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

Descriptive statistics of the study populations, showing the number of individuals (N) and percentage of the population (exact binomial 95% confidence interval), or mean (range).

Theoretical and empirical research has shown that increased variability in demographic rates often results in a decline in the population growth rate. In order to reduce the adverse effects of increased variability, life-history theory predicts that demographic rates that contribute disproportionately to population growth should be buffered against environmental variation. To date, evidence of demographic buffering is still equivocal and limited to analyses on a reduced number of age-classes (e.g. juveniles and adults), and on single sex models. Here we used Bayesian inference models for age-specific survival and fecundity on a long-term dataset of wild mountain gorillas. We used these estimates to parameterize two-sex, age-specific stochastic population projection models that accounted for the yearly covariation between demographic rates. We estimated the sensitivity of the long-run stochastic population growth rate to reductions in survival and fecundity on ages belonging to nine sex-age-classes for survival and three age-classes for female fecundity. We found a statistically significant negative linear relationship between the sensitivities and variances of demographic rates, with strong demographic buffering on young adult female survival and low buffering on older female and silverback survival and female fecundity. We found moderate buffering on all immature stages and on prime-age females. Previous research on long-lived slow species has found high buffering of prime-age female survival and low buffering on immature survival and fecundity. Our results suggest that the moderate buffering of the immature stages can be partially due to the mountain gorilla social system and the relative stability of their environment. Our results provide clear support for the demographic buffering hypothesis and its predicted effects on species at the slow end of the slow-fast life history continuum, but with the surprising outcome of moderate social buffering on the survival of immature stages. We also demonstrate how increasing the number of sex-age-classes can greatly improve the detection of demographic buffering in wild populations.

Methods The study was carried out in Volcanoes National Park in Rwanda, on the groups of habituated mountain gorillas monitored by the Dian Fossey Gorilla Fund’s Karisoke Research Center, often referred to as the Karisoke subpopulation. Since 1967, groups in this subpopulation have been monitored and protected on a near daily basis. Through the mid 2000s, the Karisoke groups generally numbered three but over the last decade, group fissions and new group formations resulted in an average of 10 groups in the region (see Caillaud et al, 2014). During daily observations, detailed demographic data were recorded, such as dates of birth and death, dates and types of individuals’ entry (immigrants) and departure (emigrants) from the study population, group composition, and maternal relatedness (for further details see Strier et al. 2010 and Granjon et al. (2020). In particular, groups were frequently monitored (daily between 2010-2016), and the arrival of a new individual to a monitored group was recorded as immigration. When individuals were lost to follow, depending on age, sex, health and group movement individuals could be classified as emigrated. However, when in doubt, the fate was recorded as unknown (Granjon et al. 2020).

1- Temporal fluctuations in growth rates can arise from both variation in age-specific vital rates and temporal fluctuations in age structure (i.e., the relative abundance of individuals in each age-class). However, empirical assessments of temporal fluctuations in age structure and their effects on population growth rate are rare. Most research has focused on understanding the contribution of changing vital rates to population growth rates and these analyses routinely assume that: (i) populations have stable age distributions, (ii) environmental influences on vital rates and age structure are stationary (i.e., the mean and/or variance of these processes does not change over time), and (iii) dynamics are independent of density. 2- Here we quantified fluctuations in age structure and assessed whether they were stationary for four populations of free-ranging vertebrates: moose (observed for 48 years), elk (15 years), tawny owls (15 years) and gray wolves (17 years). We also assessed the e...

Descriptive statistics of the traits in the study population.

As populations spread into new territory, environmental heterogeneities can shape the population front and genetic composition. We focus here on the effects of an important building block of heterogeneous environments, isolated obstacles. With a combination of experiments, theory, and simulation, we show how isolated obstacles both create long-lived distortions of the front shape and amplify the effect of genetic drift. A system of bacteriophage T7 spreading on a spatially heterogeneous Escherichia coli lawn serves as an experimental model system to study population expansions. Using an inkjet printer, we create well-defined replicates of the lawn and quantitatively study the population expansion of phage T7. The transient perturbations of the population front found in the experiments are well described by a model in which the front moves with constant speed. Independent of the precise details of the expansion, we show that obstacles create a kink in the front that persists over large distances and is insensitive to the details of the obstacle’s shape. The small deviations between experimental findings and the predictions of the constant speed model can be understood with a more general reaction-diffusion model, which reduces to the constant speed model when the obstacle size is large compared to the front width. Using this framework, we demonstrate that frontier genotypes just grazing the side of an isolated obstacle increase in abundance, a phenomenon we call ‘geometry-enhanced genetic drift’, complementary to the founder effect associated with spatial bottlenecks. Bacterial range expansions around nutrient-poor barriers and stochastic simulations confirm this prediction. The effect of the obstacle on the genealogy of individuals at the front is characterized by simulations and rationalized using the constant speed model. Lastly, we consider the effect of two obstacles on front shape and genetic composition of the population illuminating the effects expected from complex environments with many obstacles.

1Control subjects were matched for year of birth, sex and county of living during the year of first hospital discharge listing COPD.

Demographic response to short-term price fluctuations can be interpreted as an indicator of living standards in pre-modern societies. In this paper, we demonstrate how childbearing and infant and child mortality responded to changes in rye prices in two nineteenth-century Hungarian sub-regions. We conducted a micro-level demographic analysis based on family reconstitution data and multivariate statistical methods (event history analysis). Our findings reveal that both childbearing and child mortality differed between the two regions, and that both were affected by short-term economic fluctuations, but that the responses depended strongly on local economic, demographic and socio-cultural conditions. Child mortality responded markedly to rising rye prices, but in our Central Hungarian study population with high fertility and high infant and child mortality, this response was stronger than in our West Hungarian study population with more modest child mortality and fertility. At the same time, the mortality response to changing prices increased over time in both populations as a result of local industrialization in the latter and modernization of the surrounding region in the former. An immediate and presumably deliberate fertility response of the landless to rising food prices was more characteristic of the Western study population before 1870 while it was not observed in the Central population. Our results, therefore, emphasize the similarities with evidence from other European or Asian communities, and – at the same time – the importance of local context in explaining our findings.

Approximately 25% of mammals are currently threatened with extinction, a risk that is amplified under climate change. Species persistence under climate change is determined by the combined effects of climatic factors on multiple demographic rates (survival, development, reproduction), and hence, population dynamics. Thus, to quantify which species and regions on Earth are most vulnerable to climate-driven extinction, a global understanding of how different demographic rates respond to climate is urgently needed. Here, we perform a systematic review of literature on demographic responses to climate, focusing on terrestrial mammals, for which extensive demographic data are available. To assess the full spectrum of responses, we synthesize information from studies that quantitatively link climate to multiple demographic rates. We find only 106 such studies, corresponding to 87 mammal species. These 87 species constitute < 1% of all terrestrial mammals. Our synthesis reveals a strong mismatch between the locations of demographic studies and the regions and taxa currently recognized as most vulnerable to climate change. Surprisingly, for most mammals and regions sensitive to climate change, holistic demographic responses to climate remain unknown. At the same time, we reveal that filling this knowledge gap is critical as the effects of climate change will operate via complex demographic mechanisms: a vast majority of mammal populations display projected increases in some demographic rates but declines in others, often depending on the specific environmental context, complicating simple projections of population fates. Assessments of population viability under climate change are in critical need to gather data that account for multiple demographic responses, and coordinated actions to assess demography holistically should be prioritized for mammals and other taxa.

Methods For each mammal species i with available life-history information, we searched SCOPUS for studies (published before 2018) where the title, abstract, or keywords contained the following search terms:

Scientific species namei AND (demograph* OR population OR life-history OR "life history" OR model) AND (climat* OR precipitation OR rain* OR temperature OR weather) AND (surv* OR reprod* OR recruit* OR brood OR breed* OR mass OR weight OR size OR grow* OR offspring OR litter OR lambda OR birth OR mortality OR body OR hatch* OR fledg* OR productiv* OR age OR inherit* OR sex OR nest* OR fecund* OR progression OR pregnan* OR newborn OR longevity).

We used the R package taxize (Chamberlain and Szöcs 2013) to resolve discrepancies in scientific names or taxonomic identifiers and, where applicable, searched SCOPUS using all scientific names associated with a species in the Integrated Taxonomic Information System (ITIS; http://www.itis.gov).

We did not extract information on demographic-rate-climate relationships if:

A study reported on single age or stage-specific demographic rates (e.g., Albon et al. 2002; Rézoiki et al. 2016)
A study used an experimental design to link demographic rates to climate variation (e.g., Cain et al. 2008)
A study considered the effects of climate only indirectly or qualitatively. In most cases, this occurred when demographic rates differed between seasons (e.g., dry vs. wet season) but were not linked explicitly to climatic factors (e.g., varying precipitation amount between seasons) driving these differences (e.g., de Silva et al. 2013; Gaillard et al. 2013).

We included several studies of the same population as different studies assessed different climatic variables or demographic rates or spanned different years (e.g., for Rangifer tarandus platyrhynchus, Albon et al. 2017; Douhard et al. 2016).

We note that we can miss a potentially relevant study if our search terms were not mentioned in the title, abstract, or keywords. To our knowledge, this occurred only once, for Mastomys natalensis (we included the relevant study [Leirs et al. 1997] into our review after we were made aware that it assesses climate-demography relationships in the main text).

Lastly, we checked for potential database bias by running the search terms for a subset of nine species in Web of Science. The subset included three species with > three climate-demography studies published and available in SCOPUS (Rangifer tarandus, Cervus elaphus, Myocastor coypus); three species with only one climate-demography study obtained from SCOPUS (Oryx gazella, Macropus rufus, Rhabdomys pumilio); and another three species where SCOPUS did not return any published study (Calcochloris obtusirostris, Cynomops greenhalli, Suncus remyi). Species in the three subcategories were randomly chosen. Web of Science did not return additional studies for the three species where SCOPUS also failed to return a potentially suitable study. For the remaining six species, the total number of studies returned by Web of Science differed, but the same studies used for this review were returned, and we could not find any additional studies that adhered to our extraction criteria.

Description of key collected data

From all studies quantitatively assessing climate-demography relationships, we extracted the following information:

Geographic location - The center of the study area was always used. If coordinates were not provided in a study, we assigned coordinates based on the study descriptions of field sites and data collection.
Terrestrial biome - The study population was assigned to one of 14 terrestrial biomes (Olson et al. 2001) corresponding to the center of the study area. As this review is focused on general climatic patterns affecting demographic rates, specific microhabitat conditions described for any study population were not considered.
Climatic driver - Drivers linked to demographic rates were grouped as either local/regional precipitation & temperature values or derived indices (e.g., ENSO, NAO). The temporal extent (e.g., monthly, seasonal, annual, etc.) and aggregation type (e.g., minimum, maximum, mean, etc.) of drivers was also noted.
Demographic rate modeled - To facilitate comparisons, we grouped the demographic rates into either survival, reproductive success (i.e., whether or not reproduction occurre, reproductive output (i.e., number or rate of offspring production), growth (including stage transitions), or condition that determines development (i.e., mass or size). 
Stage or sex modeled - We retrieved information on responses of demographic rates to climate for each age class, stage, or sex modeled in a given study.
Driver effect - We grouped effects of drivers as positive (i.e., increased demographic rates), negative (i.e., reduced demographic rate), no effect, or context-dependent (e.g., positive effects at low population densities and now effect at high densities). We initially also considered nonlinear effects (e.g., positive effects at intermediate values and negative at extremes of a driver), but only 4 studies explicitly tested for nonlinear effects, by modelling squared or cubic climatic drivers in combination with driver interactions. We therefore considered nonlinear demographic effects as context dependent.  
Driver interactions - We noted any density dependence modeled and any non-climatic covariates included (as additive or interactive effects) in the demographic-rate models assessing climatic effects.
Future projections of climatic driver - In studies that indicated projections of drivers under climate change, we noted whether drivers were projected to increase, decrease, or show context-dependent trends. For studies that provided no information on climatic projections, we quantified projections as described in Detailed description of climate-change projections below (see also climate_change_analyses_mammal_review.R).

Demographic information about the study population.

1. Actuarial senescence, the decline of survival with age, is well documented in the wild. Rates of senescence vary widely between taxa, to some extent also between sexes, with the fastest life histories showing the highest rates of senescence. Few studies have investigated differences in senescence among populations of the same species, although such variation is expected from population-level differences in environmental conditions, leading to differences in vital rates and thus life histories.

2. We predict that, within species, populations differing in productivity (suggesting different paces of life) should experience different rates of senescence, but with little or no sexual difference in senescence within populations of monogamous, monomorphic species where the sexes share breeding duties.

3. We compared rates of actuarial senescence among three contrasting populations of the Atlantic puffin Fratercula arctica. The data set comprised 31 years (1990–2020) of parallel capture-mark-recapture data from three breeding colonies, Isle of May (North Sea), Røst (Norwegian Sea) and Hornøya (Barents Sea), showing contrasting productivities (i.e. annual breeding success) and population trends. We used time elapsed since first capture (TFC) as a proxy for bird age, and productivity and the winter North Atlantic Oscillation Index (wNAO) as proxies for the environmental conditions experienced by the populations within and outside the breeding season, respectively.

4. In accordance with our predictions, we found that senescence rates differed among the study populations, with no evidence for sexual differences. There was no evidence for an effect of wNAO, but the population with the lowest productivity, Røst, showed the lowest rate of senescence. As a consequence, the negative effect of senescence on the population growth rate (λ) was up to 3–5 times smaller on Røst (Δλ = -0.009) than on the two other colonies.

5. Our findings suggest that environmentally induced differences in senescence rates among populations of a species should be accounted for when predicting effects of climate variation and change on species persistence. There is thus a need for more detailed information on how both actuarial and reproductive senescence influence vital rates of populations of the same species, calling for large-scale comparative studies.

A discrete mathematical model was developed to study the population dynamics after a time-varying environmental disaster (R4.x261.000:0008). A 5-stage-structure matrix includes parameters for stage-specific survival and transition rates, as well as annual fecundity. This model can be used to examine the sensitivity and elasticity of the model, as well as demographic and environmental stochasticity, and many others.

The size and shape of an animal’s breeding territory are dynamic features influenced by multiple intrinsic and extrinsic factors and can have important implications for survival and reproduction. Quantitative studies of variation in these territory features can generate deeper insights into animal ecology and behavior. We explored the effect of age, breeding strategy, population density, and number of neighbors on the size and shape of breeding territories in an island population of Savannah Sparrows (Passerculus sandwichensis). Our dataset consisted of 407 breeding territories belonging to 225 males sampled over 11 years. We compared territory sizes to the age of the male territorial holder, the male’s reproductive strategy (monogamy vs. polygyny), the number of birds in the study population (population density), and the number of immediate territorial neighbors (local density). We found substantial variation in territory size, with territories ranging over two orders of magnitude from..., , , # Effects of age, breeding strategy, population density, and number of neighbors on territory size and shape in Savannah Sparrows

https://doi.org/10.5061/dryad.7d7wm383b

Three attachments are included:

1. The dataset is in CSV format.

2. The analyses in RMD format and in in HTML format

   Data for Suarez Sharma et al. 2024 Ornith.csv

   Variable list:

   - Year: Year of territory data collection

   - Male_ID: Unique code for each male in the population. The code format is "SAVS-###"". The numerical values were automatically generated.

   - Male age (1, 2, 3, or 4+): SAVS Male ages binned into categories

   \- \"1\" = one year old
   
   \- \"2\" = two years old
   
   \- \"3\" = three years old 
   
   \- \"4\" = four years old or more 
   

   - Breeding strategy: Observed breeding strategy of male SAVS.

   \- MO = monogamous
   
   \- PG = polygynous 
   
   \- NA = breeding strategy is undetermined
   

   -...

The statistic shows the total population of India from 2019 to 2029. In 2023, the estimated total population in India amounted to approximately 1.43 billion people.

Total population in India

India currently has the second-largest population in the world and is projected to overtake top-ranking China within forty years. Its residents comprise more than one-seventh of the entire world’s population, and despite a slowly decreasing fertility rate (which still exceeds the replacement rate and keeps the median age of the population relatively low), an increasing life expectancy adds to an expanding population. In comparison with other countries whose populations are decreasing, such as Japan, India has a relatively small share of aged population, which indicates the probability of lower death rates and higher retention of the existing population.

With a land mass of less than half that of the United States and a population almost four times greater, India has recognized potential problems of its growing population. Government attempts to implement family planning programs have achieved varying degrees of success. Initiatives such as sterilization programs in the 1970s have been blamed for creating general antipathy to family planning, but the combined efforts of various family planning and contraception programs have helped halve fertility rates since the 1960s. The population growth rate has correspondingly shrunk as well, but has not yet reached less than one percent growth per year.

As home to thousands of ethnic groups, hundreds of languages, and numerous religions, a cohesive and broadly-supported effort to reduce population growth is difficult to create. Despite that, India is one country to watch in coming years. It is also a growing economic power; among other measures, its GDP per capita was expected to triple between 2003 and 2013 and was listed as the third-ranked country for its share of the global gross domestic product.

--------------------------------------------------------------------------------------------------------# Description of the dataset "study-populations_location_wing-length_monitoring-method_tide.csv"#--------------------------------------------------------------------------------------------------------# The dataset contains estimates of mean female wing length for breeding and wintering populations of biparental shorebirds described from .....# Questions can be directed to: Martin Bulla (bulla.mar@gmail.com)# Values are separated by semi-colon.# Missing values are NA. 1. scinam : scientific name of the species 2. species : four letter abbreviatio of the species's English name 3. study_site : name of the study site 4. site_abbreviation : four letter abbreviation of the study site 5. type : was the study site at the breeding ground (breeding) or not (wintering) 6. lat : latitude of the study site (decimal) 7. lon : longitude of the study site (decimal) 8. tidal_habitat : is the study site at primarily tidal habitat (y=yes, n=no) 9. tidal_used : if the study site is primarily tidal habitat, do the birds use it for foraging (y=yes, n=no) 10. sexing_method : identifies the sexing method of the individuals used for the mean estimate 11. mean_female_wing : mean female wing length for the population 12. f_wing_N : sample size used for the mean estimate 13. mean_male_wing : mean male wing length for the population 14. m_wing_N : sample size used for the mean estimate 15. data_source : is the mean estimate based on the primary data ("our primary data") or literature (citation)#--------------------------------------------------------------------------------------------------------#WHEN USING THIS DATA, PLEASE CITE:#Bulla et al (2016). Data: study populations - location, wing length, monitoring method, tide. figshare. http://dx.doi.org/10.6084/m9.figshare.1536260. Retrieved ADD DATETIME.#--------------------------------------------------------------------------------------------------------

Also the welfare state of Sweden has prominent socioeconomic (SES) differences in health. These are seen for most measures of SES, i e for education, occupation and income, and also for most health outcomes: for all-cause mortality, for morbidity in most diseases and for self-rated health. SES differences are, in particular, evident for coronary heart disease (CHD) with a two-fold difference in incidence and death between high and low SES groups. Causes for this are not clear. It is well known that an unhealthy lifestyle is more common in low SES but this can only explain a part of observed SES differences.

One possible explanation is effects of psychosocial factors. High levels of psychosocial risk factors and low availability of psychosocial resources are well documented predictors of CHD and more common in low SES. We and others have demonstrated that these factors are related to poor function of the HPA axis with reduced cortisol reactivity and with higher levels of markers for inflammation and plaque vulnerability, which also are known predictors of CHD.

The overall objective of the research program is to analyse, in a prospective design, to what extent socioeconomic differences in CHD incidence and death can be explained by psychosocial factors, especially psychological resources, and if observed effects are mediated by biological markers of stress, inflammation and plaque vulnerability. Our data builds on two cohorts, using the same design of a random sample from a normal middle-aged Swedish population. Data collection: cohort I 2003-2004 (n=1007); cohort II 2012-2015 (n=2051), used a comprehensive design with broad questionnaires on SES, psychosocial risk factors, psychological resources, lifestyle and present disease, anthropometrics, saliva and blood samples. Primary outcome is symptomatic CHD. In a nested case control design data for cases shall be compared to controls.

While CHD incidence is falling, SES differences in CHD incidence and mortality remain and causes for this are not clear. This is one of few prospective studies linking the chain from SES, via psychosocial factors to biological markers of stress, inflammation and plaque vulnerability and CHD. The study has, therefore, the potential to generate important knowledge on causes behind SES disparities in CHD and on “how stress gets under your skin” More information about study design, study populations and timeline is available in document under the tab Documentation.

Purpose:

The overall objective of the research program is to analyse, in a prospective design, to what extent socioeconomic differences in Coronary Heart Disease (CHD) incidence and death can be explained by psychosocial factors, especially psychological resources, and if observed effects are mediated by biological markers of stress, inflammation and plaque vulnerability.

Cohort 2: Data collection is conducted in collaboration with 27 PHCs in Östergötland and 19 PHCs in Jönköping county council. The same methods are used for collecting data, as described for cohort 1.

This data was collected as part of a study to study population dynamics of coastal giant salamanders in Oregon. The study uses genetics to answer questions related to conservation concerns including population connectivity, sensitivity to habitat disturbances (such as logging and fires), and genetic diversity of populations.

Also the welfare state of Sweden has prominent socioeconomic (SES) differences in health. These are seen for most measures of SES, i e for education, occupation and income, and also for most health outcomes: for all-cause mortality, for morbidity in most diseases and for self-rated health. SES differences are, in particular, evident for coronary heart disease (CHD) with a two-fold difference in incidence and death between high and low SES groups. Causes for this are not clear. It is well known that an unhealthy lifestyle is more common in low SES but this can only explain a part of observed SES differences.

One possible explanation is effects of psychosocial factors. High levels of psychosocial risk factors and low availability of psychosocial resources are well documented predictors of CHD and more common in low SES. We and others have demonstrated that these factors are related to poor function of the HPA axis with reduced cortisol reactivity and with higher levels of markers for inflammation and plaque vulnerability, which also are known predictors of CHD.

The overall objective of the research program is to analyse, in a prospective design, to what extent socioeconomic differences in CHD incidence and death can be explained by psychosocial factors, especially psychological resources, and if observed effects are mediated by biological markers of stress, inflammation and plaque vulnerability. Our data builds on two cohorts, using the same design of a random sample from a normal middle-aged Swedish population. Data collection: cohort I 2003-2004 (n=1007); cohort II 2012-2015 (n=2051), used a comprehensive design with broad questionnaires on SES, psychosocial risk factors, psychological resources, lifestyle and present disease, anthropometrics, saliva and blood samples. Primary outcome is symptomatic CHD. In a nested case control design data for cases shall be compared to controls.

While CHD incidence is falling, SES differences in CHD incidence and mortality remain and causes for this are not clear. This is one of few prospective studies linking the chain from SES, via psychosocial factors to biological markers of stress, inflammation and plaque vulnerability and CHD. The study has, therefore, the potential to generate important knowledge on causes behind SES disparities in CHD and on “how stress gets under your skin” More information about study design, study populations and timeline is available in document under the tab Documentation.

Cohort 1: Data collection is conducted in collaboration with 10 Primary Health Care centers (PHCs) in Östergötland county council and sampling was done from the normal population of the catchment area for each PHC. The study is build on a comprehensive design with broad questionnaires on SES, psychosocial risk factors, psychological resources, lifestyle and present disease, anthropometrics, saliva and blood samples.

Cohort 2: Data collection is conducted in collaboration with 27 PHCs in Östergötland and 19 PHCs in Jönköping county council. The same methods are used for collecting data, as described for cohort 1.

Abstract

The Bandafassi HDSS is located in south-eastern Senegal, near the borders with Mali and Guinea. The area is 700 km from the national capital, Dakar. The population under surveillance is rural and in 2012 comprised 13 378 inhabitants living in 42 villages. Established in 1970, originally for genetic studies, and initially covering only villages inhabited by one subgroup of the population of the area (the Mandinka), the project was transformed a few years later into a HDSS and then extended to the two other subgroups living in the area: Fula villages in 1975, and Bedik villages in 1980. Data gathered include births, marriages, migrations and deaths (including their causes). One specific feature of the Bandafassi HDSS is the availability of genealogies.

Villages are quite small - 270 inhabitants in average - divided in hamlet pour a part. The population density is 19 inhabitants per km².

The population is divided in three living ethnical groups in distinct villages. In 2000, the ethnical groups are : 1 - Bedik (25 % of population). 2 - Malinke (17 %), 3 - Peul (58 %).

The housing unit is the square (or concession) which hosts members of an extended patrilineal family. It contains 17 people in average.Peul and Bedik squares are less populated (15 and 18 people in average) than Malinke squares (27 people in average). Polygamy is intense (160 maried women for 100 maried men). Women maried to the same men usually inhabit in the same square. Each wife has her own hu, sharing the same square courtyard.

Analysis unit

Individual

Universe

At the census, a person was considered a member of the compound if the head of the compound declared it to be so. This definition was broad and resulted in a de jure population under study. Thereafter, a criterion was used to decide whether and when a person was to be excluded or included in the population.

A person was considered to exit from the study population through either death or emigration. Part of the population of Mlomp engages in seasonal migration, with seasonal migrants sometimes remaining 1 or 2 years outside the area before returning. A person who is absent for two successive yearly rounds, without returning in between, is regarded as having emigrated and no longer resident in the study population at the date of the second round. This definition results in the inclusion of some vital events that occur outside the study area. Some births, for example, occur to women classified in the study population but physically absent at the time of delivery, and these births are registered and included in the calculation of rates, although information on them is less accurate. Special exit criteria apply to babies born outside the study area: they are considered emigrants on the same date as their mother.

A new person enters the study population either through birth to a woman of the study population or through immigration. Information on immigrants is collected when the list of compounds of a village is checked ("Are there new compounds or new families who settled since the last visit?") or when the list of members of a compound is checked ("Are there new persons in the compound since the last visit?"). Some immigrants are villagers who left the area several years before and were excluded from the study population. Information is collected to determine in which compound they were previously registered, to match the new and old information.

Information is routinely collected on movements from one compound to another within the study area. Some categories of the population, such as older widows or orphans, frequently move for short periods of time and live in between several compounds, and they may be considered members of these compounds or of none. As a consequence, their movements are not always declared.

Kind of data

Event history data

Frequency of data collection

One round of data collection took place annual except in 1970 and 2015.

Sampling procedure

No samplaing is done

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires: - Household book (used to register informations needed to define outmigrations) - Delivery questionnaire (used to register information of dispensaire ol mlomp) - New household questionnaire - New member questionnaire - Marriage and divorce questionnaire - Birth and marital histories questionnaire (for a new member) - Death questionnaire (used to register the date of death)

Cleaning operations

On data entry data consistency and plausibility were checked by 455 data validation rules at database level. If data validaton failure was due to a data collection error, the questionnaire was referred back to the field for revisit and correction. If the error was due to data inconsistencies that could not be directly traced to a data collection error, the record was referred to the data quality team under the supervision of the senior database scientist. This could request further field level investigation by a team of trackers or could correct the inconsistency directly at database level.

No imputations were done on the resulting micro data set, except for:

a. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is greater than 180 days, the ENT event was changed to an in-migration event (IMG). b. If an out-migration (OMG) event is followed by a homestead entry event (ENT) and the gap between OMG event and ENT event is less than 180 days, the OMG event was changed to an homestead exit event (EXT) and the ENT event date changed to the day following the original OMG event. c. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is greater than 180 days, the EXT event was changed to an out-migration event (OMG). d. If a homestead exit event (EXT) is followed by an in-migration event (IMG) and the gap between the EXT event and the IMG event is less than 180 days, the IMG event was changed to an homestead entry event (ENT) with a date equal to the day following the EXT event. e. If the last recorded event for an individual is homestead exit (EXT) and this event is more than 180 days prior to the end of the surveillance period, then the EXT event is changed to an out-migration event (OMG)

In the case of the village that was added (enumerated) in 2006, some individuals may have outmigrated from the original surveillance area and setlled in the the new village prior to the first enumeration. Where the records of such individuals have been linked, and indivdiual can legitmately have and outmigration event (OMG) forllowed by and enumeration event (ENU). In a few cases a homestead exit event (EXT) was followed by an enumeration event in these cases. In these instances the EXT events were changed to an out-migration event (OMG).

Response rate

On an average the response rate is about 99% over the years for each round.

Sampling error estimates

Not applicable

Data appraisal

CenterId Metric Table QMetric Illegal Legal Total Metric Rundate
SN011 MicroDataCleaned Starts 26293 2017-05-20 00:00
SN011 MicroDataCleaned Transitions 0 85058 85058 0 2017-05-20 00:00
SN011 MicroDataCleaned Ends 26293 2017-05-20 00:00
SN011 MicroDataCleaned SexValues 50 85008 85058 0 2017-05-20 00:00
SN011 MicroDataCleaned DoBValues 85058 2017-05-20 00:00

Estimates of population and migration from the dynamic population model (DPM) for local authority case studies, 2011 to 2022.