Geographic location of health facility and immunization program performance in Hoima district, western Uganda, a facility level assessment

doi:10.21203/rs.3.rs-25102/v3

Background

Globally immunization coverage for childhood vaccines is below the immunization target of achieving at least 90% coverage with the pentavalent vaccine. In Uganda, a recent survey shows 80% of districts had poor immunization program performance. However, there is significant variation in performance within and between districts. We hypothesized that geographic location of health facility may influence performance of their immunization programs. Therefore, the purpose of this study was to examine whether geographical location of health facility within a district is associated with performance of the immunization program in Hoima district, western Uganda.

Methods

We conducted a cross sectional study using a mixed methods approach. The study unit was a health center and we interviewed health workers in-charge of the facilities and reviewed their health facility records. We reviewed the Uganda Health Management Information System (HMIS) 105 reports of six months to obtain data on immunization program performance. Performance was categorized using World Health Organization’s Reach Every District (RED) criteria and classified as poor if a facility fell in category 3 or 4 and good if 1 or 2. We also conducted key informant interviews with immunization focal persons in the district. We examined the association between dependent and independent variables using Fisher’s exact test.

Results

We collected data at 49 health facilities. Most of the health units (55.1%) had poor immunization program performance. Proximal location to the central district headquarters was significantly associated with poor immunization program performance (p<0.05). Attitudes of health workers in the more urban areas, differences in strategies for outreach site selection and community mobilization in the rural and urban areas were suggested as possible explanations.

Conclusions

Proximal location to the urban setting near district headquarters was strongly associated with poor immunization program performance. To be able to reach larger numbers of children for vaccination, interventions to improve performance should target health facilities in urban settings.

Health Policy

Immunization

Performance

Vaccine

Coverage

associated factors

rural Uganda

According to the World Health Organization (WHO), immunization coverage for childhood vaccines is still below the optimum immunization target of achieving 90% or more for coverage with 3 doses of pentavalent vaccine. [1, 2] The WHO also approximates that two million children die every year due to vaccine preventable diseases and approximately 30% of children globally lack access to vaccines. Children in sub-Saharan Africa are ten times less likely to access vaccines than children in developed countries. [3] In Africa 81% of unvaccinated infants were found in ten countries Uganda inclusive. [4] In Uganda, 90 out of 112 districts 8had poor immunization performance. [5] In only 20% of the districts, at least 80% of the targeted children in these districts were given all the recommended doses of the lifesaving childhood vaccines

Several studies have been conducted to examine the factors associated with immunization program performance (IPP) and the challenges of vaccination programs in resource limited settings. [6-10] The key factors identified include individual factors such as access to information on maternal and child health, socio-demographic characteristics of parents or guardians and level of education. Religious beliefs, traditional remedies and mistrust of western medicine also influence program performance. [11] Health system factors such as funding constraints, human resource factors such as health worker shortages, training deficiencies, poor attitude of health workers and vaccination teams, inadequate infrastructure and equipment, and structural factors such as long distance from health facility are critical for successful programs. [12, 13]

Although these several studies have shed light on the multiple factors that may influence immunization performance, there is limited data on how geographic distribution of health facilities may influence immunization performance. Data show that even within the same district where health facilities share common structural barriers, there is variation in performance. [14, 15] We hypothesized that geographic distribution of the facilities may influence their performance. The DPT3 coverage for the rural district of Hoima on the shores of Lake Albert in Uganda was recently reported to be 19%, way below the national level. [16] Therefore, the aim of this study was to examine whether geographic distribution of health facilities influences immunization program performance by health facilities in this rural district of western Uganda.

Study design and setting

We conducted a cross sectional study with both quantitative and qualitative methods of data collection. First, we collected the quantitative data, and then the qualitative data were collected last to provide meaning and explanation to the quantitative findings. The study was conducted at both public and private health centers in Hoima district, a predominantly rural district in western Uganda, bordering with the Democratic Republic of Congo. The district has a population of 572, 896 inhabitants and 3.9% of the population is below one year. The district has 49 public and 5 private health facilities that offer immunization services and all contribute information to the District Health Information System version 2 (DHIS2) list. The sampling unit was a health facility. We included all the 49 public and private health facilities on Hoima district HMIS reporting list that offered immunization services in the previous six months prior to the study. In Uganda, health center II are units at the parish level, health center III at sub-county and health center IV is at county level. Private Health Centres levels were as follows; HC IV =1, HC III = 5, HC II =3. All HC included in this study are accredited to provide static and outreach immunization services. Private HCs are supervised by the District Health Office and health Sub District offices. UNEP also organizes support supervision visits to both private and government HCs in the District.

District Health Team (DHT) and Health Sub-district (HSD) are supposed to conducted quarterly support supervision to Health centres. Ministry of Health/UNEP conducts quarterly support supervision to Districts. Supervisors are expected to use supervision checklists developed by Ministry of Health. The tools access for planning, implementation, cold chain maintenance, management of vaccines and EPI supplies, program performance.

Data collection and measurements

Data to assess immunization program performance were obtained by reviewing HMIS 105 reports in District Health Information System version 2 (DHIS2) that spanned a period of six months from January to June 2017. The variables collected from HMIS reports included number of Expanded Program for Immunization (EPI) outreaches conducted, number of children who received the first and third dose of pentavalent vaccine and DPT3. The DPT1 and DPT3 data were used to calculate immunization program performance according to the Reach every district (RED) categorization criteria. [17, 18] The RED strategy was introduced by the WHO in 2002 with a goal of increasing immunization coverage to at least 90% with all vaccines in every district and country. It uses the pentavalent vaccine coverage and dropout rate to determine immunization program performance of health facilities. DPT1-DPT3 dropout rate was used to assess for utilization of immunization services. Dropout rate = (Cumulative DPT1- Cumulative DPT3) *100/Cumulative DPT1. DPT1 coverage = (cumulative DPT1 * 100)/population of children under 1year.

There are four categories in this classification namely; Category 1 = high coverage (>90% coverage) + low dropout rate (<10%), Category 2 = high coverage (>90%) + high dropout rate (>10%), Category 3= low coverage (<90%) + low dropout rate (<10%) and Category 4= low coverage (<90%) + high dropout rate (>10%). Health facilities in categories 3 and 4 are classified as having poor immunization performance, while categories 1 and 2 have good immunization performance.

HMIS 105 is Health unit out-patient monthly report, Reports the monthly attendance figures for Out-patient Department (OPD), OPD diagnoses, MCH, HIV/AIDS service data, Lab. data, stock outs of essential drugs and supplies and financial data.

HMIS 105 reports the monthly attendance for child immunization for every health centre. Health centres compile monthly reports that are entered in a Ministry of Health online electronic database, the (DHIS2) and we reviewed these reports to assess immunization program performance. Data at Health Centres is managed by Records Assistants and Health Centre In-charges. Records Assistants hold an Ordinary Certificate in Education (UCE); with a Certificate in Records Management while HC In-charges are Diploma and Degree holders. We engaged the records assistants as research assistants to collect data from the health centers. The records assistants conducted the data abstraction of the immunization data. The research assistants were trained on the data collection tools and these tools were pretested at three health centers. We hired an independent research assistant to conduct the health facility level assessment. The key informant interviews were conducted by the lead investigator (NMK) and supported by one research assistant with a degree in social sciences.

We also conducted interviews with health workers in-charge of the health facilities in Hoima district using structured questionnaires to assess facility level factors that support immunization such as support supervision, organization of outreaches, financing, payment for outreaches, stock out of vaccines and cold chain maintenance. We collected data on demographic characteristics of health center in-charges like age, sex, marital status, years of experience, education level, and Expanded program for Immunization (EPI) training.

We also collected data on the characteristics of the health facility such as level of health centre, ownership (government or private facility) and geographic location, presence of EPI outreach schedule, functional EPI fridge and its maintenance, means of transport, community mobilizer, payment of allowances for EPI activities, staffing level, conducting outreaches, discussing EPI performance in staff meetings, maintenance, stock out of vaccines and stock out of gas for EPI fridges.

After preliminary analysis of quantitative data to assess immunization program performance at the health facilities, we conducted key informant interviews with relevant district and health sub-district officials knowledgeable about the vaccination program using open ended questionnaires.

Data analysis

We checked the data for completeness, coded and entered them into Epi-Info version 7 and exported to Stata version 11 for analysis. The primary outcome for the analysis was immunization performance as assessed based on the WHO’s RED category. The number of facilities was small and therefore to assess the association between dependent and independent variables, we used Fisher’s exact test. This test is robust for analysis involving small sample sizes where the contingency tables have expected number of observations in the cells is less than five. [19]

Qualitative data were transcribed and no translations were necessary as data were collected in English. We manually read the transcripts and analyzed them using a thematic approach. We did not have any apriori themes. We also looked to the qualitative data to plug the gaps observed from the quantitative data.

Ethical considerations

The study was approved by Mbarara University of Science and Technology (MUST) Faculty of Medicine Research Committee and MUST Research Ethics committee (MUST REC Protocol number 07/05-17). We obtained administrative clearance from Hoima district health office before initiation of the data collection process. Participants provided written informed consent before participating in the study.

Characteristics of the health facilities

Forty-nine health centers were included in the study, 17 HC II (34.7%), 28 HCIII (57.1%), 3 HC IVs and 1 Regional referral hospital as shown in Table 1 below. Majority (81.6%) of health centers were government owned and the rest (18.4%) were private health facilities.

We found that 89.8% of the health centers had fridges for storage of vaccines. Most (87.8%) health centers reported to have experienced stock out of vaccines. All health centers had EPI focal persons. Immunization outreach schedules were present in 81.6% of health centers. Allowances for EPI had been paid in 77.6% of health centers. Support supervision had been conducted at least once in the previous six months in 89.8% of the health centers. Details are in table 1 below.

Table 1: Characteristics of health centers in Hoima District

Characteristic

Frequency (%)

p value

Total

=<25Km from District Head quarters

>25Km from District Head quarters

Health Sub District

Bugahya

Buhaguzi

Hoima Municipality

16 (32.7)

23 (46.9)

10 (20.4)

10 (37.0)

7 (26.0)

10 (37.0)

6 (27.3)

16 (72.7)

0 (0.0)

<0.001

Level of Health Centre

Regional Referral

Health center IV

Health center III

Health center II

1 ( 2.1)

3 ( 6.1)

28 (57.1)

17 (34.7)

1 (3.7)

3 (11.1)

12 (44.4)

11 (40.7)

0 (0.0)

0(0.0)

16 (72.7)

6 (27.3)

0.11

Presence of EPI fridge

No

Yes

5 (10.2)

44 (89.8)

3 (11.1)

24 (88.9)

2 (9.1)

20 (90.9)

0.99

EPI Fridge maintenance

No

Yes

10 (20.4)

39 (79.6)

3 (11.1)

24 (88.9)

7 (31.8)

15 (68.2)

0.09

Stock out of Vaccines

No

Yes

6 (12.2)

43 (87.8)

4 (14.8)

23 (85.2)

2 (9.1)

20 (90.9)

0.68

Stock out of Gas for EPI fridge

No

Yes

N/A*

21 (42.8)

9 (18.4)

19 (38.8)

14 (51.9)

4 (14.8)

9 (33.3)

7 (31.8)

5 (22.7)

10 (45.5)

0.48

Presence of EPI outreach schedule

No

Yes

9 (18.4)

40 (81.6)

5 (18.5)

22 (81.5)

4 (18.2)

18 (81.8)

0.99

Received funding for EPI activities

No

Yes

5 (10.2)

44 (89.8)

1 (3.7)

26 (97.3)

4 (18.2)

18 (81.8)

0.16

EPI allowances paid timely

No

Yes

11 (22.4)

38 (77.6)

4 (14.8)

23 (85.2)

7 (31.8)

15 (68.2)

0.18

Presence of Community mobilizer

No

Yes

1 ( 2.0)

48 (98.0)

1 (3.7)

26 (97.3)

0 (0.0)

22 (100)

1.0

Facility has means of transport

No

Yes

34 (69.4)

15 (30.6)

22 (81.5)

5 (18.5)

12 (54.5)

10 (45.5)

0.06

EPI performance discussed in staff meetings

No

Yes

10 (20.4)

39 (79.6)

7 (25.9)

20 (74.1)

3 (13.6)

19 (86.4)

0.48

Attend EPI performance review meeting

No

Yes

25 (51.0)

24 (49.0)

13 (48.1)

24 (51.9)

12 (54.5)

10 (45.5)

0.18

Have support supervision

No

Yes

5 (10.2)

44 (89.8)

1 (3.7)

26 (96.3)

4 (18.2)

18 (81.8)

0.16

* *Health centers do not have gas fridge

Demographic characteristics of respondents/health center in-charges

We interviewed one health worker per health facility included in the study and the demographics are shown in Table 2 below. Almost 50% of them were aged 30 years or less. Only 6 health workers with a degree headed a health facility and at least 60% of the health facilities were headed by diploma holders. Majority (n=38 or 77.5%) were trained in EPI. Also majority of these health workers were male

Table 2: Demographic characteristics of health facility in-charges

Characteristic

Frequency (%)

Age

≤30years

>30years

23 (46.9)

26 (54.1)

Years of experience

≤5years

>5years

25 (51.0)

24 (49.0)

Gender

Male

Female

27 (55.1)

22 (44.9)

Title position

Medical doctor

Clinical Officer

Nursing Officer

Enrolled Nurse or Midwife

Others

3 ( 6.1)

24 (48.9)

7 (14.3)

13 (26.6)

2 (4.1)

Level of Education

Certificate

Diploma

Bachelors or Masters

13 (26.5)

30 (61.2)

6 (12.3)

Marital Status

Single

Married

Divorced

10 (20.4)

38 (77.5)

1 ( 2.1)

Trained in EPI

No

Yes

11 (22.5)

38 (77.5)

Performance and distribution of health facilities in Hoima district

Thirteen or 26.5% of the health facilities scored in the highest RED category and 11 scored in the lowest as shown in Table 3 below. Over, at least 55% of the health facilities were rated as having poor immunization performance. The distribution of health facilities by their immunization performance is shown in Figure 1 below in the map of Hoima. The map shows the clustering of health facilities with poor performance around or near the district headquarters, and those with good performance away from the headquarters.

Table 3: Performance of Health centers in Hoima district.

Characteristic

Frequency (%)

WHO RED Category

1

2

3

4

13 (26.5)

9 (18.4)

16 (32.7)

11 (22.4)

Immunization performance

Good

Poor

22 (44.9)

27 (55.1)

Bivariate analysis of factors associated with immunization performance

Several factors related to vaccine delivery were not significant and included among others presence of an EPI fridge at the health facility, history of stock out of vaccines, funding for EPI activities and paid allowances and these factors are shown in Table 4 below. Although majority of the health facilities reported they received support supervision, we observed that facilities that received no support supervision were more likely to report good performance.

Discussion of EPI performance is staff meetings, attendance of EPI performance review meetings, community mobilization and having means of transport for vaccination outreaches were not related to immunization program performance.

Table 4: Bivariate analysis of factors associated with immunization performance

Characteristic

Total (n=49)

Good performance n=22 (%)

Poor performance n=27 (%)

p value

Presence of EPI fridge

No

Yes

5

44

2 (40.0)

20 (45.5)

3 (60.0)

24 (54.5)

0.82

Stock out of Vaccines

No

Yes

6

43

2 (33.3)

20 (46.5)

4 (66.7)

23 (53.5)

0.54

Stock out of gas for EPI fridge

No

Yes

N /A*

21

9

19

11 (52.4)

3 (33.3)

8 (42.1)

10 (47.6)

6 (66.7)

11 (57.9)

0.60

Presence of EPI outreach schedule

No

Yes

9

40

5 (55.6)

17 (42.5)

4 (44.4)

23 (57.5)

0.48

Received funding for EPI activities

No

Yes

5

44

2 (40.0)

20 (45.5)

3 (60.0)

24 (54.5)

0.82

EPI allowances paid

No

Yes

11

38

6 (54.5)

16 (42.1)

5 (45.5)

22 (57.9)

0.46

Have community mobilizer

No

Yes

1

48

0 (00.0)

22 (45.8)

1(100.0)

26(54.2)

0.36

Have means of transport

No

Yes

34

15

14 (41.2)

8 (53.3)

20 (58.8)

7 (46.7)

0.43

EPI performance discussed in staff meetings

No

Yes

10

39

5 (50.0)

17 (43.6)

5 (50.0)

22 (56.4)

0.72

Attending EPI performance review meeting

No

Yes

25

24

12 (48.0)

10 (41.7)

13 (52.0)

14 (58.3)

0.66

Support supervision

No

Yes

5

44

5 (100.0)

17 (38.6)

0 (0.0)

27 (61.4)

0.009

Staffing level

=<75%

>75%

39

10

18 (46.2)

4 (40.0)

21 (53.8)

6 (60.0)

0.73

Number of EPI outreaches conducted in 6 months

=<20

>20

30

19

11 (36.7)

11 (57.9)

19 (63.3)

8 (42.1)

0.14

Proportion of planned outreaches conducted (%)

=<75%

>75%

12

37

5 (41.7)

17 (45.95)

7 (58.3)

20 (54.05)

0.796

*^*Not applicable. Health centers do not have a gas fridge.

Bivariate analysis of health facility characteristics and with immunization performance

There was no significant difference in the performance by the level of the health facility or ownership type and the results are shown in Table 5 below. However, facilities located in the Municipality or the more urban part of the district were more likely to perform poorly compared to those located distally such as Buhaguzi (p=0.026).

The facilities that were located within 25km of the district headquarters were more likely to perform poorly compared to those that were located much further away (p=0.023 *using Fisher’s exact test). At least 70% (n=19) of the health facilities within 25km performed poorly compared to only 36.4% (n=8) of those more than 25km from the district headquarters.

Table 5: Bivariate analysis of health center type and geographical location with immunization program performance

Characteristic

Total (n=49)

Good performance n=22 (%)

Poor performance n=27 (%)

p value*

Level of health facility

HC II

HC III

HC IV or higher

17

28

4

7 (41.2)

13 (46.4)

2 (50.0)

10 (58.8)

15 (53.6)

2 (50.0)

0.91

Ownership

Government

Private

40

9

17 (42.5)

5 (55.6)

23 (57.5)

4 (44.4)

0.71

Support supervision

No

Yes

5

44

5 (100.0)

17 (38.6)

0 (0.0)

27 (61.4)

0.009

Health Sub District

Bugahya

Buhaguzi

Municipality

16

23

10

4 (25.0)

15 (65.2)

3 (30.0)

12 (75.0)

8 (34.8)

7 (70.0)

0.026

Distance from District Headquarters

< 25KM

>25KM

27

22

8 (29.6)

14 (63.6)

19 (70.4)

8 (36.4)

0.023

*Using Fishers exact test

Results of key informant interviews

We interviewed four key informants involved in immunization at the district and health sub-district. Overall three reasons were provided to explain the performance of the immunization program namely 1) health worker attitude 2) outreach site selection and 3) community mobilization as shown in Table 6 below.

Table 6: Reasons to explain immunization performance from key informant interviews

Reason	explanation
Health workers’ attitude	Competing interests in urban areas Less time committed to immunization activities
Outreach site selection	Convenient selection of outreach sites
Community mobilization	Outreaches conducted in the morning hours when clients are still in the gardens Less support for community mobilization Village health teams (VHTs) don’t reach all households with information on immunization dates Dates and time for outreaches not well known

Health worker attitude

The key informants indicated that the health facilities located closer to the district headquarters might put up a poor performance because the health workers there reside in town, away from their work stations and are more likely to commit less time to immunization activities like outreaches compared to their more rural counterparts. They mentioned that health workers in the rural areas reside close to the health facilities unlike their urban counterparts. The KIs mentioned that the urban health workers also tend to leave their work stations earlier to travel to town for other competing priorities as illustrated by one of the respondents:

“Those health centres located next to Hoima town, many of their health workers reside in Hoima town and commute daily to work. Many of these leave their duty stations very early to return to their businesses in town.” KI, Hoima district

“Health workers in urban areas go for immunization outreaches early morning and leave early when mothers are still in their gardens because they [health workers] want to return early to their families in town. But health workers in the rural areas commit more time from late morning [un]till evening for immunization outreaches.” KI, Hoima district

Outreach site selection

The RED strategy requires that Health Centres conduct immunization outreaches to communities in hard-to-reach areas or villages located more than 5 km from the health center. KIs indicated that, health centres in the urban and peri-urban areas tended to select immunization outreach sites closer to the health center for their convenience.

“Health workers in urban areas select outreach sites that they can easily access and many of them are located less than 5 km from their station, so they don’t delay to return.” KI, Hoima

Community mobilization

All the key informants indicated that rural areas seem to have better mechanisms for community mobilization compared to their urban counterparts through grass root mechanisms.

“The rural areas have implementing partners who support them in mobilizing communities using loud speakers and other local public address systems. In addition, these village women seem to have stronger interpersonal relationships and communication which they use to remind each other to attend immunization days”. KI, Hoima district

“Health centres in rural areas rely on VHTs to mobilize communities for immunization. I have visited many households in urban and peri-urban areas and mothers reported no visits from VHTs. I have interacted with mothers in these areas and they usually know the location of the immunization outreach sites in their locations but usually don’t know the exact dates and time when health workers go there.” KI, Hoima district

In this cross sectional review of the immunization performance of health facilities in a district in western Uganda, over 50% of them were classified as having poor immunization performance. Proximal location of the health facility to the district headquarters and support supervision were associated with performance. Several factors related to the program such as infrastructure to deliver the vaccines and staffing levels were not significantly related to performance.

The poor performance in immunization at these health centers implies that many infants in this region in Hoima district and other districts with similar performance remain unvaccinated. The performance supports the findings of Uganda Demographic and Health survey (UDHS) of 2016 which showed that at least one in three children in Bunyoro region, where Hoima is lies had not been received all basic vaccinations. [20]The same survey also found out that many regions in Uganda had significant proportion of infants who had not received all basic vaccinations. Our results are set in one district but may throw some light on performance on a much broader scale in the country. Several studies in sub Saharan Africa have reported less than the targeted levels for vaccination coverage. [21-23]

Our data showed the health facilities that were located more remotely from the district headquarters or the more rural part of the district were likely to perform better than those located closer to the district headquarters. This inverse distance and performance relationship is striking and needed to be explained. Most health workers from health centers located close to the central district headquarters were reported to reside near the town center and were more likely to pursue competing economic activities in the municipality. Key informants suggested that this may cause them to commit less time to immunization activities and health facility related activities in general. Our key informant interviews suggested that the health workers at the remote health facilities conduct immunization outreach sessions at flexible hours for the rural residents who mostly spend their morning times in the gardens. The health workers in the urban areas do not provide this flexible option to the peri-urban dwelling clients, hence the shortfall. Our results agree with other studies elsewhere that show health worker attitudes and access are key influencers of immunization programs. [24]

Studies elsewhere had shown rural-urban differences in performance of immunization programs. [25] Although urban areas are considered accessible and easy to reach, for immunization programs, populations in these areas should be considered among those with poor access to the services. For immunization to be successful, health workers need to maintain a certain level of contact with the recipient communities, [26] as a way to eliminate inequities.

The geographical differences in performance between the rural and urban counties may also be explained the role of VHTs. Buhaguzi health sub district is more rural but hosts a refugee settlement which attracts some organizations that facilitate VHTs to mobilize communities for immunization, which services are absent in the urban counties. Also, the absence of health facilities in some urban parishes may play a role. For instance some divisions of Hoima municipality had many parishes that did not have a single health center. This implies that community members travel longer distances to access immunization sessions. Since most of immunization outreach sites were selected to the convenience of health workers, many children miss the opportunity to be vaccinated.

Our findings that health facilities in rural areas perform better than those in urban areas are supported by other studies elsewhere in sub Saharan Africa. For instance, a study done in the Gambia found that children in the rural setting were more likely to be immunized based on the three antigens namely BCG, measles, and DPT and full immunization by 12 months of age, as compared those children in the urban and peri-urban areas. [27] In the Gambia, poor performance in the urban areas was attributed to long queues at health facilities which served as a deterrent to attendance. In Indonesia, poor populations crowded in peri-urban clusters were less likely to be immunized, highlighting geographic distribution in immunization coverage. [28] Our study also shows the fewer health facilities in the urban parishes may be responsible for the poor performance. The fewer health facilities create inequalities in access to immunization services. Such inequalities were observed in a comparative study of immunization coverage in the urban slums of Ouagadougou and Nairobi and were responsible for the difference in vaccine coverage.[29]

According to studies on immunization coverage in sub Saharan Africa, factors that affected immunization programs include support supervision, organization of outreaches, financing, logistics and cold chain maintenance, advocacy and communication, planning and management and use of surveillance data for decision making. [30, 31] However, our study did not find a significant association between immunization program performance and these factors.

Although support supervision has been associated with good immunization performance in studies from other sub Saharan African countries [30], our data show the opposite. We paradoxically found that facilities that did not have support supervision were all ranked as good performing. We consider this result spurious and to be taken with caution. The possible explanation for this unexpected finding is that majority of sites had regular support supervision, hence our data did not have sufficient variation to compare the sites with and without the supervision. To gain a better understanding of this finding, we triangulated these findings with qualitative data from key informant interviews and found out that support supervision was held irregularly. Also, we found that support supervision visits were integrated with other health programs and therefore never focused at details of immunization programs like review of performance data at the particular health facility. There is a potential bias in the assessment of the role of support supervision, if it was not being administered effectively. However, this finding could also have been due to bias arising from the possibility that support supervision might have targeted health centers with poor performance. Interventional studies have provided the evidence to show the benefit of support supervision in the delivery of health interventions. [32]

The major strength of our study is we assessed health facilities in a remote district, and generated novel data for a district in an approach that has not been commonly conducted. Majority of studies have examined individual patient factors, but few have examined health facilities as a unit of observation. We have mapped the performance and showed the strong, albeit paradoxical association between proximity to the central district administration and immunization performance. Our study provided valuable data that will be used to inform immunization programs in many other districts of Uganda, and similar settings outside of Uganda. Our study has some potential weaknesses. First, this is not a national study, but a survey conducted in one district, hence the number of facilities assessed is small. For this reason, we were not able to conduct multivariable analyses as the models crashed; nevertheless the descriptive and map data remain informative. However, given the similar rural urban divide of the majority of Ugandan districts, our results provide valuable lessons for other districts in the country. Second, we used routinely collected data and these have been shown to carry errors (ref) and missing information which may result in misclassification of the performance of the health facilities. Lastly, we did not assess for physical features such as mountains and roads, which are known to contribute to geographical access to services.

Future studies should conduct assessment in more districts and include a larger number of health facilities to provide a more comprehensive evaluation. Also, although we focused on health facility level assessment, more research is needed at individual level to explain performance.

In conclusion, our study has shown that majority of health facilities in this district of western Uganda had poor immunization performance. The health facilities located closer to the urban setting near the district headquarter s were associated with poorer immunization performance compared to those further away. Support supervision was negatively associated with immunization performance, although the data are small and should be interpreted with caution.

We recommend monitoring and evaluation of immunization program performance at sub county and health sub district level to identify poor performing areas and put in place targeted interventions. There should also be dialogue meetings between community members and health workers as a way of involving community members in planning for immunization services like selection of site, time and date of outreach visits with much focus on areas with poor immunization performance.

The district health team needs to be mentored or trained and supported to conducted regular and planned support supervision of immunization programs in health centres, and not simply targeting places when performance has already gone down. Interventions are needed to improve involvement of VHTs in urban areas in mobilization for vaccination.

ANC; Antenatal Care, DHIS; District Health information system, DHO; District health officer, DHT; District Health team, DPT; Diphtheria, Pertussis and Tetanus, EPI; Expanded Program on Immunization, HC; Health Center, HIS; Health Information System, HMIS; Health management information systems, HSD; Health sub district, ICT; Information communication technology, M&E; Monitoring and Evaluation, MOH; Ministry of Health, p-value; probability value, REC; Reach Every child, RED; Reach Every District, UNEPI; Uganda National Expanded Programme on Immunization, UNICEF; United Nation Children’s Fund, VHT; Village Health Team, WHO; World Health Organization

Ethics approval and consent to participate

Ethical approval for this study was sought and obtained from the Research Ethics Committee at Mbarara University of Science and Technology.

Availability of data and materials

All data supporting our findings are contained in the paper. There are some restrictions to data access and reasonable requests will be considered after approval from Mbarara University Research Ethics Committee. Data requests may be made to Nicholas Magambo Kwikiriza (corresponding author), Department of Community Health, Mbarara University of Science and Technology, PO Box 1410, Mbarara, Uganda, email: [email protected] Tel: +256783783288

Competing interests

The authors declare that there are no competing interests.

Funding

The research was financially supported by study partners.

Authors’ contributions

NMK, FB1 and FB2 conceived and designed the study, NMK supervised the data collection, implementation of field activities, NMK led the analysis of the data, interpretation of the findings and drafting of the paper. NMK made the first draft of the manuscript. All authors interpreted the data, read and revised the first draft and approved the final version of the paper.

Acknowledgements

The study team acknowledges the support and contributions of; Ms. Sarah Kabasomi, the staff of Community of Health Department of Mbarara University of Science and Technology, Hoima district Health Department staff, and Rose Kyalisiima

WHO: Immunization http://www.who.int/topics/immunization/en/. Accessed March 10, 2020.
WHO: Global immunization coverage sustained in the past five years https://www.who.int/immunization/newsroom/press/immunization_coverage_july_2016/en/ 2016 Accessed March 12, 2020.
WHO: WHO Recommendations for routine immunization summary tables: https://www.who.int/immunization/policy/immunization_tables/en/ Accessed March 15, 2020.
Gitta SN, Mwesiga A, Kamadjeu R: Proceedings of the African Field Epidemiology Network (AFENET) Scientific Conference 17-22 November 2013 Addis Ababa, Ethiopia: plenaries and oral presentations. The Pan African medical journal 2015, 21.
WHO: Uganda improves access to immunization services https://www.afro.who.int/news/uganda-improves-access-immunization-services June 2016; Accessed March 15, 2020
Lakew Y, Bekele A, Biadgilign S: Factors influencing full immunization coverage among 12-23 months of age children in Ethiopia: evidence from the national demographic and health survey in 2011. BMC public health 2015, 15:728.
Oku A, Oyo-Ita A, Glenton C, Fretheim A, Eteng G, Ames H, Muloliwa A, Kaufman J, Hill S, Cliff J et al: Factors affecting the implementation of childhood vaccination communication strategies in Nigeria: a qualitative study. BMC public health 2017, 17(1):200.
Canavati S, Plugge E, Suwanjatuporn S, Sombatrungjaroen S, Nosten F: Barriers to immunization among children of migrant workers from Myanmar living in Tak province, Thailand. Bulletin of the World Health Organization 2011, 89(7):528-531.
Mathew JL: Inequity in childhood immunization in India: a systematic review. Indian pediatrics 2012, 49(3):203-223.
Ophori EA, Tula MY, Azih AV, Okojie R, Ikpo PE: Current trends of immunization in Nigeria: prospect and challenges. Tropical medicine and health 2014, 42(2):67-75.
Pugliese-Garcia M, Heyerdahl LW, Mwamba C, Nkwemu S, Chilengi R, Demolis R, Guillermet E, Sharma A: Factors influencing vaccine acceptance and hesitancy in three informal settlements in Lusaka, Zambia. Vaccine 2018, 36(37):5617-5624.
Poy A, van den Ent M, Sosler S, Hinman AR, Brown S, Sodha S, Ehlman DC, Wallace AS, Mihigo R: Monitoring Results in Routine Immunization: Development of Routine Immunization Dashboard in Selected African Countries in the Context of the Polio Eradication Endgame Strategic Plan. The Journal of infectious diseases 2017, 216(suppl_1):S226-s236.
Olafsdottir AE, Reidpath DD, Pokhrel S, Allotey P: Health systems performance in sub-Saharan Africa: governance, outcome and equity. BMC public health 2011, 11:237.
Nsubuga F, Luzze H, Ampeire I, Kasasa S, Toliva OB, Riolexus AA: Factors that affect immunization data quality in Kabarole District, Uganda. PLoS One 2018, 13(9):e0203747.
Prinja S, Monga D, Rana SK, Sharma A, Dalpath S, Bahuguna P, Gupta R, Aggarwal AK: District Level Analysis of Routine Immunization in Haryana State: Implications for Mission Indradhanush under Universal Immunization Programme. Indian journal of community medicine : official publication of Indian Association of Preventive & Social Medicine 2018, 43(3):209-214.
Malande OO, Munube D, Afaayo RN, Annet K, Bodo B, Bakainaga A, Ayebare E, Njunwamukama S, Mworozi EA, Musyoki AM: Barriers to effective uptake and provision of immunization in a rural district in Uganda. PLoS One 2019, 14(2):e0212270.
Ryman T, Macauley R, Nshimirimana D, Taylor P, Shimp L, Wilkins K: Reaching every district (RED) approach to strengthen routine immunization services: evaluation in the African region, 2005. Journal of public health (Oxford, England) 2010, 32(1):18-25.
Vandelaer J, Bilous J, Nshimirimana D: Reaching Every District (RED) approach: a way to improve immunization performance. Bulletin of the World Health Organization 2008, 86(3):A-b.
McDonald JH: Handbook of biological statistics, vol. 2: sparky house publishing Baltimore, MD; 2009.
UBoSUa I: Uganda demographic and health survey 2016: key indicators report. UBOS, and Rockville, Maryland, USA: UBOS and ICF 2017.
Wariri O, Edem B, Nkereuwem E, Nkereuwem OO, Umeh G, Clark E, Idoko OT, Nomhwange T, Kampmann B: Tracking coverage, dropout and multidimensional equity gaps in immunisation systems in West Africa, 2000-2017. BMJ global health 2019, 4(5):e001713.
Hosseinpoor AR, Bergen N, Schlotheuber A, Gacic-Dobo M, Hansen PM, Senouci K, Boerma T, Barros AJ: State of inequality in diphtheria-tetanus-pertussis immunisation coverage in low-income and middle-income countries: a multicountry study of household health surveys. Lancet Glob Health 2016, 4(9):e617-626.
Arsenault C, Johri M, Nandi A, Mendoza Rodríguez JM, Hansen PM, Harper S: Country-level predictors of vaccination coverage and inequalities in Gavi-supported countries. Vaccine 2017, 35(18):2479-2488.
Favin M, Steinglass R, Fields R, Banerjee K, Sawhney M: Why children are not vaccinated: a review of the grey literature. International health 2012, 4(4):229-238.
Tabatabaei SM, Mokhtari T, Salari M, Mohammdi M: Rural-urban differences in reasons for incomplete vaccination in children under six years, Southeast Iran 2013. International Journal of Infection 2015, 2(3):e28109.
Chopra M, Bhutta Z, Chang Blanc D, Checchi F, Gupta A, Lemango ET, Levine OS, Lyimo D, Nandy R, O'Brien KL et al: Addressing the persistent inequities in immunization coverage. Bulletin of the World Health Organization 2020, 98(2):146-148.
Payne S, Townend J, Jasseh M, Lowe Jallow Y, Kampmann B: Achieving comprehensive childhood immunization: an analysis of obstacles and opportunities in The Gambia. Health policy and planning 2014, 29(2):193-203.
Herliana P, Douiri A: Determinants of immunisation coverage of children aged 12-59 months in Indonesia: a cross-sectional study. BMJ open 2017, 7(12):e015790.
Soura AB, Mberu B, Elungata P, Lankoande B, Millogo R, Beguy D, Compaore Y: Understanding inequities in child vaccination rates among the urban poor: evidence from Nairobi and Ouagadougou health and demographic surveillance systems. Journal of urban health : bulletin of the New York Academy of Medicine 2015, 92(1):39-54.
Bekele AT, Fiona B, Thomas K, Kassahun A, Kathleen G, Nsubuga P, Ababu Y, Lemlem A: Factors contributing to routine immunization performance in Ethiopia, 2014. The Pan African medical journal 2017, 27(Suppl 2):5.
Negussie A, Kassahun W, Assegid S, Hagan AK: Factors associated with incomplete childhood immunization in Arbegona district, southern Ethiopia: a case--control study. BMC public health 2016, 16:27.
Madede T, Sidat M, McAuliffe E, Patricio SR, Uduma O, Galligan M, Bradley S, Cambe I: The impact of a supportive supervision intervention on health workers in Niassa, Mozambique: a cluster-controlled trial. Human resources for health 2017, 15(1):58.

Geographic location of health facility and immunization program performance in Hoima district, western Uganda, a facility level assessment

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Abstract

Figures

Background

Methods

Data collection and measurements

Data analysis

Results

Discussion

Conclusions And Recommendations

Abbreviations

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References

Status:

Journal Publication

Version 3