Для начала - все-таки не понимаю, как атрибутируют смертность от СПИДа. Мне казалось, что СПИД сам по себе не может являться причиной смерти; умирает человек не от самого СПИДа, а от обычных болезней (скажем, воспаление легких), с которыми организм с ослабленным иммунитетом не может справиться. Но если так, то возникает дилемма - считать ли причиной смерти эту болезнь или СПИД? Ведь болезни убивают и людей, не имеющих СПИД. Не получается ли так, что если человек заражен вирусом СПИД, то в причины любой ненасильственной смерти, постигшей его, автоматически записывают именно СПИД?
Далее, попытка пройти по документам и источникам.
Our Commitment: The World Bank’s Africa Region HIV/AIDS Agenda for Action 2007-2011 (http://siteresources.worldbank.org/EXT
Table 1: Ten Most Common Causes of Mortality and Morbidity in sub-Saharan AfricaПолучается, что в Африке СПИД является причиной каждой пятой смерти. А как это посчитали? Таблица сопровождается двумя библиографическими ссылками, в процитированном тексте имеется третья, в итоге получаем:
The 10 Most Common Causes of Death % of Total Deaths in 2000 HIV/AIDS
Lower respiratory infections
Ischemic heart disease
Road traffic accidents
Source: World Bank (2006a) and Mathers et al. (2006)
In Africa, the HIV epidemic is far more heterogeneous than previously recognized. It can be divided into four distinct clusters <...>. The epicenter of the epidemic is southern Africa, where HIV prevalence ranges from 15 to 35 percent. The hyper-epidemic of the countries in this epicenter is a continental — and global — exception, unlikely to occur elsewhere. East Africa’s epidemics, for many years grouped with southern Africa, are far lower, ranging from 2 to 7 percent. Prevalence in West Africa, Africa’s most populous region, ranges from 1 to 5 percent. In North Africa, prevalence seldom exceeds 0.1 percent (Wilson, 2006).
- World Bank (2006a) - что расшифровывается как "Disease and Mortality in sub-Saharan Africa" World Bank, Washington: DC
- Mathers et al. (2006) - что расшифровывается как "The burden of disease and mortality by condition: data, methods and results for 2001" in: AD Lopez, CD Mathers, M Ezzati, DT Jamison and CJL Murray, Editors, Global burden of disease and risk factors, Oxford University Press, New York
- Wilson, 2006 - что расшифровывается как "HIV Epidemiology: A review of recent trends and lessons" Background Note Prepared for HIV/AIDS Agenda for Action in Sub-Saharan Africa The World Bank, Washington, DC
1. Книга "Disease and Mortality in sub-Saharan Africa" доступна по линку http://www.ncbi.nlm.nih.gov/books/bv.fc
Про смертность от СПИДа говорится в главе 17 (http://www.ncbi.nlm.nih.gov/books/bv.f
It is now the most common cause of death in the region (WHO 1999)"WHO 1999" - это "The World Health Report 1999 - Making a Difference". Geneva: WHO (http://www.who.int/whr/1999/en/index.h
В статистическом приложении к докладу (http://www.who.int/whr/1999/en/whr99_a
HIV/AIDS. The statistical methods used to estimate AIDS mortality are based in part on the imputation of missing prevalence data from sentinel sites. Further validation of the imputation results would improve the reliability of the method. Furthermore, the application of the gamma distribution to estimate the epidemic curve for the HIV/AIDS epidemic is problematic once the epidemic is established. The estimated trajectory of the epidemic in different countries based on statistical models should be compared with other epidemiological evidence to ensure that the estimates adequately reflect national experience. The estimates of AIDS mortality in sub-Saharan Africa have used national mortality statistics in order to adjust model-based figures. The results will obviously depend on the extent to which these data are reliable, especially with regard to adult mortality levels. UNAIDS and WHO will actively collaborate in further refining and developing these and other methods for reducing the uncertainty in epidemiological estimates of HIV/AIDS within the framework of the UNAIDS/WHO Working Group on Global HIV/AIDS/STD Surveillance. For sub-Saharan Africa, the estimated number of deaths from HIV/AIDS in 1998 was 1.83 million with a 95% confidence interval of 1.1 to 2.4 million deaths.При этом "national mortality statistics", которая якобы используется для оценки смертности от СПИДа в Африке, в этих странах как раз по большей части отсутствует - что следует из следующих линков:
2. "The burden of disease and mortality by condition: data, methods and results for 2001" - http://www.dcp2.org/pubs/GBD/3
В частности - "Estimating Deaths by Cause: Methods and Data" (http://www.dcp2.org/pubs/GBD/3/Sect
Complete death registration data cover only one-third of the world's population. Some information on another third is available through the urban death registration systems and national sample registration systems of China and India. For the remaining one-third of the world's population, including most countries in Sub-Saharan Africa, only partial information is available from epidemiological studies, disease registers, and surveillance systems.3. Номер 3, написанный Вильсоном, доступен по линку http://data.unaids.org/pub/ExternalDocu
Next, to estimate the number of deaths by cause we drew on the following four broad sources of data:
Death registration systems. Complete or incomplete death registration systems provide information about causes of death for almost all high-income countries and for many countries in Europe (Eastern) and Central Asia and in Latin America and the Caribbean. Some vital registration (VR) information is also available in all other regions.
- Sample death registration systems. In China and India, sample registration systems for rural areas supplement urban death registration systems. Information systems now provide data on causes of death for several other large countries for which information was not available at the time of the original GBD study.
- Epidemiological assessments. Epidemiologists have estimated deaths for specific causes, such as HIV/AIDS, malaria, and tuberculosis (TB), for most countries in the regions most affected. These estimates usually combine information from surveys on the incidence or prevalence of the disease with data on case fatality rates.
- Cause of death models. The cause of death models used in the original GBD study (Murray and Lopez 1996a) were substantially revised and enhanced for estimating deaths by broad cause group in regions with limited information on mortality. The CodMod software developed for this study and described later drew on a data set of 1,613 country-years of observation of cause of death distributions from 58 countries between 1950 and 2001.
For 55 countries, 42 of them in Sub-Saharan Africa, no information was available on levels of adult mortality. Based on the predicted level of child mortality in 2001, the most likely corresponding level of adult mortality (excluding HIV/AIDS deaths where necessary) was selected, along with uncertainty ranges, based on regression models of child versus adult mortality as observed in a set of almost 2,000 life tables judged to be of good quality (Lopez and others 2002; Murray, Ferguson, and others 2003). These estimated levels of child and adult mortality were then applied to a modified logit life table model, using a global standard, to estimate the full life table in 2001, and HIV/AIDS deaths and war deaths were added to total mortality rates as necessary. Evidence on adult mortality in Sub-Saharan African countries remains limited, even in areas with successful child and maternal mortality surveys.
Even in countries where medically qualified staff assign causes there is substantial use of coding categories for unknown and ill-defined causes. In addition to the ICD codes for "symptoms, signs, and ill-defined conditions" (ICD-9 codes 780-799 and ICD-10 codes R00-R99), a number of other ICD codes do not represent useful underlying causes from a policy perspective and their inappropriate overuse compromises the usefulness of information on causes of death. These garbage codes or ill-defined codes include deaths from injuries where the intent was not determined (ICD-9 codes E980-989 and ICD-10 codes Y10-Y34 and Y872); CVD categories lacking diagnostic meaning, such as cardiac arrest and heart failure (ICD-9 codes 427.1, 427.4, 427.5, 428, 429.0, 429.1, 429.2, 429.9, and 440.9; and ICD-10 codes I47.2, I49.0, I46, I50, I51.4, I51.5, I51.6, I51.9, and I70.9); and cancer deaths coded to categories for secondary or unspecified sites (ICD-9 codes 195 and 199 and ICD-10 codes C76, C80, and C97). The percentage of deaths coded as ill-defined causes varies from 4 percent in New Zealand to more than 40 percent in Sri Lanka and Thailand.
Table 3.3 shows the distribution of deaths assigned to ill-defined codes for the 105 WHO member states reporting data on death registrations since 1990 with at least 50 percent completeness or coverage. The median percentage of deaths coded to ill-defined causes was 12 percent; the median percentage of symptoms, signs, and ill-defined conditions was 4.0 percent; and the median of ill-defined cardiovascular causes was 5.3 percent. In more than 15 high-income countries, more than 10 percent of deaths were coded to these ill-defined conditions, not so much because of overuse of codes for symptoms, signs, and ill-defined conditions, but because of excessive use of garbage codes for CVD, cancers, and injuries (Mathers and others 2005).
The Joint United Nations Programme on HIVAIDS and WHO have developed country-specific estimates of HIV/AIDS mortality and revise them periodically to account for new data and improved methods (Schwartlander and others 1999; Walker and others 2003). For the most recent round of estimates, they used two different types of models depending on the nature of the epidemic in a particular country. For generalized epidemics, in which infection is spread primarily through heterosexual contact, they used a simple epidemiological model to estimate epidemic curves based on sentinel surveillance data on HIV seroprevalence (UNAIDS Reference Group on Estimates Model and Projections 2002). For countries with epidemics concentrated in high-risk groups, they used prevalence estimates derived from the estimated population size and prevalence surveillance data in each high-risk category, and then employed simple models to back-calculate incidence and mortality based on these estimated prevalence trends (Stover and others 2002).
Our investments in improved surveillance have yielded important results and insights. In the last five years, approximately 20 countries, including Botswana, Burkina Faso, Burundi, Cameroon, Ethiopia, Ghana, Guinea, Kenya, Lesotho, Mali, Rwanda, Senegal, South Africa, Tanzania and Zambia in Africa, the DR and Peru in Latin America and Cambodia in Asia have conducted national population-based, household HIV surveys. A major population-based HIV survey is now underway in India. These surveys have enabled us to refine previous HIV estimates derived from antenatal HIV surveys and have given us more accurate global HIV prevalence estimates.
As the figure shows, population-based estimates are lower in almost all cases (except Uganda) and significantly lower in many cases. The differences are particularly pronounced in parts of East Africa (notably Rwanda and Ethiopia) and much of West Africa (including Sierra Leone, Burkina Faso and Ghana), where population-based estimates are two- to fivefold lower than antenatal estimates. Cambodia’s population-based HIV prevalence of 0.6% is also far lower than its antenatal estimate of 2.6%.
To an even greater extent than previously believed, Southern Africa is the epicentre of the global HIV epidemic. The hyper-epidemics of Southern Africa are a continental - and a global - exception, which are unlikely to occur elsewhere. HIV epidemics elsewhere in Africa are less generalized than previously believed.
Historically, we have said that HIV epidemics are concentrated if HIV prevalence in the general population is below 1% and generalized if HIV prevalence in the general population exceeds 1%. However, population-based surveys cited above show that HIV infection in the general population has been overestimated in many cases. Recent data from Cambodia underscores this point. Whereas earlier estimates suggested that 2.6% of adults were HIV-positive, recent preliminary population-based estimates suggest that well below 1% of adults may have HIV. The historical definition is flawed in several respects. First, as noted, it is often combined with overestimated prevalence to classify countries as generalized. Second, it does not accommodate contexts in which vulnerable groups form a large enough proportion of the adult population to produce overall HIV prevalence estimates above 1%, without significant transmission in the general population. Finally, and most seriously, it is not a transmission-based definition – indeed, it tends to classify countries arbitrarily as concentrated or generalized, limiting further analysis of underlying transmission dynamics. An alternative transmission-based definition is required, which encourages greater analysis of transmission dynamics and critical intervention priorities and points. We propose the following definition: An HIV epidemic is concentrated if HIV transmission is primarily attributable to HIV-vulnerable groups and if protecting HIV-vulnerable groups would protect the wider population. In contrast, an HIV epidemic is generalized if the converse is true – HIV transmission is not primarily attributable to HIV-vulnerable groups and protecting HIV-vulnerable groups would not in itself protect the wider population.
If this definition is used, there may be no generalized epidemics outside parts of Africa and the Caribbean (whose epidemics are in any case poorly understood). Moreover, within Africa, there may be considerably more concentrated epidemics than previously recognized.
First, consider HIV in Accra, Ghana, West Africa, where HIV prevalence in the general population is 2%, HIV prevalence in sex workers approaches 80% and a recent study by Cote et al (2005) estimated that 76% of new HIV infections among adult males of aged 15-49 was attributable to sex work.
Consider the following data (Figure 5) from Nairobi, Kenya, East Africa, where HIV prevalence in the general population approaches 10%, HIV prevalence among sex workers is approximately 60% and a study by Pisani et al (2003) estimated that approximately half of infections could be attributed to sex work.
Finally, consider the following analysis from Zambia, Southern Africa. In a context where adult HIV prevalence is approximately 15% and sex worker HIV prevalence is approximately 50%, Shields (2005) estimates that less than 5% of HIV infection may be attributed to sex workers, their clients and other male bridge populations <...>
The UNAIDS 2006 Report on the Global HIV Epidemic notes that several countries in Africa reported reduced HIV prevalence rates by 2005. These declines are due to the following factors
• Improved HIV surveillance and thus reduced estimates in some cases
• HIV-related deaths, which reduce the number of people living with HIV
• Behavior change, probably partly spontaneous and partly as a result of formal HIV prevention programs
<...> Despite increased access to AIDS treatment, HIV prevention remains the cornerstone of the global HIV response.
HIV prevalence includes all HIV infections, new and old. HIV incidence is limited to new HIV infections, acquired in the last year. Because of the long duration between HIV infection and death, HIV prevalence trends lag HIV incidence trends by several years <...>. Recent US Bureau of the Census models suggest that HIV incidence began to fall in several countries in Eastern and Southern Africa in the late 1980s and early 1990s <...>
Globally, we were slow to spot declines in HIV incidence, because surveillance systems focus on HIV prevalence not incidence and HIV prevalence trends lag several years behind HIV incidence trends. However, as the UNAIDS 2006 Report on the Global HIV Epidemic notes, HIV prevalence is now declining in both generalized and concentrated epidemics.
Population based surveys conducted by WHO in Uganda from 1989 to 1995 present impressive evidence of behaviour change, with partner reduction among adults – particularly highly sexually active men - playing a major role, supported by deferred sexual inception among youth and increased condom use.
HIV prevalence among pregnant women in Kenya has fallen from a peak of 13.4% in 2001 to 6.7% in 2004.
[In Zimbabwe] HIV prevalence has fallen from 33% in 1996 to 24% in 2004 (and early reports suggest 21% in 2005).
HIV prevalence fell among sex workers in Thailand, acondom use rose to extremely high levels in commercial sex and the proportion of Thai men visiting sex workers fell steeply.