Abstract
Background
Less than 5 years remain before the 2015 mark when countries will be evaluated on their achievements for the Millennium Development Goals (MDGs). The MDG 4 and 6 call for a reduction of child mortality by two-thirds and combating malaria, HIV/AIDS, TB, and other diseases, respectively. To accelerate the achievement of these goals, focused allocation of resources and high deployment of cost-effective interventions is paramount. The knowledge of spatial and temporal distribution of diseases is important for health authorities to prioritize and allocate resources.
Methods
To identify possible significant clusters, we used SatTScan software, and analyzed 2,745 cases of under-five with 134,099 person-years for the period between 1999and 2008. Mortality rates for every year were calculated, likewise a spatial scan statistic was used to test for clusters of total under-five mortalities in both space and time.
Results
A number of significant clusters from space, time, and space–time analysis were identified in several locations for a period of 10 years in the Rufiji Demographic Surveillance Site (RDSS). These locations show that villages within the clusters have an elevated risk of under-five deaths. The spatial analysis identified three significant clusters. The first cluster had only one village, Kibiti A (p < 0.05, the second cluster involved five villages (Mtawanya, Pagae, Kibiti A, Machepe, and Kibiti B; p < 0.05), the third cluster involved one village, Jaribu Mpakani (p < 0.05). A space–time cluster of 10 villages for the period between 1999 and 2002 with a radius of 14.73 km was discovered with the highest risk (RR 1.6, p < 0.001). The mortality rates were very high for the years 1999–2002 according to the analysis. The death rates were 33.5, 26.4, 24.1, and 24.9, respectively. Total childhood mortality rates calculated for the period of 10 years were 21.0 per 1,000 person-years.
Conclusion
During the 10 years of analysis, mortality seemed to decrease in RDSS. The mortality decline should be taken with caution because the Demographic Surveillance System is not statistically representative of the whole population; therefore, inference should not be made to the general population of Tanzania. The pattern observed could be attributed to demographic and weather characteristics of RDSS. This should provide new insights for further studies and interventions toward reducing under-five mortality.
Keywords: demographic surveillance, spatial, spatial–temporal, clustering, clustering, under-five mortality, Rufiji HDSS
Less than 5 years remain before the 2015 mark when countries will be evaluated on their achievements of the Millennium Development Goals (MDGs). The MDG 4 and 6 call for a two-thirds reduction in child mortality, and to combat HIV/AIDS, malaria, and other diseases, respectively (1). Globally, there is little progress in reducing child mortality and slow progress has been mainly in sub-Saharan Africa (1).
The lack of vital registration systems in Africa have hindered progress; however, Health and Demographic Surveillance Systems (HDSS), albeit not representative, have provided a useful resource for mortality data in their respective countries. In Tanzania, data from the HDSS are used for planning and allocation of resources at the district level. This information provides a rational way of targeting scarce resources and priority problems in the districts. Innovative ways to further improve the targeting of resources in order to reach those in need of proven interventions are required.
Tanzania is in the bottom 10% of the world's economies in terms of per capital income. The economy depends heavily on agriculture, which accounts for more than 40% gross domestic product (GDP). Tanzania is located in Eastern Africa (6.00°south, 35.00°east) bordering the Indian Ocean, between Kenya and Mozambique (Fig. 1). The total area of Tanzania is about 947,000 km2. The climate varies from tropical along the coast to temperate in the highlands. The population is about 41,048,532 (2009 estimates) with an annual population growth rate of 2.04% (2009 estimates). The birth rate is 34.29 births per every 1,000 (July 2009 estimates) and the death rate of 12.59 births per 1,000 (July 2009 estimates) (2). The estimated cumulative mortality rate up to 5 years of age is reported to be 157 for males and 148 for females (3).
In recent years, Tanzania has made progress in reducing the child mortality compared to its neighbors in the region. Child mortality declined from 146 deaths per 1,000 live births to 99 deaths per 1,000 live births between 1999 and 2004 (4). A further decline in child mortality was documented for the years between 1990 and 2015 by 1.9% (1). This decline has been attributed to systemwide interventions that include improved coverage of childhood health benefits such as vitamin A, children sleeping under treated nets, and increased spending in health care by the government.
Rufiji District is one of the six districts of the Coastal Region of Tanzania. It is located in the southeast part of Tanzania. The district name comes from the Rufiji River, which bisects through the district and empties into the Indian Ocean on the eastern side of the district (Fig. 1). According to the 2002 Tanzania National Census, the population of Rufiji District was 203,102 people. The major health problems, as reported by dispensaries and health centers include: malaria, skin disease, and upper respiratory and eye infections (5).
Rufiji District has been one of the few districts in Tanzania to test some innovative ways of priority setting and resource allocating in order to maximize health benefits from available cost-effective interventions. Allocation of resources in poor settings requires knowledge of the local burden including random or clustering of events such as mortality. Statistical methods to analyze such occurrences have been documented extensively (6).
In this study, we explore the clustering of mortality in the health demographic surveillance area of Rufiji District, which is rich with data that has been collected for over 10 years.
Study population and methods
Site description
The Rufiji Demographic Surveillance Site (RDSS) is located in eastern Tanzania 7.47° to 8.03° south latitude and 38.62° to 39.17° east longitude (Fig. 1). The RDSS is in the Rufiji District of Tanzania about 178 km south of Dar-es-Salaam. The Rufiji District is among six districts in the Coastal Region of Tanzania. The district is divided into 19 wards with 94 registered villages. The RDSS constitutes 31 villages covering an area of 1,813 km2 (7).
Rufiji's vegetation is formed mainly by tropical forests and grassland. The weather is hot throughout the year and with rainy seasons. The average annual precipitation in the district is between 800 and 1,000 mm. The Rufiji River characterizes the district and it has a large flood plain and delta (7).
The population size of the Rufiji District is about 182,000 of which 85,000 (about 47% of the district) are under the DSS surveillance. The population density is 46 people per square kilometers and the mean household size for the whole district is about five people per house (Bureau of Statistics, 1994). The district is largely rural.
Rufiji DSS has a total of 18 health facilities. These include one hospital, two health centers, and 15 dispensaries. However, many people receive health services from traditional healers and traditional birth attendants (TBAs). Malaria and water-borne diseases such as cholera and diarrhea are the major health problems in the area. The major causes of mortality include acute lower respiratory infections, tuberculosis, AIDS, perinatal causes, and acute febrile illness such as malaria. Immunization coverage ranges from 85% for the Bacillus Calmette-Guérin (BCG; tuberculosis) to 66% for measles in children that are 12–23 months of age. About 89% of the population lives within 5 km of a formal health facility. All villages and health facilities in the district have been positioned by a global positioning system (GPS) and mapped in a geographic information system (GIS) database of district health resources.
The Rufiji DSS provides sentinel data for the health policy and planning and to monitor the impact of health reforms. Data and experiences from the Rufiji DSS are assessed for use by the District Health Management Teams, policy makers, and health planners. The DSS is an appropriate resource of health information for improving the health situation in the district. It provides a continuous monitoring and updating of events such as births, deaths, in-migrations, and out-migrations for all household members in the DSS area. These events are tracked through a longitudinal demographic system by a series of cycles or intervals known as ‘rounds’ every 4 months. The place or residence at time of death is derived from verbal autopsy interviews that were conducted by the field supervisor with a member of the family.
Statistical analysis and mapping
We assessed spatial, temporal, and spatial–temporal areas in order to identify clusters with high mortality for the period from 1999 to 2008 for Rufiji HDSS.
All deaths and person-years of observation by village and by year in children younger than 5 years old were extracted from the Rufiji HDSS databases. For each village, we calculated under-five mortality rates by dividing the number of deaths by the person-years of observation. Corresponding confidence intervals were estimated using exact methods based on Poisson distribution (Table 2).To identify clusters with high mortality we used SaTScan software version 7.0 developed by by Kulldorff (6).
Table 2.
Year | Cluster type | Location | Radius (km) | LLRa | Cases | Expected cases | RRb | p-Value |
---|---|---|---|---|---|---|---|---|
1999 | Most likely | Kibiti A | 0.00 | 13.0 | 50 | 23.0 | 2.4 | 0.001 |
2000 | Most likely | Mtawanya, Pagae, Kibiti A, Machepe, Kibiti B | 7.20 | 7.73 | 126 | 92 | 1.6 | 0.006 |
2001 | Most likely | Umwe South | 0.00 | 3.07 | 15 | 7 | 2.1 | 0.308 |
2002 | Most likely | Nyambili, Bungu A, Bungu B, Nyambunda, Pagae, Mtawanya, Uponda, Mlanzi, Bumba, Kibiti A, Mjawa, Jaribu Mpakani | 14.43 | 4.41 | 196 | 167 | 1.4 | 0.102 |
2003 | Most likely | Bumba, Nyambunda, Kibiti A, Kibiti B, Mtawanya, Nyambili | 12.89 | 2.07 | 70 | 56 | 1.3 | 0.679 |
Most likely | Mkupuka | 0.00 | 0.90 | 3 | 1 | 2.4 | 0.994 | |
2004 | Most likely | Kibiti B | 0.00 | 1.85 | 31 | 21 | 1.6 | 0.488 |
2005 | Most likely | Kibiti B | 0.00 | 1.85 | 41 | 31 | 1.4 | 0.772 |
2006 | Most likely | Jaribu Mpakani | 0.00 | 6.09 | 48 | 29 | 1.8 | 0.015 |
2007 | Most likely | Bumba, Nyambunda, Kibiti A, Kibiti B, Mtawanya, Nyambili, Kimbuga, Pagae, Bungu A, Bungu B, Ngulakula | 17.16 | 5.12 | 114 | 91 | 1.6 | 0.052 |
2008 | Most likely | Mchukwi A, Machepe, Mtawanya, Pagae, Kibiti B, Nyambili | 11.07 | 5.02 | 50 | 33 | 1.8 | 0.057 |
Space and time clusters | ||||||||
1999–2002 | Most likely | Nyambunda, Nyambili, Pagae, Bungu A, Bumba, Bungu B, Mtawanya, Kibiti A, Uponda, Kibiti B | 14.73 | 55.8 | 686 | 467 | 1.6 | 0.001 |
aLLR, log likelihood ratio.
bRR, relative risk.
Note: Bolded values are significant at p < 0.05; 95%CI.
The spatial scan statistic was used to test clusters with high mortality rates, whereby statistically significant clusters comprising of different sets of villages were identified. The input files for the SaTScan software include the number of cases, population, and village coordinates. Finally, a standard GIS program-MapInfo Professional version 7.5 was used to translate the space–time outputs into maps that depict clustering of under-five deaths in RDSS for the observed period.
Mortality clustering of the under-fives
Scan statistics were used to detect and evaluate clusters of cases in either a purely temporal, purely spatial, or space–time setting. This is done by gradually scanning a window across time and/or space, noting the number of observed and expected observations inside the window at each location. In the SaTScan software, the scanning window is an interval (in time), a circle (in space), or a cylinder with a circular base (in space–time) window with the maximum likelihood being that the most likely cluster is the cluster least likely to be due by chance. A p-value is assigned to this cluster (6).
SaTScan™ always runs the analysis in an iterative manner, in the first iteration it runs the standard analysis and it only reports the most likely cluster. That cluster is then removed from the dataset in the cluster while the population is set to zero for the locations and the time period defining the cluster.
In the second iteration, completely new analysis is conducted in the remaining data; this procedure is then repeated until there are more clusters with p-value less than the specified one (6).
For purely spatial, space–time analysis, SaTScan™ also identifies secondary clusters in the dataset in addition to the most likely cluster and order them according to their likelihood ratio test statistic.
Mapping areas with high mortality
A visual assessment of areas with high mortality was done by using the MapInfo software.
Results
A total of 30 villages/clusters that were geo referenced were included in the analysis. A total of 17,019 children younger than 5 years of age were identified and were followed up, retrospectively. Out of 17,019 children included in the analysis, about 16% (2,745) of them were deaths. Results presented in Table 1 and Table 2 show clusters of higher under-five rates in the RDSS. These deaths result into 134,099 person-years.
Table 1.
Year | Person year | Total number of deaths | Death ratea | 95% CI |
---|---|---|---|---|
1999 | 10,170 | 341 | 33.5 | 30.1–37.3 |
2000 | 12,629 | 334 | 26.4 | 23.8–29.4 |
2001 | 13,211 | 319 | 24.1 | 21.6–26.9 |
2002 | 13,796 | 343 | 24.9 | 22.3–27.6 |
2003 | 14,305 | 230 | 16.1 | 14.1–18.3 |
2004 | 14,087 | 280 | 19.9 | 17.7–22.3 |
2005 | 13,844 | 274 | 19.8 | 17.6–22.3 |
2006 | 14,131 | 266 | 18.8 | 16.7–21.2 |
2007 | 13,963 | 204 | 14.6 | 12.7–16.8 |
2008 | 13,254 | 154 | 11.0 | 9.4–12.9 |
1999–2008 | 134,099 | 2,745 | 21.0 | 20.2–21.8 |
aPer 1,000 person years.
Table 1 shows mortality trends for under-five children in RDSS from 1999 to 2008. The average under-five mortality rate for 10 years was 21.0 per 1,000 person-years. Results show that under-five mortality rates were high in RDSS during the first 4 years of analysis (1999–2002) with 1999 having the highest mortality rate (33.5).
The purely spatial analysis (Table 2) revealed three significant clusters, the first cluster involved Kibiti A (p = 0.001) with 50 total cases and 23 expected cases; the second cluster consisted of five villages that include Mtawanya, Pagae, Kibiti A, Machepe, and Kibiti B (p = 0.006) with 126 cases and 92 expected cases; and the last cluster consisted of one village, Jaribu Mpakani (p = 0.015) with 48 total cases and 29 expected cases. The purely spatial scan for the entire period of 10 years was also identified (Kibiti A; p = 0.001) to be a village with the highest under-five mortality rate. When we run the SaTScan for the spatial–temporal analysis (Table 2), the significant years were 1999–2002 (p = 0.001), which consisted of the villages of Nyambunda, Nyambili, Pagae, Bungu A, Bumba, Bungu B, Mtawanya, Kibiti A, Uponda, and Kibiti B (Fig. 2) with a relative risk of 1.6. The results show the overall strongly decreasing mortality in the area; however, those who had highest rates at the beginning still have the highest rates at the end of the observation period. For example, Nyambunda Village, which had the highest death rate (44) in 2000, also had the ‘peak’ death rates (69) and (27) for the years 2007 and 2008, respectively (see Supplementary Table S1).
Temporal trend results (Table 1) show significantly high under-five mortality rates for the four consecutive years from 1999 to 2002, with 1999 being the ‘peak’ year. For the last three analysis years (2006–2008), the under-five mortality rates decrease dramatically with death rates of 18.8, 14.6, and 11.0, respectively. However, more years of observation are needed before one can conclude that the childhood mortality in the DSS catchment area is decreasing significantly. The result from both Stata and SaTScan indicates that the mortality for the DSS area is decreasing substantially in RDSS.
Discussion and conclusion
Starting in 1997, the Ministry of Health and Social Welfare implemented the Tanzania Essential Health Interventions Project (TEHIP) as a reform pilot in the two large districts of Rufiji and Morogoro. A part of the reform pilot consisted of a simulated sectorwide basket funding of approximately US$1 per capita, per year that was provided to the districts. Additional tools and strategies included an annual district health profile and a district health accounts tool for budget and expenditure analysis, complemented by management training. The district also implemented the Integrated Management of Childhood Illnesses (IMCI), a strategy designed to address major causes of child mortality.
In 2002, there was a change in national policy of the first-line drug for the treatment of malaria from chloroquine to sulfadoxine pyremethamine (SP). The IMCI, to a large extent, relies on effective anti-malarial drugs since malaria is the major cause of hospital admission and mortality in Rufiji. There was also a modest increase in the coverage of insecticide-treated nets (ITNs) over the years. All these factors have contributed to a steady decline in mortality within the Rufiji district from the late 1990s. The sharp decline in mortality in 2003 was largely contributed to the year being very dry and hence less malaria transmission.
Currently the Government of Tanzania through the Ministry of Health is undertaking a number of interventions (4, 8) to reduce child mortality in the country. Furthermore mortality indicators are useful in assessing the National Strategy for Growth and Reduction of Poverty (NSGRP), as they reflect socioeconomic development and quality of life.
The 2004–2005 Tanzania Demographic and Health Survey (TDHS) data indicate a recent, rapid decline in under-five mortality (4, 8). Infant mortality estimates show a decline from 100 in the 5–9-year period preceding the survey (approximately 1995–1999) to 68 mortality rates per 1,000 births during the 2000–2004 periods (4). The 2004–2005 TDHS estimate for the 5–9-year period preceding the survey is almost identical to the 1999 Tanzania Reproductive and Child Health Survey (TRCHS) rate of 99 deaths per 1,000 births for the same period (i.e. 0–4 years preceding the survey) (8). Thus, the comparison of the two separate surveys, the 1999 TRCHS and the 2004–2005 TDHS data itself, indicate a significant decrease in infant and child mortality rates in recent years. The largest decline has occurred in the postneonatal period.
Childhood mortality data highly suffers from the effect of age misreporting at death, this seriously causes bias in estimates; specifically of age misreporting happening when transfer is made from one age bracket to another. Another data quality problem is the selective omission from the histories of births (babies who did not survive), which can lead to underestimation of mortality rates. Another potential data quality problem includes displacement of birth dates, which may cause a distortion of mortality trends (8).
Socioeconomic differentials such as household wealth and other factors like place of residence, region, or educational level of the mother may affect childhood mortality. High levels of educational attainment are generally associated with lower mortality rates, because education exposes the mother to information about better nutrition, use of contraceptives to space births, and knowledge about childhood illnesses and treatments. Birth intervals of at least 3 years are almost half the risk of death as births occurring within 2 years of the preceding birth. A child's weight at birth is an important indicator of his or her own chances of survival (8).
The ability of women to access information, make decisions, and act effectively in their own interest – or the interest of those who depend on them – are essential aspects of the empowerment of women. If women being the primary caretaker of children are empowered, the health survival of their infants will be enhanced. Household decision making is strongly associated with under-five mortality. Among children born to women who have no say in any decision, 155 per 1,000 die before their fifth birthday, compared with 124 per 1,000 children born to women who participate in all specified household decisions. Similarly, young mothers may have difficult deliveries due to physical immaturity, high-parity births, and older women (above 35) may have high mortality risk for under-fives.
In this paper, the SaTScan™ has been very useful software in assessing temporal, spatial, and space–time high-mortality clusters especially in detecting and evaluating their statistical significance. It is clear that this technique brought about an interest in further investigation on which clusters probably are chance occurrences.
Like other methods, SaTScan has limitations that have implications on results interpretation. The circular window imposed on either purely spatial scan statistic or cylindrical window for space–time statistic, usually takes various villages with high mortality. If it happens that a village with a low mortality rate is surrounded or is very close to villages with high mortality, the software will then enter this village into the high-mortality cluster villages. However, this limitation does not disqualify SaTScan from its importance in producing summarized information over conversional epidemiological methods of presenting results.
The quality of data in the Rufiji HDSS is usually assured both in the field and during data entry. However, in spite of quality assurance of the data, the status of data collection in rural parts of developing countries have their limitations. It is not possible to achieve a record of all deaths. Under-reporting of cases or incomplete recording of demographic events (births, deaths, in- and out-migration) remain a challenge and could have had an effect on our results. Irrespective of the noted challenges, information and data collected remains crucial in understanding the dynamics happening in the district for planning and evaluation of the health system performance.
This paper aimed at providing general information of clusters for high mortality rates in Rufiji HDSS. Therefore, all-cause mortality (total mortality) has been used rather than cause-specific mortality. However, in most cases – due to climatic and environmental factors like seasonal influence (hot weather and rainy periods) – diseases such as malaria, acute respiratory infections (ARI), and diarrhea, in combination with malnutrition have been the major causes of death in this area. For future purposes, conducting cause-specific mortality clustering would be important in revealing those causes of diseases that prevail in which villages within clusters. It is further recommended that similar analyses be replicated to other DSS in the country.
Acknowledgements
The authors would like to thank IDRC, INDEPTH Network, Ministry of Health and Social Welfare (MoHSW), Rockefeller Foundation, and PMI (President's Malaria Initiative) for funding the Rufiji HDSS. The authors also thank the INDEPTH Network for facilitating the publication of this paper. We also thank Professor Heiko Becher for his constant advice during the manuscript preparation. We thank the RDSS staffs (Site co-coordinator, data manager, and field manager) for granting the authors access to the data. Last but not least the authors are indebted to the villagers who shared their personal information over the years. SaTScanTM is a trademark of Martin Kulldorff. The SaTScanTM software was developed under the joint auspices of (a) Martin Kulldorff, (b) the National Cancer Institute and (c) Farzad Mostashari of the New York City Department of Health and Mental Hygiene.
Supplementary Material.
Table S1.
1999 | 2000 | 2001 | 2002 | 2003 | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Village | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI |
Bungu A | 20 | 645 | 31 | 19–48 | 19 | 776 | 25 | 15–38 | 16 | 813 | 20 | 11–32 | 26 | 852 | 31 | 20–45 | 16 | 911 | 18 | 10–29 |
Bungu B | 27 | 692 | 39 | 26–57 | 19 | 892 | 22 | 13–33 | 18 | 960 | 19 | 11–30 | 28 | 1,005 | 28 | 19–40 | 16 | 1,013 | 16 | 9–26 |
Bumba | 2 | 90 | 22 | 3– 80 | 2 | 126 | 16 | 2–57 | 2 | 125 | 16 | 2–58 | 4 | 140 | 29 | 8–73 | 3 | 133 | 23 | 5–66 |
Ikwiriri Central | 8 | 260 | 31 | 13–61 | 9 | 310 | 29 | 13–55 | 6 | 331 | 18 | 7–40 | 10 | 340 | 29 | 14–54 | 4 | 340 | 12 | 3–30 |
Ikwiriri North | 3 | 235 | 13 | 7– 37 | 9 | 272 | 33 | 15–63 | 4 | 285 | 14 | 4–36 | 7 | 293 | 24 | 10–49 | 5 | 300 | 17 | 5–39 |
Ikwiriri South | 19 | 570 | 33 | 20–52 | 13 | 646 | 20 | 11–34 | 17 | 682 | 25 | 15–40 | 15 | 683 | 22 | 12–36 | 10 | 676 | 15 | 7–27 |
Jaribu Mpakani | 20 | 747 | 27 | 16–41 | 34 | 985 | 35 | 24–48 | 24 | 1,076 | 22 | 14–33 | 35 | 1,199 | 29 | 20–41 | 22 | 1,359 | 16 | 10–24 |
Kibiti A | 50 | 686 | 73 | 54–96 | 36 | 890 | 41 | 28–56 | 29 | 898 | 32 | 22–46 | 32 | 877 | 37 | 25–52 | 19 | 862 | 22 | 13–34 |
Kibiti B | 40 | 1,133 | 35 | 25–48 | 57 | 1,660 | 34 | 26–45 | 44 | 1,690 | 26 | 19–35 | 36 | 1,709 | 21 | 15–29 | 32 | 1,757 | 18 | 12–26 |
Kimbuga | 9 | 357 | 25 | 12–48 | 7 | 380 | 18 | 7–38 | 8 | 384 | 20 | 9–41 | 8 | 402 | 20 | 9–39 | 3 | 347 | 9 | 2–25 |
Machepe | 1 | 11 | 0 | 0–335 | 0 | 20 | 0 | 0–184 | 0 | 28 | 0 | 0–132 | 2 | 29 | 69 | 8–249 | 0 | 33 | 0 | 0–112 |
Mchukwi A | 10 | 259 | 39 | 19–71 | 2 | 344 | 6 | 1–21 | 6 | 365 | 16 | 6–36 | 3 | 368 | 8 | 2–24 | 2 | 365 | 6 | 1–20 |
Mchukwi B | 14 | 339 | 41 | 23–69 | 8 | 458 | 17 | 8–34 | 13 | 483 | 27 | 14–46 | 9 | 484 | 19 | 9–35 | 6 | 492 | 12 | 5–27 |
Mgomba Central | 4 | 280 | 14 | 4–37 | 5 | 307 | 16 | 5–38 | 4 | 322 | 12 | 3–32 | 8 | 348 | 23 | 10–45 | 5 | 355 | 14 | 5–33 |
Mgomba North | 6 | 262 | 23 | 8–50 | 5 | 291 | 17 | 6–40 | 3 | 318 | 9 | 2–28 | 9 | 332 | 27 | 12–52 | 8 | 344 | 23 | 10–46 |
Mgomba South | 14 | 283 | 50 | 27–83 | 3 | 323 | 9 | 2–27 | 5 | 322 | 16 | 5–36 | 9 | 353 | 26 | 12–48 | 4 | 371 | 11 | 3–28 |
Miwaga | 5 | 128 | 39 | 13–91 | 5 | 145 | 35 | 11–81 | 2 | 137 | 15 | 2–53 | 2 | 147 | 14 | 2–49 | 3 | 153 | 20 | 4–57 |
Mjawa | 6 | 208 | 29 | 11–63 | 7 | 225 | 31 | 13–64 | 8 | 258 | 3 | 13–61 | 5 | 275 | 18 | 6–42 | 6 | 291 | 21 | 8–45 |
Mkupuka | 2 | 43 | 47 | 6–168 | 2 | 61 | 33 | 4–118 | 3 | 68 | 44 | 9–129 | 2 | 82 | 24 | 3.0–88 | 3 | 77 | 39 | 8–114 |
Mlanzi | 12 | 514 | 24 | 12–41 | 18 | 698 | 26 | 15–41 | 22 | 689 | 32 | 20–48 | 19 | 714 | 27 | 16–42 | 10 | 737 | 14 | 7–25 |
Mng'aru | 2 | 89 | 23 | 3–81 | 3 | 93 | 33 | 7–94 | 1 | 100 | 10 | 0–56 | 2 | 105 | 19 | 2–69 | 3 | 105 | 29 | 6–84 |
Mtawanya | 8 | 435 | 18 | 8–36 | 17 | 517 | 33 | 19–53 | 16 | 529 | 30 | 17–49 | 17 | 558 | 31 | 18–49 | 12 | 568 | 21 | 11–37 |
Ngulakula | 2 | 82 | 24 | 3–88 | 3 | 123 | 24 | 5–71 | 2 | 104 | 19 | 2–70 | 3 | 106 | 28 | 6–83 | 2 | 111 | 18 | 2–65 |
Nyambili | 4 | 122 | 33 | 9–84 | 3 | 123 | 24 | 5–71 | 1 | 108 | 9 | 0–52 | 1 | 117 | 9 | 0–48 | 3 | 115 | 26 | 5–76 |
Nyambunda | 1 | 50 | 20 | 0–111 | 3 | 68 | 44 | 9–129 | 2 | 69 | 29 | 4–105 | 1 | 71 | 14 | 0–79 | 1 | 73 | 14 | 0–76 |
Pagae | 12 | 370 | 32 | 17–57 | 16 | 421 | 38 | 22–62 | 8 | 432 | 19 | 8–37 | 13 | 459 | 28 | 15–48 | 6 | 480 | 13 | 5–27 |
Umwe Central | 13 | 337 | 39 | 21–66 | 8 | 393 | 20 | 9–40 | 13 | 412 | 32 | 17–54 | 5 | 440 | 11 | 4–27 | 4 | 455 | 9 | 2–23 |
Umwe North | 6 | 419 | 14 | 5–31 | 6 | 439 | 14 | 5–30 | 10 | 445 | 23 | 11–41 | 10 | 478 | 21 | 10–39 | 8 | 496 | 16 | 7–32 |
Umwe South | 9 | 225 | 40 | 18–76 | 4 | 282 | 14 | 4–36 | 15 | 307 | 49 | 27–81 | 7 | 320 | 22 | 9–45 | 3 | 305 | 10 | 2–29 |
Uponda | 13 | 302 | 43 | 23–74 | 11 | 391 | 28 | 14–50 | 17 | 469 | 36 | 21–58 | 15 | 512 | 29 | 16–48 | 11 | 583 | 19 | 9–34 |
Total | 341 | 10,170 | 34 | 31–37 | 334 | 12,629 | 27 | 24–29 | 319 | 13,211 | 24 | 22–27 | 343 | 13,796 | 25 | 22–28 | 230 | 14,305 | 16 | 14–18 |
2004 | 2005 | 2006 | 2007 | 2008 | ||||||||||||||||
Village | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI |
Bungu A | 19 | 898 | 21 | 13–33 | 13 | 870 | 15 | 8–26 | 15 | 871 | 17 | 10–28 | 9 | 825 | 11 | 5–21 | 11 | 825 | 13 | 7–24 |
Bungu B | 20 | 993 | 20 | 12–31 | 26 | 988 | 26 | 17–39 | 26 | 1,012 | 26 | 17–38 | 22 | 986 | 22 | 14–34 | 10 | 986 | 10 | 5–19 |
Bumba | 4 | 123 | 33 | 9–83 | 1 | 129 | 8 | 0–43 | 2 | 129 | 16 | 2–56 | 2 | 127 | 16 | 2–57 | 2 | 127 | 16 | 2–57 |
Ikwiriri Central | 3 | 333 | 9 | 2–26 | 4 | 317 | 13 | 3–32 | 5 | 303 | 17 | 5–39 | 1 | 305 | 3 | 0–18 | 3 | 305 | 10 | 2.–29 |
Ikwiriri North | 8 | 295 | 27 | 12–53 | 5 | 284 | 18 | 6–41 | 2 | 288 | 7 | 1–25 | 5 | 327 | 15 | 5–36 | 4 | 327 | 12 | 3–31 |
Ikwiriri South | 7 | 661 | 11 | 4–22 | 15 | 638 | 24 | 13–39 | 9 | 649 | 14 | 6–26 | 4 | 638 | 6 | 2–16 | 3 | 638 | 5 | 1–14 |
Jaribu Mpakani | 35 | 1,416 | 25 | 17–34 | 36 | 1438 | 25 | 18–35 | 48 | 1,532 | 31 | 23–41 | 26 | 1,526 | 17 | 11–25 | 19 | 1,526 | 13 | 8–19 |
Kibiti A | 26 | 836 | 25 | 20–46 | 17 | 777 | 22 | 13–35 | 22 | 773 | 29 | 18–43 | 9 | 795 | 11 | 5–22 | 3 | 795 | 4 | 1–11 |
Kibiti B | 32 | 1,635 | 20 | 13–28 | 41 | 1,542 | 27 | 19–38 | 22 | 1,549 | 14 | 9–22 | 29 | 1,544 | 19 | 13–27 | 23 | 1,544 | 14 | 9–22 |
Kimbuga | 9 | 428 | 21 | 10–40 | 6 | 445 | 14 | 5–29 | 5 | 472 | 11 | 3–25 | 8 | 452 | 18 | 8–35 | 4 | 452 | 9 | 2–23 |
Machepe | 0 | 38 | 0 | 0–97 | 1 | 40 | 25 | 1–139 | 0 | 46 | 0 | 0–80 | 0 | 45 | 0 | 0–82 | 1 | 45 | 22 | 1–124 |
Mchukwi A | 11 | 358 | 31 | 15–55 | 4 | 357 | 11 | 3–29 | 12 | 358 | 34 | 17–59 | 2 | 353 | 6 | 1–21 | 6 | 353 | 17 | 6–37 |
Mchukwi B | 10 | 491 | 20 | 10–38 | 7 | 463 | 15 | 6–31 | 10 | 472 | 21 | 10–39 | 10 | 476 | 21 | 10–39 | 8 | 476 | 17 | 7–33 |
Mgomba Central | 5 | 381 | 13 | 4–31 | 11 | 394 | 28 | 14–50 | 4 | 384 | 10 | 3–27 | 3 | 366 | 8 | 2–24 | 4 | 366 | 11 | 3–28 |
Mgomba North | 8 | 340 | 21 | 10–46 | 4 | 340 | 12 | 3–30 | 4 | 360 | 11 | 3–29 | 4 | 395 | 10 | 3–26 | 5 | 395 | 13 | 4–30 |
Mgomba South | 10 | 365 | 27 | 13–50 | 9 | 340 | 27 | 12–50 | 5 | 338 | 15 | 5–35 | 3 | 331 | 9 | 2–27 | 3 | 331 | 9 | 2–27 |
Miwaga | 0 | 152 | 0 | 0–24 | 3 | 160 | 19 | 4–55 | 2 | 175 | 11 | 1–41 | 3 | 170 | 18 | 4–52 | 0 | 170 | 0 | 0–22 |
Mjawa | 3 | 283 | 11 | 2–31 | 4 | 301 | 13 | 4–34 | 5 | 306 | 16 | 5–38 | 2 | 279 | 29 | 12–57 | 3 | 279 | 11 | 2–31 |
Mkupuka | 1 | 80 | 13 | 0–70 | 0 | 79 | 0 | 0–47 | 1 | 85 | 12 | 0–66 | 1 | 87 | 12 | 0–64 | 0 | 87 | 0 | 0–42 |
Mlanzi | 8 | 717 | 11 | 5–22 | 10 | 694 | 14 | 7–27 | 14 | 734 | 19 | 10–32 | 7 | 724 | 10 | 4–19 | 6 | 274 | 22 | 8–48 |
Mng'aru | 3 | 86 | 35 | 7–102 | 0 | 75 | 0 | 0–49 | 1 | 81 | 12 | 0–69 | 1 | 80 | 13 | 0–70 | 0 | 80 | 0 | 0–46 |
Mtawanya | 10 | 562 | 18 | 9–33 | 8 | 590 | 14 | 6–27 | 13 | 602 | 22 | 12–37 | 12 | 596 | 20 | 10–35 | 8 | 596 | 13 | 6–27 |
Ngulakula | 2 | 116 | 17 | 2–62 | 5 | 109 | 46 | 15–107 | 0 | 126 | 0 | 0–29 | 4 | 123 | 33 | 9–83 | 1 | 123 | 8 | 0–45 |
Nyambili | 0 | 102 | 0 | 0–36 | 2 | 93 | 22 | 3–78 | 1 | 95 | 11 | 0–59 | 0 | 96 | 0 | 0–38 | 1 | 96 | 10 | 0–58 |
Nyambunda | 1 | 74 | 14 | 0–75 | 2 | 70 | 29 | 4–103 | 1 | 75 | 13 | 0–74 | 5 | 73 | 69 | 22–160 | 2 | 73 | 27 | 3–99 |
Pagae | 9 | 465 | 19 | 9–37 | 9 | 463 | 19 | 9–37 | 9 | 464 | 19 | 9–37 | 11 | 455 | 24 | 12–43 | 12 | 455 | 26 | 14–46 |
Umwe Central | 9 | 449 | 20 | 9–38 | 6 | 427 | 14 | 5–31 | 9 | 340 | 21 | 10–40 | 4 | 409 | 10 | 3–25 | 2 | 409 | 26 | 14–46 |
Umwe North | 7 | 477 | 15 | 6–30 | 10 | 467 | 21 | 10–39 | 4 | 382 | 8 | 2–21 | 5 | 491 | 10 | 3–24 | 2 | 491 | 4 | 1–15 |
Umwe South | 5 | 305 | 16 | 5–38 | 3 | 298 | 10 | 2–29 | 6 | 298 | 20 | 7–44 | 2 | 297 | 7 | 1–24 | 4 | 297 | 14 | 4–35 |
Uponda | 15 | 630 | 24 | 13–39 | 12 | 657 | 18 | 9–32 | 9 | 942 | 14 | 6–27 | 4 | 594 | 7 | 2–17 | 4 | 594 | 7 | 2–17 |
Total | 280 | 14,087 | 20 | 17–22 | 274 | 13,844 | 20 | 18–22 | 266 | 13,937 | 19 | 17–22 | 204 | 13,537 | 15 | 13–17 | 154 | 13,254 | 12 | 10–14 |
PY–Person years.
DR–Death rate.
CI–Confidence interval.
Conflict of interest and funding
The author has not received any funding or benefits from industry to conduct this study.
References
- 1.Murray C, Laakso T, Shibuya K, Hill K, Lopez AD. Can we achieve Millennium Development Goal 4? New analysis of country trends and forecasts of under-5 mortality to 2015. Lancet. 2007;370:1040–54. doi: 10.1016/S0140-6736(07)61478-0. [DOI] [PubMed] [Google Scholar]
- 2.Central Intelligence Agency (CIA) The World fact book. 2000. Available from: http://www.cia.gov/library/publications/the-worldfactbook/geos/tz.html [cited 24 June 2010]
- 3.World Health Organisation. The World Health report. 2000. Available from: http://www.who.int/whr/2000/en/index.html [cited 24 June 2010]
- 4.Masanja H, De Savigny D, Schellenberg J, John T, Mbuya C, Upunda C, et al. Child survival gains in Tanzania: Analysis of data from demographic and health surveys. Lancet. 2008;371:1276–83. doi: 10.1016/S0140-6736(08)60562-0. [DOI] [PubMed] [Google Scholar]
- 5.Gasarasi DB, Premji ZG, Mujinja PGM, Mpembeni R, Calavas D. Acute adenolymphangitis due to bancroftian filariasisin Rufiji district, south east Tanzania. ActaTropica. 2000;75:19–28. doi: 10.1016/s0001-706x(99)00090-x. [DOI] [PubMed] [Google Scholar]
- 6.Kulldorff M. SaTScan™ user guide for version 8. 2008. Available from: http://www.satscan.org [cited February 2009]
- 7.Mwageni E, Momburi D, Juma Z, Irema M, Masanja H. Rufiji DSS: Tanzania essential health interventions project. Adult morbidity and mortality. Tanzania: Tanzania Ministry of Health; 1998. [Google Scholar]
- 8.The National Bureau of Statistics. Chapter 8 in: Tanzania Demographic and Health Survey. 2005
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Table S1.
1999 | 2000 | 2001 | 2002 | 2003 | ||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Village | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI |
Bungu A | 20 | 645 | 31 | 19–48 | 19 | 776 | 25 | 15–38 | 16 | 813 | 20 | 11–32 | 26 | 852 | 31 | 20–45 | 16 | 911 | 18 | 10–29 |
Bungu B | 27 | 692 | 39 | 26–57 | 19 | 892 | 22 | 13–33 | 18 | 960 | 19 | 11–30 | 28 | 1,005 | 28 | 19–40 | 16 | 1,013 | 16 | 9–26 |
Bumba | 2 | 90 | 22 | 3– 80 | 2 | 126 | 16 | 2–57 | 2 | 125 | 16 | 2–58 | 4 | 140 | 29 | 8–73 | 3 | 133 | 23 | 5–66 |
Ikwiriri Central | 8 | 260 | 31 | 13–61 | 9 | 310 | 29 | 13–55 | 6 | 331 | 18 | 7–40 | 10 | 340 | 29 | 14–54 | 4 | 340 | 12 | 3–30 |
Ikwiriri North | 3 | 235 | 13 | 7– 37 | 9 | 272 | 33 | 15–63 | 4 | 285 | 14 | 4–36 | 7 | 293 | 24 | 10–49 | 5 | 300 | 17 | 5–39 |
Ikwiriri South | 19 | 570 | 33 | 20–52 | 13 | 646 | 20 | 11–34 | 17 | 682 | 25 | 15–40 | 15 | 683 | 22 | 12–36 | 10 | 676 | 15 | 7–27 |
Jaribu Mpakani | 20 | 747 | 27 | 16–41 | 34 | 985 | 35 | 24–48 | 24 | 1,076 | 22 | 14–33 | 35 | 1,199 | 29 | 20–41 | 22 | 1,359 | 16 | 10–24 |
Kibiti A | 50 | 686 | 73 | 54–96 | 36 | 890 | 41 | 28–56 | 29 | 898 | 32 | 22–46 | 32 | 877 | 37 | 25–52 | 19 | 862 | 22 | 13–34 |
Kibiti B | 40 | 1,133 | 35 | 25–48 | 57 | 1,660 | 34 | 26–45 | 44 | 1,690 | 26 | 19–35 | 36 | 1,709 | 21 | 15–29 | 32 | 1,757 | 18 | 12–26 |
Kimbuga | 9 | 357 | 25 | 12–48 | 7 | 380 | 18 | 7–38 | 8 | 384 | 20 | 9–41 | 8 | 402 | 20 | 9–39 | 3 | 347 | 9 | 2–25 |
Machepe | 1 | 11 | 0 | 0–335 | 0 | 20 | 0 | 0–184 | 0 | 28 | 0 | 0–132 | 2 | 29 | 69 | 8–249 | 0 | 33 | 0 | 0–112 |
Mchukwi A | 10 | 259 | 39 | 19–71 | 2 | 344 | 6 | 1–21 | 6 | 365 | 16 | 6–36 | 3 | 368 | 8 | 2–24 | 2 | 365 | 6 | 1–20 |
Mchukwi B | 14 | 339 | 41 | 23–69 | 8 | 458 | 17 | 8–34 | 13 | 483 | 27 | 14–46 | 9 | 484 | 19 | 9–35 | 6 | 492 | 12 | 5–27 |
Mgomba Central | 4 | 280 | 14 | 4–37 | 5 | 307 | 16 | 5–38 | 4 | 322 | 12 | 3–32 | 8 | 348 | 23 | 10–45 | 5 | 355 | 14 | 5–33 |
Mgomba North | 6 | 262 | 23 | 8–50 | 5 | 291 | 17 | 6–40 | 3 | 318 | 9 | 2–28 | 9 | 332 | 27 | 12–52 | 8 | 344 | 23 | 10–46 |
Mgomba South | 14 | 283 | 50 | 27–83 | 3 | 323 | 9 | 2–27 | 5 | 322 | 16 | 5–36 | 9 | 353 | 26 | 12–48 | 4 | 371 | 11 | 3–28 |
Miwaga | 5 | 128 | 39 | 13–91 | 5 | 145 | 35 | 11–81 | 2 | 137 | 15 | 2–53 | 2 | 147 | 14 | 2–49 | 3 | 153 | 20 | 4–57 |
Mjawa | 6 | 208 | 29 | 11–63 | 7 | 225 | 31 | 13–64 | 8 | 258 | 3 | 13–61 | 5 | 275 | 18 | 6–42 | 6 | 291 | 21 | 8–45 |
Mkupuka | 2 | 43 | 47 | 6–168 | 2 | 61 | 33 | 4–118 | 3 | 68 | 44 | 9–129 | 2 | 82 | 24 | 3.0–88 | 3 | 77 | 39 | 8–114 |
Mlanzi | 12 | 514 | 24 | 12–41 | 18 | 698 | 26 | 15–41 | 22 | 689 | 32 | 20–48 | 19 | 714 | 27 | 16–42 | 10 | 737 | 14 | 7–25 |
Mng'aru | 2 | 89 | 23 | 3–81 | 3 | 93 | 33 | 7–94 | 1 | 100 | 10 | 0–56 | 2 | 105 | 19 | 2–69 | 3 | 105 | 29 | 6–84 |
Mtawanya | 8 | 435 | 18 | 8–36 | 17 | 517 | 33 | 19–53 | 16 | 529 | 30 | 17–49 | 17 | 558 | 31 | 18–49 | 12 | 568 | 21 | 11–37 |
Ngulakula | 2 | 82 | 24 | 3–88 | 3 | 123 | 24 | 5–71 | 2 | 104 | 19 | 2–70 | 3 | 106 | 28 | 6–83 | 2 | 111 | 18 | 2–65 |
Nyambili | 4 | 122 | 33 | 9–84 | 3 | 123 | 24 | 5–71 | 1 | 108 | 9 | 0–52 | 1 | 117 | 9 | 0–48 | 3 | 115 | 26 | 5–76 |
Nyambunda | 1 | 50 | 20 | 0–111 | 3 | 68 | 44 | 9–129 | 2 | 69 | 29 | 4–105 | 1 | 71 | 14 | 0–79 | 1 | 73 | 14 | 0–76 |
Pagae | 12 | 370 | 32 | 17–57 | 16 | 421 | 38 | 22–62 | 8 | 432 | 19 | 8–37 | 13 | 459 | 28 | 15–48 | 6 | 480 | 13 | 5–27 |
Umwe Central | 13 | 337 | 39 | 21–66 | 8 | 393 | 20 | 9–40 | 13 | 412 | 32 | 17–54 | 5 | 440 | 11 | 4–27 | 4 | 455 | 9 | 2–23 |
Umwe North | 6 | 419 | 14 | 5–31 | 6 | 439 | 14 | 5–30 | 10 | 445 | 23 | 11–41 | 10 | 478 | 21 | 10–39 | 8 | 496 | 16 | 7–32 |
Umwe South | 9 | 225 | 40 | 18–76 | 4 | 282 | 14 | 4–36 | 15 | 307 | 49 | 27–81 | 7 | 320 | 22 | 9–45 | 3 | 305 | 10 | 2–29 |
Uponda | 13 | 302 | 43 | 23–74 | 11 | 391 | 28 | 14–50 | 17 | 469 | 36 | 21–58 | 15 | 512 | 29 | 16–48 | 11 | 583 | 19 | 9–34 |
Total | 341 | 10,170 | 34 | 31–37 | 334 | 12,629 | 27 | 24–29 | 319 | 13,211 | 24 | 22–27 | 343 | 13,796 | 25 | 22–28 | 230 | 14,305 | 16 | 14–18 |
2004 | 2005 | 2006 | 2007 | 2008 | ||||||||||||||||
Village | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI | Deaths | PY | DR | CI |
Bungu A | 19 | 898 | 21 | 13–33 | 13 | 870 | 15 | 8–26 | 15 | 871 | 17 | 10–28 | 9 | 825 | 11 | 5–21 | 11 | 825 | 13 | 7–24 |
Bungu B | 20 | 993 | 20 | 12–31 | 26 | 988 | 26 | 17–39 | 26 | 1,012 | 26 | 17–38 | 22 | 986 | 22 | 14–34 | 10 | 986 | 10 | 5–19 |
Bumba | 4 | 123 | 33 | 9–83 | 1 | 129 | 8 | 0–43 | 2 | 129 | 16 | 2–56 | 2 | 127 | 16 | 2–57 | 2 | 127 | 16 | 2–57 |
Ikwiriri Central | 3 | 333 | 9 | 2–26 | 4 | 317 | 13 | 3–32 | 5 | 303 | 17 | 5–39 | 1 | 305 | 3 | 0–18 | 3 | 305 | 10 | 2.–29 |
Ikwiriri North | 8 | 295 | 27 | 12–53 | 5 | 284 | 18 | 6–41 | 2 | 288 | 7 | 1–25 | 5 | 327 | 15 | 5–36 | 4 | 327 | 12 | 3–31 |
Ikwiriri South | 7 | 661 | 11 | 4–22 | 15 | 638 | 24 | 13–39 | 9 | 649 | 14 | 6–26 | 4 | 638 | 6 | 2–16 | 3 | 638 | 5 | 1–14 |
Jaribu Mpakani | 35 | 1,416 | 25 | 17–34 | 36 | 1438 | 25 | 18–35 | 48 | 1,532 | 31 | 23–41 | 26 | 1,526 | 17 | 11–25 | 19 | 1,526 | 13 | 8–19 |
Kibiti A | 26 | 836 | 25 | 20–46 | 17 | 777 | 22 | 13–35 | 22 | 773 | 29 | 18–43 | 9 | 795 | 11 | 5–22 | 3 | 795 | 4 | 1–11 |
Kibiti B | 32 | 1,635 | 20 | 13–28 | 41 | 1,542 | 27 | 19–38 | 22 | 1,549 | 14 | 9–22 | 29 | 1,544 | 19 | 13–27 | 23 | 1,544 | 14 | 9–22 |
Kimbuga | 9 | 428 | 21 | 10–40 | 6 | 445 | 14 | 5–29 | 5 | 472 | 11 | 3–25 | 8 | 452 | 18 | 8–35 | 4 | 452 | 9 | 2–23 |
Machepe | 0 | 38 | 0 | 0–97 | 1 | 40 | 25 | 1–139 | 0 | 46 | 0 | 0–80 | 0 | 45 | 0 | 0–82 | 1 | 45 | 22 | 1–124 |
Mchukwi A | 11 | 358 | 31 | 15–55 | 4 | 357 | 11 | 3–29 | 12 | 358 | 34 | 17–59 | 2 | 353 | 6 | 1–21 | 6 | 353 | 17 | 6–37 |
Mchukwi B | 10 | 491 | 20 | 10–38 | 7 | 463 | 15 | 6–31 | 10 | 472 | 21 | 10–39 | 10 | 476 | 21 | 10–39 | 8 | 476 | 17 | 7–33 |
Mgomba Central | 5 | 381 | 13 | 4–31 | 11 | 394 | 28 | 14–50 | 4 | 384 | 10 | 3–27 | 3 | 366 | 8 | 2–24 | 4 | 366 | 11 | 3–28 |
Mgomba North | 8 | 340 | 21 | 10–46 | 4 | 340 | 12 | 3–30 | 4 | 360 | 11 | 3–29 | 4 | 395 | 10 | 3–26 | 5 | 395 | 13 | 4–30 |
Mgomba South | 10 | 365 | 27 | 13–50 | 9 | 340 | 27 | 12–50 | 5 | 338 | 15 | 5–35 | 3 | 331 | 9 | 2–27 | 3 | 331 | 9 | 2–27 |
Miwaga | 0 | 152 | 0 | 0–24 | 3 | 160 | 19 | 4–55 | 2 | 175 | 11 | 1–41 | 3 | 170 | 18 | 4–52 | 0 | 170 | 0 | 0–22 |
Mjawa | 3 | 283 | 11 | 2–31 | 4 | 301 | 13 | 4–34 | 5 | 306 | 16 | 5–38 | 2 | 279 | 29 | 12–57 | 3 | 279 | 11 | 2–31 |
Mkupuka | 1 | 80 | 13 | 0–70 | 0 | 79 | 0 | 0–47 | 1 | 85 | 12 | 0–66 | 1 | 87 | 12 | 0–64 | 0 | 87 | 0 | 0–42 |
Mlanzi | 8 | 717 | 11 | 5–22 | 10 | 694 | 14 | 7–27 | 14 | 734 | 19 | 10–32 | 7 | 724 | 10 | 4–19 | 6 | 274 | 22 | 8–48 |
Mng'aru | 3 | 86 | 35 | 7–102 | 0 | 75 | 0 | 0–49 | 1 | 81 | 12 | 0–69 | 1 | 80 | 13 | 0–70 | 0 | 80 | 0 | 0–46 |
Mtawanya | 10 | 562 | 18 | 9–33 | 8 | 590 | 14 | 6–27 | 13 | 602 | 22 | 12–37 | 12 | 596 | 20 | 10–35 | 8 | 596 | 13 | 6–27 |
Ngulakula | 2 | 116 | 17 | 2–62 | 5 | 109 | 46 | 15–107 | 0 | 126 | 0 | 0–29 | 4 | 123 | 33 | 9–83 | 1 | 123 | 8 | 0–45 |
Nyambili | 0 | 102 | 0 | 0–36 | 2 | 93 | 22 | 3–78 | 1 | 95 | 11 | 0–59 | 0 | 96 | 0 | 0–38 | 1 | 96 | 10 | 0–58 |
Nyambunda | 1 | 74 | 14 | 0–75 | 2 | 70 | 29 | 4–103 | 1 | 75 | 13 | 0–74 | 5 | 73 | 69 | 22–160 | 2 | 73 | 27 | 3–99 |
Pagae | 9 | 465 | 19 | 9–37 | 9 | 463 | 19 | 9–37 | 9 | 464 | 19 | 9–37 | 11 | 455 | 24 | 12–43 | 12 | 455 | 26 | 14–46 |
Umwe Central | 9 | 449 | 20 | 9–38 | 6 | 427 | 14 | 5–31 | 9 | 340 | 21 | 10–40 | 4 | 409 | 10 | 3–25 | 2 | 409 | 26 | 14–46 |
Umwe North | 7 | 477 | 15 | 6–30 | 10 | 467 | 21 | 10–39 | 4 | 382 | 8 | 2–21 | 5 | 491 | 10 | 3–24 | 2 | 491 | 4 | 1–15 |
Umwe South | 5 | 305 | 16 | 5–38 | 3 | 298 | 10 | 2–29 | 6 | 298 | 20 | 7–44 | 2 | 297 | 7 | 1–24 | 4 | 297 | 14 | 4–35 |
Uponda | 15 | 630 | 24 | 13–39 | 12 | 657 | 18 | 9–32 | 9 | 942 | 14 | 6–27 | 4 | 594 | 7 | 2–17 | 4 | 594 | 7 | 2–17 |
Total | 280 | 14,087 | 20 | 17–22 | 274 | 13,844 | 20 | 18–22 | 266 | 13,937 | 19 | 17–22 | 204 | 13,537 | 15 | 13–17 | 154 | 13,254 | 12 | 10–14 |
PY–Person years.
DR–Death rate.
CI–Confidence interval.