio of under-five mortality in the less developed regions remained nearly constant, around 101 to 102, from the 1970s to the 2000s (Table 3; Figure 3). This difference between the median trend and the birth-weighted trend occurs because the sex ratios of under-five mortality estimated for the two most populous countries, China and India, constitute important exceptions to the rising trend. Estimated sex ratios of under-five mortality for the first decade of the 2000s were below 100 in both countries (Table 3), indicating substantial excess female mortality. In China, the sex ratio of under-five mortality declined between the 1970s and the 2000s, while in India it remained roughly constant, suggesting that even though mortality rates were falling in both countries, girls did not share in survival improvements to the expected extent.
Dashed line is the historical sex ratio of under-five mortality for selected developed countries from Hill and Upchurch [9].
Moreover, China and India were the only two countries in the world where female infant mortality was higher than male infant mortality in the 2000s. In China, the sex ratio of infant mortality fell from 112 in the 1970s to 76 in the 2000s (Table 3), that is, from a situation where infant mortality was 12% higher for boys than for girls to one where infant mortality was 24% lower for boys.
In India, female infant mortality was roughly equal to or slightly higher than male infant mortality throughout the decades examined, but girls' survival disadvantage was particularly acute in the 1–4-y age group. In the 2000s, the ratio of male to female child mortality was estimated at 74 (Table 3), meaning that girls' mortality between ages 1 and 5 y was more than 30% higher than boys'. While the estimates suggest that the sex ratio of child mortality may have increased somewhat in India since the 1980s, girls remain disadvantaged in mortality, compared both to the sex differences found in other parts of the developing world and to the historical experience of developed countries at the same level of mortality.
The lower relative survival of girls to age 5 y in China and India has a large impact on estimates of average sex differentials for their respective regions of Asia, as well as on the average for the less developed regions. The average sex ratio of under-five mortality for eastern and southeastern Asia declined from 109 in the 1970s to 94 in the 2000s (Table 3). However, the average for the countries of the region apart from China rose from 117 to 120. The sex ratio of under-five mortality in southern Asia rose slightly from 92 to 95, but increased more steeply, from 102 to 109, in the countries of the region other than India. The estimates in this study suggest that the survival disadvantage of girls has lessened more in other countries of southern Asia than in India, with the exception of Nepal.
In many of the less developed regions, girls' past disadvantage in mortality at ages 1–4 y appears to be easing. The regions of northern Africa/western Asia, eastern/southeastern Asia, southern Asia, CIS Asia, and Latin America/Caribbean all experienced increases in the average sex ratio of child mortality of 6 or more percentage points (Table 3). In sub-Saharan Africa, however, there was essentially no change in the average sex differential of child mortality, with increasing ratios in many countries offset by decreasing ratios in others. For the less developed regions on average (excluding China and India), girls went from a situation of slight disadvantage in mortality at ages 1–4 y in the 1970s to a slight advantage in the 2000s. However, the average ratios of child mortality in all regions of the developing world remain below those expected based on the historical experience of some developed countries at similar levels of mortality (Figure 4).
Dashed line is the historical sex ratio of child mortality for selected developed countries from Hill and Upchurch [9].
The rising regional average sex ratios of child mortality mask a number of cases where the estimates suggest continued or worsening female disadvantage in mortality at ages 1–4 y. While the case of India was highlighted above because of its weight in regional and world averages, there are many other countries where mortality in this age group was higher for girls than for boys in the 2000s. The countries where excess female child mortality was apparent in the 2000s are indicated in Figure 5. While countries with excess female mortality can be found in most regions of the developing world, there are notable concentrations in southern Asia and in the western and middle regions of sub-Saharan Africa, as well as several countries in northern Africa/western Asia. While data quality issues may affect the reliability of these estimates, countries with apparent female disadvantage merit further study to see if differential treatment is an issue.
Among infants under age 1 y, girls continue to have the advantage in survival in all countries apart from China and India. However, the female survival advantage in infancy in most of the developing world is not as great as would be expected based on the historical experience of some developed countries at similar levels of mortality (Figure 6). It cannot be stated with certainty whether this finding is due to differences in the treatment of girls and boys, to factors such as differences in cause-of-death patterns or the rollout of medical interventions in different locations at a given level of mortality, or to issues with the quality of the data for some countries that affect the estimates in a systematic way.
Dashed line is the historical sex ratio of infant mortality for selected developed countries from Hill and Upchurch [9].
In the more developed regions, ratios of male to female infant mortality have been declining in recent decades (Tables 2 and 3; Figure 6), meaning that the male disadvantage in infant mortality is becoming smaller. This trend started in certain countries in the 1970s and has since spread to almost all of the developed countries and to a number of countries in the less developed regions that have relatively low levels of mortality. The change in trend may be attributable to improvements in neonatal care that have decreased deaths from prematurity and respiratory distress, causes that have a greater impact on male infants [38]. However, further study is required to confirm the causes of this trend.
Several countries had findings of unusually high sex ratios of infant mortality (greater than 130), suggesting a greater than expected degree of male disadvantage in survival. These countries, found in both more developed and less developed regions, include many of the European and Asian countries of the former Union of Soviet Socialist Republics (Figure 7). The high ratios could be due to lack of access to the advances in medical care that have led to declining sex ratios of infant mortality in most of the more developed regions, but again, more detailed examination of causes of infant deaths by age and sex is required.
Our estimates of long-term trends in the sex ratios of infant, child, and under-five mortality show that in the majority of countries in the less developed regions, the ratio of male to female under-five mortality has increased since the 1970s. This is due primarily to increases in the sex ratio of mortality at ages 1–4 y, while changes in the sex ratio of infant mortality have been smaller. There remain, however, a number of developing countries where girls have higher mortality than boys at ages 1–4 y, with concentrations in middle and western sub-Saharan Africa, northern Africa/western Asia, and southern Asia. Estimated infant mortality was higher for girls than for boys in only two countries, India and China. Meanwhile, in the more developed regions, a reversal of the historically rising trend in sex ratios of infant mortality has been observed as countries approach very low levels of mortality.
Estimates of under-five mortality levels are receiving intense focus as the world nears the 2015 target date for the Millennium Development Goals. The target for Millennium Development Goal 4 calls for reducing under-five mortality by two-thirds from its 1990 level, and efforts to strengthen child survival programs are intensifying. In many areas of the world, advances in survival appear to be accruing relatively equitably to girls and boys, in line with the changes in sex differentials expected given the changing cause-of-death patterns that accompany mortality decline. However, this is not universally the case. Findings of low or declining sex ratios of infant or child mortality in a number of countries that still have relatively high mortality may merit concern, as they suggest that girls in these countries may not be sharing fully in the recent improvements in survival. Further study is needed to confirm these findings, to identify why girls' relative survival may not be keeping pace in some countries, and to assess interactions with other barriers to care such as poverty or marginalization.
Studying Regions with Excess Female Mortality
Regions where concentrations of excess female child mortality were found would benefit from in-depth cross-national or sub-national studies of cause-specific mortality and mortality determinants by sex. Regions such as middle and western sub-Saharan Africa, northern Africa/western Asia, and southern Asia each have a number of countries with excess female child mortality, but also have countries where mortality is (or has become) higher for boys. Case studies from countries that have been successful in reducing inequalities in the survival of girls and boys—whether this was a conscious policy choice or an indirect outcome of generally expanded access to interventions—could provide useful insights and guidance for the planning of child health interventions and health system improvements.
The situation of girls in China and India, already well documented in the literature, merits continued study, as there is evidence that girls are not benefiting as much as boys from the mortality declines in these countries. The interaction of strong son preference and declining fertility has continued implications for the health and survival of girls in these countries. Both countries have implemented policies and programs intended to improve the status of girls and women as well as directly influence families' treatment of girls [2], but the available data, which refer most recently to 2005 for China and 2009 for India (Table S1), do not indicate significant change as yet in girls' relative survival to age 5 y, and trends in these countries should be reassessed as new data become available. In both countries, media and policy attention in recent years have concentrated largely on sex-selective abortion—that is, prenatal discrimination—but differences in postnatal treatment still have mortality consequences for large numbers of girls, particularly in India, where relatively high infant and child mortality rates mean that a significant number of excess deaths still occur.
The methods presented here have the benefit of producing comparable results for countries in a wide variety of data situations. They are valuable for making sense of noisy data from the often patchy collection of survey, census, and vital registration sources for many developing countries. Yet, they are also useful for analyzing sex differentials even in situations of good data quality and when mortality is low, because year-to-year fluctuations in the sex ratio of mortality can be substantial when numbers of infant and child deaths are low, as is the case even in very large countries with low mortality levels.
The methods are subject to a number of limitations. Despite the use of robust regression methods to limit the influence of extreme data points that are due to sampling error, the nature of the data—which for many countries can be variable, sparse, or from sources for which data collection quality cannot be adequately assessed—does not permit strong conclusions. Particularly in the case of countries with only a few sources of data, addition of a new source may change the results substantially. Future work should aim to quantify the uncertainty of the estimates.
Another limitation, from an analytical standpoint, is that sex differentials in overall under-five mortality cannot be explained well without a nuanced understanding of mortality in the component age groups. The reliance on the sex ratio of 4q1 as an important indicator of sex differentials in this study is somewhat problematic. This ratio is calculated from sex-specific estimates of 4q1 that are derived after fitted trends in the sex ratios of infant and under-five mortality are used to disaggregate both-sexes estimates of infant and under-five mortality. Thus, the sex ratio of 4q1 is sensitive both to the fitted sex ratios of infant and under-five mortality, and to the relative levels of both-sexes infant and under-five mortality used as inputs. However, in cases where direct data on 4q1 by sex were available for comparison, the derived trends in the sex ratio of 4q1 were generally consistent with the empirical trends.
The estimates derived here will be useful for incorporation into life tables for the estimation of mortality and population change from the 1970s until today. In this way, they represent an advance over earlier studies of sex differentials in child mortality. However, the usefulness of the fitted trends for the projection of sex differentials in individual countries may be limited, particularly in cases where the estimated increases or decreases are steep and such rapid change cannot sensibly be projected into the future.
The results obtained here could be a first step in developing a model based on the experience of countries with low mortality, or on regional trends, to blend with the estimates, for purposes of projection. Such a model will also be useful for countries with little or no information on which to base estimates of sex differentials in mortality. However, I believe there is merit in using country-specific data to the extent possible, especially if the intent is to have policy-relevant monitoring of sex differences in mortality. Models based strictly on levels of mortality or regional dummies could mask findings that should prompt further investigation, such as the different trends in sex differentials among countries within regions such as northern Africa/western Asia and western Africa.
Despite the limitations detailed above, the methods developed here may be useful for the international community. The methods can be easily implemented by researchers or national authorities working with standard registration or survey data in their own countries. Applied cross-nationally, the methods can advance understanding of the dynamics of childhood mortality by sex around the world.
Supporting Information
Table S1.
Data sources.
Table S2.
Sex ratios and levels of infant, child, and under-five mortality for countries, 1970s–2000s.
Table S3.
Regional groupings used in the study.
The author recognizes colleagues in the United Nations Population Division/Department of Economic and Social Affairs, and other agencies participating in the UN IGME for their work in producing the mortality time series on which mortality levels in this article are based. Additional thanks are due to Leontine Alkema, Sara Hertog, François Pelletier, and John Wilmoth for detailed comments on earlier versions of the paper.
Author Contributions
Conceived and designed the experiments: CCS. Performed the experiments: CCS. Analyzed the data: CCS. Wrote the first draft of the manuscript: CCS. Contributed to the writing of the manuscript: CCS. ICMJE criteria for authorship read and met: CCS. Agree with manuscript results and conclusions: CCS.
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text:
At a Glance
Heparin-induced thrombocytopenia (HIT) should be suspected if the platelet count decreases by 50% (at least 30%) during or within 2 weeks after administration of unfractionated heparin or low molecular weight heparin or fondaparinux, especially if associated with a new thrombosis. The platelet count typically starts falling 5-14 days after the start of heparin.
Patients who were pre-exposed to heparin within the previous 4 weeks may have circulating PF4/heparin antibodies. In these patients, the platelet count falls within hours after re-exposure. In a subset of these patients who receive a heparin bolus, generalized reactions, which are caused by immune complexes, may occur.
Because HIT is a prothrombotic condition, bleeding complications are rare. In addition, the platelet count in HIT usually does not decline below 20G/L. In patients with very low platelet counts and especially in bleeding patients, other causes of thrombocytopenia are much more likely than HIT. Clinical scoring systems are helpful in assessing the probability that HIT is the cause for the low platelet count. The 4 T-score results in 0-8 points. HIT should be considered unlikely if the resulting score is less than or equal to 3 points. If the 4 T-score is 4-6 points, HIT is moderately or very likely (7-8 points) and laboratory work-up should be initiated to rule out HIT (See Table 1).
Table 1.
|Thrombocytopenia (acute)
|>50% platelet fall (nadir ≥20 x109/l)
|30-50% platelet fall (or >50% fall due to surgery); or nadir 10-19 x109/l
|<30% platelet fall; or nadir ≤10 x109/l
|Timing of platelet count fall, thrombosis, or other sequelae (first day of heparin course=day 0)
|Onset between days 5 and10 or ≤1 day (if heparin exposure within past 30 days)
|Consistent with day 5-10 fall, but not clear (e.g. missing platelet counts or ≤ 1 day with heparin exposure within the past 31-100 days; or platelet count fall after day 10)
|Platelet count fall ≤ 4 days without recent heparin exposure
|Thrombosis or other sequelae (e.g., skin lesions, ASR)
|New thrombosis, skin necrosis; acute systemic reaction after IV heparin bolus
|Progressive or recurrent thrombosis; erythematous skin lesions; suspected thrombosis (not yet proven); asymptomatic upper-limb DVT
|Other cause of thrombocytopenia
|No explanation (besides HIT) for platelet count fall is evident
|Possible other cause is evident
|Definite other cause is present
What Tests Should I Request to Confirm My Clinical Dx? In addition, what follow-up tests might be useful?
Patients should only undergo testing if clinical features reasonably suggest a diagnosis of HIT. The "pathological" criterion is the detectability of "HIT antibodies" in acute patient serum or plasma. HIT is caused by heparin-dependent platelet-activating antibodies that, in almost all patients, recognize complexes of platelet factor 4 (PF4) bound to heparin. There are two types of assays: antigen assays measuring binding of antibodies against PF4/heparin complexes and functional assays measuring heparin dependent platelet activation by the patient serum.
Sensitivity of enzyme-immunoassays (EIAs) for anti-PF4/heparin antibodies is very high (~99%). A negative result of a HIT antibody test generally rules out HIT. However, the high sensitivity of the PF4/heparin EIAs causes the problem of detecting clinically-insignificant anti-PF4/heparin antibodies, which can be present coincidentally in patients with thrombocytopenia caused by non-HIT factors. This leads to "overdiagnosis" of HIT, which has become a serious clinical issue.
Diagnosis for HIT is optimized by combining the anti-PF4/heparin EIA with a functional (platelet activation) test. Platelet activation assays (especially using "washed" platelets, such as the serotonin-release assay (SRA) or the heparin-induced platelet activation (HIPA) test), are much more specific for HIT. Only about 50% of those who test EIA-positive also test positive in a washed platelet activation assay. Functional assays based on platelet-rich plasma or whole blood are less sensitive with potential for false-negative results. If the first-line test is an immunoassay, the functional assay could be restricted to patients testing positive in the EIA.
Are There Any Factors That Might Affect the Lab Results? In particular, does your patient take any medications – OTC drugs or Herbals – that might affect the lab results?
Patients should be tested for IgG class antibodies only, as only this isotype class activates platelets.
The strength of a positive EIA test result (expressed in optical density (OD) units) correlates strongly with the presence of platelet-activating antibodies and the risk of thrombosis. A weak-positive result (e.g., an OD <1.0 in the EIA together with a low 4T-score) is very unlikely in HIT.
Showing inhibition of antibody binding in the presence of high heparin concentrations (100 IU/mL) increases specificity of the EIA. (CAUTION: Strong platelet-activating antibodies may not be inhibited by high heparin.)
What Lab Results Are Absolutely Confirmatory?
There is no laboratory test absolutely confirmatory of HIT. The strength of HIT testing is exclusion of HIT. A negative EIA excludes HIT by 99%; a negative washed platelet functional assay excludes clinically relevant antibodies by more than 90%. The best approach, currently, is to combine the clinical score with the laboratory test result.
Additional Issues of Clinical Importance
After a negative antigen test, routine repeat testing a few days later is not indicated, unless a new platelet count decline or thrombosis occur. Such testing risks detecting clinically-irrelevant antibodies, which is especially relevant after major surgery and in intensive care unit (ICU) patients.
Over-diagnosis of HIT is potentially dangerous, because it may lead to withholding of certain diagnostic and therapeutic interventions not only during the acute event, but also at future admissions. Further, the diagnosis of HIT usually prompts treatment with alternative anticoagulants, which may increase bleeding risk.
Errors in Test Selection and Interpretation
HIT antibodies are transient. Testing a blood sample obtained several weeks after the clinical manifestation of HIT can cause false-negative results.
The most common mistake made in the interpretation of test results for HIT is interpretation of a positive antigen test as confirmation of HIT. These assays can only exclude HIT. Especially anti-PF4/heparin IgM and IgA antibodies do not confirm HIT. A positive antigen assay requires confirmation by a functional assay.
Copyright © 2017, 2013 Decision Support in Medicine, LLC. All rights reserved.
No sponsor or advertiser has participated in, approved or paid for the content provided by Decision Support in Medicine LLC. The Licensed Content is the property of and copyrighted by DSM.
- At a Glance
- What Tests Should I Request to Confirm My Clinical Dx? In addition, what follow-up tests might be useful?
- Are There Any Factors That Might Affect the Lab Results? In particular, does your patient take any medications - OTC drugs or Herbals - that might affect the lab results?
- What Lab Results Are Absolutely Confirmatory?
- Additional Issues of Clinical Importance
- Errors in Test Selection and Interpretation

subset: commoncrawl


text:
In a world where Two And A Half Men rules, it doesn't seem like a brilliantly satirical show like Reno 911! should have lasted this long. But the improvised fake-reality show, which follows the bumbling officers of the fictional Reno Sheriff's Department, debuted on Comedy Central in 2003, and it's still going strong, with three seasons now on DVD and the big-screen spin-off Reno 911!: Miami debuting in theaters this week.
Creators Ben Garant, Kerri Kenny, and Tom Lennon all cut their teeth on MTV's mid-'90s sketch-comedy show The State, and on Viva Variety, The State's stranger, short-lived spin-off. Reno 911!'s ensemble has a real improvisational chemistry, and the characters are surprisingly sympathetic, considering their often-disturbing antics. Like Borat's Sacha Baron Cohen, Lennon and Garant have done all press for the film in character, as Lt. Jim Dangle and Deputy Travis Junior, respectively. But for the first time, they broke character to talk to The A.V. Club about the film, Reno's surprise success, punching up movie scripts, and Lindsay Lohan's breasts. Their alter egos pitched in to discuss law enforcement in Reno here.
The A.V. Club: Why do you think Reno has caught on where your previous shows didn't?
Tom Lennon: There's a widely misperceived notion that The State was canceled, which is not true. Actually, MTV wanted to renew it, what I heard, for a minimum of 65 more episodes. We were very, very badly advised at the time. [Laughs.] We never really heard about that offer until much later. We had been advised to try to do these specials for CBS, which seemed like a big opportunity. But had it been up to MTV—to exonerate them somewhat in history, they actually did want to do many more State episodes, and in hindsight, it probably would have been a good thing to do. We were just hitting a stride. But it's fun to be The Police of sketch comedy, who couldn't keep it together. [Laughs.]
Ben Garant: When we were doing Viva Variety, we knew, "Boy, this really cracks us up, but this is not for everybody. This is a little weird." [Laughs.] In sketch comedy, people had characters that they loved and characters that they didn't love, but the characters never came back. But for Reno, I feel like we've stumbled on characters we really like. It's still basically a sketch show, but you have these main guys—you can pick a favorite one and grab onto and watch, so I think that's a big part of it. Sketch shows change gears so drastically every two minutes. I think sketch shows are for sketch fans; they're not really for everybody. I think Reno combines what we do, which is sketch, but makes it into a situation comedy so people can tune into their favorite guys every week.
AVC: On a related note, what have you learned from your friends' shows, like Stella and Dog Bites Man, that didn't catch on?
BG: Honestly, if Stella had been on longer, it probably would have found an audience. We found an audience gradually. Stella should have been cheaper; Stella was twice the budget of our show—and honestly, it's that simple. If they had shot it at a production model more like their shorts that they did on the Internet, it would still be on.
TL: You know, it's really, really hard to keep a show on the air, and honestly, I think the formula we've figured out for Reno—"have characters that people want to keep seeing what's happening to them"—is totally by accident. Honestly, Reno was not ever supposed to have that. It was supposed to be just a sketch show in the Cops format, where we played both the cops and all the perps, exactly like a Python film. But we accidentally added this sort of soap-opera aspect to it, really not by choice. It just sort of happened. [Laughs.] People have always been telling us, "You've gotta have characters people wanna see, that people are going to want to hear their stories." And that worked. We play eight stereotypes, but very, very sincerely.
BG: If we keep it under budget, [Comedy Central doesn't] give us notes. We didn't get a single note on the comedy. As long as we stay under budget, we're totally under their radar, and we can do anything we want.
AVC: So Comedy Central has never put the kibosh on the racier material?
BG: No, the first season before we had aired, we had to iron out what we thought the sensibility was, and what they thought the sensibility was. We had a lot of gay stuff and a lot of very dark stuff, which at first they were a little reluctant to do. Now they can see that our characters are basically likeable guys, even though they're doing stuff that's really off what most people would think of sitcom-y characters. It took some convincing, but now they let us do what we want.
AVC: You don't work from a script on the show. Was the film the same way?
BG: No, we have outlines. We know basically the beginning of the sketch and the end. We know we're going to walk up to a whale because we think it's a topless beach, and then when we get there, it's not topless chicks that need help, it's a whale. We're gonna try and push the whale around, and then at the end, we're gonna blow it up. But we don't write dialogue, and we don't rehearse it. We get an outline, and that's it. The outline is really for production purposes, so that we know the whale's going to take a long time to build, we've got to trigger it to blow, we need the hottest topless chick we can find, she needs to speak a foreign language so it gets us to the beach not knowing there's a whale there. But we don't rehearse. We just hire people we know are going to be funny.
AVC: When you watch the first season now, do you notice changes in your improvisation skills?
TL: Oh my God. When we started with the first season and the pilot, our improv skills were zero; we'd never done it, ever. Everything we'd done, we'd spent a lot of time on the scripts, rewriting. We never really riffed on them ever. So it was pretty much on-the-job training out of the gate, although our format of the improvisation is directly against the traditional "yes and" format. We just tell people "Stop," which means, "Keep doing what you're doing." Luckily we're doing a very easy kind of improv. [Laughs.]
AVC: Did you approach the film as a feature-length version of the show?
BG: Well, we knew we had to do a couple of things. We knew we needed to have a bigger plot. We knew we couldn't actually solve the mystery at the end, because that's not really what we do. But we knew we had to have some kind of big, overarching, unfolding plot that would keep people watching for an hour and a half. As a fan of sketch movies like [Monty Python And The] Holy Grail—there's a point 50 minutes into Holy Grail where you start to get a little bored, even if you really like it. So we knew that, and we knew we had to show the characters doing things that we've never done before on the show.
AVC: What's the ratio of stuff that gets filmed to what makes the final cut?
BG: On the show, we were scheduled last year to shoot 20 episodes, and we turned in 27. There's still a kill-board of stuff that doesn't make it. Like when we walk up to Big Mike, who's played by Toby Huss, we know he's stealing newspapers, so me and Tom arrest him for stealing newspapers. So then we say "Cedric, Carlos, you go up and arrest him for something." We don't really plan it. Usually we're ahead of schedule. And then me and Tom will go up and arrest him for something else. So we have Toby there to shoot one sketch, and I think last year we got seven. Same with Patton Oswalt. Throughout the course of the season, we've learned to shoot extra runners, extra plot pieces, because we know we're going to end up with extra episodes. For the movie, we accidentally shot twice as much as we needed. [Laughs.] The DVD is going to come with we call "the lost version." It's like an hour and 12 minutes, and it's not different takes; it's totally different everything. It's got a whole scene with Oscar Nunez that's not in the movie, and a long dinner scene with us and Patton Oswalt and Mindy Sterling, who plays his mom. It's all hilarious, but we couldn't have a Kill Bill Parts One And Two Reno movie. [Laughs.]
AVC: Ben, you directed the film. How much new ground did you have to cover?
BG: I think I aged a year during the movie. There was one point where we were on a bridge, and there were two explosions that were going to happen, and a helicopter had to be coordinated to come up. We had all these different departments we had to run it by: Homeland Security, Harbor Patrol, Miami Police, Miami Beach Police, and the Coast Guard. I've never really felt that much pressure, with that many people looking over my shoulder, and knowing if the helicopter hurts somebody, it's 100 percent my fault… We've never tried anything that big before. My hair is going grey, and I'm losing it. I knew making a movie would be stressful; I had no idea it would be as stressful as it was.
AVC: Reno has an ensemble method, so how does it work being both part of the ensemble and the person who has the last say?
BG: There has to be somebody on set people know to go to for an answer, because if you go to three different people, it's really confusing. I think with any movie, directing is more in the preparation. You just have to prepare for everything that's going to happen. Because after we start shooting, every actor, especially the guest stars, gets an equal vote in how the scene's going to turn out. It's not like we start a scene, and the director comes in, and I say, "No, you need to be more angst-filled. Everybody's not getting the point of the scene." We don't really know what the point of a scene is until the camera starts rolling. So it's fun. I think all movies are very ensemble, but none of them are nearly as ensemble as Reno. We bring in the Toby Husses and the Oscar Nunezes so they can do whatever they want, and not so we can tell them exactly how to do the joke. Which is great, which is fun. I think we get really good performances out of people, because nobody asks them to do that anywhere else.
AVC: You've done a lot of punch-ups for movie scripts, from Starsky & Hutch to The Pacifier and Taxi. Does working on scripts open up doors for you elsewhere?
BG: Working on scripts really opens up doors.
TL: Everybody hated Taxi, but Ben Stiller read the script of Taxi, and it got us the production rewrite of Starsky & Hutch, so it's like your credibility in one affects the other a tremendous amount. People don't have to guess what our sense of humor is like; they can watch Reno. So even when I think we've survived in the movie industry a Taxi, a Pacifier, a Herbie Fully Loaded, it's because you can look and see our sensibility. It's on every week—what we meant is on every week.
BG: Sometimes when you do a whole script, you hand it off and it gets rewritten by a bunch of people, and people don't really get that much of a sense of how funny you can be. But when you do punch-up work, you're sitting around a room, always with a director, usually with the producer, and sometimes the star who demanded that the script be funnier. So it's a good chance to really show people, "Oh, okay, now I know Lennon and Garant's voice." We didn't do as much rewriting the past couple of years as we have previously, just because we've been too busy, but it was a really good way to get us established.
AVC: Do you enjoy doing that? Is it tougher working within a script's framework?
BG: Oh no, it's great. Thinking up jokes is easy; it's thinking up characters and plot that's hard. [Laughs.] When you're just there to think up jokes, it's great. I think we have a big advantage over a lot of writers. There's a lot of writers who've never had anything produced. This town is full of writers who never got to really write a joke and then get on a set and direct that joke and see whether it works—and it's very informative. It's very difficult to be funny if you've never actually seen one of your jokes performed, so I think it gives us a good insight on how to censor ourselves, especially. We've seen a lot of jokes fail. [Laughs.] I think a lot of people are much more confident in their material because they haven't had a cable show and watched their jokes land like fucking lead balloons for 10 years.
AVC: How hard is it when you watch a script you worked on turned into something else entirely, like Herbie Fully Loaded?
BG: The first time we saw Herbie Fully Loaded, it was really painful. As much as people on the Internet hate that movie, Tom and I hate it worse. Our script of it was so good. We wrote a script that was greenlit off the very first draft, and it was great. Then they hired, I think, 24 other writers after us to change it, and God, our script was good. Then when we sat and watched it the first time, other than being amazed at how great Lindsay Lohan's boobs were, we were just cringing that they got every single joke wrong. And every single scene was the opposite of the way that we had intended it to be. Aw, man. We wrote a really funny movie that I think kids would have liked, but I think State fans would have loved. It is what it is, but we ain't writing novels. It's always painful. It's why we do Reno. In Reno, if something sucks, it's 100 percent our fault, which we totally accept, and it's not nearly as painful as handing your stuff off to someone else to fuck up.
AVC: Do you worry that the reputation will follow you, though? Like "Oh, those are the guys who did Herbie Fully Loaded," even though what you did isn't what they saw?
BG: We're getting more and more used to that. Depending on who's writing the article, they'll either say "It's the guys from Reno 911!", or they'll say, "It's those two turkeys who wrote Herbie Fully Loaded." Now we're used to it. I think that's fair that people like some things that we do and don't like some things that we do. I think that's totally okay. I really don't expect every single person in the world to love Reno and Night At The Museum, and I don't care. I'm just doing my best here.


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Stalin's Curse
By Robert Gellately
By Robert Gellately
By Robert Gellately
By Robert Gellately
Category: European World History | Politics
Category: European World History | Politics
Absolute War
Killing Hitler
The Dark Valley
My Father's Country
The Perfect Nazi
Eichmann Before Jerusalem
On the Natural History of Destruction
Rendezvous with Destiny
Praise for Robert Gellately's Stalin's Curse
"Masterly. . . . Gellately's latest work has a good claim to be the best single-volume account of the darkest period in Russian history."
—The Economist
"Impeccably researched and cogently argued . . . Gellately's intimate knowledge of the sources across Eastern European and of Russian archives compels us to accept his conclusions. . . . The blame for the barren cul-de-sac down which global history strayed for nearly half a century has never been better diagnosed: It was Stalin's curse."
—The Wall Street Journal
"Well-researched. . . . [Gellately's] grasp of the literature is tremendous, especially his expertise with the Soviet archives. I know of only two or three other books that can rival Stalin's Curse in terms of its penetrating use of Russian sources. . . . Gellately does an excellent job of showing how the Soviet leader took his country from a backwards nation to a global superpower."
—Jordan Michael Smith, Christian Science Monitor
"Incisive. . . . [Gellately] dashes once and for all the claims of blame-America academicians and faux historians that Washington was responsible for the Cold War."
—The Washington Times
"Outstanding. . . . A prominent historian of Nazi Germany and Soviet Russia, Gellately offers a panoramic view of Stalin's political, diplomatic and psychological manoeuvres that allowed the USSR to achieve superpower status. The author has an encyclopedic knowledge of his subject and provides a compelling narrative of deception, brutality, foolishness and betrayed idealism."
—Times Higher Education
"Masterful . . . historian Robert Gellately takes us back to square one. Whodunit? Stalin."
"A compelling argument. . . . Gellately traces Stalin's career in a new work that takes dead-aim at debunking historiographical views that Stalin's political post-World War II posturing originated from any obsession with national security."
—The Advocate
"A refreshingly frank analysis. Not for [Gellately] is the revisionist notion that the US was as much to blame for the cold war as the Soviet Union . . . a powerful work of synthesis."
—New Statesman
"The complicated story . . . is graphically and succinctly told. The narrative is compelling. . . . Although Stalin's Curse emphasizes the unprecedented ruthlessness of Stalin, it has implications for today."
—Literary Review (UK)
"Florida State University's Gellately (Lenin, Stalin and Hitler) adds to his distinguished body of work on 20th-century totalitarianism with this analysis of Stalin's conduct in international relations between 1939 and 1953 . . . Interweaving scholarship and the testimonies of those who suffered under Stalin's rule, Gellately's history is political and personal."
—Publishers Weekly
"Gellately here indicts Stalin as the primary instigator of the Cold War, marshaling evidence from Communist archives that undermines the revisionist case for Western responsibility for starting the confrontation . . . Gellately's fine contribution to Cold War studies will engage readers with its inside-the-Kremlin detail."
"Masterful. . . . This book should become a go-to read on how the Cold War developed."
—Library Journal
"Gellately has written a fascinating study of Stalin's motivation. Using rich sources from archives to memoirs, [he] convincingly shows that Stalin was a committed follower of Marxism-Leninism and worked constantly to expand the international revolution under Russia's leadership. . . . Highly recommended."
—Choice Magazine
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Accountant In Laurel Hollow
Looking for Accounting Services in Laurel Hollow, NY? Important Factors to Consider Before Hiring an Accountant
Garcia & Associates, C.P.A. PLLC provides outstanding accounting services to the Nassau County, NY area. Our combination of expertise, experience and dedicated staff ensures each client receives personal and professional attention. Offering personal tax account services, bookkeeping, audit defense among our services in Laurel Hollow, Garcia & Associates, C.P.A. PLLC is your trusted partner to help guide your tax and financial decisions.
Why Should You Work with an Accounting Professional?
Many people like to save money where they can but when it comes to your tax and financial matters it is always better to work with experienced professionals. Let's face it, there is enough to worry about without having to add figuring out tax laws and financial accounting. An accountant in Laurel Hollow is much better equipped and experienced to guide you in accounting matters for your business. An accountant can help minimize the risk of being audited and will make sure the appropriate forms are filed each quarter. Your accounting firm can handle bookkeeping for you or help you set up internal processes to ensure proper records are being kept and maintained to make things easier at tax time.
4 Types of Accounting
The four main types of accounting include corporate, public, government, and forensic accounting.
1- Public Accounting
Public accountants work with companies, corporations, or individuals. Their function is to help ensure a client's financial statements, records, and filings are accurate. Accountants work closely with tax regulations and financial reporting and must maintain current knowledge of generally accepted accounting principles (GAAP) and the tax code in Nassau County, NY. They must also understand, and be able to apply, industry-standard accounting frameworks and best practices.
2- Corporate Accounting
Corporate accounting involves the use, handling, and filing of a company's financial data. This data is often for the purpose of external reporting and tax compliance. Corporate accountants must adhere to certain widely held accounting methodologies to maintain accurate records and to ensure their financial and tax submissions are in compliance with appropriate rules, regulations, and standards.
Among these common types of accounting principles, standards, and procedures are Generally Accepted Accounting Principles (GAAP), International Financial Reporting Standards (IFRS) and the Internal Revenue Code (IRC). The average business, especially a small business will not be able to keep up with these and it is best to find an accountant in Laurel Hollow who is experienced in state and local laws.
3- Government Accounting
Government accountants work within the area of local, state or federal government entities. They often work within frameworks that differ from those employed by public accountants. Government accountants are also more strictly vetted, and in some instances are responsible for keeping privileged or confidential information.
4- Forensic Accounting
Forensic accountants collect, recover, and reconstruct financial data when it is difficult or impossible to obtain. In addition to understanding accounting principles and practices, forensic accountants must be resourceful, creative, and able to solve often-complex problems. When dealing with complex business situations forensic accountants are sometimes called in.
What Can an Accountant Do?
Whether you're just in the planning stages, or already have employees advice from a professional can be helpful. Looking for an accounting firm in Laurel Hollow will ensure your tax and financial needs are met.
- An accountant can advise what type of business structure to create if you are a start up. Should you set up as a sole proprietorship, corporation or an LLC? They can also handle the paperwork to get your business entity registered and up and running.
- An accountant can issue invoices and keep track of bookkeeping.
- An accountant can manage payroll. Your accountant can track and calculate employee pay, plus file quarterly payroll taxes.
What are the Types of Accountants?
Though there are many specialized areas of accounting and functions they play, some distinctions to keep in mind are:
Bookkeeper. Bookkeepers handle a range of activities for your business, from bill payments to weekly reports. While bookkeepers are skilled and qualified to handle plenty of financial responsibilities, they're not required to hold licenses that allow them to represent you to the IRS or prepare your taxes.
Enrolled Agent. Enrolled agents must pass an examination that covers individual and business tax laws. They're allowed to prepare your tax returns, but they are not allowed to sign them. They must also meet continuing education requirements to maintain their licenses, and they can represent you before the IRS if you're audited.
Certified Public Accountant (CPA). CPAs are certified through the state through specific examinations and licensing requirements. They're allowed to prepare and sign tax returns for individuals and businesses and can represent taxpayers before the IRS for audits. It is good to keep this in mind when you are searching for your accounting needs in Nassau County.
Looking for Personal and Professional Accounting Services Near You?
If you are looking to speak to someone about your personal or business tax account, the professionals at Garcia & Associates, C.P.A. PLLC are looking forward to taking your call. Our accountants provide personal and professional attention and are ready to discuss your tax and financial situation. Please give us a call at 718-533-0300 or visit https://www.garciagroup.com and submit our online contact form. We take pride in our reputation for quality service, integrity and professionalism.
- Learn more about Laurel Hollow, NY 11791
- Open a Laurel Hollow, NY map
- Find the Laurel Hollow, NY United States Post Office
- Locate nearby Laurel Hollow, NY pharmacies
- View the current Laurel Hollow, NY weather report
- Browse a list of Laurel Hollow, NY public and private schools
- Laurel Hollow, NY is located in Nassau county in New York State


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Ruby Velvet Studded Dress Red
€17,99 Regular price €42,95 Sale price
- In stock
- Inventory on the way
- Ordered before 17:00hr = sent same business day!
* Fits true to size
* Model is 1.69m tall, normally wears size XS/S and is here also wearing size XS/S
Not Your Baby offers free shipping on orders in The Netherlands over €80. All areas in The Netherlands can expect delivery of 1-2 business days.
We offer returns and money back within 14 days of package delivery for all non-SALE items only. Money will be refunded within 14 days after we have received your return. Easy!
All items on SALE can only be returned and exchanged for store credit.


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Effective Literacy and Numeracy Practices Database – LitBase
Welcome to LitBase, the Global Literacy and Numeracy Database. Here you will find case studies of youth and adult literacy programmes representing some of the best practice around the world.
The truly global nature of the collection – documenting 226 case studies from 97 countries in all world regions – makes it a unique resource for providers and practitioners seeking to develop programmes that compare to the best in terms of design, implementation, monitoring and evaluation, and impact.
Filtering Table


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This post looks a bit off-topic, but it's not. It is about a small but important part of my daily life.
At times that everything (including me) in my world is crowded and noisy, I visit a 'room' in my mind where it is completely silent and peaceful. I meditate every morning and it makes me happy, strong and carefree.
My friend Sandra from BijzonderMOOI* gave me this link: Get some Headspace. Former Tibettan monk and founder of Get some Headspace Andy Puddicombe states: "Headspace is a project designed to demystify meditation. Using the wonders of science and technology, we make it easy-to-learn, fun-to-do, and relevant to your everyday life." According to the New York Times Andy Puddicombe is doing for meditation what Jamie Oliver has done for food.
Have fun!


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It might be a few more days before I post again since it's super busy around these parts: Unpacking the new place, cleaning the old place, Thanksgiving, Apple Cup, the fiance's parents coming to town, preparing for my cross-country road trip, still no Internet at the new house ... So I wanted to share a glimpse of the early stages of the living room pre boxes and miscellaneous furniture overflow. This picture might be more for my sanity and serve as a reminder it did look semi-normal at one time.
By no means am I a professional interior designer, and the living room and office are proving to be a little challenging. But I like to keep it real around here, so I present to you the unfinished and unstyled living room.
And now it's back to cleaning the old place ... Maid service anyone?!
Sunset from the backyard ... Pardon the leaves.