Identification Survey ID. Study type. Series Information. This dataset provides waves 1 to 4 of the panel Kagera Health and Development Survey. A fifth wave was conducted in The primary objective of the Kagera Health and Development Survey KHDS was to estimate the economic impact of the death of prime-age adults on surviving household members. This impact was primarily measured as the difference in well-being between households with and without the death of a prime-age adultover time.
An additional hypothesis was that households in communities with high mortality rates might be less successful in coping with a prime-age adult death. Thus, the research de called for collecting extensive socioeconomic information from households with and without adult deaths in communities personaos high and low adult mortality rates.
The KHDS was an economic survey.
It did not attempt to measure knowledge, attitudes, behaviors or practices related to HIV infection or AIDS in households or communities. It also did not collect blood samples or attempt to measure HIV seroprevalence; this would have substantially affected the costs and complexity of the research and possibly the willingness of households to participate. Information on the cause of death iin the KHDS household survey is based on the reports of surviving household members; the researchers maintained that household coping will respond to the perceived cause of death, irrespective of whether the deceased actually died of AIDS.
The sampling frame for the survey was based on the Tanzania Census, which also provided information on adult death rates by ward within Kagera region. While it was possible to determine which communities had relatively high and low adult death rates from the census data, two additional problems arose that led to the decision for a stratified sample of households based on personalss criteria: - First, despite the high rates of HIV infection in Kagera and the large of deaths over time due to AIDS, the death of a prime-age adult is still a relatively rare event over a short time period.
This meant that a very large sample would have had to be selected in order to ensure that the survey could interview enough families suffering our about to suffer the rating of a prime-age adult. A survey de stratified only on mortality rates might confound the effects of high mortality nauhhty different agricultural, soil, and rainfall patterns. Thus, the sample of households was selected from a stratified random sample of communities from the census stratified on agroclimatic zone and adult mortality rate.
Within communities, the household sample was stratified according to the anticipated risk of each household of suffering a prime-age adult death. One additional concern was that the high mortality of households might lead to attrition from the sample that is systematically related to household coping. For example, if out-migration is an important coping behavior, then the most severely affected households might leave the sample and the analysis of the remaining pereonals would understate the economic impact of adult deaths.
For this reason, at the conclusion of the fieldwork, interviewers attempted to locate and interview all of the individuals who were members of households that dropped out of the longitudinal survey between the first and last interviews, and who were still resident in the region. The final longitudinal household survey followed households at month intervals, over a month period from Because household coping behavior is conditioned on local prices, services, and available programs, the KHDS also collected data from the communities from which households were drawn, local markets, the nearest source of modern medical care, and all of the primary schools in the community.
This information was collected longitudinally, with the exception of a questionnaire for traditional healers, which was administered only once. While households were drawn from a stratified random sample of households, the health facilities, schools, markets and healers interviewed represent those closest to each community and thus personalls not random samples that are statistically representative of Kagera facilities. The panel survey was conducted in a total of five waves. Kind of Data. Unit of Analysis.
Scope Notes. Coverage Geographic Coverage. Producers and sponsors Primary investigators. Sampling Sampling Procedure. However, policymakers need to know which households are suffering the most, the size of the impact, the extent to which they suffer more than other households in a poor country, and the potential costs and effects of assistance programs.
For this purpose, the sample of households must be representative of the population, a random sample for which the probability of selecting each household from the whole population is known. The KHDS used a random sample that was stratified geographically and according to several measures of adult mortality risk. This strategy datlng the team to ensure an adequate of households with an adult death in the sample while retaining the ability to extrapolate the to the entire population.
The from the household survey show that stratification of the sample on mortality risk at both the community and household level proved to be worthwhile. Among the households in the original sample that began the survey in the first passage, 91 had an adult death in the course of the survey—more than three times the expected 25 had the households been drawn at random with no stratification.
The households that began the survey in the first passage were observed, on average, for 1. The average probability of an adult death per household per year, according to the Tanzania Census, is 0. Thus, the expected of deaths from a random sample of households observed for 1. Because households were added to the sample to compensate for attrition, a total of households were eventually interviewed at least once. Between the first and last interview, of these households had an adult death, compared to 27 households that would have been expected to have a death from from a non-stratified sample.
First Stage: Selection of communities and clusters In the first stage of selecting the sample, the primary sampling units PSUs in Kagera region were classified according to eight strata defined over four agronomic zones and, within each zone, the level of adult mortality high and low. A PSU is a geographical area delineated by the Tanzanian Census that usually corresponds to a community or, in the case of a town, to a neighborhood. Clusters of households were drawn randomly from the PSUs in each stratum, with a probability of selection proportional to the size of the PSU.
This zone is in the middle of the region; most of its communities are in Karagwe and Bukoba Rural Districts and a few in Muleba District. This is in the southern part of Kagera in Biharamulo and Ngara Districts. The zone labels were chosen for simplicity. They suggest the characteristic, though by no means exclusive, agricultural pattern.
Within each agronomic zone, PSUs were classified according to the level of adult mortality. The Tanzanian Census asked a 15 percent sample of households about recent adult deaths.
Those answers were aggregated at the level of the "ward", an administrative area that is smaller than a district. Because the adult mortality rates were much higher in some zones than others and the distribution was quite different within zones, "high" and "low" mortality PSUs were defined relative to other PSUs within the same zone. A PSU was classified as in the "high" mortality category if its ward adult mortality rate was at the 90th percentile or higher of the ward adult mortality rates within a given agronomic zone.
Based on experience with other LSMS surveys, this is the of households that could reasonably be interviewed by a field team of one supervisor, two interviewers, and an anthropometrist in a week. The probability that a PSU was selected within each stratum was proportional to its size the of householdsaccording to the following formula [note: in the high-mortality urban stratum, eight clusters had to be selected from only six PSUs.
Divided across the eight strata, this would imply the need to enumerate roughly se PSUs in each of six strata and 7 PSUs in two strata. However, to guard against attrition of entire communities and the possibility that actual mortality rates would be found to be quite different from those nauvhty in the census, more PSUs were enumerated than would be needed for the survey. A total of 62 PSUs were selected from the in the region to be enumerated PSUs were selected at random from each of seven strata and all 6 PSUs in the high-mortality urban stratum were selected.
However, the field teams successfully enumerated only 52 PSUs, from which 54 clusters could be drawn. Ten Naughyy were not enumerated, generally because they were inaccessible or the teams ran out of time. Two PSUs were on islands and one was in a game park. The rainy season substantially slowed down the enumeration and made some PSUs inaccessible.
Among the 10 PSUs not enumerated, three were in the riverine zone, six were in the annual crop zone, and one was in the urban zone. Of the 52 PSUs that were enumerated, only 48 were needed allowing for selection of two clusters from each of two PSUs in the urban high-mortality zone. In zone 3, where fewer PSUs were enumerated than were anticipated in the research de, all 10 enumerated PSUs were accepted into the sample.
To compensate, it was decided to select datng total of 14 PSUs in the tree crop zone, and 12 each in the annual crop and urban zones, for a total of In deciding which PSUs to drop, the PSUs were ordered within each zone, from highest to lowest adult death rate based on the enumeration.
For example, in the riverine zone, where datinng PSUs were enumerated, the PSU with the median adult mortality rate from the enumeration was dropped. Using this method, one PSU each was dropped from the riverine and urban zones and two were dropped from the tree crop perzonals, leaving 48 PSUs from which 50 clusters were selected. A 51st cluster from the highmortality tree crop stratum was added toward the end of the first passage of field work, to ensure that an adequate sample size would be maintained should an entire cluster drop out later during the panel.
Second Stage : Selection of Households In the second stage, households within each of the selected PSUs were ased to one of two strata "sick" or "well" based on the of an enumeration of all households in each community. Sixteen households were selected at random per cluster, of which 14 were selected from the "sick" group and 2 from the "well" group.
In addition to recording perxonals name of the head of each household, the of adults in the household 15 and olderand the of children, the enumeration form asked: "Are any adults in this household ill at this moment and unable to work? If nauyhty, the age of each adult and the cause of death illness, accident, childbirth, other.
Since AIDS is sexually transmitted, other adults in the same household with an AIDS patient may sirveyor become infected, either through sexual contact with the HIV-infected person or because of similarities in sexual behavior.
Thus, Daitng morbidity and deaths are likely to be clustered in households. Information on illness and deaths on the enumeration form could be recorded for a maximum of three people for each question per household. Of the more than 29, households enumerated, only 3. Only 77 households had both an adult death due to illness and a sick adult.
This underscores the point that, even with some stratification based on community mortality rates and in an area with very high adult mortality due to an AIDS epidemic, a very large sample would have had to have been selected to observe a sufficient of households that would experience an adult death during the two-year survey. It was assumed that in communities suffering from an HIV syrveyor, a history of prior adult death or illness in a household might predict future adult deaths in the same household.
In selecting the 16 households to be interviewed surveyoe each PSU from which a cluster was drawn, 14 were selected at random pedsonals among the "sick" households in that Personlas and 2 were selected at random from among the "well" households. In one cluster, where the of "sick" households available was less than 14, all available sick households were included in the sample and the balance were from well households. The final sample drawn for the first passage was therefore households in 51 clusters drawn from 49 PSUs.
Attrition from the household nsughty a Attrition between the enumeration and the first passage Among the original households selected from the enumeration, 47 5. In about a third of these cases, the move was related to the death of a household member. This included five cases in which the household moved following a death and two cases in which the persknals who died was a single-person household.
The presence of the household head was not necessary to conduct the interview, unless a household was a single-person household. This group included the "original" households selected from the enumeration or their replacements and 24 "extra" households. The field teams added these households, taken from the list of replacement households, when they sensed that another continuing household in the sample was likely to drop out or was a poor source of information.
An additional 75 households began the survey in later passages, completing a wave 1 questionnaire at the first interview. Their subsequent attrition 5 households is not studied here. In 80 percent of the cases, the reason for attrition was that the household moved; about a third of those moves were related to an adult death in the household, including one case in which a single-person household died. Taken over all households interviewed during the first passage, only 1.
Were households with an adult death more likely to drop out of the sample? In fact, households surveyod an adult death in the 12 months before the enumeration were less likely to drop out before the first passage than were households without a death see Table III.