10. F UENTES BIBLIOGRAFÍAS
10.1. Bibliografía citada
Household Census Data
Household information, including data on each household member and the birth history sub-modules A and B, had already been cleaned by the ITPi project team when the data set was received. Major data cleaning and manipulation was thus limited to the detailed birth histories (sub-module C) and the geographical information data (see p 111)
The checking of the module C of the birth history (antenatal, intrapartum and postpartum care) found little inconsistent information. Inconsistencies found were, for example, that a positive answer was given for “having received antenatal care” but if asked about the number of visits “null” visits were recorded. In cases like this, the first answer in the questionnaire, in this case “having received any ANC” was treated as the correct answer and mismatched information was set as missing.
Inconsistency was observed for Caesarean section. The expression used in Swahili for Caesarean section is ambiguous’13and it is likely that some episiotomies are included in the total numbers. In our data set, 99 out of the 1008 (9.8%) reported Caesarean sections were among mothers who reported not having delivered in a hospital. None of the first-line health facilities in the two regions where the study was carried out were equipped or had the staff to do a Caesarean section in 2007. Thus, Caesarean sections indicated as being done in first-line facilities or at home were recorded as missing, in line with recommendations found in the literature [347]. Four Caesarean sections in mothers who reported to have delivered at
“another place” were left in the data set, as these may describe private maternity institutions somewhere else in the country.
Another variable for which inconsistencies were observed was the question on assistance during delivery. Women were asked about the person who assisted during their last delivery allowing multiple answers for the people present. It is likely that women have difficulties distinguishing the profession and training level of health workers. The expression “Mama Mkunga” which translates to midwife is likely to be used also for MCHA, who are auxiliary midwifes or nurse assistants. The analysis of uptake of care thus did not use the indicator skilled attendant, but only birth in a health facility, which was thought to be less biased by a woman’s ability to distinguish provider categories.
13 The Swahili expression for Caesarean section means literally translated ‘by the way of operation’
111 Preparation of the analysis of pregnancy-related mortality
Information on pregnancy-related deaths was available from two sources: 1) the household module of the census; and 2) from the verbal autopsy questionnaire. The analysis of pregnancy-related mortality used primarily the reports at household level (head of household reporting that the deaths occurred in pregnancy, while delivering or two months postpartum) but not the confirmed “maternal deaths” as available from the verbal autopsy. This decision was made because verbal autopsies were missing for 14% of reported pregnancy-related deaths (see p 145) and the distinction between indirect (maternal) and a coincidental (non-maternal) cause of death using verbal autopsies is uncertain. Sub-group analysis for confirmed direct and indirect maternal deaths is presented.
Three full years of ascertainment of deaths and livebirths were used. Thus, deaths recorded in the period from January to May 2004 were excluded, firstly to have three full years (1st June 2004 to 31st May 2007) for data analysis, and secondly, to reduce the edge effect of incomplete documentation14 around the recording limits.
Geographical Information Data
The cleaning of the geographical information data was a major part of the data preparation.
The initial investigation disclosed that the GIS information and household identifiers had inconsistencies. To analyse and correct the observed inconsistencies, systematic investigations into all GIS coordinates became necessary. Moreover, the geographical data were incomplete, most likely due to problems downloading GIS coordinates to the laptops every day during the fieldwork.
The GIS data cleaning and consistency checking was based on information from the household file, which included sub-village and a household identification numbers and included information on the respective ward, division and district to which each household belonged.
The GIS file included information on longitude and latitude together with the sub-village and household numbers.
The GIS file was merged with the household file using the household numbers and sub-village identifiers. 184,945 records had information from both data files, and household information and GIS information could be merged. There were 62,521 records with household information
14The edge effect describes the phenomenon of lower recording of vital event around the recoding limits, which might partly be due to event displacement to reduce the work load during interviews
112 but no GIS information. The GIS file had 14,235 records with coordinates, but no corresponding household information (Figure 16 ).
All the records with both household and GIS information (184,945 records in 2,362 sub-villages) were checked to investigate consistency by displaying them in Arc-GIS (version 9.2;
ESRI, Redlands, CA, USA) and using a map of ward boundaries from the National 2002 Tanzania Census for comparison [348]. The aim was to examine whether the position of a specific household coincided geographically with the ward indicated in its sub-village identifier code.
Four major problems were identified: 1) GIS coordinates and sub-village identifiers did not correspond to each other (problem 1,”mismatch of sub-village identifier and GIS coordinates”); 2) some households had more than one pair of GIS coordinates (problem 2,
“doubles and triples”); 3) some households within a sub-village with GIS coordinates were located far outside the sub-village/ward (problem 3, “outliers”); and finally 4) there were missing household GIS coordinates (259 sub-villages without any GIS coordinates) (Problem 4,
“missing GIS information”) (see Figure 16).
Algorithms were developed to systematically deal with the four problems and inconsistencies found when examining the GIS data. A variable for the quality of the data with five categories was added to the GIS data set to tag the GIS data by the cleaning and manipulation procedure.
The decisions taken for the four major problems described above were as follows:
1. Mismatch of sub-village identifiers and GIS coordinates
This problem was caused by a transcription error when downloading and storing the GIS data.
The GIS information was taken by a mapper, who visited the sub-village one day before the household interviewers came to complete the interviews. The mapper recorded the position of each household and prepared detailed maps of each sub-village. The GIS data were uploaded every evening onto a laptop and the file should have been given a respective sub-village identification number. However, some of the GIS data were allocated to the wrong sub-village identification number.
These incorrect GIS coordinates were deleted unless they could be used for other sub-villages.
To decide whether the GIS data could be used for other villages, additional investigations were made. These included the consultation of a study preparation document in which all sub-villages were listed with names, the number of households, and the date they were visited. By using the GIS visualisation, the number of households expected in the sub-village—according to the national 2002 census—and the date the village was visited, GIS data from some
sub-113 villages with coordinates allocated to the wrong ward were reassigned to sub-villages where GIS household information was missing. A cautious stand was taken and only 3,348 coordinates were reassigned (see also Table 23 and Table 74 in annex).
2. Doubles and Triples
In the GIS data set, a total of 3,781 households had two or three coordinate pairs (doubles and triples). These were deleted at random if they were lying only 100 m apart from each other, as this cut-off was perceived as a measurement irregularity. All other doubles and triples (3,697) were examined. To decide which of the coordinates most likely belonged to the respective sub-village, a median of all of the coordinates that had no doubles within the sub-village was computed, the coordinates closest to the median were selected.
3. Outliers
This problem was typically observed for the first or the first few coordinate pairs of a sub-village, probably due to coordinates being taken before the geographical positioning had properly located the current position. These coordinates were replaced by the median of the next five household coordinates belonging to the same sub-village.
4. Missing sub-village coordinates
Out of the 2,621 sub-villages in the census area, 259 sub-villages had no coordinates before the cleaning and re-assignment (10%). The re-assignment of wrong and doubles or triples coordinates to households with missing coordinates was only possible for a few sub-villages due to the cautious stand that was taken. In only a few cases, the list of sub-villages, the date of visiting the village and the projected location provided a clear indication of which sub-village the additional coordinates belonged to. More often, the additional coordinates were the same or very similar to those from sub-villages where no inconsistencies were found. In total, 272 sub-villages had no coordinates after the cleaning and re-assignment procedure (see steps to reduce missing data Figure 16).
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199,180 records in 2,373 sub-villages
184,945 households in 2,362 sub-villages with both information
Problem 1: Mismatch of sub-village identifier and GIS information Problem 2: 3,781 double and triple coordinates for one household Problem 3: “outliers”
219,438 households in 2,362 sub-villages with both information
Steps to reduce missing data 1) Cleaning
2) Inclusion of INSIST survey sub-village coordinates
Figure 16: Flowchart describing cleaning and manipulation of the GIS data
Inclusion of coordinates from an INSIST 2011 sample survey
During an INSIST sample survey carried out in 2011, central sub-village GIS coordinates were taken. A total of 13 village central coordinates could be used to fill missing data for sub-villages. These sub-village coordinates did not represent the actual coordinates of a single household.
Computation of missing GIS coordinates for single household when coordinates from neighbouring households were available
The final step was to fill coordinates from single households where other coordinates from neighbouring households within the same sub-villages were available. This was done by computing the median of available coordinates within a sub-village and filling missing coordinates with these median coordinates. Using this approach, a large number of missing GIS coordinates were imputed (36,214 or 15% of all coordinates in the data set).
Final GIS data set
In the final GIS data file, 24,174 (10%) out of 243,612 identified households had no GIS coordinates (Figure 16). The assessment of the cleaning and replacement procedure is presented in the section on Data Quality (see Table 23).
115 The households without GIS data were included in all analysis except for the assessment of the effect of distance. A row specifying effect estimates in the group of women from households without any GIS data has been added in tables analysing the effect of distance.
Health Facility Census 2009 Data
The health facility census information was carefully checked with regards to consistency and the likelihood of the reported responses in view of previous knowledge of the resource situation. This previous knowledge of health facilities of the region came from having visited many facilities in the period 2001-2003. In addition information was counterchecked using administrative records and discussion with regional administrative health staff during a visit to Lindi and Mtwara regions in March 2011.
Data manipulation included correction of the level of health facility (dispensary, health centre and hospital) and designation (private, public, private not-for-profit/mission facility). In addition, a few incorrect responses to availability of health staff were corrected. For example, according to the un-cleaned data set, two dispensaries were headed by a medical officer.
However, cross-checking with the regional administrative staff confirmed that no health centre and dispensary in the region ever had any medical officer employed.
Travel to Lindi and Mtwara region in 2011 also made it possible to verify other unexpected findings such as the lack of blood pressure machines, even at the hospital level. The staff in the maternity ward in Newala confirmed that during the time of the survey, they had difficulties with blood pressure machines because the supplies received from the Medical Stores Department were of low quality and new machines broke within three weeks.
The availability of Caesarean section, vacuum extraction and MVA for removal of retained products of abortion were also verified and corrected if they were inconsistent with information from administrative records.
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4. Outcome and Exposure Variables: Construction and Categorization
Outcome Variables Pregnancy-related mortality
Pregnancy-related mortality was the main outcome for this study. Pregnancy-related mortality was defined as a death of a female household member aged 13– 49 where the head of the household reported that she had been pregnant, died in childbirth or within two months after delivery or termination of pregnancy.
Verbal autopsies were done to conform the pregnancy-related deaths and to establish a cause of deaths diagnosis. However, for 69 pregnancy-related deaths, the cause of death diagnosis was missing (Figure 30). For a further 30% of cases, the cause of death remained unresolved.
Information from the verbal autopsies is analysed and presented in the results section. A sub-analysis for the effect of socio-demographic factors distance on mortality is presented also for a sub-group of ‘maternal deaths’ which are all pregnancy-related deaths but excluding the deaths where the verbal autopsy did not confirm that the mothers died during pregnancy or postpartum. Also some analysis is given for confirmed direct and indirect maternal deaths.
Pregnancy-related mortality was calculated based on the deaths recorded as “pregnancy-related” and the livebirths recorded during the birth history interviews (sub-module B) administered to all women of reproductive age (13– 49 years) and expressed as deaths per 100,000 livebirths. Thus, whereas deaths were recorded at the household level by asking the head of household if any deaths had occurred, livebirths were recorded as part of the births histories.
The maternal mortality ratio was constructed by dividing the pregnancy-related deaths by the number of reported livebirths in the same period expressed per 100,000 livebirth, and adjusted for missing birth histories. The adjustment factor was based on the age-adjusted birth rate observed in women of reproductive age during the period which was applied to the number of missing birth histories.
Variables of uptake of care
The variable four or more antenatal care visits was constructed using the question of how many times the women had gone for ANC. Information was collected on total numbers of visits. A binary variable was constructed.
117 Delivery in a Hospital/Delivery in First-Line Facilities: The place of delivery was assessed using a variable with five categories. Two binary variables were computed, each giving the value 1 for women who delivered in hospitals and first-line facilities (health centres/dispensaries), respectively and the value 0 to all other births. There was no missing information, but one
“don’t know” answer was coded as missing. Thus, the analysis distinguished delivery in a hospital and delivery in a first-line facility instead of combining both for a measurement of institutional birth. Uptake of hospital/first-line facility delivery and not skilled attendance was used as the main outcome variable for two reasons. Firstly, some response bias was assumed in regard to women’s responses to the question of who was present at the time of delivery.
Secondly, as the analysis focused on the influence of distance to a health facility, delivery in a hospital and delivery in a first-line facility was the main interest.
Birth by Caesarean section was assessed using a three answer option: yes, no, don’t know.
Three answers were missing. The answer options were re-coded into a binary variable after cleaning of the information on Caesarean sections as described earlier. Data cleaning resulted in 98 (0.4%) missing data points.
Postnatal care was assessed asking mothers about a check up done postpartum and a binary variable was constructed.
Exposure Variables
Administrative Information/Location
All households could be linked to the five districts, 24 divisions, 114 wards, and 2621 sub-villages. There was no missing information concerning the administrative location of any household. The variables district, division and wards were used in the analysis. In addition, a variable “region” was created to combine the three districts from Lindi region (Lindi Rural, Ruangwa and Nachingwea districts) and the two districts from Mtwara region (Newala and Tandahimba districts).
Ethnic group was assessed using a ten-category question including the most common ethnic groups (nine options) and the option “other”. A new categorical variable was constructed to reduce the ten categories to five. The first category represents the predominant ethnic group, the Makonde. Three more represented the second, third and fourth most common group, with the smaller ethnic groups combined into a fifth category.
118 For the descriptive analysis of predictors of “four of more ANC visits” and “postnatal care”, the ethnic groups were further reduced to two groups comprised of the three most dominant ethnic groups (Makonde, Mwera and Makuwa), and the minority ethnic groups Yao and others.
Household wealth was assessed using a household asset score. Principle component analysis was used to reduce ten asset indicators of household-owned consumer durables such as a bicycle, a radio, animals or poultry, indicators of housing characteristics like roofing materials and cooking materials and income other than farming into one single score (see Table 27). This reduction allowed for the construction of five wealth quintiles. The score used weighted sums of the household assets [104, 349, 350]. The asset score used the same indicators as employed for other studies in the same area [343]. The distribution of ownership of the assets within the five wealth quintiles is shown in Table 27.
The wealth quintiles were used as an individual level variable [335] which is widely accepted [73, 351]. For the descriptive analysis of predictors of “four or more ANC visits” and “PNC”
wealth quintiles were grouped into two groups comprised of the three poorest quintiles and the two least poor quintiles to keep information for presentation purposes limited.
The sex of the head of household was also assessed at household level with a categorical variable (male/female). This variable was included because another study in the same area had indicated that women living in a female compared to male headed household were more likely to deliver in a health facility [326].
Education of mother was assessed as part of the household module in full years. For the analysis of the birth histories for predictors of uptake of care, a categorical variable was constructed in line with categories used in the DHS in Tanzania [255] using 0 years = no education, 1– 6 years = primary education incomplete, 7-12 years = primary education complete and 13 or more years = secondary or higher education.
The assessment of the education level for mothers who died was done as part of the verbal autopsy questionnaire. The interviewee was asked about the total number of years the woman had spent in school and in a second question about the highest education level of the deceased. Answers were only available for women for whom a verbal autopsy questionnaire was available (see Methods section p 145).
There were more missing answers on the question of total years of schooling than for the question of highest education level within the verbal autopsies. Cross-tabulation of the two
119 variables revealed that missing answers for the question on “years of schooling” all fell in the
119 variables revealed that missing answers for the question on “years of schooling” all fell in the