andre_melo_mpp project_final_rev
TRANSCRIPT
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THE UNIVERSITY OF UTAH
2011
Policy Implications of the Integration
of HIV/AIDS Services into Primary
Health Care in Mozambique An Applied Project for a Master’s Degree in
Public Policy
André Joaquim Melo
C E N T E R F O R P U B L I C P O L I C Y A N D A D M I N I S T R A T I O N ( C P P A )
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Dedication
This work is dedicated to Manuel Fortuna, for being true to self and to
others. He did everything in his duty and related with everyone he met as
was necessary. You did not need to solve riddles to understand the man
that he was. What is most remarkable about him is that he had the heart to
promote the good and rebuke the evil with individuals and groups at all
levels of society. May his soul be at peace in eternal repose!
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Acknowledgements
Resulting from research to constitute my Master of Public Policy capstone
project, this piece of work, like all others, would not take on the face it did
without the help of other people. First of all, my sincere gratitude goes to
my professors and other members of staff at the University of Utah whose
work inspired me, each in a unique way, to carry out my duties as a student
in the right magnitude and direction to bring my plans to fulfilment.
Secondly, I would like to thank the community at St. Catherine of Siena
Newman Centre and all friends in the State of Utah who provided a social
and spiritual platform for me to feel at home away from home. Many thanks
go to my family and friends home in Angola, Zambia and around the globe
for all the moral support they provided me with during my stay in Utah. Last
and most importantly I thank the US Department of State for granting me
and the Institute for International Education for administering the Fulbright
scholarship which provided for my legal and economic upkeep in Utah.
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Abstract
Aim: The aim of this study is to assess the impact of the integration of
HIV/AIDS services into primary health care on HIV prevalence and the
delivery of HIV/AIDS services in Mozambique. The study also projects
some possible policy actions to maximize any perceived benefits of the
integration program in Mozambique and other countries in similar
situations.
Rationale/Significance: Although the study conducted by Pfeiffer and
others shows the strides made by the initiative in terms of expansion of
access, decline of loss to follow-up from antenatal and TB testing to Anti
Retro Viral Therapy (ART) services, and the efficiency in the transition from
HIV testing to ART initiation, it does not provide comprehensive statistical
evidence for the findings to quantitatively back up their qualitative claims.
This project seeks to bridge this gap by analyzing a select set of variables,
quantifying the impact of the integration program in terms of magnitude and
direction, and indicating how this can be translated into sustainable policy
actions.
Methodology: Data from 43 Sub-Saharan African countries were collected
on a number of variables over a period of 8 years (2002 – 2009). Some of
the variables included in the Analytical models are HIV incidence, HIV
prevalence, number of Voluntary Counselling and Testing (VCT)/ART
facilities, and number of people on Anti Retro Viral Therapy. Others are
incidence of tuberculosis per 100000 people, and number of physicians per
1000 people. An integration variable was generated in the factor analysis
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model. This serves as the Dependent Variable (DV) in the final regression
model.
Findings and Conclusion: Results of this study show that the integration
of HIV/AIDS services into Primary Health Care has substantially
contributed to the reduction in the HIV prevalence rate in Mozambique
(74% variance explained in both the factor and regression analysis).
Although the integration program has led to a reduced HIV prevalence rate,
the time trend graph shows that this is happening still at high rates and
slow pace. The study also indicates that insufficient human resources still
threaten the adequate delivery of HIV/AIDS services in Mozambique even
in the face of the integration program (integration – physician/1000people
coefficient of determination = 0.226). This leads to the conclusion that an
adequate number of VCT/ART facilities and physicians is needed to
enhance effectiveness and efficiency of the integration program which in
turn lowers the HIV prevalence rate in Mozambique, R2 = 0.049.
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Table of Contents
Content page
Dedication……….……………………………………………………….....1
Acknowledgements………………………………………………………...2
Abstract…………….………………………………………………………..3
CHAPTER ONE: BACKGROUND
1. Country background..…………………………………………………….9
1.1 Political, economic and social background………………………….10
1.2 Mozambique‟s healthcare system.…...………………………………11
1.3 Access to healthcare in Mozambique.……………………………….13
CHAPTER TWO: INTRODUCTION
2. Introduction to the study………………………………………………….15
2.1 Statement of the problem……………...…….…………………………18
2.2 Aim and objectives of the study……………………………………….21
2.3 Hypothetical statements……….……………………………………….22
2.4 Rationale/Significance………….……………………………………....22
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CHAPTER THREE: LITERATURE REVIEW
3. Literature review…………………….…………………………………..24
CHAPTER FOUR: DATA AND METHODOLOGY
4. Data and methodology…………………………………………………27
4.1 Data analysis process..………………………………………………28
CHAPTER FIVE: DATA ANALYSIS
5. Data analysis..………………………………………………………….31
5.1 Factor analysis..……………………………………………………...33
5.2 Regression analysis.………………………………………………...36
5.2.1 Regression diagnostic tests………………………………………37
5.2.2 Test of hypothesis…..……………...………………………………38
CHAPTER SIX: POLICY IMPLICATIONS
6. Policy implications..…………………………………………………….41
CHAPTER SEVEN: CONCLUSION
7. Conclusion.…………..…………………………………………………….44
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7.1 Weaknesses and inadequacies………………………………….….45
7.2 Suggestions/recommendations……...………………………………46
References………..…………………………………………………….…..48
APPENDIX A: Correlations output……………………………………….50
APPENDIX B: Regression output I………………………………………54
APPENDIX C: Factor analysis output……………………………………61
APPENDIX D: Regression output II……………………………………...68
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1. Country Background
Mozambique is located on the South-Eastern part of the African continent.
She shares borders with Tanzania on the North; Malawi, Zambia and
Zimbabwe on the West and South Africa and Swaziland on the South as
well as a coast line of the Indian Ocean on the East. She occupies a total
area of 801,590 square kilometers on this region of Africa. The total
population of this country as of 2012 was estimated at 25,203,000 of which
1,150,000 live in the capital city, Maputo. They use Metical (MZN) for their
currency
Map of Mozambique, its neighbors, flag and location in Africa
Source: BBC archives
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1.1. Political, Economic and Social Background
Since independence from Portugal in 1975, Mozambique has been
battered by civil war, economic mismanagement and famine. A peace deal
in 1992 ended 16 years of civil war, and the country has made much
progress in economic development and political stability. Mozambique
emerged as a country from the Portuguese colonial rule that began in the
early 16th century. An anti-authoritarian coup in 1974 in Portugal ended the
colonial rule and its ten-year war with the Frelimo independence movement
leading to the dawn of independence in 1975.
Mozambican support for armed groups fighting the white-minority rule
governments in Rhodesia and South Africa led to those two countries
sponsoring the Renamo movement, which fought Frelimo in the 1977-1992
civil war. This conflict, combined with Rhodesian and South African
intervention and central economic planning by the Marxist leadership of
Frelimo left the country in chaos. About a million people died in the civil war
and millions more fled abroad or to safer parts of the country.
An attempt to secure a ceasefire with South Africa in the Nkomati Accord of
1984 broke down and the government and Renamo eventually began talks
brokered first by Christian groups and then by the United Nations. Frelimo
inaugurated a new constitution in 1990 that enshrined free elections, and
both sides signed the resulting Rome Peace Accords of 1992 that instilled
a relatively peaceful environment in the country. Frelimo has won all
subsequent elections, some of which have been disputed by Renamo and
smaller opposition groups. Political life has nonetheless remained stable,
with Renamo continuing to work within the constitutional system.
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The country has emerged as one of the world's fastest growing economies,
with foreign investors showing interest in Mozambique's untapped oil and
gas reserves. Coal and titanium are a growing source of revenue. Most of
the population tills the land, however, and infrastructure nationwide still
suffers from colonial neglect, war and under-investment. The economy
suffered serious setbacks when in 2000 and 2001 Mozambique was hit by
floods which affected about a quarter of the population and destroyed much
of its infrastructure. Furthermore, in 2002 a severe drought hit many central
and southern parts of the country, including previously flood-stricken areas.
Poverty remains widespread, with more than 50% of Mozambicans living
on less than $1 a day.
1.2. Mozambique’s Healthcare System
According to the International Insulin Foundation, about seventy percent of
Mozambique‟s health budget is financed through basket funding by an
estimated 25 donors. Some of these donors provide direct financial
assistance to the Ministry of Health while others to specific areas of the
country or disease areas. A World Health Organization‟s (WHO) estimate
shows that in 2006 Mozambique spent 56 US Dollars per person at
Personal Purchasing Parity (PPP) on health, which represents 4.7% of
Gross Domestic Product (GDP).
The Mozambique‟s Ministry of Health (Ministério da Saúde - MISAU) is the
public institution that is responsible for running the health sector. The
Ministry runs 652 health posts and 435 health centers which provide health
services at the primary level. At the secondary level, health services are
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provided by 27 rural hospitals and eight district hospitals. At tertiary level
they are provided by five general hospitals and seven provincial hospitals,
with three central hospitals providing services at the quaternary level. This
is equivalent to one health unit per 15,000 inhabitants with only 40% of the
population having access to these health facilities. The remainder of the
population is covered by traditional medicine, community health agents and
traditional birth attendants. A small part of the population is covered by
private healthcare mainly concentrated in the big cities.
Despite these constraints Mozambique has been able to improve its core
health indicators. In addition, soma government measures have been
implemented that benefit people with chronic illnesses. Specific examples
are Presidential Decree 16/88 (which ordains discounts on the total value
of the prescription) and the more recent Ministerial Dispatch Nr. 42/2007
(ordaining a unitary price of 5.00 Mts = US$0.20 per prescription). These
are clear measures aimed at benefiting people with chronic diseases.
These positive measures, however, place a heavy burden on the health
system and on the country as the burden of the health care costs shifts
from the individual to the country.
Communicable diseases continue to pose the greatest health challenges in
Mozambique. HIV/AIDS is now responsible for one in three deaths and the
death rate due to malaria in children under five years of age is equivalent to
1,150 per 100,000 people. However, Non Communicable Diseases (NCDs)
are also increasing in burden. In a recent study by Damasceno et al, a
prevalence of 33.1% for hypertension in Mozambique was found, of which
only 18.4% were aware of their condition. About half of these individuals
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aware of their condition were under treatment and control was found to be
extremely low.
NCDs are not only affecting adults, but are also starting to impact children.
In a 10 – year study of the causes of death of children under the age of 15
years in Manhica province, Communicable Diseases are still the most
prominent cause of mortality with 73.6%. Nonetheless, NCDs represent
13.4% of the total with 9.5% because of chronic conditions and 3.9% due to
injuries.
1.3. Access to Healthcare in Mozambique
In a country like Mozambique people face numerous barriers when
accessing the health services that they need. Mozambique is a country that
– even if all of its international and national commitments to health
spending are met – still needs an extra $35.2 USD per person per year to
ensure that all of the population has access to basic healthcare. The
burden of making up for this financing gap inevitably falls on the population
through direct and indirect out-of-pocket payments for health services. This
is an impossible situation for a country that is still ranked at 184 out of 187
nations on the UN‟s Human Development Index, and that has millions of
people living in poverty.
A film was made that looks at all of the barriers that people face in
accessing healthcare. Urban and rural settings present different
challenges, but the film addressed the rural setting of Tsangano in the
province of Tete, a huge region in the centre of the country.
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The examples of Tsangano and Tete clearly show that all parts of a health
system need to come together in order for the system as a whole to
function. Tete has two million inhabitants and just 63 doctors. That means
that there is just one doctor for 30,000 people, and one nurse for 8,000
people. When we advocate for an end to out-of-pocket payments we must
ensure that the „key ingredients‟ which make user fee removal a success
are also addressed – the financing for the system as a whole and ensuring
increased investment in transport and infrastructure, particularly in rural
areas, the health workforce, access to medicines and better information for
the population to demand their right to health.
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2. Introduction to the Study
HIV began hitting the global scene in the early 1980s and since then the
devastating impact of the HIV/AIDS epidemic has not gone unnoticed in the
whole world, giving way to various studies and initiatives aimed at
mitigating its spread. The Joint United Nations Program on HIV/AIDS
(UNAIDS) 2007 AIDS Epidemic Update reports a decline in the number of
the world‟s people living with HIV from 39.5 million in 2006 to 33.2 million in
2007. The latest country estimates from UNAIDS and the World Health
Organization (WHO) indicate that in most sub-Saharan African countries,
the HIV/AIDS prevalence has stabilized, though still at high levels. In
addition, some countries such as Uganda have begun to experience
declines.
At last, there is evidence of reduced risky behavior in some parts of the
Sub-Saharan region. According to Lule and others (2008), this is the
rationale of The Changing HIV/AIDS Landscape…for Action in Africa. Over
the last half a decade, most countries in the region have developed
national responses to HIV/AIDS through National AIDS Commissions,
legislation, programs, and services which need to be sustained through
locally and nationally adaptive multi-sector strategies and enhanced policy
frameworks. Although we have such positive indicators of mitigation, the
current AIDS prevalence rates in Africa still compel us to re-examine how
mitigation efforts are being aggregated.
Persson and Sjöstedt (2010) discuss that until now public opinion has
ascribed the persistent high HIV/AIDS prevalence rates in Africa to the lack
of sufficient financial and human capital, and the absence of necessary
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policies and political leadership. In their study, Pfeiffer et al (2010) have
clearly indicated that the integration program in Mozambique took
advantage of the increasing international funding for disease specific
programs.
Other authors like Cohen and Tate (2005) and Gillespie (2005) also talk
about increased funding, both domestic and external, giving way to various
studies and HIV/AIDS initiatives in Uganda, Kenya, Rwanda, and Zambia.
This is to say that the lack of financial capital is not as much of a problem in
the fight against the HIV/AIDS epidemic as are other factors like necessary
policies and political leadership. Such are the factors that can be attributed
to the persistent high levels of HIV prevalence in most African countries.
Persson and Sjöstedt (2010) argue that the situation described in the
previous paragraph is a result of mismatch between research and policy.
They say that AIDS in Sub-Saharan Africa needs to be addressed, unlike
other diseases, as a cultural phenomenon paying attention to indigenous
perceptions of socio-cultural aspects such as sexual behavior, self-esteem,
the cost of sacrifice, both materially and immaterially, and how much of
private behavior individuals are willing to discuss. This served as ground for
their hypothesis that a viable solution for the AIDS epidemic in Sub
Saharan Africa lies in the proper application of a sustainable social
mobilization conceptual framework1. Although this can be done in various
1 The social mobilization conceptual framework is one of the volumes of the Integrating
Reproductive Health and HIV/AIDS for NGOs, FBOs, and CBOs Series developed by the Center
for Development and Population Activities. It is a five-day curriculum manual designed to
impart skills in advocacy, behavior change communication and social marketing at national and
grassroots levels to promote social mobilization as a means for communities to communities to
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ways, Pfeiffer et al (2010) suggest that their case study on the HIV/AIDS
integration into primary health care services in Mozambique is one such
viably sustainable strategy, especially for African countries.
According to UNICEF, the first case of HIV/AIDS in Mozambique was
diagnosed in 1986. This was followed by a steady increase in the
prevalence rate up to an estimated 16.2% among the population aged 15 to
49 years in 2004 leading to Government‟s declaring HIV/AIDS a national
emergency in July the same year. As of 2007, the country had an
estimated (more precise) adult HIV/AIDS prevalence rate of 12.5%. Like
many other countries devastated by this epidemic, Mozambique has sought
ways of mitigating the epidemic.
One of the efforts in this respect is government initiative of a national scale-
up of Anti Retroviral Therapy (ART) and HIV care through a vertical “Day
Hospital” approach, as described by Pfeiffer et al (2010) – case study. This
approach involves people who are affected and infected by HIV seeking
services at centralized hierarchically managed centers which are which are
set specifically for HIV/AIDS services. Even when accessible, they were not
utilized due to stigmatization fears.
Supported by large increases in international disease-specific funding, the
vertical “Day Hospital” model diverted scarce, especially human, resources
away from the Primary Health Care (PHC) system. Given the increase in
the number of people being served with the ART services, Mozambique‟s
Ministry of Health (MOH) adopted a strategy that used HIV/AIDS therapy
increase local participation and women’s empowerment in addressing HIV/AIDS. Although
developed specifically for Nepal, it is said that lessons are applicable to a variety of contexts
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and care resources as a means to strengthen their PHC system in 20052
with the hope that the would-have-been-lost human capital still serves in
both service sectors simultaneously.
According to Pfeiffer et al (2010), the MOH worked closely with a number of
Non Governmental Organizations (NGOs) to integrate HIV programs more
effectively into existing public-sector PHC services. From their findings,
they concluded that using aid (mostly international) resources to integrate
and better link HIV care with existing services can strengthen wider PHC
systems. This project investigates how this integration program has
impacted the health care delivery system in Mozambique relative to the HIV
prevalence rate, assesses the magnitude and direction of the change in
quantifiable terms, and projects some policy implications of that impact.
2.1. Statement of the Problem
Admittedly, there is a lot of policy to facilitate resource mobilization and
promote research on the HIV/AIDS epidemic. However, if there is any
2 According to the authors, the initial approach to ART scale-up in 2004 focused on a vertical, donor-initiated, day hospital model in which new freestanding HIV treatment hospitals were constructed in large population centers alongside existing hospital compounds. Day hospitals included their own pharmacies, data systems, health workforce, waiting areas and receptions. Using this separate infrastructure, patients identified as HIV positive from other sectors of the health system (VCT, PMTCT, blood bank and laboratory) were referred to day hospitals to register for HIV care, and to follow a sequence of visits for clinical staging, CD4 testing, social worker visits, treatment for opportunistic infections, and initiation and follow up of ART. The day hospitals also included specifically allocated staff (often expatriate) and better working conditions than other sectors. This vertical approach may have contributed to high loss-to-follow-up rates and missed opportunities that limited the uptake of patients initiating ART.
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literature on the diagonal implementation framework3, it is not widespread.
As used by Pfeiffer and others (2010), diagonal implementation framework
refers to hierarchical/multi-level implementation programs which are broad
based. Most appropriately, they would be called Broad Based Triangular
Frameworks. Mozambique‟s experience with the integration of HIV/AIDS
care services into its public sector Primary Health Care system is a strategy
that can provide lessons for most, if not all, Sub-Saharan African countries
in the HIV/AIDS mitigation quest.
Pfeiffer et al (2010) state that the integration program has taken advantage
of the thriving HIV/AIDS program (in terms of funds and infrastructure) to
strengthen both itself and the PHC system as a whole. They, in the case
study, found that decentralization and integration of HIV/AIDS care services
into the existing PHC system has improved mainly three health care
factors. One of these factors is access to care through expansion of sites
and services. The second is service quality through reduced loss to follow
up (LTFU) and improved patient flow. The program is also said to have
improved system efficiency by linking services and improving referral rates,
while accelerating the pace at which services can be expanded.
With due credit to the interesting findings, Pfeiffer et al (2010) do not
address the policy implications of those improvements due to the
integration program. They could have addressed, among others, three
major policy issues. First, they could quantify the relationship between the
integration program and the HIV prevalence, and show how this
3 This framework is a combination of the vertical approach and the social mobilization
framework or other similar horizontally tailored approaches.
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relationship interacts with other factors that potentially impact the program.
Second, they could measure and/or discuss the strategic capacity to
address the need for more health care personnel and other challenges
associated with the expansion of services. Third, they could illustrate how
significant the marginal benefits of the integration program are to the nation
as whole. These aspects are crucial to making research findings easily
usable by governments and other consumers of public policy in general
and health policy in particular.
In this regard, It is important to allude to Persson‟s and Sjöstedt‟s (2010)
point that there is a policy disconnect among the various donors and
governments on the prevention philosophy. Logically speaking, such a
mismatch is more unlikely to produce significant prevention and mitigation
results. This disconnect can be looked at as a spectrum.
On one end of the spectrum there are too many conflicting interests by
corporate organizations, and too little political willingness by governments,
on the other end, to address the issue by adopting policies that translate
research into the implementation of viable and sustainable programs.
Searching from the US government documents catalogue, one of the most
elaborate and inclusive legislative documents in the world, none of the
latest congressional hearings addresses framing a policy related to
HIV/AIDS care – Primary Health Care integration or other strategies, in
relation to existing research. The major problem in the HIV/AIDS policy is
that there is a lack of implementation research to provide the necessary link
between basic research and outcomes.
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It is, therefore, imperative that governments and other policy makers are
united on the best preventive methods, and pay attention to research on
the epidemic when deliberating policy. It is also important that policy
makers adopt and promote those implementation strategies which are
proven or seen to have the potential for effective and efficient interventions
in the fight against the HIV/AIDS epidemic. This study attempts to assess
the potential effectiveness and efficiency of one such intervention program
based on a thorough quantitative analysis of existing research and data on
which basis some interesting recommendations are made.
2.2. Aim and Objectives
This project aimed to assess the impact and magnitude of the integration of
HIV/AIDS services into primary health care in Mozambique. It sought to
attain the following specific objectives:
a) Find statistical indicators pointing to some evidence that the
integration of HIV/AIDS services into primary health care has caused
the change in HIV prevalence in Mozambique;
b) Show/illustrate the magnitude and direction of that change in the rate
of HIV prevalence due to the integration program in Mozambique;
c) Elicit and quantify the capacity of Mozambique‟s Ministry of Health to
adequately face the challenges of scaling up activities due to the
program, and;
d) Project some possible policy actions to maximize any perceived
benefits of the integration program in Mozambique and other
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countries in similar situations especially within the Southern region of
Africa.
2.3. Hypothetical Statements
The following set of hypothetical statements guided the methodological and
analytical processes of this project:
a) The integration of HIV/AIDS services into Primary Health Care
services led to a reduction in the HIV prevalence rate in Mozambique;
b) Insufficient human resource is still a threat to adequate delivery of
HIV/AIDS services in Mozambique even in the face of the integration
program;
c) Increasing the number of Voluntary Counseling and Testing/Anti
Retroviral Therapy facilities will further drive down the prevalence rate
in Mozambique;
d) The diagonal framework of the integration program has the potential
of responding to the needs of preventing the HIV/AIDS epidemic in
Mozambique and other African countries.
2.4. Rationale/Significance
The study conducted by Pfeiffer and others (2010) shows the strides made
by the initiative in terms of expansion of access, decline of loss to follow-up
from antenatal and TB testing to ART services, and the efficiency in the
transition from HIV testing to ART initiation. However, the study does not
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provide statistical evidence to support their findings and make them
consumable by a wider range of the public. This project sought to bridge
this gap by compiling a dataset from existing databanks and repositories
including variables deemed to be principal factor of the integration program
from 2002 to 2009. This is expected to provide a clear picture about the
benefits of this integration program and enhance its possibility of being
developed into a sustainable model for Mozambique.
The study may have broader implication if replicated in other African
countries, especially in the Southern region, and the world at large where
the HIV/AIDS epidemic poses a big threat to national development. In
addition, it contributes to policy direction in the application of cost-effective
and efficient utilization of resources in developing countries.
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3. Literature Review
Over the years various initiatives and studies have come up in an effort to
mitigate the spread of HIV/AIDS following its devastating impact on society
with particular focus on Sub-Saharan Africa. One of the potentially reliable
multilateral studies on the epidemic is the one edited by Lule and others
(2008) in which the UNAIDS 2007 AIDS Epidemic Update is quoted
reporting a decline in the number of the world‟s people living with HIV from
39.5 million in 2006 to 33.2 million in 2007.
It is said that new methodological approaches, improved HIV surveillance,
and changes in the key epidemiological assumptions used to calculate
prevalence are making it possible for estimates to be closer to reality now
than ever before. This is being facilitated by increased funding both
domestic and international, including the U.S. President‟s Emergency Plan
for AIDS Relief (PEPFAR). The PEPFAR program mandated increases in
international funding for HIV/AIDS programs especially for Sub-Saharan
Africa. A good number of reports by the World Health Organization (WHO),
United Nations Children‟s Fund (UNICEF), and the joint United Nations
Program on HIV/AIDS (UNAIDS) show in various ways what strides are
being made in the effort to contain the Global AIDS problem.
In addition to the multinational reports on the status of HIV/AIDS in Sub-
Saharan Africa, smaller and specific studies have been conducted by
individuals and/or groups of people. Studies like those conducted by
EQUINET (2007) discuss in general terms Africa‟s health status and the
potential African countries have to solve their health problems had their
wealth of resources been adequately and appropriately utilized. Others like
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Made and Morna (2006) discuss the importance of the media being
supportive of diversity as a way to reach a wider spectrum of society on
HIV/AIDS issues and other social perils. Crush et al (2007) discuss the role
of such aspects as migration and household food security in dealing with
the HIV/AIDS issue.
In another study Ondimu (2005) analyzes the sexual behaviors of specific
groups of people, the tea plantation workers in Kenya to indicate how multi-
faceted the HIV/AIDS epidemic is. The common denominator to all these
different kinds of literature is the indication that taking a multidimensional
and integrated approach4 to attack the various facets of the prevention and
mitigation efforts of the HIV/AIDS epidemic will be more viable for most of
Sub-Saharan Africa.
Like other Sub-Saharan African countries, there are numerous literatures
on HIV/AIDS in Mozambique, but befitting our purpose and the scope of
this project, reference is made only to a few selected works. Newman et al
(2001) talk about the HIV situation among the military personnel of
Mozambique, an area in which, according to the authors, little was then
known about the state of the virus and, consequently, the epidemic.
Gillespie (2005) advocates for the use of evidence from studies that relate
HIV/AIDS to food and nutrition security conducted in Mozambique and
other countries like Zambia and Rwanda to implement intervention
4 The multidimensional aspect of this approach involves the design and implementation of
programs which attack the issue, HIV/AIDS prevention and mitigation in this case, from various
standpoints. Whereas, the integrated aspect involves the inclusion of the various actors on the
issue in their diversity of expertise and specialization to holistically address the issue within the
temporal, spatial, and other relevant contexts.
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programs. Other authors like Haacker (2004) discuss the interactive
relationships between sexually Transmitted Diseases (STDs), AIDS, and
cultural mentalities with respect to traditional healers. Green and Jurg
(1993) add to the wealth of the literature by showing how the relationships
discussed by Haacker (2004) impact the social fabric and the economy in
Mozambique. As much as all these pieces of research talk about the
different components of the HIV/AIDS epidemic, their relevance and value
will not be tangible until they are translated into positive policy actions
through programs tailored to prevention and mitigation. This project is an
attempt to make contributions toward this goal.
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4. Data and Methodology
The dataset used in this study was built using existing data from the World
Bank‟s Nutritional Health and Population databank and the World Health
Organization‟s Health Observatory on HIV/AIDS. It consists of 344
observations representing 43 Sub-Saharan African countries over a period
of 8 years (2002 – 2009). Variables used in the empirical framework
include cases diagnosed with HIV (incidence), HIV prevalence, number of
Voluntary Counselling and Testing (VCT/ART) facilities, number of people
on Anti Retro Viral Therapy (ARV/T), number of physicians per 1000
people, and incidence of tuberculosis per 100000 people.
The data set consists of these and other variables like beneficiaries of
VCT/ART facilities, number of people in need of the therapy, country GNI,
number of AIDS deaths, and incidence of malnutrition. It was initially
planned that the data set would include data from 1994 through 2009.
However, reality showed that prior to 2002, VCT/ART services were not
available in a uniformly documented manner for all African countries,
hence, the decision to begin from 2002.
It was also practically impossible to obtain an existing dataset on these
specific variables which are ideally necessary for the scope of this study.
As such, the manner in which the dataset was put together is one of the
best possibilities of having data relevant to the study given its scope and
the general difficulty of obtaining readily available datasets for Sub-
Saharan Africa. SPSS was exclusively used as the software program for
the statistical analysis.
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4.1. Data Analysis Process
The data analysis process began with a run of correlation matrices for the
variables that were thought to have a relationship with Mozambique‟s
integration program and its impact on HIV prevalence in that country.
These variables include HIV incidence, prevalence as a standardized
measure, number of VCT/ART facilities, number of seropositives (people
with HIV) on ART, Tuberculosis incidence per 100000 people, and the
number of physicians per 1000 people. A complete table of this output can
be found in appendix A. This correlation matrix table provides a quick
numerical impression of the nature of the data by showing the correlation
coefficients, their significance at the 0.01 or 0.05 levels, and the number of
observations accounted for in each relationship. Table 1 below shows
descriptive statistics of these variables. Graph 1 is a time trend bar graph of
HIV prevalence rate averages for the 43 countries.
Then an initial regression model was built including the six variables
described in the prior paragraph with HIV prevalence as the dependent
variable. The variables were chosen on theory that they represent the best
relationship between the prevalence of HIV and the integration of HIV/AIDS
services into Primary health Care in Mozambique. Doing this preliminary
regression analysis created the possibility of carrying out statistical
diagnosis of the model to ensure that the combination of the variables of
interest does not violate the regression assumptions. In this regard the best
model was obtained doing away with the incidence variable as the
standardized prevalence rate variable was a better representation of the
same measurement. The output for this regression model is contained in
appendix B. These five variables were used to construct the factor analysis
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model from which two principal factors were generated to represent the
underlying variable for the integration program. Appendix C contains a
complete output of the factor analysis model, and the next two paragraphs
explain how the integration variable was generated.
The integration variable is a dummy variable generated from the two
principal components (factors) extracted from the factor analysis model.
Factor analysis is a data reduction tool. In the absence of a primary and
comprehensive measurement of the effectiveness of Mozambique‟s
integration program, it is hard to quantify the concept integration. Even in
the presence of primary data comprehensively collected in the field,
integration as a variable would still have to be quantified in terms of other
quantitatively measurable variables because of its abstract nature.
Therefore, as already stated in the previous paragraph, the variables
selected for the preliminary regression model were deemed the best
representatives of the underlying concept integration.
Table 2 shows the total variance explained by the factor analysis model.
From the table you can see that the initial (observed) variables with Eigen
values (variances) equal to or more than 1 constitute the extracted factors.
In this model we have 2 such factors (also known as principal
components). The 2 extracted factors represent the underlying concept
integration. These factors were further reduced to a single dummy
integration variable by recoding (in the dataset) the observations that
contributed to either of the two factors a variance of 1 or more into a
dummy value of 1, and the remainder a dummy value of 0. The dummy
value 1 means the variance of that observation from the perfect line of
points representing the underlying integration variable is close enough to
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have a significant effect on it. Whereas, 0 means the variance is too far
from the line to be significantly effective. Appendix C shows a detailed
output for this analysis.
Integration was used as the dependent variable in the model for the final
regression analysis of this study with all the other variables used in the
previous models serving as independent variables. Tables 3a – 3c show
the major results of this analysis and its complete output appears in
appendix D. The following section presents and discusses both the factor
analysis results (subsection 4.1) and final regression analysis results
(subsection 4.2).
5. Data Analysis
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From the Correlations table in appendix A, we can see a good number of
bivariate relationships with very strong coefficients and significances. This
also served as confirmation that the variables selected for the models were
statistically fitting in their relationship with one and the other before they
were used in the preliminary regression model to set grounds for the factor
analysis model. Since in the previous subsection (3.1) emphasis was
placed on describing the process of the different analytical models
employed in the study, it is important to highlight here the order in which
the analytical models were executed. After the correlations table was
generated, the preliminary regression model was set from which the
descriptive statistics table below (table 1) was generated. As mentioned
earlier, the HIV incidence variable was eliminated and prevalence retained
as the dependent variable. Then the time trend graph of HIV prevalence
country average was generated. Next, after running the factor analysis, the
retained principal component factors of the factor analysis model were
recoded into dummies to create the integration variable. The integration
variable served as the dependent variable in the final regression model, the
model on which the hypotheses of this study were tested. Table 1 is self
explanatory, but comments on the time trend graph are found below the
graph.
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Table 1: Descriptive Statistics for the Model
Variable N Minimum Maximum Mean
Std. Deviation
HIV Incidence 344 100 5600000 487758.72 948005.773
HIV Prevalence 344 .1 26.3 5.901 6.9897
# VCT/ART Facilities 344 0 4326 162.11 447.572
# on Anti Retroviral Therapy 344 0 971556 34727.28 90687.132
Tuberculosis Incidence/100000people 344 22 1260 370.22 236.803
Physicians/1000people 344 .006 1.600 .16552 .220703
Valid N (listwise) 344
Graph 1: Time trend of HIV Prevalence Country Average
From table 1 above, we can see that the minimum rate of HIV prevalence
for all the observations in the dataset is 0.1 and the maximum is 26.3.
Having an average of 5.9 for this range, is an indication that the rates are
fairly low for a good number of countries in Sub-Saharan Africa. From this
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time trend graph we see how rates on average have been declining over
the years, although the pace at which the decline is taking place is still very
slow.
5.1. Factor Analysis
As a data reduction tool, factor analysis was used to quantify the
integration concept from the selected variables of the dataset which are
seen to best represent the relationship between HIV prevalence and the
integration of HIV/AIDS services into Primary Health Care, which would
otherwise be hard to do. Factor analysis was chosen as preferable to other
data reduction methods because it fits best the nature of the data most of
which are estimates and all of which involve such large
spatial/geographical entities as countries. The test also provides the benefit
of having a chi-square value and significance and descriptive statistics in
the same test as shown in appendix C where the complete output of the
factor analysis test appears.
The 764.825 Bartlett‟s Test of Sphericity (approx. chi-square) which is
significant at P < 0.001 indicates that the partial correlations among
variables are decent and that the matrix is not an identity matrix. In other
words, the matrices are not correlated. This confirms the premise that using
these variables as observed variables for factor analysis is appropriate.
This is reinforced by the 0.531 KMO Measure of Sampling Adequacy which
is good enough a value.
From the Scree plot (graph 2) and the table of total variance explained
(table 2) we see that two component factors were generated from the
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observed variables as sufficient representatives of the underlying
integration factor as their extracted variance factor loadings (Eigen values)
are > 1. These were retained as principal components and they
cumulatively explain 74.009% of the variance of the uncorrelated matrices
of the model.
The unrelatedness of the principal component factors is further justified in
the factor Score Covariance Matrix table (Appendix C). The extraction
column in the table of communalities in appendix C shows the proportion of
each observed variable‟s variance that can be explained by the principal
components (resultant factors). The component matrix table shows the
proportions the variables contribute to each retained principal component.
A rotation did not cause any significant change to the model as shown both
in table 2 and in appendix C.
Table 2 (below) shows that component 1 has the maximum variance (Eigen
value) of 2.312, and component 2 contains the next highest variance 1.389.
The two components constitute the extracted factors and their cumulative
factor loadings explain 74.009% of the total variance. This means it is a
good enough model to account for the underlying concept integration from
the listed observed variables.
As stated earlier in this report, the two extracted principal components are
the factors from which the integration variable was generated. From the
explanation in this subsection of the report and the full output of the factor
analysis model in appendix C, it can be seen that enough diagnostics were
carried out to insure that the model was appropriately employed. As such,
substantial internal validity was sought so that using the resultant
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integration variable as the dependent variable for the final regression
analysis provides a statistically well grounded model.
Table 2: Total Variance Explained
Component Initial Eigenvalues
Extraction Sums of Squared Loadings
Rotation Sums of Squared Loadings
Total % of
Variance Cumulative
% Total % of
Variance Cumulative
% Total % of
Variance Cumulative
%
1 2.312 46.237 46.237 2.312 46.237 46.237 1.866 37.320 37.320
2 1.389 27.772 74.009 1.389 27.772 74.009 1.834 36.689 74.009
3 .919 18.385 92.394
4 .208 4.153 96.547
5 .173 3.453 100.000
Graph 2: Variance Extraction Plot for each Observed Variable
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5.2. Regression Analysis
As indicated in the first table in appendices B and D, I used the Enter
method for the multivariate regression analysis. I found this method more
relevant as I could run several tests including the ANOVA, partial
correlations, and the test of regression assumptions. Table 3c lists the
independent variables of this regression model whose dependent variable
is integration. From the model summary table below (table 3a), we see that
73.7% of the variance in the integration of HIV/AIDS services into Primary
Health Care can be explained by its relation with the predictor variables.
These include HIV prevalence rates, the number of VCT/ART facilities, the
number of seropositives5 on Anti Retroviral Therapy, tuberculosis incidence
per 100,000 people, and the number of physicians per 1,000 people. The F
value in the ANOVA table (table 3b) – 80.073 – is significant at the 0.001
level.
Table 3a: Model Summary
Model
R R
Square Adjusted R Square
Std. Error of the
Estimate
1 .737 .543 .536 .28325
Table 3b: ANOVA
Model Sum of Squares df
Mean Square F Sig.
1 Regression 32.122 5 6.424 80.073 .000
Residual 27.038 337 .080
Total 59.160 342
5 These are people who live with the Human Immunodeficiency Virus (HIV) also known as HIV
positive.
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5.2.1. Regression Diagnostic Tests
The preliminary regression analysis included a diagnostic test of regression
assumptions. These diagnostics will be discussed with a little more detail in
this subsection unlike in the final regression model. Apart from table 3c
below, all the graphs and tables referred to in this subsection are found in
appendix D. From the histogram, we see quite a decent picture of normality
in the distribution.
The Normal P-P Plot of Regression Standardized Residuals shows the
distribution of the data points along the regression line. We see that they
are quite normally distributed along the regression line, though some of the
variance differences are quite big below the upper half of the line.
The scatter plot of standardized residual and predicted values accounts for
the regression assumption of constant variability across all values of the
predictor variables (homoscedasticity). The randomness of the data points
is not very typical but the fact that the dependent variable is represented by
two absolute dummy values (1 and 0), it is a legitimate pattern of data
points, and the relevant assumption is still fulfilled. We seem to have one
outlier, though.
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Table3c: Coefficients
Model
Unstandardized Coefficients
Standardized Coefficients
t Sig.
Collinearity Statistics
B Std. Error Beta
Tolerance VIF
(Constant) .017 .034
.497 .620
HIV Prevalence .040 .004 .666 10.281 .000 .324 3.090
#VCT Facilities .000 .000 .244 4.172 .000 .395 2.532
#on Anti Retroviral Therapy .000 .000 .008 .126 .900 .337 2.971
Tuberculosis Incidence/100000 people
-.001 .000 -.291 -4.473 .000 .321 3.114
Physicians/1000 people .732 .074 .389 9.915 .000 .882 1.134
Table 3c (coefficients table) shows that outcomes for the predictor
variables are independent. The t- statistics are significant at the 0.001 level
for all the variables except for seropositives on Anti Retroviral Therapy, and
its relevance in the model is discussed in the test of hypotheses subsection
below. The Variance Inflection Factors (VIFs) of the predictor (independent)
variables are all low (< 5.0). This is an indication that there is no multi-
collinearity of variables in the model. These diagnostic results all testify that
the regression model applied does not violate any regression assumption.
As said earlier in the subsection, see appendix D for the full output of the
model including the partial correlation graphs, collinearity diagnostics,
casewise diagnostics, and residual statistics tables.
5.2.2. Test of Hypotheses
To begin with, it is helpful to note that although the number of seropositives
on ART is statistically insignificant with t = 0.126, it was advantageous to
keep it in the model because it adds to the overall explanatory power of the
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model. It is considered an important factor of the integration program. What
is more interesting about this variable is that its partial correlation plot with
the integration variable has the line of fit running horizontal at almost zero.
This is an intriguing phenomenon which insinuates a need for a more
focused study to measure the relationship between the integration program
and the number of seropositives on ART in the absence of new HIV
incidences.
Looking at the t-statistics for the variables in table 3c and their levels of
significance, we can recall the hypotheses and reiterate that:
a) The integration of HIV/AIDS services into Primary Health Care
services has a bearing in the reduction of the HIV prevalence rate in
Mozambique. The time trend graph confirms this conclusion;
b) Insufficient human resource still threatens the adequate delivery of
HIV/AIDS services in Mozambique even in the face of the integration
program. The integration – physician/1,000 people partial regression
plot shows a coefficient of determination of 0.226;
c) Increasing the number of Voluntary Counseling and Testing/Anti
Retroviral Therapy facilities facilitates the effectiveness of the
integration program which in turn contributes to the lowering of the
HIV prevalence rate in Mozambique, R2 = 0.049;
A bivariate analysis showed that more Tuberculosis treatment leads to
lower HIV prevalence (R2 = 0.002). The final regression model also shows
that the integration program is associated with lower tuberculosis
incidences. This implies that the diagonal framework of the integration
program has the potential of containing the HIV/AIDS epidemic in
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Mozambique and other African countries by addressing the factors of the
epidemic in a holistic approach.
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6. Policy Implications
After all the methodological and analytical processes, what policy
implications do we draw from the findings of this study? Having found that
the integration of HIV/AIDS services into Primary Health Care services has
a bearing in the reduction of the HIV prevalence rate in Mozambique,
attention is called to the Mozambican and other African governments to
seriously consider investing in the program. As can be seen from the time
trend graph, there has been considerable reduction in the HIV prevalence
rates over the past eight years. However, the fact that the rates are still
high, though reducing, and the slow pace of reduction is enough reason for
the government of Mozambique to scale up the integration services and
corresponding resources in all aspects.
What about the insufficiency of human resource which still threatens the
adequate delivery of HIV/AIDS services in Mozambique even in the face of
the integration program? One quick solution to this problem is to maximize
the use of the existing number of human resource in the integration
program as has been the case in 2004. The disadvantage with this strategy
is that the scale up will keep increasing the burden on the health personnel
which will soon or later result in their being overburdened and,
consequently, compromise the quality of service delivery.
However, training of more health personnel and retaining them is the most
tangible solution. The 0.226 integration – physician/1000 people coefficient
of determination indicates that having 0.226 more health personnel per
1000 people will place the integration program at its fullest capacity and
bring HIV prevalence to 0, assuming that this was the only determining
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variable. The fact that there are other variables that influence HIV
prevalence and the effectiveness of the integration program, this rate will
have to be divided by whatever the number of variables is considered as
contributing factors. The same will apply to the rates of the other variables.
Increasing the number of health care facilities is the other tangible way the
Mozambican government can support the burden of expanded access to
AIDS/primary health care. The findings of this study say that increasing the
number of Voluntary Counseling and Testing/Anti Retroviral Therapy
facilities facilitates the effectiveness of the integration program which in turn
contributes to the lowering of the HIV prevalence rate in Mozambique, with
a coefficient of determination = 0.049. Assuming that the five variables
included in the analytical models of this study were the only real factors of
the effectiveness of the integration program, Mozambique will need 0.049/5
= 0.0098 more health facilities per 1000 people to make the integration
program fully effective.
If Mozambique meets the demand for the two major resources mentioned
above, she will afford to adequately deal with diseases like Tuberculosis
and other HIV opportunistic diseases in terms of prevention and treatment
and, eventually, mitigate the HIV/AIDS epidemic. This means that more
than providing adequate clinical services, there will be enough health
personnel to engage in other non-clinical preventive strategies and
processes that will address the socio-cultural and other non-medical/clinical
contexts of medicine and public health at all levels and in various horizontal
directions. In the final analysis, the benefit is greater if other African
countries adopt the model because it will eliminate the external threat to the
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effectiveness and efficiency of the model resulting from cross-border
migration.
It is important to note that what is suggested in this study of the integration
model is more likely to take shape in terms of effectiveness and efficiency if
there is commitment, especially when it comes to implementation, by all
stakeholders. It may look like a very huge task, but we will come to realize
that all it takes, in some cases, is an enabling environment which, in most
national contexts, is determined by government. Given the opportunity and
resources, more likely than not citizens will do what it takes to create
conditions that add value to their lives and wellbeing.
There are numerous institutional and individual entities willing to partner
with various governments on a variety of Public Health programs many of
whom have to face the obstacles created by lack of political will on the part
of policy makers and, sometimes, policy implementers. Such partnerships
alleviate the pressure on government to mobilize the resources necessary
to carry out objective needs assessments and program implementations.
This facilitates the maximization in the utility of existing resources and the
mobilization of new resources which is one of the most essential aspects of
the integration model pursued in this study. It is this factor that principally
makes the model worth being adopted and implemented in other African
countries than Mozambique.
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7. Conclusion
The findings of this study point to a conclusion that the integration of
HIV/AIDS services into Primary Health Care has substantially contributed
to the reduction in the rate of HIV prevalence in Mozambique. Both the
factor analysis and the final regression analysis models of this study
indicate that 74% of the variance in the integration of HIV/AIDS services
into Primary Health Care can be explained by its relation with HIV
prevalence rates, the number of VCT/ART facilities, the number of people
living with HIV on Anti Retroviral Therapy, tuberculosis incidence per
100000 people, and the number of physicians per 1000 people. The
interaction among these explanatory variables accounts for the
effectiveness of the integration program in reducing the rate of HIV
prevalence at 1% level of significance.
Although the integration of HIV/AIDS services into Primary Health Care
services has led to the reduction of the HIV prevalence rate, the time trend
graph shows that this is happening still at high rates and slow pace. The
study also concludes that insufficient human resource still threatens the
adequate delivery of HIV/AIDS services in Mozambique even in the face of
the integration program. Finally, increasing the number of Voluntary
Counseling and Testing/Anti Retroviral Therapy facilities enhances the
effectiveness of the integration program which in turn leads to the lowering
of the HIV prevalence rate in Mozambique. From the overall model of this
study, it can be safely said that full utilization of the integration program
enables health administrators and all stakeholders address the HIV/AIDS
epidemic from various perspectives including socio-economic, political, and
cultural.
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7.1. Weaknesses and Inadequacies
The major weakness of this project is the fact that most of the data are
annual estimates of the Sub-Saharan African countries which affects
precision. However, given the difficulty in carrying out primary data
collection to satisfy the needs of this study given its scope and
circumstance, the compiled data set constitutes the best representation.
It may be argued that the use of data for all Sub-Sahara African countries
could detract from the model‟s quantitative relevance to Mozambique. The
counter argument to this is the fact that the model fits the Hierarchical
Linear Model‟s aggregation technique. In this respect, the model can
actually be reciprocated. Lessons learned from Mozambique‟s integration
program can be disaggregated to other Sub-Saharan African countries,
taking into account the sub-variables unique to each country.
The other possible weakness of this study is the fact that too many aspects
of the HIV/AIDS epidemic are subjective/qualitative factors which makes it
hard to have them fully accounted for in quantitative methods. However,
the use of factor analysis is actually meant to account for such qualitative
and intangible concepts in quantitative terms. And, as can be seen from the
models, a good job is done on this.
The final inadequacy that would be registered is the fact that it was not
possible, within the study‟s scope, to elicit and measure the capacity of
Mozambique‟s Ministry of Health to adequately face the challenges of
scaling up activities due to the program, as stated in objective (c). It would
be said, however, that this is embedded in the integration variable and can
be considered as part of the two major policy actions necessary to meet the
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needs of a fully fledged, effective and efficient integration program
discussed in the policy implications section. To adequately address this as
a specific research question, we need a field based primary data collection
process.
7.2. Suggestions/Recommendations
With reference to the outcomes of the analytical models and the policy
implications, a number of recommendations can be made. First and
foremost, Mozambique needs to fully utilize the integration model. This
means going beyond scaling up HIV/AIDS services which in turn boosts
primary health care services in terms of quality and access. It involves
refurnishing the entire health care system by training more health care
personnel and expanding the capacity of health facilities to meet the
demands of expanded access. As such other African countries can draw
lessons from the Mozambican integration program and adopt the model
which is more likely to yield huge marginal benefits to the continent and its
peoples.
There is need to have more studies conducted on the epidemic in Sub-
Saharan African countries and build comprehensive and publicly accessible
data repositories, especially for Lusophone Africa6 on which there are lots
of missing data or not any data at all. Beside Ethiopia, Seychelles, and the
Democratic Republic of Congo, Lusophone African countries presented the
highest amount of missing data which resulted in the exclusion of two
6 This term refers to African countries in which Portuguese is the official language and widely
spoken across the country.
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(Cape Verde and Sao Tome and Principe) of the five Lusophone African
countries from the study. This awakens a great awareness for the need to
pursue this almost systematic disparity among these members of the
Portuguese speaking community in Africa. It will be interesting to carry out
studies that probe why we find, almost consistently, incomplete data about
the HIV/AIDS epidemic on these countries.
The need for more research is almost imperative because it will facilitate
the improvement of the model. Policy research is a very critical economic
development factor for all nations that seek to have their legislative and
other decision making processes guide by scientific evidence.
Governments that do not have this as a priority are urged to seriously
consider it. For countries like Angola which are working towards making the
national higher education system meet the needs of students in a
globalized system of academic standards, the need for more research
practice and institutions cannot be overemphasized. For this to happen
well, legislators of these countries should make full usage of available
research findings in this and other relevant areas and be politically willing to
not only support academic and professional research, but to implement the
resulting objective recommendations too.
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References
Cohen and Tate (2005), The less they Know the Better: Abstinence-Only
HIV/AIDS Programs in Uganda; Human Rights Watch
Crush, J. et al (2007), Linking Migration, HIV/AIDS, and Urban Food
Security in Southern and Eastern Africa; Idasa Publishing, Cape Town
Damasceno, B.C.M. et al (2008), Structure of the Glucanase Inhibitor
Protein (GIP) Family from Phytophthora Species Suggests Coevolution
with Plant Endo-β-1,3-Glucanases; MPMI Vol. 21, No. 6 of The American
Phytopathological Society
EQUINET – Regional Network for Equity in Health in East and Southern
Africa (2007), Reclaiming the Resources for Health: a Regional Analysis of
Equity in Health in East and Southern Africa; Training and Research
Support Center, Harare, Zimbabwe
Gillespie, S. and Kadiyala, S. (2005), HIV/AIDS and Food and Nutrition
Security: from Evidence to Action;
Green, E.C. (1994), AIDS and STDs in Africa: Bridging the Gap between
Traditional Healing and Modern Medicine; Westview Press,
Haacker, M. (2004), The Macroeconomics of HIV/AIDS; International
Monetary Fund (IMF) Publication Services, Washington D.C.
Lule, E. et al (2008), The Changing AIDS Landscape: Selected Papers for
the World Bank’s Agenda for Action in Africa 2007 – 2011; World Bank,
Washington D.C.
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50
Made, P.A. and Morna, C.L, (2006), Diversity in Action: HIV and AIDS and
Gender Policies in Newsrooms; Gender Links and MMP, Johannesburg
Newman, L.M. et al (2001), Seropositives among Military Blood Donors in
Manica Province – Mozambique; International Journal of STDs and AIDS
Persson and Sjöstedt (2010), A Deadly Mismatch? The Problem of
HIV/AIDS in
Research and Policy; QoG Working Paper Series 2010:7, Gothenburg
Pfeiffer et al (2010), Integration of HIV/AIDS services into African primary
health care: lessons learned for health system strengthening in
Mozambique - a case study, Journal of the International AIDS Society
Ondimu, K. N. (2005): Risky Sexual Behaviors among Migrant Tea
Workers in Kenya, Organization for Social Science Research in Eastern
and Southern Africa, Addis Ababa.
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Appendix A
Correlations
Correlations
IntegrationVaria
ble HIVIncidence HIVPrevalence
IntegrationVariable Pearson Correlation 1 .339** .523
**
Sig. (2-tailed) .000 .000
N 343 343 343
HIVIncidence Pearson Correlation .339** 1 .342
**
Sig. (2-tailed) .000 .000
N 343 344 344
HIVPrevalence Pearson Correlation .523** .342
** 1
Sig. (2-tailed) .000 .000
N 343 344 344
@#VCTFacilities Pearson Correlation .351** .328
** .123
*
Sig. (2-tailed) .000 .000 .022
N 343 344 344
@#onAntiRetroviralTherapy Pearson Correlation .424** .655
** .319
**
Sig. (2-tailed) .000 .000 .000
N 343 344 344
Incidenceoftuberculosisper1
00000people
Pearson Correlation .310** .335
** .816
**
Sig. (2-tailed) .000 .000 .000
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N 343 344 344
Physiciansper1000people Pearson Correlation .505** .333
** .160
**
Sig. (2-tailed) .000 .000 .003
N 343 344 344
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Correlations
@#VCTFacilitie
s
@#onAntiRetrov
iralTherapy
Incidenceoftube
rculosisper1000
00people
IntegrationVariable Pearson Correlation .351** .424
** .310
**
Sig. (2-tailed) .000 .000 .000
N 343 343 343
HIVIncidence Pearson Correlation .328** .655
** .335
**
Sig. (2-tailed) .000 .000 .000
N 344 344 344
HIVPrevalence Pearson Correlation .123* .319
** .816
**
Sig. (2-tailed) .022 .000 .000
N 344 344 344
@#VCTFacilities Pearson Correlation 1 .759** .103
Sig. (2-tailed) .000 .056
N 344 344 344
@#onAntiRetroviralTherapy Pearson Correlation .759** 1 .315
**
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Sig. (2-tailed) .000 .000
N 344 344 344
Incidenceoftuberculosisper1
00000people
Pearson Correlation .103 .315** 1
Sig. (2-tailed) .056 .000
N 344 344 344
Physiciansper1000people Pearson Correlation .125* .283
** .078
Sig. (2-tailed) .021 .000 .148
N 344 344 344
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Correlations
Physiciansper1
000people
IntegrationVariable Pearson Correlation .505**
Sig. (2-tailed) .000
N 343
HIVIncidence Pearson Correlation .333**
Sig. (2-tailed) .000
N 344
HIVPrevalence Pearson Correlation .160**
Sig. (2-tailed) .003
N 344
@#VCTFacilities Pearson Correlation .125*
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Sig. (2-tailed) .021
N 344
@#onAntiRetroviralTherapy Pearson Correlation .283**
Sig. (2-tailed) .000
N 344
Incidenceoftuberculosisper1
00000people
Pearson Correlation .078
Sig. (2-tailed) .148
N 344
Physiciansper1000people Pearson Correlation 1
Sig. (2-tailed)
N 344
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
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Appendix B
Regression Output I
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT HIVPrevalence
/METHOD=ENTER @#VCTFacilities @#onAntiRetroviralTherapy
Incidenceoftuberculosisper100000people Physiciansper1000people
/PARTIALPLOT ALL
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
/CASEWISE PLOT(ZRESID) OUTLIERS(3).
Variables Entered/Removedb
Model Variables
Entered
Variables
Removed Method
d
i
m
e
n
s
i
o
n
0
1 Physiciansper1
000people,
Incidenceoftube
rculosisper1000
00people,
@#VCTFacilitie
s,
@#onAntiRetro
viralTherapya
. Enter
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a. All requested variables entered.
b. Dependent Variable: HIVPrevalence
Model Summaryb
Model
R R Square
Adjusted R
Square
Std. Error of the
Estimate
d
i
m
e
n
s
i
o
n
0
1 .823a .677 .673 3.9980
a. Predictors: (Constant), Physiciansper1000people,
Incidenceoftuberculosisper100000people, @#VCTFacilities,
@#onAntiRetroviralTherapy
b. Dependent Variable: HIVPrevalence
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 11339.180 4 2834.795 177.353 .000a
Residual 5418.560 339 15.984
Total 16757.740 343
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a. Predictors: (Constant), Physiciansper1000people, Incidenceoftuberculosisper100000people,
@#VCTFacilities, @#onAntiRetroviralTherapy
b. Dependent Variable: HIVPrevalence
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) -3.329 .445 -7.484 .000
@#VCTFacilities .000 .001 -.009 -.189 .851
@#onAntiRetroviralTherapy 4.080E-6 .000 .053 .996 .320
Incidenceoftuberculosisper1
00000people
.023 .001 .794 23.772 .000
Physiciansper1000people 2.651 1.032 .084 2.570 .011
a. Dependent Variable: HIVPrevalence
Coefficientsa
Model Collinearity Statistics
Tolerance VIF
1 (Constant)
@#VCTFacilities .395 2.533
@#onAntiRetroviralTherapy .337 2.964
Incidenceoftuberculosisper1
00000people
.856 1.169
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Physiciansper1000people .899 1.112
a. Dependent Variable: HIVPrevalence
Collinearity Diagnosticsa
Model Dimension
Eigenvalue Condition Index
Variance Proportions
(Constant)
@#VCTFacilitie
s
@#onAntiRetro
viralTherapy
d
i
m
e
n
s
i
o
n
0
1
dimension1
1 3.009 1.000 .02 .02 .02
2 1.122 1.637 .04 .12 .07
3 .518 2.410 .04 .01 .00
4 .239 3.546 .16 .49 .43
5 .112 5.193 .75 .36 .48
a. Dependent Variable: HIVPrevalence
Collinearity Diagnosticsa
Model Dimension Variance Proportions
Incidenceoftube
rculosisper1000
00people
Physiciansper1
000people
d
i
m
e
n
1
dimension1
1 .02 .03
2 .03 .03
3 .08 .81
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s
i
o
n
0
4 .16 .01
5 .71 .12
a. Dependent Variable: HIVPrevalence
Casewise Diagnosticsa
Case Number Std. Residual HIVPrevalence Predicted Value Residual
dimension0
161 3.302 24.3 11.097 13.2028
162 3.134 24.1 11.569 12.5312
163 3.000 23.8 11.805 11.9952
a. Dependent Variable: HIVPrevalence
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value -1.845 26.754 5.901 5.7497 344
Residual -9.5874 13.2028 .0000 3.9746 344
Std. Predicted Value -1.347 3.627 .000 1.000 344
Std. Residual -2.398 3.302 .000 .994 344
a. Dependent Variable: HIVPrevalence
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Charts
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Appendix C
Factor Analysis
FACTOR
/VARIABLES HIVPrevalence @#VCTFacilities @#onAntiRetroviralTherapy
Incidenceoftuberculosisper100000people Physiciansper1000people
/MISSING LISTWISE
/ANALYSIS HIVPrevalence @#VCTFacilities @#onAntiRetroviralTherapy
Incidenceoftuberculosisper100000people Physiciansper1000people
/PRINT UNIVARIATE INITIAL KMO EXTRACTION ROTATION FSCORE
/PLOT EIGEN
/CRITERIA MINEIGEN(1) ITERATE(25)
/EXTRACTION PC
/CRITERIA ITERATE(25)
/ROTATION VARIMAX
/SAVE REG(ALL)
/METHOD=CORRELATION.
Factor Analysis
Descriptive Statistics
Mean Std. Deviation Analysis N
HIVPrevalence 5.901 6.9897 344
@#VCTFacilities 162.11 447.572 344
@#onAntiRetroviralTherapy 34727.28 90687.132 344
Incidenceoftuberculosisper1
00000people
370.22 236.803 344
Physiciansper1000people .16552 .220703 344
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KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .531
Bartlett's Test of Sphericity Approx. Chi-Square 764.825
df 10
Sig. .000
Communalities
Initial Extraction
HIVPrevalence 1.000 .905
@#VCTFacilities 1.000 .832
@#onAntiRetroviralTherapy 1.000 .880
Incidenceoftuberculosisper1
00000people
1.000 .904
Physiciansper1000people 1.000 .179
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
dimension0
1 2.312 46.237 46.237 2.312 46.237 46.237
2 1.389 27.772 74.009 1.389 27.772 74.009
3 .919 18.385 92.394
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4 .208 4.153 96.547
5 .173 3.453 100.000
Extraction Method: Principal Component Analysis.
Total Variance Explained
Component Rotation Sums of Squared Loadings
Total % of Variance Cumulative %
dimension0
1 1.866 37.320 37.320
2 1.834 36.689 74.009
3
4
5
Extraction Method: Principal Component Analysis.
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Component Matrixa
Component
1 2
HIVPrevalence .760 -.572
@#VCTFacilities .632 .658
@#onAntiRetroviralTherapy .808 .478
Incidenceoftuberculosisper1
00000people
.738 -.599
Physiciansper1000people .371 .204
Extraction Method: Principal Component Analysis.
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Component Matrixa
Component
1 2
HIVPrevalence .760 -.572
@#VCTFacilities .632 .658
@#onAntiRetroviralTherapy .808 .478
Incidenceoftuberculosisper1
00000people
.738 -.599
Physiciansper1000people .371 .204
Extraction Method: Principal Component Analysis.
a. 2 components extracted.
Rotated Component Matrixa
Component
1 2
HIVPrevalence .149 .940
@#VCTFacilities .912 -.034
@#onAntiRetroviralTherapy .913 .218
Incidenceoftuberculosisper1
00000people
.115 .944
Physiciansper1000people .408 .111
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 3 iterations.
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Component Transformation Matrix
Component 1 2
dimension0
1 .719 .695
2 .695 -.719
Extraction Method: Principal
Component Analysis.
Rotation Method: Varimax with Kaiser
Normalization.
Component Score Coefficient Matrix
Component
1 2
HIVPrevalence -.050 .525
@#VCTFacilities .526 -.151
@#onAntiRetroviralTherapy .490 -.005
Incidenceoftuberculosisper1
00000people
-.070 .532
Physiciansper1000people .217 .006
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
Component Scores.
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Component Score Covariance Matrix
Component 1 2
dimension0
1 1.000 .000
2 .000 1.000
Extraction Method: Principal Component
Analysis.
Rotation Method: Varimax with Kaiser
Normalization.
Component Scores.
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Appendix D
Regression Output II (with Integration as the Dependent Variable)
/COMPRESSED.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT IntegrationVariable
/METHOD=ENTER HIVPrevalence @#VCTFacilities @#onAntiRetroviralTherapy
Incidenceoftuberculosisper100000people Physiciansper1000people
/PARTIALPLOT ALL
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID)
/CASEWISE PLOT(ZRESID) OUTLIERS(3).
Variables Entered/Removedb
Model Variables
Entered
Variables
Removed Method
d
i
m
e
n
s
i
o
n
0
1 Physiciansper1
000people,
Incidenceoftube
rculosisper1000
00people,
@#VCTFacilitie
s,
@#onAntiRetro
viralTherapy,
HIVPrevalencea
. Enter
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a. All requested variables entered.
b. Dependent Variable: IntegrationVariable
Model Summaryb
Model
R R Square
Adjusted R
Square
Std. Error of the
Estimate
d
i
m
e
n
s
i
o
n
0
1 .737a .543 .536 .28325
a. Predictors: (Constant), Physiciansper1000people,
Incidenceoftuberculosisper100000people, @#VCTFacilities,
@#onAntiRetroviralTherapy, HIVPrevalence
b. Dependent Variable: IntegrationVariable
ANOVAb
Model Sum of Squares df Mean Square F Sig.
1 Regression 32.122 5 6.424 80.073 .000a
Residual 27.038 337 .080
Total 59.160 342
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a. Predictors: (Constant), Physiciansper1000people, Incidenceoftuberculosisper100000people,
@#VCTFacilities, @#onAntiRetroviralTherapy, HIVPrevalence
b. Dependent Variable: IntegrationVariable
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig. B Std. Error Beta
1 (Constant) .017 .034 .497 .620
HIVPrevalence .040 .004 .666 10.281 .000
@#VCTFacilities .000 .000 .244 4.172 .000
@#onAntiRetroviralTherapy 3.650E-8 .000 .008 .126 .900
Incidenceoftuberculosisper1
00000people
-.001 .000 -.291 -4.473 .000
Physiciansper1000people .732 .074 .389 9.915 .000
a. Dependent Variable: IntegrationVariable
Coefficientsa
Model Collinearity Statistics
Tolerance VIF
1 (Constant)
HIVPrevalence .324 3.090
@#VCTFacilities .395 2.532
@#onAntiRetroviralTherapy .337 2.971
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Incidenceoftuberculosisper1
00000people
.321 3.114
Physiciansper1000people .882 1.134
a. Dependent Variable: IntegrationVariable
Collinearity Diagnosticsa
Model Dimension
Eigenvalue Condition Index
Variance Proportions
(Constant) HIVPrevalence
@#VCTFacilitie
s
d
i
m
e
n
s
i
o
n
0
1
dimension1
1 3.631 1.000 .01 .01 .01
2 1.193 1.744 .01 .01 .13
3 .591 2.478 .00 .05 .00
4 .361 3.172 .27 .13 .11
5 .166 4.676 .07 .14 .71
6 .057 7.963 .64 .66 .04
a. Dependent Variable: IntegrationVariable
Collinearity Diagnosticsa
Model Dimension Variance Proportions
@#onAntiRetro
viralTherapy
Incidenceoftube
rculosisper1000
00people
Physiciansper1
000people
d
i
m
1
dimension1
1 .01 .01 .02
2 .08 .01 .01
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e
n
s
i
o
n
0
3 .00 .01 .71
4 .07 .00 .10
5 .79 .01 .10
6 .05 .97 .06
a. Dependent Variable: IntegrationVariable
Casewise Diagnosticsa
Case Number
Std. Residual
IntegrationVaria
ble Predicted Value Residual
dimension0
65 3.292 1.00 .0675 .93247
66 3.237 1.00 .0832 .91681
67 3.155 1.00 .1064 .89360
68 3.100 1.00 .1221 .87795
69 3.018 1.00 .1450 .85498
288 -3.005 1.00 1.8510 -.85104
a. Dependent Variable: IntegrationVariable
Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value -.1781 1.8510 .2216 .30647 343
Residual -.85104 .93247 .00000 .28117 343
Std. Predicted Value -1.304 5.317 .000 1.000 343
Std. Residual -3.005 3.292 .000 .993 343
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Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value -.1781 1.8510 .2216 .30647 343
Residual -.85104 .93247 .00000 .28117 343
Std. Predicted Value -1.304 5.317 .000 1.000 343
Std. Residual -3.005 3.292 .000 .993 343
a. Dependent Variable: IntegrationVariable
Charts
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Descriptive Statistics
N Minimum Maximum Mean Std. Deviation
HIVIncidence 344 100 5600000 487758.72 948005.773
HIVPrevalence 344 .1 26.3 5.901 6.9897
@#VCTFacilities 344 0 4326 162.11 447.572
@#onAntiRetroviralTherapy 344 0 971556 34727.28 90687.132
Incidenceoftuberculosisper1
00000people
344 22 1260 370.22 236.803
Physiciansper1000people 344 .006 1.600 .16552 .220703
Valid N (listwise) 344