Actual value and historical data chart for Guatemala Mortality Rate Under 5 Per 1 000

Guatemala GT: Mortality Rate: Under-5: Male: per 1000 Live Births data was reported at 31.400 Ratio in 2016. This records a decrease from the previous number of 32.500 Ratio for 2015. Guatemala GT: Mortality Rate: Under-5: Male: per 1000 Live Births data is updated yearly, averaging 38.800 Ratio from Dec 1990 (Median) to 2016, with 5 observations. The data reached an all-time high of 87.100 Ratio in 1990 and a record low of 31.400 Ratio in 2016. Guatemala GT: Mortality Rate: Under-5: Male: per 1000 Live Births data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank: Health Statistics. Under-five mortality rate, male is the probability per 1,000 that a newborn male baby will die before reaching age five, if subject to male age-specific mortality rates of the specified year.; ; Estimates Developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted Average; Given that data on the incidence and prevalence of diseases are frequently unavailable, mortality rates are often used to identify vulnerable populations. Moreover, they are among the indicators most frequently used to compare socioeconomic development across countries. Under-five mortality rates are higher for boys than for girls in countries in which parental gender preferences are insignificant. Under-five mortality captures the effect of gender discrimination better than infant mortality does, as malnutrition and medical interventions have more significant impacts to this age group. Where female under-five mortality is higher, girls are likely to have less access to resources than boys.

In 2023, the infant mortality rate in deaths per 1,000 live births in Guatemala stood at 17.9. Between 1960 and 2023, the figure dropped by 137.6, though the decline followed an uneven course rather than a steady trajectory.

Mortality rate, under-5, male (per 1,000 live births) in Guatemala was reported at 23.5 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Guatemala - Mortality rate, under-5, male (per 1,000 live births) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Guatemala GT: Probability of Dying at Age 5-9 Years: per 1000 data was reported at 1.600 Ratio in 2019. This records a decrease from the previous number of 1.700 Ratio for 2018. Guatemala GT: Probability of Dying at Age 5-9 Years: per 1000 data is updated yearly, averaging 2.900 Ratio from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 7.600 Ratio in 1990 and a record low of 1.600 Ratio in 2019. Guatemala GT: Probability of Dying at Age 5-9 Years: per 1000 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank.WDI: Health Statistics. Probability of dying between age 5-9 years of age expressed per 1,000 children aged 5, if subject to age-specific mortality rates of the specified year.; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Weighted average; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

Guatemala GT: Number of Deaths Ages 5-9 Years data was reported at 635.000 Person in 2019. This records a decrease from the previous number of 651.000 Person for 2018. Guatemala GT: Number of Deaths Ages 5-9 Years data is updated yearly, averaging 1,054.500 Person from Dec 1990 (Median) to 2019, with 30 observations. The data reached an all-time high of 2,190.000 Person in 1990 and a record low of 635.000 Person in 2019. Guatemala GT: Number of Deaths Ages 5-9 Years data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank.WDI: Health Statistics. Number of deaths of children ages 5-9 years; ; Estimates developed by the UN Inter-agency Group for Child Mortality Estimation (UNICEF, WHO, World Bank, UN DESA Population Division) at www.childmortality.org.; Sum; Aggregate data for LIC, UMC, LMC, HIC are computed based on the groupings for the World Bank fiscal year in which the data was released by the UN Inter-agency Group for Child Mortality Estimation.

This scatter chart displays suicide mortality rate (per 100,000 population) against urban population living in areas where elevation is below 5 meters (% of total population) in Guatemala. The data is about countries per year.

Guatemala GT: Mortality Rate Attributed to Unintentional Poisoning: Female: per 100,000 Female Population data was reported at 0.800 Ratio in 2016. This stayed constant from the previous number of 0.800 Ratio for 2015. Guatemala GT: Mortality Rate Attributed to Unintentional Poisoning: Female: per 100,000 Female Population data is updated yearly, averaging 0.900 Ratio from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 1.300 Ratio in 2000 and a record low of 0.800 Ratio in 2016. Guatemala GT: Mortality Rate Attributed to Unintentional Poisoning: Female: per 100,000 Female Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank: Health Statistics. Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Time series data for the statistic Survival to age 65, female (% of cohort) and country Guatemala. Indicator Definition:Survival to age 65 refers to the percentage of a cohort of newborn infants that would survive to age 65, if subject to age specific mortality rates of the specified year.The indicator "Survival to age 65, female (% of cohort)" stands at 80.32 as of 12/31/2023, the highest value at least since 12/31/1961, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.83 percent compared to the value the year prior.The 1 year change in percent is 2.83.The 3 year change in percent is 4.24.The 5 year change in percent is 2.12.The 10 year change in percent is 2.19.The Serie's long term average value is 62.97. It's latest available value, on 12/31/2023, is 27.55 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1960, to it's latest available value, on 12/31/2023, is +124.30%.The Serie's change in percent from it's maximum value, on 12/31/2023, to it's latest available value, on 12/31/2023, is 0.0%.

Guatemala GT: Suicide Mortality Rate: Male data was reported at 3.700 NA in 2016. This records a decrease from the previous number of 4.300 NA for 2015. Guatemala GT: Suicide Mortality Rate: Male data is updated yearly, averaging 4.300 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 4.900 NA in 2000 and a record low of 3.700 NA in 2016. Guatemala GT: Suicide Mortality Rate: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank.WDI: Health Statistics. Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

Time series data for the statistic Cause of death, by non-communicable diseases, ages 5-14, female (% of female population ages 5-14) and country Guatemala. Indicator Definition:Number of female deaths ages 5-14 due to non-communicable diseases divided by number of all female deaths ages 5-14, expressed by percentage. Non-Communicable diseases include cancer, diabetes mellitus, cardiovascular diseases, digestive diseases, skin diseases, musculoskeletal diseases, and congenital anomalies.

Guatemala GT: Mortality Rate Attributed to Unintentional Poisoning: Male: per 100,000 Male Population data was reported at 1.400 Ratio in 2016. This records a decrease from the previous number of 1.500 Ratio for 2015. Guatemala GT: Mortality Rate Attributed to Unintentional Poisoning: Male: per 100,000 Male Population data is updated yearly, averaging 2.000 Ratio from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 2.300 Ratio in 2005 and a record low of 1.400 Ratio in 2016. Guatemala GT: Mortality Rate Attributed to Unintentional Poisoning: Male: per 100,000 Male Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank: Health Statistics. Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

This dataset is about politicians in Guatemala. It has 256 rows. It features 5 columns: birth date, death date, country, and gender.

Guatemala GT: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data was reported at 47.100 % in 2015. This records a decrease from the previous number of 48.700 % for 2009. Guatemala GT: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data is updated yearly, averaging 54.550 % from Dec 1987 (Median) to 2015, with 6 observations. The data reached an all-time high of 64.200 % in 1987 and a record low of 47.100 % in 2015. Guatemala GT: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank.WDI: Health Statistics. Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Guatemala GT: Death Rate: Crude: per 1000 People data was reported at 4.833 Ratio in 2016. This records a decrease from the previous number of 4.861 Ratio for 2015. Guatemala GT: Death Rate: Crude: per 1000 People data is updated yearly, averaging 9.069 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 18.695 Ratio in 1960 and a record low of 4.833 Ratio in 2016. Guatemala GT: Death Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank.WDI: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Aim: To assess the burden of respiratory syncytial virus-associated disease in older adults (RSV OA), ≥50 years old, in World Health Organization region countries in Africa, the Americas (except for Canada and the USA), Eastern Mediterranean, Europe (except for European Union countries and the UK), South-East Asia, and Western Pacific (except for Australia, China, Japan, New Zealand, and South Korea). Materials & methods: English-language publications published and indexed in Embase (2012 to 29 September 2022) and MEDLINE (2012 to 29 September 2022) were reviewed. RSV OA outcomes of interest: epidemiology, testing/diagnostic methods, clinical/humanistic burden. Results: Nine RSV OA-burden statistics publications were identified from Africa (Kenya, Madagascar, and South Africa), the Americas (Guatemala), Eastern Mediterranean (Morocco), South-East Asia (Thailand), and Western Pacific (Hong Kong). One study described RSV hospital course (9-day median length of stay, 9.4% intensive-care admission rate), and seven studies reported case-fatality ratios (range, 7.5–15.9%). A third (3/9) of the studies investigated uniquely older adult disease. Conclusion: The limited details of RSV OA burden suggest nonetheless that RSV-associated hospitalization courses and mortality rates are substantial. While considerable surveillance needs remain, optimizing RSV vaccine access through immunization programs in these WHO region countries are essential steps to control RSV OA disease burden. This review article describes studies that were done between 2012 and 2022 about the serious illness caused by the respiratory syncytial virus (RSV) in older adult (OA) patients. We wanted to see what these studies said about how many adults tested positive (and how often the virus was identified), how long people infected by this virus stayed in the hospital, and how many died. We focused on countries that were not looked at in previous review articles to identify nine studies with information about RSV OA disease across five WHO regions in seven different countries – Guatemala, Hong Kong, Kenya, Madagascar, Morocco, Thailand, and South Africa. Only three of the nine studies gave us information specifically about older adults. One of these three studies showed that adults usually stayed in the hospital for about 9 days, and about 9% of them had to go to the intensive-care unit. Seven of the nine studies told us about how frequently hospitalized adults infected by the virus died, which was between 8 and 16%. This review tells us that more research is needed about how this virus affects older adults, especially in Africa, Asia, Latin America, and the Middle East. Although consequential for older adult (OA) populations, the respiratory syncytial virus (RSV) acute respiratory infection (ARI) burden is often underestimated, as compared with the well-established risk for neonates and young infants. The objective of this systematic literature review is to present a gap analysis of RSV OA disease-burden publications among countries within the six WHO regions where the epidemiology is not fully documented, while excluding from the review North America, Europe, and some countries of the Western Pacific that are already well represented in the international RSV OA disease-burden literature. This review included studies published between 2012 and 2022 investigating RSV outcomes of interest including epidemiology, testing/diagnostic methods, and clinical/humanistic burden. The entire systematic literature review included both children (0–5 years old) and adults; this current paper focused on publications from the adult population, particularly in older adults. Geographical range comprised countries classified within the six WHO regions: Africa, the Americas (except for Canada and the USA), Eastern Mediterranean, Europe (except for European Union countries and the UK), South-East Asia, and Western Pacific (except for Australia, China, Japan, New Zealand, and South Korea). RSV outcomes of interest included hospitalization course, mortality and case fatality, RSV-testing positivity rates, and incidence. This review revealed substantial gaps: RSV OA-burden evidence identified only nine published studies, 4.7% (7/149) of the countries of interest, and among these nine studies there were only three, from Thailand and Hong Kong, that reported uniquely adult estimates. Publications describing RSV OA hospitalization course showed a 9% intensive-care admission rate (one study) and a mortality/case fatality up to 15.9% (seven studies). RSV positivity rates varied between studies, likely due to patient factors such as age range, presence of comorbid conditions, respiratory infection case definitions, and sampling timing, as well as differences in sensitivity between the chosen laboratory tests. Our findings suggest that the research gaps in the RSV OA literature, although multifactorial, can be attributed mainly to insufficient/imprecise information and to biased information. RSV OA disease incidence values are confounded by underestimation due to inadequate case definitions that require presence of fever, incomplete testing (by use of lower sensitivity immunofluorescence assay testing and of single site specimen sampling), and insufficient surveillance by enrolling few adult patients. A better understanding of the burden of RSV OA disease across Africa, Asia, Latin America, and the Middle East can be accomplished in epidemiologic studies by initiation or enhancement of surveillance systems, improvement of RSV testing methods and sampling rates, and application of statistical modeling methods. The positive results from clinical trials have led to regulatory approvals of different RSV vaccines in several countries, presenting an opportunity to lower the OA disease burden across Africa, Asia, Latin America, and the Middle East in the near future through immunization. Standardizing RSV OA epidemiology methods (to establish hospitalization course, to determine mortality, to test positivity rate, and to establish incidence values) and tackling under-ascertainment of RSV OA disease burden (through enhanced RSV positivity testing and by applying statistical modeling) across the six WHO regions would enhance cross-study comparison analyses. Implementation of RSV OA vaccination strategies could potentially provide indirect protection, through control of carriage and transmission, while directly preventing RSV ARI in older adults.

GT：死亡率：5岁以下儿童：女性：每1000名新生儿在12-01-2017达24.600Ratio，相较于12-01-2015的26.400Ratio有所下降。GT：死亡率：5岁以下儿童：女性：每1000名新生儿数据按年更新，12-01-1990至12-01-2017期间平均值为31.900Ratio，共5份观测结果。该数据的历史最高值出现于12-01-1990，达75.700Ratio，而历史最低值则出现于12-01-2017，为24.600Ratio。CEIC提供的GT：死亡率：5岁以下儿童：女性：每1000名新生儿数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的危地马拉 – 表 GT.世界银行：卫生统计。

Guatemala GT: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data was reported at 14.900 NA in 2016. This stayed constant from the previous number of 14.900 NA for 2015. Guatemala GT: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data is updated yearly, averaging 14.900 NA from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 15.600 NA in 2005 and a record low of 14.800 NA in 2010. Guatemala GT: Mortality from CVD, Cancer, Diabetes or CRD between Exact Ages 30 and 70: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank.WDI: Health Statistics. Mortality from CVD, cancer, diabetes or CRD is the percent of 30-year-old-people who would die before their 70th birthday from any of cardiovascular disease, cancer, diabetes, or chronic respiratory disease, assuming that s/he would experience current mortality rates at every age and s/he would not die from any other cause of death (e.g., injuries or HIV/AIDS).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;