andre_melo_mpp project_final_rev

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 )

2

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!

3

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.

4

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

7

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

8

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

14

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

24

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

26

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.

27

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.

28

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

30

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

31

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

32

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.

33

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

34

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

35

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

36

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

37

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.

38

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.

39

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

40

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

41

Mozambique and other African countries by addressing the factors of the

epidemic in a holistic approach.

42

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

43

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

44

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.

45

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.

46

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

47

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.

48

(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.

49

References

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EQUINET – Regional Network for Equity in Health in East and Southern

Africa (2007), Reclaiming the Resources for Health: a Regional Analysis of

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Support Center, Harare, Zimbabwe

Gillespie, S. and Kadiyala, S. (2005), HIV/AIDS and Food and Nutrition

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Green, E.C. (1994), AIDS and STDs in Africa: Bridging the Gap between

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Haacker, M. (2004), The Macroeconomics of HIV/AIDS; International

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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.

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.

51

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

52

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

**

53

Sig. (2-tailed) .000 .000

N 344 344 344


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



Correlations

Physiciansper1

000people

IntegrationVariable Pearson Correlation .505**

Sig. (2-tailed) .000

N 343

HIVIncidence Pearson Correlation .333**


N 344

HIVPrevalence Pearson Correlation .160**


N 344

@#VCTFacilities Pearson Correlation .125*

54


N 344

@#onAntiRetroviralTherapy Pearson Correlation .283**


N 344


00000people

Pearson Correlation .078


N 344

Physiciansper1000people Pearson Correlation 1

Sig. (2-tailed)

N 344



55

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

56

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


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

57

a. Predictors: (Constant), Physiciansper1000people, Incidenceoftuberculosisper100000people,

@#VCTFacilities, @#onAntiRetroviralTherapy


Coefficientsa

Model


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


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


00000people

.856 1.169

58

Physiciansper1000people .899 1.112


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



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

59

s

i

o

n

0

4 .16 .01

5 .71 .12


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


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


60

Charts

62

Appendix C

Factor Analysis

FACTOR

/VARIABLES HIVPrevalence @#VCTFacilities @#onAntiRetroviralTherapy


/MISSING LISTWISE

/ANALYSIS HIVPrevalence @#VCTFacilities @#onAntiRetroviralTherapy


/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


00000people

370.22 236.803 344

Physiciansper1000people .16552 .220703 344

63

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


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

64

4 .208 4.153 96.547

5 .173 3.453 100.000


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


65

Component Matrixa

Component

1 2

HIVPrevalence .760 -.572

@#VCTFacilities .632 .658

@#onAntiRetroviralTherapy .808 .478


00000people

.738 -.599

Physiciansper1000people .371 .204


66

Component Matrixa

Component

1 2

HIVPrevalence .760 -.572

@#VCTFacilities .632 .658



00000people

.738 -.599



a. 2 components extracted.

Rotated Component Matrixa

Component

1 2

HIVPrevalence .149 .940

@#VCTFacilities .912 -.034



00000people

.115 .944



Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 3 iterations.

67

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


00000people

-.070 .532



Rotation Method: Varimax with Kaiser Normalization.

Component Scores.

68

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.

69

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


/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

70

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


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

71

a. Predictors: (Constant), Physiciansper1000people, Incidenceoftuberculosisper100000people,

@#VCTFacilities, @#onAntiRetroviralTherapy, HIVPrevalence


Coefficientsa

Model


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


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

72


00000people

.321 3.114

Physiciansper1000people .882 1.134



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



Model Dimension Variance Proportions

@#onAntiRetro

viralTherapy

Incidenceoftube

rculosisper1000

00people

Physiciansper1

000people

d

i

m

1

dimension1

1 .01 .01 .02

2 .08 .01 .01

73

e

n

s

i

o

n

0

3 .00 .01 .71

4 .07 .00 .10

5 .79 .01 .10

6 .05 .97 .06


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




Predicted Value -.1781 1.8510 .2216 .30647 343

Residual -.85104 .93247 .00000 .28117 343


Std. Residual -3.005 3.292 .000 .993 343

74



Predicted Value -.1781 1.8510 .2216 .30647 343

Residual -.85104 .93247 .00000 .28117 343


Std. Residual -3.005 3.292 .000 .993 343


Charts

78

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


00000people

344 22 1260 370.22 236.803

Physiciansper1000people 344 .006 1.600 .16552 .220703

Valid N (listwise) 344

andre_melo_mpp project_final_rev

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