mpoma health profile 2011

8/3/2019 Mpoma Health Profile 2011

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M p o m a C o m m u n i t y H I V / A I D S I n i t i a t i v e

Lukojjo Village, Mukono District, Uganda ! +256 775364300 [email protected]

November! 11

Mpoma Community Health ProfileBaseline health survey of Nama Subcounty conducted in October 2011

Ashley L. RogersMpoma Community HIV/AIDS Initiative


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Table of Contents1.0 EXECUTIVE SUMMARY 3

2.0 METHODOLOGY 4

2.1 OBJECTIVES 42.2 ASSESSMENT DESIGN 42.3 HOUSEHOLD SURVEY 42.4 PARTICIPATORY ASSESSMENT 5

3.0 ANALYSIS 6

3.1 PROXY INDICATOR OF ECONOMIC STATUS 63.2 ENVIRONMENTAL HEALTH 73.2.1 HYGIENE INFRASTRUCTURE 73.3.1 TYPE OF WATER SOURCE 83.3.2 NUMBER OF WATER SOURCES 93.3.3 WATER TREATMENT 10

3.4.1 FOOD SOURCE 113.4.2 ENERGY SOURCE 113.5 HEALTH FACILITIES 123.5.1 TYPE OF HEALTH FACILITY 123.5.2 DIVERSITY OF HEALTH SERVICES 133.5.3 SATISFACTION WITH HEALTH SERVICE 133.6 FAMILY PLANNING 143.6.1 PREVALENCE OF FAMILY PLANNING 143.6.2 FAMILY PLANNING METHODS 153.7 MALARIA 163.7.1 MALARIA INCIDENCE 16

3.7.2 PREVALENCE OF SLEEPING UNDER NETS 173.8 HIV/AIDS 193.8.1 HIV STATUS AWARENESS 193.8.2 HIV PREVALENCE 20

4.0 CONCLUSION 20

5.0 APPENDIX 22


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1.0 Executive SummaryThe villages that form Mpoma Community have a unique health profile that is driven by socio-economic, environmental, and behavioral factors. This report sketches the health status of thiscommunity as a rough, but informative, guide to local policy makers, project implementers, andhealth workers. Using a survey of 247 households (covering 1200 individuals) and a participatory

assessment engaging community members and Village Health Teams (VHT), this study finds:

Water-borne Illnesses Poorer households are at greater risk of water-borne illnesses because they are 1) 13% more likely

to have access to only one water source and 2) 7% less likely to treat drinking water. Half of all households in Mpoma and Lutengo villages rely on contaminated water sources.Food Security Households of lower economic status are more vulnerable to crop failure as they are 13% more

likely to only source food from their gardens. Conversely, households of higher economic status are more vulnerable to food price fluctuations

as they are 11% more likely to only source food through purchase.Energy Consumption Fifty-seven percent of households rely solely on firewood, while another 25% use both firewood

and charcoal. Households with lower economic status are 30% more likely to use only firewood than

households with higher economic status.

Access to Health Services Households with lower economic status are 7% more likely to self-medicate. Proximity is the most significant determinant of the type of health services households access. Households from Mpoma and Lwanyonyi villages are particularly vulnerable because they have

the lowest economic status and face considerable obstacles to reach health centers. Due mostly to drug shortages in government facilities, households that only use government

health services are 30% less likely to be satisfied with the care they receive.Demand for Family Planning Services Only 40% of households practice a method of family planning. Forty-five percent of households that arent using a method of family planning are interested in

receiving family planning services. Sixty-nine percent of households that practice family planning are interested in further services. Households with lower economic status are 23% less likely to use a method of family planning.

Malaria Prevention Over one-third of individuals report suffering from malaria in the last two months.

Fifty-seven percent of individuals report sleeping under a bed net, however bed net use is 6%higher amongst pregnant women and 8% higher amongst children under five. Though 82% of households name cost as the major limitation to using a bed net, net use is driven

by community-wide socio-economic factors, rather than household economic status.HIV Status Awareness Only one-third of the overall population is aware of their HIV status. The rate of awareness varies significantly by village and is connected to access to free testing. Wakiso is especially in need of HIV testing, as only 20% of residents are aware of their status.


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2.0 Methodology

2.1 ObjectivesIn October 2011 Mpoma Community HIV/AIDS Initiative (Mpoma) conducted a health assessmentof Nama subcounty. The objectives of this assessment were to:

1. Create baseline data of community health2. Understand socio-economic and environmental factors that contribute to health outcomes3. Ascertain levels of sensitization in areas of: family planning, malaria, and HIV/AIDS4. Estimate prevalence of illness and prevention with special emphasis on malaria

2.2 Assessment DesignMpoma collected quantitative and qualitative data through an in-person household survey of 247participant households. Additionally, Mpoma collected qualitative data through a participatoryassessment process conducted with community members and Village Health Teams (VHTs).Community Integrated Development Initiative (CIDI) commissioned the participatory assessmentwith a special interest in malaria prevention. This analysis combines the results of both studies.

Mpoma decided to use a mixed-method design because such an approach: allowed for greater validity and credibility, reducing the threat of methodological error by

accessing data from groups and from individuals in two different settings, created a more robust portrait of health status by allowing local leaders to inform the analysis

of the quantitative data, and increased the depth of inquiry by opening up the range of questions enumerators could ask.i

2.3 Household SurveyIn October 2011 Mpoma enumerators surveyed 245 households from 3 parishes within Nama sub-county. Mpoma Community HIV/AIDS Initiative chose Mpoma, Bulika, and Namubiru parishes torepresent variation across the following characteristics:

rural/urban, number of villages, population, income levels, and distance from health centers.

Mpoma then selected two villages from each parish. Again, selection was based on representing thegreatest possible variance on the aforementioned characteristics. At the village level, the enumeratorsfirst met with the Local Council 1 (LC1) to map the households in the community. From thatmapping, approximately forty households were randomly selected, as seen in Figure 1.

Figure 1: Number of respondents by villageParish Village # of Respondents

Bulika Lutengo 49

Bulika Wakiso 38

Mpoma Mpoma 40

Mpoma Nsanvu 39

Namubiru Lwanyonyi 41

Namubiru Takajunge 39


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Enumerators targeted heads of households as the key respondents and asked them to provideinformation concerning the entire household. Thus, as seen in Figure 2, a total of 1200 people arerepresented in the survey.

Figure 2: Respondent profile

Respondent informationNumber of households 246

Average age of respondent 40.4

Average household size 4.9

Average number of children 3.5

Average number of children under 5 1.34

Total population covered in survey 1200

The survey itself includes recall data collected from the participants to answer questions coveringhousehold environment, knowledge of household members illness prevention methods, frequencyof household members illnesses, and household members health practices. There are inherent

reliability issues with head of household reporting because participants may forget, overestimate,underestimate, or intentionally skew data. Additionally, the respondent may have inaccurateinformation concerning other members of the household. Thus, the survey also employedobservational data via an observation guide. This was a useful data collection tool to collectbenchmark and descriptive data of the household environment. The method was adapted fromexamples found in Stake (1995), University of Wisconsin (1996), and Yin (2003).ii

2.4 Participatory AssessmentMpoma also conducted a participatory assessment with funding from and in coordination with CIDI.This assessment occurred in two stages. The first was through a series of focus groups meetings heldwith Village Health Teams (VHTs). The second stage consisted of meetings with community

members who had been involved in a maternal malaria prevention program implemented throughan Mpoma-CIDI partnership. According to the World Bank (2009), The group process tends to elicitmore information than individual interviews,because people express different views and engage indialogue with one another. iiiAccordingly, this assessment uses data from the participatoryassessment to corroborate and explain survey data.


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3.0 Analysis

3.1 Proxy Indicator of Economic StatusPoverty is often strongly correlated with health outcomes. However, measuring incomes levels orother quantitative indicators of poverty is extremely technical and costly. Self-reported income is not

reliable within this population because most people do not receive a set salary nor keep track ofinflows and outflows of financial resources. Additionally, much of the wealth of a household is heldin assets like farm animals, agriculture inputs, housing and land, rather than currency.

Thus, this study employs floor type (either brick or mud) as an indicator of economic status. Thesurvey also recorded occupation, wall type, tenancy, and house permanence. However, afterreviewing the data with community members through our participatory assessment, we found that ofall these variables, floor type is most consistent with local delineation of economic status. Even more,the population is split almost exactly in half by floor type, providing a simple way to bifurcate thepopulation by wealth. Though this is a rough measurement, it offers insight to the role of poverty inhealth outcomes. Figure 3 illustrates floor type by village, and accordingly shows a rough idea of therelative wealth of each village.

Ttakajjunge is the most economically secure village with 77% of households having cemented floors.Mpoma and Lwanyonyi villages are the least, with only 35% and 38% respectively. Lutengo, Wakiso,and Nsanvu all hover around the average of 49%.

Figure 3: Floor type as proxy for economic status

35% 38%46% 50%

53%

77%

49%

63% 60%51% 50%

47%

20%

49%

Mpoma Lwanyonyi Nsanvu Wakiso Lutengo Ttakajjunge All

Households

Floor type by village

Cement Mud


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3.2 Environmental Health

3.2.1 Hygiene InfrastructureObservational data from the survey found that a majority of the population lacks drying racks andrubbish pits. Conversely, over 95% of households have cooking facilities and latrines. However,

about one third of these are of poor quality (See Figure 4).Figure 4: Hygiene infrastructure

Figure 5: Wall type disaggregated by village

3.2.2 Wall TypeNearly, three quarters ofhouseholds have brickwalls. Ttakajjunge hasthe highest incidents ofbricks walls, at 94% ofhouseholds, whileLutengos has the

lowest, with only 65% ofhouseholds. Theremaining villagescluster around theaverage.

None Poor Average Good

Cooking Facility 12% 35% 28% 25%

Latrine 5% 30% 43% 22%

Drying Rack 67% 9% 14% 10%

Rubbish Pit 74% 5% 7% 14%

0%

10%

20%

30%

40%

50%

60%

70%

80%

Households

Percent of Households at each Level of HygieneInfrastructure

75% 80% 78% 82%

65%

94%79%

25% 20% 23% 18%35%

6%22%

Households

Wall Type by Village

Mud

Brick


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3.3 Water Access

3.3.1 Type of water source Figure 6: Types of water sources (all villages)As seen in Figure 6, the most commontypes of water sources are rainwater,

41%; spring water, 30%; and boreholes18%. There is no statisticallysignificant difference in type of watersources between those with lowereconomic status and those withhigher. However, water source doesvary significantly by village (SeeChart 1, Appendix).

Figure 7 shows that Mpoma Village isespecially reliant upon well water,

with 51% of the population using wellwater. Qualitative data reveals that this well is open and contaminated. Even more, enumerators andresidents report that the water flows under trees where birds and monkeys drop waste. LutengoVillage has a high proportion of its population (53%) using borehole water. However, the water fromthe borehole comes out brown, leaving residents to believe the pipes have rusted.

Figure 7: Types of water sources disaggregated by village

Mpoma LutengoLwanyon

yiNsanvu

Ttakajjunge

Wakiso All

Spring water 21% 1% 48% 39% 46% 29% 30%

Tapped water 2% 0% 2% 0% 0% 0% 1%

Well water 51% 0% 2% 2% 0% 0% 10%

Borehole 0% 53% 2% 20% 4% 20% 18%

Rainwater 26% 47% 47% 39% 50% 51% 41%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Households

Types of Water Sources by Village

Rainwater41%

Borehole18%Well water

10%

Tappedwater

1%

Springwater30%

Types of Water Sources


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3.3.2 Number of water sourcesAs seen in Figure 8, 68% of households use exactly two sources of water; 27% use only one. There is astatistically significant relationship between economic status and access to more than one source ofwater.iv Households with higher economic status are 13% more likely to have access to two or moresources of water than households with lower economic status (See Figure 9). VHTs explain that

wealthier families are more likely to buy catchment tanks to supplement local sources of water.

Figure 8: Number of water sources households are able to access

Figure 9: Diversity of water sources disaggregated by economic status

1 water source27%

2 water sources68%

3 water sources4%

4 water sources1%

Number of water sources

73%

68%

81%

Overall Lower Econ Status Higher Econ Status

Hous

eholds

Households with access to two or more types ofwater sources


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3.3.3 Water treatmentAs seen in Figure 10, 77% of households report treating their drinking water. There is a statisticallysignificant difference in rates of water treatment between economic groups.v Figure 11 illustrates thathouseholds with higher economic status are 7% more likely to treat their drinking water thanhouseholds with lower economic status. Of those who treat their water, 93% do so through boiling.

Only 7% use a product like Aqua Safe or Water Guard.

Figure 10: Percent of households and drinking water treatment

Figure 11: Water treatment disaggregated by economic status

Yes77%

NO23%

Do you treat your drinking water?

77%

81%

74%

All Higher economic status Lower Economic Status

Households

Percent of households treating drinking water


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3.4 Food and CookingFigure 12: Food source and economic status

3.4.1 Food sourceMore than half of all

households source food boththrough buying and gardening.This rate remains constantacross economic status.However, for those with onlyone source of food, householdswith lower economic status are13% more likely to only sourcefood from their gardens.Conversely, households with

higher economic status are 11%more likely to only source foodthrough purchase (See Figure12). vi

Figure 13: Energy source for cooking and economic status

3.4.2 Energy sourceFirewood is the most common

source of energy used forcooking, with 57% of thepopulation relying solely uponfirewood and another 25%using a combination offirewood and charcoal.Households with lowereconomic status aresignificantly more reliant onfirewood with 71% using onlyfirewood compared to 44% of

households with highereconomic status. vii

25% 32% 19%

24%18%

29%

51% 50% 51%

All Lower EconomicStatus

Higher EconomicStatus

Households

Food source and economic status

Only Gardening Only Buying Both Gardening and Buying

18%8%

28%

57%71%

44%

25% 21% 28%

All Lower EconomicStatus

Higher EconomicStatus

Households

Energy source and economic status

Only Charcoal Only Firewood Both Charcoal and Firewood


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3.5 Health Facilities

3.5.1 Type of health facilityWhen asked to report the types of health facilities they use, 44% of households reported usinggovernment health centers, 40% reported using private health centers, 12% reported self-medicatingand buying drugs from a shop, and 5% reported using herbal or traditional remedies. Householdswith lower economic status are 7% more likely to self-medicate. However, economic status does notaffect use of other types of health facilities (See Chart 2, Appendix). Rather, the type of health facilitya household accesses varies significantly by village. For example, Lutengo households reported amuch higher rate of use of government health centers at 52%, while Mpoma, Lwanyonyo, Nsanvu,and Ttakajjunge range between 33% and 37%. Ttakajjunge has the highest use of private healthcenters, at 57%, while Mpoma has the highest use of self-medication at 21% (See Chart 3, Appendix).

Data from the participatory assessment reveals that much of these discrepancies can be explained byproximity to health centers. Lutengo is very near a government health center III that is currently

being upgraded to a health center IV. On the other hand, Ttakkajjunge is very near Good Samaritan, aprivate clinic. Mpoma is far from a health center and also has the lowest economic status, thus,residents self-medicate to circumvent consultation costs. Households in Lwanyonyi underusegovernment services because they have to cross the Metha sugar cane field to reach the nearestgovernment health center. Many Lwanyonyi residents fear for their personal safety when passingthrough the fields and are thus reluctant to take the risk to visit the health center.

Figure 14: Household use of varying types of health facilities disaggregated by village

Overall

HH

usingonly 1facility

Mpoma

Lwanyonyi

Nsanvu

Wakiso

Lutengo

Ttakajjunge

Herbal or Traditional Healing 5% 2% 2% 8% 8% 5% 2% 2%

Self Medication/ Shops 12% 2% 21% 17% 11% 11% 3% 6%

Private Health Center 40% 41% 45% 38% 44% 31% 27% 57%

Government Health Center 44% 56% 33% 37% 36% 52% 68% 34%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Households

Use of varying types of health facilities


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3.5.2 Diversity of health servicesMost households (55%) use only one type of Figure 15: Diversity of health facilitieshealth facility. Economic status does notaffect the diversity of health facilities ahousehold accesses. Households that only

use one type of health service are more likelyto use government centers and less likely touse self-medication or herbal/traditionalremedies. (Compare Figures 14 & 15). ix

If respondents reported transparently, thisdata suggests that most households use self-medication and traditional remedies only assupplementary forms of healthcare.However, through the participatoryassessment, VHTs raised doubts about thisdata. They noted that respondents likely feltstigmatized and that use of these services islikely much higher than the survey data suggests.

3.5.3 Satisfaction with health serviceWhen asked how often they receive the services they need from a given health facility, 61% ofhouseholds selected the option always, with the remainder selecting sometimes. Only onehousehold reported that they never receive the services they need.

Figure 16: Satisfaction with health center disaggregated by facility type and village

0%

10%20%30%40%50%60%70%80%90%

100%

Households

When you go to a health center, how often do you getthe services you need?

Sometimes

Always

1 type ofhealth care

55%

2 types ofhealth care

33%

3 or moretypes of

health care12%

Diversity of health facilities used


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As seen in Figure 16, satisfaction with health services varies by facility type and village. More than80% of households that only use private clinics reported that they always receive the services theyneed, compared to just 50% of those who use only government clinics.viii Over three quarters of allhouseholds that express dissatisfaction with government health services mentioned drug shortages asa major complaint.

When comparing Figures 14 & 16 Mpoma, Lwanyonyo, Nsanvu, and Ttakajjunge use governmentfacilities at a similar rate, yet their levels of satisfaction vary greatly. The participatory assessmentsuggests that the difference may be related to the timing of visits to the government health center,which could affect the stock of drugs.

3.6 Family Planning

3.6.1 Prevalence of family planningJust less than 40% of all households report using a method of family planning. As seen in Figure 17,this rate varies significantly with economic status. Households with lower economic status are 23%

less likely to report using family planning than households with higher economic status.ix Theparticipatory assessment points out that those with higher economic status are more likely to havejobs in town where family planning information is more available and workplace sensitizationprograms are more common.

Figure 17: Family planning use disaggregated by economic status

39%

27%

50%

All Lower economic status Higher economic status

Households

Households practicing family planning


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3.6.2 Family planning methodsOf households that practice Figure 18: Methods of family planning__________________family planning, the Depo-Proverainjection is by far the most popular,

with 55% of the market. Qualitativedata indicates that this is because theDepo-Provera injection is the mostconvenient. Pills and condoms are thenext most popular at 17% and 16%,respectively. Rhythm method,Norplant, and IUDs cover only asmall portion of the population (SeeFigure 18). The participatoryassessment suggests that peopleperceive norplants and IUDs as more

invasive and are more fearful of theirside-effects.

As seen in Figure 19, family planning methods vary greatly with economic status. Households withhigher economic status are 25% more likely to use the Depo-Provera injection than poorerhouseholds. Conversely, households with lower economic status rely more heavily on pills andcondoms. Type of health service is less of an influence on family planning method as methods varyonly slightly from households that use only government health services to households that use onlyprivate services (See Chart 4, Appendix).

Figure 19: FP method disaggregated by economic status and health service type

16%25%

12%22% 18%

55%38%

63%44% 45%

17%33%

10%

28%18%

All Lower economicstatus

Higher economicstatus

Only governmenthealth services

Only private healthservices

Hou

seholds

Family planning method by economic status and health service

Condoms Depovera Pills

Rythmn5%

Condoms16% IUD

4%

Depo-Provera

55%

Norplant3%

Pills17%

Methods of family planning


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3.6.3 Interest in addition family planning servicesOf households that already practice a method of family planning, 69% say that they are interested inadditional services. Of households that are not practicing family planning, 45% say they areinterested in receiving family planning services.

Many of these individuals asked about specific family planning methods they would like to access.Specifically, fifteen individuals asked to know more about the norplant and another ten asked tolearn more about the injection plan. Others reported wanting to receive education that compared thedifferent family planning options available.

Figure 20: Interest in additional family planning services

3.7 Malaria

3.7.1 Malaria incidenceAccording to the survey, 1.81 people per household or 32.5% of all household members, sufferedfrom malaria in the last two months. Mpoma has the highest reported rate of malaria at 37%, whileLutengo has the lowest at 26.6% (See Figure 21). However, variation by village and the 4% differencebetween higher economic status and lower economic status are not statistically significant (Chart 5,Appendix). Therefore based on survey data alone it is not possible to make reliable comparisonsbetween groups.

69%

45%

Households already using a familyplanning method

Households not currently using a familyplanning method

Households

Households interested in receiving additionalfamily planning services


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However the participatory assessment suggests that differential access to health facilities maycontribute to variation in malaria rates by village. VHTs explain that when patients visit governmenthealth facilities they are often only given a portion of the malaria treatment and told to return later toreceive the remainder of the supply. Patients from distant villages are less likely to return for thebalance of the treatment, increasing the likelihood and severity of malaria.

Figure 21: Malaria rates disaggregated by economic status and village

3.7.2 Prevalence of sleeping under netsAs illustrated in Figure 21, households report that 57% of individuals always sleep under a bed net.The rate is higher for individuals who are pregnant (63%) and under five years of age (65%).

VHTs attribute this to government programs that distributed free bed nets to these two vulnerablegroups. In the qualitative portion of the survey 11% of people who dont use bed nets explained thereason was because the government didnt provide free nets to their demographic. Similarly, another71% reported that cost was the limiting factor in using a bed net.

There is not a statistically significant difference in the rate of always sleeping under a bed net byeconomic status. x This suggests that economic access is not the only limiting factor to net use. Forexample, data from the participatory assessment points out that some pregnant women are reluctant

32.5%

38.5%

34.3%37.0%

31.6%

36.4%

33.0%

26.6%

31.0%

Individualsw

ithmalaria

Malaria rate over last two months


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to use nets because of the strong smell they give off. Through the household survey, 18% ofindividuals not using nets reported one of the limiting factors below:

nets are too hot (4%) prefer slashing bush, closing doors early, or boiling water (4%) nets spoil easily (3%) lack of space (2%) nets smell bad (1%) have heard stories about negative side-effects (1%)

Also, the participatory assessment revealed that many people could not tell the difference betweentreated and untreated nets. Therefore, it is not possible to distinguish how many individualsrepresented in Figure 21 are sleeping under treated nets.

Figure 21: Bed net use disaggregated by age group, economic status, and village

All

Notpregnant andover 5

Pregnant

Under5

Lowereconomic

status

Highereconomic

status

Mpoma

Lwanyonyi

Nsanvu

WakisoLuteng

o

never 38% 38% 31% 29% 43% 33% 47% 55% 42% 29% 31%

sometimes 5% 5% 6% 5% 3% 8% 3% 3% 9% 8% 3%

always 57% 56% 63% 65% 55% 58% 50% 42% 48% 63% 67%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Individuals

Frequency of sleeping under a mosquito net


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Bed net use also varies by village. Figure 22 shows that villages with a larger percentage ofhouseholds in the higher economic group also have larger rates of bed net use. When coupled withthe aforementioned weak relationship between household-level economic status and bed net use, thisdata suggests that bed net use is motivated by community-wide socio-economic factors, rather thanhousehold economic status. This hypothesis is supported by the participatory assessment, in which

members explained that as a critical mass of village residents use and value nets, households,regardless of resources, are more likely to seek out and value nets.

Figure 22: Bed net use and economic status by village

3.8 HIV/AIDS3.8.1 HIV status awarenessOnly one-third of the overall population is aware of their HIV status. The rate of awareness variessignificantly by village. As seen in Figure 23, there is a 49-percentage point gap between Ttakajjungeand Wakiso (See Chart 5, Appendix).

49.8%

42.1%

48.1%

63.3% 66.5%

72.2%

35%38%

46%50%

53%

77%

Individuals

Bed net use and economic status

% of individuals "always" sleepingunder a bed net

% of households in higher economicgroup


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Participatory assessment data suggests that theses differences are connected to access to free testingservices. Good Samaritans free weekly testing likely contributes to Takajjunges high awarenessrates. Nnsanvu residents report that an NGO (though they cant remember the name) went house tohouse offering free testing. Also, Lutengo hosted a short-term HIV testing outreach program that wasopen to surrounding villages. Wakisos low awareness rate may be connected to the fact that it is

distant from clinic-based free services and from outreach programs like the one held in Lutengo.

Figure 23: HIV status awareness disaggregated by economic status and village

3.8.2 HIV prevalenceOf the 1200 individuals covered by the household survey only 18 were reported to be HIV positive.This number is extremely low compared to the 6.5% regional HIV prevalence rates released in the2009 UNGASS Country Progress Report.xi This suggests that Mpomas household survey did notaccurately capture HIV rates. This is not surprising considering the stigma and vulnerabilityconnected to HIV/AIDS. A much more methodologically targeted study is needed to accuratelyassess HIV/AIDS prevalence.

4.0 ConclusionBuilding from this assessment, Mpoma Community HIV/AIDS Initiative looks forward tocollaborating with local government, partner organizations, and outside funders to implement healthand social programs that address the specific need of our community. We welcome our stakeholdersto use and share this data for the promotion, education and general advancement of the Nama sub-county.

31%

40%

47%

30%

43%

59%

20%

45%

69%

Individuals

HIV Status Awareness


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iJohnson, Burke and Anthony J. Omwuegbuzie (2004) Mixed Methods Research: A Research Paradigm Whose

Time Has Come, Educational Researcher Vol 33 No. 7, 14-26

iiStake, Robert E. (1995). The Art of Case Study Research. Thousand Oaks, CA: Sage Publications.

University of Wisconsin, Cooperative Extension (1996). Program Development and Evaluation, CollectingEvaluation Data: Direct Observation. http://learningstore.uwex.edu/pdf/G3658-5.pdf

Colorado State University. Writing Guide: Ethnography, Observational Research, and Narrative Inquiry.

http://writing.colostate.edu/guides/research/observe/

iiiMorra, L. G., & Rist, R. C. (2009). The Road to Results: Designing and Conducting Effective Development

Evaluations. Washington, D.C: World Bank. Chapter 8.

ivThis is a statistically significant difference based on a two-tailed t-test comparing the mean number of households

with access to more than one water source in the lower economic group to the mean in the higher economic group.Analysis used a .05 significance level, with a p-value of .0209.

vThis is a statistically significant difference based on a two-tailed t-test comparing the mean number of households

that treat their water in the lower economic group to the mean that does so in the higher economic group. Analysisused a .05 significance level, with a t-value of 1.9899 and a p-value of .0478

viThis is a statistically significant difference based on a two-tailed t-test comparing the mean number of households

that rely on gardening as a food source in the lower economic group to the mean that do so in the higher economicgroup. Analysis used a .05 significance level, with a t-value of 3.1467 and a p-value of less than .0021

viiThis is a statistically significant difference based on a two-tailed t-test comparing the mean number of households

that rely on firewood in the lower economic group to the mean that does so in the higher economic group. Analysis

used a .05 significance level, with a t-value of 4.5821 and a p-value of less than .0001

viiiThis is a statistically significant difference based on a two-tailed t-test comparing the mean number households

satisfied with government facilities to the mean satisfied with private facilities. Analysis used at a .05 significancelevel, with a t-value of 4.5941 and a p-value of less than .0001

ixThis is a statistically significant difference based on a two-tailed t-test comparing the mean number of households

that practice family planning in the lower economic group to the mean that does so in the higher economic group.Analysis used a .05 significance level, with a t-value of 3.7362 and a p-value of less than .0002

xThis is a statistically significant difference based on a two-tailed t-test comparing the mean number of households

that reported always sleep under a bed net in the lower economic group to the mean that reported this in thehigher economic group. Based on a two-tailed t-test at a .05 significance level, with a t-value of 1.2965 and a p-

value of 0.1966

xiUNGASS. (2010) Country Progress Report. http://www.unaids.org/en/data

analysis/monitoringcountryprogress/2010progressreportssubmittedbycountries/uganda_2010_country_progress_report_en.pdf


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5.0 Appendix

Chart 1: Difference in water source by village

Mpoma Lutengo Lwanyonyi Nsanvu Ttakajjunge Wakiso All

Rainwater 26% 47% 47% 39% 50% 51% 41%

Borehole 0%*** 53%*** 2%** 20% 4%*** 20% 18%

Well water 51%*** 0%*** 1.5%** 2% 0%*** 0%*** 10%

Tappedwater 2% 0% 2% 0% 0% 0% 1%

Spring

water 21% 1%*** 48%** 39% 46.4%* 29% 30%

* p-value less than .05 ** p-value less than .005 *** p-value less than .0001

Notes: Based on two-tailed t-tests comparing village means in each category to the averagemean (Re: Figure 7)

Chart 2: Difference in facility type by economic status

Lower economic status Higher economic status

Government Health Center 42% 47%

Private Health Center 38% 40%

Self Medication/ Shops 16%* 9%*

Herbal or TraditionalHealing 5% 3%


Notes: Based on two-tailed t-tests comparing usage means of the lower economicgroup to the higher economic group for each type facility


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Chart 3: Difference in health facility type by village

Overall

HHusingonly 1 Mpoma Lwanyonyi Nsanvu Wakiso Lutengo Ttakajjunge

GovernmentHealthCenter 44% 55%* 33% 37% 36% 52% 68%*** 34%***

PrivateHealthCenter 40% 41% 45% 38% 44% 31% 27% 57%***

SelfMedication/Shops 12% 2%* 21% 17% 11% 11% 3% 6%

Herbal orTraditionalHealing 5% 2%* 2% 8% 8% 5% 2% 2%


Notes: Based on two-tailed t-tests comparing village means in each category to the average mean(Re: Figure 14)

Chart 4: Difference in family planning usage by economic status and facility type

All

Lowereconomicstatus

Highereconomicstatus

Onlygovernmenthealthservices

Only privatehealthservices

Condoms 16% 25%* 12%* 22% 18%

Depovera 55% 37.5%* 62.7%* 44% 45%

Pills 17% 33.3%* 9.80%* 28% 18%

* p-value less than .05 ** p-value less than .005 *** p-value less than .0001Notes: Based on two-tailed t-tests comparing usage means of the lower economic group tothe higher economic group and only government to only private health services(Re: Figure 19)


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Chart 5: Malaria rate by economic status and village

All

Lower

economic

status

Higher

economic

status Mpoma Lwanyonyi Nsanvu Wakiso Lutengo Ttakajjunge

Malariarate 32.5% 38.5% 34.3% 37.0% 31.6% 36.4% 33.0% 26.6% 31.0%

p-value 0.3791 0.422 0.8551 0.4846 0.9217 0.1139 0.7582

t-stat 0.08815 0.8115 0.1838 0.7058 0.099 1.6104 0.3104

None of the comparisons are significant at a 5% level

Notes: Based on two-tailed t-tests comparing malaria rates of the lower economic group to the highereconomic group and then each village malaria rate by the average malaria rate (Re: Figure 21)

Chart 5: HIV status awareness by economic status and village

All

Lowereconomic status

Highereconomic status Mpoma

Lwanyonyi Nsanvu Wakiso Lutengo

Ttakajjunge

Awareof HIVstatus 31% 35% 47% 30% 43% 59%** 20%* 45%* 69%***


Notes: Based on two-tailed t-tests comparing HIV awareness rates of the lower economic group to thehigher economic group and each village awareness rates by the average awareness rate (Re: Figure 21)

mpoma health profile 2011

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