DEMOGRAPHIC

AND

HEALTH

RESEARCH

DHS WORKING PAPERS

2010 No. 72

Melissa Latigo

September 2010

This document was produced for review by the United States Agency for 

International Development.

Health Facility Characteristics Affecting 

the Early Adoption of Malaria Rapid Tests 

in Rwanda, Tanzania, and Uganda
The DHS Working Papers series is a prepublication series of papers reporting on research in progress that 

is based on Demographic and Health Surveys (DHS) data. This research is carried out with support 

provided by the United States Agency for International Development (USAID) through the MEASURE 

DHS project (#GPO-C-00-08-00008-00). The views expressed are those of the authors and do not 

necessarily reflect the views of USAID or the United States Government. 

MEASURE DHS assists countries worldwide in the collection and use of data to monitor and evaluate 

population, health, and nutrition programs. Additional information about the MEASURE DHS project can 

be obtained by contacting MEASURE DHS, ICF Macro, 11785 Beltsville Drive, Suite 300, Calverton, 

MD 20705 (telephone: 301-572-0200; fax: 301-572-0999; e-mail: reports@measuredhs.com; internet: 

www.measuredhs.com).
Health Facility Characteristics Affecting the Early Adoption of Malaria 

Rapid Tests in Rwanda, Tanzania, and Uganda 

Melissa Latigo 

ICF Macro

Calverton, Maryland, USA

September 2010 

Corresponding author: Melissa Latigo, Case Western Reserve University, School of Medicine, Cleveland 

OHIO; E-mail: mal45@case.edu 
ACKNOWLEDGEMENT 

I am grateful to Dr. Fred Arnold (ICF Macro), Dr. Caroline Asiimwe (FIND), Dr. Rathavuth 

Hong (ICF Macro), Dr. Siran Koroukian (Case Western Reserve University) and Dr. Alfred 

Latigo (UNECA) for their feedback and input during the development of this paper, other 

ICF Macro staff and USAID for their educational and funding support under the MEASURE 

DHS fellowship, and Dr. Paul Bakaki (Case Western Reserve University) who informed me 

of this fellowship opportunity. 

Suggested citation: 

Latigo, Melissa. 2010. Health Facility Characteristics Affecting the Early Adoption of 

Malaria Rapid Tests in Rwanda, Tanzania, and Uganda. DHS Working Papers No. 72. 

Calverton, Maryland, USA: ICF Macro.
ABSTRACT 

Background: In 2003, World Health Organization recommendations to scale up the 

use of malaria rapid tests in resource-limited settings were accompanied by identification of 

key operational research needs to assess the factors that affect the implementation or 

expansion of use of malaria rapid tests at the national level. Such research findings can help 

guide the scale-up process and ensure high-quality testing. To date, there is limited literature 

assessing factors affecting the adoption of simple diagnostic technologies in resource-limited 

settings at the macro-level. 

Objective: This study evaluated the health facility characteristics associated with the 

early adoption of malaria rapid tests in Rwanda, Tanzania and Uganda. 

 Methods: We used data from the MEASURE DHS Service Provision Assessment 

(SPA) surveys conducted in Rwanda (2007), Tanzania (2006) and Uganda (2007). The 

surveys included a total of 538 facilities in Rwanda, 611 in Tanzania and 491 in Uganda. We 

conducted univariable and multivariate logistic regression analysis to evaluate the impact of 

organizational and contextual factors on malaria rapid test use among health facilities with 

malaria diagnostic capacity. 

Results: Our analysis included 482 facilities in Rwanda, 233 in Tanzania and 157 in 

Uganda. In Rwanda 9% of facilities used rapid tests, in Tanzania 6% and in Uganda 9%. In 

Rwanda and Uganda malaria rapid tests were more likely to be used in capital city regions, 

while in Tanzania test use was not associated with region. In Tanzania malaria rapid tests 

were more likely to be used in private health facilities than in government facilities, while in 

Rwanda and Uganda there was little difference in test use between private and government 

facilities. 

Conclusion: Study findings suggest that region and operating authority are influential 

factors in the adoption of simple health technology devices in resource-limited settings. 

However, the extent to which these factors are important varies by country. Further analysis 

is recommended to find out why types of facilities identified in this study adopted malaria 

rapid tests and to develop adoption indicators applicable to resource-limited settings. 

1

BACKGROUND 

In 2001, the World Health Organization (WHO) issued guidelines that encouraged 

artemisinin combination therapy (ACT) as the first line treatment for uncomplicated malaria 

in endemic areas, due to the increased resistance of the malaria parasite to chloroquine-based 

therapies (WHO 2001). The new treatment policy brought the need to improve upon the less 

accurate clinical diagnostic methods used in resource-limited settings that lack access to 

laboratory diagnostic capacity. More accurate diagnoses would in turn prevent the 

unnecessary prescription of costly ACT and discourage the development of resistance to the 

new drug (WHO 2003). 

While microscopy was (and still is) the gold standard in the diagnosis of malaria, its 

use was largely limited by its expense and by the inadequate number of skilled technicians in 

resource-poor health facilities. Malaria rapid tests were becoming a popular alternative to 

microscopy in areas with limited diagnostic capacity, due to their simplicity and ability to 

provide results quickly and hence to ensure appropriate and timely treatment of patients 

(WHO 2003). 

In 2003 WHO‟s Training in Tropical Diseases group (TDR) identified key operational 

research gaps concerning malaria rapid test use, such as the need to better understand the 

factors that affect the implementation or expansion of use of rapid tests at the national level 

(WHO 2003). In furthering knowledge in this area, one approach includes the identification 

of health facilities that are likely to be early adopters of malaria rapid tests. Typically in 

resource-limited settings such as Rwanda, Tanzania and Uganda, wide-scale adoption to a 

new technology by health facilities is contingent upon approval of the technology by an 

international agency, such as WHO, policy development for technology use at the national 

level, and adequate funding for technology procurement (Free 2004). Some facilities adopt 

new technologies before or during the early stages of national policy development. 

Knowledge of early adopters can help to identify health facilities where the new 

technology can be evaluated, to assess the quality of the new technology and determine 

whether it is being applied correctly in the clinical setting, to identify an initial market for 

other upcoming technologies and, based on the facility‟s catchment area and patient profile, 

to understand the characteristics and proportion of the population with access to the new 

technology. Early adopters can provide information that can assist in developing guidelines 

for technology use and, in addition, encourage the adoption of the technology by other health 

facilities (Berwick 2003; Free 2004). 
2

To date, few studies have evaluated the characteristics of facilities that are early 

adopters of simple diagnostic technologies in resource-limited settings. In a working paper 

aimed at determining the impact of new diagnostic technologies, Girosi and colleagues 

suggested that when new technologies are introduced into a country they are initially 

accessible to higher-level health facilities with the best infrastructure (Girosi, Olmsted et al. 

2006). Previous reports and qualitative studies also have suggested that key factors that 

facilitate adoption of technologies by facilities in resource-limited settings include proximity 

to a supply/distribution chain, affiliation with non-governmental organizations (NGOs) or 

donor agencies/partnerships, and private sector affiliation (Peeling 2007; Singer, Berndtson et 

al. 2007; Kamau 2008). 

Our study seeks to evaluate the association between health facility characteristics and 

the adoption of malaria rapid tests, as an example of a simple diagnostic technology in 

resource-limited settings. In our study, adoption is defined as reported use of the diagnostic 

technology. 

We use data from health facility surveys conducted in Tanzania in 2006, and in 

Uganda and Rwanda in 2007. At the time of the surveys, malaria rapid test policy guidelines 

in Uganda, Rwanda and Tanzania were still in their infancy, while some health facilities 

already had begun to adopt rapid tests independent of a formal policy framework (Lynch et al 

2006). Given the timeframe in which the surveys were conducted, their findings are likely to 

depict an informal or unplanned adoption process, as opposed to one characterized by formal 

policies and scientific methods (Greenhalgh, Robert et al. 2004). 
3

METHODS 

Study Design 

We used data from the MEASURE DHS Service Provision Assessment (SPA) 

surveys conducted in Rwanda (2007), Tanzania (2006) and Uganda (2007). The SPA surveys 

are cross-sectional health-facility-based surveys, typically conducted over a four to five 

month period to assess the type and quality of health care infrastructure and services offered 

by a sample of representative facilities in a country. 

In the Tanzania and Uganda surveys, health facilities were selected by facility type 

from a sampling frame of functioning health facilities in each country. Facilities accounting 

for a small proportion of facilities in the country were oversampled, whereas those 

accounting for a large proportion were undersampled. Therefore, all government hospitals in 

Tanzania were included in the survey, because they account for a small proportion of the total 

number of health facilities. Health centers, dispensaries, stand-alone facilities and private 

hospitals were selected so that they were representative of facilities at the national level and 

zonal level. Zones are unofficial groupings of regions in Tanzania that are used by the 

Ministry of Health and Social Welfare. In Uganda, all hospitals and half of all Health Centre 

IVs were included in the survey. Health Centre Is, IIs and IIIs were selected to be 

representative of facilities at the national and regional levels. Each facility was weighted by 

facility type so that it represented that actual number of that type of facility in the country. 

Because Rwanda has fewer health facilities than the other two countries, almost all facilities 

(97%) were included in the Rwanda survey. The study sample was not weighted, because it 

was representative of the health facilities in the country. 

A total of 538 facilities were included in the survey in Rwanda, 611 in Tanzania and 

491 in Uganda. Further details of the survey design have been described elsewhere (Tanzania 

NBS and Macro 2007; Rwanda MOH, Macro et al. 2008; Uganda MOH and Macro 2008). 

In our study, we conducted a secondary data analysis using variables from the facility 

checklist, laboratory and pharmacy datasets, to evaluate the association between health 

facility characteristics and the type of malaria test used. The final study sample included 

health facilities that were reported to have a malaria test (microscopy, rapid test or other) 

available in the laboratory dataset. The study was exempted from approval by the University 

Hospitals of Cleveland Institutional Review Board. 
4

Conceptual Framework 

The factors associated with the adoption of health technologies can be characterized 

as individual, organizational and contextual. Individual factors are the characteristics of 

authoritative figures in the organization or health facility. Organizational factors describe 

how the health facility is structured and operates. Contextual factors are characteristics of the 

policy and regional environment in which the health facility is located (Kimberly and 

Evanisko 1981). We focus on the organizational and contextual factors that are associated 

with the adoption of malaria rapid tests, because the SPA surveys collected this type of 

information.

Study Outcome 

The main outcome measure was a dichotomous variable indicating whether the 

facility used malaria rapid tests in a laboratory unit, or in at least one of its laboratory units if 

it had more than one laboratory. In each laboratory unit in which a survey was conducted, the 

provider was asked whether malaria rapid tests were used and whether they were available, 

recorded as „observed‟, „reported, not seen‟ or „normally available, not today‟. If the response 

to whether the malaria rapid test was used in at least one laboratory unit was „yes‟, we 

categorized the facility as one that uses rapid tests, and vice versa. 

Independent Variables 

Organizational factors 

Health facilities in our study samples follow a tiered structure in which hospitals are 

classified as the highest level, followed by health centers and dispensaries or stand-alone 

facilities. Unique to Uganda is the classification of health centers, from Health Centre I (HCI) 

to Health Centre IV (HCIV), with HCI being the lowest level. Higher-level health facilities 

tend to serve larger populations and to be more autonomous. The type of facility has been 

suggested as a factor influencing the adoption of diagnostic technologies (Girosi, Olmsted et 

al. 2006). We included “type of facility” as recorded in the facility checklist dataset as a 

variable in our analysis. 

Previous reports have suggested that malaria rapid test use is more common in the 

private sector than in the public sector (Kamau 2008). We included “managing authority” 
5

from the facility checklist as a variable in our analysis. Facilities operated by the government 

included public facilities and, in Tanzania, parastatal facilities. Private health facilities 

included private-for-profit health facilities, not-for profit health facilities (faith-based 

organizations, community-based organizations) and government-assisted facilities. 

Government-assisted facilities are not-for-profit health facilities, in Rwanda, that have similar 

management structures to public health facilities. 

Health facility size has been shown to be an influential factor in the adoption of 

health technologies in high income countries and can be reliably estimated by the total 

number of health workers (Kimberly and Evanisko 1981). We calculated the total number of 

health workers in each health facility by summing the “actual number in post” in Tanzania, 

and the “actual number of full-time and part-time males and females” in Rwanda and 

Uganda, as observed in the facility checklist. 

Because malaria rapid test use was recommended following the introduction of ACT 

(or Coartem), we included “Coartem availability” as a variable in our analysis. Coartem was 

considered to be available in the health facility if it was recorded as „all valid‟, „at least one 

valid‟ or „available but none valid‟ in at least one pharmacy unit in the pharmacy dataset. The 

use of routine user fees by health facilities has been associated with improved quality of care. 

We used the variable “facility charges routine user fees” from the facility checklist dataset. 

To explore the potential effect of additional revenue resources and NGO affiliation on 

test adoption, we used variables in the analysis specific to the Uganda facility checklist 

dataset indicating whether the facility received any funding from NGOs/donors or out of 

pocket revenue from clients in the 2006/2007 financial year. At the time of the survey, 

malaria diagnosis and clinical guidelines in Uganda discouraged routine use of malaria rapid 

tests, due to uncertainty about their accuracy. We therefore evaluated the association between 

test use and guideline availability in at least one inpatient or outpatient unit, using data from 

the inpatient and outpatient datasets. 

Contextual factors 

Geographic location is likely to influence health technology adoption, because 

location affects proximity to infrastructure, supply chain and financial resources (Hikmet, 

Bhattacherjee et al. 2008). We used the regions, or zones in the case of Tanzania, identified in 

the facility checklist to represent geographic location. The nine regions in Uganda were 
6

regrouped to create four regions: Kampala, Central and Eastern (East and East Central), 

Northern (North Central, West Nile, Northeast) and Western (Western and Southwestern). 

Analytic Strategy 

We conducted separate analyses for each country. The facility served as the unit of 

analysis in this study. In the descriptive analysis we calculated the weighted distribution 

(Tanzania and Uganda) or unweighted distribution (Rwanda) of the total sample of facilities 

by facility characteristic. We conducted univariate logistic regressions to evaluate the 

association between facility characteristics and malaria rapid test use. All variables were 

included in the multivariable logistic model to estimate the adjusted odds of malaria rapid test 

use by facility characteristic. Data were analysed using SAS Software for Windows, version 

9.2 (SAS Institute Inc. Cary, NC, USA) and STATA Software for Windows, version 10.1 

(StataCorp, TX, USA).
7

RESULTS 

Study Sample Characteristics and Test Use 

The analysis included a total of 482 facilities in Rwanda, 233 in Tanzania and 157 in 

Uganda (see Tables 1a, 1b and 1c). As the tables show, at the time of each survey 9% of 

facilities in Rwanda used malaria rapid tests, 6% in Tanzania and 9% in Uganda. The 

majority of health facilities had coartem therapy available in Rwanda (78%) and Uganda 

(81%), while the proportion of facilities with coartem availability was much lower in 

Tanzania (13%). In Rwanda 96% of facilities charged routine user fees, and 91% in 

Tanzania, while in Uganda only 47% of facilities charged routine user fees. In Uganda 

malaria management guidelines were available in 73% of health facilities, and clinical 

guidelines were available in 82%. In Rwanda and Uganda, malaria rapid test use was more 

common in the capital cities (Kigali City, 16%; Kampala, 33%) than in other regions of the 

countries. In Tanzania, however, test use was highest in the lake zone (11%), surpassing the 

eastern zone (6%) in which the capital city, Dar es Salaam, is situated. In all three countries 

studied, rapid test use was highest among hospitals and declined as health facility level 

decreased. In Uganda, however, malaria rapid test use among Health Centre IIs (13%) was 

slightly higher than among Health Centre IVs (10%). 
8

Table 1a: Distribution of Rwandan study sample and test use by facility characteristic

Facility characteristic Number Percent

Percentage with 

malaria rapid test

Region

Kigali City 77 16.0 16.9

Northern 80 16.6 8.8

Southern 109 22.6 9.2

Eastern 102 21.2 2.9

Western 114 23.7 7.0

Facility Type

Hospital 39 8.1 10.3

Health Center, Polyclinic 360 74.7 8.6

Dispensary/Health Post/Clinic 83 17.2 7.2

Total Number of Health Workers

<= 25 377 78.1 6.9

25 - 75 73 15.1 17.8

 > 75 33 6.8 6.1

Managing Authority

Public 284 58.9 6.7

Agrees/Private/NGO/Community 198 41.1 11.1

Coartem availabilitya

Yes 378 78.4 8.5

No/Unknown 104 21.6 8.7

Facility charges routine user fees

Yes 464 96.3 8.6

No 18 3.7 5.6

Total 482 100.0

a Represents first line therapy availability in at least one unit in the health facility.
9

Table 1b: Distribution of Tanzanian study sample and test use by facility characteristic

Facility characteristic NumberaPercent

Percentage with 

malaria rapid test

Region

Western 26 11.2 0.0

Northern 53 22.7 5.6

Central 6 2.5 1.9

Southern Highlands 25 10.9 9.3

Lake 29 12.4 11.2

Eastern 63 26.9 6.4

Southern 20 8.5 1.3

Zanzibar 12 5.0 1.9

Facility Type

Hospital 25 10.5 15.2

Health Centre 44 18.7 12.1

Dispensary/Stand-Alone 165 70.8 2.5

Total Number of Health Workers

<= 20 199 85.4 3.6

> 20 34 14.6 17.9

Managing Authority

Government/Parastatal 71 30.5 3.2

Non-Government 162 69.5 6.7

Coartem availabilityb

Yes 31 13.3 13.7

No 202 86.7 4.4

Facility charges routine user fees

Yes 212 91.0 6.2

No 21 9.0 0.0

Total 233 100.0

a Weighted 

b Represents first line therapy availability in at least one unit in the health facility.
10

Table 1c: Distribution of Ugandan study sample and test use by facility characteristic

Facility characteristic NumberaPercent

Percentage with 

malaria rapid test

Region

Kampala 7 4.6 32.8

Central and Eastern 63 40.3 8.3

Northern 38 24.3 1.7

Western 48 30.8 12.2

Facility type

Hospital 19 12.0 14.8

Health Centre IV 25 16.0 10.1

Health Centre III 70 44.9 4.9

Health Centre II 42 27.0 12.7

Total number of health workers

<= 20 112 71.6 8.6

> 20 44 28.4 10.2

Managing authority

Government Public 88 56.2 7.2

Private 69 43.8 11.4

Received any funding from donor 

agencies/NGOs in 2006/2007 

financial year

Yes 33 21.0 3.9

No/Unknown 124 79.0 10.4

Received any funding from out of 

pocket revenue (client charges) in 

2006/2007 financial year

Yes 53 33.7 5.9

No/Unknown 104 66.3 7.7

Coartem availabilityb

Yes 125 80.1 10.3

No 31 19.9 4.1

Management of uncomplicated 

malaria guideline availabilityc

Yes 115 73.7 8.0

No 41 26.3 11.8

Uganda clinical guidelines availabilityc

Yes 129 82.3 8.6

No 28 17.7 11.1

Facility charges routine user fees

Yes 74 47.1 10.5

No 83 53.0 7.7

Total 157 100.0

a Weighted 

b Represents first line therapy availability in at least one unit in the health facility.

c Represents guideline availability in at least one unit (inpatient or outpatient) in the health facility.
11

Univariate and Multivariable Analyses 

Tables 2a, 2b and 2c present results of the univariate and multivariable analyses. In 

the univariate analysis, the odds of test use were lower in Rwanda‟s Eastern (OR = 0.15; p 

<0.01) and Western (OR = 0.37; p<0.05) regions compared to Kigali City. In Uganda the 

odds of test use were significantly lower across all regions compared to Kampala. In Rwanda 

the odds of test use were higher among health facilities with 25 to 70 health workers 

compared to facilities with 25 or fewer health workers (OR = 2.92; p<0.01). 

In the multivariable analyses, test use remained associated with region in the study 

samples from Rwanda and Uganda. In Uganda, the odds of test use were lower among Health 

Centre IIIs compared to hospitals (OR = 0.22; p <0.05). In the study sample from Tanzania, 

the odds of test use among private facilities increased by twofold and became statistically 

significant (OR = 2.88; p = 0.045). Test use was not associated with the total number of 

health workers in the three study samples. 
12

Table 2a: The association of health facility characteristics with malaria rapid test use in the 

Rwandan study sample

Variable

Unadjusted model Adjusted model

Odds 

ratio

pvalue

95% 

Lower 

CI

95% 

Upper 

CI

Odds 

ratio

pvalue

95% 

Lower 

CI

95% 

Upper 

CI

Region

Kigali City 1.00 — — — 1.00 — — —

Northern 0.47 0.13 0.18 1.26 0.34 0.05 0.11 0.99

Southern 0.50 0.12 0.21 1.20 0.32 0.03 0.12 0.87

Eastern 0.15 0.00 0.04 0.54 0.11 0.00 0.03 0.45

Western 0.37 0.04 0.15 0.95 0.24 0.01 0.09 0.68

Facility type

Hospital 1.00 — — — 1.00 — — —

Health Center, Polyclinic 0.82 0.73 0.27 2.47 0.38 0.29 0.06 2.25

Dispensary/Health Post/Clinic 0.68 0.57 0.18 2.57 0.20 0.18 0.02 2.11

Total number of health workers

<= 25 1.00 — — — 1.00 — — —

25 - 75 2.92 0.00 1.42 5.99 1.94 0.15 0.78 4.80

 > 75 0.87 0.85 0.20 3.83 0.24 0.24 0.02 2.57

Managing authority

Public 1.00 — — — 1.00 — — —

Agrees/Private/NGO

/Community 1.74 0.09 0.92 3.32 1.54 0.25 0.73 3.22

Coartem availabilitya

Yes 1.00 — — — 1.00 — — —

No/Unknown 1.02 0.95 0.47 2.22 0.70 0.49 0.26 1.92

Facility charges routine user fees

Yes 1.00 — — — 1.00 — — —

No 0.62 0.65 0.08 4.81 0.89 0.92 0.10 7.74

a

 Represents first line therapy availability in at least one unit in the health facility. 
13

Table 2b: The association of health facility characteristics with malaria rapid test use in the 

Tanzanian study sample

Variable

Unadjusted model Adjusted model

Odds 

ratio

pvalue

95% 

Lower 

CI

95% 

Upper 

CI

Odds 

ratio

pvalue

95% 

Lower 

CI

95% 

Upper 

CI

Region

Northern 1.00 — — — 1.00 — — —

Western — — — — — — — —

Central 0.85 0.88 0.09 7.76 1.23 0.86 0.12 12.60

Southern Highlands 0.60 0.55 0.11 3.16 0.61 0.57 0.11 3.41

Lake 1.70 0.39 0.51 5.69 1.63 0.47 0.44 6.03

Eastern 0.85 0.80 0.24 2.94 0.90 0.88 0.23 3.53

Southern 0.75 0.73 0.14 3.96 0.90 0.90 0.15 5.28

Zanzibar 0.23 0.19 0.03 2.01 0.33 0.36 0.03 3.55

Facility type

Hospital 1.00 — — — 1.00 — — —

Health Centre 0.93 0.90 0.29 2.97 1.16 0.88 0.17 7.93

Dispensary/Stand-Alone 0.15 0.00 0.04 0.52 0.14 0.12 0.01 1.61

Total number of health workers

<= 20 1.00 — — — 1.00 — — —

> 20 3.61 0.01 1.38 9.43 1.59 0.68 0.19 13.19

Managing authority

Government/Parastatal 1.00 — — — 1.00 — — —

Non-Government 1.49 0.37 0.62 3.56 2.88 0.05 1.02 8.08

Coartem availabilitya

Yes 1.00 — — — 1.00 — — —

No 0.58 0.30 0.20 1.65 2.77 0.13 0.75 10.31

Facility charges routine user fees

Yes — — — — — — — —

No — — — — — — — —

a

Represents first line therapy availability in at least one unit in the health facility.
14

Table 2c: The association of health facility characteristics with malaria rapid test use in the 

Ugandan study sample

Variable

Unadjusted model Adjusted model

Odds 

ratio

pvalue

95%

Lower 

CI

95% 

Upper 

CI

Odds 

ratio

pvalue

95% 

Lower 

CI

95% 

Upper 

CI

Region

Kampala 1.00 — — — 1.00 — — —

Central and Eastern 0.14 <0.0001 0.05 0.39 0.13 0.00 0.04 0.43

Northern 0.08 <0.0001 0.02 0.30 0.07 <0.0001 0.02 0.31

Western 0.37 0.04 0.14 0.94 0.31 0.04 0.10 0.94

Facility type

Hospital 1.00 — — — 1.00 — — —

Health Centre IV 0.69 0.41 0.28 1.69 0.57 0.35 0.17 1.88

Health Centre III 0.51 0.22 0.18 1.47 0.22 0.04 0.05 0.94

Health Centre II 0.96 0.95 0.30 3.12 0.46 0.34 0.09 2.27

Total number of health workers

<= 20 1.00 — — — 1.00 — — —

> 20 1.39 0.41 0.64 3.05 1.35 0.61 0.42 4.33

Managing authority

Government Public 1.00 — — — 1.00 — — —

Private 1.50 0.27 0.73 3.10 1.38 0.64 0.35 5.49

Received any funding from 

donor agencies/NGOs in 

2006/2007 financial year

Yes 1.00 — — — 1.00 — — —

No/Unknown 2.01 0.12 0.84 4.81 2.10 0.15 0.78 5.68

Received funding from out of 

pocket revenue (client charges) 

in 2006/2007 financial year

Yes 1.00 — — — 1.00 — — —

No/Unknown 0.62 0.19 0.30 1.28 0.73 0.64 0.20 2.69

Coartem availabilitya

Yes 1.00 — — — 1.00 — — —

No/Unknown 1.24 0.65 0.48 3.22 1.03 0.96 0.34 3.11

Management of uncomplicated 

malaria guideline availability

Yes 1.00 — — — 1.00 — — —

No 1.09 0.85 0.47 2.54 0.86 0.79 0.28 2.65

Uganda clinical guidelines 

availability

Yes 1.00 — — — 1.00 — — —

No 2.01 0.14 0.80 5.04 0.68 0.20 0.66 7.11

Facility charges routine 

user fees

Yes 1.00 — — — 1.00 — — —

No 0.79 0.52 0.38 1.62 1.69 0.51 0.35 8.19

a Represents first line therapy availability in at least one unit in the health facility.
15

Other Analyses 

We collapsed the region variable into two categories, capital region and other region, 

because test use was comparatively low across regions outside of the capital cities in Rwanda 

and Uganda. In a multivariable logistic regression analysis adjusting for managing authority, 

facility type and total number of health workers, the odds of test use were greater in Kigali 

City (OR = 3.55; p = 0.004; 95% CI = 1.49, 8.48) and Kampala (OR = 7.51; p < 0.0001; 95% 

CI = 2.87, 19.63) compared to other regions. In a multivariable logistic regression analysis 

stratified by managing authority, the odds of test use were greater in Kampala compared to 

other regions (OR = 4.91; p = 0.052; 95% CI = 0.99, 24.49). The overall model was not 

statistically significant (p = 0.29). Among private facilities, the odds of test use were greater 

in Kampala compared to other regions (OR = 11.36; p<0.0001; 95% CI = 3.08, 41.90), and 

the overall model was statistically significant (p <0.01) (Results not shown). 

We also conducted a stratified regression analysis using the study sample from 

Rwanda. However, we collapsed the variable facility type to two categories, hospital and 

other health facility, because there were too few observations for analysis for the 

dispensary/health post/clinic category. We dropped the total number of health workers 

variable from the analysis, because it was strongly correlated with facility type in this study 

sample. Among government facilities, the odds of test use were greater in Kigali City 

compared to other regions (OR = 5.13; p = 0.005; 95% CI = 1.63, 16.16), and the overall 

model was statistically significant (p <0.05). Among private facilities, the odds of test use 

were not associated with region (OR = 1.83; p = 0.22; 95% CI = 0.70, 4.77), and the overall 

model was not statistically significant (p = 0.31) (Results not shown). 
16

DISCUSSION

Findings from this study provide quantitative evidence for the association between 

health facility characteristics and the adoption of malaria rapid tests in study samples from 

Rwanda, Tanzania and Uganda. The findings suggest important associations between test use 

and geographic region in Rwanda and Uganda, at the time of the surveys. While there was no 

association between geographic region and test use in Tanzania, private health facilities in 

Tanzania were more likely than public facilities to use malaria rapid tests. 

 When new health technologies are introduced in countries similar to those in our 

study sample, it is expected that the technologies will first become available to capital city 

regions, due to better accessibility to the distribution chain. However, the lack of significant 

regional disparity in test use among health facilities in Tanzania may reflect a more equitable 

distribution of per capita income and resources across the country than in Rwanda and 

Uganda, where resources tend to be concentrated in capital city regions (BJ Ndulu and CK 

Mutalemwa 2002). 

It is also expected that new technologies are more likely to become available first to 

the private sector. Study findings confirm this hypothesis in Tanzania, but not in Rwanda and 

Uganda. The slower adoption of malaria rapid tests among public compared to private health 

facilities in Tanzania might be attributed to the notably centralized decision-making process 

in the country‟s public health sector (DELIVER 2007). However, in Rwanda and Uganda 

public health facilities may have demonstrated minimal disparities in test adoption compared 

to their private counterparts due to a greater degree of autonomy in the public sector. In 

Rwanda this autonomy may stem from a decentralized system in which public facilities set 

their own user-fee levels and determine how revenue is spent (Soeters, Habineza et al. 2006). 

In Uganda, despite a no user-fee policy in the decentralized public health system, increased 

affiliations with non-governmental agencies and research institutions may have facilitated the 

decision to adopt malaria rapid tests. The general difference in outcomes observed in 

Tanzania might reflect an earlier stage of the adoption process, given that at the time of 

survey Tanzania had not officially adopted the ACT treatment policy, whereas Uganda and 

Rwanda and Uganda had adopted the policy (PMI 2006; PMI 2007; PMI 2007). 

Further analysis of the effect of managing authority on test adoption in Rwanda and 

Uganda suggests that this variable modifies the association between region and test use. In 

Uganda a stronger estimate of association between region and test use was observed among 
17

private facilities compared to government facilities, although the confidence limits around 

these estimates were considerably wider. The wide confidence limits might be attributed to 

the fewer number of cases per variable in the regression analysis. A stronger association 

between region and test use in private facilities may be an indication of the greater 

opportunity and incentive for the private sector to provide services in Kampala, due to the 

higher income per capita compared to other regions of the country. Conversely, in Rwanda a 

stronger estimate of association between region and test use was observed among government 

health facilities compared to private facilities. This difference could be a result of the fact that 

the private sector played a limited role in health services delivery at the time of the survey. 

While the total number of health workers, a measure of health facility size and proxy 

for resource availability, has been a particularly important variable associated with adoption 

of health technologies in high income countries, our study found that facilities with more 

health workers were not more likely to use malaria rapid tests. We hypothesize that there are 

other factors in addition to facility size that need to be accounted for to better reflect resource 

availability in resource-limited settings. Such factors may include availability of electricity, 

clean water, trained laboratory technicians and decently equipped laboratories, which are 

commonly observed among higher-level health facilities, such as hospitals (Girosi, Olmsted 

et al. 2006). „Type of facility‟ might therefore be a better measure of resource availability 

than the number of health workers. Our findings showed that hospitals were more likely than 

other health facilities to use rapid tests, although this result was not statistically significant. 

 There are several limitations in this study. First, the small sample of facilities using 

malaria rapid tests may have prevented the detection and precision of important associations 

in the regression analysis. Therefore, we acknowledge that the study findings are only 

suggestive of the associations that exist between facility characteristics and test use in 

resource-limited settings. Second, given the cross-sectional nature of the survey design, 

timing of adoption was not captured in the study, a factor that can help distinguish between 

the “early-early adopters” and the “late-early adopters,” which may have different 

characteristics (Berwick 2003). 

Also, in our study malaria rapid test use was only captured in the laboratory setting, 

while the simple nature of these devices means that they could potentially be used outside of 

this setting. Moreover, additional individual-level factors that may affect test use, such as the 

educational level, age and „cosmopolitanism‟ (having outside professional contacts) of the 

facility decision-makers, were not included in the study (Kimberly and Evanisko 1981). 
18

It is also important to acknowledge that our study samples are limited to facilities with 

malaria diagnostic capacity, with the majority of these facilities already using microscopy. 

Therefore, the decision to adopt a malaria rapid test may have been influenced by factors that 

reflect an improvement over microscopy-based diagnosis, for example reduced patient 

waiting time in high-volume facilities. Similarly, the decision not to adopt rapid tests may 

have been influenced by factors that would have reduced the effectiveness of this testing 

method, such as frequent stock-outs of laboratory reagents and lack of finances to procure 

test kits. Further evaluation of the group of early adopters at the qualitative level can provide 

more insight into why health facilities chose to adopt malaria rapid tests. 
19

CONCLUSIONS 

 This study has demonstrated that region and operating authority are influential factors 

in the adoption of simple health technology devices in resource-limited settings. However, 

the extent to which these factors are important depends on the country in question, possibly 

as result of differences in the decision-making context and/or the structure and influence of 

the private and public sectors in health service delivery. Accounting for interactions among 

these factors might better explain their association with test adoption. 

To our knowledge, this study is one of the few that provide quantitative evidence at 

the macro-level for test device adoption patterns in resource-limited settings. To better 

evaluate the association between health technology adoption and health facility 

characteristics in resource-limited settings, we recommend longitudinal and more 

comprehensive collection of facility-level data. As a follow-up to this study, we recommend a 

more detailed analysis of the facilities adopting the malaria rapid tests and their associated 

decision-makers in the respective countries. The follow-up can help to explain the adoption 

patterns suggested in this study and can provide guidance on additional relevant indicators of 

adoption that can be collected in upcoming SPA surveys. In order to allow for the inclusion 

of larger sample sizes in subsequent analyses, collection of similar adoption indicators across 

countries will allow more efficient pooling of data, in the event that independent country 

analyses depict similar findings. 
20

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