Populations of North American river otters (Lontra canadensis) declined throughout large portions of the continent during the early 1900s due to habitat degradation and unregulated trapping. River otters had been extirpated in North Dakota (ND), but the Red River Valley has since been recolonized, with potential source populations including the neighboring states of Minnesota or South Dakota, or the Canadian province of Manitoba (MB). We genotyped 9 microsatellite loci in 121 samples to determine the source population of river otters in the Red River Valley of ND, as well as to assess population structure and diversity of river otters in central North America. Overall, genetic diversity was high, with an average observed heterozygosity of 0.58. Genetic differentiation was low (F ST < 0.05) between river otters in ND and those of Minnesota, suggesting that eastern ND was recolonized by river otters from Minnesota. River otters from MB were genetically distinct from all other sampled populations. Low genetic differentiation (F ST = 0.044) between South Dakota and Louisiana (LA) suggested that reintroductions using LA stock were successful. The genetic distinctiveness of river otters from different geographic regions should be considered when deciding on source populations for future translocations.

Plan Description: Map F-1 Revised - SFV One District is a modified map designed to take the complaints of the San Fernando Valley and keep them in one district as much as possible. It also meets the Armenian community desire to attach North Hollywood to their communities of interest to the east in Burbank and Glendale. Plan Objectives: Map F-1 Revised - SFV One District is a modified map designed to take the complaints of the San Fernando Valley and keep them in one district as much as possible. Unfortunately, you can't keep them in one district and have enough for the North County to attach themselves to the remaining cities in the foothill communities. Also, the Armenian community expressed an interest to attach North Hollywood to their communities of interest to the east in Burbank and Glendale.This map does the following:SD 1 stays the same as previous in the San Gabriel Valley except moves Covina to SD 5 to meet with their city elected official's request as stated in their letter to the commission and in comments at a San Gabriel Valley COG meeting. Covina has historically always been in SD 5 in the 1991, 2001, and 2011 redistrictings, unlike Walnut which was in SD 5 in 1991 and in SD 1 in 2001 and 2011 and so was not moved. It also adds La Habra Heights. On the City of LA side it keeps Pico-Union whole by jutting west north of the 10 freeway.SD 1 now links Chinatown with Little Tokyo, adds Koreatown, and connects all to the Asian communities along the 10 and 60 corridors. It also adjusts the northern boundary to match the Glendale/LA City limit in response to commissioner Obregon's concerns. It adds La Habra Heights and Pico Rivera for population balance. Pico Rivera was a member of the ACE Construction Authority and has connections to SGV interests such as the Whittier Narrows and railroad traffic so it is not unusual to add them to a SGV focused district. It connects Sunset Junction and Silver Lake and does not split this historically LGBT community. SD 2 keeps the Westside Neighhborhood Council whole and regularizes the boundary to cover the portions of Mid City with a significant Black population. It now stretches to the coast so that the South Bay COG is only split into two pieces. It does not have an odd strip connecting through Dockweiler Beach. Blacks remain the second largest group at about 27% compared to 30% in the baseline map so they remain able to influence their supervisor. It includes Watts, South Central, Ladera Heights, Crenshaw, Inglewood, and Compton, the core of the Black community. South of the 10, it cuts off at the 405 freeway (with the exception of Culver City's extension to the west) as communities west of the 405 tend to be more affluent. It continues to maintain a community of interest along UCLA and covers denser communities of Sawtelle Japantown, Palms, and West Los Angeles near the Expo Line and 405 freeway heavily populated by UCLA students. Although the Beach Cities are also affluent this is more compact and does not take them on a district all the way to the San Fernando Valley. In Downtown LA it regularizes the boundary along 5th Street.SD 3 consists of the San Fernando Valley west of North Hollywood, the Las Virgenes/Malibu COG, Santa Monica, Venice, Mar Vista, Playa Vista, homeowner rich areas of the Westside and areas west of the 405, West Hollywood, Hollywood, Marina Del Rey, the Miracle Mile, Park La Brea, and Beverly Hills. It keeps as much of the SFV as whole as possible while respecting the carveouts requested by members of the community. It does connect some communities south of the hill but they will not be the largest influence and the SFV will be the majority of this district, and does not go south of the airport. SD 4 consists of the SELA communities and the Gateway Cities except Pico Rivera. It also has the Palos Verdes Peninsula. Although not ideal, they are better suited to SD 4 than SD 2 to not further dilute Black influence in SD 2. It keeps Long Beach whole and now includes Lomita, which is more of a working class community tied into the ports than the beach cities to its west. Lomita is also part of LAUSD and Narbonne High School in Harbor City serves Lomita. It includes Carson for population balance and relatively high Latino population (32% CVAP). If it is desired to strengthen the Black CVAP in SD 2 the City of Carson Council District 1 (generally consisting of the part of Carson north of the 405 freeway and Del Amo Boulevard) could be placed in SD 2 and the rest stay in SD 4. SD 5 consists of the foothill cities, north County, and links Armenian-American dominant communities of North Hollywood, Burbank and Glendale. It links the tri cities of Burbank, Glendale, and Pasadena together. It links the 210 corridor together except for Azusa, which is grouped with central SGV cities in the state legislative district maps because of commonality of interest. It places Shadow Hills and Kagel Canyon in a more rural district. For population balance it includes the Los Feliz and Griffith Park neighborhoods, as well as the Hollywood Hills and Hollywood east of the 101 freeway. It maintains two CVAP Latino districts (1 and 4 at 53.25% and 51.17% respectively), one Black influence district (2 at 27.16%, after Latinos at 34.56% and above Whites at 25.25%) and creates an Asian influence district (1 at 26.72%). It is more compact than original F-1 on the western edge and central city, with more regular boundaries other than the UCLA extension.A pro or con is that it splits the coastline into three districts. This could mean more focus on the coast, or a situation where the coast is neglected because there is no champion. It should also be noted that due to the use of Redistricting Data Units it is impossible to make clean cuts at freeways or neighborhood council boundaries. Commissioner Holtzman's comments should be addressed in the 2031 cycle.

This dataset includes live births, birth rates, and fertility rates by Hispanic origin of mother in the United States since 1989. National data on births by Hispanic origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; New Hampshire and Oklahoma in 1990; and New Hampshire in 1991 and 1992. Birth and fertility rates for the Central and South American population includes other and unknown Hispanic. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf). SOURCES NCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/). REFERENCES National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I–Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.

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